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VOLUME 13, ISSUE 6, JUNE 2026
Publication is in progress..
Development and Utilization of Structural Panel with Indian Almond Seed
Anamae V. Balatayo
Semantic-Aware LLM-Based Test Generation: A Closed-Loop Framework for Requirement Alignment and Fault Detection
Sooraj Jacob, Rajeew Vishvakarma
The K – 12 MAPEH Program: Working Conditions, Challenges, and Professional Development Opportunities
Maria Carmen M. Ubal
Review on the Cam Follower Based Power Generation
Prof. R.N. Deshmukh, Mr. Ashish Kshirsagar, Mr. Amer Londhe, Mr. Digambar Mandale
Experimental Study of Cam Follower Based Power Generation
Prof. R.N. Deshmukh, Mr. Ashish Kshirsagar, Mr. Amer Londhe, Mr. Digambar Mandale
Review on Waste Oil Powered Thermal Electric Energy Generation System
Prof. V.H. Shivsharan, Rhishikesh Sanjay Mahadik, Ajay Malhari Riswadkar, Vikas Pawar
A simple, low cost, three-octave frequency gen- erating system, using two passive buzzers and thirty-six touch capacitive switches, implemented with FPGA and VHDL, suitable for hands-on practice of music beginners
Dr Evangelos I. Dimitriadis, Theodora Dimitriadou, Leonidas Dimitriadis, Stergios Michalakopoulos
A STUDY ON RECRUITMENT METRICS AND THEIR IMPACT ON ORGANIZATIONAL EFFICIENCY
Mr.AJAYRATHNA S, Ms.GAYATHRI M
SSI ANALYSIS OF G+6 RCC BUILDING ON STIFF SOIL USING ETABS AND SAFE
Harsh S. Patle, Aaquib Ansari
Effect of Roof Shape on Wind Response of High- Rise Building Using IS 875:2025 Draft in ETABS
Pratik B. Patle, Rahul Hinge
Effect of Floor Height Variation on Seismic Performance of RCC Buildings Using ETABS
Sagar D. Dule, Mahesh Raut
A SURVEY ON AI – BASED PLANT HEALTH MONITORING SYSTEM
Nandini P Gowda, Deepthi K, Koka Mahitha, Nelbiya N, Sumashree Pulagurla
A Review on Diet, Non-Communicable Disease and Public Health Policy
Mr. Shaik Atheeb Abdullah A & Dr. V Krishnaprabha
MnemoX: An AI-Powered Personalized Memory Assistant with Mood Tracking, Goal Monitoring, and Multi-Modal Interaction
T. Amalraj Victoire, A. Gopinath
Leave Management System for Efficient Employee Leave Processing
V. Udhayakumar, S. Kaviya
Digital Marketing Strategies Using Data Analytics Tools
Ms. R. Priyanka, Ms. S. Swathi
Women Safety SOS Application Using Android Technology
Mrs. M. Vasuki, Dr. T. Amalraj Victoire, B. Madhusri*
MULBERRY LEAVES DISEASES DETECTION AND CLASSFICATION
V. Udhayakumar V.Shamini
Caught Between Fate and Freedom: An Existential Reading of Rosencrantz and Guildenstern Are Dead
Dr. Sanju Jhajharia
A STUDY ON JOB SATISFACTION AND ITS IMPACT ON EMPLOYEE RETENTION IN MAYILADUTHURAI
Ms. R. Priyanka, Ms. K. Arthi
Cost Analysis of RC Buildings in Different Seismic Zones
Yogesh Tarare, Vijay Kotapati
ChargePulse: A Real-Time EV Charging Station Discovery and Management System
Snehal Dudhal, Soham Deshpande, Shreepad Aalekar
EFFECT OF CORROSIVE ENVIRONMENT ON BOND STRENGTH OF FRP BARS IN DIFFERENT GRADE OF CONCRETE
Hardik B. Bele, Deepa Telang
A STUDY ON DIGITAL MARKETING IN TOURISM AT EPIC HOLIDAY
Mr. V. Vishal, Dr. S.N. Kanagarathinam
Stray Buddies: Stray Animal Rescue and Shelter Connect
Vaishnavi Jadhav, Priti Tamhane, Shreya Mate, Anjali Gavane, Prof. A. D. Gujar
A Study on Job Expectation Among College Students at Karaikal
Ms. R. Sangeetha, Ms. N. Swathi
A Study on the Effectiveness of Employee Engagement on Organizational Performance
R. Sangeetha, T. Udhaya Kumari
Experimental Investigation on Reactive Powder Concrete
Pushkar Borse, Tushar Shende
TRUSTNET.AI – DETECTION OF FAKE IMAGES AND VIDEOS
Ms. Bhargavi H G, Himani B L, Sandra C, Navya Manosri S, Ananya V
Dynamic Trust Evaluation Framework for IoT
Nishant Sanghani and Bhavesh Borisaniya
EXPERIMENTAL STUDY: STRENGTHENING OF REINFORCED CONCRETE MEMBERS USING EXTERNALLY BONDED FRP COMPOSITES
Vikrant Dhoke, Monika Jain
Development of a 4-DOF Robotic Arm for Lightweight Precision Automation
Salunkhe Rohan, Sabale Shantanu, Shete Yuvraj, Dalvi Kishore, Prof G.P.Kavhekar, Dr. D.P.Kamble
SELF CHARGING ELECTRIC BICYCLE USING SOLAR ENERGY
MATTAM AMRUTH KUMAR SWAMY, K USHA RANI, SHAIK SAMIULLA, POLURU RAHUL, P VASANTH KUMAR, SAKE SIREESHA, GADDAM MAHESH BABU
When Biodiversity Persists but Protection Weakens: Community Stewardship, Encroachment and the Future of Orans in the Thar Desert — Evidence from Dhok and Jajawa Sacred Groves, Rajasthan
Dr. Devendra Singh Chouhan, Mehtab Singh Rathore
A Serverless Event-Driven Architecture for Automated Customer Feedback Management Using AWS SNS, Lambda, and CodePipeline
LAVANYA GUBBALA, Smt A.N. RAMA MANI*
A Serverless Event-Driven Architecture for Intelligent Personal Finance Management with Automated Continuous Deployment on AWS Lambda
KARRI HEMA LAKSHMI KANAKA DURGA, Mr.B.N. SRINIVASA GUPTA *
A Smart Peer-Learning Platform with AI-Driven Skill Matching and a Credit-Based Reciprocal Exchange Mechanism
PASUPULETI PRAMEELA VISALAKSHI, Mr. KARRI LAKSHMANA REDDY*
Cloud-Integrated Automated Academic Scheduling System: A Scalable Web-Based Approach for Conflict-Free Timetable Generation
CHELLABOYINA SINDHU, DR. CHIRAPARAPU SRINIVASA RAO *
A Cloud-Native Complaint Management Platform with Real-Time Business Intelligence and NLP-Driven Automated Triage Using Amazon QuickSight
TAMANAMPUDI LAKSHMI KALYANI, PADALA SRINIVASA REDDY*
A Cloud-Native Full-Stack Java Framework for Secure and Transparent Online College Election Management on AWS
VEERA SIVANI, K. LAKSHMI SAI SREE*
“Prevalence of Overweight and Obesity in Balangir District: Age and Sex Analysis”
John Soumyashree Collet*, Jyotismita Satpathy, Dr. Arvind Kumar Ojha, Dr. Kumar Sambhav Chopdar
A SUMMARY OF CUTTING-EDGE RESEARCH ON MORPHING AIRPLANE BASED ON INTELLIGENT MATERIALS AND STRUCTURES
K Veeranjaneyulu
Review on Numerical Analysis of Phase Change Materials for Battery Thermal Management Systems
Miss. Priyadarshani K. Dure and Mr. Avinash S. Patil
Abstract
Development and Utilization of Structural Panel with Indian Almond Seed
Anamae V. Balatayo
DOI: 10.17148/IARJSET.2026.13601
Abstract: This study investigated the potential of processed Indian Almond (Terminalia catappa) seed shell particles, locally known as Talisay, as reinforcement for sustainable cement-based structural panels. Using an experimental developmental design, three panel formulations were developed by varying the proportions of ground Talisay seed shells and sand while maintaining a constant cement-to-water ratio. The panels underwent mechanical testing and qualitative evaluation by 30 industry professionals and construction practitioners. Results showed that all treatments exhibited a flexural strength of approximately 0.1 MPa. However, Treatment B, composed of equal proportions of Talisay seed shells and sand, demonstrated the highest performance with a peak load capacity of 857 N, compressive strength of 8.8 MPa, and low water absorption rate of 11.25%. Panel densities ranged from 1.34 to 1.41 g/cm³, classifying them as lightweight materials. Evaluators rated the panels as “Very Acceptable” and “Very Applicable” for interior wall and ceiling applications. The findings support the use of Talisay seed shells as eco-friendly construction materials.
Keywords: Indian Almond Seed shells, Talisay seed shells, Terminalia catappa, Structural Panel, sustainable building materials, lightweight composites.
Keywords: Indian Almond Seed shells, Talisay seed shells, Terminalia catappa, Structural Panel, sustainable building materials, lightweight composites.
Abstract
Semantic-Aware LLM-Based Test Generation: A Closed-Loop Framework for Requirement Alignment and Fault Detection
Sooraj Jacob, Rajeew Vishvakarma
DOI: 10.17148/IARJSET.2026.13602
Abstract: Large language models (LLMs) increasingly generate software tests from source codes, natural language requirements, and API specifications. While LLMs can produce readable test cases with plausible assertions, the generated tests may fail to validate requirement intent, omit boundary conditions, duplicate scenarios, or provide weak fault detection. This study proposes a semantic-aware closed-loop framework for LLM-based test generation. The framework combines requirement ingestion, retrieval-augmented context construction, constraint extraction, LLM-based test generation, semantic coverage scoring, mutation-style feedback and refinement. The main contribution is a Semantic Coverage Metric (SCM) that evaluates requirement-test alignment using semantic similarity, constraint satisfaction, and assertion quality. To strengthen the framework beyond conceptual design, this study includes an executable pilot study using StringUtils-style utility functions, repeated generated test suites, coverage analysis, and mutation-style fault injection. The pilot study shows that semantic-guided generation improves line coverage and mutation effectiveness compared with naive prompting and slightly outperforms structured prompting in coverage while maintaining explicit requirement traceability. This study demonstrates that LLM-generated tests should be evaluated using both traditional quality metrics and requirement-alignment measures before adoption in engineering workflows.
Keywords: LLM-generated tests, software testing, semantic coverage, mutation testing, requirement traceability, AI- assisted testing, test generation, software quality.
Keywords: LLM-generated tests, software testing, semantic coverage, mutation testing, requirement traceability, AI- assisted testing, test generation, software quality.
Abstract
The K – 12 MAPEH Program: Working Conditions, Challenges, and Professional Development Opportunities
Maria Carmen M. Ubal
DOI: 10.17148/IARJSET.2026.13603
Abstract: The K-12 MAPEH (Music, Arts, Physical Education, and Health) program in the Philippines requires teachers to handle four distinct subject areas, creating unique professional challenges related to working conditions, instructional delivery, and professional development. This study examined the working conditions, instructional challenges, and professional development opportunities of Junior High School MAPEH teachers in the Division of Roxas City, Capiz, and determined the relationships among these variables. A descriptive-correlational research design was employed with 50 Junior High School MAPEH teachers selected through simple random sampling from public secondary schools in Roxas City during School Year 2025-2026. A researcher-made validated questionnaire (Cronbach's α = 0.85) measured working conditions, challenges, and professional development opportunities using a five-point Likert scale. Data were analyzed using frequency counts, percentages, weighted means, and Pearson correlation.Working conditions ranged from moderate to moderately high, with administrative duties impact (M=3.60) and multi-specialization requirements (M=3.55) rated highest, while adequacy of facilities (M=2.61) rated lowest. Physical Education presented the highest instructional challenges (M=3.59), followed by Arts (M=3.40), Music (M=3.28), and Health (M=3.24). Professional development opportunities (M=2.72) and institutional support (M=2.96) were moderately available yet showed moderately high positive impact on teacher readiness (M=3.27). Significant positive correlations were found among working conditions, challenges, and professional development opportunities (r=0.684, 0.571, 0.623; p<0.001). MAPEH teachers face considerable workload demands, multi-specialization pressures, and facility limitations that significantly affect instructional effectiveness. Strengthening institutional support, resource allocation, and sustained professional development programs is essential for improving MAPEH instruction under the K-12 curriculum.
Keywords: MAPEH teachers, working conditions, instructional challenges, professional development, K-12 program, Roxas City
Keywords: MAPEH teachers, working conditions, instructional challenges, professional development, K-12 program, Roxas City
Abstract
Review on the Cam Follower Based Power Generation
Prof. R.N. Deshmukh, Mr. Ashish Kshirsagar, Mr. Amer Londhe, Mr. Digambar Mandale
DOI: 10.17148/IARJSET.2026.13604
Abstract: The rapid depletion of conventional fossil fuels and the escalating global demand for clean energy have encouraged researchers to investigate small-scale, distributed electromechanical power generation systems. This project presents the design, fabrication, and experimental evaluation of a cam-follower-based power generation prototype that converts controlled reciprocating mechanical motion into usable electrical energy.
The system employs a 12 V DC geared motor operating at 45 RPM as the prime mover. The motor drives a profiled 90 mm cam, which actuates a spring-loaded follower shaft (12 mm diameter, 150 mm spring). A rack machined onto the follower shaft meshes with a pinion mounted on the shaft of a DC permanent-magnet generator, thereby converting the linear reciprocating motion into rotary generator input. The electrical output is rectified, stored in two series-connected 6 V DC batteries (12 V bus), and used to illuminate a 12 V DC LED module. The complete mechanism is mounted on a rigid frame fabricated from 1-inch mild-steel square pipes, with metal bushes providing precision linear guidance for the follower shaft.
Keywords: Cam-Follower Mechanism, Rack and Pinion, DC Generator
The system employs a 12 V DC geared motor operating at 45 RPM as the prime mover. The motor drives a profiled 90 mm cam, which actuates a spring-loaded follower shaft (12 mm diameter, 150 mm spring). A rack machined onto the follower shaft meshes with a pinion mounted on the shaft of a DC permanent-magnet generator, thereby converting the linear reciprocating motion into rotary generator input. The electrical output is rectified, stored in two series-connected 6 V DC batteries (12 V bus), and used to illuminate a 12 V DC LED module. The complete mechanism is mounted on a rigid frame fabricated from 1-inch mild-steel square pipes, with metal bushes providing precision linear guidance for the follower shaft.
Keywords: Cam-Follower Mechanism, Rack and Pinion, DC Generator
Abstract
Experimental Study of Cam Follower Based Power Generation
Prof. R.N. Deshmukh, Mr. Ashish Kshirsagar, Mr. Amer Londhe, Mr. Digambar Mandale
DOI: 10.17148/IARJSET.2026.13605
Abstract: The rapid depletion of conventional fossil fuels and the escalating global demand for clean energy have encouraged researchers to investigate small-scale, distributed electromechanical power generation systems. This project presents the design, fabrication, and experimental evaluation of a cam-follower-based power generation prototype that converts controlled reciprocating mechanical motion into usable electrical energy.
The system employs a 12 V DC geared motor operating at 45 RPM as the prime mover. The motor drives a profiled 90 mm cam, which actuates a spring-loaded follower shaft (12 mm diameter, 150 mm spring). A rack machined onto the follower shaft meshes with a pinion mounted on the shaft of a DC permanent-magnet generator, thereby converting the linear reciprocating motion into rotary generator input. The electrical output is rectified, stored in two series-connected 6 V DC batteries (12 V bus), and used to illuminate a 12 V DC LED module. The complete mechanism is mounted on a rigid frame fabricated from 1-inch mild-steel square pipes, with metal bushes providing precision linear guidance for the follower shaft.
Design calculations validate the cam kinematics, spring selection, rack-pinion gear ratios, frame structural integrity, and shaft stress. Experimental measurements of open-circuit voltage, loaded output voltage, and output current yield an average overall system efficiency of approximately 14%, consistent with expectations for a multi-stage electromechanical prototype. The project demonstrates a feasible, low-cost approach to mechanical energy harvesting and serves as a valuable educational platform for studying electromechanical conversion principles.
Keywords: Cam-Follower Mechanism, Energy Harvesting, Spring Design, Mechanical Power Generation.
The system employs a 12 V DC geared motor operating at 45 RPM as the prime mover. The motor drives a profiled 90 mm cam, which actuates a spring-loaded follower shaft (12 mm diameter, 150 mm spring). A rack machined onto the follower shaft meshes with a pinion mounted on the shaft of a DC permanent-magnet generator, thereby converting the linear reciprocating motion into rotary generator input. The electrical output is rectified, stored in two series-connected 6 V DC batteries (12 V bus), and used to illuminate a 12 V DC LED module. The complete mechanism is mounted on a rigid frame fabricated from 1-inch mild-steel square pipes, with metal bushes providing precision linear guidance for the follower shaft.
Design calculations validate the cam kinematics, spring selection, rack-pinion gear ratios, frame structural integrity, and shaft stress. Experimental measurements of open-circuit voltage, loaded output voltage, and output current yield an average overall system efficiency of approximately 14%, consistent with expectations for a multi-stage electromechanical prototype. The project demonstrates a feasible, low-cost approach to mechanical energy harvesting and serves as a valuable educational platform for studying electromechanical conversion principles.
Keywords: Cam-Follower Mechanism, Energy Harvesting, Spring Design, Mechanical Power Generation.
Abstract
Review on Waste Oil Powered Thermal Electric Energy Generation System
Prof. V.H. Shivsharan, Rhishikesh Sanjay Mahadik, Ajay Malhari Riswadkar, Vikas Pawar
DOI: 10.17148/IARJSET.2026.13606
Abstract: The global demand for clean, affordable, and sustainable energy has driven researchers toward innovative methods of harvesting energy from unconventional and waste sources. This paper presents a comprehensive literature review on the design and implementation of a Waste Oil Powered Thermoelectric Energy Generation System, which integrates a Peltier module (thermoelectric generator), a waste oil burner as the heat source, an aluminium cooling block as the cold-side heat sink, and a complete operational circuit comprising a 6V battery, LED lights, a 9V water pump, and a supporting tank frame structure.
Waste cooking and lubricating oils, being abundant by-products of industrial and domestic activity, represent a significant untapped energy reservoir. When combusted, these oils generate substantial thermal gradients which can be directly converted into electrical energy via the Seebeck effect, as exploited in Peltier/thermoelectric modules. The proposed system offers a compelling low-cost, decentralised energy solution particularly suited for rural electrification, off-grid communities, and emergency power applications.
This review synthesises current research across thermoelectric generator (TEG) performance optimisation, waste oil combustion characteristics, heat exchanger and cooling system design, and practical low-voltage application integration. Key findings indicate that system efficiency is strongly governed by the quality of the thermal gradient maintained across the Peltier module, the effectiveness of the cooling block design, and the calorific properties of the waste oil fuel. The paper concludes with identified research gaps, future directions, and a consolidated understanding of how this integrated system can contribute to sustainable and decentralised energy solutions.
Keywords: Waste Oil, Seebeck Effect, Thermoelectric Generator, Peltier Module
Waste cooking and lubricating oils, being abundant by-products of industrial and domestic activity, represent a significant untapped energy reservoir. When combusted, these oils generate substantial thermal gradients which can be directly converted into electrical energy via the Seebeck effect, as exploited in Peltier/thermoelectric modules. The proposed system offers a compelling low-cost, decentralised energy solution particularly suited for rural electrification, off-grid communities, and emergency power applications.
This review synthesises current research across thermoelectric generator (TEG) performance optimisation, waste oil combustion characteristics, heat exchanger and cooling system design, and practical low-voltage application integration. Key findings indicate that system efficiency is strongly governed by the quality of the thermal gradient maintained across the Peltier module, the effectiveness of the cooling block design, and the calorific properties of the waste oil fuel. The paper concludes with identified research gaps, future directions, and a consolidated understanding of how this integrated system can contribute to sustainable and decentralised energy solutions.
Keywords: Waste Oil, Seebeck Effect, Thermoelectric Generator, Peltier Module
Abstract
A simple, low cost, three-octave frequency gen- erating system, using two passive buzzers and thirty-six touch capacitive switches, implemented with FPGA and VHDL, suitable for hands-on practice of music beginners
Dr Evangelos I. Dimitriadis, Theodora Dimitriadou, Leonidas Dimitriadis, Stergios Michalakopoulos
DOI: 10.17148/IARJSET.2026.13607
Abstract: A frequency generating system, capable of producing 36 different sound frequencies, corresponding to tones and semitones of 3rd, 4th and 5th music octaves, is manufactured, programmed and presented here. The system uses two passive buzzers from which the left one is programmed to produce 3rd octave sound frequencies, while the right buzzer produces middle 4th and 5th octave sound frequencies. Our system also uses two external LEDs related to cor- responding buzzers and each LED lights up if signal is sent to its relative buzzer. An additional information is given to system user by the VHDL program, showing in FPGA board’s seven-segment displays, letters FA or SOL correspond- ing to FA or SOL keys, if left or right buzzer, respectively, is activated. Both FA and SOL are shown in case that user plays a chord from 3rd – 4th or 3rd – 5th octaves. The system is implemented using DE10-Lite FPGA board and 36 touch-capacitive switches, acting as inputs to the board and obviously related to corresponding notes frequencies. Our system has low manufacturing cost and it is ideal for use in hands-on practicing method of music beginners or in any other application where specific acoustic music notes sounds are needed. The VHDL program used here, also provides the ability of expanding our system by incorporating 36 instead of 2 different output buzzers, each one corresponding to specific tone or semitone notes of the three octaves mentioned above. Our system’s capabilities can also be expanded due to the flexibility of the VHDL code, by programming it to generate frequency sounds corresponding to quarter tones.
Keywords: Octaves, music notes, acoustic frequency generator FPGA, VHDL, Buzzer, LEDs.
Keywords: Octaves, music notes, acoustic frequency generator FPGA, VHDL, Buzzer, LEDs.
Abstract
Adaptive Neuro-Fuzzy Control-Based Multi- Objective Energy Management for Solar- Integrated Battery–Supercapacitor Electric Vehicles
Adel Elgammal
DOI: 10.17148/IARJSET.2026.13608
Abstract: Integrating electric vehicles with renewable energy is a core direction for building sustainable transportation systems, and it is also the central research focus in the current new energy transportation sector. This paper targets solar electric vehicles that integrate on-board photovoltaics and are equipped with a hybrid energy storage system (HESS) composed of power batteries and supercapacitors, and proposes a novel adaptive neuro-fuzzy inference system (ANFIS) energy management strategy. This hybrid energy storage system operates based on the complementary characteristics of its two component types: power batteries provide continuous basic power supply via their high energy density, while supercapacitors, relying on their high-power density, meet the fast charging and discharging demands of vehicle acceleration, deceleration, and braking energy recovery. As an auxiliary energy source, on-board photovoltaics can effectively extend driving range and reduce the whole vehicle’s reliance on the public power grid. The ANFIS controller in this paper combines the independent learning capability of neural networks and the interpretability of fuzzy logic. It can respond in real time to three core dynamic working condition variables: driving mode, solar irradiance level, and state of charge, and simultaneously achieve four optimization goals: minimizing battery degradation through intelligent power allocation, maximizing solar energy collection efficiency, optimizing the whole vehicle’s equivalent fuel economy, and maintaining the supercapacitor’s state of charge within a compliant operating range. This paper uses the MATLAB/Simulink platform to conduct simulation verification with three standard driving cycles: UDDS, HWFET, and US06. The proposed strategy is compared with two baseline strategies, namely a traditional rule-based controller and a pure fuzzy logic controller, and its practicality is also tested using real-world scenarios extracted from a natural driving database. The results show that compared with the baseline strategies, the proposed scheme reduces battery current stress by 23%, increases energy efficiency by 18%, lifts solar energy utilization by 31%, and extends the estimated battery cycle life by 35%. Meanwhile, it exhibits good robustness under different irradiance and temperature conditions, achieves fast convergence in controller training, and has outstanding cross-condition generalization ability. This study advances the development of next-generation intelligent energy management systems for solar-powered electric vehicles, and puts forward a highly robust adaptive scheme that can balance multiple conflicting objectives and adapt to the inherent uncertainties in two types of scenarios.
Keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS), Electric Vehicles, Solar-Assisted EVs, Hybrid Energy Storage System, Multi-Objective Optimization, Energy Management System (EMS).
Keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS), Electric Vehicles, Solar-Assisted EVs, Hybrid Energy Storage System, Multi-Objective Optimization, Energy Management System (EMS).
Abstract
A STUDY ON RECRUITMENT METRICS AND THEIR IMPACT ON ORGANIZATIONAL EFFICIENCY
Mr.AJAYRATHNA S, Ms.GAYATHRI M
DOI: 10.17148/IARJSET.2026.13609
Abstract: This paper examines how social media platforms simplify and enhance employee recruitment. Networks such as LinkedIn, Facebook, and Instagram enable organizations to reach a large pool of applicants—both active and passive job seekers—within a short time frame. By facilitating rapid communication and streamlined application processes, social media reduces recruitment costs and time. Moreover, it supports employer branding and assists organizations in identifying qualified candidates, thereby making the recruitment process more efficient and effective.
Keywords: Social Media Recruitment, Employer Branding, Human Resource Management, Cost and Time Efficiency, Talent Acquisition
Keywords: Social Media Recruitment, Employer Branding, Human Resource Management, Cost and Time Efficiency, Talent Acquisition
Abstract
Development and Effectiveness of Electrical Trainer in Basic Electrical Connection
MARVIN C. JORE
DOI: 10.17148/IARJSET.2026.13610
Abstract: An Electrical Trainer was developed and tested to serve as an instructional aid for Basic Electrical Connection. It includes authentic electrical equipment, safety elements (such as circuit breakers), analog multitester, and transportation frame to allow students to practice wiring, switching, measuring and fault detection. The purpose of the study was to identify technical features; to evaluate pre/post test student performance; to assess if there is a significant difference between pre/post test performances; and to establish acceptability for composition, operating attributes, and safety. Developmental method of research was utilized. The device were designed, manufactured, tested and evaluated through both engineers and students. Evaluation of the Trainer included members evaluating technical features, quality of design, operating performance and safety. Data were collected through; engineer manufacturing evaluations, student performance evaluations prior to use of Trainer vs. after use; acceptability; through statistical analysis of means and paired sample t- tests. Evaluation results indicated that an Electrical Trainer manufactured with wood frame/base, plywood wiring board, switching devices, electrical outlets (supply), incandescent bulbs, alarm device (sound and visual), residual current circuit breaker, prototype analog multitester, junction boxes, tool/personal protective equipment compartment, casters, and fault detection capability. Student performance improved from Satisfactory (Pre Test) to Excellent (Post Test) by use of the Trainer to develop practical skills in wiring, switching, measuring, troubleshooting. Statistical analysis supports that the improvement in performance from Pre Test vs. Post Test was statistically significant. Because of this, it has been determined that the increase in performance is directly correlated to use of the Trainer. The Electrical Trainer has been rated Very Acceptable in terms of design, operating performance, and safety. Conclusions from this study support the theory that an Electrical Trainer for Basic Electrical Connection is functional, safe, acceptable, and a means to provide improvement in practical competency for students studying for a career in Electrical.
Keywords: Electrical Trainer; Basic Electrical Connection; Electrical Installation; Electrical Maintenance; Technical- Vocational Education; Hands-on Learning; Instructional Device; Skill Development; Competency-Based Learning.
Keywords: Electrical Trainer; Basic Electrical Connection; Electrical Installation; Electrical Maintenance; Technical- Vocational Education; Hands-on Learning; Instructional Device; Skill Development; Competency-Based Learning.
Abstract
SSI ANALYSIS OF G+6 RCC BUILDING ON STIFF SOIL USING ETABS AND SAFE
Harsh S. Patle, Aaquib Ansari
DOI: 10.17148/IARJSET.2026.13611
Abstract: Soil-Structure Interaction (SSI) changes the seismic response of reinforced concrete buildings because the soil, foundation, and superstructure act together as one system. This paper studies a G+6 RCC moment-resisting frame building resting on stiff soil using ETABS for the building model and SAFE for the raft foundation model. Two cases were compared: a conventional fixed-base model and a flexible-base SSI model. The analysis was carried out according to IS 1893 (Part 1): 2016 for Seismic Zone III, with load details taken from IS 875 and material properties from IS 456:2000. The results show that considering SSI increases the fundamental time period by about 18–24% and the lateral displacement by about 28–35%, while reducing the base shear by about 12–18% compared to the fixed-base case. The SAFE results also show non-uniform soil pressure beneath the raft and noticeable settlement variation, which means the foundation flexibility affects the building response even on stiff soil. Overall, the study shows that fixed-base analysis can miss important serviceability effects such as drift and settlement, and that combined ETABS-SAFE modelling gives a more realistic way to assess mid-rise RCC buildings.
Keywords: Soil-Structure Interaction, ETABS, SAFE, G+6 RCC Building, Stiff Soil, Raft Foundation.
Keywords: Soil-Structure Interaction, ETABS, SAFE, G+6 RCC Building, Stiff Soil, Raft Foundation.
Abstract
Effect of Roof Shape on Wind Response of High- Rise Building Using IS 875:2025 Draft in ETABS
Pratik B. Patle, Rahul Hinge
DOI: 10.17148/IARJSET.2026.13612
Abstract: High-rise buildings are increasingly common in Indian cities because of limited land and growing population. In such buildings, wind load often controls the design, especially when the building height increases. This study examines how roof shape affects the wind response of a high-rise RCC building using ETABS and the draft provisions of IS 875 (Part 3): 2025. Different roof forms such as flat, sloped, stepped, pyramidal, dome, and tower shapes are compared under the same loading conditions. The results show that roof shape has a clear effect on displacement, storey drift, base shear, and overall wind response. Flat roofs generally show higher wind effects, while more aerodynamic roof forms perform better by reducing pressure concentration and improving flow behavior. The study shows that roof geometry is an important design factor and should be considered during the planning stage of tall RCC buildings.
Keywords: High-rise building, wind load, roof shape, ETABS, RCC structure, IS 875:2025 draft, displacement, base shear.
Keywords: High-rise building, wind load, roof shape, ETABS, RCC structure, IS 875:2025 draft, displacement, base shear.
Abstract
Effect of Floor Height Variation on Seismic Performance of RCC Buildings Using ETABS
Sagar D. Dule, Mahesh Raut
DOI: 10.17148/IARJSET.2026.13613
Abstract: This paper studies how floor height variation affects the seismic behavior of a G+10 RCC building using ETABS.In modern cities, reinforced concrete buildings are often built with different floor heights for parking, commercial space, or architectural needs. While this is useful in practice, it can change how the building behaves during an earthquake. This study examines the effect of floor height variation on the seismic performance of a G+10 RCC building using ETABS. Three cases are studied: a uniform-height building, a soft-storey building with a taller ground floor, and a building with alternate floor height variation. The results show that floor height variation increases the fundamental time period and lateral displacement, while reducing base shear. It also increases drift at the irregular storey, especially in the soft-storey model. These findings show that vertical irregularity should be carefully considered in seismic design, because even if strength demand seems lower, serviceability and safety may become critical.
Keywords: Floor height variation, seismic performance, G+10 RCC building, soft storey, ETABS, response spectrum analysis, inter-storey drift, vertical irregularity.
Keywords: Floor height variation, seismic performance, G+10 RCC building, soft storey, ETABS, response spectrum analysis, inter-storey drift, vertical irregularity.
Abstract
A SURVEY ON AI – BASED PLANT HEALTH MONITORING SYSTEM
Nandini P Gowda, Deepthi K, Koka Mahitha, Nelbiya N, Sumashree Pulagurla
DOI: 10.17148/IARJSET.2026.13614
Abstract: The increasing demand for sustainable agriculture and early stress management in crops has led to the exploration of advanced technologies such as Artificial Intelligence (AI) for plant monitoring and communication. Plants continuously emit biochemical and biophysical signals in response to environmental stresses including drought, nutrient deficiency, pest attacks, and diseases. Recent advancements in AI, sensor technologies, and Internet of Things (IoT) systems enable the detection, interpretation, and translation of these signals into actionable insights for farmers.This study focuses on the development of an AI-based plant communication and stress detection system that integrates Machine Learning algorithms, sensor networks, and data analytics to monitor plant health in real time. The system captures parameters such as Volatile Organic Compounds (VOCs), leaf temperature, soil moisture, and electrical signaling patterns, which are analyzed using AI models to identify stress conditions at an early stage. By enabling plants to communicate their stress signals, this approach facilitates timely intervention, reduces crop losses, and minimizing excessive use of water, fertilizers, and pesticides.The proposed framework supports precision agriculture by improving decision-making and promoting resource-efficient farming practices. Furthermore, it contributes to sustainable crop management by enhancing resilience against climate variability and environmental challenges. The integration of AI in plant health monitoring represents a transformative step toward smart agriculture and improved global food security.
Keywords: Artificial Intelligence, Plant communication, Stress detection, Precision agriculture, IoT, Plant health.
Keywords: Artificial Intelligence, Plant communication, Stress detection, Precision agriculture, IoT, Plant health.
Abstract
A Review on Diet, Non-Communicable Disease and Public Health Policy
Mr. Shaik Atheeb Abdullah A & Dr. V Krishnaprabha
DOI: 10.17148/IARJSET.2026.13615
Abstract: The global burden of Non-Communicable Diseases (NCDs) is escalating, primarily driven by the widespread adoption of modern, Westernized dietary patterns. This public health crisis places immense strain on both individual well-being and global healthcare systems. This paper presents a comprehensive review of the public health implications of the nutrition transition and outlines evidence-based policy interventions, including food reformulation, fiscal policies, and front-of-pack labelling, necessary to reshape food environments. Furthermore, it identifies critical research gaps, emphasizing the need for advanced dietary assessment methods, long-term intervention studies, and the integration of systems epidemiology to better understand diet-disease interactions. Ultimately, mitigating this epidemic requires a multifaceted approach that accounts for the socioeconomic and cultural determinants of diet to foster a healthier, sustainable future.
Keywords: Non-Communicable Diseases, Nutrition Transition, Public Health Policy, Systems Epidemiology, Dietary Assessment.
Keywords: Non-Communicable Diseases, Nutrition Transition, Public Health Policy, Systems Epidemiology, Dietary Assessment.
Abstract
MnemoX: An AI-Powered Personalized Memory Assistant with Mood Tracking, Goal Monitoring, and Multi-Modal Interaction
T. Amalraj Victoire, A. Gopinath
DOI: 10.17148/IARJSET.2026.13616
Abstract: This paper presents MnemoX, a web-based AI-powered personal memory assistant designed to help users store, recall, and analyze their daily activities, goals, and cognitive patterns. Built on the Django framework and integrated with the Google Gemini 2.0 API, MnemoX provides a conversational interface that supports natural language memory storage, mood detection, goal tracking, image and PDF analysis, multi-language translation, Wikipedia-backed knowledge retrieval, real-time news aggregation, and voice interaction. The system employs a structured relational database via Django ORM to persist user memories, daily logs, reminders, and goals, while leveraging large language model (LLM) capabilities for intelligent summarization and contextual responses. Experimental observations demonstrate that MnemoX effectively bridges personal productivity tools and conversational AI, offering a unified, user-friendly platform for cognitive augmentation. The system addresses limitations of existing memory tools by combining multi-modal input (text, voice, image, PDF), automatic mood detection, and AI-generated reminders within a single coherent framework.
Keywords: AI memory assistant, natural language processing, mood detection, goal tracking, Django, Google Gemini API, multi-modal interaction, cognitive augmentation, conversational AI
Abstract
Leave Management System for Efficient Employee Leave Processing
V. Udhayakumar, S. Kaviya
DOI: 10.17148/IARJSET.2026.13617
Abstract: Leave Management System is a web-based application developed to automate and simplify the process of employee leave management within an organization. Traditional leave management methods rely on manual paperwork and spreadsheets, which often lead to errors, delays, and inefficient record keeping. The proposed system enables employees to apply for leave online, track leave status, and view leave balances, while administrators can approve, reject, and monitor leave requests efficiently. The system maintains centralized records, improves transparency, reduces administrative workload, and enhances organizational productivity. The application is developed using modern web technologies and a database management system to ensure secure and reliable leave processing.
Keywords: Leave Management System, Employee Management, Leave Approval, Web Application, Human Resource Management, Database Management.
Keywords: Leave Management System, Employee Management, Leave Approval, Web Application, Human Resource Management, Database Management.
Abstract
Digital Marketing Strategies Using Data Analytics Tools
Ms. R. Priyanka, Ms. S. Swathi
DOI: 10.17148/IARJSET.2026.13618
Abstract: Digital marketing has transformed business-customer interactions by providing measurable, targeted, and real-time communication channels. With the proliferation of online platforms including websites, search engines, and social media, organizations can effectively reach wider audiences and improve customer engagement. This study examines digital marketing strategies using data analytics tools and investigates how platforms such as Google Analytics, Google Ads, SEMrush, and Instagram Insights help organizations analyze customer behavior, monitor campaign performance, and improve decision-making. The study highlights the importance of key performance indicators (KPIs) including click-through rate (CTR), conversion rate, bounce rate, and return on investment (ROI) in evaluating marketing effectiveness. A descriptive cross-sectional survey design was employed with 150 respondents using a structured 5-point Likert scale questionnaire. Findings reveal that data-driven marketing strategies significantly improve campaign performance, customer engagement, and business growth. Real-time analytics enable faster and more accurate decisions, while targeted advertising and personalized communication increase conversion rates and customer satisfaction. The study concludes that effective use of data analytics tools helps businesses optimize marketing campaigns, enhance brand visibility, and achieve competitive advantage in the digital marketplace.
Keywords: Digital Marketing, Data Analytics, Online Campaigns, Customer Behavior Analysis, Return on Investment
Keywords: Digital Marketing, Data Analytics, Online Campaigns, Customer Behavior Analysis, Return on Investment
Abstract
Women Safety SOS Application Using Android Technology
Mrs. M. Vasuki, Dr. T. Amalraj Victoire, B. Madhusri*
DOI: 10.17148/IARJSET.2026.13619
Abstract: Women’s safety has become an important concern due to the increasing number of crimes and emergency situations faced by women in society. Immediate communication and location sharing are essential during dangerous situations. This paper presents a Women Safety SOS Application developed using Android Studio. The application enables users to send emergency SMS alerts along with their current GPS location to pre-selected emergency contacts. The proposed system provides one-touch SOS activation, live location sharing, emergency calling, and quick communication services. The application is designed to be simple, reliable, and easy to use during emergencies. Experimental testing shows that the system can effectively transmit emergency alerts and location details within a short period, thereby improving personal safety and reducing response time.
Keywords: Women Safety, Android Application, SOS Alert, GPS Tracking, Emergency SMS, Mobile Security.
Keywords: Women Safety, Android Application, SOS Alert, GPS Tracking, Emergency SMS, Mobile Security.
Abstract
MULBERRY LEAVES DISEASES DETECTION AND CLASSFICATION
V. Udhayakumar V.Shamini
DOI: 10.17148/IARJSET.2026.13620
Abstract: The Mulberry Leaf Disease Detection and Classification System is an intelligent application developed to identify and classify diseases affecting mulberry leaves using Deep Learning techniques. Mulberry plants play a crucial role in the sericulture industry as they serve as the primary food source for silkworms. Diseases in mulberry leaves can significantly reduce leaf quality and silk production. This system uses image processing and Convolutional Neural Networks (CNNs) to analyze leaf images and accurately detect diseases at an early stage. The proposed solution helps farmers, agricultural experts, and researchers monitor plant health efficiently, reduce crop losses, and improve productivity. The application provides disease identification, classification, and treatment suggestions, making disease management faster and more reliable.
Keywords: Mulberry Leaf Disease Detection, Deep Learning, Convolutional Neural Network (CNN), Image Processing, Disease Classification, Agriculture, Plant Health Monitoring, Artificial Intelligence, Sericulture, Crop Protection.
Keywords: Mulberry Leaf Disease Detection, Deep Learning, Convolutional Neural Network (CNN), Image Processing, Disease Classification, Agriculture, Plant Health Monitoring, Artificial Intelligence, Sericulture, Crop Protection.
Abstract
Caught Between Fate and Freedom: An Existential Reading of Rosencrantz and Guildenstern Are Dead
Dr. Sanju Jhajharia
DOI: 10.17148/IARJSET.2026.13621
Abstract: Tom Stoppard's Rosencrantz and Guildenstern Are Dead (1966) is widely regarded as one of the most significant dramatic works of the twentieth century. Drawing upon William Shakespeare's Hamlet, Stoppard transforms two minor characters into central figures and explores profound philosophical questions concerning identity, freedom, fate, and human existence. This paper examines the play through the lens of existential philosophy, particularly the ideas of Jean-Paul Sartre, Albert Camus, and Søren Kierkegaard. The study argues that Rosencrantz and Guildenstern embody the existential condition of modern humanity, trapped between predetermined circumstances and the desire for personal freedom. Through absurd dialogue, uncertainty, and the inevitability of death, Stoppard reveals the tension between human agency and determinism. The play ultimately demonstrates the existential struggle to create meaning in an apparently indifferent universe.
Keywords: Existentialism, Tom Stoppard, absurd theatre, freedom, fate, identity, Rosencrantz, Guildenstern.
Keywords: Existentialism, Tom Stoppard, absurd theatre, freedom, fate, identity, Rosencrantz, Guildenstern.
Abstract
A STUDY ON JOB SATISFACTION AND ITS IMPACT ON EMPLOYEE RETENTION IN MAYILADUTHURAI
Ms. R. Priyanka, Ms. K. Arthi
DOI: 10.17148/IARJSET.2026.13622
Abstract: Employee retention has become one of the major challenges faced by organizations in the current competitive business environment. Employee job satisfaction plays a significant role in influencing retention and reducing employee turnover. The present study aims to examine the impact of job satisfaction on employee retention among employees in Mayiladuthurai. The study focuses on various factors influencing job satisfaction, including compensation and benefits, work environment, career growth opportunities, leadership support, and work-life balance. Primary data were collected from 150 respondents through a structured questionnaire using a simple random sampling method. The collected data were analyzed using Percentage Analysis, One Sample T-Test, Chi-Square Test, and Correlation Analysis. The findings reveal that job satisfaction has a positive and significant relationship with employee retention. Employees satisfied with organizational practices, supportive management, and career development opportunities demonstrate stronger retention intentions. The study concludes that organizations should focus on improving employee satisfaction to enhance retention and organizational performance.
Keywords: Job Satisfaction, Employee Retention, Employee Engagement, Human Resource Management, Organizational Commitment
Keywords: Job Satisfaction, Employee Retention, Employee Engagement, Human Resource Management, Organizational Commitment
Abstract
Cost Analysis of RC Buildings in Different Seismic Zones
Yogesh Tarare, Vijay Kotapati
DOI: 10.17148/IARJSET.2026.13623
Abstract: Reinforced cement concrete buildings are widely used in India because they offer a practical balance of strength, durability, and economy. However, seismic zone classification has a major influence on structural design, reinforcement demand, and overall construction cost. This paper presents a comparative study of a G+6 RCC framed building designed for Seismic Zones II, III, IV, and V as per IS 1893:2016 and IS 13920:2016. The building was analyzed using standard seismic design concepts to evaluate changes in base shear, storey displacement, drift, reinforcement requirement, concrete consumption, and total structural cost. The findings show a clear increase in seismic demand as the zone severity increases. Base shear, member sizes, and reinforcement quantities all rise progressively from Zone II to Zone V, which leads to a noticeable increase in construction cost. The study confirms that seismic design is not only a safety requirement but also a cost-driving factor in multi-storey RCC construction. The results are useful for engineers who need to achieve a practical balance between structural performance and economy in earthquake-prone regions.
Keywords: RCC buildings, seismic zones, cost analysis, base shear, storey drift, reinforcement demand, seismic design, ETABS, IS 1893, construction cost.
Keywords: RCC buildings, seismic zones, cost analysis, base shear, storey drift, reinforcement demand, seismic design, ETABS, IS 1893, construction cost.
Abstract
ChargePulse: A Real-Time EV Charging Station Discovery and Management System
Snehal Dudhal, Soham Deshpande, Shreepad Aalekar
DOI: 10.17148/IARJSET.2026.13624
Abstract: In the rapidly evolving electric vehicle (EV) ecosystem, the accessibility and reliability of charging infrastructure remain critical barriers to widespread adoption. However, the absence of a centralized, real-time tracking system leads to range anxiety, fragmented station data, and inefficient trip planning for drivers. This paper presents ChargePulse, a comprehensive digital platform designed to locate, book, and manage EV charging stations through a unified network. The system integrates EV users, station administrators, and energy providers into a seamless ecosystem using a robust full-stack architecture built with Next.js and MongoDB. Secure user authentication is implemented for profile and booking management, while an integrated AI-driven assistant provides dynamic cost estimations and intelligent station recommendations based on real-time data. Experimental evaluation demonstrates improved booking efficiency, reduced driver wait times, and enhanced accuracy for live station availability. The proposed system bridges the gap between fragmented EV infrastructure and user needs, offering a scalable solution aligned with modern sustainable transportation requirements.
Keywords: EV Charging Station Network, Full-Stack Architecture, AI-Driven Assistant, Real-Time Availability Tracking, Sustainable Transportation System
Keywords: EV Charging Station Network, Full-Stack Architecture, AI-Driven Assistant, Real-Time Availability Tracking, Sustainable Transportation System
Abstract
EFFECT OF CORROSIVE ENVIRONMENT ON BOND STRENGTH OF FRP BARS IN DIFFERENT GRADE OF CONCRETE
Hardik B. Bele, Deepa Telang
DOI: 10.17148/IARJSET.2026.13625
Abstract: Corrosion of steel reinforcement severely compromises the longevity of concrete infrastructure in aggressive environments. Fiber-Reinforced Polymer (FRP) bars offer a corrosion-resistant alternative; however, maintaining exceptional bond integrity at the bar–concrete interface is critical for overall structural stability. This study systematically evaluates the impact of aggressive environmental conditioning on the compressive and bond strength of FRP bars embedded across three ordinary concrete grades: M30, M35, and M40. Pull-out specimens and cubes were subjected to accelerated curing in marine (3.5% NaCl) and industrial acidic (3.5% HCl) solutions for up to 28 days. Destructive testing revealed that acidic exposure inflicted the most severe deterioration, reducing bond strength by up to 30% and causing high susceptibility in lower-grade (M30) mixes. Conversely, higher-grade concrete (M40) demonstrated superior mechanical resilience due to its dense microstructural matrix, which effectively limited degradation, preserved critical confinement pressure, and ensured long-term interfacial load transfer efficiency.
Keywords: FRP bars, Bond strength, Corrosive environment, Concrete grade, Pull-out test, Compressive strength, Durability, Marine exposure, Acidic degradation, Structural performance.
Keywords: FRP bars, Bond strength, Corrosive environment, Concrete grade, Pull-out test, Compressive strength, Durability, Marine exposure, Acidic degradation, Structural performance.
Abstract
A STUDY ON DIGITAL MARKETING IN TOURISM AT EPIC HOLIDAY
Mr. V. Vishal, Dr. S.N. Kanagarathinam
DOI: 10.17148/IARJSET.2026.13626
Abstract: The rapid spread of digital technologies has fundamentally transformed the global tourism industry. Tourism companies are quickly shifting their marketing & promotion activities from the traditional offline channels to dynamic digital channels, creating revolutionary changes in consumer behaviour, brand engagement and sales conversion. The project study “A Study on Digital Marketing in Tourism an Epic Holidays” in aimed at exploring and evaluating the role of digital marketing strategies in the tourism sector. Epic Holidays is a leading travel and tourism company based in Nagapattinam and has been taken as the reference organisation for the study. The research examines various facets of digital marketing in the tourism sector including Search Engine Optimization (SEO), Social Media marketing, Email Marketing, Content Marketing and Online Booking platform management. The Findings show that most today’s travellers prefer to use digital platforms for travel research, planning and booking and the social media platforms, such as Instagram, Facebook, and YouTube play a significant role in influencing travel decisions and brand perceptions.
Keywords: Digital Marketing, Tourism, Epic Holidays, Social Media Marketing, SEO, Instagram, E-Tourism, Tamil Nadu, Travel Industry, Online Booking
Keywords: Digital Marketing, Tourism, Epic Holidays, Social Media Marketing, SEO, Instagram, E-Tourism, Tamil Nadu, Travel Industry, Online Booking
Abstract
Stray Buddies: Stray Animal Rescue and Shelter Connect
Vaishnavi Jadhav, Priti Tamhane, Shreya Mate, Anjali Gavane, Prof. A. D. Gujar
DOI: 10.17148/IARJSET.2026.13627
Abstract: The increase in number of stray animals and the lack of an organized rescue system create significant challenges in urban areas. This paper presents Stray Buddies, a web-based platform designed to support stray animal rescue, symptom-based health prediction, veterinary assistance, and secure user management. The system allows users to report stray animals through a structured questionnaire and location details, enabling early identification of possible health conditions. Built using React, TypeScript, Node.js, Express.js, Supabase, and PostgreSQL, the platform provides secure authentication and real-time data management. By combining cloud technologies with intelligent symptom analysis, Stray Buddies aims to improve rescue coordination and promote animal welfare.
Keywords: Animal Rescue system, Disease Prediction, Symptom Analysis, Questionnaire Based Health Analysis, Web Platform
Keywords: Animal Rescue system, Disease Prediction, Symptom Analysis, Questionnaire Based Health Analysis, Web Platform
Abstract
A Study on Job Expectation Among College Students at Karaikal
Ms. R. Sangeetha, Ms. N. Swathi
DOI: 10.17148/IARJSET.2026.13628
Abstract: This study examines the job expectations of college students in Karaikal and identifies the factors influencing their career preferences. The research focuses on students’ expectations regarding salary, job position, work environment, career growth opportunities, and job security. Primary data were collected from 150 college students through a structured questionnaire using convenience sampling. Statistical tools such as percentage analysis, mean score analysis, chi-square test, and correlation analysis were used for data analysis. The findings reveal that students highly prefer attractive salaries, career growth opportunities, and positive work environments. Internship experience and training programs significantly influence students' job expectations and career readiness. The study concludes that career awareness, practical exposure, and skill development play a crucial role in shaping employment expectations among college students.
Keywords: Job Expectation, College Students, Career Growth, Internship, Employability, Job Market Awareness.
Keywords: Job Expectation, College Students, Career Growth, Internship, Employability, Job Market Awareness.
Abstract
A Study on the Effectiveness of Employee Engagement on Organizational Performance
R. Sangeetha, T. Udhaya Kumari
DOI: 10.17148/IARJSET.2026.13629
Abstract: This study investigates how employee engagement influences overall organizational performance. Drawing on primary data gathered from 120 employees spanning multiple departments, the research reveals that workforce engagement has a constructive bearing on productivity, task effectiveness, and employee retention. A majority of respondents acknowledged that engagement elevates work quality and nurtures long-term organizational commitment. Statistical tools including Pearson Correlation, Chi-Square Analysis, and One-Way ANOVA were applied to examine the relationships between engagement-related variables. Correlation analysis showed that managerial support and productivity improvement share a weak, statistically non-significant association. The Chi-Square test confirmed no meaningful link between employment type and job satisfaction. ANOVA results demonstrated that engagement levels remain consistent across all departments. Despite these statistical outcomes, descriptive findings highlight that engaged employees display greater motivation, willingness to assume additional duties, and a stronger inclination to remain with the organization. The study concludes that while individual demographic variables may not independently determine engagement outcomes, cultivating an environment of recognition, transparent communication, and managerial support collectively strengthens the workforce's emotional investment and drives sustainable organizational growth.
Keywords: Employee Engagement, Organizational Performance, Tube Investments of India, Productivity, Employee Retention, Work-Life Balance, Managerial Support, Recognition.
Keywords: Employee Engagement, Organizational Performance, Tube Investments of India, Productivity, Employee Retention, Work-Life Balance, Managerial Support, Recognition.
Abstract
Experimental Investigation on Reactive Powder Concrete
Pushkar Borse, Tushar Shende
DOI: 10.17148/IARJSET.2026.13630
Abstract: This paper studies how floor height variation affects the seismic behavior of a G+10 RCC building using ETABS.In modern cities, reinforced concrete buildings are often built with different floor heights for parking, commercial space, or architectural needs. While this is useful in practice, it can change how the building behaves during an earthquake. This study examines the effect of floor height variation on the seismic performance of a G+10 RCC building using ETABS. Three cases are studied: a uniform-height building, a soft-storey building with a taller ground floor, and a building with alternate floor height variation. The results show that floor height variation increases the fundamental time period and lateral displacement, while reducing base shear. It also increases drift at the irregular storey, especially in the soft-storey model. These findings show that vertical irregularity should be carefully considered in seismic design, because even if strength demand seems lower, serviceability and safety may become critical.
Keywords: Reactive Powder Concrete (RPC), Rice Husk Ash (RHA), Quartz powder, Micro steel fibres, High-performance concrete, Compressive strength, Particle packing, Durability.
Keywords: Reactive Powder Concrete (RPC), Rice Husk Ash (RHA), Quartz powder, Micro steel fibres, High-performance concrete, Compressive strength, Particle packing, Durability.
Abstract
TRUSTNET.AI – DETECTION OF FAKE IMAGES AND VIDEOS
Ms. Bhargavi H G, Himani B L, Sandra C, Navya Manosri S, Ananya V
DOI: 10.17148/IARJSET.2026.13631
Abstract: The artificial intelligence technology is getting better and better. This has led to the creation of deepfake technologies. These deepfake technologies are causing problems for cybersecurity and digital trust. They are also making it hard to verify identities. There have been a lot of cases of deepfake fraud in North America. In fact, the number of cases has increased by about 1,740%. This is a jump. 40% Of all biometric authentication attacks worldwide are because of deepfakes.
India is also facing a lot of problems with deepfakes. The number of deepfake-related cybercrimes in India has increased by 550% since 2019. This is a concern. It is estimated that the financial losses due to deepfakes will be than ₹70,000 crore in 2025. These losses will be because of identity impersonation, financial fraud and social engineering attacks. We need to find a way to stop these deepfakes. We need a system that can detect deepfakes with accuracy.
There are already some deepfake detection systems. These systems use something called Convolutional Neural Network (CNN)-based architectures. They are trained on datasets that contain about 15,000 images. These systems are good at detecting deepfakes. They can even work in time. However they have some limitations. They rely on single-model architectures. They do not have comparative analysis capabilities. They also do not have user authentication mechanisms.
To solve these problems, we are proposing a system called the Tri-Model Hybrid Deepfake Detection Pipeline (TMHDPP). This system uses deep learning architectures. It combines the features of these architectures to improve deepfake classification performance. The TMHDPP system uses something called InceptionV3 and EfficientNet. It also uses an architecture that combines the convolutional layers of both models. This helps to capture and aggregate -scale spatial features. The system is. Evaluated using a dataset that contains 2,041 training images and 2,041 testing images.
The TMHDPP system is not a detection system. It also has a web-based application. This application has role-based access control. This means that administrators and users have functionalities. Administrators can upload datasets manage users, train models, test models and monitor performance. They can also visualize the results using graphs.
Users can upload images or videos for deepfake analysis. They can get the classification results along with the evaluation metrics. The TMHDPP system is designed to be scalable, accurate and user-centric. It aims to provide a solution to the growing problem of deepfakes.
The TMHDPP system is an improvement over the existing systems. It uses a - model architecture. It has benchmarking capabilities. It also has integrated management features. All these features make the TMHDPP system a powerful tool for detecting deepfakes. We hope that this system will help to reduce the number of deepfake-related cybercrimes. We also hope that it will help to improve cybersecurity and digital trust. The deepfake detection technology is still evolving. We need to keep working on it to make it better. The TMHDPP system is a step in the direction. It has the potential to make a difference in the fight, against deepfakes.
India is also facing a lot of problems with deepfakes. The number of deepfake-related cybercrimes in India has increased by 550% since 2019. This is a concern. It is estimated that the financial losses due to deepfakes will be than ₹70,000 crore in 2025. These losses will be because of identity impersonation, financial fraud and social engineering attacks. We need to find a way to stop these deepfakes. We need a system that can detect deepfakes with accuracy.
There are already some deepfake detection systems. These systems use something called Convolutional Neural Network (CNN)-based architectures. They are trained on datasets that contain about 15,000 images. These systems are good at detecting deepfakes. They can even work in time. However they have some limitations. They rely on single-model architectures. They do not have comparative analysis capabilities. They also do not have user authentication mechanisms.
To solve these problems, we are proposing a system called the Tri-Model Hybrid Deepfake Detection Pipeline (TMHDPP). This system uses deep learning architectures. It combines the features of these architectures to improve deepfake classification performance. The TMHDPP system uses something called InceptionV3 and EfficientNet. It also uses an architecture that combines the convolutional layers of both models. This helps to capture and aggregate -scale spatial features. The system is. Evaluated using a dataset that contains 2,041 training images and 2,041 testing images.
The TMHDPP system is not a detection system. It also has a web-based application. This application has role-based access control. This means that administrators and users have functionalities. Administrators can upload datasets manage users, train models, test models and monitor performance. They can also visualize the results using graphs.
Users can upload images or videos for deepfake analysis. They can get the classification results along with the evaluation metrics. The TMHDPP system is designed to be scalable, accurate and user-centric. It aims to provide a solution to the growing problem of deepfakes.
The TMHDPP system is an improvement over the existing systems. It uses a - model architecture. It has benchmarking capabilities. It also has integrated management features. All these features make the TMHDPP system a powerful tool for detecting deepfakes. We hope that this system will help to reduce the number of deepfake-related cybercrimes. We also hope that it will help to improve cybersecurity and digital trust. The deepfake detection technology is still evolving. We need to keep working on it to make it better. The TMHDPP system is a step in the direction. It has the potential to make a difference in the fight, against deepfakes.
Abstract
Dynamic Trust Evaluation Framework for IoT
Nishant Sanghani and Bhavesh Borisaniya
DOI: 10.17148/IARJSET.2026.13632
Abstract: The rapid expansion of the Internet of Things (IoT) has introduced significant security and trust management challenges due to the heterogeneous and dynamic nature of interconnected devices. Conventional trust evaluation approaches often rely on static parameters or recommendation-based mechanisms, making them vulnerable to malicious behaviors, false feedback, and evolving cyberattacks. This paper presents a dynamic trust evaluation framework that combines Quality of Service (QoS)-based metrics with entropy-driven weighting and reputation-based trust updating to provide adaptive trust assessment in IoT environments.
The proposed framework consists of five major stages: data collection, data processing, trust evaluation, trust validation, and trust updating. Network performance indicators including Packet Delivery Ratio (PDR), latency, and throughput are extracted from IoT communication traces generated through simulation environments. The collected metrics are normalized and weighted using an entropy-based method to determine their relative importance dynamically. A trust score is then computed for each device and validated against an adaptive threshold to classify nodes as trusted or untrusted. The proposed approach enhances trust assessment accuracy, reduces susceptibility to manipulation attacks, and supports adaptive decision-making in heterogeneous IoT networks. By integrating information-theoretic trust computation with dynamic reputation management, the framework provides a scalable foundation for secure and resilient IoT deployments.
Keywords: Dynamic Trust, Entropy, Packet Delivery Ratio, Quality of Service
The proposed framework consists of five major stages: data collection, data processing, trust evaluation, trust validation, and trust updating. Network performance indicators including Packet Delivery Ratio (PDR), latency, and throughput are extracted from IoT communication traces generated through simulation environments. The collected metrics are normalized and weighted using an entropy-based method to determine their relative importance dynamically. A trust score is then computed for each device and validated against an adaptive threshold to classify nodes as trusted or untrusted. The proposed approach enhances trust assessment accuracy, reduces susceptibility to manipulation attacks, and supports adaptive decision-making in heterogeneous IoT networks. By integrating information-theoretic trust computation with dynamic reputation management, the framework provides a scalable foundation for secure and resilient IoT deployments.
Keywords: Dynamic Trust, Entropy, Packet Delivery Ratio, Quality of Service
Abstract
EXPERIMENTAL STUDY: STRENGTHENING OF REINFORCED CONCRETE MEMBERS USING EXTERNALLY BONDED FRP COMPOSITES
Vikrant Dhoke, Monika Jain
DOI: 10.17148/IARJSET.2026.13633
Abstract: The rapid deterioration of reinforced concrete (RC) infrastructure due to corrosion, increased service loads and environmental exposure has created a strong demand for efficient strengthening and retrofitting techniques. Fiber reinforced polymer (FRP) composites have emerged as a promising alternative to conventional steel plate bonding because of their high strength-to-weight ratio, corrosion resistance and ease of installation. This paper presents a concise review of the application of externally bonded FRP systems for the flexural and shear strengthening of RC beams and for the confinement of RC columns.
Key experimental and analytical studies are discussed to highlight improvements in strength, stiffness and ductility, as well as changes in failure modes associated with FRP retrofitting. Particular attention is given to bond and debonding behaviour, the role of structural adhesives, and practical aspects such as constructability and durability.
Existing international guidelines and design provisions for FRP-strengthened RC members are briefly outlined, together with current limitations and gaps. The review shows that externally bonded FRP systems can significantly enhance the performance of deficient RC members, but design is often governed by brittle debonding or FRP rupture, and long-term durability data and unified standards are still evolving.
Keywords: Fiber reinforced polymer (FRP); Reinforced concrete (RC); Retrofitting; Flexural strengthening; Shear strengthening; Column confinement; Structural adhesives.
Key experimental and analytical studies are discussed to highlight improvements in strength, stiffness and ductility, as well as changes in failure modes associated with FRP retrofitting. Particular attention is given to bond and debonding behaviour, the role of structural adhesives, and practical aspects such as constructability and durability.
Existing international guidelines and design provisions for FRP-strengthened RC members are briefly outlined, together with current limitations and gaps. The review shows that externally bonded FRP systems can significantly enhance the performance of deficient RC members, but design is often governed by brittle debonding or FRP rupture, and long-term durability data and unified standards are still evolving.
Keywords: Fiber reinforced polymer (FRP); Reinforced concrete (RC); Retrofitting; Flexural strengthening; Shear strengthening; Column confinement; Structural adhesives.
Abstract
Development of a 4-DOF Robotic Arm for Lightweight Precision Automation
Salunkhe Rohan, Sabale Shantanu, Shete Yuvraj, Dalvi Kishore, Prof G.P.Kavhekar, Dr. D.P.Kamble
DOI: 10.17148/IARJSET.2026.13634
Abstract: As consumer interest in cost-effective and space-efficient automated solutions grows, there is an increasing requirement for versatile robot arms designed specifically for micro-industries and academic settings. The document outlines the creation and implementation of an affordable four-degree-of-freedom robot hand designed specifically for tasks requiring light weight in picking up objects and placing them down. A new design incorporates an integrated mechanical framework constructed of fused elements made from 5 millimeter-thick laser-etched acrylic panels paired with metallic parts for improved stability, longevity, and ease of installation. Integrating high-torque servomotors alongside an ESP32-controlled system enabled highly accurate, consistent, and coordinated movement of joints. Using SolidWorks/Fusion 360 for computer-aided design and kinematic simulation purposes was aimed at verifying the workspace range, power demands, and structural soundness of machinery.
This system utilizes pulse-width modulation for precise motor operation through an exclusive controller, facilitating coordinated movement and adaptable scaling capabilities. The experimental assessment showcased dependable management capabilities for objects ranging in weight from 150 grams upwards while maintaining stable positioning precision and optimizing energy usage effectively. A newly engineered robot hand offers economical solutions tailored for lab efficiency, academic studies, and micro-industrial jobs, potentially improving performance via sensors, wireless connections, and smart visual control systems.
This system utilizes pulse-width modulation for precise motor operation through an exclusive controller, facilitating coordinated movement and adaptable scaling capabilities. The experimental assessment showcased dependable management capabilities for objects ranging in weight from 150 grams upwards while maintaining stable positioning precision and optimizing energy usage effectively. A newly engineered robot hand offers economical solutions tailored for lab efficiency, academic studies, and micro-industrial jobs, potentially improving performance via sensors, wireless connections, and smart visual control systems.
Abstract
SELF CHARGING ELECTRIC BICYCLE USING SOLAR ENERGY
MATTAM AMRUTH KUMAR SWAMY, K USHA RANI, SHAIK SAMIULLA, POLURU RAHUL, P VASANTH KUMAR, SAKE SIREESHA, GADDAM MAHESH BABU
DOI: 10.17148/IARJSET.2026.13635
Abstract: Nowadays and in future rising interest in sustainable and eco-friendly transportation has sparked the creation of self-charging electric bicycles that harness renewable energy. This project is all about designing and building a self- charging electric bike that features solar panels to recharge its battery while it's in use or parked, and even generates power through pedal rotation while riding. The system makes use of photovoltaic cells to turn sunlight into electrical energy, which is stored in a lithium-ion battery to drive the electric motor. This won't have to depends on external charging stations, allowing for a longer travel range and better energy efficiency. Our main goals include finding the best spots for solar panel placement to maximize energy capture, enhancing battery management, and ensuring the bicycle is both cost-effective and portable. The design aims to encourage green mobility by cutting down on fossil fuel use and greenhouse gas emissions, providing a sustainable option for urban transportation. It's an innovative and eco- friendly approach to getting around that reduces the need for outside charging sources. The growing demand for sustainable transport has spurred the development of energy-efficient and eco-friendly mobility solutions.
Keywords: The design reduces dependence on external charging sources and promotes eco-friendly transportation. It aims to improve energy efficiency, reduce pollution, and support sustainable urban mobility.
Keywords: The design reduces dependence on external charging sources and promotes eco-friendly transportation. It aims to improve energy efficiency, reduce pollution, and support sustainable urban mobility.
Abstract
When Biodiversity Persists but Protection Weakens: Community Stewardship, Encroachment and the Future of Orans in the Thar Desert — Evidence from Dhok and Jajawa Sacred Groves, Rajasthan
Dr. Devendra Singh Chouhan, Mehtab Singh Rathore
DOI: 10.17148/IARJSET.2026.13636
Abstract: Orans, the sacred groves of western Rajasthan, represent unique socio-ecological landscapes that have historically contributed to biodiversity conservation, livestock-based livelihoods, and cultural continuity in the Thar Desert. However, increasing pressures from encroachment, agricultural expansion, infrastructure development, and weakening traditional institutions have raised concerns regarding their long-term sustainability. Recent legal developments, including the recognition of Orans within the broader forest governance debate, have further highlighted the need to reassess existing conservation approaches. Against this background, the present study examines the ecological significance, conservation challenges, and future management of Dhok and Jajawa Orans in Barmer district, Rajasthan.
The study is based on household surveys conducted among 100 respondents (50 from each village), field observations, and geospatial analysis using Land Use/Land Cover (LULC) mapping and vegetation indices. The findings reveal important contrasts between the two sacred groves. Dhok Oran continues to benefit from strong religious significance, active community stewardship, and relatively lower levels of encroachment. In contrast, Jajawa Oran exhibits better vegetation condition, higher NDVI values, and considerable grazing potential, but faces greater pressure from agricultural expansion, residential development, and infrastructure growth. Survey results indicate that declining religious faith (95%), encroachment (91%), agricultural expansion (86%), and weak legal protection (82%) are perceived as the most significant threats to Oran conservation.
The study demonstrates that ecological quality alone cannot guarantee long-term conservation. While biodiversity and vegetation cover persist in many Orans, weakening cultural institutions and increasing land-use pressures threaten their sustainability. The findings further indicate strong public support for legal protection, official mapping, and boundary demarcation, alongside continued community participation in management. The study concludes that the future of Oran conservation lies in a hybrid governance framework that combines legal recognition, biodiversity conservation, community stewardship, and protection of traditional grazing rights. Such an approach can strengthen ecological resilience while safeguarding the cultural and livelihood functions of sacred landscapes in the Thar Desert.
Keywords: Orans, Sacred Groves, Community Stewardship, Biodiversity Conservation, LULC, NDVI, Thar Desert
The study is based on household surveys conducted among 100 respondents (50 from each village), field observations, and geospatial analysis using Land Use/Land Cover (LULC) mapping and vegetation indices. The findings reveal important contrasts between the two sacred groves. Dhok Oran continues to benefit from strong religious significance, active community stewardship, and relatively lower levels of encroachment. In contrast, Jajawa Oran exhibits better vegetation condition, higher NDVI values, and considerable grazing potential, but faces greater pressure from agricultural expansion, residential development, and infrastructure growth. Survey results indicate that declining religious faith (95%), encroachment (91%), agricultural expansion (86%), and weak legal protection (82%) are perceived as the most significant threats to Oran conservation.
The study demonstrates that ecological quality alone cannot guarantee long-term conservation. While biodiversity and vegetation cover persist in many Orans, weakening cultural institutions and increasing land-use pressures threaten their sustainability. The findings further indicate strong public support for legal protection, official mapping, and boundary demarcation, alongside continued community participation in management. The study concludes that the future of Oran conservation lies in a hybrid governance framework that combines legal recognition, biodiversity conservation, community stewardship, and protection of traditional grazing rights. Such an approach can strengthen ecological resilience while safeguarding the cultural and livelihood functions of sacred landscapes in the Thar Desert.
Keywords: Orans, Sacred Groves, Community Stewardship, Biodiversity Conservation, LULC, NDVI, Thar Desert
Abstract
A Serverless Event-Driven Architecture for Automated Customer Feedback Management Using AWS SNS, Lambda, and CodePipeline
LAVANYA GUBBALA, Smt A.N. RAMA MANI*
DOI: 10.17148/IARJSET.2026.13637
Abstract: Organizations increasingly depend on timely customer feedback to refine services, yet conventional feedback- handling pipelines are often built on continuously running servers that are costly to operate, slow to react to demand surges, and burdensome to maintain. This paper presents the design and empirical evaluation of a fully serverless, event- driven platform that automates the ingestion, analysis, routing, and escalation of customer feedback. The architecture is realized on Amazon Web Services, where submissions enter through an API gateway, propagate as events through a publish-subscribe notification service, and are processed by stateless functions written in Python that perform validation, sentiment classification, and category-based routing. A lightweight web client built with Node.js provides submission and administrative interfaces, while processed records are persisted in a managed NoSQL store and surfaced through an analytics dashboard. The entire delivery lifecycle build, test, and deployment is automated through a managed continuous-integration and continuous-delivery pipeline governed by Infrastructure-as-Code. Experimental evaluation under synthetic event load shows that the platform sustains an end-to-end latency of approximately 210 ms at 1000 events per second, scaling elastically where a server-based baseline degrades beyond 1400 ms. Pipeline automation reduced deployment lead time from roughly 75 minutes to under 10 and lowered processing cost per million requests by nearly sixfold owing to consumption-based billing. The contributions comprise an integrated serverless reference architecture for feedback automation, a reproducible CI/CD strategy, and a quantitative comparison establishing the efficiency of event-driven designs for this class of workload.
Keywords: Serverless computing; Event-driven architecture; AWS Lambda; Amazon SNS; Continuous delivery; Sentiment analysis; Feedback management; Cloud automation
Keywords: Serverless computing; Event-driven architecture; AWS Lambda; Amazon SNS; Continuous delivery; Sentiment analysis; Feedback management; Cloud automation
Abstract
A Serverless Event-Driven Architecture for Intelligent Personal Finance Management with Automated Continuous Deployment on AWS Lambda
KARRI HEMA LAKSHMI KANAKA DURGA, Mr.B.N. SRINIVASA GUPTA *
DOI: 10.17148/IARJSET.2026.13638
Abstract: Personal and small-business financial management increasingly demands software that is responsive, intelligent, and inexpensive to operate, yet conventional server-based applications burden developers with continuous provisioning, idle-capacity cost, and manual scaling. This paper presents a fully serverless, event-driven financial management platform that records expenses and income, enforces budgets, and delivers predictive insight while eliminating standing infrastructure. The system couples a stateless application programming interface, implemented with the FastAPI framework and adapted to a function-as-a-service runtime, to a constellation of ten independent worker functions that handle receipt optical-character recognition, categorization, budget evaluation, forecasting, notification, and audit logging in response to events. A lightweight machine-learning engine performs automatic expense categorization, regression-based forecasting, and anomaly detection, while multi-tenant isolation, token-based authentication, role-based access control, and request rate limiting protect each account. Persistence is provided by a managed NoSQL store in the cloud and a relational engine for local development, and the entire stack is released through an automated continuous-integration and continuous-deployment pipeline that executes tests, security scanning, and packaging before promotion. Experimental evaluation shows that the serverless deployment sustains a 95th-percentile latency of 640 ms at 1,000 requests per second, compared with 4,300 ms for a provisioned monolithic baseline, while warm-invocation latency averages 95 ms and the categorizer attains 88.5% accuracy. The principal contributions are a decomposed event-driven design, an embedded intelligence layer, and a reproducible automated release pipeline that together yield an elastic, cost-efficient, and maintainable financial platform.
Keywords: Serverless computing, AWS Lambda, event-driven architecture, continuous deployment, FastAPI, machine learning, expense forecasting, cloud-native applications.
Keywords: Serverless computing, AWS Lambda, event-driven architecture, continuous deployment, FastAPI, machine learning, expense forecasting, cloud-native applications.
Abstract
A Smart Peer-Learning Platform with AI-Driven Skill Matching and a Credit-Based Reciprocal Exchange Mechanism
PASUPULETI PRAMEELA VISALAKSHI, Mr. KARRI LAKSHMANA REDDY*
DOI: 10.17148/IARJSET.2026.13639
Abstract: Peer-to-peer learning lets individuals teach skills they possess and learn skills they lack, but informal arrangements suffer from poor discoverability, unbalanced reciprocity, and a lack of trust between strangers. Existing online learning platforms are predominantly one-directional and monetary, offering little support for equitable, non-cash exchange among peers. This paper presents a smart peer-learning platform that pairs an artificial-intelligence skill- matching recommender with a credit-based trading mechanism, enabling learners to earn credits by teaching and spend them to receive instruction. A hybrid recommender combines content-based skill similarity with collaborative signals to suggest high-quality learner-mentor pairs, a reputation engine aggregates peer ratings to build trust, and a credit ledger with escrow guarantees fair settlement of each session. A Python back end implements the recommendation, reputation, and ledger logic, while a Node.js layer delivers the marketplace and session interfaces. Evaluated against popularity, content-based, and collaborative-filtering baselines, the hybrid recommender achieved a precision-at-five of 0.88 and a recall-at-five of 0.85, and simulated platform activity exhibited sustained growth in completed sessions and circulated credits. The principal contributions are a hybrid skill-matching recommender tailored to bidirectional learning, a credit- with-escrow exchange protocol that enforces reciprocity, and a reputation mechanism that fosters trust in an open peer marketplace.
Keywords: Peer learning; recommender systems; skill matching; credit-based exchange; reputation systems; collaborative learning; hybrid recommendation; online education.
Keywords: Peer learning; recommender systems; skill matching; credit-based exchange; reputation systems; collaborative learning; hybrid recommendation; online education.
Abstract
Cloud-Integrated Automated Academic Scheduling System: A Scalable Web-Based Approach for Conflict-Free Timetable Generation
CHELLABOYINA SINDHU, DR. CHIRAPARAPU SRINIVASA RAO *
DOI: 10.17148/IARJSET.2026.13640
Abstract: Academic timetable management remains a persistent administrative bottleneck in educational institutions worldwide. Conventional scheduling practices rely on manual allocation procedures that are error-prone, time- intensive, and incapable of dynamically responding to evolving constraints such as faculty availability, subject hour requirements, and classroom occupancy. This paper presents the design, development, and deployment of a cloud- integrated automated academic scheduling system built upon the Flask web framework, a relational SQLite database engine, and Amazon Web Services (AWS) infrastructure, leveraging AWS CodePipeline and AWS Elastic Beanstalk for continuous integration and scalable deployment. The proposed system introduces a constraint-aware slot-filling algorithm that distributes subjects across a five-day weekly schedule while respecting per-subject credit-hour allocations assigned by the administrator. The platform provides a web-based administrative interface encompassing faculty registration, course configuration, departmental class management, subject-faculty assignment, automatic schedule generation, inline schedule editing, and CSV-based export functionality. Experimental evaluation on three academic cohorts-comprising undergraduate and postgraduate programs-demonstrates that the system successfully generates complete, conflict-free timetables within sub-second response times. The architecture's serverless deployment model inherently provides horizontal scalability and high availability without infrastructure overhead. The proposed solution reduces timetable preparation time by an estimated 85% compared to manual methods, offering a compelling advancement for the domain of intelligent academic resource management.
Keywords: Academic timetable management, automated scheduling system, cloud computing, AWS Elastic Beanstalk, Flask framework, constraint-based scheduling, educational resource planning, web-based administration
Keywords: Academic timetable management, automated scheduling system, cloud computing, AWS Elastic Beanstalk, Flask framework, constraint-based scheduling, educational resource planning, web-based administration
Abstract
A Cloud-Native Complaint Management Platform with Real-Time Business Intelligence and NLP-Driven Automated Triage Using Amazon QuickSight
TAMANAMPUDI LAKSHMI KALYANI, PADALA SRINIVASA REDDY*
DOI: 10.17148/IARJSET.2026.13641
Abstract: Organizations across the public and private sectors handle large volumes of grievances, yet many still rely on email threads, spreadsheets, and disconnected ticketing tools that scatter information and obscure operational insight. This fragmentation delays resolution, weakens accountability, and prevents managers from observing service quality as it unfolds. This paper presents a cloud-native complaint management platform that unifies grievance capture, intelligent routing, lifecycle tracking, and live business intelligence within a single elastic system. The back end is implemented in Python using the Flask micro-framework and exposes a stateless REST interface, while a responsive front end built with standard web technologies serves both complainants and administrative staff. A lightweight natural-language- processing component automatically classifies incoming complaints by category and severity, reducing manual triage effort and standardizing prioritization. Persistent data is stored in a managed relational database, attachments reside in object storage, and an analytics pipeline continuously feeds Amazon QuickSight to render interactive dashboards covering volume, category mix, agent workload, and turnaround time. Experimental evaluation under simulated concurrent load shows that the platform sustains an average response time of 430 ms at 800 concurrent users, while the classifier attains 91.6% accuracy and an F1-score of 89.8%. Compared with a conventional monolithic baseline, the proposed system reduces mean resolution time and improves throughput scalability. The principal contributions are an integrated cloud reference architecture, an automated triage workflow, and an embedded real-time analytics layer that converts raw grievance data into actionable managerial intelligence.
Keywords: Complaint management, cloud computing, real-time analytics, Amazon QuickSight, natural language processing, REST API, business intelligence, service automation.
Keywords: Complaint management, cloud computing, real-time analytics, Amazon QuickSight, natural language processing, REST API, business intelligence, service automation.
Abstract
A Cloud-Native Full-Stack Java Framework for Secure and Transparent Online College Election Management on AWS
VEERA SIVANI, K. LAKSHMI SAI SREE*
DOI: 10.17148/IARJSET.2026.13642
Abstract: The digitization of democratic processes within higher education institutions demands robust, scalable, and tamper-resistant platforms. Conventional paper-based and legacy digital election systems in academic environments are susceptible to vote manipulation, ballot stuffing, administrative overhead, and limited accessibility. This paper presents a cloud-native, full-stack election management framework designed specifically for college environments, deployed on Amazon Web Services (AWS) Elastic Beanstalk. The proposed system integrates a Spring Boot 3.x RESTful backend with a React-based single-page application (SPA) frontend, secured through JSON Web Token (JWT) stateless authentication, BCrypt-based password hashing (cost factor 12), and SHA-256 voter fingerprinting to enforce strict one-person-one-vote integrity. The relational data model, managed through Flyway versioned migrations on PostgreSQL 16, enforces vote immutability via database-level triggers, preventing any post-cast modification or deletion. Role-based access control (RBAC) segregates student voters from election officers, each operating within a strictly scoped permission boundary. The system supports complete election lifecycle management encompassing scheduling, active-period enforcement, result tallying, and controlled verdict publication. Experimental evaluation demonstrates sub-300 ms average API response times under concurrent load, zero duplicate-vote incidents across simulated test scenarios, and a fully automated CI/CD pipeline through AWS CodeBuild. The architecture is extensible to multi-institution environments and positions the framework as a replicable model for transparent, auditable academic e-governance.
Keywords: online voting system; cloud computing; AWS Elastic Beanstalk; JWT authentication; Spring Boot; role- based access control; SHA-256 fingerprinting; election management; academic e-governance; PostgreSQL
Keywords: online voting system; cloud computing; AWS Elastic Beanstalk; JWT authentication; Spring Boot; role- based access control; SHA-256 fingerprinting; election management; academic e-governance; PostgreSQL
Abstract
Understanding the Chemistry, Bioactive Components, and Health Implications of Kashmiri Salt Tea (Noon Chai) -A Critical Review
Ashaq Hussain
DOI: 10.17148/IARJSET.2026.13643
Abstract: Kashmiri Salt Tea, commonly known as Noon Chai, is a traditional beverage that occupies a central place in the cultural, social and dietary practices of Kashmir. Unlike conventional tea preparations, Noon Chai is prepared by boiling green tea leaves with sodium bicarbonate and salt, followed by the addition of milk, resulting in a characteristic pink-colored beverage with a distinctive taste and chemical composition. The unique preparation process induces a series of physicochemical transformations that influence the beverage's sensory properties, nutritional profile and biological activity. Growing scientific interest in functional foods and traditional beverages has highlighted the need for a comprehensive evaluation of Noon Chai from the perspectives of food chemistry, nutrition and public health. This review critically examines the chemistry of Kashmiri Salt Tea, emphasizing the role of tea polyphenols, catechins, flavonoids, amino acids, minerals and milk-derived nutrients. Particular attention is given to the chemical reactions responsible for color formation, the influence of alkaline processing on phytochemical stability and the interactions between tea constituents and milk proteins. The review further explores the potential health benefits associated with antioxidant, anti- inflammatory, cardioprotective, metabolic and neuroprotective activities of tea-derived bioactive compounds while considering concerns related to excessive sodium intake. By integrating evidence from contemporary literature, this review aims to provide a scientific framework for understanding the health implications of Noon Chai and to identify future research priorities that may help preserve its cultural significance while promoting evidence-based dietary recommendations.
Keywords: Antioxidants; Bioactive Compounds; Health Implications; Kashmiri Salt Tea; Noon Chai
Keywords: Antioxidants; Bioactive Compounds; Health Implications; Kashmiri Salt Tea; Noon Chai
Abstract
“Prevalence of Overweight and Obesity in Balangir District: Age and Sex Analysis”
John Soumyashree Collet*, Jyotismita Satpathy, Dr. Arvind Kumar Ojha, Dr. Kumar Sambhav Chopdar
DOI: 10.17148/IARJSET.2026.13644
Abstract: Overweight and obesity have emerged as major public health concern worldwide due to their increasing prevalence and association with various diseases. Excess body weight is linked to cardiovascular diseases, diabetes, hypertension and reduced quality of life. Developing countries are experiencing a rapid rise in obesity rates because of changes in dietary habits, urbanization and sedentary lifestyles. A sectional study was conducted among people in Western Odisha, Balangir district. The study revealed thatoverall16% of people were overweight and obese as per BMI classification. Among them, 11.6% were overweight and 4.4% were obese. Gender-wise analysis showed that 20.33% of males and 11.96% of females were affected by overweight and obesity. Using WHO factors such as age, marital status, drink, calorie intake and physical adequacy all were significantly associated with overweight and obesity. The study highlights the contribution of these socio-biological factors to the increasing prevalence of overweight and obesity in the population.
Keywords: Overweight; Obesity; Body Mass Index (BMI); Prevalence; Age and Sex Differences; Socio-biological Factors.
Keywords: Overweight; Obesity; Body Mass Index (BMI); Prevalence; Age and Sex Differences; Socio-biological Factors.
Abstract
A SUMMARY OF CUTTING-EDGE RESEARCH ON MORPHING AIRPLANE BASED ON INTELLIGENT MATERIALS AND STRUCTURES
K Veeranjaneyulu
DOI: 10.17148/IARJSET.2026.13645
Abstract. This article reviews current innovations in the use of intelligent materials and structures for morphing airplane covering specific applications of actuators, sensors, and controllers. Morphing refers to a gradual transformation of an object's appearance or form. In the past, electrical actuators were used to do morphing; now, advanced materials like SMA and piezoelectric materials are used. A typical aircraft's flying envelope is designed to be optimized for just one or two flight conditions, not for all of them. A bird's wings, on the other hand, can be repositioned to fly optimally in any situation. Aerodynamic performance can be improved by making modifications to the aircraft wing, and it is possible to find the best configurations for any flight scenario. A wide range of flight conditions can be improved using morphing technology. It is assumed that extra weight of the morphing components is considered acceptable in order to justify the strengths of a morphing aircraft. Modern mechanical and hydraulic technologies are not thought to be the best options for morphing aircraft. The benefits of "smart" materials and constructions include their high energy density, controllability, variable stiffness, and capacity to withstand significant strain. These characteristics equip researchers and designers with novel design ideas for morphing aircraft.
Keywords: Bird’s wings, Morphing Airplane, Piezoelectric, Smart structures
Abstract
Review on Numerical Analysis of Phase Change Materials for Battery Thermal Management Systems
Miss. Priyadarshani K. Dure and Mr. Avinash S. Patil
DOI: 10.17148/IARJSET.2026.13646
Abstract: The rapid expansion of electric vehicles (EVs) has intensified the need for efficient battery thermal management systems (BTMS) to ensure safety, performance, and longevity of lithium-ion batteries. Phase Change Materials (PCMs) have emerged as a promising passive cooling solution due to their high latent heat storage capacity and cost-effectiveness. This review presents a comprehensive analysis of PCM-based BTMS, highlighting their advantages, limitations, and enhancement strategies. Key challenges, such as low thermal conductivity, are addressed through composite structures incorporating expanded graphite, biochar, nanoparticles, carbon nanotubes, and metallic coatings, as well as integration with fins for improved heat dissipation. Hybrid cooling systems combining PCMs with liquid cooling, air cooling, and heat pipes are discussed, demonstrating superior thermal uniformity and stability compared to standalone PCM systems. Numerical methods, including finite volume simulations, enthalpy-porosity, and apparent heat capacity approaches, supported by tools such as ANSYS FLUENT, COMSOL Multiphysics, and STAR- CCM+, are reviewed for their role in optimizing PCM-BTMS configurations. Results indicate that hybrid PCM systems significantly reduce maximum battery temperatures, enhance uniformity, and improve exergy efficiency, particularly under high discharge rates. The findings underscore the importance of material innovation, geometric optimization, and numerical modeling in advancing PCM-based BTMS for next-generation EV applications.