VOLUME 12, ISSUE 10, OCTOBER 2025

Abstract: The theory of p-valent functions is an important subject in the geometric function theory. Recently, many researchers have shown great interests in the study of p-valent functions. The aim of this paper is to investigate several results concerning the subordination of multivalent functions in the open unit disc U; which are associated with derivative operator D_(δ,β,ʎ,p)^(k,α) f(z).

Keywords: analytic functions, multivalent functions, differential operator, subordination.

Abstract: This paper focuses on how to AI apply to analysis of natural source of Tinospora Cordifolla (GURJO). To apply AI for analysis, the data is absolutely is needed. GURJO has a very good pharmacological effect for human well-being. Tinospora Cordifolia has chemical constituents like terpenoids, alkaloids, steroids, lignans, flavonoids and glycosides. Therefore, it has many pharmacological activities such as immunomodulation, anti-diabatic, antifungal, in hepatotoxicity (hepatic disorder), anti- cancer, anti-HIV potential, antitoxic effect, and in Parkinson disease because there are phenol and tannins saponins, glycosides, steroids, steroids. To analysis and use, we should know which natural source has impact and what relation among natural sources through data and AI based analysis to develop a new paradigm and products. This paper has the purpose data build for AI application for this purpose. Because AI Thinking has a multi-purpose meaning about various aspects of AI user and it has a wide range of the management, the production, the training, and use of AI systems, it is absolutely needed to try systematically to understand and learn. This paper has also purpose AI Thinking for practice, methodological, and context of GURJO.

Keywords: AI Application, Food, Tinospora Cordifolla (GURJO), Data, Smart food.

Abstract: We study the manner in which solar and interplanetary characteristics correlate with geomagnetic indices during solar cycle 25's ascending phase. Solar cycle 25's ascending phase's sunspot number, interplanetary magnetic field, and geomagnetic indices are all related to that of cycles 22, 23, and 24. There is a periodic in the sunspot number, geomagnetic parameters, and interplanetary parameters (IMF). However, during solar cycle 25's ascending phase, the IMF and geomagnetic parameter values differ. We determined that all of the geomagnetic parameters, IMF, and sunspot number were in good agreement. This phase has unique minimum and maximum durations. Compared to the ascending phase of solar cycle 25, the cosmic ray intensity magnitude was lower during this ascending phase. We discovered that the geomagnetic indices, sunspot number, and IMF all show larger spectrum fluctuations than at the start and finish of the cycle. These factors have a continuous spectrum energy of medium to week magnitude, according to our analysis & three different neutron monitor Oulu, Moscow and Athens are observed the highest peak down in year of 2020 in the ascending phase of Solar Cycle 25. We propose that solar activity plays a role for the unique circumstances of solar and interplanetary parameters as well as geomagnetic indices.

Keywords: Sunspot number, interplanetary parameters (IMF), geomagnetic index.

Abstract: A vehicle is a machine that can convey people and goods from a source to a destination. When vehicles ply on roads, it can lead to traffic. Traffic has to be managed on a day-to-day basis in villages, towns, cities and the freeway. A vehicle license plate is affixed to the vehicle at the time of registration or at some other possible event. A vehicle license plate can help in identification, management and routing of vehicles. Lately, high security registration plates (HSRP) have been assigned in India. Vehicle license plate localization is a well-known and well-studied problem. By localizing the license plate on a vehicle, it is possible to get the image of the license plate. In this research article, a new algorithm - license plate localization applying contours (LILY) is presented

Keywords: Vehicle, License plate, HSRP, LILY

Abstract: Karnataka's agricultural sector is very sensitive to climatic variability with regard to timing and distribution of monsoon rainfall. In response to the urgent need for reliable Agro-Meteorological forecasting, this study investigated the potential use of two supervised machine learning models Decision Tree and Random Forest for precipitation forecasting in Karnataka. The models were trained using historical meteorological datasets that were developed from the results of a number of climate parameters related to, for example, atmospheric circulation patterns, sea surface temperature, and specific monsoon-related indices. For both models, the combination of recursive partitioning and ensemble learning incorporated numerous distributed climate factors and their associated nonlinear relationships. Both models were then critically compared, with Random Forest being determined as most superior and a better overall estimation in terms of quality and reliability, trend association and being more robust to overfitting. In summary, these results demonstrate that data-driven approaches can dramatically improve the geographic and temporal quality of forecasts in this region. The framework proposed is a scalable, interpretable, and practical tool to aid climate resilient agricultural planning in Karnataka, and more generally Agro-climatic zones in India.

Keywords: Karnataka agriculture, climatic variability, monsoon rainfall forecasting, agro-meteorology, machine learning, Decision Tree, Random Forest, precipitation prediction, climate parameters, ensemble learning, nonlinear relationships, overfitting robustness, data-driven forecasting, climate-resilient agriculture, agro-climatic zones of India

Abstract: For a long time, businesses tried to understand why a person buys one thing and not another. In history, people would just watch customers and make guesses. This was not very accurate. The main problem is that it is very hard to know what a customer will do. So many things like their culture, their friends, their age and their money change their choices. This makes it difficult for companies to sell their products and they waste lots of money on advertisements that dont work. This project gives a solution using machine learning. A system is proposed that uses data about consumers to predict their buying decision. The data includes personal factors, social factors, and economic factors. The system will use a machine learning algorithm like a Decision Tree to learn from past data. This model will then predict if a new customer is likely to buy a product or not. This helps companies to focus their efforts and understand their customers in a much better way.

Keywords: Consumer Behavior, Predictive Modeling, Data Analytics, Marketing Strategy, Economic Factors, Social Factors, Psychological Factors, Customer Segmentation

Abstract: The problem of global warming is world widely known. One of the causes of this situation is the increase in the emission of greenhouse gasses from the utilization of fossil fuel. One of the possible solution to this environmental degradation is the production of biofuel from lignocellulosic biomass. Bioethanol is a widely liquid biofuel which is sustainable and environmentally friendliness. Mango seeds, a renewable and abundant resources can serve as an alternative lignocellulosic biomass for the production of bioethanol which solved the problem of energy required and environmental degradation to more sustainable source of energy. This study was carried out to produce bioethanol from mango (Mangifera indica) seeds. The seeds was sundried, grounded into fine powdered and pretreated using combination of different concentrations of organic and inorganic dilute acids hydrolysis . Physico-chemical analysis of the raw sample and pretreated sample was analyzed. Reducing sugar was determined after hydrolysis using UV Spectrophotometer at 540nm with p H of 4.5. The results of the physic-chemical analysis revealed high carbohydrate, moisture and crude fibre content with significant ash content. Also, 40.97%, 24.30% of cellulose and hemicelluloses content respectively. The results of the pretreated sample shows decrease in lignin and hemicelluloses content and increase in cellulose content. The high reducing sugar was noticed with 20.97 in 15%/10% of formic acid/hydrochloric acid pretreatment. The highest yield of bioethanol was observed in 15%/10% with 39% bioethanol yield. The results indicates the suitability of mango seeds as a good potential resources for quality bioethanol.

Keywords: Lignocellulosic biomass, Renewable energy, Mango seeds, Fossil fuel., Bioethanol

Abstract: A major environmental concern of the 21st century, marine pollution jeopardizes human health, marine biodiversity, coastal ecosystems, and ocean health. The main causes of marine pollution, including plastic waste, oil spills, untreated sewage, agricultural runoff, and industrial discharge, are thoroughly examined in this paper along with the short- and long-term effects they have. It is noted that plastic pollution, in particular microplastics, is a widespread hazard that has contaminated food chains and isolated ocean areas, endangering both human health and marine life. This study further examines the ecological effects of chemical contaminants, including the bioaccumulation and biomagnification of heavy metals and persistent organic pollutants, which can cause immune system problems, reproductive failure, and mass mortality in marine species. Fertilizer-induced nutrient pollution leads to oxygen-depleted dead zones and toxic algal blooms, significantly reducing biodiversity and disrupting food webs. Coastal communities, tourism, and fisheries face significant financial implications as a result of these effects. The article emphasizes the necessity of an integrated response through global collaboration, stricter laws, cutting-edge technologies, and public involvement. It examines international frameworks, such as SDG 14 and the MARPOL Convention, and advocates for sustainable solutions, including improved waste management and the development of biodegradable substitutes. Ultimately, the study advocates for a global commitment to equitable, science-based marine stewardship.

Keywords: Pollution, Plastic waste, Microplastic, Health and environmental impact, Environment policy, Awareness.

Abstract: Artificial Intelligence is the branch of computer science that focuses on creating machine capable of performing tasks that typically require human intelligence. AI ethics refers to a system of principles and techniques that responsible and development and use of AI technologies. Human-AI interaction involves the ways human and intelligent system communicate, collaborate and perform task together. AI enhances human capabilities by supporting decision making, creativity and problem solving. However, the interaction of AI into everyday life raises significant ethical challenges such as fairness, transparency, accountability and privacy.

Keywords: Artificial Intelligence, Human-AI Interaction, AI Ethics, AI Transparency, Responsible AI, AI Decision-Making.

Abstract: The interpretation of sign language into English has emerged as a critical research domain in Artificial Intelligence (AI) and Natural Language Processing (NLP), aiming to reduce communication barriers for the deaf and hard-of-hearing community. Sign languages are unique in their grammar, visual modality, and cultural variations, which makes translation into spoken or written languages highly complex. Recent developments in computer vision, deep learning, and large language models (LLMs) have significantly advanced recognition and translation capabilities. This review synthesizes 23 research studies published between 2021 and 2025, focusing on gloss-based methods, gloss-free architectures, and transformer-driven models such as SignBERT, SignBERT+, and Sign2GPT. A dedicated emphasis is given to Indian Sign Language (ISL), a low-resource language with limited datasets and benchmark systems. The paper also examines real-time mobile solutions, dataset availability, and ethical considerations in system design. Comparative analysis reveals that gloss-free transformer and LLM-based models outperform traditional methods but face challenges of computational cost and dataset scarcity. Finally, the review outlines key future directions, including large-scale ISL dataset creation, multilingual support, edge AI deployment, and inclusive co-design with deaf communities.

Keywords: Natural Language Processing (NLP), Deep Learning (DL), Machine Learning (ML), Computer Vision, Gloss Translation, SignBERT, Sign2GPT, Transformer Models

Abstract: Infertility remains a global health concern, and in-vitro fertilization (IVF) has become a widely used assisted reproductive technology for achieving pregnancy. However, predicting IVF success continues to be a major challenge due to biological variability, subjective embryo evaluation, and limited data integration. To address these issues, recent research has adopted Artificial Intelligence and Machine Learning paradigms to enhance prediction accuracy and automate embryo assessment. This study synthesizes and extends five advanced AI-based approaches that integrate deep learning, transformer architectures, and multi-modal data fusion for embryo grading and live birth outcome prediction. The unified framework leverages clinical, morphological, and temporal embryo features, applying models such as Convolutional Neural Networks (CNNs), Particle Swarm Optimization (PSO), and Tab-Transformers to extract interpretable and clinically relevant patterns. Comparative analysis shows that AI-driven systems can achieve accuracy levels exceeding, outperforming traditional embryologist evaluations. By providing explainable, data-driven insights, these methods have the potential to improve decision-making, reduce human subjectivity, and personalize IVF treatment outcomes.

Keywords: In-Vitro Fertilization, Artificial Intelligence, Machine Learning, Deep Learning, Transformer Models, Embryo Grading, Outcome Prediction, Multi-Modal Data Fusion, Explainable AI, Clinical Decision Support.

Abstract: As global environmental challenges intensify, Artificial Intelligence (AI) has emerged as a promising tool to enhance environmental sustainability and optimize resource management. However, the effectiveness of AI-driven solutions relies heavily on consumer acceptance and attitudes. This study investigates consumer perceptions toward the use of AI in promoting sustainable environmental practices and efficient resource utilization. This study aims to investigate consumer attitudes toward the use of Artificial Intelligence in environmental sustainability and resource management in Erode District. Employing a quantitative research design, data were collected from residents of Erode District through structured questionnaires and analyzed using multiple regression techniques to examine the influence of socio-demographic, psychological, and informational factors on consumer attitudes. Findings reveal that awareness, trust, perceived benefits, and ethical considerations significantly shape consumer acceptance of AI applications in sustainability. The study highlights the critical need for transparent communication and educational initiatives to foster positive perceptions and facilitate wider adoption of AI technologies in environmental management. These insights can guide policymakers, technology developers, and environmental advocates in designing AI solutions that are socially accepted and environmentally effective. A convenience sampling technique was employed to select a sample of 100 consumers from the Erode District. Data were collected using a structured questionnaire designed to capture relevant information regarding the study variables. To analyze the relationships and effects among these variables, regression analysis was conducted, enabling the identification of significant predictors and the strength of their influence on the outcome measures.

Keywords: Artificial Intelligence, Consumer, Solutions, Technology, Environmental.

Abstract: This paper explores the growing importance of Corporate Social Responsibility (CSR) and sustainability as integral components of modern business strategy. It emphasizes how organizations balance profitability with social and environmental considerations to create long-term value. The study highlights CSR's multidimensional aspects-economic, ethical, philanthropic, and environmental-and analyzes its impact on corporate image, stakeholder trust, and business performance. Using theoretical insights and supporting literature, this paper concludes that CSR and sustainability are vital for achieving organizational development goals and ensuring ethical and responsible business growth.

Keywords: Corporate Social Responsibility, Sustainability, Business Ethics, Stakeholder Trust, Environmental Responsibility, Organizational Strategy

Abstract: This paper details a structured methodology for designing and validating a coil-spring cab suspension system for a heavy commercial vehicle (HCV) to enhance driver ride comfort. The design process focuses on isolating the cabin from road-induced chassis vibrations. Key inputs include vehicle weight distribution and data obtained through road load data acquisition (RLDA). A single-degree-of-freedom base excitation model is utilized to set a target natural frequency of 2 Hz for the cab suspension. Detailed calculations determine the stiffness and geometrical parameters for the front and rear coil springs. The performance of the designed system is virtually validated using Multi-Body Dynamics (MBD) analysis in MSC ADAMS. Simulations for standard ride events, such as rough road and low-speed bumps, yield a Ride Quality Number (RQN) between 6.5 and 7.5, indicating satisfactory performance. A comparative analysis confirms the superiority of the four-point coil spring configuration over alternative design schemes. The study concludes that an integrated approach, combining theoretical calculations with advanced CAE tools, is effective for developing a cost-effective and performance-optimized cab suspension.

Keywords: Cab Suspension, Ride Quality, Vehicle Dynamics, Spring Design, Finite Element Analysis, Heavy Commercial Vehicle.

Abstract: Mental health challenges such as stress, anxiety, and depression are increasing globally, yet access to timely and affordable psychological support remains limited. To address this gap, AI-powered mental health catboats have emerged as a scalable and accessible solution. These catboats leverage Natural Language Processing (NLP), sentiment analysis, and machine learning techniques to engage users in empathetic conversations, provide self-help resources, and offer coping strategies such as mindfulness and cognitive behavioural therapy (CBT)-based exercises. Available 24/7, they ensure privacy, anonymity, and continuous support, thereby reducing stigma and encouraging users to seek help. While not a replacement for professional therapy, such catboats can complement existing mental health services by providing early intervention, mood tracking, and crisis management features.

Keywords: AI Chatbots, Mental Health Support, Natural Language Processing, Sentiment Analysis, Cognitive Behavioral Therapy, Emotional Well-being.

Abstract: Forest fire is a major environmental concern affecting biodiversity, forest health, and ecological balance. In India, the frequency and intensity of forest fires have been steadily rising, particularly in ecologically fragile regions such as the Aravalli Hills in Rajasthan. This study aims to analyze the spatio-temporal assessment of forest fire occurrences and associated land use and land cover (LULC) changes in Girwa Tehsil of Udaipur District during the period 2018-2023. MODIS FIRMS active fire point data were analysed to evaluate the distribution and frequency of fire incidents, while hotspot analysis was carried out using overlay mapping to identify high-risk fire-prone zones. The results indicated that fire events were not uniformly distributed but clustered in specific belts, particularly the Sajjangarh-Badi forest corridor and parts of central Girwa, with peak five years recorded in 2019 and 2021. To understand the land transformation associated with recurrent fires, SAGA GIS was employed for supervised classification of Sentinel-2 imagery from 2018 and 2023. Comparative LULC mapping revealed a significant decline in dense forest cover and a corresponding increase in degraded or wasteland categories, along with slight expansion of built-up areas due to urban growth. The integration of fire hotspot mapping with LULC dynamics provides evidence that recurrent fires have accelerated vegetation degradation and land transformation in Girwa. The finding highlights the urgent need for proactive forest fire management, afforestation in degraded regions, and the integration of geospatial monitoring for sustainable ecological conservation.

Keywords: Forest Fire, MODIS FIRMS, Hotspot Mapping, SAGA GIS.

Abstract: As a consequence of the escalating amounts of user-generated text available today, including tweets, comments, reviews, and blog posts, researchers can examine and derive knowledge about public opinion. Sentiment analysis, a pivotal application area of Natural Language Processing (NLP), interprets and categorizes opinions to assess emotions and attitudes that are expressed in text. This study presents a review of former studies regarding prediction accuracy using sentiment analysis (with a focus on supervised machines, deep learning, and combining of approaches). For example, we analyze various supervised algorithms including Support Vector Machines, Logistic Regression, K-Nearest Neighbor, Naïve Bayes, and Random Forest, and introduce advanced neural models including Recurrent Neural Networks and Long Short-Term Memory networks. Also, some of our research describes new hybrid architectures that combine keyword extraction algorithm based on graph methods and machine learning models that improve performance on contextual text using the first applications of statistical models of sentiment for real datasets. We also examine data preprocessing and feature selection efforts, as well as the use of ensemble models for classification performance. Our review finds that hybrid models and those using graph-based techniques may be advantageous compared to standard supervised models, as well scalable, adaptive models for sentiment mining, brand communication monitoring, and decision support.

Keywords: Sentiment Analysis, Natural Language Processing (NLP), Machine Learning, Deep Learning, Hybrid Models, Support Vector Machine (SVM), Recurrent Neural Network (RNN), LSTM, Graph-Based Approach, Text Classification, Opinion Mining.

Abstract: The vast amount of information on YouTube is often embedded in lengthy videos, making it time-consuming for users to extract key insights. To address this inefficiency, this project introduces a Smart YouTube Video Summarizer that generates quick and meaningful summaries. The system uses the YouTube API to fetch video details and transcript, then processes the content using Natural Language Processing (NLP) and the LLaMA3 model to create concise summaries. Users can paste a video URL, view the transcript, and get the summary instantly in a Streamlit web application. The project reduces the time required to watch long videos, provides an option to view the transcript with timestamps, and supports multiple languages. This system is efficient, user-friendly, cost-effective, and helpful for students, researchers, and general users who need information quickly.

Keywords: YouTube Summarizer, Transcript Extraction, Natural Language Processing, Abstractive Summarization, LLaMA3, Streamlit.

Abstract: Wireless networks are increasingly vulnerable to attacks that exploit weaknesses in the IEEE 802.11 protocol, particularly through the misuse of unencrypted management frames. One such attack is the WiFi deauthentication attack, which forcibly disconnects clients from access points by sending forged deauth packets. This project presents the design and implementation of a portable WiFi deauthentication device using the ESP8266 microcontroller, an SSD1306 OLED display, and button-based navigation.The device enables users to scan nearby WiFi networks, select targets, and initiate deauthentication attacks in a controlled and ethical environment. It serves as a practical tool for security researchers, ethical hackers, and educators to demonstrate wireless vulnerabilities and promote awareness of network security best practices. The system is fully standalone, requiring no external computer or software, and is built using open-source libraries and firmware.

Keywords: "WiFi Deauthentication, ESP8266, Ethical Hacking, Wireless Security, 802.11 Protocol".

Abstract: Three roller bending machines are widely used for shaping metallic components critical in industries such as automotive and aerospace. This review analyzes the effects of roller diameter and number of passes on power consumption in these machines. Larger roller diameters enhance contact mechanics by increasing the contact area, which reduces bending forces, torque requirements, and consequently power consumption. Additionally, multi-pass bending distributes deformation incrementally, lowering peak power demands per pass but increasing total operation time and energy use. The interaction of these parameters influences energy efficiency, dimensional accuracy, surface finish, and springback behavior. Theoretical models rooted in bending moments and torque-speed relationships corroborate empirical findings. Advancements in Industry 4.0 and AI-driven process control offer promising avenues for real-time optimization of these parameters, potentially enabling significant energy savings and quality improvements. Despite progress, research gaps persist in adaptive control systems for power optimization. This article synthesizes current knowledge, discusses practical implications for machine design and process management, and suggests future research directions focused on intelligent, data-driven bending operations.

Keywords: Three roller bending machines, Power consumption, Roller diameter, Number of passes, Energy efficiency

Abstract: The evolution of e-commerce and digital trade is entering a new phase with the advent of Industry 5.0, characterized by human-centric, sustainable, and resilient industrial practices. This research explores the opportunities and challenges that arise as digital trade integrates advanced technologies such as artificial intelligence, blockchain, 5G, and the Internet of Things (IoT) with a renewed focus on human-machine collaboration.

Keywords: Industry 5.0, e-commerce, digital trade, human-machine collaboration, AI, blockchain, 5G, IoT, sustainability.

Abstract: Weather forecast is essential for agriculture, transportation, disaster management, and environmental planning. Traditional numerical and statistical models are helpful, but they do not capture the complex, nonlinear, and dynamic relationships among numerous atmospheric variables. To address this problem, the study combines and evaluates several approaches for multivariate weather forecast using Artificial Intelligence (AI) and Deep Learning (DL). By applying meteorological datasets with different parameters, including temperature, humidity, wind speed, and precipitation levels, the study evaluates more sophisticated architectures for deep learning (e.g., Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit based (GRU), and a hybrid Convolutional Neural Networks-Recurrent Neural Networks (CNN-RNN) model), as well as machine learning regressors (e.g., Random Forest, Gradient Boosted Regression). The contribution of the hybrid CNN-RNN model shows that it better captured the underlying spatial-temporal dependencies in the dataset, yielding the best forecasting accuracy and precision compared to all other models. In general, results showed a greater decrease in prediction error (RMSE, MAE) and improved consistency across various climatic conditions. Overall, work serves as a unified framework showing the potential of AI-based forecasting systems to enhance the accuracy, reliability, and representation of weather prediction systems.

Keywords: Weather forecasting, Artificial Intelligence, Deep Learning, Machine Learning, CNN, RNN, LSTM, GRU, Hybrid Models, Multivariate Prediction, Atmospheric Parameters, Time-series Analysis, Meteorology, Predictive Modeling.

Abstract: Fermented food condiments play a vital role in enhancing flavor and nutritional quality in traditional African cuisines. Among these, Daddawa is a widely consumed seasoning in West Africa, traditionally produced from African locust beans (Parkia biglobosa). However, the seasonal scarcity and labor-intensive processing of locust beans have led to the use of soybeans (Glycine max) as an alternative raw material (Falang et al., 2022). This study was conducted in Birnin Kebbi Metropolis, Kebbi State, Nigeria, to compare the proximate composition of Daddawa made from soybeans and locust beans. Samples of each type were collected from local producers and markets, air-dried, milled, and analyzed in triplicate using AOAC (2019) standard methods to determine moisture, crude protein, crude fat, crude fiber, ash, and carbohydrate contents. Results showed that soybean Daddawa contained higher crude protein (39.2%) and crude fat (17.6%) than locust bean Daddawa (33.5% and 12.4%, respectively). Conversely, locust bean Daddawa exhibited higher crude fiber (6.2%), ash (6.8%), and carbohydrate (30.3%) compared to the soybean variant (3.8%, 5.4%, and 24.5%, respectively). Both condiments had low moisture contents (9.5-10.8%), indicating good shelf stability. The findings demonstrate that soybean Daddawa provides higher protein and energy value, while locust bean Daddawa remains richer in fiber and minerals. Overall, soybean serves as a viable, affordable substitute for locust bean in the production of nutritious fermented condiments in Northern Nigeria.

Keywords: Soybean, Locust Bean, Food Condiment, Daddawa.

Abstract: Purpose: The purpose of this field survey is to examine the opportunities and challenges available for youth entrepreneurs in some selected rural areas in Suru local Government of Kebbi State Nigeria, in order to drive economic and showcase untapped opportunities Design/methodology/approach: Several successful young entrepreneurs were interviewed. The principles of case study design and method were followed. Data collection involved the use of structured questionnaire Findings: The analysis shows that although in the areas of youth participation, significant progress has been recorded in the number of youths now engaged in entrepreneurship in rural areas of Suru Local government especially among different genders. Limited access to finance, information, mentorship, training and poor infrastructural facilities remains the major challenges. Agricultural related entrepreneurial business, vehicle mechanic, phone repairs/accessories still remain untapped business opportunities in the area. The findings also revealed that availability of local resources in the area, growing local market, youth access to digital technology is a prospect for the future of entrepreneurial opportunity in the area. Conclusion: we therefore concluded that access to finance and improve infrastructure and access to information and training can bring youth entrepreneurship potentials in the area. Recommendation: Government improve access to infrastructure in rural area such as electricity. Government entrepreneurship program should well implement to reach to the real beneficiates. Community program to support youth entrepreneurs should be established.

Abstract: Three roller bending machines are widely used for shaping metallic components critical in industries such as automotive and aerospace. This review analyzes the effects of roller diameter and number of passes on power consumption in these machines. Larger roller diameters enhance contact mechanics by increasing the contact area, which reduces bending forces, but increase torque requirements and more power. Additionally, multi-pass bending distributes deformation incrementally, lowering peak power demands per pass but increasing total operation time and energy use. The interaction of these parameters influences energy efficiency, dimensional accuracy, surface finish, and springback behavior. Theoretical models rooted in bending moments and torque-speed relationships corroborate empirical findings. Advancements in Industry 4.0 and AI-driven process control offer promising avenues for real-time optimization of these parameters, potentially enabling significant energy savings and quality improvements. Despite progress, research gaps persist in adaptive control systems for power optimization. This article synthesizes current knowledge, discusses practical implications for machine design and process management, and suggests future research directions focused on intelligent, data-driven bending operations.

Keywords: Three roller bending machines, Power consumption, Roller diameter, Number of passes, Energy efficiency

Abstract: GreenWing is transforming precision agriculture with state-of-the-art drone technology in order to increase crop yield, reduce resource waste, and enhance sustainable farming practices. GreenWing gives farmers real-time information about crop health, soil conditions, and field variability by using drones equipped with imaging systems and multispectral sensors. This essay explores the application of drone technology in agriculture, including how well it manages and monitors crops and how it has the potential to transform modern farming practices. The results of several field tests demonstrate that drone precision agriculture can boost crop yields while lessening its negative effects on the environment.

Abstract: This project proposes an Automated Vehicle Access Control System that leverages Number Plate Recognition (NPR) technology to identify and authorize vehicles entering a restricted area, such as a university campus or corporate facility. The system captures the vehicle's number plate image using a high-resolution camera positioned at the entry gate. The captured image undergoes grayscale conversion to simplify data processing and reduce computational complexity. Using image processing techniques such as noise removal, edge detection (e.g., Canny or Sobel operators), and segmentation, the number plate region is isolated. Once extracted, the Optical Character Recognition (OCR) module converts the plate's characters into a machine readable text format. This alphanumeric string is then cross-referenced with a predefined database containing authorized vehicle numbers. If a match is found, the system retrieves and displays the stored image of the corresponding vehicle, and the gate control mechanism permits entry. In case of a mismatch, the system displays a "No Access" message and denies entry to the premises.

Keywords: Number Plate Recognition, OCR, Edge Detection, Grayscale Conversion, Access Control System.

Abstract: Cloud computing has definitively transitioned from an organizational option to the indispensable foundation of modern IT, driven primarily by its non-negotiable value propositions of elasticity, Opc Ex efficiency, and accelerated business agility. This paper Analyze the evolution of the cloud, focusing on the core benefits that continue to drive mass adoption and the emerging architectural paradigms-namely Serverless Computing, Edge Computing, and deep AI/ML integration-that define its future. Critically, the study evaluates the shift in strategic challenge: moving from the technical hurdle of initial migration to the organizational hurdle of governance and complexity management. Major challenges include financial sprawl (necessitating FinOps), the operational overhead of hybrid and multi-cloud environments, and the critical risk of security misconfiguration within the shared responsibility model. Finally, the paper concludes by detailing the strategic solutions required for future resilience, proposing that success hinges on mastering AIOps for automation, adopting Confidential Computing for enhanced security, and leveraging open standards to mitigate vendor lock-in.

Keywords: Cloud Computing, Multi-Cloud, Serverless, FinOps, AIOps, Edge Computing, Confidential Computing, Vendor Lock-in, Data Sovereignty.

Abstract: By providing a customer-friendly online meal ordering platform, our technology addresses the shortcomings of traditional queening systems. It makes it easy for customers to order food from restaurants and mess services. The technology enhances the process of receiving customer orders by providing an online food menu that makes it easy for customers to select what they want and track their orders. Additionally, patrons have the option to review the food they have consumed, which aids hotel staff in identifying areas for improvement. The system can also recommend restaurants and accommodations based on user reviews. There are options for online or pay-on-delivery payment methods, and each user has an account, guaranteeing safe ordering with distinct IDs. And passwords.

Keywords: food ordering system, dynamic database management, and smartphone.

Abstract: The arrival of independent drones, powered by Artificial Intelligence (AI), has the implicit to revise diligence ranging from husbandry and logistics to disaster operation and environmental monitoring. This exploration explores the integration of AI technologies in independent drones, fastening on their capabilities for independent decision- timber, navigation, and real- time data processing. The design delves into the core AI factors, including computer vision, machine literacy, and underpinning literacy, which enable drones to perform complex tasks without mortal intervention. By exercising deep literacy models like Convolutional Neural Networks (CNNs) for object recognition and underpinning literacy (RL) for path planning, drones can autonomously descry obstacles, optimize flight paths, and acclimatize to dynamic surroundings. likewise, the design investigates the operation of independent drones in real- world scripts similar as perfection husbandry, disaster relief, and structure examination, demonstrating their eventuality to ameliorate functional effectiveness, reduce mortal threat, and give critical real- time data for decision- timber. Despite these advancements, challenges similar as nonsupervisory enterprises, safety issues, and technological limitations still hamper wide relinquishment. This exploration aims to punctuate both the implicit and the hurdles in the deployment of AI- driven independent drones, furnishing a comprehensive understanding of how AI can enhance their capabilities and shape the future of drone- grounded operations.

Keywords: Autonomous Drones, Unmanned Aerial Vehicles (UAVs), Drone Navigation Algorithms, Artificial Intelligence in Drones

Abstract: The Web page login system is a web-based application intended for online registration. The main objective of this application is to make it interactive and its ease of use and provide security. It would make information of students / employees and selection about their category easier. The application also provides a multiple option feature so that a user can fill proper information. The main emphasis lies in providing a user-friendly search engine for effectively showing the desired results. With the rapid evolution of the wireless communication technology, user authentication is important in order to ensure the security of the wireless communication technology. Password play an important role in the process of authentication. Due to the issues, there are many solutions has been proposed to improve the security of wireless communication technology. In this paper, the previously proposed solution will be used to enhance the security of the system. The solution adopted is the one-time password, hashing and two-factor authentication. In this context, we have tried to explain how we have aimed to bring security to a common online system. And how we have limited the number of users with the help of the login system. And we have brought restrictions to those who are outside abusers. In short, we have made it clear how we can strengthen the login system in the future.

Abstract: Deepfake can distort our perception of the truth and we need to develop a strategy to improve their detection. Deep Fakes are increasingly detrimental to privacy, social security, and democracy. We plan to achieve better accuracy in predicting real and fake videos.

Keywords: Deepfake, Deepfake detection System, Face Forensic, AI arms race, Computer Vision

Abstract: The rapid growth of the laptop industry has led to a wide variation in product specifications and prices, making price prediction a challenging task. Consumers often struggle to identify whether a laptop is fairly priced, while retailers face difficulties in determining competitive pricing strategies. To address this challenge, this study proposes an integrated machine learning approach for laptop price prediction using Linear Regression, Decision Trees, and Random Forests. The dataset consists of key laptop features such as brand, processor type, RAM size, storage capacity, graphics card, display characteristics, and operating system, which significantly influence the overall price. Before model development, preprocessing steps such as data cleaning, feature encoding, and normalization are performed to ensure consistency and accuracy. Three predictive models are applied and compared: Linear Regression provides a baseline by establishing a linear relationship between features and price. Decision Trees capture non-linear relationships and offer rule-based interpretability. Random Forests, as an ensemble method, combine multiple decision trees to enhance accuracy and reduce overfitting. The performance of these models is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² Score. The results indicate that Random Forest outperforms the other models, achieving higher accuracy and robustness, while Linear Regression and Decision Trees provide valuable interpretability and feature insights. This integrated approach demonstrates the effectiveness of combining multiple machine learning techniques for price prediction tasks. The findings can assist consumers in making informed purchase decisions, e-commerce platforms in optimizing pricing strategies, and manufacturers in competitive market analysis. Furthermore, this research highlights the potential of machine learning in real world pricing applications and lays the foundation for future exploration with advanced models such as Gradient Boosting and Deep Learning.

Abstract: Online voting system in which the election data is stored and processed. To achieve higher level of security one levels of authentication techniques are used. The authentication technique used is a Face Detection and Recognition system. In this method of authentication, the voters face image captured during the registration is compared with the image captured by the webcam at the time of casting vote. After the first level of authentication is done a voter can casting the vote. These techniques provide a more secure platform thus overcoming vulnerabilities of the traditional voting system. The Online Voting System is a web-based application. The system has a centralized database to keep records of all the Voters and Candidates and Final Results. This Online Voting System is based on SMS sending to voters, to confirmation of Vote. This web-based system is time saving, workload reduced information available at time and it provide security for the data.

Keywords: Image Processing, Voting System, Face Recognition, OTP

Abstract: The automation of emotion recognition using Artificial Intelligence (AI) has seen great interest in the past decade as a step towards making machines more sympathetic with humans emotions. In which way the AI model identifies people emotions through their audio-visual data is conceptually outlined in this paper mainly focusing on design on some theoretical foundations. It describes the integration of video based facial expression analysis and audio based speech tone recognition to form a multimodal emotion recognition system. In the conceptual framework, phases like data pre-processing, feature extraction, model building and multimodal fusion have been introduced. As a supplement, it emphasizes theoretical performance benefits, potential risks such as ethical bias and interpretability, and use-cases in associated HCI, learning and mental health areas. The research indicates that when video and audio data is brought together, the emotional intelligence of AI is given a conceptual boost, thereby laying the groundwork for emotionally aware computing.

Abstract: The rapid growth of online art platforms has created a challenge in helping users discover artworks that match their interests. Personalized recommendation systems have become essential to enhance user engagement and provide a seamless browsing experience. This research focuses on developing an intelligent artwork recommendation system using both content-based and collaborative filtering techniques...

+91-7667918914 iarjset@gmail.com 0 Items International Advanced Research Journal in Science, Engineering and Technology A Monthly Peer-Reviewed Multidisciplinary Journal ISSN Online 2393-8021 ISSN Print 2394-1588 Since 2014 Home About About IARJSET Aims and Scope Editorial Board Editorial Policies Publication Ethics Publication Policies Indexing and Abstracting Citation Index License Information Authors How can I publish my paper? Instructions to Authors Benefits to Authors Why Publish in IARJSET Call for Papers Check my Paper status Publication Fee Details Publication Fee Mode FAQs Author Testimonials Reviewers Topics Peer Review Current Issue & Archives Indexing FAQ’s Contact Select Page A STUDY ON DIGITAL BANKING AND AN INDIAN ATTITUDE Dr. D. SUNDARAMOORTHI

Abstract: In the present situation, the interest of banking is whenever, anyplace banking, this requires inventive strong secure enhanced and prepared to meet the desires for engaged and educated clients. Computerized change is simply moving from customary banking to an advanced world. It is a crucial change in how banks and other money related organizations find out about, how to associate with and fulfil the clients. A powerful advanced change starts with a comprehension of computerized client conduct, inclinations, decisions, prerequisites, and yearnings and so forth. This change prompts the significant changes in the associations from item driven to client driven view. This paper covers job of digitization in Indian banking, factors influence the extent of computerized banking in India, advanced financial patterns in India, innovative achievements in Indian banks. The present investigation depends on optional information. The information has been extricated from the different sources like research articles, productions from administration of India, different announcements of RBI and confirmed sites. The examination found that, computerized banking has radically decreased the working expenses of banks. This has made it workable for banks to charge lower expenses for administrations and furthermore offer higher loan fees for stores. Lower working expenses have implied more benefits for the banks. The investigation likewise found that, advanced banking is having the capacity to change the scene of monetary consideration. Simple utilization of computerized banking can quicken the mix of unbanked economy to the standard. Downloads: | DOI: 10.17148/IARJSET.2025.121037 How to Cite: [1] Dr. D. SUNDARAMOORTHI, "A STUDY ON DIGITAL BANKING AND AN INDIAN ATTITUDE," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2025.121037 Copy Citation Call for Papers Rapid Publication 24/7 April 2026 Submission: eMail paper now Notification: Immediate Publication: Immediately with eCertificates Frequency: Monthly Downloads Paper Format Copyright Form   Submit to iarjset@gmail.com or editor@iarjset.com   Submit My Paper Author CenterHow can I publish my paper? Publication Fee Why Publish in IARJSET Benefits to Authors Guidelines to Authors FAQs (Frequently Asked Questions) Author Testimonials IARJSET ManagementAims and Scope Call for Papers Editorial Board DOI and Crossref Publication Ethics Editorial Policies Publication Policies Subscription / Librarian Conference Special Issue Info ArchivesCurrent Issue & Archives Conference Special Issue Copyright © 2026 IARJSET This work is licensed under a Creative Commons Attribution 4.0 International License. Open chat

Abstract: The Smart Career Predictor is an Artificial Intelligence (AI)-based framework designed to assist students and professionals in selecting suitable career paths through data-driven insights. The primary purpose of this research is to develop a predictive system that leverages machine learning algorithms to analyze an individual's academic performance, skills, interests, and psychometric attributes for accurate career guidance. The scope of the study encompasses the design and implementation of a web-based model capable of integrating multiple data sources and providing personalized recommendations. The methodology employs supervised machine learning techniques, including Decision Tree and Random Forest classifiers, trained on verified datasets to predict optimal career domains. The system architecture integrates user input modules, a predictive engine, and a visual recommendation dashboard. Findings from the prototype demonstrate that the AI-based model significantly enhances the accuracy, efficiency, and objectivity of career counseling, thereby bridging the gap between education and employment through intelligent, scalable, and personalized career prediction.

Keywords: Artificial Intelligence (AI); Machine Learning (ML); Career Prediction; Data-Driven Guidance; Educational Technology; Decision Tree; Random Forest; Personalized Recommendation System.

Abstract: Generative AI, particularly Large Language Models (LLMs) such as ChatGPT, has rapidly transformed the landscape of higher education. This project investigates how the integration of such AI tools affects student academic performance, drawing on meta-analyses, empirical studies, and survey-based insights. Evidence suggests that AI-assisted learning can improve academic outcomes, learning perceptions, and higher-order thinking. Yet, challenges remain around assessment integrity, teacher preparedness, and ethical use. The report develops a practical framework for integrating GenAI in university environments, emphasizing teacher support, equitable access, and rigorous evaluation. Key recommendations include enhancing faculty training, redesigning assessments for the AI age, and continuous monitoring of student engagement and outcomes.

Abstract: The crucial role of Technical and Vocational Education and Training (TVET) in developing human capital by equipping individuals with knowledge, skills, and capabilities needed to succeed in a rapidly changing world cannot be gainsaid. In Kenya, TVET sub-sector is currently implementing a new Competency-Based Education and Training (CBET) curriculum that focuses on 50% theory and 50% industrial training as a panacea. However, there has been increased reluctance to implement CBET courses. The purpose of this study was to examine the implementation of CBET in electrical and electronics engineering courses in Technical and Vocational Colleges in Uasin Gishu County, Kenya. In this regard, the study sought to determine the extent to which student support services affects implementation of CBET system in electrical engineering courses in Technical and Vocational Colleges in Uasin Gishu County. This study adopted a pragmatic paradigm with an explanatory research design. The target population was 119 respondents who were drawn from the Ziwa, Moiben, Rift Valley Technical Training Institute, Ngeria, Kipkabus, Turbo Technical Training Institute and Koshin Technical Training Institute in Uasin Gishu County. The study used census for the enumeration of all the 119 electrical and electronic engineering trainers and HoDs of the 7 TVET institutions. Questionnaire was used to collect the primary data and analyzed using inferential statistical techniques using Statistical Package for Social Sciences (SPSS) version 25 software. From the findings, the coefficient of determination (R square) of 0.489 indicated that student support services explained only 48.9% of the variation or change in implementation of CBET system in electrical and electronics engineering courses in Technical and Vocational Colleges in Uasin Gishu County, Kenya. The study findings further indicated that students support services significantly influence implementation of CBET system in electrical and electronics engineering courses in Technical and Vocational Colleges in Uasin Gishu County, Kenya. Therefore, TVET institutions should consider strengthening students support services in line with the CBET requirements using a policy framework to enhance implementation of CBET system. The findings of the study are of significance to TVET and the stakeholders in improving the implementation of CBET system.

Keywords: Student support, Competency-Based Education and Training

Abstract: This abstract proposes a contactless method for monitoring respiratory rate (RR) using facial video analysis, which overcomes the discomfort of traditional sensors for continuous monitoring in clinical and home settings.

Keywords: Contactless monitoring, Vital sign monitoring (or vital sign detection), Respiratory rate (RR), RGB camera, Optical flow algorithm, Skin segmentation, Filtering or noise reduction

Abstract: This paper presents a comparative study of seismic design provisions as per Indian Standards IS 1893:2002, IS 1893:2016, and the draft IS 1893:2023. A G+14 RCC building was modeled and analysed using ETABS software under the respective codes. The comparison focuses on key seismic parameters-base shear, storey displacement, and storey drift. Results show that the 2023 draft code introduces a performance-based approach, with revised seismic zoning, updated response spectra, and improved site classification. These revisions enhance building safety and align Indian practices with international standards.

Keywords: IS 1893, Seismic Analysis, RCC Building, ETABS, Base Shear, Storey Drift, Response Spectrum.

Abstract: Malnutrition is an emerging health problem that has challenged healthcare authorities worldwide. This study aims to determine anthropometric measurements and the malnutrition status children of Anaikatti tribal area of Coimbatore district. A cross-sectional study was conducted among 97 tribal school children (48 boys and 49 girls) aged 5-14 years in Anaikatti village. Socioeconomic data were collected using a structured questionnaire; anthropometric measurements were assessed using standard WHO procedures. All participants belonged to the lower middle-income group. Among boys aged 5-9 years, 8% exhibited thinness, 24% were overweight, 4% were obese and 64% belongs to normal anthropometric status. In the 10-14-year group, 35% were with normal anthropometrics, 13% showed severe thinness, 26.1% thinness, 27.1% overweight, and 8.7% obesity. Among girls aged 5-9 years, 7.7% were thin, 61.6% with normal anthropometrics, 26.9% overweight, and 3.8% obese, while in the 10-14-year group, only 17.4% were with normal anthropometrics, 26.1% had severe thinness, 43.5% thinness, and 8.7% overweight, and 4.3% obesity. Despite belonging to the same socioeconomic group, both under-nutrition and over-nutrition coexisted, indicating a double burden of malnutrition. Personalized nutrition education and counseling sessions were conducted for all participants and their parents to improve awareness and promote healthy eating practices. The study highlights a significant coexistence of thinness and overweight among tribal school children in Anaikatti, emphasizing the need for school based targeted nutritional interventions and continuous community-based education.

Keywords: anthropometry, nutrition education, obesity, thinness, Tribal children, nutrition education

Abstract: The study examines the recruitment, training, performance evaluation, and retention practices of private school teachers in Mysore District, focusing on how these HR functions influence teacher satisfaction and institutional effectiveness. Data were collected from 150 teachers and 30 administrators through structured surveys and analyzed using descriptive statistics and chi-square tests. The findings reveal that over 90% of schools prioritize educational qualifications, communication skills, subject expertise, and demo-class performance during teacher selection. Training programs were rated as moderately to highly effective by 70-80% of respondents, particularly in pedagogical and digital-learning areas, though most teachers reported inadequate follow-up evaluation. Performance appraisals continue to rely heavily on traditional observation based methods, with 65% of teachers indicating limited linkage to career advancement. Retention challenges persist due to factors such as salary dissatisfaction and limited professional growth, contributing to a notably high turnover rate reported by school administrators. The study highlights the need for structured, transparent, and development-oriented HR practices to strengthen teacher motivation, reduce attrition, and improve institutional performance. The research offers localized insights into enhancing teacher management systems in Mysore's private schools.

Keywords: Private Schools, Teacher Selection Criteria, Recruitment Practices, Training and Development, Performance Evaluation, Retention Strategies, Mysore District.

Abstract: Background: Incident recurrence following prior investigation and corrective action represents a preventable and persistent failure in construction occupational safety management, suggesting systemic deficiencies in corrective action quality and organizational learning. Methods: This study presents a secondary analysis of four publicly available federal datasets: the Occupational Safety and Health Administration (OSHA) Enforcement Database (2018 to 2023), OSHA Injury Tracking Application (ITA) (2018 to 2022), Bureau of Labor Statistics (BLS) Census of Fatal Occupational Injuries (CFOI) (2018 to 2022), and National Institute for Occupational Safety and Health (NIOSH) Fatality Assessment and Control Evaluation (FACE) reports (2018 to 2022), to characterize the scope and root cause determinants of incident recurrence in U.S. construction. Results: OSHA enforcement data document a 29.9% increase in construction repeat citations between 2018 and 2023. BLS CFOI analysis identifies five fatal event categories that appeared in the top five causes of construction fatalities every year from 2018 to 2022. OSHA ITA data reveal structural steel contractors exhibited the highest same-category recurrence rate (41.7%). Directed content analysis of 117 NIOSH FACE reports identifies absence of fall protection (68.4%), inadequate hazard communication (61.2%), and insufficient supervisory oversight (57.9%) as the most recurring causal categories, with 43.6% of reports documenting recurrence of a previously investigated incident type. Conclusion: The findings provide government-validated evidence for standardizing systemic corrective action governance in construction safety management.

Keywords: Root cause analysis, incident recurrence, construction safety, OSHA enforcement, repeat citations, corrective action quality, NIOSH FACE, BLS CFOI, fatality prevention, organizational learning.