VOLUME 12, ISSUE 2, FEBRUARY 2025

Abstract: In this paper a probabilistic inventory model with deterministic and stochastic environments is discussed. We analyze the model by geometric programming and fuzzy geometric programming techniques. During the entire discussion the model is discussed under Uniform and exponential lead time demands. Finally it is concluded that fuzzy geometric programming gives us more optimized result than geometric Programming Technique.

Keywords: Geometric Programming, Fuzzy Geometric Programming, Stochastic Environment.

Abstract: The study investigated seasonal variation of particulate pollutant concentrations in parts of Port Harcourt Metropolis, Rivers State, Nigeria. The study adopted descriptive research design which involved the description of particulate pollutant concentrations in the study area. The study assessed PM2.5, PM10 and PM1.0 as well as meteorological parameters in eight (8) locations (Rumuola, Rukpokwu, Elioparanwo, Mgbuoshimini, Rumuokurushi, Oroworukwo, Trans-Amadi and Port Harcourt Township) for both the wet and dry seasons. It was reported that, PM2.5 during the wet season in all location was above WHO permissible limit. Also, the concentrations of PM2.5 and PM10 in the dry season for all the locations were above WHO permissible limit of air quality guidelines. It was recommended that, legislation measure should be put in place to curb this menace, alternative sources of fuel should also be adopted.

Keywords: Atmosphere, Environment, Particulate Matter, Pollutant Concentrations, Port Harcourt, Rivers State.

Abstract: This qualitative exploratory research combines data from six professionals working in the fields of cybersecurity, education, and medicine with in-depth analysis of selected white papers, reports, and case studies. The findings reveal huge detection challenges as regards the sophistication of adversarial inputs and limitations to traditional detection mechanisms. Some of the mitigation strategies discussed in the paper include adversarial training, hybrid models for detection, and the integration of watermarking technologies. Further, this study has shed light on the need for deep learning-especially of CNNs and transformers-in automating feature extraction that could improve resilience in deep learning models against adversarial types of threats. The resolution of the challenges presented here will provide the ability to contribute toward developing scalable, transparent, and adaptive frameworks capable of ensuring cybersecurity resilience of generative AI systems throughout their lifecycle against evolving adversarial threats. In this paper, consideration is taken of some of the adversarial attacks against generative AI systems and some strategies that in efforts towards strengthening cybersecurity are made for mitigation. Qualitative exploratory research was done, combining data from six professionals working in the fields of cybersecurity, education, and medicine, coupled with in-depth analysis of selected white papers, reports, and case studies. Results pointed to big detection challenges about the sophistication of adversarial inputs and limitations to traditional detection mechanisms. Adversarial training, detection by hybrid models, and integrating watermarking technologies are some of the mitigation strategies discussed in the paper. Further, this study identified the need for deep learning, especially of CNN and transformers, in automating feature extraction, which could give better resilience for deep learning models against adversarial kinds of threats. Anchoring on game theory, adversarial training, and explainable AI, this covers a very strong optimization approach with a view to model transparency and interpretability of the decisions of detection. Given the modular system design and distributed computing, this work enables scalability and efficiency in Anomaly Detection, Representation Learning, and Robust Optimization methods. In view of the challenges presented, these contributions become possible for the development of scalable, transparent, and adaptive frameworks that can ensure cybersecurity resilience in generative AI systems against dynamically evolving adversarial threats throughout their whole life cycle.

Keywords: Cybersecurity, Deep Fakes, Machine Learning, Artificial Intelligence, Economic Impact

Abstract: Moment marketing has gained immense popularity in India as brands leverage real-time events, trends, and cultural moments to engage with their audience. This marketing approach involves capitalizing on viral content, social media trends, and major happenings to create timely and relevant campaigns that resonate with consumers.

Keywords: Moment marketing, social media influencers

Abstract: This project investigates the changes in tyre surface temperature during aircraft touchdown when pre-rotation is applied to the landing gear. During the landing phase, tyres undergo rapid temperature fluctuations due to the sudden interaction with the runway, which can affect tyre performance, longevity, and safety. Pre-rotation involves rotating the tyres before touchdown to reduce the relative speed between the tyre and the runway, potentially minimizing the thermal shock upon initial contact. The project aims to analyse how pre-rotation influences the temperature distribution across the tyre surface at touchdown, by using experimental method. The results will help to identify the potential benefits of incorporating pre-rotation into landing procedures, providing insights into improved tyre durability, enhanced safety, and more efficient landing dynamics.

Keywords: Pre-Rotation, Aircraft Touchdown, Friction, Heat Generation and Tyre Surface Temperature

Abstract: Handloom weaving has been a foundation of traditional textile production across India, supporting the livelihoods of rural artisans. The sector is mostly decentralized. The dyeing operations associated with this industry generate substantial volumes of wastewater, contributing to environmental pollution and water scarceness. This study aims to develop a cost-effective and eco-friendly decentralized wastewater treatment system personalized to the needs of handloom clusters. The research focuses on the treatment of sulphur and reactive dye wastewater, emphasizing colour removal and chemical oxygen demand (COD) reduction using a combination of coagulation/flocculation (CF) followed by multilayer membrane filtration. The study systematically evaluates the performance of neutral metal salts, including Magnesium chloride (MgCl₂), Aluminum sulphate (Al₂(SO₄)₃), Ferric chloride (FeCl₂), and Calcium carbonate (CaCO₃), to determine the most effective coagulant for optimal dye removal and pH neutralization. The proposed treatment approach aims to enable the safe reuse of treated water in the dyeing process, reducing freshwater dependency and promoting sustainability in the handloom sector. Findings indicate that integrating efficient coagulation-flocculation mechanisms with gravity-based filtration systems can provide a scalable and decentralized wastewater treatment solution for rural handloom clusters. The study underscores the potential of low-cost, environmentally sustainable water management practices, offering a viable alternative to conventional wastewater disposal methods in Impoverished regions.

Keywords: wastewater treatment, handloom industry, dye effluent, coagulation-flocculation, metal salts, COD reduction, colour removal, sustainability.

Abstract: The process of protecting networks, computers, mobile devices, servers, electronic systems and data from malicious attacks is called Cyber Security. It's also referred as Information Security (INFOSEC) or Information Assurance (IA) or System Security. In cyber world threats are constantly new, malevolent hackers are not going to give up anytime soon. As long as there are hackers, the cyber security will remain a trending technology. And to provide the strong need of cyber security professionals, the number of cyber security jobs is growing three times faster than other technical jobs. AI has enabled us to develop useful tools such as speech recognition (Siri), search engines (Google), and facial recognition software (Facebook) etc. With strong public-private partnerships and cross-pollination among industry, academia, and international partners, we can build an unshakeable cyber security foundation based on sensor-embedded systems, data, and AI-driven predictive analytics. According to Gartner, by 2025, 60% of organization will use cyber security risk as a primary determinant in conducting a third party transaction. This article shows the implementation of artificial intelligence on antivirus as both beneficial and detrimental.

Keywords: Cyber Attacks, Cyber Security, Artificial Intelligence, Antivirus, Machine Learning

Abstract: Pyrazolines are well known and important nitrogen containing five membered heterocyclic compounds. Pyrazoline derivatives have been extensively studied because of their ready accessibility through synthesis, diverse chemical reactivity, various biological activities and variety of industrial applications. In the present study, Novel pyrazoline derivatives were carried out by cyclization of acrylamide derivatives with hydrazine hydrate in the presence of benzoic acid. Structures of the newly synthesized compounds were assigned on the basis of elemental analysis, IR, 1H NMR, and 13C NMR.

Keywords: Pyrazoline derivatives, spectral analysis, Substituted pyrazoline, Synthesis.

Abstract: The rapid advancement of artificial intelligence (AI), particularly in generative models, has led to an exponential increase in the need for efficient handling of high-dimensional vector data. This paper explores the critical role of vector databases in modern AI applications, focusing on their capabilities, use cases, and the challenges they address.Vector databases have emerged as a critical component in the development of generative AI applications. This paper provides a comprehensive review of the role of vector databases in generative AI, focusing on their ability to store, manage, and retrieve high-dimensional vector data efficiently. This paper explores the critical role of vector databases in modern AI applications, focusing on their capabilities, use cases, and the challenges they address. We examine the fundamental limitations of relational databases in handling vector data, contrasting them with specialized vector databases that are optimized for high-dimensional data storage and similarity search. The paper surveys various vector database solutions, including those offered by major cloud providers like Google, AWS, and Microsoft, and highlights their integration with generative AI frameworks such as Lang Chain, Semantic Kernel, and Vertex AI. We also discuss the impact of vector databases on retrieval-augmented generation (RAG) and other AI-driven applications, emphasizing their ability to enhance the accuracy and relevance of large language models (LLMs). Additionally, the paper provides insights into future trends, including scalability improvements, integration with knowledge graphs, and ethical considerations in AI development. By addressing performance, cost, and implementation challenges, this paper aims to provide a comprehensive understanding of how vector databases are shaping the future of generative AI.

Keywords: Vector Databases, Generative AI, Retrieval-Augmented Generation (RAG), High-Dimensional Data, Machine Learning, AI Applications Vector Databases, Large Language Models

Abstract: This study explores how machine learning can be used to anticipate cybercrimes, with an emphasis on detecting attack techniques and possible offenders. The dataset used includes comprehensive records of criminal activity, including the characteristics of criminals and the methods used in attacks. The study compares these algorithms' performance in order to ascertain how accurate they are at forecasting the kind of cyberattack as well as the characteristics of the attacker. The study also looks at the potential effects of a number of variables on the forecasts, including gender, income level, work position, and the seriousness of the crime. Additionally, it looks into how feature selection and preprocessing methods can improve model performance. This work's ultimate objective is to assist law enforcement organizations in improving their capacity to foresee and stop cyberattacks.

Keywords: Cyber Attack Prediction using Machine Learning involves cybersecurity concepts such as phishing, malware detection, data breaches, and intrusion detection systems (IDS). It utilizes machine learning techniques like logistic regression, random forests, SVM, and deep neural networks to analyze network traffic and detect anomalies. Key processes include feature extraction, dimensionality reduction (PCA, t-SNE), and data augmentation to enhance model accuracy. Performance evaluation metrics such as precision, recall, F1- score, and cross-validation are crucial for ensuring reliable threat detection. The project also involves tools and techniques like supervised learning, hyperparameter tuning, and behavior-based threat intelligence to improve predictive capabilities.

Abstract: In this research paper, design and simulation of 4T-GNRFET Cascode Amplifier at 45 Nanometer Technology Node has been performed. DC voltage gain, average power, bandwidth and output resistance have been computed using HSPICE Software. Further, the low voltage Cascode Op Amp has better DC Gain, output resistance and less power dissipation. DC voltage gain is 39.5dB, average power is 30 nW, bandwidth is 3.6MHz, Phase Margin 89.30 and Output Resistance 35.15 K-Ohms as obtained from simulation results of HSPICE Software. The proposed circuit of 4T-GNRFET based Cascode Amplifier at 45 Nanometer Technology Node is suitable for A.I. Applications due to astonishing electronic properties of proposed circuit.

Keywords: 4T-GNRFET-Cascode amplifier, Output Resistance, Band width, Average Power, DC Gain

Abstract: Voice assistants, which use natural language processing and speech recognition to facilitate smooth conversation, have completely changed human-computer interaction. The development of AI-powered systems that can automate daily operations has been made possible by the rise of intelligent personal assistants like Siri, Alexa, Google Assistant, and Cortana. This paper provides a thorough review of the literature on the creation of JANI AI (Just in Time Assistant for Necessary Insights), an AI-based virtual assistant that can handle a variety of user-centric tasks, including music recommendations, real-time information retrieval, speech recognition, face recognition, optical character recognition (OCR), app automation, and customized voice-based note-taking. The paper examines current voice assistant systems and technologies, such as IoT-based smart home automation (Keerthana et al., 2018) [5], natural language processing (Nil Göksel et al., 2018) [4], and speech recognition (Nguyen et al., 2007) [2].The interactive capabilities of voice assistants have been greatly improved by the combination of gTTS (Google Text-to-Speech) and AIML (Artificial Intelligence Markup Language) for creating dynamic conversational assistants (Gawand et al., 2020) [9].The application of OCR for text extraction from photos and Convolutional Neural Networks (CNNs) for face recognition broadens the capabilities of JANI AI and provides flexible features for both visually impaired and non-visually impaired users.In order to create a multipurpose AI-powered virtual assistant, this review examines several existing systems, highlights developments in the field, and suggests a hybrid design that makes use of open-source AI models and machine learning techniques.

Keywords: Voice Assistant, Conversational AI, Speech Recognition, Natural Language Processing (NLP), Optical Character Recognition (OCR), Face Recognition, Text-to-Speech (TTS), App Automation, Virtual Personal Assistant, Intelligent Personal Assistant, Human-Computer Interaction (HCI), Information Retrieval, Python Automation, Open-Source LLMs, Smart AI Assistant, Audio-Based Search, Real-time Data Extraction, Multi-Modal AI Systems, AI-Based Notes Management, Document Summarization, AI-Powered Chatbot, Interactive Games.

Abstract: This research explores the potential of stepper motors on Computer Numerical Control (CNC) machines focusing on simulation and control using Arduino. Stepper motors offer advantages like easier control and lower costs, but they face challenges like oscillations and limited torque. This study presents a novel, low-cost, closed-loop control system to address these issues. . The design and implementation of a stepper motor circuit, along with relevant Arduino code, enabled precise motor movement. The simulation results showed that optimizing delay and speed settings significantly improved the motor's performance, achieving faster and more reliable responses suitable for practical CNC applications. Keynote: CNC, STEPPER MOTOR, CLOSED LOOP, LOW COST.

Abstract: This study investigates the effects of fiber length and content on the mechanical properties of jute fiberreinforced polypropylene composites through advanced statistical modeling approaches. Jute fibers of varying lengths (3mm, 5mm, and 10mm) and weight contents (10%, 20%, and 30%) were incorporated into polypropylene matrices, and the resulting composites were systematically characterized for their tensile, flexural, and impact properties. Through Response Surface Methodology (RSM), Weibull statistical analysis, and Artificial Neural Network (ANN) modeling, we established that composites with 10mm fiber length and 10% fiber content exhibit optimal mechanical performance, with a tensile strength of 30.3 MPa. Weibull analysis confirmed significantly higher reliability for 10mm fiber composites (β=30.68) compared to shorter fiber alternatives, while ANN modeling effectively captured non-linear behaviors, particularly the distinctive dip-and-recovery pattern observed in 5mm fiber samples. Economic and environmental analyses demonstrate that these optimized composites offer substantial benefits, including a 26% reduction in global warming potential and a 10% cost advantage compared to conventional glass-fibre-reinforced alternatives. This research validates jute fiber-reinforced polypropylene composites as environmentally advantageous and economically viable options for applications where their specific performance characteristics are sufficient.

Keywords: Jute fiber, Polypropylene composites, Response Surface Methodology (RSM), Weibull statistical analysis, Artificial Neural Network (ANN) modelling, Sustainable composites

Abstract: After making significant strides from its first uses to help the blind and visually impaired, optical character recognition (OCR) has evolved into a vital tool for automated data extraction from photos. The necessity to efficiently handle massive amounts of image-based data and the increasing digitization of information have been the driving forces behind this change. The advancement of OCR is examined in this research, with a focus on the contribution of Convolutional Neural Networks (CNNs) to increased text extraction job accuracy. Conventional OCR methods, which mostly used rule-based strategies and manually created features, had trouble handling differences in font sizes, styles, and image quality, especially when dealing with intricate backgrounds. OCR has been greatly impacted by the paradigm shift in computer vision brought about by the development of deep learning, particularly CNNs. CNNs, which draw inspiration from the human visual system, are skilled at automatically deriving complex patterns and features from image data without the need for intensive feature engineering. OCR performance has significantly improved as a result of this capacity, allowing computers to more accurately handle a variety of font types, scales, and even difficult backdrop conditions. The current literature on CNN-powered OCR systems is reviewed in this work, which looks at diverse architectures, methods, and language-specific applications. It also describes a brand-new system architecture that achieves reliable and effective text extraction from pictures. The suggested design aims to address the shortcomings of current instruments while highlighting the wider societal advantages of developing OCR technology.

Keywords: Utilising OCR and CNN to extract text from images includes concepts like Convolutional neural network, Deep learning, Optical Character Recognition, and Feature Extraction and Key words like Text extraction, Text comments, and Image extraction using CNN, Text detection, text recognition, CNN, Text Extraction, and Pre-Processing.

Abstract: This survey paper presents a comparative study of recent advancements in vision-language models, focusing on their methodologies, applications, and impact on tasks such as image captioning and multimodal understanding. It analyzes three key research papers: Gemini AI for Vision-Language Tasks (2024), BLIP: Bootstrapped Language- Image Pretraining (2022), and Transformers for Image Captioning (2020), each contributing uniquely to the field of artificial intelligence and computer vision. The paper on Gemini AI introduces a state-of-the- art multimodal large language model designed for seamless integration of text, images, audio, and video. Its optimized transformer-based architecture enables extensive contextual understanding, making it highly effective for real-world multimodal tasks. However, its high computational requirements and potential challenges in handling complex real-world scenarios pose limitations. The BLIP framework addresses the challenge of leveraging noisy web data for effective language- image pretraining. It implements a bootstrapped learning approach by combining synthetic caption generation and a filtering mechanism to improve dataset quality. This technique significantly enhances vision-language model performance across multiple benchmarks but remains dependent on the accuracy of its filtering strategy. The study on Transformers for Image Captioning explores the application of self- attention mechanisms in generating coherent and contextually rich image descriptions. The transformer-based architecture allows for improved relationship modeling within images, leading to higher-quality captions. Despite its success, the model's high computational demands and dependency on large-scale datasets present challenges for practical deployment. Through this comparative analysis, the paper highlights the evolution of vision-language models, discussing their strengths, limitations, and future research directions. By understanding the advancements in multimodal AI, researchers can develop more efficient and inclusive assistive technologies, particularly in fields such as accessibility, content generation, and human- computer interaction.

Abstract: Agriculture is the pillar of world food security, but it is confronted with challenges like climate fluctuation, soil erosion, pest attacks, water shortages, and unstable market prices. AgriPulse is a smart agriculture platform pow-ered by AI that combines Crop Recommendation, Yield Forecasting, Plant Disease Detection, Soil Health Monitoring, Weather Forecasting, Market Connectivity, and AI-based Decision Support to improve agricultural productivity and sustainability. This system utilizes Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), and Ex-plainable AI (XAI) to offer real-time insights as well as predictive analytics. AgriPulse uses Random Forest, Genetic Algorithms, and Support Vector Machines (SVMs) for classification of crops as well as the prediction of yields with high accuracy in suggesting the most appropriate crops according to soil and weather factors. Plant disease detection system based on deep learning using CNNs, GANs, and Self-Supervised Learning (SSL) guarantees early detection of plant infections, minimizing losses from pests and diseases. IoT-based soil health monitoring system continuously monitors moisture, pH, and nutrients, maximizing fertilizer usage and irrigation management. Agri Pulse also has an AI-based market prediction module, with Time Series Forecasting (ARIMA) and Regression models, to forecast crop prices and enable farmers to make informed sales decisions. In addition, weather prediction algorithms examine mete-orological data to offer early warnings of unfavourable conditions to help farmers manage risks. The platform is ac-companied by an AI chatbot that provides localized, personalized recommendations in local languages for ease of access and use. By combining precision agriculture technology, Agri Pulse seeks to optimize crop production, im-prove resource management, lower environmental footprint, and enhance farmers' connectivity with markets. Through this integrative strategy, stakeholders are empowered with data-driven decision-making, leading to a sustainable and resilient food industry future.

Keywords: Machine Learning (ML) in Agriculture, Deep Learning (DL) for Crop & Disease Prediction, Crop Recom-mendation System, Yield Prediction, Soil Health Monitoring, Precision Farming, AI for Plant Disease Diagnosis, Pest Detection using Computer Vision, AI-powered Market Intelligence, Crop Price Forecasting, Time Series Forecasting (ARIMA, LSTM), Remote Sensing in Agriculture, AI-based Weather Prediction, Climate-Smart Agriculture, Self-Supervised Learning (SSL) for Pest & Disease Identification, Automated Decision Support Systems, AI-powered Agri-cultural Chatbots.

Abstract: Forti Chat is a secure messaging app that uses machine learning to integrate sophisticated intrusion detection and prevention while shielding messages from man-in-the-middle (MITM) assaults. Growing cyberthreats in today's digital environment necessitate strong security measures. Forti Chat uses machine learning to analyse user behavior and identify anomalies in real-time, improving threat detection and response. It also uses end-to-end encryption, which guarantees that messages are only accessible by the intended receivers. One of the main features is ephemeral messaging, which improves privacy by reducing data retention by deleting messages after a predetermined amount of time. Users may have private, secure talks without sacrificing convenience because to the platform's robust security measures and user-friendly design. While adaptive machine learning capabilities handle changing threats, thorough security audits and frequent updates provide resilience against new vulnerabilities. With a focus on privacy and innovation, Forti Chat is a dependable tool for both personal and professional communication, successfully protecting users from online risks and becoming a market leader in private messaging solutions.

Keywords: Secure Messaging, Man-in-the-Middle (MITM) Attacks, Intrusion Detection, Machine Learning, End-to-End Encryption, Ephemeral Messaging, Cybersecurity

+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 SYNTHESIS AND CHARACTERIZATION OF SOME NEW SERIES OF PYRAZOLINE DERIVATIVES Vijay V. Dabholkar*, Rahul Jaiswar, Dinesh Udawant

Abstract: Pyrazolines are well known and important nitrogen containing five membered heterocyclic compounds. Pyrazoline derivatives have been extensively studied because of their ready accessibility through synthesis, diverse chemical reactivity, various biological activities and variety of industrial applications. In the present study, Novel pyrazoline derivatives were carried out by cyclization of acrylamide derivatives with hydrazine hydrate in the presence of benzoic acid. Structures of the newly synthesized compounds were assigned on the basis of elemental analysis, IR, 1H NMR, and 13C NMR.

Keyword: Pyrazoline derivatives, spectral analysis, Substituted pyrazoline, Synthesis. Downloads: | DOI: 10.17148/IARJSET.2025.12209 How to Cite: [1] Vijay V. Dabholkar*, Rahul Jaiswar, Dinesh Udawant, "SYNTHESIS AND CHARACTERIZATION OF SOME NEW SERIES OF PYRAZOLINE DERIVATIVES," International Advanced Research Journal in Science, Engineering and Technology (IARJSET), DOI: 10.17148/IARJSET.2025.12209 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: A direct one-pot three-component reaction of 4-Acetamidocyclohexanone, aromatic aldehydes, and ethyl acetoacetate under microwave irradiation afforded a series of new cyclo¬hexenone derivatives in the presence of potassium carbonate. One-pot reaction, high efficiency, short reaction time are some of the considerable advantages of this procedure. Microwave irradiation facilitates better thermal management of chemical reactions. The Microwave heat transfer allowed the reaction to be carried out faster and gives better yield.

Keywords: 4-Acetamidocyclohexanone, cyclo¬hexenone, one-pot reaction, potassium carbonate.

Abstract: This study explores the synthesis and characterization of a Carbon Quantum Dots (CQDs)/CdS/ZnS photoanode thin-film solar cell with a Cu-CuS counter electrode, targeting eco-friendly and efficient photovoltaic applications. The CQDs were synthesized via a hydrothermal method, while CdS and ZnS layers were deposited using the SILAR technique. The fabricated solar cell, comprising FTO/d-TiO2/m-TiO2/ CQD/CdS/ZnS/ /CuS-Cu, demonstrated a short-circuit current density (J_sc) of 26.30 µA, an open-circuit voltage (V_oc) of 0.470 V, and a power conversion efficiency (PCE) of 1.88%. The Cu-CuS counter electrode show cased desirable electronic properties, including a low resistivity (ρ) of 2.161×10^(-2) Ω⋅cm and high conductivity (σ) of 4.628×10^2 S/cm, Hall coefficient (RH) of value of 5.659 cm^3/C and sheet resistance (Rs) value of 3.087 Ω/sq. These findings suggest that incorporating green materials and innovative design can significantly contribute to sustainable energy solutions, though further optimizations are required to enhance device performance.

Abstract: Stock market prediction is a challenging task due to the market's complexity and volatility. Recent literature has turned to sentiment analysis - extracting opinions or emotions from news and social media - as a complementary signal for forecasting stock movements. This survey reviews existing approaches that combine sentiment analysis with machine learning to predict stock prices or trends. We outline the spectrum of sentiment analysis techniques (from lexicon-based to deep learning-based methods) and the variety of predictive models (regression, SVM, neural networks, etc.) employed. We summarize key findings from prior studies, which largely indicate that incorporating sentiment features can improve predictive accuracy ([1010.3003] Twitter mood predicts the stock market) ([The impact of microblogging data for stock market prediction: Using ...]), while also highlighting inconsistencies and mixed results. The survey further discusses practical deployments in industry - including hedge funds and financial data services leveraging sentiment - and examines the persistent challenges (noisy data, alignment of sentiment signals with price movements, market non-stationarity, and model interpretability) that limit performance. We conclude by identifying gaps and suggesting future research directions to develop more robust, interpretable, and effective sentiment-enhanced stock prediction models.

Keywords: Stock Market Prediction; Sentiment Analysis; Machine Learning; Natural Language Processing; Financial News; Social Media; Deep Learning; Predictive Modeling.

Abstract: Detection of blood groups is a vital process in transfusion medicine, as well as in emergency care and individualized treatment strategies. Traditional methods of blood typing include serological testing through the use of blood samples and laboratory-based antigen-antibody reactions, which are time-consuming, invasive, and resource intensive. To address these issues, the following research suggests a deep learning approach to detection of blood groups using two different techniques: fingerprint patterns and images of blood smears. Through the use of Convolutional Neural Networks (CNNs) to perform feature extraction and classification, this system hopes to offer a quick, painless, and precise complement to mainstream blood typing procedures. Fingerprint typing relies on the theory that dermatoglyphic patterns are associated with genetic components and may be related to blood groups. High-resolution fingerprint images have high-resolution imaging processed through CNNs to identify the low-level features that can distinguish between them. Additionally, deep learning models interpret images of blood smears to determine cell morphology patterns that identify particular blood types. The system under consideration is developed with Python as backend processing, Flask for web-based communication, and HTML, CSS, and JavaScript for interface purposes. The process minimizes reliance on physical blood sample collection, thus making it extremely applicable to remote and resource-scarce regions. It increases access, reduces errors in blood typing, and accelerates emergency medical response. However, difficulties including availability of datasets, bias in algorithms, and generalizability of models need to be resolved for clinical deployment. Future efforts will involve enlarging training datasets, improving deep learning architectures, and incorporating real-time mobile apps for general adoption. The suggested system represents a milestone in AI-based medical diagnostics, providing a practical and scalable method for detecting blood groups.

Keywords: Blood Group Prediction, Machine Learning in Healthcare, Neural Networks, Image Processing, Deep Learning-Based Biometrics, AI in Medical Diagnostics, Pattern Recognition in Medicine, Fingerprint Recognition, Medical Image Analysis, Healthcare AI Applications, Feature Extraction Techniques, Biometric Authentication, Automated Blood Typing, CNN-Based Classification, Digital Pathology, Non-Invasive Medical Testing, Blood Type Identification Using AI, Healthcare Informatics, Medical Data Processing

Abstract: We introduce a deep learning system for automatic floorplan generation from layout graphs. Our system combines generative modeling with user-in-the-loop design in which users can add sparse constraints like room numbers, connectivity, and other layout adjustments. The system relies on a core deep neural network that takes an input building boundary and layout graph to generate realistic and constraint-abiding floorplans. The system utilizes a graph neural network (GNN) to encode layout patterns and convolutional neural networks (CNNs) for processing building contours and rasterized floorplan images. The model, trained on RPLAN, a 80K-annotated floorplan dataset, outputs varied floorplan layouts consistent with user inputs. We measure its performance via qualitative and quantitative analysis, ablation experiments, and comparison against state-of-the-art techniques, proving its effectiveness and flexibility in floorplan synthesis with constraints. Keywords-floorplan generation, layout graph, deep generative modeling

Abstract: AI-based regulatory compliance is transforming industries in the best way possible by giving it a boost in adhering to legal frameworks in a very more robotic and efficient manner. This paper discusses emerging trends, applications in banking, healthcare and finance, and challenges in the form of ethical concerns as well as governance issues. Based on recent research, it makes arguments related to the European AI Act, the way that explainability in general and in relation to AI-driven compliance is increasingly important. Innovations that are promised for the future will be further enhancements of transparency, reduced risk, and more refined regulatory oversight.

Keywords: Microsoft, Audit, Reporting, Regulatory

Abstract: Biomedical applications require efficient wireless communication systems, particularly in the ISM band (2.4-2.48 GHz), for applications such as patient monitoring and implantable medical devices. In this paper, we propose a dual-band Frequency Selective Surface (FSS) integrated microstrip patch antenna to enhance the gain and bandwidth while minimizing interference. The antenna is designed using a rectangular patch configuration and optimized for biomedical applications. CST Microwave Studio is used for design simulations, evaluating return loss, gain, radiation patterns, and bandwidth performance. The fabricated prototype is measured using a Vector Network Analyzer (VNA) to validate the simulation results. The proposed antenna demonstrates improved performance, making it a suitable candidate for wireless biomedical applications.

Keywords: ISM band, Microstrip Patch Antenna, Frequency Selective Surface (FSS), Biomedical Applications, Wireless Communication, Wearable Devices.

Abstract: The childcare industry has undergone a transformation thanks to the use of cutting-edge technology, which has improved parent-caregiver communication, security, and efficiency. A greater level of care and safety is now possible for day-care facilities thanks to the development of digital technologies, including automated attendance systems, real-time CCTV surveillance, and mobile applications for immediate updates. Caretakers may concentrate more on raising and educating children thanks to these technologies, which also simplify administrative duties and provide parents peace of mind. Adoption of cutting-edge technological solutions will be essential to addressing the growing demand for high-quality childcare services and improving the environment for kids to flourish by making it safer, better, and more organized.

Keywords: Daycare management, Technology integration, Efficiency, Care quality, Early childhood education.

Abstract: This paper provides an in-depth analysis of learning dashboards, particularly focusing on Low Code. It explores the growing popularity of dashboards due to their widespread use in educational technologies such as e-training sys-tems and online courses. Low/No-code development is highlighted as a sig-nificant system, allowing individuals to perform operations without exten-sive coding knowledge. The paper discusses the benefits for companies and associations seeking software solutions in the technology-driven era. It ana-lyzes the advantages and disadvantages of Low/No-code development and examines the latest industry platforms. Additionally, it discusses potential enhancements to this development methodology and offers insights into its future impact on society and related industries. By assessing the trajectory of this trend, the paper predicts significant changes in software development practices and the dynamics of digital transformation. In summary, it sug-gests that Low/No-code development is a promising trend with the potential to significantly influence the broader technological landscape.

Keywords: Software engineering, Digital evolution, Development with minimal coding, Development without coding.

Abstract: Robotic Operating System (ROS) has emerged as a pivotal middleware for developing applications in modern robotic systems, extending beyond industrial use to various real-world applications. As the adoption of ROS-based systems grows, ensuring their security becomes critical due to the increasing risk of cyber-attacks. Intelligent attack detection frameworks leveraging machine learning have proven effective in mitigating these threats. This research explores advanced attack detection techniques using the ROS cyber-attack dataset and evaluates the performance of multiple machine learning models, including Random Forest, Support Vector Machine (SVM), Naive Bayes, Logistic Regression, K-Nearest Neighbours (KNN), and AdaBoost. Additionally, deep learning architectures, such as Long Short-Term Memory (LSTM) networks and 2D Convolutional Neural Networks (CNN2D), are employed to enhance detection accuracy. Among the evaluated models, CNN2D demonstrates superior performance, leveraging its ability to extract intricate spatial and temporal features from input data. The study highlights the potential of deep learning-based solutions for robust security in ROS-based systems, providing a significant step toward resilient and intelligent attack detection in robotic environments. These findings underscore the importance of integrating advanced detection mechanisms to safeguard the integrity and reliability of robotic systems.

Keywords: ROS Security, Cyber-Attack Detection, Machine Learning, Deep Learning, CNN2D, Robotic Systems.

Abstract: With increasing security concerns in various fields, automation in surveillance and threat response has gained significant attention. This paper presents the development of an autonomous security vehicle equipped with a real-time face recognition system. The vehicle is trained with a predefined set of faces and is programmed to identify and respond to unauthorized individuals. When an unknown face is detected, the system activates an automated gun mechanism for defensive action. The implementation of artificial intelligence for face recognition, microcontroller-based hardware for control, and an actuation mechanism for response ensures a real-time and efficient security system. This research highlights the design, methodology, experimental setup, and results of this project, providing insights into its feasibility, limitations, and potential improvements for future developments.

Abstract: We present a solution method for a general Riccati differential equation by imposing relationships between the coefficients of the Riccati differential equation and explaining them through proofs and examples, we can find the general solution to the various forms of Riccati's equation by integration directly after transforming it into a separable differential equation.

Keywords: Riccati equation; Exact solution; General solution; Integrable differential Equations.

Abstract: The integration of Face Recognition Technology (FRT) into police operations offers a promising solution for enhancing public safety, identifying suspects, and locating missing persons. However, the deployment of this technology presents several challenges that must be addressed to ensure its effectiveness, reliability, and ethical use. Key technical hurdles include ensuring accuracy across diverse conditions and seamless integration with existing police databases. Data quality, privacy concerns, and the risk of bias further complicate FRT's implementation. Additionally, the absence of clear regulatory frameworks poses legal risks, and substantial resources are required for both development and personnel training. This project aims to develop robust FRT algorithms that ensure high accuracy, minimize bias, and handle diverse conditions, while advocating for comprehensive legal frameworks and privacy protections. Public engagement initiatives will be essential to build trust and transparency. Through a balanced approach that addresses both operational and ethical concerns, FRT can enhance law enforcement capabilities while maintaining public trust and protecting civil rights.

Keywords: Face Recognition Technology, Law Enforcement, Public Safety,Missing Persons Tracking.

Abstract: The project "AI Based Automated Email Spam Classification for Fast Growing Company" has been developed using JAVA as front end and MySQL server as backend. The project helps to identify the spam message (unwanted message) automatically in user mail after successful of spam detection message will block automatically based on user customized spam keyword. Spam detection is becoming a big challenge for network resources and users because of some negative effects. Spam causes annoyance and wastes user's time to regularly check and delete this large number of unwanted messages. Main aim of proposed application develop identify the spam message (unwanted message) automatically in user mail after successful of spam detection message will block automatically. Initially mail user need to register with the application by submitting their details. After that user login this application using their username and password. After successful of login user can able to upload no of spam keyword based on their interest level. User can do the mailing process all the mail Store in data server. Before receiver receive the mail, this proposed application check weather mail is normal mail or spam for particular receiver. Spam checking process initially compose mail string is divided into one unit or token. And this token is matching with user spam keyword database using keyword matching technique. Finally based on keyword spam message identify Automatically and filtering the emails by reading one-by-one.

Keywords: Artificial neural network, Email matching network, keyword detection, spam detection

Abstract: Railway accidents pose risks to passenger safety, infrastructure, and economic stability. Traditional accident prevention methods rely on rule-based systems and human intervention, often failing to address real-time risks effectively. This paper integrates Deep Learning (DL), Machine Learning (ML), and Global Positioning System (GPS) tracking to enhance railway accident prevention. By leveraging historical accident data and knowledge-based analysis, we propose an intelligent system capable of real-time anomaly detection, predictive maintenance, and automated decision-making.

Keywords: Artificial Intelligence, Data Processing, Deep Learning, GPS, Machine Learning

Abstract: Herbal shampoos are gaining popularity due to their natural ingredients and minimal chemical composition. This study explores the green synthesis of an herbal shampoo using plant extracts and evaluates its physicochemical properties. The formulated shampoo was developed using herbal extracts such as Reetha, Shikakai, Amla, Aloe vera, and Neem, combined with natural surfactants. Various quality parameters, including pH, foam stability, wetting time, transparency, and odor, were assessed. The results indicated that the shampoo had a pH of 6.5, dense and stable foam, a clear brown appearance, and excellent cleansing efficiency. The herbal formulation offers significant benefits, including antifungal and antibacterial properties, making it a safer and eco-friendly alternative to synthetic shampoos. This study highlights the potential of herbal shampoos in sustainable personal care and encourages further research into their long-term stability and consumer acceptability.

Keywords: Reetha, Herbal formulation, Antifungal and antibacterial properties

Abstract: Artificial Intelligence (AI) has revolutionized our daily lives, seamlessly integrating human intellect with machine capabilities. Over the past few years, AI has experienced significant growth, expanding its influence across various fields. Advancements and innovations driven by AI continue to emerge, shaping a future of automation and intelligence. Its applications are not confined to specific domains but span everything from minor tasks to groundbreaking developments. With numerous AI-powered technologies and devices already transforming the world, many more innovations are on the horizon. This paper provides a comprehensive survey of the latest advancements and current trends in Artificial Intelligence.

Keywords: Artificial intelligence, Machine learning, IOT, Augumented Reality, Virtual Reality

Abstract: This paper presents a fuzzy logic-based Maximum Power Point Tracking (MPPT) controller for a single-stage solar PV grid-integrated system. The proposed scheme integrates MPPT, grid synchronization, voltage and current regulation, and ripple mitigation to enhance inverter performance. A Fuzzy Logic Controller (FLC) optimizes MPPT for efficient power extraction, while an H-bridge inverter with dual-loop control ensures DC-link voltage stability and unity power factor operation. Simulation results confirm that the inverter output current meets IEEE 519 and IEC 61727 THD standards.

Keywords: Photovoltaic (PV); single-stage; grid-interfaced PV system; FLC MPPT.

Abstract: Emotion recognition plays a crucial role in advancing artificial intelligence (AI) systems, enabling more human-like interactions in fields such as mental health, security, customer experience, and human-computer interaction. Traditional methods of emotion detection rely on a single modality, limiting the accuracy and depth of emotional understanding. This study presents a multimodal emotion detection system that integrates image, video, audio, and text analysis using deep learning models. The proposed system leverages Convolutional Neural Networks (CNNs) for facial expression analysis, Long Short-Term Memory (LSTM) for video-based emotion recognition, Mel-Frequency Cepstral Coefficients (MFCCs) with deep learning for speech emotion detection, and Natural Language Processing (NLP) for sentiment analysis in text. The system is deployed as a Flask-based web application, enabling real-time emotion classification. Key challenges such as data privacy, model bias, and real-time efficiency are addressed using ethical AI practices and optimized deep learning architectures. The paper explores the impact of multimodal emotion detection in mental health diagnostics, AI-driven assistants, security systems, and customer engagement platforms, highlighting its potential to enhance machine understanding of human emotions.

Keywords: Multimodal Emotion Detection, Deep Learning,Convolutional Neural Networks ,Facial Expression ,Sentiment Analysis , Speech Emotion Detection

Abstract: Driver drowsiness and distractions are leading causes of accidents, making real-time detection essential for safety. This work employs machine learning and deep learning to monitor drivers through facial and behavioral cues. Real-time video processing analyzes Blink Frequency, Maximum Eye Closure Time, and PERCLOS to detect prolonged eye closure, while Yawning Frequency helps assess fatigue and trigger alerts.Head Pose estimation tracks Euler angles to identify distractions like backseat conversations, and the system detects mobile phone usage without Bluetooth. EAR ensures the driver remains focused. By combining video analysis, image processing, and deep learning, the system enhances road safety, tackling efficiency and accuracy challenges and advancing intelligent transportation.

Keywords: Driver monitoring, drowsiness detection, distraction detection, machine learning, deep learning, image processing, head pose estimation, PERCLOS, Eye Aspect Ratio (EAR), real-time video analysis, road safety.

Abstract: As the field of human resource management continues to evolve, fostering inclusion and embracing diversity have become essential for building organizations that are both sustainable and high performing. This research, entitled "A Study on the Role of HR Diversity Practices Influencing Work Group Inclusion in Selected Pharma Companies", is an investigation of how strategic HR diversity programs affect attitudes towards inclusion in work groups in the pharma sector. Based on Optimal Distinctiveness Theory and organizational justice models, this study foregrounds the co-requirement that employees feel both belongingness and uniqueness to truly be included in the workplace. Each of several HR diversity practices is assessed through the lenses of their strength in creating an inclusive climate. It also considers the key mediating role of leadership reaction, showing how aligning or not with diversity policy can have a strong effect on the success of inclusion practices. Shore et al. (2011) and Buengeler et al. (2018) provide theoretical contributions illustrating how inclusion is not merely an HR-based outcome but rather a process enabled through leadership, determined by how managers make sense of and enact HR diversity policies. By a qualitative and/or quantitative method (on primary or secondary data), this research examines the connection between HR diversity practices and employee outcomes like job satisfaction, psychological safety, well-being, and team cohesion in sample pharmaceutical companies. The results highlight that when HR diversity practices are accompanied by inclusive leadership behaviors and organizational fairness like procedural, interpersonal, and informational justice, employees perceive greater inclusion and engagement. This study contributes to the emerging literature in the workplace by providing actionable guidance for HR professionals and business leaders within the pharmacy industry to create inclusive workplaces that maximize the value of workforce diversity.

Keywords: HR diversity practices, work group inclusion, inclusive leadership, organizational justice, pharmaceutical industry.

Abstract: In today's digital era, video marketing has emerged as a powerful tool for brands to capture consumer attention and enhance engagement. This study explores the impact of video marketing on consumer engagement and brand recall. It investigates how video content influences consumers' emotional responses, purchasing behaviour, and their ability to remember brands. Data was collected through a structured questionnaire distributed to a sample of consumers exposed to video advertisements. The findings reveal that visually appealing, emotionally resonant, and informative video content significantly boosts consumer engagement levels and positively influences brand recall. Regression analysis indicates a moderate positive relationship between consumers' interaction with video ads and their perceived connection with brands. The study concludes that video marketing is an effective strategy for fostering stronger consumer-brand relationships and recommends that marketers invest in high-quality, targeted video content to maximize impact. Limitations and suggestions for future research are also discussed.

Keywords: Video Marketing, Emotion, Social media and Promotion

Abstract: The center of gravity (CoG) is a fundamental biomechanical concept that plays a decisive role in determining efficiency, balance, and performance in sports. This study explores the relationship between CoG and sports performance across multiple disciplines, emphasizing how variations in body positioning, mass distribution, and stability influence athletic outcomes. A systematic review and biomechanical analysis were employed to examine how CoG interacts with balance, agility, strength, and skill execution in sports such as gymnastics, athletics, football, wrestling, and basketball. Findings suggest that lowering the CoG enhances stability and defensive performance, while elevating or dynamically shifting the CoG supports explosive movements, vertical jumps, and agility-based skills. Athletes who successfully manipulate their CoG demonstrate superior movement efficiency, reduced injury risk, and improved technical performance. The study highlights that optimizing CoG through training interventions-such as strength conditioning, flexibility, and sport-specific drills-can significantly enhance performance. Furthermore, gender, body composition, and anthropometric differences were identified as key factors influencing CoG and its application in sports. The discussion integrates biomechanical theory with practical implications for training, coaching, and injury prevention. The paper concludes that a deep understanding of CoG mechanics provides athletes and coaches with actionable strategies to maximize performance and efficiency in competitive sports.

Keywords: Center of Gravity, Balance, Sports Performance, Biomechanics, Stability, Agility, Athletic Training

Abstract: Lithium bromide-water (LiBr-H₂O) absorption refrigeration systems (VARS) are widely used for cooling applications due to their ability to operate on low-grade energy sources. However, a major operational limitation is *crystallization*, which occurs when the solution becomes supersaturated with LiBr. Crystallization can block system components, reduce efficiency, and lead to system failure. This paper presents a thermodynamic analysis of the crystallization phenomenon, supported by simulation results using Engineering Equation Solver (EES). A crystallization condition equation is derived, and a crystallization curve relating generator and absorber temperatures is formulated to define the safe working zone of the system.

Keywords: Crystallization Boundary, Lithium Bromide (LiBr), Working Concentration Zone, Vapour Absorption Refrigeration System (VARS).