Volume 14 | Issue 4

 Abstract

The Internet of Things is based on the concept of layered design. A range of technologies are used by each tier for information transmission, capacity, and preparation. This study aims to assess the present Internet of Things architecture with respect to the risks and vulnerabilities associated with IoT-enabled devices, as well as potential assurance procedures in light of equipment limits and novel information transfer methodologies. We then discuss IOT applications and architecture. A list of successful real-time IOT applications now in use is as follows: Emerging technologies include things like self-driving cars, smart grids, traffic management systems, logistic management hierarchies, environment monitoring, building safety applications, and many more.

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Artificial Intelligence (AI) has moved the practice of marketing from a traditional activity into a domain that is intensely driven by intelligence and data. The framework combines behavioral insights, neuromarketing, and AI-driven predictive analytics to offer an innovative approach for consumer intelligence and strategic decision-making. Neuromarketing is a method for exploring unconscious emotional and cognitive responses; behavioral insights explain decision-making biases and preferences while predictive analytics employs massive data in order to anticipate consumer behavior. This research proposes a conceptual framework between marketing performance, decision quality, and customization effectiveness with Artificial Intelligence (AI)-based consumer intelligence. The Effects of Integrated Consumer Intelligence Series Using Survey Data, Evidence from Neuromarketing Experiments and AI-Based Predictive Modeling: This mixed-method study employing survey data and experimental evidence from neuromarketing as well as AI-based predictive modelling to unlock the strategic marketing effects. We anticipate that the findings can help advance theory through the can provide a bridge between cognitive neuroscience in conjunction with AI-decomposed today, precision marketing, consumer engagement, and competitive advantage in the digital economy.

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Artificial Intelligence, Consumer Intelligence, Neuromarketing, Predictive Analytics, Behavioral Insights, Strategic Marketing, Decision Intelligence

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The rapid growth of social media platforms has significantly increased the spread of misinformation and fake news. Manual verification of news content is slow, expensive, and unsuitable for large- scale data processing. This paper presents an automated fake news detection system using Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning techniques. Textual news data is preprocessed using tokenization, stop-word removal, and lemmatization. Feature extraction is performed using TF-IDF vectorization and word embeddings. Multiple classification models including Logistic Regression, Support Vector Machine (SVM), Naive Bayes, and Long Short-Term Memory (LSTM) networks are trained and evaluated. Experimental results show that deep learning models outperform traditional machine learning methods, achieving an accuracy of up to 96%. The proposed system provides a scalable and efficient solution for identifying fake news in digital platforms.

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Fake News Detection, NLP, Machine Learning, Deep Learning, Text Classification, LSTM, TF-IDF.

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The rapid growth of mobile technologies and the increasing demand for contactless, efficient, and personalized services have significantly transformed the hospitality industry. Traditional restaurant management systems rely heavily on manual processes such as printed menus, verbal order taking, and counter-based billing, which often lead to inefficiencies, longer waiting times, human errors, and limited customer engagement. This paper proposes a Smart Dining Experience System, an Android-based intelligent restaurant management application designed to automate menu management, order processing, billing, and customer interaction. Developed using Android Studio, the system employs Java and XML for front-end development and SQLite for backend data management. Restaurant administrators can dynamically manage digital menus, while customers can browse food items, place orders, track preparation time, receive personalized food recommendations, and make digital payments using smart phones. A frequency-based recommendation algorithm analyzes customer order history to enhance personalization. Experimental evaluation indicates reduced service time, improved order accuracy, and increased customer satisfaction. The study demonstrates that mobile-based smart dining solutions can effectively modernize restaurant operations and enhance overall service quality.

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Smart Dining Experience, Restaurant Automation, Android Application, Digital Menu, Food Recommendation System, SQLite, Mobile Payment

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Attendance monitoring is an essential administrative task in educational institutions for tracking student participation and academic engagement. Conventional attendance methods such as manual roll calls and signature-based systems are time-consuming, error-prone, and vulnerable to proxy attendance. This paper presents a face recognition-based automated attendance management system using computer vision and deep learning techniques. The proposed system captures real-time facial images through a camera and performs face detection and recognition using convolutional neural network-based models. Facial features are extracted and matched against a pre-trained student database, and attendance records are automatically updated in a centralized storage system. The system eliminates manual intervention and ensures secure, contactless attendance recording. Experimental evaluation was conducted in a controlled classroom environment using a dataset of enrolled students. Performance was measured using recognition accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). The results demonstrate that the proposed system achieves high recognition accuracy and significantly reduces attendance processing time compared to traditional methods. The system provides a reliable and scalable solution for intelligent attendance management in educational institutions. The system achieved a recognition accuracy of 96.2%, with a False Acceptance Rate of 1.8% and a False Rejection Rate of 2.0%.

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Face Recognition, Automated Attendance, Deep Learning, Computer Vision, Biometric Authentication, CNN, Image Processing.

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The rapid advancement of digital technologies has significantly transformed modern computing environments, particularly through the integration of Artificial Intelligence (AI) with cloud computing. Cloud computing enables organizations to access scalable computing resources, storage, and services over the internet, eliminating the need for costly on-premise infrastructure. When combined with AI technologies such as machine learning, deep learning, and big data analytics, cloud platforms become powerful environments capable of processing massive datasets, generating insights, and supporting intelligent decision-making.This paper examines the evolution of AI cloud computing, beginning with early cloud infrastructure models and progressing through stages such as big data integration, machine learning adoption, and the development of AI-as-a-Service platforms. It also highlights the core technologies that enable AI cloud systems, including machine learning, deep learning, big data analytics, Internet of Things (IoT), and automated machine learning tools. The study further discusses the major benefits of AI cloud computing, such as cost efficiency, scalability, faster innovation, and improved data management.In addition, the paper explores key real-world applications across industries including healthcare, finance, transportation, smart cities, and e-commerce. While the adoption of AI cloud computing continues to grow, challenges such as data privacy concerns, cybersecurity risks, high data dependency, and technical complexity remain significant considerations.Finally, the paper outlines future trends shaping the next generation of AI cloud systems, including edge AI, intelligent automation, quantum computing integration, and advanced privacy-preserving technologies.

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Artificial Intelligence, Cloud Computing, Machine Learning, Deep Learning, Big Data Analytics, AI-as-a-Service (AIaaS), Internet of Things (IoT), Edge Computing, Intelligent Automation, Digital Transformation

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Honey pots have evolved from static decoy systems into intelligent, adaptive components of modern cyber security architectures. This survey paper presents a comparative analysis of traditional and modern honey pot technologies, emphasizing their integration with artificial intelligence (AI), machine learning (ML), block chain, reinforcement learning, and cloud-native orchestration. We synthesize recent advancements and categorize honey pot systems by interaction level, deployment strategy, and technological augmentation. Comparative tables highlight the evolution of capabilities, scalability, and operational effectiveness. The paper concludes with insights into best practices and future research directions for deploying deception-based defenses in dynamic threat environments.

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Honeypots, Cyber security, Deception Technology, Machine Learning, Reinforcement Learning, Block chain,Cloud Security.

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The Real-Time Disaster Management system is designed to improve emergency response efficiency through automated processing of disaster-related information. Traditional disaster management systems rely on manual communication and delayed decision-making, which often result in slow response and increased damage. The proposed system uses rule-based artificial intelligence techniques to analyze emergency messages, determine severity levels, assign priority, and generate alerts in real time [5]. The system also detects duplicate messages to avoid repeated alerts and maintains structured records in a centralized database [7]. An admin dashboard enables monitoring of emergency messages, alert delivery status, and disaster history. The system reduces response time, improves coordination, and enhances disaster preparedness and management effectiveness.

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Real-Time Disaster Management , Emergency Response , Artificial Intelligence, Rule-Based System ,Severity Analysis , Priority Assignment , Automated Alerts, Duplicate Detection , Centralized Database , Disaster Preparedness

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The Smart Dormitory Management Tracking System is a comprehensive web-based application designed to modernize and automate hostel administration processes within educational institutions. Traditional dormitory management systems rely heavily on manual record-keeping, including physical registers and spreadsheet-based documentation. These conventional approaches often result in data inconsistency, delayed updates, difficulty in monitoring occupancy, and increased administrative workload. The proposed system introduces a centralized digital platform that integrates multiple hostel management functions such as student registration, room allocation, attendance monitoring, fee management, complaint handling, and reporting. The application is structured with role-based access control to ensure that administrators, wardens, and students can access only authorized modules. The system enhances operational efficiency by automating repetitive tasks, maintaining structured database records, and enabling real-time monitoring of room occupancy and student activities. Secure authentication mechanisms are implemented to protect sensitive data and prevent unauthorized access. By reducing manual dependency and improving transparency, the Smart Dormitory Management Tracking System provides a scalable and reliable solution suitable for institutional deployment.

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Dormitory Management System, Web-Based Automation, Occupancy Tracking, Access Control, Student Information System, Real-Time Monitoring

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With the rapid growth of digital communication platforms such as SMS, email, and social media, cyber threats like spam, phishing, and scam messages have become increasingly sophisticated and context-driven. Traditional detection systems mainly rely on keyword matching and predefined patterns, which often fail to identify the underlying intent behind malicious communications, leading to reduced detection accuracy. This paper proposes an Intent-Aware Artificial Intelligence approach for proactive threat detection in digital communications by focusing on understanding the sender's intent using Natural Language Processing (NLP), contextual analysis, and machine learning techniques. The system analyzes linguistic patterns, behavioral cues, and contextual semantics to detect manipulation strategies such as urgency, deception, and fraudulent intent before user interaction. Additionally, the proposed model supports adaptive learning to handle evolving threat patterns and can be deployed using edge Intelligence to ensure privacy preservation and real-time processing without heavy cloud dependency. This approach aims to enhance detection accuracy, reduce false positives, and provide a more intelligent, adaptive, and human-like security mechanism for modern communication systems.

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Intent-Aware AI, Cyber Threat Detection, Natural Language Processing, Contextual Analysis, Phishing Detection, Adaptive Learning, Edge Intelligence, Spam Detection, Fraud Detection, Behavioral Analysis, Real-Time Processing

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The Web Based Voting Platform for Institute Election is a secure and centralized web application designed to automate and manage institutional election processes with accuracy, transparency, and controlled access. The system replaces traditional manual voting methods by providing a structured digital environment that ensures voter authentication, vote uniqueness, and administrative control. The platform consists of two independent interfaces: an administrator module and a student voting module. The administrator module enables authorized users to securely log in, register and manage candidate information, maintain election records, and monitor final voting results. It also supports effective management of both current and historical election data. The student voting module ensures secure participation by validating a unique student identification number against the institutional database. Upon Successful verification, an OTP-based authentication mechanism is used to authorize the voting process through the registered mobile number. The system strictly enforces a one-vote-per-student policy, preventing duplicate voting attempts and maintaining election integrity. Students are restricted from accessing vote counts or intermediate results to avoid bias and influence. This web-based platform enhances election reliability, minimizes human intervention, prevents unauthorized access, and provides a scalable and efficient solution suitable for institutional election management.

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Web-Based Voting System, Secure Voting, OTP Authentication, Voter Verification, Election Management, One Vote Policy, Access Control, Data Integrity, Digital Elections, Authentication System.

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Student dropout is a significant challenge faced by educational institutions, affecting both academic outcomes and institutional reputation. This study proposes a machine learning-based approach to predict students who are at risk of dropping out at an early stage. The model utilizes features such as attendance, academic performance, assignment completion, travel distance, and family support. Multiple algorithms including Random Forest, Logistic Regression, and Decision Tree are implemented and compared. The results demonstrate that the Random Forest model provides better prediction accuracy. The proposed system enables early identification of at-risk students, allowing institutions to take timely intervention measures and improve retention rates.

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Student Dropout Prediction, Machine Learning, Random Forest, Early Intervention, Academic Performance, Predictive Analytics, Classification Models, Educational Data Mining

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Student Performance Prediction, Academic Achievement, Machine Learning, Random Forest, Regression Models, Data Visualization, Early Academic Intervention, Predictive Analytics

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Online hotel reviews provide valuable insights into customer experiences; however, such reviews are often short and informal, making automated sentiment analysis challenging. Single classification models frequently struggle to capture sentiment accurately due to limited contextual information and lexical variability. To address this issue, this paper presents a comparative study of voting-based and stacking-based ensemble learning methods for sentiment analysis of hotel reviews. Reviews are preprocessed and decomposed into bigram phrases to analyze the sentimentsin hotel reviews. Multiple feature representations including TF-IDF, Word2Vec, and BERT embeddings are employed to capture lexical, semantic, and contextual information. In the voting ensemble, independent classifiers aggregate predictions using soft voting, while the stacking ensemble integrates base learner outputs through a meta-classifier. Experimental results demonstrate that stacking ensembles achieve more consistent performance on ambiguous hotel review phrases, whereas voting ensembles provide a computationally efficient and reliable baseline. The findings highlight the effectiveness of ensemble strategies for short-text sentiment analysis in hospitality review systems.

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Sentiment Analysis, Hotel Reviews, Ensemble Learning, Voting Ensemble, Stacking Ensemble ,Short Text Classification, Bigrams, Deep Language Models

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Managing sports achievement records in educational institutions often involves significant manual effort, leading to data redundancy and lack of immediate accessibility. This paper presents the "NCW Sports Achievement Tracker," a robust web-based application developed using the Flask framework and MongoDB NoSQL database. The system is designed to digitalize student-athlete records, providing features such as dynamic search, batch-wise filtering, and an automated points-based leaderboard. By leveraging a non-relational database schema, the system ensures high scalability and flexibility for diverse sporting disciplines. Experimental results demonstrate that the system effectively manages 191 records with high retrieval accuracy, enhancing the recognition and motivation of student athletes.

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NCW Sports Achievement Tracker: A Scalable Web-Based System for Athletic Recognition and Data Management

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Green synthesis of metal oxide nanoparticles has gained considerable attention due to its eco-friendly nature and reduced environmental impact. In the present review, Taguchi optimization methodology is discussed as an effective statistical tool for optimizing microwave-assisted green synthesis parameters of cobalt oxide (Co?O?) nanoparticles using plant leaf extract. Key variables such as precursor concentration, extract volume, microwave irradiation time, and annealing temperature were optimized using an L9 orthogonal array design. Structural characterization was performed using X-ray diffraction (XRD), confirming the formation of phase-pure cubic spinel Co?O? nanoparticles. Crystallite size was calculated using the Debye-Scherrer equation. The Taguchi approach significantly minimized experimental runs while maximizing crystallinity and structural uniformity. The optimized green microwave-assisted synthesis method provides an energy-efficient, scalable route for producing cobalt oxide nanoparticles for catalytic, electrochemical, and environmental applications.

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Green synthesis; Cobalt oxide nanoparticles; Taguchi method; Microwave irradiation; XRD characterization

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Nanomaterials have emerged as a revolutionary class of materials with unique physicochemical properties such as high surface area, quantum effects, and enhanced reactivity. These properties have enabled wide applications in environmental remediation, medicine, agriculture, electronics, and energy sectors. Despite their significant advantages, nanomaterials also pose potential risks to ecosystems and human health due to their toxicity, persistence, and bioaccumulation tendencies. This review critically examines both the positive and negative impacts of nanomaterials on the environment. Beneficial applications include water purification, air pollution control, soil remediation, and green energy solutions. However, unintended environmental exposure can lead to toxicity in aquatic systems, soil microorganisms, and human tissues. The study compiles data on commonly used toxic nanoparticles, their environmental pathways, and mechanisms of toxicity. It also discusses safer alternatives and sustainable approaches such as green synthesis and biodegradable nanomaterials. The balance between technological advancement and environmental safety is essential for sustainable development. The findings emphasize the need for stringent regulations, risk assessment frameworks, and eco-friendly nanotechnology practices to minimize adverse effects while maximizing benefits.

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Nanomaterials; Environmental Impact; Toxicity; Nanoparticles; Sustainable Nanotechnology

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Climate change has emerged as a critical global concern affecting various sectors, including tourism. The Thane district of Maharashtra, characterized by its diverse geography, coastal ecosystems, forested regions, and cultural heritage, is particularly vulnerable to climatic variations. This review examines the impact of climate change on tourism in Thane district by integrating geographical, socio-cultural, and environmental perspectives. Thane's proximity to the Arabian Sea, its position within the Konkan region, and the presence of rivers such as Ulhas and Vaitarna contribute to its tourism potential but also increase its vulnerability to flooding, rising temperatures, and extreme weather events [1][2]. Over the past decade, observable changes in rainfall patterns, increasing temperatures, and deteriorating air quality have influenced tourist inflow and destination sustainability. Tribal communities and local cultural traditions, including Tarpa dance and Warli art, form an integral part of tourism, yet face disruption due to ecological stress. This study employs qualitative and quantitative methods, including literature survey, field observations, and statistical analysis, to assess climate trends and their implications for tourism and employment. The findings indicate that climate change affects natural attractions, infrastructure, seasonal tourism, and livelihoods. Sustainable tourism planning, climate-resilient infrastructure, and community participation are essential to mitigate adverse impacts and promote eco-tourism. The study concludes that integrating environmental conservation with tourism development is crucial for the long-term sustainability of Thane district.

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Climate Change; Tourism; Thane District; Tribal Culture; Sustainable Development

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A novel series of halogen-substituted heterocyclic derivatives based on the 1,2,4-triazole scaffold was synthesized and evaluated for their biological potential. In the present investigation, chloro-, bromo-, and fluoro-substituted derivatives of 5-(4,7-dihydrothiazolo[5,4-c]pyridin-2-yl)-4-phenyl-4H-1,2,4-triazole-3-thiol were successfully prepared through an efficient synthetic protocol. The structures of the synthesized compounds were confirmed using various spectroscopic techniques, including FT-IR, 1H NMR, 13C NMR, and mass spectrometry. The spectral data were found to be in good agreement with the proposed molecular structures. The newly synthesized derivatives were further screened for their biological activities against selected microbial strains to evaluate their antimicrobial potential. The results revealed that several halogen-substituted compounds exhibited significant inhibitory activity compared to the standard drugs. The presence of electron-withdrawing halogen substituents such as chloro, bromo, and fluoro on the aromatic ring was found to influence the biological activity of the synthesized molecules. These findings suggest that the incorporation of halogen atoms into the triazole-thiazolopyridine framework may enhance biological efficacy and could provide promising lead structures for the development of new bioactive heterocyclic compounds.

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1,2,4-Triazole; Thiazolopyridine; Halogenated derivatives; Chloro, bromo and fluoro substituents; Spectral characterization; Antimicrobial activity; Heterocyclic compounds; Bioactive molecules.

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Multi-walled carbon nanotubes; Spray pyrolysis; Cobalt oxide catalyst; Castor oil precursor; XRD characterization

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