International Journal of Information Technology Research Studies (IJITRS)
The International Journal of Information Technology Research Studies (IJITRS) is a cutting-edge platform dedicated to exploring and promoting the latest advancements in the field of information technology. As a leading publication in the industry, International Journal of Information Technology Research Studies (IJITRS) provides a comprehensive and interdisciplinary forum for researchers, academics, and professionals to share their innovative research and insights on a wide range of topics related to information technology. This journal adheres to the guidelines, policies and ethical standards prescribed by the UGC-CARE. With its rigorous double-blind peer-review process and commitment to excellence, International Journal of Information Technology Research Studies (IJITRS) ensures the highest quality of scholarship and encourages collaboration and exchange of ideas among experts in the field. The journal welcomes contributions from scholars and practitioners from around the world, making it a truly global platform for knowledge dissemination and academic discourse. Whether your research focuses on Artificial Intelligence, Cyber security, Data Analytics, or any other area within the broad spectrum of Information Technology, International Journal of Information Technology Research Studies (IJITRS) offers a prestigious and reputable platform to showcase your work and contribute to the advancement of the field. Join us in shaping the future of information technology research and studies by submitting your work to International Journal of Information Technology Research Studies (IJITRS) today. Title International Journal of Information technology Research Studies (IJITRS) Frequency Quarterly ISSN XXXX Publisher Eduschool Academic Research and Publishers Chief Editor Dr. R. Pugazhenthi Copyright Eduschool Academic Research and Publishers Starting Year 2025 Subject Information Technology Language English Publication Format Online Phone No 0484-2929383, 0484- 2003366 Email Id info@eduresearchpublication.com,ex_editor@eduresearchpublication.com Website www.eduresearchpublication.com Address Eduschool Academic Research and Publishers Airport Road, Nayathode Kavala, Angamaly -72, Kerala, India
Eduschool Academic Research and Publishers
The forthcoming issue of the International Journal of Information Technology Research Studies (IJITRS) presents a comprehensive exploration of core and emerging domains in information technology, reflecting the discipline’s rapid evolution and expanding influence across sectors. The articles in this volume collectively bridge foundational concepts with applied insights, offering both theoretical clarity and practical relevance for researchers, practitioners, and technology leaders.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
What is Edge Computing and Why Should Your Business Care?
Edge computing represents a paradigm shift in distributed computing architecture, bringing computational resources closer to data sources and end-users. This paper provides a comprehensive analysis of edge computing technologies, architectures, and business implications. We examine the fundamental principles underlying edge computing, including its distributed architecture, latency optimization mechanisms, and data processing capabilities. Through systematic evaluation of performance metrics, we demonstrate that edge computing achieves latency reductions of 89-93% compared to traditional cloud computing while reducing bandwidth consumption by 40-60%. Our analysis encompasses key application domains including manufacturing, healthcare, retail, smart cities, and transportation, representing a combined market value of $13.5 billion in 2024. We present empirical evidence supporting edge computing's advantages in real-time processing, data privacy, and operational efficiency. Implementation considerations are discussed, including infrastructure requirements, security protocols, and integration strategies. The findings indicate that businesses implementing edge computing solutions experience significant improvements in response times, reduced operational costs, and enhanced data sovereignty. This research contributes to the understanding of edge computing's transformative potential and provides actionable insights for organizational adoption strategies.
Business Intelligence Tools Comparison: Tableau, Power BI, and Alternatives A Comprehensive Technical Analysis of Enterprise BI Platforms
Business Intelligence (BI) tools have become critical infrastructure for data-driven decision-making in modern enterprises [1], [2]. This paper presents a comprehensive technical comparison of leading BI platforms Tableau, Microsoft Power BI, and emerging alternatives including Qlik Sense and open-source solutions. Through systematic evaluation of technical architecture, performance metrics, integration capabilities, and deployment models, this research identifies distinct strengths and limitations across platforms. The methodology encompasses multi-dimensional analysis of visualization capabilities, data processing performance, scalability, cost structures, and ecosystem integration. Results indicate that while Tableau maintains advantages in advanced visualization and exploratory analytics [3], [4], Power BI demonstrates superior enterprise integration within Microsoft ecosystems [5], [6], and alternatives like Qlik Sense excel in associative data modeling [7]. The findings provide practitioners with empirical evidence for platform selection aligned with organizational requirements, technical infrastructure, and analytical maturity. This research contributes to the growing body of knowledge on enterprise BI tool evaluation and provides actionable insights for technology decision-makers [8].
The Art of Technical Debt: Strategic Approaches to Code Maintenance
Technical debt represents a fundamental challenge in software engineering, affecting long-term maintainability, development velocity, and system quality. This paper examines strategic approaches to managing technical debt through systematic code maintenance practices. Beginning with Ward Cunningham's foundational metaphor introduced in 1992, we explore how technical debt has evolved into a comprehensive framework for understanding software quality trade-offs. Through analysis of verified academic literature and industry practices, this research identifies key strategies for debt identification, measurement, prioritization, and remediation. Our findings indicate that organizations benefit most from strategic debt management rather than debt elimination, with continuous refactoring practices and cross-functional collaboration emerging as critical success factors. This work contributes to software engineering practice by synthesizing established approaches and providing evidence-based recommendations for technical debt management. We demonstrate that effective technical debt management requires integrating automated tools, economic decision-making frameworks, and organizational practices that balance immediate business value with long-term system health.
Measuring IT Success: Key Performance Indicators That Actually Matter A Comprehensive Framework for Strategic IT Performance Measurement
The measurement of information technology (IT) success has evolved from simple operational metrics to complex, multi-dimensional frameworks that align with strategic business objectives. This paper presents a comprehensive analysis of key performance indicators (KPIs) that genuinely reflect IT value creation and business impact. Through systematic review of contemporary literature and industry practices, we identify critical gaps in traditional IT measurement approaches and propose an integrated framework that encompasses strategic, tactical, and operational dimensions. Our research synthesizes findings from 47 peer-reviewed studies and industry reports to establish evidence-based KPI categories including financial performance, customer satisfaction, internal processes, and organizational learning. We introduce a maturity model for KPI implementation and demonstrate the practical application through case analysis. The proposed framework addresses the fundamental challenge of connecting IT investments to measurable business outcomes while maintaining operational excellence. Our findings indicate that organizations employing multi-dimensional KPI frameworks achieve 34% higher IT value realization compared to those relying solely on traditional efficiency metrics. This research contributes to both academic discourse and practical implementation by providing actionable guidelines for CIOs and IT leaders seeking to demonstrate and enhance IT's strategic contribution.
Understanding Machine Learning: Real-World Examples That Make Sense
Machine learning has emerged as a transformative technology that enables computers to learn from data and make intelligent decisions without explicit programming. This paper provides a comprehensive examination of machine learning fundamentals through practical, real-world examples that bridge theoretical concepts with tangible applications. We explore the core paradigms of supervised, unsupervised, and reinforcement learning, demonstrating how each approach solves distinct classes of problems across diverse domains including healthcare, finance, e-commerce, and autonomous systems. Through detailed case studies of email spam filtering, medical diagnosis, recommendation systems, and autonomous vehicles, we illustrate how machine learning algorithms process data, extract patterns, and generate predictions. The paper analyzes the complete machine learning workflow from data collection and preprocessing through model training, evaluation, and deployment. We examine popular algorithms including neural networks, decision trees, support vector machines, and ensemble methods, providing concrete examples of their application. Additionally, we discuss contemporary challenges including data quality, algorithmic bias, model interpretability, and computational requirements. This work serves as a practical guide for understanding how machine learning transforms abstract mathematical concepts into solutions for complex real-world problems.
The International Journal of Information Technology Research Studies presents its next issue, showcasing research that reflects the accelerating evolution of computing, cybersecurity, and intelligent systems. The contributions span some of the most critical technological frontiers shaping today’s digital world from quantum computing and machine learning to cyber defence and organisational transformation.
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Digital Transformation Strategy: Where to Start and What to Avoid
Digital transformation represents a fundamental restructuring of organizational operations through strategic integration of digital technologies. Despite global investments exceeding $2 trillion annually, empirical evidence reveals that 70% of digital transformation initiatives fail to achieve intended objectives. This paper investigates critical success factors and common pitfalls in initiating digital transformation strategies through systematic analysis of peer-reviewed literature. The research identifies strategic alignment, organizational culture, and leadership commitment as primary enablers of successful transformation. Conversely, technology-centric approaches, inadequate change management, and insufficient stakeholder engagement emerge as principal failure factors. A synthesized framework for transformation initiation is proposed, emphasizing phased implementation, comprehensive readiness assessment, and balanced attention to technological, organizational, and human dimensions. This study contributes to digital transformation literature by providing evidence-based guidance for organizations initiating transformation journeys while highlighting critical risks requiring mitigation.
Ransomware Evolution: How AI is Changing Both Attack and Defence Strategies
The proliferation of artificial intelligence (AI) technologies has fundamentally transformed the cybersecurity landscape, particularly in the domain of ransomware attacks and defense mechanisms. This paper examines the evolutionary trajectory of ransomware through the lens of AI integration, analyzing how machine learning algorithms, automated decision-making systems, and neural networks are being weaponized by adversaries while simultaneously empowering defensive capabilities. Through systematic analysis of current literature, attack taxonomies, and defense frameworks, this research identifies critical inflection points where AI technologies have altered the ransomware threat landscape. The study reveals that AI-enhanced ransomware demonstrates increased sophistication in target selection, evasion techniques, and encryption methods, while AI-driven defense systems show promise in predictive threat detection, behavioral analysis, and automated response mechanisms. However, significant asymmetries persist between offensive and defensive capabilities, with attackers often exploiting AI advantages more rapidly than defenders can implement countermeasures. This paper proposes a comprehensive framework for understanding AI's dual role in ransomware evolution and offers strategic recommendations for organizations seeking to leverage AI technologies for enhanced cybersecurity resilience. The findings underscore the urgency of developing adaptive defense strategies that can respond to the accelerating pace of AI-enabled threats while addressing ethical considerations and resource constraints inherent in AI implementation.
Zero-Shot Learning: Enhancing Models to Recognize Unseen Classes with Minimal Data
Zero-shot learning (ZSL) represents a paradigmatic shift in machine learning that enables models to classify instances from classes that were not observed during training. This paper presents a comprehensive analysis of contemporary zero-shot learning methodologies, with particular emphasis on generative adversarial networks (GANs) and semantic embedding approaches for enhancing recognition of unseen classes with minimal data requirements. We evaluate state-of-the-art techniques across benchmark datasets including Animals with Attributes (AWA2), Caltech-UCSD Birds (CUB-200-2011), and SUN Attribute datasets. Our experimental analysis demonstrates that generative feature synthesis combined with semantic attribute learning achieves superior performance in both conventional zero-shot and generalized zero-shot settings. The proposed framework shows significant improvements of 3-7% over existing methods in harmonic mean accuracy across multiple benchmark datasets. These findings contribute to the advancement of adaptive learning systems capable of robust performance in dynamic environments with continuously emerging object categories.
Quantum Computing Explained: A Beginner's Guide to the Future of Processing
Quantum computing represents a paradigmatic shift in computational methodology, leveraging quantum mechanical phenomena to process information in fundamentally different ways than classical computers. This paper examines the theoretical foundations, architectural implementations, and practical applications of quantum computing systems. Through analysis of current quantum computing platforms including superconducting, trapped-ion, and photonic implementations, we evaluate the technology's potential to solve computationally intractable problems in cryptography, optimization, and simulation. Key findings indicate that while quantum computers demonstrate quantum supremacy in specific domains, significant technical challenges including decoherence, error rates, and scalability limitations currently restrict practical applications. The analysis reveals that quantum computing's transformative potential lies in hybrid classical-quantum algorithms rather than complete replacement of classical systems. Current implementations show promise for near-term applications in quantum chemistry simulation and certain optimization problems, while fault-tolerant quantum computing remains a long-term objective requiring substantial technological advancement.
AI-Driven Network Security: Using Deep Learning to Detect and Mitigate Cyber Threats in Real Time
The exponential growth of cyber threats necessitates advanced security mechanisms capable of real-time detection and mitigation. Traditional signature-based security systems struggle with sophisticated attacks, zero-day exploits, and advanced persistent threats (APTs). This paper presents a comprehensive analysis of artificial intelligence (AI)-driven network security systems utilizing deep learning methodologies for real-time cyber threat detection and mitigation using the NSL-KDD dataset. We examine convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid deep learning architectures applied to network intrusion detection systems (NIDS). Our analysis encompasses recent developments in deep learning applications, including multi-layer perceptron (MLP) models, long short-term memory (LSTM) networks, and autoencoder-based anomaly detection systems. The paper evaluates performance metrics on the widely-used NSL-KDD benchmark dataset, demonstrating superior detection accuracy rates of 99.24% for binary classification and 98.73% for multiclass classification while maintaining low false-positive rates of 1.2%. Key contributions include a systematic evaluation of deep learning architectures for network security, analysis of real-time implementation challenges using the NSL-KDD dataset, and identification of emerging research directions in AI-powered cybersecurity. Results indicate that hybrid CNN-LSTM models achieve optimal performance for sequential network traffic analysis with 99.24% accuracy, outperforming traditional machine learning approaches by 15-20%.
It is with great pride that we present the inaugural issue of the International Journal of Information Technology Research Studies (IJITRS), a platform dedicated to advancing knowledge and innovation in the dynamic field of information technology. This first issue brings together a collection of cutting-edge research articles that address some of the most pressing challenges and transformative opportunities in today’s digital landscape.
Privacy-Preserving Techniques in Data Mining: A Comprehensive Analysis of Homomorphic Encryption and Differential Privacy Approaches
The proliferation of big data analytics has raised significant privacy concerns regarding the protection of sensitive information during data mining processes. This research investigates the effectiveness of homomorphic encryption (HE) and differential privacy (DP) as privacy-preserving techniques in data mining applications. Through systematic analysis of existing implementations, this study evaluates the performance, security guarantees, and practical applicability of these approaches across various data mining tasks including classification, clustering, and association rule mining. Our findings reveal that while fully homomorphic encryption offers comprehensive security guarantees, it suffers from prohibitive computational overhead for large-scale data mining applications. In contrast, somewhat homomorphic encryption schemes provide a more practical balance between security and efficiency. Differential privacy demonstrates superior performance in terms of computational efficiency, though with varying utility-privacy tradeoffs dependent on privacy budget allocation. We propose a hybrid framework that leverages the strengths of both approaches, demonstrating improved privacy protection without significant utility loss on benchmark datasets. This research contributes to advancing privacy-preserving data mining techniques that balance analytical utility with robust privacy guarantees.
Fake News Detection: Mining Social Media Data to Detect and Classify Misinformation
The proliferation of misinformation on social media platforms poses significant challenges to information integrity and democratic discourse. This paper presents a comprehensive analysis of computational approaches for fake news detection, examining current methodologies that leverage natural language processing, machine learning, and network analysis to identify and classify misinformation. Through a systematic review of empirical studies published between 2017 and 2024, we identify key features and techniques used in fake news detection systems, evaluate their effectiveness, and discuss limitations and future research directions. Our findings reveal that ensemble methods combining linguistic, network, and temporal features achieve accuracy rates of 85-95%, though challenges remain in cross-domain generalization and detecting sophisticated deepfakes. We propose a unified framework for understanding fake news detection methodologies and provide recommendations for developing more robust and scalable systems.
Edge Computing in Networks: Reducing Latency Using AI-Driven Edge Computing Strategies
Research Question: How can AI-driven strategies in edge computing architectures effectively reduce network latency while maintaining system performance and resource efficiency using real-world IoT datasets? This paper presents a comprehensive experimental analysis of AI-driven edge computing strategies designed to minimize network latency using concrete datasets and rigorous benchmarking methodologies. We conducted extensive experiments using three primary datasets: the IEEE DataPort "Benchmark Dataset for Generative AI on Edge Devices" containing 1,000+ experimental runs on Raspberry Pi clusters, an industrial IoT machinery vibration monitoring dataset with 100 experimental runs generating 350KB per 10-second interval, and MQTT broker performance measurements spanning 360+ hours across cloud and edge deployments. Our experimental testbed consisted of distributed Raspberry Pi 4B devices orchestrated by Kubernetes (K3s), NVIDIA Jetson AGX Xavier edge nodes, and cloud instances on AWS EC2. Results demonstrate that AI-driven edge computing achieves 4.2ms average latency compared to 31.7ms for cloud-only processing (86.7% reduction) while maintaining 94.3% model accuracy. The hybrid AI architecture combining model quantization (INT8), neural architecture search, and reinforcement learning-based task scheduling processed 75.4% of data locally, reducing network traffic by 68.2% and improving energy efficiency by 42.8%. Performance evaluation using 22.8 trillion IoT sensor readings across temperature, vibration, and environmental monitoring scenarios validates the practical applicability of our approach for latency-critical applications including industrial automation, smart cities, and autonomous systems.
Green Networking: Energy-Efficient Routing Algorithms for Sustainable Networking
The exponential growth of network traffic and the proliferation of Internet of Things (IoT) devices have significantly increased energy consumption in modern communication networks, contributing to rising carbon emissions and operational costs. This paper presents a comprehensive analysis of energy-efficient routing algorithms designed to optimize power consumption while maintaining quality of service in sustainable networking infrastructures. Through systematic evaluation of contemporary green routing protocols, including tree-based, fuzzy logic-enhanced, and machine learning-driven approaches, we demonstrate that optimized energy-aware routing can achieve up to 40% reduction in power consumption compared to traditional routing methods. Our analysis employs established datasets including NSL-KDD and UNSW-NB15, and utilizes simulation frameworks NS-3 and OMNeT++ for performance validation. The results indicate that hybrid optimization algorithms combining particle swarm optimization with fuzzy clustering show superior performance in balancing energy efficiency with network reliability. This research contributes to the development of sustainable networking solutions essential for reducing the carbon footprint of information and communication technology infrastructure.
Explainable AI (XAI) – Enhancing Interpretability of Deep Learning Models for Critical Applications
Deep learning models have achieved remarkable performance across critical applications including healthcare, finance, and autonomous systems. However, their black-box nature poses significant challenges for deployment in high-stakes domains where transparency and accountability are paramount. This paper presents a comprehensive technical framework for enhancing interpretability of deep learning models through explainable artificial intelligence (XAI) methodologies. We evaluate multiple XAI techniques including SHAP, LIME, Grad-CAM, and layerwise relevance propagation across diverse datasets from healthcare and financial domains. Our approach demonstrates significant improvements in model interpretability while maintaining predictive accuracy, achieving faithfulness scores of 0.87±0.05 and stability metrics exceeding 0.82 across tested applications. The proposed methodology addresses critical requirements for regulatory compliance and trustworthy AI deployment in mission-critical systems. Results indicate that post-hoc explanation methods combined with rigorous evaluation frameworks provide viable pathways for transparent AI implementation in critical applications.
Copyright (c) 2025 Saritha E (Author) https://creativecommons.org/licenses/by/4.0
We are delighted to present the second issue of the International Journal of Information Technology Research Studies (IJITRS), which showcases innovative and impactful research addressing the technological challenges and opportunities of our time. This issue brings together forward-thinking studies that span artificial intelligence, cybersecurity, Internet of Things (IoT), human-computer interaction, and data-driven decision-making systems—demonstrating the breadth and depth of ongoing advances in the information technology domain.
Securing the Internet of Things: A Comprehensive Analysis of Lightweight Cryptographic Approaches for Resource-Constrained Devices
The rapid proliferation of Internet of Things (IoT) devices has introduced significant security challenges due to their resource constraints and widespread deployment in critical applications. This research examines lightweight cryptographic approaches that can provide robust security for IoT communication while operating within the severe computational, memory, and energy limitations of IoT devices. Through systematic analysis of existing lightweight cryptographic primitives, protocols, and frameworks, this paper identifies the most promising solutions for securing IoT ecosystems. Our findings indicate that optimized implementations of established algorithms like AES, novel lightweight block ciphers such as PRESENT and SIMON, and emerging post-quantum resistant schemes offer viable security options for different IoT deployment scenarios. The research also evaluates implementation challenges, performance metrics, and security-efficiency tradeoffs across various IoT application domains. This comprehensive analysis contributes to the growing body of knowledge on IoT security by providing a structured evaluation framework for selecting appropriate lightweight cryptographic solutions based on specific IoT device constraints and security requirements.
NIST Lightweight Cryptography Standardization
Recommender Systems: Enhancing Prediction Accuracy Through Hybrid Data Mining Techniques
This research explores the integration of multiple data mining approaches to improve recommendation accuracy in modern recommender systems. Despite significant advancements in recommendation algorithms, challenges persist in addressing the cold-start problem, data sparsity, and preference volatility. This study investigates how hybrid techniques combining collaborative filtering, content-based filtering, and knowledge-based approaches can overcome these limitations. Using a comprehensive dataset from an e-commerce platform with 2.3 million user-item interactions, we implemented a novel hybrid framework that dynamically switches between recommendation strategies based on contextual factors. Results demonstrate that our hybrid approach achieves a 27.4% improvement in recommendation accuracy compared to single-method approaches, with particularly strong performance in cold-start scenarios (41.2% improvement). The findings contribute to recommender systems theory by establishing an adaptive framework that optimizes recommendation strategies based on real-time data characteristics and user behavior patterns. This research has significant implications for e-commerce platforms, digital content providers, and social networks seeking to enhance user experience through more accurate and contextually relevant recommendations.
The Evolution of In-Vehicle Intrusion Detection Systems through Deep Learning: A Systematic Study
The security of in-vehicle networks is jeopardized by the advancement of sophisticated automotive electronics. Historically, intrusion detection systems have been employed to safeguard these networks. Nevertheless, they cannot recognize advanced dangers; hence, recent advancements in artificial intelligence provide more refined and efficient detection systems. This systematic study emphasizes the shift from traditional methods to deep learning, examining the latest deep learning-based intrusion detection systems for in-vehicle networks. We assess the efficacy of various deep learning-based intrusion detection systems in detecting and preventing cyberattacks, including denial-of-service and spoofing, by examining their applicability, performance metrics, advantages, and disadvantages. The future of DL in in-vehicle security is also examined in our assessment, which suggests possible lines of inquiry
Development of Explainable AI (XAI) Based Model for Prediction of Heavy/High Impact Rain Events Using Satellite Data
A significant development in meteorological science has been the creation of Explainable AI (XAI) models that use satellite data to predict heavy/high-impact rain events. There is a growing demand for predictive models that not only provide accurate forecasts but also provide insights into the underlying decision-making process due to the increasing frequency and severity of extreme weather patterns. It is difficult to use traditional machine learning (ML) models in high-stakes situations like weather forecasting due to their lack of transparency. XAI fills this void by improving the interpretability of models, which is essential for comprehending the factors that lead to extreme rainfall events. This paper looks at how convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models can be used to combine satellite-based data with XAI models to predict heavy rain events. The spatiotemporal transformer framework, attention mechanisms, and capsule networks are some of the cutting-edge deep learning architectures that we are looking into for ways to boost prediction accuracy and dependability. The study also emphasizes how model explainability helps end-users like meteorologists, emergency response teams, and policymakers build trust. This study aims to provide a comprehensive framework for deploying satellite-driven rain prediction models in real-world applications by examining recent advancements in XAI for climate risk assessment. In addition, we address issues related to data quality, model interpretability, computational efficiency, and scalability when integrating these models with existing weather forecasting systems. The findings suggest that XAI models have a lot of potential to change weather forecasting practices by making it easier to be prepared for extreme rainfall events and providing clear insights into the decisions made by the models. This paper paves the way for the widespread use of weather prediction tools that are more reliable, actionable, and interpretable in disaster management and climate adaptation strategies.
Copyright (c) 2025 Janvi S. Zamre, Tamanna M. Nebhani, Tanushree D. Raut, Sanskruti S. Deshmukh, Mayuri A. Pohane (Author) https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Incoming Calls for Helpdesk
In the evolving landscape of customer service, understanding the emotional tone of interactions is vital for delivering empathetic, efficient, and personalized support. This thesis proposes a real-time sentiment analysis system for incoming helpdesk calls, transforming raw audio into actionable emotional insights. The framework integrates Automatic Speech Recognition (ASR) to transcribe calls, followed by Natural Language Processing (NLP) and machine learning-based sentiment analysis to classify emotions as positive, negative, or neutral. The system architecture features a Python- based backend pipeline, a Flutter-based customer feedback interface, and a JavaScript- driven admin dashboard for real-time monitoring. Hybrid modeling approaches—combining lexicon-based methods, traditional machine learning algorithms, and deep learning models like Transformers—are used to capture both linguistic and acoustic sentiment features. The system facilitates early detection of customer dissatisfaction, optimizes call handling, and identifies recurring issues and sentiment trends. Performance evaluation with synthetic data demonstrates its reliability and responsiveness. Key challenges addressed include transcription accuracy, emotional nuance detection, and ethical concerns related to privacy and bias. Future enhancements aim to support multilingual sentiment analysis, CRM integration, and finer-grained emotion detection, offering a scalable and ethically sound framework for embedding emotional intelligence into helpdesk operations.
Shreyash Nandurkar, Shivam Lad, Yashswi Gandhak, Shravani Kshirsagar, A. R. Ladole, A. B. Gadicha
Copyright (c) 2025 Shreyash Nandurkar, Shivam Lad, Yashswi Gandhak, Shravani Kshirsagar, A. R. Ladole, A. B. Gadicha (Author) https://creativecommons.org/licenses/by/4.0
WATER ELECTRICITY MONITORING WEB-BASED APPLICATION
Access to clean water and reliable electricity is fundamental to the well-being of individuals and the sustainability of modern societies. As utility consumption rises in urban and semi-urban areas, traditional resource monitoring systems are proving inefficient, delayed, and inadequate for large-scale or real-time management. Manual meter readings and delayed billing processes offer no insights into usage trends or abnormalities, leaving users unaware of excessive consumption or leaks until the end of the billing cycle. This research presents a smart and scalable solution using an Internet of Things (IoT)-based monitoring system that provides real-time tracking and analytics for both water and electricity consumption. The system is built using an ESP32 microcontroller integrated with calibrated sensors — a YF-S201 water flow sensor and an ACS712 current sensor — which continuously measure utility usage at the household or unit level. Sensor data is transmitted to a centralized MongoDB database using the MQTT protocol, ensuring efficient and secure communication. A cloud-based dashboard, developed using Streamlit, visualizes the collected data in real-time, offering users an intuitive interface to monitor usage, compare trends, receive anomaly alerts, and download historical reports. The dashboard includes modules like daily/weekly/monthly analytics, personalized usage leaderboards, billing breakdowns, and a dark mode for enhanced accessibility. Field deployment and validation of the prototype confirm the system’s accuracy, low-latency performance, and user-friendliness. Designed to operate in both resource-constrained and developed environments, this solution demonstrates the potential of IoT for promoting sustainable living through informed utility usage. The proposed system supports the global agenda of smart cities and environmental conservation through data-driven decision-making and resource optimization.
Copyright (c) 2025 Prathamesh Deshmukh, Prajwal Dilip Kale, Lokesh Prabhakar Nandanwar, Swayam Dyaneshwar Dehanikar , Prof. Ajay B, Gadicha (Author) https://creativecommons.org/licenses/by/4.0