International Journal of Data Science and IoT Management System
Editor- In-Chief: Dr. Jorge K Title proper: International Journal of Data Science and IoT Management System (IJDIM) Subject: Engineering, Computer Science, IOT, Management, Science and Technology Publisher Name: IJDIM Publications ISSN: 3068-272X Frequency: Quarterly Language: English Country: USA Medium: Online Indexed by: CROSSREF, MDPI scilit, Google Scholar Ext Title DOI of the Journal: http://doi.org/10.64751/issn.3068-272X E-mail: editor@ijdim.com Impact Factor : 6.125
ADVANCED DDOS DETECTION IN IOT NETWORKS USING ENSEMBLE MACHINE LEARNING TECHNIQUES
The rapid growth of Internet of Things (IoT) devices has significantly increased the attack surface for cyber threats, particularly Distributed Denial of Service (DDoS) attacks. These attacks disrupt network services by overwhelming systems with malicious traffic. Traditional intrusion detection systems and basic machine learning techniques fail to provide accurate and real-time detection due to the high volume and dynamic nature of IoT traffic. This paper proposes an advanced DDoS detection system using ensemble machine learning techniques. The proposed framework combines Random Forest, XGBoost, and K-Nearest Neighbors (KNN) algorithms to improve detection accuracy and efficiency. The model preprocesses IoT network traffic data, extracts important features, and performs real-time monitoring to identify malicious activities. Experimental analysis shows that the ensemble learning approach achieves higher accuracy, faster response time, and improved scalability compared to traditional detection systems. The proposed system effectively detects multiple DDoS attack types and provides real-time alerts and mitigation support for secure IoT environments.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Real-time Traffic Surveillance and Detection using Deep Learning and Computer Vision Techniques
"Real-time traffic surveillance and detection play a crucial role in modern transportation systems for ensuring safety, efficiency, and security. This study proposes a novel approach that leverages deep learning and computer vision techniques for real-time traffic surveillance and detection. By employing state-of-the-art convolutional neural networks (CNNs) and object detection algorithms, the proposed system is capable of accurately detecting and tracking vehicles, pedestrians, and other objects in live video streams from traffic cameras. The system utilizes advanced techniques such as transfer learning and data augmentation to adapt pre-trained CNN models to the specific requirements of traffic surveillance tasks. Additionally, it incorporates methods for crowd density estimation, anomaly detection, and traffic flow analysis to provide valuable insights for traffic management and decision-making. Through extensive experimentation and evaluation on real-world traffic datasets, the proposed approach demonstrates superior performance in terms of detection accuracy, speed, and scalability compared to traditional methods. This research contributes to the advancement of intelligent transportation systems by offering an efficient and reliable solution for real-time traffic surveillance and detection, with potential applications in traffic monitoring, congestion management, and public safety."
CLIP - Guided Generative Adversarial Network for Semantic Text-to-image synthesis
Text-to-image synthesis has emerged as one of the most significant applications of deep learning, enabling the automatic generation of realistic images from natural language descriptions. Traditional Generative Adversarial Networks (GANs) have demonstrated promising capabilities in image generation; however, maintaining semantic consistency between textual descriptions and generated images remains a major challenge. Recent advancements in Contrastive Language– Image Pre-training (CLIP) have provided powerful multimodal representations that effectively capture the relationship between textual and visual information. This project presents a CLIP-Guided Generative Adversarial Network (GAN) framework for semantic text-to-image synthesis. The proposed approach integrates the semantic understanding capability of CLIP with the image generation strength of GANs to produce visually realistic and textually aligned images. CLIP is employed to evaluate and guide the generator by measuring the similarity between generated images and their corresponding textual descriptions. The generator learns to create images that not only exhibit high visual quality but also preserve the semantic meaning embedded in the input text. The discriminator further enhances image authenticity by distinguishing between real and synthesized samples.
AUTOMATIC EPILEPSY DETECTING USING MACHINE LEARNING BY NAIVE BAYES TECHINIQUE
Epilepsy is one of the most common neurological disorders characterized by recurrent and unpredictable seizures caused by abnormal electrical activity in the brain. Early and accurate detection of epileptic seizures is crucial for timely medical intervention and effective treatment planning. Traditional epilepsy diagnosis primarily relies on manual interpretation of Electroencephalogram (EEG) signals by neurologists, which is time-consuming, labor-intensive, and susceptible to human error. Therefore, there is a growing need for automated and intelligent systems that can assist healthcare professionals in detecting epilepsy with high accuracy and efficiency. This project proposes an Automatic Epilepsy Detection System using Machine Learning based on the Naive Bayes Technique. The system utilizes EEG signal datasets obtained from patients and extracts relevant features that represent the characteristics of normal and epileptic brain activities. The collected data undergo preprocessing steps such as noise removal, normalization, and feature selection to improve the quality of the input data. The Naive Bayes classifier is then trained using these extracted features to classify EEG signals into epileptic and nonepileptic categories. Despite its assumption of feature independence, the Naive Bayes algorithm is computationally efficient, easy to implement, and capable of delivering reliable classification performance for medical diagnostic applications.
1.M. RATNA KUMARI 1 ,2. KOMALIKA CHANDOLU 2
Predicting Rainfall using Machine Learning Techniques
Rainfall prediction is one of the challenging and uncertain tasks which has a significant impact on human society. Timely and accurate predictions can help to proactively reduce human and financial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.
Spondylitis Detection Using Deep Learning Architecture
Spondylitis is a chronic inflammatory disease that primarily affects the spine and can lead to severe pain, stiffness, and eventually spinal deformity. Traditional methods for diagnosing spondylitis rely heavily on radiographic interpretation by specialists, which can be time-consuming and subjective. With the growing availability of medical imaging data and advancements in computational capabilities, there is a rising interest in the automation of diagnostic processes through deep learning. This project presents a deep learning-based solution for the automated detection of spondylitis using spinal X-ray and MRI images. The proposed system employs advanced convolutional neural networks (CNNs) capable of learning hierarchical features from medical images. By training the model on labeled datasets, it can differentiate between healthy and affected spinal structures with high accuracy. The system is designed to reduce diagnostic delays and improve reliability by minimizing human error. In conclusion, this deep learning architecture provides a promising tool for early and accurate detection of spondylitis. It serves as a decision support system for radiologists and orthopedic specialists, streamlining the diagnostic workflow in clinical settings. Future enhancements may include multidisease classification, integration with hospital systems, and deployment in mobile health applications for remote diagnosis.
B. AmarnathReddy1 , C.AKSHAYA SABITHA JASMINE 2
ML Based Scraper To Navigate Through Search Queries, Extracts Relevant Listings, and Data Such As Business Names, Categories, Addresses, Phone Numbers, Ratings, And Review Counts.
The exponential growth of location-based services has made geographic and businessrelated data crucial for a wide range of applications, including market analysis, travel planning, and service optimization. Google Maps, one of the most comprehensive mapping platforms available today, contains a vast repository of realtime, location-specific data such as business names, ratings, addresses, and user reviews. This project focuses on programmatically accessing such data using Python, not through web scraping, which violates Google's Terms of Service, but by leveraging the official and authorized Google Places API. Through the API, developers can legally and efficiently retrieve structured data about places, including restaurants, hospitals, tourist spots, and more, using simple HTTP requests. The project involves integrating the API with Python to send queries, parse JSON responses, and extract relevant fields such as name, location, rating, and user reviews. The retrieved data can then be stored in databases or visualized for further analysis. By adhering to legal and ethical data usage practices, this project highlights the importance of responsible data access while demonstrating practical skills in Python programming, RESTful APIs, and JSON data handling. Additionally, alternative data sources like OpenStreetMap and Foursquare are explored for open and free access to similar geographic datasets.
M.Ratna Kumari 1 , Shaik.Nayab Rasool 2,
Banana Leaf Disease Detection using ML & Computer Vision Algorithm
Banana cultivation is highly susceptible to various leaf diseases, which significantly reduce crop yield and quality. This project presents a web-based application for automatic banana leaf disease detection using Machine Learning (ML) and Computer Vision algorithms. The system allows users to load and process banana leaf image datasets, train detection models, and predict diseases through a user-friendly interface. Upon image upload, the trained model classifies the leaf condition and provides a disease prediction with an associated accuracy score. As demonstrated in the application, diseases like Black Sigatoka can be detected with notable precision. The project integrates efficient image processing techniques and machine learning classifiers, enabling early and accurate diagnosis to assist farmers and agronomists in disease management.
AI POWERED PERSONAL FITNESS COACH USING DEEP LEARNING
In recent years, the intersection of artificial intelligence and personal health has led to the development of innovative fitness technologies. This project presents an AIpowered personal fitness coach that utilizes deep learning techniques to provide personalized and interactive fitness training. The system is designed to emulate the role of a human fitness coach by recognizing exercises, analyzing posture, tracking performance, and delivering real-time feedback. By integrating computer vision and deep neural networks, the coach ensures that users perform exercises correctly, reducing the risk of injury and improving overall workout efficiency. The core of the system leverages advanced pose estimation models such as MediaPipe or PoseNet to identify human skeletal keypoints during workouts. These keypoints are analyzed using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify exercises and assess user form. The AI model is trained on labeled fitness datasets to recognize different types of workouts, detect errors in posture, and calculate metrics such as repetitions, angles, and stability. This allows the coach to provide accurate corrections and guidance in real time, mimicking the support provided by a personal trainer. This AI-powered fitness coach demonstrates how deep learning can revolutionize personal health by making fitness guidance more accessible, personalized, and effective. By reducing dependency on human trainers and expensive gym subscriptions, the system empowers users to take control of their health with the help of intelligent automation. The proposed model can be expanded in the future to include wearable integration, diet tracking, AR/VR enhancements, and even mental wellness recommendations, creating a holistic virtual fitness assistant.
RIFD-NET: A ROBUST IMAGE FORGERY DET ECTION NETWORK
With the widespread use of digital imaging and social media platforms, the authenticity of images has become increasingly critical. Image forgery, which involves manipulating or tampering with digital images, poses significant threats in various domains including journalism, legal evidence, and security. Traditional forgery detection methods often struggle with robustness and accuracy, especially against sophisticated editing techniques. To address these challenges, we propose RIFD-NET, a novel deep learning-based framework designed to detect various types of image forgeries with high precision and resilience. RIFD-NET leverages a multi-branch convolutional neural network architecture that integrates both spatial and frequency domain features. This hybrid feature extraction approach enhances the model’s ability to identify subtle inconsistencies introduced by forgery processes. Additionally, the network incorporates attention mechanisms to focus on manipulated regions, improving detection performance even under adverse conditions such as compression, noise, and scaling. The design allows RIFD-NET to generalize effectively across diverse forgery types including copy-move, splicing, and removal attacks. In conclusion, RIFD-NET represents a significant advancement in the field of image forgery detection by delivering enhanced robustness, accuracy, and adaptability. Future work will focus on extending the framework to video forgery detection and incorporating explainability features to provide better interpretability of detected manipulations. The proposed network offers a promising direction for strengthening trust and authenticity in digital visual content.
Accurate Cloud Data Center Workload Forecasting Using CEEMDAN-VMD and BiLSTM-BiGRU Networks
Managing cloud data center resources— high-dimensional, chaotic operational data—requires forecasting workload. In order to enhance prediction without temporal repeating features in the CVCBM model, this paper presents a lightweight Bidirectional GRU (BiGRU) and Bidirectional LSTM (BiLSTM). Workload signals are concurrently denoised and decomposed in two phases (CEEMDAN and VMD). While K-Means clustering favors high-workload data, SE identifies key components. Conv1DBiLSTM-BiGRU is a hybrid approach that acquires both short-term and long-term temporal patterns. Using input datasets, the Flask-built trained model forecasts workloads in real time. According to experimental study, the improved model offers reliable, scalable, and real-time forecasts for cloud data center resource allocation while lowering computing costs and improving forecast accuracy.
Heterogeneous Network-on-Chip Design with Adaptive Key Rotation Aware Obfuscation
The global semiconductor industry surpassed USD 790 billion in revenue during 2025, while modern multicore and AI processors integrate hundreds of processing elements that generate massive on-chip communication traffic. Industry studies indicate that communication latency and interconnect overhead account for a significant portion of overall System-on-Chip (SoC) performance degradation, making efficient and secure Network-on-Chip (NoC) communication a critical design requirement. NoC architectures are extensively utilized in artificial intelligence accelerators, cloud data-center processors, autonomous vehicles, high-performance computing systems, edge devices, and advanced mobile SoCs, where reliable and secure packet transmission is essential for maintaining computational efficiency. Existing NoC security architectures based on centralized logic obfuscation and distributed interconnect obfuscation provide basic protection against unauthorized access and hardware attacks; however, they suffer from limitations such as static key management, increased routing complexity, higher latency, congestion sensitivity, scalability constraints, excessive Look-Up Table (LUT) utilization, increased power consumption, and limited adaptability to dynamic attack scenarios. To address these challenges, this work proposes a Heterogeneous Network-on-Chip (HNoC) with Adaptive Key Rotation Aware Obfuscation (AKRAO) architecture. The proposed framework integrates Input Switching Allocation with Buffer Management, Forwarding Buffer, Dynamic Look-Ahead Bypass Route Computation, Shortest Path Computation, Parallel Virtual Channel and Switch Allocation, Adaptive Key Rotation, and Crossbar Switching with Obfuscation within a unified routing architecture. The adaptive key rotation mechanism continuously updates security credentials to enhance resistance against key compromise, traffic analysis, and side-channel attacks, while intelligent routing and forwarding strategies reduce congestion and communication delays. Consequently, the proposed AKRAO-enabled HNoC improves throughput, routing efficiency, security robustness, scalability, resource utilization, and energy efficiency, making it highly suitable for next-generation heterogeneous multicore and manycore VLSI systems.
Adaptive Key Rotation, Buffer Management, Crossbar Switching, Heterogeneous Network-on-Chip (HNoC), Network-on-Chip (NoC), Obfuscation
High-Throughput Molecular Data Storage Readout Using Reconfigurable Lightweight Hybrid Symbolic Encoding
The exponential growth of global digital data is creating unprecedented storage challenges, with worldwide data generation expected to exceed 180 zettabytes by 2025, while conventional storage media suffer from limited lifespan and increasing energy consumption. Recent studies indicate that Deoxyribonucleic Acid (DNA)-based molecular storage can achieve storage densities exceeding 200 PB per gram and preserve data for hundreds to thousands of years, making it a promising solution for long-term archival applications. However, existing hardware-accelerated DNA data readout platforms rely on fixed primer identification, static index verification, conventional majority voting, and iterative Low Density Parity Checking (LDPC) decoding, resulting in limited adaptability, high memory traffic, increased bandwidth requirements, substantial hardware complexity, elevated power consumption, and longer decoding latency. Furthermore, the absence of early-stage quality-aware filtering allows lowconfidence sequencing reads to propagate through the recovery pipeline, reducing reconstruction accuracy and scalability. To address these limitations, this work proposes a Hardware-Accelerated Data Readout Platform for Molecular Digital Archive (MDA) using heterogeneous CPU computing. The proposed architecture incorporates Primer and Index Reconfigurable Templates (PIRT) for adaptive fragment identification, Base Quality-Aware Filtering (BQAF) for early elimination of unreliable sequencing reads, and On-Chip Lightweight Compression (OLC) to reduce memory traffic and storage overhead. Additionally, a novel Hybrid Symbolic Consensus Coding (HSCC) decoder replaces conventional LDPC decoding by combining symbolic consensus generation with parity-aware correction to efficiently handle substitution, insertion, and deletion errors with lower hardware complexity. Through deep pipelining, FIFO-based decoupling, and parallel hardware execution, the proposed framework achieves improved throughput, reduced latency, enhanced scalability, lower resource utilization, and reliable data reconstruction for next-generation molecular digital archive systems
Deoxyribonucleic Acid (DNA) Data Storage, Molecular Digital Archive (MDA), Hardware-Accelerated Data Readout, Heterogeneous CPU Computing, Hybrid Symbolic Consensus Coding (HSCC), Base Quality-Aware Filtering (BQAF)
In the age of exponential information growth, effective document retrieval has become essential for knowledge-based systems. This project presents a Document Retrieval System using RetrievalAugmented Generation (RAG), an advanced architecture that combines traditional retrieval mechanisms with the generative power of transformer-based language models. RAG enhances the retrieval process by dynamically integrating external documents into the response generation pipeline, allowing the system to produce more accurate and contextually relevant answers. The system first retrieves relevant passages from a pre-indexed document corpus using vector similarity search (e.g., FAISS or Elasticsearch). These retrieved documents are then passed to a generative language model (like BERT or GPT) that synthesizes the final output based on both the input query and the retrieved content. This hybrid approach ensures factual accuracy while maintaining the flexibility of generative models. The solution is particularly effective for question answering, summarization, and knowledge base augmentation. It demonstrates improved performance over traditional retrieval or generation-only models, offering a scalable and intelligent document access system suitable for enterprise, academic, and personal use
IOT - Enabled Fire Detection And Risk Prediction Using ML
Fire incidents in residential, commercial, and industrial environments can lead to severe loss of life, property damage, and environmental harm if not detected at an early stage. Conventional fire alarm systems primarily respond only after fire or smoke reaches a predefined threshold, often resulting in delayed emergency response. To overcome these limitations, this study proposes an IoT-Enabled Fire Detection and Risk Prediction System Using Machine Learning, which integrates real-time sensor monitoring with intelligent predictive analytics. The proposed system employs Internet of Things (IoT) devices equipped with temperature, smoke, gas, flame, and humidity sensors to continuously collect environmental data. The sensed information is transmitted to a cloud platform for storage, monitoring, and analysis. Machine learning algorithms are utilized to analyze historical and real-time sensor readings to predict the probability of fire occurrence before it becomes critical. By identifying abnormal environmental patterns and classifying risk levels, the system generates early warnings and sends instant alerts to users and emergency authorities through mobile or web-based applications. This proactive approach enables timely preventive actions, minimizes false alarms, and enhances overall safety. Furthermore, cloud-based data storage supports continuous learning and improves prediction accuracy over time. The proposed framework offers a scalable, cost-effective, and reliable solution for smart buildings, industrial facilities, warehouses, and public infrastructures. Experimental evaluation demonstrates that the integration of IoT and machine learning significantly improves fire detection accuracy, reduces response time, and enhances risk prediction compared to traditional fire alarm systems. This intelligent fire safety solution contributes to the development of smart cities by providing continuous monitoring, predictive decision-making, and efficient emergency management.
High-Speed Low-Power Arithmetic Logic Unit (ALU) Design Using CMOS Technology
The High-Speed Low-Power Arithmetic Logic Unit (ALU) Design Using CMOS Technology focuses on developing an efficient digital processing unit capable of performing arithmetic and logical operations with reduced power consumption and improved operating speed. As the demand for portable, battery-powered, and high-performance electronic devices continues to grow, designing energy-efficient hardware has become a critical challenge in modern Very Large Scale Integration (VLSI) systems. CMOS (Complementary MetalOxide-Semiconductor) technology is widely adopted due to its low static power dissipation, high noise immunity, and excellent scalability, making it an ideal choice for implementing advanced ALU architectures. This project proposes a CMOS-based ALU that integrates optimized arithmetic operations such as addition, subtraction, increment, decrement, multiplication, and logical operations including AND, OR, XOR, NOT, NAND, NOR, and comparison functions. The design emphasizes minimizing propagation delay, switching power, and silicon area through transistor-level optimization and efficient logic implementation. Performance evaluation is carried out using VLSI design and simulation tools to analyze key metrics such as power consumption, delay, area utilization, and power-delay product (PDP). The experimental results demonstrate that the proposed CMOS ALU achieves faster computation speeds while consuming significantly less power compared to conventional ALU designs. This design is well suited for applications in microprocessors, digital signal processors (DSPs), embedded systems, and Internet of Things (IoT) devices, where energy efficiency and high computational performance are essential.
BEYOND TRANSLATION: BUILDING HINDI-CENTRIC INTELLIGENCE FOR HUMAN–AI COLLABORATION
Artificial Intelligence (AI) has become a powerful tool for communication and collaboration across languages. However, most AI systems remain English-centric, treating non-English languages as secondary through translation. For Hindi, one of the most widely spoken languages globally, this approach often results in the loss of cultural meaning, idiomatic richness, and socio-pragmatic depth. Translation tools can provide basic communication, but they fail to capture the nuances of Hindi expressions, dialects, and cultural references, leading to interactions that feel artificial or incomplete. This paper argues for a paradigm shift toward building Hindi-centric intelligence that treats Hindi as a primary language of thought rather than a peripheral translation target. We explore frameworks for embedding Hindi semantics, pragmatics, and socio-cultural cues directly into AI models. Through surveys of 200 participants across academia, industry, and government, we identify strong dissatisfaction with translation-based systems and a clear demand for authentic Hindi-centric AI. Experimental comparisons between translation-based tools and Hindi-trained models reveal significant improvements in accuracy, idiomatic interpretation, and user satisfaction. A key case study is India’s sovereign AI initiative, Sarvam LLM, developed under the IndiaAI Mission. Sarvam LLM demonstrates the feasibility of large-scale Hindifirst intelligence, achieving over 20% improvement on Indic benchmarks compared to global models.
Hybrid Machine Learning and Federated Learning Framework for Intelligent Task Scheduling, Load Balancing, and Resource Allocation in Fog Computing
Fog computing has become a successful technology which processes data at edge locations for Internet of Things (IoT) systems that require quick response times. The fog environment encounters challenges in task scheduling and load distribution and resource management because of its fluctuating operational demands and its various node operational strengths and its limited resource capacity and increasing privacy concerns. The research paper presents a Federated Learning system which uses machine learning with hybrid technology to manage resources efficiently in fog computing environments. The hybrid model uses predictive algorithms together with classification algorithms to forecast workload requirements and determine task importance and assess fog node capabilities which support better scheduling choices. Through Federated Learning multiple distributed fog nodes can train a global model together without sharing their original local data which protects user privacy and minimizes required data transfer. The trained model establishes a dynamic scheduling system which allocates tasks while balancing workloads and adapting to network changes in real time. The proposed framework operates to reduce latency while increasing throughput and achieving maximum resource efficiency and delivering improved system scalability. The experimental results show that our approach achieves better performance than traditional resource management methods which improve response times and load distribution and system performance to meet the requirements of advanced smart technologies used in healthcare and smart cities and industrial automation.
Hafeena Mohammad1 , Dr.Voruganti Naresh Kumar2
BEYOND ACADEMIC METRICS: PREDICTING STUDENT PERFORMANCE USING ENGAGEMENT AND CONTEXTUAL INDICATORS
This study investigates the role of engagement and contextual indicators in predicting student academic performance using machine learning techniques. Traditional academic metrics such as attendance and grades often fail to capture behavioral and psychological aspects of learning. Using a dataset of 177 student records, this study evaluates Logistic Regression, Decision Tree, and Random Forest models to analyze the influence of engagement-related factors on academic outcomes. Experimental results show that psychological engagement is the most influential predictor of performance. Logistic Regression and Random Forest achieved the highest prediction accuracy of 83%. The findings highlight the importance of incorporating engagement indicators into modern educational analytics systems.
VOICE OF THE CUSTOMER MEETS THE MANUAL: BUILDING QFD WITH DATA-DRIVEN INTELLIGENCE
Quality Function Deployment (QFD) is a customer-oriented product development methodology that translates customer requirements into engineering characteristics for designing high-quality products and services. Traditional QFD relies heavily on expert judgment, manual evaluation, and subjective decisionmaking, which often lead to inconsistencies, increased development time, and limited adaptability to rapidly changing customer expectations. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), Data Analytics, and Natural Language Processing (NLP) have introduced data-driven approaches that enhance the efficiency and accuracy of Quality Function Deployment. This paper proposes a datadriven intelligent QFD framework that integrates customer feedback analytics, sentiment analysis, machine learning, and predictive modeling to automatically capture the Voice of the Customer (VoC) and transform it into engineering design requirements. The proposed framework combines structured and unstructured customer data collected from surveys, product reviews, social media platforms, and online feedback systems to generate intelligent House of Quality (HoQ) matrices. Comparative evaluation demonstrates that the proposed approach significantly improves customer requirement prioritization, decision-making accuracy, product quality, development efficiency, and customer satisfaction compared with conventional manual QFD techniques. Furthermore, the framework enables continuous learning from evolving customer preferences through intelligent analytics and automated recommendation mechanisms. The proposed research contributes to modern quality engineering by bridging traditional Quality Function Deployment methodologies with AI-powered data intelligence, thereby supporting customer-centric product innovation, strategic decision-making, and sustainable competitive advantage in intelligent manufacturing and service industries.
Quality Function Deployment, Voice of the Customer, Artificial Intelligence, Machine Learning, Data Analytics, Natural Language Processing, House of Quality, Customer Satisfaction, Intelligent Decision Support, Product Development.
EXPLAINABLE DEEP LEARNING FOR BREAST CANCER DETECTION: BRIDGING ACCURACY AND INTERPRETABILITY
Breast cancer is one of the leading causes of cancer-related mortality among women worldwide, making early and accurate diagnosis essential for improving patient survival and treatment outcomes. Conventional breast cancer diagnosis primarily relies on mammography, ultrasound, magnetic resonance imaging (MRI), and histopathological examination interpreted by experienced radiologists. Although Deep Learning (DL) has achieved remarkable success in automated breast cancer detection, many existing models operate as "black-box" systems, limiting clinicians' trust due to the lack of interpretability and transparency in their predictions. Explainable Artificial Intelligence (XAI) has emerged as a promising solution for addressing this challenge by providing visual and interpretable explanations for deep learning decisions. This paper proposes an explainable deep learning framework that integrates advanced Convolutional Neural Networks (CNNs) with Explainable AI techniques for accurate and transparent breast cancer detection. The proposed framework incorporates image preprocessing, deep feature extraction, tumor classification, Grad-CAM visualization, attention mechanisms, and interpretable decision support to assist clinicians in understanding model predictions. Comparative evaluation demonstrates that the proposed approach significantly improves diagnostic accuracy, precision, recall, F1-score, and model transparency while maintaining high computational efficiency. The generated visual explanations enable radiologists to verify detected tumor regions and increase confidence in AI-assisted diagnosis. The proposed framework contributes to trustworthy medical AI by bridging the gap between predictive performance and clinical interpretability, supporting intelligent breast cancer diagnosis, precision medicine, and reliable clinical decision-making.
Breast Cancer Detection, Explainable Artificial Intelligence, Deep Learning, Convolutional Neural Networks, Explainable Deep Learning, Grad-CAM, Medical Image Analysis, Mammography, Computer-Aided Diagnosis, Precision Healthcare.
CHARTING CONNECTIONS: A SOCIAL NETWORK ANALYSIS OF RESEARCH COLLABORATION IN AI-DRIVEN HUMAN RESOURCE MANAGEMENT
The rapid adoption of Artificial Intelligence (AI) in Human Resource Management (HRM) has transformed traditional workforce management by enabling intelligent recruitment, employee performance evaluation, talent analytics, workforce planning, and organizational decision-making. Simultaneously, the growing volume of research publications in AI-driven HRM has created complex collaboration networks among researchers, institutions, and countries. Social Network Analysis (SNA) provides an effective methodology for exploring these collaborative relationships by identifying influential authors, research communities, institutional partnerships, and knowledge diffusion patterns. This paper presents a comprehensive Social Network Analysis framework for examining research collaboration in AI-driven Human Resource Management. The proposed framework integrates bibliometric analysis, network construction, graph analytics, and visualization techniques to investigate co-authorship, institutional collaboration, countrylevel partnerships, keyword co-occurrence, and citation relationships. Graph-based metrics including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, network density, and modularity are utilized to evaluate collaboration strength and identify influential research communities. Comparative analysis demonstrates that AI-driven HRM research has experienced significant global collaboration growth, interdisciplinary knowledge sharing, and increased institutional connectivity over recent years. The findings provide valuable insights into emerging research trends, collaborative structures, influential contributors, and future research opportunities within AI-enabled Human Resource Management. The proposed framework contributes to strategic research planning, policy formulation, and international scientific collaboration by providing a data-driven understanding of the evolving research ecosystem.
Artificial Intelligence, Human Resource Management, Social Network Analysis, Bibliometric Analysis, Research Collaboration, Co-authorship Network, Citation Analysis, Knowledge Graph, Network Analytics, Research Mapping
MOOD MAPPING IN THE DIGITAL WORLD: ML AND DL-BASED INSIGHTS INTO DEPRESSION DETECTION ON SOCIAL PLATFORM
The widespread adoption of social media platforms has transformed digital communication by enabling individuals to share thoughts, emotions, and daily experiences in real time. These online interactions generate valuable textual, visual, and behavioral data that can provide meaningful insights into users’ mental health conditions. Depression is one of the most prevalent mental health disorders worldwide, and early identification is crucial for timely intervention and improved psychological well-being. Conventional depression diagnosis primarily depends on clinical interviews, psychological assessments, and self-reported questionnaires, which may delay detection and limit continuous monitoring. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have enabled automated analysis of large-scale social media data for intelligent mental health assessment. This paper proposes an ML and DL-based framework for depression detection using social platform data by integrating natural language processing, sentiment analysis, behavioral feature extraction, and deep neural networks. The proposed framework analyzes textual posts, user engagement patterns, emotional expressions, and linguistic features to identify depressive tendencies. Comparative evaluation using traditional machine learning algorithms and advanced deep learning models demonstrates that deep learning significantly improves prediction accuracy, precision, recall, and early depression identification. The proposed system provides an intelligent decision-support framework that can assist healthcare professionals in large-scale mental health monitoring while preserving scalability and efficiency. This research contributes to the advancement of AI-driven digital mental healthcare by enabling early depression detection through intelligent social media analytics.
Depression Detection, Social Media Analytics, Machine Learning, Deep Learning, Natural Language Processing, Sentiment Analysis, Mental Health, Artificial Intelligence, Text Classification, Behavioral Analytics
FUZZY-DRIVEN KIDNEY TUMOR DETECTION: INTEGRATING TWIN TRANSFERABLE NETWORKS WITH WEIGHTED ENSEMBLE MACHINE LEARNING
Kidney cancer is one of the most prevalent urological malignancies worldwide, and its early detection plays a vital role in improving patient survival and treatment outcomes. Conventional diagnostic methods based on manual interpretation of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans are often time-consuming, subjective, and dependent on radiologists’ expertise, leading to diagnostic variability and delayed clinical decisions. Recent advances in Artificial Intelligence (AI), Deep Learning (DL), and Transfer Learning have significantly enhanced automated medical image analysis by enabling accurate tumor detection and classification. This paper proposes a fuzzy-driven kidney tumor detection framework that integrates Twin Transferable Networks (TTNs) with a Weighted Ensemble Machine Learning (WEML) model for intelligent medical diagnosis. The proposed framework combines fuzzy logicbased preprocessing, medical image enhancement, transfer learning, feature extraction, and weighted ensemble classification to accurately identify kidney tumors from CT images. Twin Transferable Networks automatically learn discriminative image representations, while the weighted ensemble model combines predictions from multiple classifiers to improve robustness and diagnostic accuracy. Experimental evaluation demonstrates that the proposed approach significantly outperforms conventional machine learning and standalone deep learning models in terms of accuracy, precision, recall, F1-score, and computational efficiency. The integration of fuzzy logic further enhances image quality by reducing uncertainty and improving feature discrimination. The proposed framework provides a reliable and scalable computer-aided diagnosis system capable of supporting radiologists in early kidney tumor detection, clinical decision-making, and precision healthcare applications.
Kidney Tumor Detection, Fuzzy Logic, Twin Transferable Networks, Transfer Learning, Weighted Ensemble Machine Learning, Deep Learning, Medical Image Analysis, CT Imaging, Artificial Intelligence, Computer-Aided Diagnosis.
A NEW INNOVATIVE AND EFFECTIVE SYSTEM FOR TRAINS AND RAILWAY STATIONS FOR AUTO ANNOUNCEMENTS OF TRAINS, POWER SAVING AND AUTO RAILWAY GATE CONTROL
The proposed system introduces an innovative approach to modern railway station management by combining automatic train announcements, energy-efficient station operations, and automated railway gate control using IoT and embedded systems. Delays in manual announcements, unnecessary power consumption, and human-operated gates often reduce operational efficiency and safety. This system integrates sensors, microcontrollers, and IoT connectivity to automate key operations. Train detection sensors such as infrared modules monitor train arrivals, departures, and track occupancy. The system triggers automated voice announcements for upcoming trains, platform changes, and safety instructions using pre-recorded audio or text-to-speech conversion. microcontrollers process sensor inputs, , and manage station appliances based on real-time schedules. Power-saving mechanisms are implemented by automatically controlling lighting, fans, and other electrical equipment depending on passenger presence and train schedules. Automated railway gates are controlled using relay-driven motors based on train proximity data, reducing the risk of accidents and improving traffic flow near crossings. IoT integration allows centralized monitoring via web or mobile dashboards, displaying train schedules, gate status, platform occupancy, and power consumption. Real-time alerts notify station authorities of anomalies or emergency conditions. Historical logs of train movements, power usage, and gate operations enable trend analysis, predictive maintenance, and operational optimization. Security mechanisms ensure authorized access, encrypted communication, and device authentication to prevent unauthorized control. Modular design allows integration of additional smart devices such as CCTV cameras, passenger counters, and emergency alarms. The system enhances passenger safety, reduces human error, improves station operational efficiency, and contributes to energy conservation. Overall, this solution combines embedded electronics, IoT connectivity, cloud monitoring, and automation to create a modern, intelligent, and efficient railway station environment.
IOT-BASED SMART KITCHEN WITH ENHANCED AND AUTOMATED SAFETY MEASURES
The IoT-Based Smart Kitchen with Enhanced and Automated Safety Measures is a modern solution designed to improve kitchen safety, efficiency, and convenience through intelligent automation and IoT connectivity. Kitchen accidents, such as gas leaks, fire hazards, and appliance malfunctions, pose significant risks to households. This system integrates sensors, microcontrollers, and IoT technologies to monitor cooking appliances, detect hazardous conditions, and take preventive actions automatically. Gas leakage is detected using MQ2 or MQ5 gas sensors, while smoke detection is achieved via smoke sensors. Temperature sensors monitor stovetop and oven temperatures to prevent overheating and fire incidents. Smart relays connected to appliances enable automatic switching OFF in hazardous conditions. Motion sensors or proximity sensors detect human presence to ensure operational safety. The ESP32/ESP8266 microcontroller processes sensor inputs and communicates with cloud servers using MQTT or HTTP protocols. Real-time alerts and notifications are sent to users via mobile apps or dashboards in the event of unsafe conditions, gas leaks, or smoke detection. The dashboard provides live monitoring of appliance status, historical data on safety incidents, and remote control for appliances. IoT integration allows users to control kitchen devices from anywhere, enhancing convenience and operational control. Safety mechanisms include automatic shutdown of gas stoves or ovens in emergencies, alert notifications, and integration with smart locks for kitchen access. Data analytics enables trend analysis, predictive maintenance, and identification of recurring hazards. Modular design allows expansion to include additional smart appliances, cameras, or sensors. Security is ensured through encrypted communication, authentication, and device-specific credentials. Overall, this IoT-based smart kitchen system reduces risks, prevents accidents, enhances operational efficiency, and provides a safer cooking environment for households. The system combines embedded electronics, IoT connectivity, cloud monitoring, and automated safety mechanisms to create a modern, intelligent, and user-friendly kitchen environment.
TRAFFIC MONITORING AND CONTROL SYSTEM FOR SMART CITY POLLUTION REGULATION USING IOT.
The Traffic Monitoring and Control System for Smart City Pollution Regulation Using IoT is an advanced urban traffic management solution designed to optimize vehicular flow while reducing environmental pollution. Rapid urbanization and increasing vehicle density have led to severe traffic congestion and rising air pollution levels in modern cities. This project integrates IoT-enabled sensors, microcontrollers, and cloud computing to monitor traffic density, vehicle speed, and emission levels in real-time. Traffic flow is analyzed using ultrasonic sensors, infrared vehicle counters, and air quality sensors deployed at major intersections. ESP32 or ESP8266 microcontrollers process data locally and transmit it to a cloud dashboard using MQTT or HTTP protocols. The system generates real-time alerts for congestion, pollution spikes. Traffic lights and dynamic signs are controlled automatically based on vehicle density and pollution levels, ensuring smooth traffic flow and minimizing emissions. Historical data is stored for trend analysis, predictive modeling, and long-term urban planning. The system supports remote monitoring by city authorities via mobile apps or web dashboards, offering live visualization of traffic conditions, emission levels, and traffic alerts. Safety mechanisms prevent accidents by dynamically controlling traffic signals and alerting drivers. mitigation of environmental impact. Modular design permits scalability, integration of additional sensors, cameras, and smart devices. Security features include encrypted communication, authentication, and device-specific credentials to prevent unauthorized access. Overall, this IoT-based traffic monitoring and pollution regulation system promotes smart city development, reduces environmental pollution, enhances road safety, and improves urban living standards.
INTELLIGENT TRAIN ENGINE TO AVOID ACCIDENTS AND CONTROLLING RAILWAY GATE AUTOMATICALLY
The Intelligent Train Engine and Automatic Railway Gate Control system is an IoT and sensor-based safety solution designed to reduce railway accidents and enhance operational efficiency. The system integrates sensors, microcontrollers, and IoT connectivity to monitor train movement, detect obstacles on tracks, and control railway gates automatically. Ultrasonic sensors, infrared sensors, and RFID tags are deployed along tracks to detect obstacles or incoming trains. The train engine is equipped with a microcontroller, such as ESP32 or Arduino, which continuously communicates with sensors and the railway gate system. Upon detecting a train approaching a crossing, the system automatically signals the railway gate to close, preventing vehicles and pedestrians from entering the tracks. If obstacles are detected on tracks, an alert is sent to the train driver and control center to prevent collisions. The system leverages IoT communication protocols like MQTT or HTTP to transmit real-time data to cloud servers and monitoring dashboards. Train speed, location, and gate status are continuously monitored, ensuring timely intervention and enhanced safety. The dashboard allows railway authorities to remotely monitor crossings, track train schedules, and receive automated alerts for anomalies. Intelligent control algorithms process sensor data to optimize train braking and gate operation, reducing the risk of accidents. Historical data logging facilitates analysis of near-misses, gate operations, and train movements for safety improvement. Modular design allows integration of additional sensors, cameras, and IoT-enabled devices. The system improves efficiency by reducing human intervention, minimizing delays, and automating critical safety operations. Security features such as encrypted communication, authentication, and device credentials prevent unauthorized control. By combining embedded electronics, IoT, cloud monitoring, and intelligent sensors, this system provides a reliable, scalable, and cost-effective solution for railway safety. Overall, the Intelligent Train Engine and Automatic Railway Gate Control system ensures timely operation, reduces accidents, and enhances the safety and efficiency of railway networks.
SMART GATE: IOT-ENABLED RFID-BASED VEHICLE ENTRY AND REAL-TIME COUNT MANAGEMENT SYSTEM
The Smart Gate system is an advanced IoT-enabled solution designed for automated vehicle entry and real-time monitoring in residential complexes, offices, parking areas, and restricted zones. The system combines RFID technology, microcontroller intelligence, and cloud-based IoT connectivity to replace traditional manual access methods. Each vehicle is assigned a unique RFID tag that carries identification information. When a vehicle approaches the gate, the RFID reader scans the tag and sends the data to the ESP8266 or ESP32 microcontroller. The microcontroller verifies the vehicle information against a pre-stored database of authorized entries. Upon successful verification, the gate motor is activated via a relay module, allowing the vehicle to enter or exit. Simultaneously, the vehicle count and status are updated to the cloud server using MQTT or HTTP protocols, enabling real-time monitoring via a web or mobile dashboard. Unauthorized vehicles trigger an alert system to notify administrators, ensuring enhanced security. By integrating IoT, the system allows administrators to access real-time data from any location, reducing dependency on on-site security personnel and manual logbooks. The automated approach minimizes congestion, increases accuracy, and ensures fast and reliable operation even during peak hours. Multiple gates can be managed simultaneously without interference, ensuring scalability for large complexes. Historical data, such as vehicle entry and exit timestamps, is maintained in the cloud for reporting and analytics. Sensor-based triggers and scheduling rules can automate gate closing, reducing energy consumption and enhancing operational efficiency. Security mechanisms, including encrypted communication, device authentication, and secure MQTT topics, prevent unauthorized access and hacking attempts. The system’s modular design allows easy expansion, maintenance, and integration with additional IoT devices. By eliminating human error, the solution ensures accuracy in vehicle management and enhances safety. Administrators can track occupancy, generate automated reports, and optimize traffic flow based on historical data analytics. The system is cost-effective, reliable, and suitable for modern smart city implementations. It leverages low-power microcontrollers, lightweight communication protocols, and cloud platforms to deliver a high-performance, efficient, and user-friendly smart gate solution. This IoT-based system provides a comprehensive vehicle management platform that enhances convenience, security, and efficiency for all stakeholders. It integrates seamlessly with existing infrastructure and supports future expansion, including integration with automated parking systems, RFID-enabled smart cards, and cloud analytics tools. Overall, the Smart Gate system represents a significant improvement over traditional methods, offering a fully automated, scalable, and secure solution for vehicle access and real-time monitoring in modern facilities.
Military operations often involve dangerous tasks such as surveillance, border monitoring, and intruder detection, which can put soldiers' lives at risk. To overcome these challenges, a wireless military defense robot is proposed to perform surveillance and monitoring activities in hazardous environments. The system utilizes NodeMCU, Wi-Fi communication, sensors, and mobile technologies to provide both automatic and manual control. It incorporates ultrasonic, PIR, infrared, flame, and gas sensors for threat detection and monitoring. The robot also uses Skype video calling for real-time surveillance, reducing the need for expensive camera systems. The proposed solution offers a cost-effective, energyefficient, and reliable approach for military and defense applications.
1Lachi Hari Krishna,2 Inguva Venkata Naga Manikanta Sai Raman, 3Kosuri Vishnu Vardhan, 4Keerthi Prem Kumar, 5Y. John Varakumar