International Journal of Information Technology and Computer Engineering
JOURNAL SUMMARY: Editor- In-Chief: Dr. Abdul Wasay Title proper: International Journal of Information Technology and Computer Engineering ISSN : 2347-3657 Subject: Computer Sciences & Information Technology, AI & ML, Data Science Publisher: Nisaab Publishers Dates of publication: 2013 Frequency: Quarterly Language: English Country: India Medium: Online Indexed by: CROSSREF Indexed by: ROAD Indexed by: THE KEEPERS Title DOI of the Journal: http://doi.org/10.62647/issn.2347-3657 APC: $100/-
Mathematical Modeling of Social Networks: A Graph Theoretical Approach
Social networks play a vital role in shaping information dissemination and interaction dynamics in modern societies. Understanding the structural properties of such networks is essential for identifying influential individuals and optimizing communication strategies. This study presents a graph-theoretical framework for modeling and analyzing social networks using a real-life–inspired university social system. The network is represented as an undirected graph, where nodes correspond to individuals and edges represent academic interactions. To address privacy and ethical concerns, synthetic data is employed to simulate realistic interaction patterns. Classical graph-theoretical measures, including degree, betweenness, and closeness centrality, are utilized to evaluate node importance and influence. The analysis reveals that nodes with high centrality values significantly enhance information dissemination efficiency within the network. The proposed approach demonstrates the effectiveness of graph theory as a mathematical tool for analyzing social structures and provides a transparent, application-oriented methodology suitable for both research and educational purposes. The study further highlights the potential for extending the framework to dynamic, weighted, and uncertainty-based graph models.
Social Networks, Graph Theory, Centrality Measures, Information Diffusion, Mathematical Modeling, Synthetic Data
Copyright (c) 2026 S .Hemalatha, Dr. C. Vasudev, Dr. R. Venugopal, Mr. G.Ragupathy, Dr.K.Soundarraj, Mr.P.Sriram (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning–Based Intrusion Detection Framework for Industrial Internet of Things Environments
The Industrial Internet of Things has transformed manufacturing and critical infrastructure, yet introduces significant cybersecurity vulnerabilities. This research proposes a comprehensive machine learning-based intrusion detection framework tailored for IIoT environments. The framework integrates multiple ML algorithms including Random Forest, Support Vector Machines, and deep learning architectures to detect diverse cyberattacks. Using the Edge-IIoTset benchmark dataset containing 2.2 million instances across 15 attack categories, the proposed framework achieved 99.60% detection accuracy with minimal false positive rates. The study hypothesizes that hybrid ML models combining Convolutional Neural Networks with ensemble methods outperform traditional signature-based detection systems. Experimental validation demonstrates the framework's capability to identify DDoS attacks, Man-in-the-Middle intrusions, injection attacks, and malware threats in real-time. Results confirm superior performance across multiple IIoT protocols including MQTT, Modbus TCP/IP, and HTTP, establishing the framework's effectiveness for protecting Industry 4.0 infrastructures against evolving cyber threats.
Industrial Internet of Things, Machine Learning, Intrusion Detection System, Cybersecurity, Deep Learning
Copyright (c) 2025 Lakshmi Prasanna P, Dr. Abha Tamrakar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Early-Stage Hot Event Prediction In Social Networks Using A Bayesian Modeling Framework
Social media platforms have become primary channels for information dissemination, making early prediction of hot events crucial for marketing, advertising, and recommendation systems. Traditional prediction models require long-term observations and extensive feature extraction, rendering them ineffective during initial event stages. This study proposes a Bayesian modeling framework utilizing Semi-Naive Bayes Classifiers to predict hot events at their early stages in social networks. The research addresses challenges of limited data availability, high noise levels, and complex network structures characteristic of early-stage events. The framework incorporates both temporal and structural features through distribution modeling, enabling accurate predictions with minimal observation time. Experimental validation using Twitter and Weibo datasets demonstrates significant improvements over conventional approaches. The Semi-Naive Bayes methodology achieved 87.3% accuracy in hot event classification within the first hour of event emergence. Results indicate that Bayesian inference effectively handles uncertainty in sparse data environments, providing robust predictions when traditional methods fail. This framework offers practical applications for real-time trend detection, viral content identification, and strategic decision-making in digital marketing ecosystems.
Bayesian modeling, hot event prediction, social networks, early-stage detection, information cascade
Copyright (c) 2025 Er. Rishabh Aryan, Dr. Bhanu Priya (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Lightweight Blockchain Framework With Proof-of-Authority(POA) For Secure And Scalable IOT Communication
The exponential growth of Internet of Things (IoT) devices has intensified challenges in ensuring secure communication, authentication, and scalability due to distributed deployments and constrained computational resources. Traditional centralized security models suffer from single points of failure and limited scalability, making them unsuitable for large-scale IoT networks. This paper presents a lightweight blockchain-based framework utilizing a Proof-of-Authority (PoA) consensus mechanism combined with smart contract–driven authentication to enhance data integrity, confidentiality, and decentralized trust. The proposed hybrid architecture leverages off-chain storage to significantly reduce costs and improve performance. Experimental evaluations on a private Ethereum network demonstrate that the system achieves up to 65% lower data management costs, reduced latency under increasing device loads, and stable transaction throughput compared to Proof-of-Work (PoW) approaches. Energy consumption analysis further validates the suitability of the proposed model for resource-constrained IoT environments, making it a viable solution for secure and scalable IoT deployments.
Blockchain, Internet of Things, Proof-of-Authority, Smart Contracts, Hybrid Architecture, Security, Scalability.
Copyright (c) 2025 S.Gomathi, Dr.N.Balakumar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Context-Aware Harmful Language Identification on Social Media Using Machine Learning and Explainable AI
The proliferation of social media platforms has transformed digital communication, enabling real-time interaction and large-scale content sharing. However, this growth has simultaneously increased the prevalence of harmful language, including abusive, offensive, and toxic expressions that negatively affect individuals and online communities. Manual moderation and rule-based systems are insufficient due to scalability limitations and poor contextual understanding. This paper proposes a comprehensive and interpretable framework for context-aware harmful language identification using classical machine learning techniques, extended with transformer-based modeling and explainable artificial intelligence (XAI). The framework incorporates a complete natural language processing pipeline, including data preprocessing, feature extraction, model training, and extensive evaluation. Multiple classifiers are systematically compared using accuracy, precision, recall, F1-score, AUC–ROC, confusion matrices, and Precision–Recall curves. Experimental results demonstrate that ensemble-based models provide strong baseline performance, while transformer-based approaches improve contextual discrimination. The integration of explainable AI techniques enhances transparency, accountability, and trust, making the system suitable for responsible real-world content moderation.
Harmful Language Detection, NLP, Machine Learning, Explainable AI, Transformer Models
Copyright (c) 2025 Ahmed Qudsi Ghouse Ali Khan, Sadia Kausar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Thyroid Disease Prediction Using Machine Learning
Thyroid disease is a common endocrine disorder affecting millions of people worldwide. The thyroid gland, a small butterfly-shaped organ located in the neck, plays a crucial role in regulating various metabolic processes in the body by secreting hormones such as thyroxine (T4) and triiodothyronine (T3). When the thyroid produces too much hormone (hyperthyroidism) or too little (hypothyroidism), it leads to metabolic imbalances that can significantly affect overall health. Early and accurate diagnosis of thyroid disorders is essential for effective treatment and management. Traditional diagnostic methods rely heavily on clinical evaluations and lab-based test results such as TSH, T3, and T4 levels. However, interpreting these tests can sometimes be complex and prone to human error. Moreover, symptoms of thyroid disease often overlap with other conditions, making diagnosis challenging. In this context, Machine Learning (ML) offers a promising solution. By analyzing patterns in large datasets of patient information, ML algorithms can assist in the early detection and classification of thyroid conditions with high accuracy. This project explores the development of a predictive model for thyroid disease using Machine Learning techniques. The model aims to classify whether a patient has a thyroid disorder based on clinical parameters, enabling faster diagnosis and treatment.
Copyright (c) 2025 Ms. B Jyothsna, K Anupama Reddy, P Anya Reddy, I Ashwitha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
An Innovative Ensemble Deep Learning Clinical Decision Support System For Diabetes Prediction
Diabetes is a significant global health concern, with an increasing number of diabetic individuals at risk. Early prediction of diabetes is essential for preventing its progression and reducing the risk of severe complications such as kidney and heart diseases. This study proposes an innovative Ensemble Deep Learning (EDL) clinical decision support system for diabetes prediction with high accuracy. Three diabetes datasets are utilized: the Pima Indian Diabetes Dataset, the Diabetes Dataset from Frankfurt Hospital, Germany, and the Iraqi Diabetes Patient Dataset (IDP, to train the novel EDL models. The Extra Tree Classifier (ETC) approach is employed to extract relevant features from the data. The performance of the proposed EDL models is rigorously evaluated based on key metrics such as accuracy, precision, recall, and F-score. The study employs a variety of algorithms, including ANN, CNN, LSTM, Stack-LSTM, Stack-ANN, Stack-CNN, and CNN + LSTM, with further potential to enhance performance by exploring additional ensemble techniques such as CNN + LSTM.
Ensemble Deep Learning (EDL), ANN, CNN, LSTM.
Copyright (c) 2025 Nada Fayaz , Dr. Ijteba Sultana (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Design and Evaluation of a Low-Cost Quadcopter Drone ControlSystem with Real-Time IMU-Based Stabilization
Quadcopter technology has gained attention due to applications in aerial surveillance, delivery, and research, yet many existing studies focus on either complex autonomous control algorithms or highcost hardware, limiting accessibility for educational and experimental purposes. A basic quadcopter control system integrating a LiPo battery, flight controller, BLDC motors, ESC units, and a wireless transmitter was implemented to demonstrate stable flight using real-time command processing and sensor feedback. The transmitter commands were decoded and processed by the flight controller, which generated PWM signals to control motor speed, producing thrust for maneuvering. IMU sensors provided continuous orientation feedback, maintaining stability under disturbances. Experimental results indicated stable hover with ±2° orientation deviation, maximum motor RPM of 8500, average PWM duty cycle of 60%, hover current draw of 4.5 A, and a flight time of approximately 12 minutes, outperforming typical small-scale educational drones in response time and stability. The system validates efficient integration of power, control, and feedback mechanisms for reliable quadcopter operation.
Battery management, Flight controller, Quadcopter control, Sensor feedback, Unmanned aerial vehicle stability
Copyright (c) 2025 Dr.R.Manikandan, L.Muthu kumar, S.Sivachandran, V.Jeyaram, V.Adlin Preethi, V.Jebitha, A.Monisha, V.Preethi, K.Stefy seles (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Tricopter Based Aerial Transport Maintaining Organ ViabilityThrough Controlled Flight and Temperature
Emergency organ transportation demands rapid, reliable, and thermally controlled delivery systems to preserve organ viability. Conventional logistics and existing unmanned aerial vehicle studies are limited by traffic dependency, simulation-only validation, lack of active cooling, and insufficient emergency handling under real-flight conditions. A tricopter-based aerial system integrating precision flight control, a medical payload container, and thermoelectric cooling is presented to address these limitations. The system employs PID-based stabilization, real-time sensor monitoring, and a Peltier module to maintain container temperature during flight. Experimental evaluation demonstrates stable operation with nominal battery voltage of 11.1 V, hover current draw of 4.5 A, maximum motor speed of 8500 RPM, and attitude response times below 0.2 s. Orientation deviation remains within ±2 degrees, achieving a maximum flight duration of 12 minutes. Compared to prior simulationcentric or vision-only studies, the results confirm improved real-world stability, payload safety, and emergency readiness. Overall findings validate the feasibility of tricopter-based emergency organ transport for short-range medical missions.
Autonomous Aerial Vehicle, Emergency Medical Transportation, Organ Preservation, Precision Flight Control, Thermoelectric Cooling
Copyright (c) 2025 Dr.R.Manikandan , Arthi.S, Architha.N, Ponshalini.R, Esakki Muthu.A, Sudalai Mani.N, Issam Ratib.T.U, Elson Santra.E, K.Suba kala, Jebaselvi.D (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
An Advanced Signal Jammer Interference System DeliveringComplete Connectivity Suppression in Controlled ApplicationZones
The proliferation of LTE networks has increased vulnerabilities to unauthorized communication and security breaches, necessitating controlled signal suppression in sensitive areas. Existing studies on LTE jamming and detection often focus on selective interference, reinforcement learning-based detection, or data-driven anomaly analysis, yet they are limited by support for only LTE, computational complexity, environment-specific deployment, or dependency on KPI availability. The present study implements a 4G signal jammer using an oscillator for precise LTE carrier generation, a noise generator to produce broadband interference, an RF amplifier for stable output, and an omnidirectional antenna for uniform coverage. Field evaluation demonstrated complete connectivity loss within a 12-meter radius, a 41 dB reduction in RSSI, and harmonic suppression greater than 38 dB, outperforming previous approaches that either lacked controlled coverage or produced partial disruption. Results confirm reliable and uniform jamming performance, validating the system’s applicability for controlled environments such as examination halls, research laboratories, and secure facilities.
Controlled Jamming, Interference Signal, Mobile communication disruption, Radio frequency amplification, Signal Suppression
Copyright (c) 2025 Dr.R.Manikandan , K.subakala, J.Antony Bobin John, S.Augesto, M.Glarance Wiseman, S.Stanly Stuvet (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Robust Real-Time Control System for a Compact 15-DOF Biped Robot
This study presents a low-cost 15-DOF humanoid robot designed using Arduino to overcome the limitations of existing humanoid systems that rely on complex controllers, high-end processors, and computationally heavy whole-body coordination methods. The objective is to develop a compact, efficient humanoid capable of stable walking, turning, and head-tracking using lightweight motion planning and sensor-based corrections. The proposed method integrates simplified keyframe gait generation, servo-angle computation, and a real-time feedback loop implemented through Arduino IDE, enabling precise actuation across 15 metal-gear servos. Experimental evaluation shows improved servo angle accuracy by 12% and enhanced motion stability by 9% compared to baseline manual-tuned control. No external dataset is required, as all data are generated from robot trials. Results confirm that the proposed architecture ensures reliable, repeatable movement, offering a promising foundation for compact humanoid development.
Degrees of Freedom, Humanoid Robot, Metal Servo Actuators, Motion Control, Microcontroller-Based System
B.Dinesh, S.Bala Arun, W. Godlin Sharon , G.kathirvel
Copyright (c) 2025 R.Manikandan, K.subakala, B.Dinesh, S.Bala Arun, W. Godlin Sharon , G.kathirvel (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Real-Time Automated Radar-Based Intruder Detection and Response System Using Embedded Sensing and Actuation
The increasing need for efficient perimeter defense has led to development of automated surveillance systems, though existing solutions are costly, computationally intensive, or require constant human monitoring. Many ultrasonic or radar-based systems also suffer from limited detection accuracy and lack real-time automated response. This study proposes a low-cost, Arduino-based automated radar detection and firing system integrating an ultrasonic sensor, dual servo motors, and a relay-controlled shooter. The system continuously scans the monitored area, detects intruders in real-time, aligns the shooter via servo motors, and executes an automatic firing response. Implemented using Embedded C on Arduino, experiments demonstrate detection accuracy exceeding 95%, improving over previous ultrasonic-only systems by approximately 20%. Results confirm reliable, autonomous monitoring and rapid response, offering a practical solution for military and defense application.
M.Selvabharathi, M.Vennila , T.Kaviya , S.Prashiba
Copyright (c) 2025 R.Manikandan, K.Subakala, M.Selvabharathi, M.Vennila , T.Kaviya , S.Prashiba (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
IoT Enabled Intelligent Digital Notice System for Real-Time Campus Communication
The proposed study addresses the inefficiency of traditional manual notice boards and overcomes limitations in prior IoT signage systems, which often rely on small displays, lack multi‑device synchronization, and offer poor update responsiveness. The objective is to develop a network‑based Smart TV notice board using Raspberry Pi 4 connected to an LED TV, with a secure web application and MySQL backend. We implement the system using Embedded C (for display controller) and Python + HTML/JavaScript (for server and web client), enabling real-time communication via WebSocket/MQTT. In prototype testing with 100 sample notices, display latency remains low (2–3 seconds) with 100% successful propagation and CPU utilization under 50%. The result proves the system’s reliability, scalability, and real-time performance. This cost‑effective, energy-efficient solution modernizes campus communication, enabling synchronized notice access on Smart TV, mobile, and laptop devices inviting the research community to further build on its architecture and scalability.
Automated Message, Campus Communication System, Embedded System Design, Raspberry Pi, Smart TV Interface
K. Subramaniyan, C. Shiva Thanu, A. Prakash, S. Maruthesh
Copyright (c) 2025 R.Manikandan, K.Subakala, K. Subramaniyan, C. Shiva Thanu, A. Prakash, S. Maruthesh (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Multi-Layer Secure and Quality-Preserving Image Sharing Framework Using LSB Steganography, AES Encryption, and Shamir’s Secret Sharing
The growing use of digital images in sensitive applications demands secure transmission and storage mechanisms. Conventional encryption protects data confidentiality but reveals the existence of secret communication, while steganography conceals information without strong protection once detected. Secret sharing schemes provide distributed security but often generate image-based shares with increased overhead. This paper proposes a multi-layer secure image sharing framework that integrates LSB steganography, AES encryption, and Shamir’s Secret Sharing Scheme. Secret data is first embedded into a cover image using LSB steganography, encrypted using AES, and then divided into threshold-based binary shares using Shamir’s scheme. The proposed approach ensures imperceptibility, strong confidentiality, and threshold-based access control while preserving image quality and reducing storage overhead, making it suitable for secure image communication and distributed storage applications.
Image Security, LSB Steganography, AES Encryption, Shamir’s Secret Sharing, Threshold Cryptography, Secure Image Sharing.
Copyright (c) 2025 Bilal Ahmed Siddique , Dr Md. Zainlabuddin (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Multilingual, Generative AI-Based Food Calorie Estimation Method: Algorithms, Advantages, and Comparative Analysis
The rapid evolution of artificial intelligence (AI) and deep learning has transformed the field of nutritional analysis, offering significant improvements over traditional methods in food recognition and calorie estimation. Conventional techniques based on convolutional neural networks (CNNs) have shown promise yet remain limited by extensive data requirements, language dependence, and inadequate nutritional insights. In this paper, we propose a novel, multilingual, generative AI-based approach that leverages large multimodal models (LMMs) such as Google’s Gemini Pro Vision and OpenAI’s GPT-4 Vision. Our solution integrates robust image validation, dynamic prompt engineering, and multilingual natural language processing to deliver detailed calorie estimates and nutritional breakdowns while overcoming the challenges inherent in CNN-based systems. We detail the underlying algorithms, provide a conceptual system flowchart, and present comparative analyses against traditional approaches. Finally, our consolidated “Proposed Solution and Future Directions” section describes the system architecture, implementation details, and outlines the future research agenda.
Food calorie estimation, generative AI, multi- modal models, multilingual natural language processing, deep learning, nutritional analysis.
Copyright (c) 2025 Manan Vikrambhai Patel (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Product Recommendation SystemUsing Autoencoders
The rapid expansion of e-commerce has made recommendation systems essential for helping users navigate vast product catalogs and discover items that match their preferences. Traditional approaches, such as collaborative filtering and content-based filtering, often struggle with challenges like the cold-start problem, where new users or items lack sufficient data, and scalability issues in large datasets. Deep learning techniques, particularly autoencoders, have emerged as powerful tools to address these limitations by learning compact, latent representations of data in an unsupervised manner This paper presents a product recommendation system that leverages an autoencoder to generate recommendations based on product metadata, including descriptions, categories, stores, and prices, from the Amazon Electronics dataset. Our approach involves preprocessing the dataset to create a feature matrix using TF-IDF for text descriptions, one-hot encoding for categorical attributes, and Min-Max Scaling for prices The autoencoder, consisting of an encoder and decoder with dense layers and ReLU activations, compresses this feature matrix into a lower-dimensional latent space and reconstructs it to capture essential patterns Bayesian Optimization is employed to tune hyperparameters, such as the number of layers, units, and learning rate, ensuring optimal model performance The model is trained with Mean Squared Error (MSE) as the loss function and Cosine Similarity as an additional metric, using early stopping to prevent overfitting.The system was evaluated on a subset of 50,000 products, Visualizations of training and validation loss curves confirmed model convergence, while cosine similarity trends validated the quality of learned representations. This work highlights the potential of autoencoder-based recommendation systems in e-commerce, offering accurate and scalable solutions. Future improvements could incorporate user interaction data or explore advanced architectures like variational autoencoders to enhance personalization. These results underscore the system's effectiveness in addressing traditional recommendation challenges, paving the way for more robust e-commerce solutions.
Recommendation Systems, Autoencoders, Collaborative Filtering, Content-Based Filtering, Hybrid Recommender, Amazon Electronics Dataset.
Prof. Dr. M. Sreenivasulu, Pavan Suraj, Sudarshan, Rai Manoj
Copyright (c) 2025 Prof. Dr. M. Sreenivasulu, Pavan Suraj, Sudarshan, Rai Manoj (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Information-Seeking Behaviour of College Students: An Analytical Study
Information-seeking behaviour among college students has become a critical area of investigation in the digital era, where access to information resources has transformed dramatically. This study aims to analyse the information-seeking patterns, preferred sources, and challenges faced by college students in fulfilling their academic and personal information needs. The primary objectives include identifying the most frequently used information sources, examining the purpose behind information seeking, and understanding barriers encountered during the information search process. A descriptive survey methodology was employed, utilizing a structured questionnaire administered to 450 college students across undergraduate and postgraduate programmes. The hypothesis posited that digital resources have become the predominant source of information among college students, superseding traditional library resources. Results indicate that 78.4% of students prefer internet-based resources over print materials, with search engines being the most utilized tool. However, significant challenges including information overload, credibility assessment difficulties, and inadequate information literacy skills were identified. The study concludes that while digital transformation has enhanced information accessibility, structured information literacy programmes are essential for effective information utilization among college students.
Information-seeking behaviour, college students, digital resources, information literacy, academic information needs.
Copyright (c) 2023 Khanjana Deka (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Improving Fake News Detection Through Word Embedding-Based Text Augmentation and XGBoost Ensemble Learning
By combining state-of-the-art data augmentation, ensemble learning, and deep learning approaches, the extended system for classifying fake news hopes to conquer the obstacles presented by small text datasets and intricate language patterns. In order to increase variety and decrease overfitting, text augmentation strategies including Function Word Reduction, Synonym Replacement, and Back Translation are utilized to broaden the training corpus. To further improve the capture of significant word associations and contextual information, the Word2Vec Skip-gram model is used to convert the enhanced text into numerical representations that are rich in semantic information. The enhanced system improves classification performance by combining deep learning architectures that offer strong feature extraction and better decision-making skills with powerful ensemble algorithms including LightGBM, CatBoost, and XGBoost. By absorbing intricate patterns that more conventional ML models miss, these models dramatically improve accuracy, precision, recall, and F1-score. More than that, a graphical user interface built on Flask is created to give people an easy-to-use platform where they can input news stories and have them categorized instantly. An accurate, scalable, and practically useful solution for trustworthy false news detection across various digital platforms is provided by the extended system through the integration of augmentation, advanced modeling, and user-centered design.
Fake News Detection, Text Data Augmentation, Word Embedding, Word2Vec Skip-gram, Back Translation, Synonym Replacement, Function Word Reduction, Machine Learning Classifiers, Natural Language Processing, WEL Fake News Dataset.
Copyright (c) 2025 Dr Sheik Meerasharif, Gubbala Madhu Bhushan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Bidirectional GRU-Integrated Seq2Seq–ConvLSTM Framework for Improved Network Intrusion Detection
In order to improve computational efficiency and train temporal-spatial features more effectively, this study expands the hybrid Seq2Seq-ConvLSTM intrusion detection model by adding GRU and Bidirectional layers. The GRU layer expedites training and prediction without compromising accuracy, while the Bidirectional layer improves identification of complicated sequential attack patterns by capturing forward and backward temporal relationships. A web interface built on Flask allows users to submit test datasets and view classification results instantaneously, enabling real-time intrusion detection. This is how the upgraded model is implemented. Results from experiments reveal that the suggested extended hybrid strategy performs better than the baseline Seq2Seq-ConvLSTM and Random Forest models in contemporary network security settings, in terms of accuracy, latency, and resilience.
Network Intrusion Detection, Hybrid Deep Learning, Seq2Seq, ConvLSTM, Bidirectional Layer, GRU, Temporal-Spatial Features, Real-Time Detection, Flask Deployment, Cybersecurity, Deep Sequential Models, Feature Optimization, Anomaly Detection, Intrusion Prediction, Neural Networks.
Copyright (c) 2025 Ganga Bhavani Billa, Navyasri Vipparthi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Leveraging Generative AI And Zero Trust Countermeasures In Critical Infrastructure: From Vulnerability To Resilience
Generative artificial intelligence (GenAI) has had a transformative effect on the automation and decision-making responsibilities in numerous industry sectors, including healthcare, industrial control systems, and finance. However, the rapid expansion of GenAI has inherently added complexity to the global cybersecurity threat landscape. The objective of this research is to investigate how adversaries are leveraging the capabilities of GenAI to conduct scalable fraud, manipulate healthcare data, and subject security-sensitive operational processes to exploit. In order to support the objective, a qualitative analysis is performed that also utilizes sector-based risk mapping, adversarial scenario modeling, and safety system framework validation. The research found that continuous identity verification, context-aware Zero Trust controls, Human-in-the-Loop (HITL) monitoring, and data provenance assurance significantly mitigate vulnerabilities associated with natural-language processing (GenAI) systems. These contributions are significant for lawmakers, cybersecurity architects, and organizational leaders who want to construct reliable artificial intelligence governance to support critical infrastructure systems.
Generative AI; Zero Trust Measures; Critical Infrastructure; Vulnerability; Artificial Intelligence.
Copyright (c) 2025 Dr. Aniket Deshpande (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Next-Generation Handwriting Recognition Powered By AI
Character segmentation and recognition are essential tasks in image processing and computer vision, with various applications in text recognition, document analysis, and optical character recognition (OCR) systems. The procedure entails isolating individual characters from an input image, succeeded by the identification of these segmented characters. This study provides a thorough examination of character segmentation and recognition techniques, examining both conventional methods and contemporary innovations. Character segmentation approaches are classified into two primary categories: linked component-based methods and contour-based methods. Connected component-based techniques depend on recognizing distinct characters through connected regions in the image, whereas contour-based techniques emphasize character segmentation via edge detection and contour analysis. Furthermore, we investigate diverse methodologies for character identification, encompassing template matching, feature-based approaches, and deep learning techniques. Template matching entails the comparison of segmented characters against established templates to ascertain correspondences, whereas feature-based approaches derive pertinent properties from characters and utilize classifiers for recognition. Deep learning approaches have garnered considerable interest for their capacity to autonomously acquire discriminative features from unprocessed data, attaining superior performance in character recognition challenges. Furthermore, we examine the obstacles and prospective avenues in character segmentation and recognition, including managing intricate backdrops, addressing diverse fonts and writing styles, and enhancing performance on degraded or handwritten text. We emphasize the significance of dataset diversity and robustness in creating precise and adaptable segmentation and identification algorithms. DOI: https://doi-ds.org/doilink/12.2025-57716868
optical character recognition (OCR), AI.
Copyright (c) 2025 Mr.S.Sathiyanathan, Mrs.S.Sasipriya, H.fathima, Dr.K.K.Savitha, Ms. M. Leelavathi, veldandi Srikanth (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Leather Defect Detection System Using Machine Learning
The quality of leather products is significantly influenced by the presence of surface defects such as scratches, wounds, insect bites, tanning irregularities, and texture variations. Traditionally, leather inspection is performed manually by trained workers, a process that is highly time-consuming, inconsistent, and subjective. With increasing demand for high-quality leather goods and the need for standardization in quality assurance, automated defect detection systems have become essential. This project proposes a deep learning–based approach for automated leather defect detection using the MobileNetV2 Convolutional Neural Network (CNN) architecture, specifically designed for efficient image classification tasks. The system utilizes a comprehensive data set of leather images, categorized into defective and non-defective classes. The methodology includes image acquisition, preprocessing, augmentation, feature extraction, and model training. MobileNetV2 is chosen for its lightweight structure, fast computation, and strong performance on image recognition tasks. The model architecture is enhanced with additional dense layers, dropout regularization, and a soft max classifier to improve generalization and accuracy. A systematic training and validation process is conducted using augmented datasets to ensure robustness against variations in lighting, orientation, and texture. Experimental results demonstrate that the proposed model achieves 98% accuracy on the MVTEC anomaly detection data set, effectively distinguishing between defective and non-defective leather surfaces. The system not only identifies defects but also ensures consistent evaluation standards across all inspected samples. A user-friendly interface is developed to enable real-time detection, allowing users to upload an image and receive instant defect classification results. The automated approach significantly reduces manual labor, minimizes inspection time, and increases overall production efficiency. This research validates the effectiveness of deep learning in industrial quality inspection and highlights its potential for large-scale deployment in leather manufacturing units. Future work may include incorporating advanced AI models, expanding the defect categories, integrating segmentation for precise defect localization, and deploying the system on edge devices for real-time industrial automation.
Convolutional Neural Network (CNN), AI, ML.
Ramini Bhavitha, Mamidala Gouthami, Japa Nandhu
Copyright (c) 2025 G.Sudharkar Raju, Ramini Bhavitha, Mamidala Gouthami, Japa Nandhu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Route Optimization for Logistics and Transportation in IoT Networks: A Machine Learning Approach with Cybersecurity Implications
The rapid expansion of Internet of Things (IoT) technologies has transformed modern logistics by enabling real-time monitoring of vehicles, traffic conditions, and delivery operations. However, efficiently optimizing delivery routes in such dynamic environments remains a challenge, particularly when traffic variability, operational constraints, and cybersecurity threats are considered. This study proposes a hybrid framework that integrates machine learning–based travel time prediction with a Genetic Algorithm (GA) for route optimization in IoT-enabled logistics networks. Synthetic IoT data capturing distance, vehicle speed, and traffic congestion are used to train a prediction model, which then informs the optimization phase to generate efficient multi-vehicle delivery routes. The results demonstrate significant improvements in total travel time, fleet utilization, and operational efficiency when compared to conventional routing methods. Additionally, the study evaluates the impact of cybersecurity vulnerabilities such as GPS spoofing and sensor tampering, highlighting their potential to distort route decisions and proposing mitigation mechanisms. The findings underscore the importance of combining predictive analytics, optimization algorithms, and cybersecurity strategies to support secure, adaptive, and efficient logistics operations. This framework can be generalized to various logistics applications including e-commerce, urban delivery, and smart transportation systems.
IoT Networks; Route Optimization; Machine Learning; Genetic Algorithm; Logistics and Transportation; Travel Time Prediction; Cybersecurity; GPS Spoofing; Traffic Congestion Modeling; Smart Supply Chain.
Copyright (c) 2025 Er. Manpreet Kaur, Gurjinderpal Singh, Mrs.M.Mahalakshmi, Dr.R.venugopal, V.dharani, Dr.N.Balakumar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Online hostel fee payment system is a web-based application and designed to simplify and automate the hostel fee collections and Management. Traditional fee payment methods often involve long queues, and delayed processing, and leading to inconvenience for both students and hostel administrations. This system addresses these challenges by enabling students to securely login, view fee details, and make payments online using multiple payment methods. In this Online Hostel Fee payment System using to solve the Time conception and Lack of Transparency by digitalizing the hostel fee payment process, this system reduces administrative workloads. This system is built using modern technologies are full stack using python, node.js and database, its ensuring scalability and user-friendliness and efficient performance.
Mongo DB, flask, Node.js, Payment gateways
Busam Rani, Amara Akkala Lalitha Sri, Mindala Poojitha
Copyright (c) 2025 Busam Rani, Amara Akkala Lalitha Sri, Mindala Poojitha, Dr. A. Tirupatiah (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
This project presents an AI-powered solution for automating medical report summarization using Natural Language Processing (NLP), designed to reduce the documentation workload for healthcare professionals. advanced transformer models and domain- specific medical knowledge, the system extracts key clinical information from complex medical texts and generates concise, accurate summaries to true and exact information. It supports multi- modal data integration like text and lab results and enables real-time report generation to accelerate clinical decision making. The entire process is secured, ensuring patient data privacy while enhancing efficiency and accuracy in healthcare documentation.
Diagnosis, Medications, Procedures, Symptoms, Tests, Recommendations.
Kagitha Monasri, Amara Sri Sai Sudha Amrutha Varshini, Jaggarapu Venkat
Copyright (c) 2025 Kagitha Monasri, Amara Sri Sai Sudha Amrutha Varshini, Jaggarapu Venkat, Mrs. D. Madhuri (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Hostel Management System With Room Allocation
The Hostel Management System is a web- based application developed to digitalize and simplify hostel operations such as student registration, secure login, room allocation, and fee management. The system provides separate dashboards for students and administrators, ensuring clear role-based access and smooth data flow. Students can view their personal and room details, track fee records, and access information conveniently through a user- friendly interface. Administrators can manage student records, allocate or deallocate rooms, and monitor hostel occupancy efficiently. Developed using HTML, CSS, JavaScript, Node.js, and MongoDB, the system ensures scalability, security, and data reliability. MongoDB serves as the centralized database for storing all student and room data. JWT authentication and encryption techniques safeguard user information. The system significantly reduces manual workload and eliminates errors common in traditional hostel record management. Overall, it enhances transparency, accuracy, and operational efficiency in hostel administration.
Hostel Management System, Room Allocation, Web Application, Room Deallocation Authentication and Data Security.
Gandikota Bala Ayyappa, Gowrabathuni Vidyasri, Ganji L Venkata Naga Siva Sai Ram
Copyright (c) 2025 Gandikota Bala Ayyappa, Gowrabathuni Vidyasri, Ganji L Venkata Naga Siva Sai Ram, Dr. P.V.S.Sarma (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A web-based program called the Complaint Management System makes managing customer concerns easier. It provides a safe login and registration process that enables users to file complaints via an easy-to-use interface. MongoDB Atlas, a scalable cloud database, is where complaints are kept, guaranteeing dependable data access. The solution has an admin dashboard for effective case management and a user dashboard for real-time tracking. Unresolved complaints that exceed time limitations are flagged by an automatic escalation process. By adjusting to different organizational circumstances and setting the stage for future improvements like mobile application support and AI-based complaint classification, the system improves openness, accountability, and efficiency in complaint management.
Complaint Management, Escalation Management, Python Flask, MongoDB, Automation, Accountability, Service Quality.
Bhargavi Mohana Maddu, Evuri Hari Krishna, Chadalavada Rajkumar
Copyright (c) 2025 Bhargavi Mohana Maddu, Evuri Hari Krishna, Chadalavada Rajkumar, Mrs.M. Anusha (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Developing an Artificial Intelligence- Driven Resume Builder with a Feedback System: Enhancing Career prospects in the Digital Age
Artificial Intelligence is now become a large concept in which everyone depends on it to do their work. By using it, we can simply finish our application within the prescribed time. Ai Resume Builder with Feedback System is an application of AI in which students and professionals can build their resumes effectively by giving their details to the system. The main objective is to make the students to get shortlisted for multiple companies. Compared to traditional methods, it is very beneficial to the students. Although there are online tools like Word to create their own resumes, there are not ATS- friendly. Most of the companies now using ATS (Applicant Tracking System) to shortlist the resumes of students. Ai Resume Builder is the one which generates the resume that can pass ATS.
Applicant Tracking System (ATS), Artificial Intelligence (AI), Resume Optimization, Recruitment Technology, Automated Resume Generation.
Kamparaju Sindhura, Bussa Santhi, Buddi Swapna
Copyright (c) 2025 Kamparaju Sindhura, Bussa Santhi, Buddi Swapna, DR.Y. Chitti Babu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Creating academic schedules manually in educational institutions often results in class conflicts, errors, and inefficient use of faculty and classroom resources. To address these issues, the Smart Timetable Suggestion App is developed as a web- based solution capable of automatically generating accurate, conflict-free, and well-organized schedules. The system utilizes the MERN stack (MongoDB, Express.js, React.js, Node.js) to manage data, backend logic, and UI rendering. The application considers teacher availability, subject load, and classroom constraints to suggest optimized timetables. By shifting from conventional manual scheduling to an automated approach, the system saves time, reduces workload, minimizes human error, and enhances scheduling efficiency for administrators, faculty, and students.
Smart Timetable Generation, Automated Scheduling, MERN Stack, MongoDB, Express.js, React.js, Node.js, Web-Based Scheduling System, Conflict-Free Timetable, AI-Based Scheduling, Time Management Automation, Academic Scheduling, Optimization Techniques, User-Friendly Interface, Data-Driven Timetable, Educational Technology.
Akula Lakshmi Priyanka, Chelli Asha Jyothi, Ambati Pavan Tanwesh
Copyright (c) 2025 Akula Lakshmi Priyanka, Chelli Asha Jyothi, Ambati Pavan Tanwesh, Mrs. V L N. Swapnika (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Human Pose Estimation For Fitness Feedback
This project is about creating a human pose detection system using artificial intelligence. It can recognize and show the name of a person’s body pose in real time. Many people doing exercises or yoga at home find it hard to keep the correct posture without a trainer. When the camera is turned on, the system captures live video and tracks body parts like shoulders, elbows, and knees. Using these points, it identifies which pose the person is doing and shows its name on the screen. If the pose doesn’t match any known pose, it shows “None.” The project uses Python, OpenCV, and Media Pipe to detect poses accurately. It can be helpful for fitness tracking, yoga pose detection, and movement analysis.
OpenCV, Media Pipe, Posture Recognition, Machine Learning. Human Tracking, Deep Learning
Bethapudi Dorka, Avvaru Ujwal, Dasari Divya
Copyright (c) 2025 Bethapudi Dorka, Avvaru Ujwal, Dasari Divya, DR.Y. Chitti Babu (Author) https://creativecommons.org/licenses/by-nc-nd/4.0