International Journal of Multidisciplinary Engineering In Current Research
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Enhanced Neural Text Summarization With Syntactic And Headline Insights
Text summarization plays a vital role in condensing large volumes of information into concise and coherent summaries. Traditional extractive methods often fail to capture semantic richness, while abstractive models face challenges in grammatical correctness and factual accuracy. The base paper introduced a syntax-augmented, headline-aware neural model that leverages syntactic features and headline guidance to improve summary generation. In this work, we extend this idea by evaluating both classical and modern approaches on the CNN/DailyMail dataset. First, we establish a baseline using the unsupervised Text Rank algorithm, which provides extractive summaries but achieves limited ROUGE scores. We then explore a state-of-the-art pretrained Transformer, DistilBART, which has been fine-tuned on CNN/DailyMail. Without additional training, DistilBART generates abstractive summaries with significantly higher accuracy, outperforming the base model on ROUGE-1 and ROUGE-2 metrics. Our experimental results show that incorporating headline cues and syntactic awareness (as in the base paper) improves traditional LSTM-based models, but modern pretrained Transformers demonstrate superior performance and efficiency. The findings highlight the evolution of summarization methods from handcrafted features to large-scale pretrained architectures, offering both practical insights for deployment and a strong foundation for future research.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Property-Based Token Attestation With Homomorphic Encryption (PTA-HE) for Secure Mobile Computing
This paper proposes PTA-HE, an enhanced Property-based Token Attestation scheme integrated with Homomorphic Encryption for secure mobile cloud computing environments. Traditional PTA protocols lack sufficient protection during active data processing, leading to confidentiality risks. The proposed system enables computations directly on encrypted data, ensuring privacy and integrity throughout the attestation workflow. Trusted Third Parties manage verification securely, while experimental evaluation analyzes computational overhead, latency, scalability, and communication cost. Formal verification using the Scyther tool proves resistance against replay and man-in-the-middle attacks. The solution provides strong security guarantees for modern mobile cloud systems.
Property-based Token Attestation,Homomorphic Encryption ,Mobile Cloud Computing, Security Privacy, Scyther ,Verification.
Ripple: A Decentralized Edge Based Data Deduplication Frame Work
With its advantages in ensuring low data retrieval latency and reducing backhaul network traffic, edge computing is becoming a backbone solution for many latency-sensitive appli cations. An increasingly large number of data is being generated at the edge, stretching the limited capacity of edge storage sys tems. Improving resource utilization for edge storage systems has become a significant challenge in recent years. Existing solutions attempt to achieve this goal through data placement optimization, data partitioning, data sharing, etc. These approaches overlook the data redundancy in edge storage systems, which produces substantial storage resource wastage. This motivates the need for an approach for data deduplication at the edge. However, existing data deduplication methods rely on centralized control, which is not always feasible in practical edge computing environments. This article presents Ripple, the first approach that enables edge servers to deduplicate their data in a decentralized manner. At its core, it builds a data index for each edge server, enabling them to deduplicate data without central control. With Ripple, edge servers can 1) identify data duplicates; 2) remove redundant data without violating data retrieval latency constraints; and 3) ensure data availability after deduplication. The results of trace driven experiments conducted in a testbed system demonstrate the use fulness of Ripple in practice. Compared with the state-of-the-art approach,Ripple improves the deduplication ratio by upto 16.79% and reduces data retrieval latency by an average of 60.42%. ,
A Blockchain-Based Zero Trust Model for Privacy-Centric IoT Cybersecurity
The rapid expansion of Internet of Things (IoT) ecosystems has intensified the need for robust, scalable, and privacy-preserving security solutions. This research introduces a novel Unified Quantum-Resilient Blockchain– Zero Knowledge Proofs Privacy Authentication Framework (QBC-ZKPAF) aimed at enhancing security in decentralized IoT environments. The proposed framework integrates blockchain technology, Zero Trust Architecture (ZTA), and post-quantum cryptography to enable secure communication, fine-grained access control, and privacy-preserving authentication. It employs a hybrid Reinforcement-Lattice Blockchain Key Generation mechanism to produce quantum-resilient cryptographic keys, while a Deep Q-Network Multi-Factor Secure Key (DQN-MFSK) model dynamically selects optimal keys based on system conditions. Furthermore, Zero-Knowledge Proofs (ZKPs) are utilized to validate identities without revealing sensitive information, ensuring strong privacy guarantees. The architecture ensures confidentiality, integrity, auditability, and traceability of all operations within the IoT network. Leveraging the immutability of blockchain, all access requests, data transactions, and device interactions are recorded in a tamper-proof ledger, enabling transparent monitoring and reliable post-event analysis. In the event of suspicious activities or security breaches, the system supports precise source tracing through a secure tracing key maintained within the audit server under the Zero Trust framework. Additionally, decentralized identity management combined with multi-factor authentication minimizes reliance on centralized authorities and reduces vulnerability to single-point failures.
Enhanced Key Exchange And Lightweight Encryption For IOT Security Using Timestamp-Based OTP And Sit
In today’s digital era, secure data transmission between a data owner and users is critical. This paper presents a novel framework that combines One-Time Password (OTP) generation with an RSA-based key exchange mechanism to ensure robust data protection. Each time the data owner initiates data sharing; a unique OTP is dynamically generated and encrypted using the RSA algorithm. This OTP acts as a secure access key, ensuring that only authorized users can decrypt and retrieve the shared information. The RSA encryption not only secures the OTP but also establishes a secure communication channel between the data owner and the user, preventing unauthorized access or interception during transmission. By integrating dynamic OTPs with asymmetric encryption, this scheme enhances the confidentiality and integrity of data in environments where security.
Enhancing User Feedback Analysis With Review Text Granularity For Better Sentiment And Rating Prediction
The exponential growth of user-generated content on digital platforms presents significant challenges in extracting meaningful insights from large-scale textual data. In particular, analyzing user sentiments and accurately predicting product ratings from online reviews require advanced natural language processing (NLP) techniques. This paper proposes a novel framework that leverages Long Short-Term Memory (LSTM) networks to capture contextual and sequential dependencies within review texts. Unlike conventional approaches that perform binary or categorical sentiment classification, the proposed model generates continuous and fine-grained sentiment scores, enabling a more nuanced understanding of user opinions. The integration of this sentiment representation with predictive modeling enhances the accuracy of rating prediction systems. Experimental results demonstrate that the proposed approach effectively captures complex linguistic patterns and dynamic emotional variations in textual data. The framework is applicable across multiple domains, including e-commerce, entertainment, and social media, and contributes to improving recommendation systems by enabling more personalized and context-aware user experiences.
Enhancing Fire Detection With Yolov10: Advanced Techniques For Flame And Smoke Recognition
Fire detection has become a critical research area due to its importance in protecting human life, infrastructure, and environmental resources. Traditional fire detection systems such as smoke sensors and heat alarms are often limited by delayed response, false alarms, and poor performance in large or complex environments. With the increasing use of surveillance cameras in industries, public spaces, forests, and residential areas, vision-based fire detection has emerged as an effective solution for early fire identification. This research presents a deep learning-based approach for real-time flame and smoke detection using the YOLOv10 (You Only Look Once) object detection model. The proposed system improves detection accuracy by addressing challenges such as cluttered backgrounds, low visibility, varying fire intensities, overlapping objects, and smoke diffusion patterns. The system processes image and video streams, detects flames and smoke, tracks fire-related regions, and provides accurate localization through improved bounding box regression. An enhanced feature extraction mechanism and attention-based learning strategy are incorporated to improve the model’s focus on critical fire regions while reducing background interference. Experimental results show that the proposed YOLOv10-based system outperforms existing models such as YOLOv5s and YOLOv8 in terms of accuracy, precision, recall, and F1-score while maintaining real-time performance. The system offers a practical and scalable solution for industrial safety, smart surveillance, forest fire monitoring, and public safety applications.
Dual Detection Of License Plates And Helmets Using An Optimized YOLO And Neural Networks
This project presents an advanced computer vision system for real-time Safety Helmet Detection and License Plate Recognition using the latest YOLOv10 object detection architecture. The primary objective is to enhance workplace safety and vehicle monitoring by automatically identifying individuals without safety helmets in industrial zones and capturing vehicle license plates for surveillance and regulation purposes. YOLOv10, known for its superior accuracy and speed, enables efficient multi-object detection in complex environments. The system is trained on annotated datasets containing diverse helmet types and vehicle plates under varying conditions, ensuring robust performance. Wearing safety helmets can effectively reduce the risk of head injuries for construction workers in high-altitude falls. In order to address the low detection accuracy of existing safety helmet detection algorithms for small targets and complex environments in various scenes, this study proposes an improved safety helmet detection algorithm based on YOLOv10.
Enhancing Credit Card Fraud Detection in Banking Using Neural Networks
Credit card fraud is one of the most serious threats in today’s digital banking systems, where thousands of transactions occur every second. Detecting fraudulent transactions in real time is challenging due to the high imbalance between legitimate and fraudulent records and the constantly evolving nature of fraud patterns. In this project, a deep learning-based fraud detection system is developed using the TabNet model, which efficiently handles large-scale, high-dimensional tabular transaction data. The model learns to focus on the most important features of each transaction using sequential attention, achieving high precision and recall while minimizing false alarms. The system integrates advanced pre-processing, balanced data handling, model training, evaluation, and visualization. Finally, a Flask-based web application is built to provide an interactive user interface with modules for registration, login, real-time prediction, and visual analytics of model performance. The proposed system achieves excellent accuracy, faster inference, and robust fraud detection capabilities compared to traditional approaches.
A Deep Neural Network Approach for Classifying Pulmonary Diseases from Respiratory Sounds
This study presents an advanced approach to detecting lung auscultation sounds using Mel-frequency Cepstral Coefficients (MFCC), Chroma features, and neural networks. Lung auscultation, a key diagnostic tool in identifying respiratory conditions, often relies on the expertise of medical professionals to interpret subtle sound patterns. However, automated systems that accurately classify these sounds can greatly assist in early diagnosis and treatment. To achieve this, we employed MFCC, which captures the power spectrum of sounds and effectively models the way humans perceive auditory signals, focusing on the critical frequency ranges for lung sounds. Additionally, Chroma features, which represent the tonal content of audio signals, were used to capture harmonic aspects that could be indicative of specific lung conditions. These features were then fed into a neural network designed to classify lung sounds into various diagnostic categories, such as normal breathing, wheezing, crackles, and other abnormal respiratory sounds. The neural network, trained on a comprehensive dataset of lung sounds, was able to learn complex patterns and correlations within the MFCC and Chroma features, leading to high accuracy in sound classification. This automated approach offers a powerful tool for enhancing the precision of lung sound diagnosis, potentially leading to earlier detection of respiratory conditions and improved patient outcomes.
Time Series Traffic Prediction With Vehicle-Type Suggestions
Urban traffic congestion is a major challenge in modern cities, leading to increased travel times, fuel consumption, and air pollution. To address this issue, Deep Traffic-VTS presents an intelligent hybrid system that integrates Long Short-Term Memory (LSTM) networks for time series forecasting with YOLO (You Only Look Once) for vehicle detection. The LSTM model analyzes historical traffic data—such as vehicle count, average speed, and congestion levels—to predict traffic conditions over upcoming time intervals. Simultaneously, YOLO processes live video feeds to detect and classify vehicle types on the road, including two-wheelers, cars, buses, and emergency vehicles. Based on both predicted and real-time traffic conditions, the system provides adaptive suggestions on the most suitable vehicle types for efficient navigation—for example, recommending two-wheelers in high-congestion zones due to their maneuverability, while advising larger vehicles to reroute or delay travel. This combined approach enables more effective traffic management, emergency response optimization, and smart urban mobility planning. Based on both predicted and real-time traffic conditions, the system provides adaptive suggestions on the most suitable vehicle types for efficient navigation. Specifically, when congestion levels are high, the system recommends small vehicles such as two-wheelers due to their maneuverability; for medium congestion, it suggests medium-sized vehicles like cars; and for low congestion, it supports the use of larger vehicles such as buses and trucks for efficient mass transportation. In addition, emergency vehicles like ambulances and fire trucks can be given priority-based navigation routes, reducing response times in critical situations.
Predicting Adolescent Concern Toward Unhealthy Food Advertisements Using Deep Neural Networks With Explainable AI
Predicting adolescent concern over unhealthy food advertisements is critical for promoting health awareness and guiding public policy. This study utilizes XGBoost, a gradient boosting machine learning model, to predict concern levels among adolescents based on demographic and behavioral features. Survey data from 1030 adolescents were collected, including age, parental education, and advertisement exposure types, such as celebrity endorsements and free toys. The model is trained with hyperparameter tuning and synthetic oversampling to handle imbalanced classes. Explainable AI techniques (LIME and SHAP) are applied to interpret feature importance, providing insights into which factors most influence adolescent concern. Results demonstrate that XGBoost achieves high predictive accuracy, offering an effective and interpretable solution for understanding and mitigating the impact of unhealthy food advertisements.
Mindfulpath: An AI-Enhanced Platform For Mental Wellbeing Using NLP And CBT Principles
The growing global mental health crisis demands accessible, immediate, and stigma-free support mechanisms. MindfulPath is an AI-enhanced web platform for mental wellbeing that integrates Natural Language Processing (NLP)-based sentiment analysis with Cognitive Behavioral Therapy (CBT) principles to deliver real-time therapeutic support. Built on Node.js and Express.js with an SQLite relational database (seven tables), the system employs the AFINN-165 lexicon through the npm sentiment package for live emotional-state assessment of user messages. The platform supports three role-based user types — Admin, Therapist, and User — secured via JWT authentication and bcryptjs password hashing. Core features include an AI CBT chatbot with crisis detection and helpline surfacing, daily mood journaling with auto-computed sentiment scores visualized as Chart.js line charts, a curated library of ten guided meditations across six categories, a therapist directory with session booking, and comprehensive role-based dashboards. Mathematical formulations of the AFINN scoring model, comparative score normalization, CBT sentiment-to-response mapping, JWT authentication flow, and bcrypt cost function are derived. System architecture, NLP pipeline flowchart, algorithmic pseudocode, and comparative performance tables are presented alongside results analysis with bar and line graphs.
Mental Wellbeing, NLP, Sentiment Analysis, AFINN, CBT, Crisis Detection, Chatbot, Mood Tracking, Node.js, Express.js, SQLite, JWT, Bootstrap 5, Chart.js, Docker
Syed Asim, Syed Mazher, Syed Rayyan Ahmed
AI Assistance For Healthcare Using NLP, Machine Learning
This study presents an AI-based medical chatbot that utilizes Natural Language Processing (NLP) and Machine Learning (ML) to provide preliminary disease diagnosis from user-reported symptoms. The system employs a multistage symptom matching approach, including syntactic similarity (Jaccard), semantic similarity (WordNet WUP), and synonym mapping, to interpret natural language inputs effectively. A K-Nearest Neighbors (KNN) classifier trained on 4,920 records with 132 symptom features predicts diseases across 41 conditions. The chatbot is implemented using a Flask-based conversational interface that collects user inputs, performs disease prediction, and provides relevant descriptions and precautions. Experimental results demonstrate the effectiveness of combining NLP techniques with ML models for accessible and efficient healthcare support systems.
NLP, Machine Learning, KNN, Medical Chatbot, Disease Prediction
Syed Yousuf Pasha Quadri, Mohd Amaanuddin, Mohd Abdul Sameer, Syed Abdul Jauvad
Blockchain-Based Medical Report Management System: A Secure And Decentralized Approach To Healthcare Data Exchange
Healthcare data management faces critical challenges including unauthorized access, lack of patient control, absence of verifiable audit trails, and vulnerability to data tampering. Centralized storage architectures create single points of failure, compromising patient privacy and data integrity. This paper presents a blockchain-based Medical Report Management and Distribution System that leverages cryptographic hashing and decentralized ledger technology to provide secure, tamper-proof storage and sharing of medical records. The system implements a custom blockchain using SHA-256 cryptographic hashing, Flask web framework, and SQLite database. Patients retain full ownership and control over their medical reports through a granular access control mechanism, granting and revoking permissions to healthcare providers. Every significant system action—report upload, access grant, access revocation, and file download—is recorded as an immutable block in the blockchain, creating a comprehensive audit trail. The system achieves average page response time under 150ms, database query time under 10ms, and blockchain verification time of 15ms for 100 blocks. Security features include Werkzeug password hashing, parameterized SQL queries preventing injection attacks, and file integrity verification through SHA-256 checksums. A responsive Bootstrap 5 interface with Chart.js visualizations provides analytics on report distribution, user roles, upload trends, and access patterns. Experimental results demonstrate successful integration testing with 100% pass rate across authentication, blockchain recording, access control, and security features.
Blockchain, Medical Records, Healthcare Data Security, SHA-256, Cryptographic Hashing, Access Control, Audit Trail, Flask, Patient Privacy, Data Integrity, Decentralization.
Mohammed Zaid, Rayan Mohammed Khaled, Saad Ahmed Aijaz, Syed Ahmed Ali
Cloud-Based Comprehensive Bus Ticketing And Reservation System
Public road transport is the backbone of urban and inter-city mobility in developing nations, yet the overwhelming majority of bus operators still depend on manual counter-based ticketing — a model plagued by long queues, limited operating hours, overbooking errors, and the absence of real-time seat information. This paper presents BusBook, a Cloud-Based Comprehensive Bus Ticketing and Reservation System — a full-stack web application architected on the Model-View-Controller (MVC) pattern using Python Flask 3.x, SQLite 3, and Bootstrap 5. The system implements role-based access control for passengers and administrators, atomic seat management via SQLite transactions to eliminate race-condition overbooking, a dynamic source-destination bus-search engine, a complete booking lifecycle (search → book → confirm → cancel → refund), and a real-time admin analytics dashboard. The application is containerized with Docker for one-command deployment on AWS, Azure, or Google Cloud Run. Mathematical formulations of fare computation, seat availability constraints, atomicity invariants, and revenue aggregation are derived. System architecture, MVC layering, booking flowchart, and four pseudocode algorithms are presented. Results analysis includes bar charts of comparative system evaluation, page-load benchmarks, monthly booking and revenue trends, seat utilization, and a comprehensive feature-comparison table against existing solutions. All 15 functional and security test cases passed, confirming system correctness.
Cloud Computing, Bus Ticketing, Flask, SQLite, Bootstrap 5, Docker, MVC Architecture, Seat Management, Atomic Transactions, RBAC, Reservation System, REST API, Session Authentication
Anas Mohammed Nayeemuddin · Syed Aaqib Raahil · Syed Sana Ullah · Abdul Hannan
A Theoretical Study On The Electrodynamics Properties Of Superconducting Ultra-Thin Films And Their Optical Response In The Terahertz Domain
Superconducting ultra-thin films exhibit distinctive electrodynamics behaviour governed by reduced dimensionality, Cooper-pair condensation, and the formation of an energy gap at the Fermi level. This paper presents a theoretical investigation into the optical conductivity and THz-frequency response of conventional metallic superconducting thin films, with niobium nitride (NbN) and tantalum nitride (TaN) as primary model systems. The Mattis–Bardeen (MB) theoretical framework within BCS formalism is adopted to analyse real (σ₁) and imaginary (σ₂) components of complex conductivity across the 0.1–1.1 THz spectral window. The central hypothesis posits that as film thickness diminishes below the London penetration depth (λL), disorder-induced modifications to the superconducting gap produce measurable deviations from BCS-predicted optical responses. Secondary datasets from terahertz frequency-domain spectroscopy (THz-FDS) experiments are reanalysed to validate theoretical predictions. Results demonstrate that NbN films of 3.9–5.0 nm exhibit Tc suppression, penetration depth enhancement, and spectral weight redistribution consistent with disorder-driven pair-breaking, while TaN films adhere closely to weak-coupling BCS predictions. These findings carry implications for the rational engineering of kinetic inductance detectors, superconducting nanowire single-photon detectors, and quantum sensing platforms. DOI: https://doi-ds.org/doilink/04.2026-89282224
Superconducting ultra-thin films, THz optical conductivity, Mattis–Bardeen theory, London penetration depth, kinetic inductance detectors
A Research Analysis Of Intelligent Tutoring Systems Based On Generative Artificial Intelligence And Adaptive Learning Approaches
The integration of Generative Artificial Intelligence (GenAI) into Intelligent Tutoring Systems (ITS) has opened a transformative pathway for personalized, adaptive education. This study examines how GenAI-powered ITS influence learning outcomes across educational levels, focusing on cognitive performance improvement and embedded adaptive mechanisms. The research objectives are: (1) to evaluate GenAI-ITS effectiveness on student learning outcomes, and (2) to analyze adaptive learning mechanisms embedded within GenAI-driven platforms. A secondary research design was employed, synthesizing meta-analyses, randomized controlled trials, and systematic reviews published between 2016 and 2025. The hypothesis posits that GenAI-integrated ITS yield significantly superior academic outcomes compared to conventional instruction. Findings from five verified data tables reveal effect sizes ranging from d = 0.66 to g = 0.76, statistically significant pre/post-test gains (p < 0.001) in problem-solving and critical thinking, and GenAI academic adoption exceeding 60% among higher education students globally in 2023–2024. The study affirms that adaptive feedback, LLM-based interactivity, and personalized learning pathways drive these outcomes, while underscoring ethical and equitable implementation imperatives for responsible deployment. DOI: https://doi-ds.org/doilink/04.2026-81221952
Intelligent Tutoring Systems, Generative Artificial Intelligence, Adaptive Learning, Large Language Models, Personalized Education
Underwater Net: Efficient Visual Detection Of Marine Garbage For Eco Monitoring
Marine pollution poses a severe threat to the sustainability of aquatic ecosystems and the blue economy. Effective detection and classification of underwater debris are crucial for enabling timely interventions and supporting marine conservation efforts. In this project, we present an advanced underwater garbage detection system based on YOLOv10n, a cutting-edge, lightweight object detection model optimized for resource-constrained IoT and underwater robotic platforms. Building on the challenges identified in traditional detection models—such as high computational costs and deployment complexity—we replace older backbones like CSPDarknet with the more efficient YOLOv10n architecture. YOLOv10n is designed with an emphasis on speed, low parameter count, and high accuracy, making it ideal for real-time underwater applications. Our system achieves robust debris detection with high precision, while significantly reducing memory and processing requirements, thereby facilitating deployment on embedded and mobile devices. This project demonstrates the feasibility and effectiveness of using YOLOv10n for scalable and eco-friendly marine monitoring solutions, providing a practical approach to combat marine pollution through intelligent automation.
Marine Pollution, YOLOv10n, Underwater Garbage Detection, Object Detection, Deep Learning, IoT, Real-Time Monitoring, Underwater Robotics.
A.Manogna, G.Dheeraj Narasimha, K.Archana
Enhancing Agricultural Decision-Making with Combined Language Models
Agricultural advisory systems play a crucial role in supporting farmers by providing timely and accurate solutions to crop-related problems. However, building reliable question-answering systems for agriculture is challenging due to the diverse, domain-specific, and often noisy nature of farmer queries. In this project, a retrieval-based agricultural question-answering framework is proposed to deliver accurate and trustworthy responses by leveraging historical expert-curated data. The system utilizes a pretrained sentence embedding model to convert farmer queries into dense vector representations, followed by similarity-based retrieval using FAISS to identify the most relevant past questions and their corresponding expert answers. Unlike generative models, which often produce vague or hallucinated responses, the proposed approach ensures factual correctness by reusing validated advisory content. Experiments conducted on a large farmer query dataset demonstrate that the system achieves a training accuracy of 100% and a test accuracy of 95.9%, with qualitative evaluation confirming high relevance and agricultural validity of the retrieved responses. The results indicate that retrieval-based question answering is a practical, efficient, and reliable solution for real-world agricultural advisory applications.
Agricultural Advisory System, Question Answering, Retrieval-Based Learning, Sentence Embeddings, FAISS, Information Retrieval, Natural Language Processing, Dense Vector Representation, Similarity Search, Expert Knowledge Base, Farmer Queries, Domain-Specific QA, Machine Learning, Semantic Search, Agricultural Decision Support.
Sripada Naga Venkata Sudheer, Nenavath venkatesh, Syed Asif Ali
Enhancement Of Wider Impedance Bandwidth Monopole Cpw-Fed Pentagonal Patch Antenna With Slits For SRR Applications
This paper presents the design of a monopole CPW-fed pentagonal patch antenna with slits for Short Range Radar (SRR) applications. The proposed antenna is implemented on an FR4 substrate with dimensions of 28 × 23 × 1.6 mm³ and utilizes a Co-planar Waveguide (CPW) feeding technique. The antenna is optimized through a stepwise geometric evolution process using slot-loading technique which resonates at 9.73 GHz with reflection coefficients of –38.05 dB. It operates over a frequency band of 8.85–11.69 GHz producing a bandwidth of 2.84 GHz. The observed peak gain is 3.6 dBi. This paper presents the analysis of S11 parameters, far-field radiation patterns, 3D gain characteristics, and surface current distributions at the resonant frequency. The structure was optimized for stable reflection coefficient, omnidirectional radiation, and compatibility with compact sensing systems.
Pentagonal patch antenna, Short Range Radar applications, Co-planar Waveguide, Slot-loading technique, S11 parameters, Gain characteristics, Radiation patterns, Surface current distributions
Intelligent Protection And Fault Management In Electrical Power Systems Using Ai Techniques
Electrical power systems face increasing complexity with renewable energy integration and expanding transmission networks. Traditional protection schemes struggle with dynamic fault scenarios, necessitating advanced intelligent solutions. This research investigates artificial intelligence techniques for enhancing power system protection and fault management. The study examines machine learning algorithms including Neural Networks, Support Vector Machines, and Deep Learning models for fault detection, classification, and localization. Through systematic analysis of AI-based protection systems, this research evaluates performance metrics across various fault scenarios. The hypothesis proposes that AI techniques significantly improve fault detection accuracy and response time compared to conventional methods. Results demonstrate that ensemble machine learning models achieve 99.96% accuracy in fault detection, with neural networks showing 99.52% classification accuracy. Deep learning approaches reduce false alarm rates while maintaining specificity above 98%. Implementation of reinforcement learning for adaptive protection demonstrates substantial improvements in grid resilience. The findings establish AI techniques as transformative tools for modern power system protection, offering enhanced reliability, faster fault isolation, and improved system stability essential for smart grid infrastructure.
Structural and Topological Perspectives on Four-Dimensional Manifolds
Four-dimensional manifolds occupy a uniquely complex position in modern topology. This paper examines their structural and topological properties through intersection forms, Betti numbers, Euler characteristics, and gauge-theoretic invariants. The primary objective is to systematically compare smooth and topological 4-manifolds using standard examples and established invariants. The study employs a descriptive-analytical methodology, drawing on foundational results from Freedman, Donaldson, and Witten to analyze topological invariants of key 4-manifolds including S⁴, CP², S²×S², K3, and the four-torus T⁴. The hypothesis posits that the divergence between smooth and topological categories in dimension four is uniquely determined by the arithmetic of intersection forms and gauge-theoretic constraints. Results demonstrate that Euler characteristics range from 0 (T⁴) to 24 (K3), while Seiberg-Witten invariants sharply distinguish diffeomorphism types within homeomorphism classes. The discussion affirms that no single invariant suffices for complete classification, and that the interplay between intersection forms and gauge theory remains the central framework for understanding 4-manifold topology. The conclusion highlights open problems including the smooth four-dimensional Poincaré conjecture.
Four-dimensional manifolds, intersection forms, Seiberg-Witten invariants, Euler characteristic, smooth topology
A Mathematical Analysis of Diabetes Progression Using Differential Equation Models
Diabetes mellitus is among the fastest-growing chronic metabolic disorders globally, with 589 million adults affected as of 2024. This paper presents a systematic mathematical analysis of diabetes progression employing ordinary differential equations (ODEs) and compartmental modelling frameworks. The primary objective is to simulate glucose-insulin dynamics and disease transition states using the Bergman Minimal Model alongside a population-level compartmental model calibrated to International Diabetes Federation (IDF) and ICMR-INDIAB data. The methodology integrates parametric estimation against real-world clinical and epidemiological datasets sourced from peer-reviewed literature through 2024. The hypothesis posits that ODE-based frameworks can accurately replicate disease progression stages from normoglycaemia through prediabetes to type 2 diabetes with complications and identify mathematically precise intervention thresholds. Results demonstrate that the basic reproduction number (R₀) governs stability of disease-free and endemic equilibria, while insulin sensitivity index (Sᵢ) is the primary physiological predictor. Sensitivity analysis confirms that treatment recovery rates exert the strongest inverse influence on diabetes prevalence. Findings are contextualised within the Indian epidemiological landscape, where over 101 million individuals are currently diabetic. The conclusions affirm ODE models as indispensable predictive instruments for evidence-based national diabetes policy.
Differential equations; diabetes progression; Bergman minimal model; insulin sensitivity index; compartmental epidemiological modelling
Analysis of Surface Wave Generation and Propagation in Layered Geological Media
Surface waves, particularly Rayleigh and Love waves, constitute the dominant component of seismic energy recorded at the Earth's surface and carry crucial diagnostic information about subsurface geological structure. This study investigates the generation mechanisms and propagation behavior of surface waves in horizontally layered geological media through a synthesis of numerical simulation, multi-channel analysis of surface waves (MASW), and dispersion inversion techniques. The primary objective is to characterize how elastic wave parameters shear-wave velocity (Vs), layer thickness, and Poisson's ratio govern surface wave dispersion characteristics across multilayered subsurface profiles. The hypothesis posits that Rayleigh wave phase velocity at high frequencies (>10 Hz) is predominantly sensitive to shallow-layer Vs, whereas low-frequency components penetrate deeper geological horizons. Results demonstrate statistically significant agreement (R² > 0.91) between numerically simulated and field-observed wave parameters across five geological units, with Vs30 values ranging from 145 to 1,750 m/s. Dispersion curves confirm strong depth-dependent phase velocity variation. These findings reinforce the viability of MASW-based inversion for precision subsurface characterization applicable to seismic hazard assessment, foundation engineering, and resource exploration in geologically complex terrains.
Surface wave dispersion, Rayleigh waves, layered geological media, MASW inversion, shear-wave velocity
Trust-Aware Clustering for Enhanced Routing Security and Performance in Sensor-Enabled Mobile Ad Hoc Networks
Mobile Ad Hoc Networks (MANETs) play a vital role in sensor-based and Internet of Things (IoT) applications operating in infrastructure-less and dynamically changing environments such as disaster recovery, military operations, and remote monitoring. However, frequent topology variations, limited node energy, and the absence of centralized control make MANETs highly vulnerable to routing inefficiencies and security threats, leading to degraded sensor data delivery performance. This paper proposes a trust-aware clustering-based routing framework for sensor-enabled MANETs aimed at enhancing both routing security and network performance. The proposed approach employs a hybrid cluster head (CH) election mechanism that integrates node degree, mobility, residual energy, and a dynamically computed trust value to ensure stable, energy-efficient, and secure cluster formation. The trust model evaluates node behavior based on packet forwarding reliability and communication consistency, enabling the identification and isolation of malicious or selfish nodes. Secure inter-cluster communication is further strengthened through lightweight symmetric encryption and authentication mechanisms, while a trust-assisted intrusion detection component at the CH level facilitates early threat detection. The proposed protocol is evaluated using NS-3 simulations under varying node densities and mobility conditions. Performance analysis demonstrates significant improvements in packet delivery ratio, routing overhead, end-to-end delay, and malicious node detection accuracy compared to AODV, CBRP, and Secure-AODV protocols. The results indicate that integrating trust-aware clustering with lightweight security mechanisms provides an effective and scalable solution for reliable and secure data transmission in sensor-enabled MANET and IoT environments.
A Comprehensive Energy–QoS Trade-off Modeling Framework for Heterogeneous Cloud Data Centers
Modern cloud data centers operate under constant pressure to deliver high performance while simultaneously reducing energy consumption and operational costs. As cloud infrastructures grow increasingly heterogeneous—comprising diverse servers, virtualization layers, and multi-tier architectures—maintaining an optimal balance between Quality of Service (QoS) and energy efficiency has become a significant challenge. Traditional resource management strategies often focus on either minimizing power usage or satisfying Service Level Agreements (SLAs), but rarely address the dynamic trade-off between these two competing objectives in an integrated manner. This research proposes a comprehensive Energy–QoS trade-off modeling framework tailored for heterogeneous cloud data centers. The framework establishes mathematical relationships between workload characteristics, resource utilization, energy consumption, and SLA compliance. By incorporating multi-objective optimization principles, the model quantifies the impact of resource allocation decisions on both performance and power efficiency. It also introduces a penalty-based SLA violation function to capture real-world service constraints. The expected outcome of this study is a flexible modeling foundation that enables cloud operators to make informed, adaptive decisions that balance energy savings with service reliability. The framework aims to reduce operational costs, minimize SLA violations, and enhance sustainability, thereby supporting the development of intelligent and environmentally responsible cloud computing systems.
Carbon-aware computing, Cloud data centers, Energy efficiency, Energy modeling Heterogeneous systems, multi-objective optimization, Quality of Service (QoS), Resource allocation Service Level Agreement (SLA), Workload characterization
Improving Medical Diagnosis Systems using Efficient Machine Learning System
This paper proposes a unified and mathematically grounded hybrid framework for disease prediction by integrating machine learning, applied mathematics, and fuzzy logic. The study focuses on predictive modeling of infectious and non-communicable diseases, including COVID-19, lung cancer, swine flu, and dengue, using structured clinical, epidemiological, and climatic datasets. Data preprocessing techniques such as cleaning, normalization, and train–test splitting are employed to ensure reliability and consistency. Multiple supervised learning algorithms Support Vector Machine, Random Forest, k- Nearest Neighbors, Decision Tree, and Artificial Neural Network are implemented and comparatively evaluated using accuracy, precision, recall, F1-score, mean squared error, and confusion matrices. To address uncertainty in epidemiological parameters, fuzzy mathematical modeling and bifurcation analysis are incorporated, enabling uncertainty-aware interpretation of disease dynamics through the fuzzy basic reproduction number. Experimental results indicate that Random Forest achieves the most stable and reliable performance, while SVM and ANN show competitive outcomes. Overall, the proposed hybrid analytical–ML framework enhances interpretability, robustness, and scalability, making it suitable for epidemiological studies and clinical decision support systems.
Machine Learning, Fuzzy Logic, Disease Prediction, Epidemiological Modeling, Clinical Decision Support.
Secure Cloud Enabled Platform for AI-Powered Computer Vision Syndrome Detection
Computer vision syndrome detection has become a significant discovery in the current healthcare system, as a way of diagnosing genetic and development disorders at an early stage with the help of facial features. Nevertheless, in practice, such systems must have high-level security, why privacy protection, and scalability in the case of sensitive patient information. This paper describes a safe cloud-based system that incorporates AI-based computer vision algorithms to detect syndromes automatically. The given framework makes use of using convolutional neural networks (CNNs) to extract facial features and make deep learning classifications using the Softmax probability-based syndrome prediction. Experimental evaluation was done using a 90 sample of faces images that consisted of Down Syndrome (33.3%), Williams Syndrome (22.2%), Turner Syndrome (16.7%), and other syndromes (27.8%). The gender representation was also fairly equal at 53.3/46.7 % males/females respectively. The site also has security features like encryption and privacy learning methods to secure the information of the patients in the cloud implementation.