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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Sandeepa, A.G.R. Mohottala, Sanka General Sir John Kotelawala Defence University Dept. of Computer Engineering Ratmalana Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331530983

This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach. © 2025 IEEE.

关键词： Students

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IoT-Enabled Piezoelectric Energy Harvesting System for Mobile Charging with DC-DC Power Regulation and Supercapacitor Storage

IoT-Enabled Piezoelectric Energy Harvesting System for Mobil...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Libina M Gopinath P Duraimurugan K Dept. of Information Technology IFET College of Engineering Tamilnadu

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The piezoelectric energy harvesting system functions as an environmentally powered system which converts mechanical vibrations in the environment into electrical power. The energy will subsequently power mobile charging systems. Piezoelectric crystals form an integral part of the system because mechanical stress causes them to produce output voltages. The system uses a microcontroller to monitor continuous voltage and current readings from the piezoelectric crystal before the IoT platform receives this data for remote monitoring. The data transmission system ensures both the accuracy and top quality standards of transmitted information. A DC-DC converter functions to prepare the collected energy. The system accepts steady input while creating outputs which have constant and variable components. The constant input guides effective power supply systems with voltage stabilizers without any changes in input levels. The rechargeable battery-based charger functions as the main power supply for mobile device charging operations. The application collects smaller power volumes through this system which makes it suitable for remote sites unable to connect to standard power distribution networks. Users now have convenient access to view real-time data about energy generation and storage thanks to the Internet of Things (IoT) while their system delivers an optimal working condition. This power system design featuring piezoelectric energy harvesting power moulding and supercapacitor caching leads to an environment friendly solution for mobile equipment energy needs in regions with restricted power supplies.

关键词： Vibrations Power supplies DC-DC power converters Crystals Supercapacitors Regulation Real-time systems Energy harvesting Internet of Things Standards

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Deep Neural Architecture for Context-Aware Tamil Text-To-Speech Synthesis with Multilingual Scalability

Deep Neural Architecture for Context-Aware Tamil Text-To-Spe...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： R. Thendral R. Sugumar S. Dhineshkumar Dept. of Information Technology IFET College of Engineering Tamilnadu

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The morphological abundance and high contextuality of Tamil, it is extremely challenging for Text-To-Speech (TTS) synthesis. This paper introduces a new Deep Neural Architecture for Context-Aware Tamil TTS Synthesis that improves intelligibility, naturalness, and multilingual flexibility through three major innovations: Multilingual Phoneme Embedding Network (MPEN) for better phoneme prediction from cross-lingual features, Prosody-Aware Transformer (PAT) for tone variation modeling and context-aware intonation, and Hybrid Spectrogram-Waveform Generator (HSWG) based on WaveNet and HiFi-Gan for high-fidelity speech synthesis. The resulting system achieves 98% intelligibility and 97.5% naturalness, surpassing state-of-the-art systems like Tacotron 2 and HiFi-Gan. The Mean Opinion Score (MOS) test verifies that the produced speech is extremely natural and human-like, and prosodic errors were decreased by 33% when compared with the older Tamil TTS models. Additionally, the model can be extended to other Indic languages and utilized in assistive technologies, AI-based voice assistants, real-time translation, and multilingual speech synthesis. The findings show that the system is efficient, scalable, and linguistically accurate, which is a valuable contribution to Tamil speech technology.

关键词： Technological innovation Accuracy Translation Computational modeling Personal voice assistants Transformers Natural language processing Multilingual Text to speech Context modeling

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Artificial Intelligence Techniques for the Ransomware Detection: Current Methods and Challenges 1

Artificial Intelligence Techniques for the Ransomware Detect...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Rakibe, Priya Jawale, Madhuri Computer Engineering Research Centre Sanjivani College of Engineering Kopargaon Savitribai Phule Pune University Pune India Sanjivani College of Engineering Kopargaon Savitribai Phule Pune University Dept. of Information Technology Pune India

ISBN: (纸本)9798350355611

Recently, Artificial Intelligence (AI) Techniques have received significant interest across the different fields for data analysis and decision-making. Ransomware detection using AI has been a challenging research problem for the last decade in hybrid communication systems like blockchainintegrated Internet of Things (IoT) architectures. Ransomware is a form of malicious software that restricts access to a victim's resources by altering system login credentials or encrypting digital data. Consequently, typical approaches used to identify other types of threats are inadequate for recognizing ransomware. Different AI techniques recently have been used for the detection of such threats, but an appropriate level of accuracy and minimum false rate with automation are vital challenges. This study presents a systematic review of the current methods for ransomware detection using different AI techniques. The ransomware detection methods under categories such as rule-based, machine learning-based, and deep learning-based are reviewed in this study. The comparative analysis of these methods is provided quantitatively and qualitatively. As per the recent progress in the domain of ransomware detection, the current research problems are identified and discussed as the outcome of the study. The roadmap for future work is described as the research gap analysis output. © 2025 IEEE.

关键词： Adversarial machine learning

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Optimizing Healthcare(Nephrology) Automation to Enhance Patient Care and Operational Efficiency

Optimizing Healthcare(Nephrology) Automation to Enhance Pati...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Latha L Amaresh M Ashwath A Krithinka M Lokesh P Sai Yeswant S Dept. of Information Technology Kumaraguru College of Technology Coimbatore TamilNadu Dept. of Computer Science and Engineering Kumaraguru College of Technology Coimbatore TamilNadu Dept. of Information Science and Engineering Kumaraguru College of Technology Coimbatore TamilNadu

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Healthcare automation is rapidly transforming the landscape of medical care, particularly in specialized fields like nephrology, which deal with the diagnosis and treatment of kidney-related diseases. This study explores the integration of automation technologies in nephrology to enhance patient care and improve operational efficiency. Nephrology practices can leverage the advancements in AI, machine learning, robotics, and data analytics to streamline administrative workflows, optimize resource allocation, and deliver more personalized and precise care. Automated systems, such as AI-powered diagnostic tools, predictive analytics for disease progression, and automated dialysis machines, may reduce diagnostic errors, improve patient outcomes, and minimize clinician workload. This research shows how automation of routine processes, including patient scheduling, updating medical records, and treatment planning, decreases administrative burdens and allows healthcare providers to focus on critical decision-making and direct patient interaction. In addition, automation in nephrology supports the early detection and management of chronic kidney disease (CKD) by analysing large datasets to identify at-risk individuals and recommend timely interventions. Automating tasks such as laboratory data processing and dialysis monitoring will enable healthcare facilities to save a lot of money and be more operationally efficient while maintaining regulatory standards. This research study addresses the healthcare automation issues associated with data security, patient privacy, and upskilling of the workforce for operation and management of complex systems. Thus, this paper stresses the point that a multi-stakeholder approach involving providers of healthcare services, developers of technology, and policymakers is significant in achieving sustainable and effective automation strategies. This research indicates that in the field of nephrology, optimizing automation brings clinical and

关键词： Automation Nephrology Medical services Chronic kidney disease Telecommunication computing Resource management Stress Standards Robots Diseases

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Cryptography Innovations for Securing Data in the Quantum Computing Era: Integrating Machine Learning for Enhanced Security

Cryptography Innovations for Securing Data in the Quantum Co...

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2025 International Conference on Computer, Electrical and Communication engineering, ICCECE 2025

作者： Temara, Sheetal Bhagyalakshmi, L. Suman, Sanjay Kumar Shakunthala, M. Chitra, N. Thulasi Golla, Kishor University of the Cumberlands Department of Computer and Information Sciences United States Rajalakshhmi Engineering College Dept. of ECE TN Chennai India St. Martin's Engineering College Dept. of Ece and Dean R&d Telangana Secunderabad India R.M.D. Engineering College Dept. of ECE TN Thiruvallur India Mlr Institute of Technology Dept. of CSE Hyderabad India St. Martin's Engineering College Dept. of CSE Telangana Secunderabad India

ISBN: (纸本)9798331513894

The rise of quantum computing introduces substantial risks to traditional cryptographic protocols, which are vulnerable to quantum decryption methods such as Shor's algorithm. To address these emerging threats, this paper proposes an innovative cryptographic framework that integrates machine learning (ML) techniques to enhance data security in the quantum computing era. Our approach leverages ML-driven anomaly detection, adaptive key management, and predictive analytics to create a flexible and resilient cryptographic defense. The system's anomaly detection module utilizes neural networks to identify potential quantum-based decryption attempts, while reinforcement learning optimizes key generation and distribution in response to detected threats. Experimental results demonstrate that the proposed ML-augmented framework significantly improves anomaly detection accuracy and reduces vulnerability to quantum decryption attempts by dynamically adjusting cryptographic parameters. These findings underscore the potential of machine learning to strengthen cryptographic systems, making them adaptable to the advanced threats posed by quantum computing. © 2025 IEEE.

关键词： Predictive analytics

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Adaptive Input Design for Nonlinear System Identification with Operational Constraints

arXiv

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arXiv 2025年

作者： Hu, Jingwei Zachariah, Dave Wigren, Torbjörn Stoica, Petre Div. Systems and Control Dept. Information Technology Uppsala University Sweden

We consider the problem of joint input design and parameter estimation for identifying nonlinear system models through the sequential acquisition of measurements while adhering to system constraints. We utilize a receding horizon approach and propose a new scale-invariant input design criterion, which is tailored to continuously updated parameter estimates, along with a new sequential parameter estimator. We demonstrate the ability of the method to design informative experiments online, while steering the system within operational constraints. © 2025, CC BY-NC-SA.

关键词： Nonlinear simulations

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Hybrid Feature engineering and Sampling Strategies for Enhanced Diabetic Retinopathy Classification 3

Hybrid Feature Engineering and Sampling Strategies for Enhan...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vanlalnunpuia, C. Lalhmingliana Ambeth Kumar, V.D. Vanlalawmpuia, R. Khiangte, David Lalmuanpuia Kumar Khan, Ajoy Mizoram University Dept. of Computer Engineering Aizawl India Mizoram University Dept. of Information Technology Aizawl India Eye Care Hospital Ophthalmologist Aizawl India

ISBN: (纸本)9798331523893

This study presents a machine learning approach for Diabetic Retinopathy (DR) classification, integrating advanced preprocessing, feature extraction, and adaptive sampling. Preprocessing techniques, including CLAHE, gamma correction, and noise reduction, standardize image quality while preserving anatomical details. Class imbalance is addressed with ADASYN, generating synthetic samples in sparse regions for balanced class representation. Feature extraction focuses on clinical markers like vascular density, microaneurysms, exudates, and texture descriptors such as GLCM and LBP. Recursive Feature Elimination (RFE) refines features for better interpretability and reduced computational load. This binary classification framework achieves 92.91% accuracy on test data and a cross-validated ROC AUC of 0.994, demonstrating the effectiveness of hybrid feature engineering and intelligent sampling in DR detection. © 2025 IEEE.

关键词： Diabetic Retinopathy Fundus Image Image Processing Machine Learning Medical Image Diagnostics

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A Large-Scale Dataset for Small and Tiny Object Detection from Aerial Imagery

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Procedia Computer Science 2025年 258卷 4228-4237页

作者： Sourabh Verma Himanshu Gupta Om Prakash Verma Dept. of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India Dept. of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India 576104

Aerial object detection has gained significant attention due to its broad applications in areas such as security, surveillance, and agriculture. However, existing datasets present several challenges, such as annotation errors and limited scale, which hinder the accuracy and speed of current detection models. To address this, the NITJ-AIR dataset—an open-source, large-scale dataset has been proposed that aims to advance research in small and tiny object detection. Baseline results are provided for several state-of-the-art detectors, including Faster R-CNN, SSD, YOLO (v3, v4, and v5s), and CornerNet. Further, the performance of these detectors on NITJ-AIR is compared against the VisDrone and AU-AIR datasets. The comparative analysis demonstrates enhanced feature extraction capabilities, yielding a substantial minimum improvement of 8.37% in average precision and 7.65% in mean average precision across models. The developed dataset is available at https://***/himanshugiriraj/NITJ-AIR .

关键词： Aerial object detection NITJ-AIR dataset Overhead imagery Small tiny object detection Visual surveillance

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AgroConnect: A Flutter-Based E-Commerce Platform for Enhancing Agricultural Supply Chain Efficiency and Transparency

AgroConnect: A Flutter-Based E-Commerce Platform for Enhanci...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R. Thendral Selva Ganapathy J Dept. of Information Technology IFET College of Engineering Villupuram Tamilnadu

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

AgroConnect is a completely Flutter-based mobile app that acts as the much-needed “connection” between farmers and retailers, providing an e-commerce platform streamlined specifically for the agricultural sector. In this way, it empowers the farmer to manage and showcase his products effectively to thrive in a competitive marketplace while providing retailers with the ease of smooth purchasing. These include real-time inventory tracking that prevents wastage and maximizes sales together with secure payment processing that earns the parties maximum trust. Effective communication tools ensure interaction at the point. Some of the communication tools that ensure on-spot discussions about the available products, negotiations over prices, and inquiries are the ones that enhance accessibility to the markets and optimize the agricultural chain of supply. Available on Android and iOS, AgroConnect strives to revolutionize the farming industry by fostering greater transparency and efficiency throughout the agricultural ecosystem, ultimately paving the way for a more sustainable, interconnected marketplace for all farmers, retailers, and consumers.

关键词： Scalability Operating systems Supply chains Ecosystems Real-time systems Mobile applications Electronic commerce Reliability Sustainable development Farming

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