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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 IEEE.

关键词： Recommender systems

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SWATI AI : Advancing AI Models from Rule-Based Frameworks to NLP-Driven Prescriptive Analytics to Assist Victims of Domestic Abuse 1

SWATI AI : Advancing AI Models from Rule-Based Frameworks to...

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

作者： Awasekar, Dipali Dilip Lobo, L.M.R.J. WIT-Solapur Punyashlok Ahilyadevi Holkar Solapur University Dept. of Computer Science and Engg. Maharashtra Solapur India Walchand Institute of Technology Dept. of Information Technology Maharashtra Solapur India

ISBN: (纸本)9798350355611

SWATI AI (Support and Welfare Assistance through technology Interface) is an AI-driven chatbot designed to assist victims of domestic abuse by providing accessible, non-judgmental, and actionable legal guidance. This paper introduces the LAMP2 (Legal Analytics Model for Prediction and Prescription), the core framework that powers SWATI, progressing through four distinct research phases aimed at enhancing decision-making capabilities for victims of domestic violence under the Protection of Women from Domestic Violence Act, 2005. Phase 0 focuses on the creation of a curated dataset, PROTECT-JC, which compiles judgments from district courts, high courts, and the Supreme Court of India between 2008 and 2024. This dataset, developed in collaboration with legal experts and judicial professionals, serves as the foundation for training models to predict legal outcomes. Phase 1 introduces a rule-based decision framework in LAMP2 1.0, designed to extract and map predefined intents and entities from user inputs. This phase enables the structured documentation of complaints, providing victims with initial guidance based on the legal provisions of the Domestic Violence Act, 2005. The rule-based approach achieved accuracy of 80%, but with limited customization for individual cases. In Phase 2, LAMP2 2.0 leverages supervised machine learning models to predict legal outcomes, such as protection orders, custody orders, and monetary relief. These models, including Random Forest, XGBoost, and Decision Trees, achieved impressive results, with the Random Forest model showing 96.5% accuracy in predicting protection orders. Phase 3 evolves the model further with NLP-driven predictive analytics to process unstructured legal narratives, such as case judgments, transforming them into structured data for improved predictions. Models like Llama and Mixtral demonstrated strong summarization capabilities and show promise in predicting legal results. The integration of NLP and machine learning te

关键词： Decision making

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Sivasanjith G Mohan Prabhu N Hemaswathi S Rajkumar P Rajeshkannan C R. Dhivya Dept. of Information Technology KARE Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Dept. of Computer Science and Engg. Mount Zion College of Engg. and Tech. Pudukottai India Dept. of Computer Science and Engg. Suguna College of Engineering Coimbatore India Dept. of Information Technology M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy; however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers’ review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase.

关键词： Drugs Sentiment analysis Reviews Statistical analysis Urban areas Stars Smart homes Electronic commerce Older adults Recommender systems

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Assessing the Accuracy of Regression and Classification Models in Air Quality Prediction

Assessing the Accuracy of Regression and Classification Mode...

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Advances in Electrical, Computing, Communication and Sustainable technologies (ICAECT), International Conference on

作者： Harsh Kumar Vishwakarma Guru Prasad M S Vanshika Mittal Mudit Mittal PavanKumar Naik Chandrappa S Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India School of Computing and Information Technology REVA University Bangalore India Dept. of Computer Science and Engg. JAIN (DEEMED-TO-BE UNIVERSITY) Bengaluru India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This research paper addresses the pressing issue of air quality monitoring and prediction using machine learning algorithms. With a dataset comprising pollutant levels from various states, we employed a comprehensive approach to data analysis, visualization, and preprocessing. The study aimed to predict the Air Quality Index related to key pollutants such as SO2, NO2, RSPM, and SPM. We utilized linear regression, decision tree regression, and random forest regression for prediction tasks and logistic regression, decision tree classification, random forest classification, and k-nearest neighbors for classification tasks. The proposed method was evaluated using different performance matrixes for regression and classification tasks, respectively. The results demonstrated the usefulness of the proposed models in predicting and classifying air quality levels. The proposed model provides a robust framework for air quality management and can be a valuable tool for environmental monitoring and policymaking.

关键词： Logistic regression Accuracy Machine learning algorithms Atmospheric modeling Predictive models Air quality Environmental monitoring Public healthcare Regression tree analysis Random forests

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Shieldmaiden: A maternity and baby care assistance smartphone application with pregnancy detection, healthcare support, baby product purchasing features

Shieldmaiden: A maternity and baby care assistance smartphon...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Mahfuzulhoq Chowdhury Arupa Barua Suriya Sultana Dept. of Computer Science and Engg. CUET Chittagong Univ. of Eng. And Tech. Bangladesh Department of Computer Science and Engg. CUET Chittagong Univ. of Eng. And Tech. Bangladesh

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

A nation’s development and well-being depend on a healthy mother and newborn child. There were no ICT-based smartphone applications with pregnancy detection, health status prediction for the mother and newborn, or online mom and baby goods buying features in previous maternity and infant care-related works. Additionally, they did not offer any support systems for the detection of postpartum depression in women, search for health professionals, ambulances, infant day care centers, caregivers, and clinic search, as well as pregnancy calendars and recommendations, help for maternity-related queries, and emergency messaging features. To resolve these problems, this paper offers a smartphone application for maternity and baby care assistance with pregnancy detection, health status detection, mom and baby item purchase, search for health professionals and clinics, post-partum depression detection, exercise and diet advice, baby day care center search, query assistance, and emergency chat features. According to the app’s merit and impeccability study results, over 62% of reviewers provided spectacular feedback on the app’s suitability.

关键词： Pregnancy Pediatrics technological innovation Transfer learning Medical services Feature extraction Depression Search problems Method of moments Smart phones

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Multimodal Data-Driven Classification of Mental Disorders: A Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia

arXiv

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arXiv 2025年

作者： Singh, Himanshi Tiwari, Sadhana Agarwal, Sonali Chandra, Ritesh Sonbhadra, Sanjay Kumar Singh, Vrijendra Dept. of Information Technology IIIT Allahabad Uttar Pradesh Prayagraj India Dept. of Computer Science and Engg. Siksha’O’ Anusandhan University Odisha Bhubaneswar India

This study investigates the potential of multimodal data integration, which combines electroencephalogram (EEG) data with sociodemographic characteristics like age, sex, education, and intelligence quotient (IQ), to diagnose mental diseases like schizophrenia, depression, and anxiety. Using Apache Spark and convolutional neural networks (CNNs), a data-driven classification pipeline has been developed for big data environment to effectively analyze massive datasets. In order to evaluate brain activity and connection patterns associated with mental disorders, EEG parameters such as power spectral density (PSD) and coherence are examined. The importance of coherence features is highlighted by comparative analysis, which shows significant improvement in classification accuracy and robustness. This study emphasizes the significance of holistic approaches for efficient diagnostic tools by integrating a variety of data sources. The findings open the door for creative, data-driven approaches to treating psychiatric diseases by demonstrating the potential of utilizing big data, sophisticated deep learning methods, and multimodal datasets to enhance the precision, usability, and comprehension of mental health diagnostics. © 2025, CC BY.

关键词： Electroencephalography

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Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions

arXiv

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arXiv 2025年

Individuals’ general well-being is greatly impacted by mental health conditions including depression and Post-Traumatic Stress Disorder (PTSD), underscoring the importance of early detection and precise diagnosis in order to facilitate prompt clinical intervention. An advanced multimodal deep learning system for the automated classification of PTSD and depression is presented in this paper. Utilizing textual and audio data from clinical interview datasets, the method combines features taken from both modalities by combining the architectures of LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional Long Short-Term Memory).Although text features focus on speech’s semantic and grammatical components;audio features capture vocal traits including rhythm, tone, and pitch. This combination of modalities enhances the model’s capacity to identify minute patterns connected to mental health conditions. Using test datasets, the proposed method achieves classification accuracies of 92% for depression and 93% for PTSD, outperforming traditional unimodal approaches and demonstrating its accuracy and robustness. © 2025, CC BY.

关键词： Semantics

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MediVault DApp: A Electronic Health Record Application Using Blockchain technology

MediVault DApp: A Electronic Health Record Application Using...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Lingeswara Reddy Peddu Aditya Mishra Amirineni Rama L Padmaja Rekha G Palanikumar S R. K. Pongiannan Dept. of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of ECE Geethanjali College of Engg. And Tech. Hyderabad India Dept. of Computer science and business systems Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engg. College Avadi Chennai Tamilnadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The healthcare industry stands to benefit greatly from blockchain technology due to its robust security, privacy protection, confidentiality, and decentralized nature. This study outlines how blockchain can revolutionize current medical data systems and proposes a framework for its implementation. The primary objective of this framework is to ensure secure storage of electronic records by establishing comprehensive access protocols for users. When patients seek medical care, hospitals generate and store patient records in a decentralized application (DApp), which is a blockchain- based platform for maintaining electronic medical records. Medical professionals can modify patient records, while patients can retrieve their medical information from the DApp using public keys. Electronic Health Records (EHRs) allow healthcare providers to monitor information necessary for compliance with insurance companies and government regulations. EHRs serve as a centralized data repository for storing doctors' orders related to laboratory tests, x-rays, and other diagnostic procedures. The proposed system aims to optimize individual medical information management, minimize redundant testing, reduce treatment delays, and equip patients with adequate information to make informed decisions.

关键词： Privacy Scalability Medical services Decentralized applications Regulation Blockchains InterPlanetary File System Electronic medical records Usability Medical diagnostic imaging

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SWATI AI : Advancing AI Models from Rule-Based Frameworks to NLP-Driven Prescriptive Analytics to Assist Victims of Domestic Abuse

SWATI AI : Advancing AI Models from Rule-Based Frameworks to...

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AIML-Applications for Engineering & technology (ICAET), International Conference on

作者： Dipali Dilip Awasekar L.M.R.J. Lobo Dept. of Computer Science and Engg. WIT-Solapur Punyashlok Ahilyadevi Holkar Solapur University Solapur Maharashtra India Dept. of Information Technology Walchand Institute of Technology Solapur Maharashtra India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

SWATI AI (Support and Welfare Assistance through technology Interface) is an AI-driven chatbot designed to assist victims of domestic abuse by providing accessible, non-judgmental, and actionable legal guidance. This paper introduces the LAMP 2 (Legal Analytics Model for Prediction and Prescription), the core framework that powers SWATI, progressing through four distinct research phases aimed at enhancing decision-making capabilities for victims of domestic violence under the Protection of Women from Domestic Violence Act, 2005. Phase 0 focuses on the creation of a curated dataset, PROTECT-JC, which compiles judgments from district courts, high courts, and the Supreme Court of India between 2008 and 2024. This dataset, developed in collaboration with legal experts and judicial professionals, serves as the foundation for training models to predict legal outcomes. Phase 1 introduces a rule-based decision framework in LAMP 2 1.0, designed to extract and map predefined intents and entities from user inputs. This phase enables the structured documentation of complaints, providing victims with initial guidance based on the legal provisions of the Domestic Violence Act, 2005. The rule-based approach achieved accuracy of 80%, but with limited customization for individual cases. In Phase 2, LAMP 2 2.0 leverages supervised machine learning models to predict legal outcomes, such as protection orders, custody orders, and monetary relief. These models, including Random Forest, XGBoost, and Decision Trees, achieved impressive results, with the Random Forest model showing 96.5% accuracy in predicting protection orders. Phase 3 evolves the model further with NLP-driven predictive analytics to process unstructured legal narratives, such as case judgments, transforming them into structured data for improved predictions. Models like Llama and Mixtral demonstrated strong summarization capabilities and show promise in predicting legal results. The integration of NLP and machine learning

关键词： Training Support vector machines Analytical models Accuracy Law Predictive models Chatbots Decision trees Protection Random forests

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Hybrid Quantum Approximate Optimization Algorithm (HQAOA) for Efficient Blockchain Transaction Scheduling

Hybrid Quantum Approximate Optimization Algorithm (HQAOA) fo...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Soumyadip Paul Sarit Chakraborty Dept. Of Information Technology Government College of Engg. & Ceramic Technology Kolkata WB India Dept. of Computer Science and Engineering Government College of Engineering and Leather Technology Kolkata WB India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Quantum technology offers a transformative approach to solving complex computational challenges in decentralized systems, particularly in blockchain transaction scheduling. Efficient transaction scheduling is critical in distributed ledger systems, which often face challenges like dynamic transaction loads. This work proposes a novel approach namely Hybrid Quantum Approximate Optimization Algorithm (HQAOA) which combines Multi-Agent QAOA (MA-QAOA) and Adaptive Layer QAOA (AL-QAOA) to provide a robust solution to such issues. HQAOA leverages a decentralized optimization framework where multiple agents, each representing a node in the network, optimize local transaction scheduling while considering global constraints. Additionally, HQAOA adapts to varying transaction loads by dynamically adjusting the number of quantum layers for each agent, ensuring computational efficiency under different conditions. Such dynamic layer adjustment mechanisms mitigate the adverse effects of quantum noise and ensures optimal performance in scenarios with fluctuating transaction loads. In contrast to traditional QAOA, which struggles with noise and scaling issues, HQAOA provides improved performance by balancing the complexity of the problem with available quantum resources. Experimental results signify the future potential of HQAOA to outperform QAOA, particularly in noisy environments and irregular transaction conditions. This work demonstrates the potential of HQAOA to optimize blockchain transaction scheduling in decentralized systems.

关键词： Quantum computing Distributed ledger Processor scheduling Heuristic algorithms Scalability Noise Dynamic scheduling Approximation algorithms Blockchains Complexity theory

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