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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pavan Kalyan, S. Arun Kumar, B.R. Satish Kumar, T. Prem Raj, S. Patil, Pratik B. Harshini, Pothireddy Bms Institute of Technology and Management Dept. Computer Science and Engineering Karnataka Bangalore5600119 India

ISBN: (纸本)9798331538538

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments. © 2025 IEEE.

关键词： Digital differential analyzers

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An optimal group-wise non-rigid image registration technique based clinical decision support for dementia prediction

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Multimedia Tools and Applications 2025年 1-26页

作者： Mary, F. Sheeja Asanambigai, V. Fred, A. Lenin Assistant Professor Senior Grade School of Computing Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Computer Science and Engineering Annamalai University Chidambaram India Dept. of CSE Mar Ephraem College of Engineering and Technology Marthandam India

The fundamental challenge in clinical practice is predicting whether patients with cognitive symptoms or impairment will improve or stay stable. Population ageing is a worldwide phenomena with various impacts. Among dementia, Alzheimer disease (AD) is common in the elderly which is the most common form of dementia and may contribute to 60–70% of cases. An early and precise AD diagnosis enhances patient quality of life. In this study, we propose an optimal group-wise non-rigid image registration technique for dementia (AD) prediction. We introduce an improved cuckoo search optimization (ICSO) algorithm to search optimal group-wise dense correspondence in large image sets in non-rigid image registration which reduce dimensionality of the search space. Then, we illustrate a teacher-student learning based optimization algorithm which cooperates with PLS regression to extract deformation which separates the relationship information suitable for each other from the raw relationship information. After that, we compute the distribution of morphologies of age by fitting smooth percentile curves to these scores using hybrid capsule neural network with least Median Square (hybrid CNN-LMS) curve fitting model. Finally, the simulation results of proposed hybrid CNN-LMS technique are compared to the existing methods to analyze the measures. We observed that the accuracy of our proposed hybrid CNN-LMS technique is 96.23% and 95.82% for RSS and ANDI datasets respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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An Analysis of Various Classification Algorithms for Fake News Detection 2

An Analysis of Various Classification Algorithms for Fake Ne...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Khare, Sakshi Singh, Pushpa Kumar, Pankaj Dept. of Computer Science and Engineering G. L. Bajaj Institute of Technology & Management Greater Noida India

ISBN: (纸本)9798331530389

With the expansion of social media and advanced stages, the spread of fake news has ended up a noteworthy societal issue. This paper presents a comprehensive outline of machine learning strategies utilized for the detection of fake news. We talk about different approaches such as Natural Language Processing (NLP), unsupervised learning calculations, and machine learning models utilized within the recognizable proof of fake news. We dive into highlight designing, opinion investigation, and organize examination as key components of fake news detection frameworks. Moreover, we look at datasets, assessment measurements, and challenges related with detection of fake news. Through this analysis, we point to supply bits of knowledge into the current state-of-the-art techniques and headings for future investigate in combating fake news dispersal. This paper mainly used K-Nearest Neighbor, Jaccard similarity, LSTM, Decision Tree, and Logistic regression Classifiers for the classification. Main objective of this paper to verify the accuracy of models by performing the pre-processing the data and handle the unbalanced data more appropriately. The results of this papers show comparison of all classifiers with accuracy. Our results shows that KNN achieves 83%, 84% for Logistic Regression, 84% for Decision Tree, 85% for Jaccard Similarity & Random Forest is 85% and 98% for LSTM in our experiment. © 2025 IEEE.

关键词： Adversarial machine learning

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Edge Node Load Balancing Using Game Theoretic Approaches 5

Edge Node Load Balancing Using Game Theoretic Approaches

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Haldar, Rajdeep Rooj, Saptadip Sharma, Neeraj Das, Ritambhar Mandal, Arnab Bandyopadhyay, Anjan Dept. of Information Technology Bhubaneswar India Lovely Professional University School of Computer Science and Engineering Jalandhar India School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

This paper explores the application of game theoretic approaches to load balancing across edge nodes within distributed computing systems. Focusing on a non-cooperative game model, we aim to enhance system efficiency by strategically optimizing resource allocation and load distribution among autonomous edge nodes. Initial simulations demonstrate that our approach not only addresses the inherent complexities of edge environments but also outperforms traditional load balancing mechanisms in terms of both operational efficiency and system responsiveness. © 2025 IEEE.

关键词： Edge computing

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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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Decision Support System for Crop Management using Convolutional Neural Network 3

Decision Support System for Crop Management using Convolutio...

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

作者： Immaculate Mercy, A. Ruby, D. Logesh, T. Shahidha Shireen, S. Periyar Maniammai Institute of Science & Technology Dept. of Computer Applications Tamilnadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Effective crop management is critical for enhancing agricultural productivity and sustainability. Traditional farming methods often face challenges in accurately forecasting yields, optimizing resources, and determining the right harvest timing. To address these challenges, this study develops a decision support system for rice farming in South India by utilizing deep learning techniques, specifically convolutional neural networks (CNN s), combined with dimensionality reduction methods. The system uses a comprehensive dataset that includes factors such as rainfall, temperature, soil type, and rice variety to predict rice yield. Additionally, it optimizes the use of resources such as water, fertilizer, and labor on the basis of expected yields. The proposed CNN model, coupled with squared exponential kernel PCA (SEKPCA), achieved a prediction accuracy of 83.12% and an F1 score of 0.63, enabling farmers to make informed decisions and improve productivity. The system also evaluates resource requirements, offering insights into water usage, fertilizer, and labor needs. This approach enhances agricultural efficiency, reduces waste, and promotes sustainable farming practices. By integrating advanced deep learning technologies into traditional agriculture, the system contributes to more precise yield predictions and better resource management, paving the way for improved crop management and sustainability in rice farming in South India. © 2025 IEEE.

关键词： CNN Crop yield Dimensionality reduction DSS Rice crops SEKPCA

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Exploring the Impact of Conditional Deep Convolutional Generative Adversarial Networks in Brain Tumor Image Classification: A Novel Approach

Exploring the Impact of Conditional Deep Convolutional Gener...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kuj Bihari Dhondiyal, Shiv Ashish Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331508685

In this paper, we explore the impact of conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) with brain tumor image classification and introduce a new way to improve diagnosis accuracy. Shortage of Data/Data Imbalance: In Medical Imaging datasets, it helps in surging the data scarcity and class imbalance with mimicking generation images using cDCGAN. The detailed investigation demonstrates that synthetic data effectively improves classifier performance by acting as a form of augmentation, providing additional training examples for classifiers and serving as ground truth. From the computational efficiency analysis, we see that there is also scalability in training time with respect to dataset size. Consequently, this study presents cDCGANs as an important asset with which to improve diagnostic accuracy through brain tumor categorization in clinical contexts. © 2025 IEEE.

关键词： Generative adversarial networks

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Term Deposit Customer Prediction: A Blending Approach Using Bank Marketing Data

Term Deposit Customer Prediction: A Blending Approach Using ...

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

作者： Prasad, Rahul Choubey, Mohit Baghel, R.K. Naruka, Aaditya Upes Dehradun Cluster of Computer Science India Maulana Azad National Institute of Technology Dept. of Ece Bhopal India Poornima University Dept. of Se Jaipur India

ISBN: (纸本)9798331529833

The global COVID-19 pandemic led to a significant economic downturn, severely impacting financial systems and economies around the world. Widespread lockdowns and travel restrictions disrupted supply chains, forced business closures, and triggered job losses, highlighting the importance of sound financial planning. Term deposits have emerged as a preferred strategy for risk mitigation. In marketing, direct customer outreach has increasingly replaced traditional intermediary-based approaches. Using data from a Portuguese bank, this study explores the predictive accuracy of various machine learning models in identifying customers most likely to invest in term deposits. Machine learning techniques applied include Support Vector Machine (SVM), Gaussian Naïve Bayes, bagging, blending, Light Gradient Boosting Machine (GBM), bagging, and Extreme Gradient Boosting (XGBoost). Results demonstrate that bagging algorithms provide superior accuracy in predicting customer behavior, offering insights to enhance targeted marketing efforts." © 2025 IEEE.

关键词： Sales

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Enhancing Livestock Supply Chains with Blockchain Traceability from Source to Market: A Survey 5

Enhancing Livestock Supply Chains with Blockchain Traceabili...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dahariya, Tukendra Kumar Sharma, Suraj Ray, Niranjan Kumar Guru Ghasidas Vishwavidyalaya Dept. Computer Science & Engineering Chhattisgarh Bilaspur India Kalinga Institute of Industrial Technology School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

This document provides an in-dept. analysis of how blockchain technology can greatly improve transparency, traceability, and accountability in fish and livestock supply chains, presenting the potential to transform this industry. Utilizing blockchain's decentralized framework, which guarantees a secure and unchangeable record of transactions, problems associated with inefficiencies, fraud, and data manipulation in conventional supply chains can be efficiently resolved. This survey analyzes the present condition of blockchain applications in fish and livestock management, emphasizing their advantages and drawbacks. Furthermore, it highlights significant technical and regulatory challenges that impede wider acceptance, while recognizing potential future research avenues that could enhance processes from the production stage to the retail stage. The results intend to assist stakeholders in developing supply chains that are more sustainable, efficient, and transparent, fostering trust and complying with regulatory requirements. © 2025 IEEE.

关键词： Records management

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Botanical Bio Vision: Advancing Plant Leaf Disease Classifications with Convolutional Neural Network 6

Botanical Bio Vision: Advancing Plant Leaf Disease Classific...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Madhuvarshini, V. Iphiya, Magdiel D. Dept. of Computer Science and Engineering St. Joseph 's Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

In recent years, the agricultural sector has encountered major challenges due to the widespread presence of plant leaf diseases, which pose serious risks to crop yields and food security. Advancements in artificial intelligence, particularly through Convolutional Neural Networks (CNNs), offer a promising solution for the precise and efficient classification of plant leaf diseases. This study delves into the development and application of a CNN model built using TensorFIow to classify a variety of plant leaf diseases. By leveraging an extensive dataset containing images of both healthy and diseased leaves, the model is trained to accurately recognize and distinguish between several disease types. This approach not only automates the process of disease identification but also significantly improves diagnostic precision and speed, enabling swift and targeted interventions. Experimental findings reveal the CNN model's effectiveness, achieving notable improvements over traditional image processing methods. This research highlights the potential for incorporating deep learning methods into agricultural practices, paving the way for more intelligent and resilient crop management systems. © 2025 IEEE.

关键词： Bacterial Count Dairy Farming Fat Content Machine learning Milk Quality Predictive Models Protein Levels Sustainability

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