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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Anbumalar Pandian S Shruthi V Dhanush Sriranganayaki Sundarajan P Abishek Sutha Subbian Pappa Natarajan Dept. of Instrumentation Engineering MIT Campus Anna University Chennai India Dept. of Artificial Intelligence and Data science Meenakshi Sundararajan Engineering college Chennai India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Precise measurement of State of Charge (SoC) in Liion battery is crucial for effective utilization of energy storage system. However, it is challenging with physics-based model due to computation complexity while data-driven model often lacks generalization for unseen data. To improve the robustness of data-driven model, hybrid algorithms are employed. This paper focuses on combining Temporal Fusion Transformer (TFT)Long Short-Term Memory (LSTM) to improve the generalization of state-of charge estimation of unseen drive cycles. By leveraging the attention mechanism feature, temporal feature extraction of TFT along with memory capability of LSTM TFT-LSTM model is developed with optimal hyperparameters using Optuna framework. This model is used to estimate the SoC during the discharging cycle of a Turnigy Graphene Li-Ion Battery ($5000 \mathrm{mAh}, \mathbf{6 5 C}$) for the LA92 drive cycle at an ambient temperature of $40^{\circ} \mathrm{C}$. Additionally, uncertainty analysis is performed against US06 drive cycle. Finally, the performance of TFT-LSTM is compared with LSTM model. The results demonstrate that TFT-LSTM achieves better performance compared to LSTM with Root Mean Square Error (RMSE) of $\mathbf{1. 1 9 6 2}$ for unseen US06 drive cycle. These findings demonstrate the robustness of hybrid TFTLSTM against unseen drive cycle and highlight its potential under realistic conditions with variability.

关键词： Lithium-ion batteries Adaptation models Uncertainty computational modeling Estimation Drives Transformers Robustness Thin film transistors State of charge

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Can Large Language Models Predict the Outcome of Judicial Decisions?

arXiv

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arXiv 2025年

作者： Kmainasi, Mohamed Bayan Shahroor, Ali Ezzat Al-Ghraibah, Amani Computer Science and Engineering Dept. Qatar University Doha Qatar School of Computing and Data Science OUC in partnership with LJMU Doha Qatar Biomedical Engineering Dept. Jordan University of Science and Technology Irbid Jordan

Large Language Models (LLMs) have shown exceptional capabilities in Natural Language Processing (NLP) across diverse domains. However, their application in specialized tasks such as Legal Judgment Prediction (LJP) for low-resource languages like Arabic remains underexplored. In this work, we address this gap by developing an Arabic LJP dataset, collected and preprocessed from Saudi commercial court judgments. We benchmark state-of-the-art open-source LLMs, including LLaMA-3.23B and LLaMA-3.1-8B, under varying configurations such as zero-shot, one-shot, and fine-tuning using LoRA. Additionally, we employed a comprehensive evaluation framework that integrates both quantitative metrics (such as BLEU, ROUGE, and BERT) and qualitative assessments (including Coherence, Legal Language, Clarity, etc.) using an LLM. Our results demonstrate that fine-tuned smaller models achieve comparable performance to larger models in task-specific contexts while offering significant resource efficiency. Furthermore, we investigate the impact of fine-tuning the model on a diverse set of instructions, offering valuable insights into the development of a more human-centric and adaptable LLM. We have made the dataset, code, and models publicly available to provide a solid foundation for future research in Arabic legal NLP1 Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Improving the accuracy of sentiment analysis using a linguistic rule-based feature selection method in tourism reviews

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Measurement: Sensors 2023年 29卷

作者： Saraswathi, N. Sasi Rooba, T. Chakaravarthi, S. Dept. of Computer Science and Engineering Annamalai University Annamalainagar 608002 India Dept. of Artificial Intelligence and Data Science Panimalar Engineering College Poonamallie Chennai600123 India

Sentiment Analysis technique involves extracting the relevant information from Unstructured User Reviews (UUR) dataset fetched from online and classifying them into appropriate positive and negative comments for making decisions. In UUR, data may be in noisy state, irrelevant features exist which creates high dimensional feature space. To design an effective sentiment learning model, users are required to extract the most relevant sentiment features from UUR. To overcome the issue, we proposed a Linguistic rule based feature selection method for extracting and selecting the sentiment features for Sentiment Analysis as it improves the predictive performance of classification algorithms. The proposed novel feature selection method involves identifying the various sentiment features in the review dataset by using filtering methods such as POS tags, n-grams. In the ensemble model, where the Random Forest classification algorithm is trained for textual sentiment classification, the chosen sentiment feature sets are used. Finally, we test our approach using the real-time review dataset that was collected from a multitude of sources, and the results demonstrate prediction accuracy that is superior to that of existing Sentiment analysis techniques. © 2023 The Authors

关键词： Sentiment analysis

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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2024 International Conference on Advances in data engineering and Intelligent Computing Systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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Image to Audio Using LLM for Blind People

Image to Audio Using LLM for Blind People

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data, Computation and Communication (ICDCC), International Conference on

作者： Kumaresan T Geetha A Rajeshkumar G Benita Gracia Thangam J Malathi Eswaran Thivya Rajkumar Dept. of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Dept. of Computer Science and Engineering Presidency University Bangalore India Dept. of Artificial Intelligence and Data science Velalar college of Engineering and Technology Erode India Dept. of Computer Technology Kongu engineering College Perundurai India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

Blind people face many difficulties while engaging with their surroundings. This project is all about creating innovative solutions for the blind people by providing the information in the format of audio. Image-to-Audio transformation system specifically designed for natural language understanding using Large Language Model (LLM).The system will capture a picture or video of the situation present in front of the blind person and then provide descriptions about the images. The objective is to provide guidelines for blind people by understanding their surroundings through an LLM model and provide audio response. The CLIP model analyzes the objects, scenes, text etc.. and provides relevant information. The extracted visual data is then passed through GTTs (Google-Text-To-Speech) to create rich audio descriptions. It provides accurate and detailed representation of the visual content, allowing blind people to understand their surroundings. The project involves integrating convolutional neural networks and Large Language Model (e.g GPT-4) technologies to create a real-time image-to-text system for blind individuals. The CNN model is used to calculate the distance of the object from the user. We are integrating the Google Assistants to open the app automatically by the blind people with help of their voice.

关键词： Visualization Navigation Large language models Blindness Real-time systems Natural language processing Internet Convolutional neural networks Multimedia communication Monitoring

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Voice AI-Intelligence Based Voice Assistant

Voice AI-Intelligence Based Voice Assistant

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Pooja Garai Aditya Nikam Suyash Kerkar Samruddhi Deshmukh Atharva Mane Suvarna Bhise Dept. of Artificial Intelligence & Data Science Vasantdada Patil College Of Engineering & Visual Arts Mumbai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's Machine Learning Kit Machine learning(MLKit) for Optical Character Recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications.

关键词： Measurement Productivity Accuracy Optical character recognition Personal voice assistants Machine learning Internet Time factors Smart phones Bars

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The Loser’s Curse and the Critical Role of the Utility Function

arXiv

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arXiv 2024年

作者： Brill, Ryan S. Wyner, Abraham J. Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania United States Dept. of Statistics and Data Science The Wharton School University of Pennsylvania United States

A longstanding question in the judgment and decision making literature is whether experts, even in high-stakes environments, exhibit the same cognitive biases observed in controlled experiments with inexperienced participants. In their seminal work, Massey and Thaler (2013) provide a notable example of bias and irrationality in expert decision making: general managers’ behavior in the National Football League draft pick trade market. They argue that general managers systematically overvalue top draft picks, which generate less surplus value on average than later first-round picks, a phenomenon known as the loser’s curse. Their conclusion hinges on the assumption that general managers should use expected surplus value as their utility function for evaluating draft picks. This assumption, however, is neither explicitly justified nor necessarily aligned with the strategic complexities of constructing a National Football League roster. In this paper, we challenge their framework by considering alternative utility functions, particularly those that emphasize the acquisition of transformational players––those capable of dramatically increasing a team’s chances of winning the Super Bowl. Under a decision rule that prioritizes the probability of acquiring elite players, which we construct from a novel Bayesian hierarchical Beta regression model, general managers’ draft trade behavior appears rational rather than systematically flawed. More broadly, our findings highlight the critical role of carefully specifying a utility function when evaluating the quality of decisions. © 2024, CC BY.

关键词： Hierarchical systems

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Advancements in Environmental data Analysis for Climate-Resilient Agriculture Using Remote Sensing and Deep Learning

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 670-680页

作者： Nirmal, Khyati R. Reddy, Chilukala Mahender P, Jose Tulasi, Rajesh Maheswari, G. Uma Sapthami, I. SNJB’S Late Sau KBJ CoE Maharashtra Nashik India Department of Artificial Intelligence and Data Science GITAM Deemed to Be University Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai Avadi India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P Vaddeswaram Guntur Dist. India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Pudhuvoyal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Weather significantly influences agricultural productivity. Plant biotic and abiotic stressors are primarily induced by climate change, resulting in a detrimental effect on worldwide agricultural productivity. These two components are closely interconnected. This paper presents an innovative approach in smart agriculture for climate change combining remote sensing and a deep learning algorithm. The input is obtained as a multispectral environmental image and then subjected to noise reduction and normalisation processing. The image has been retrieved using a primary convolutional component with a stacked encoder model. The retrieved features are identified using ResNet graph reinforcement neural networks. The resulting classification output displays environmental imagery depicting climate variations. The agriculture sector has been analysed based on the classified climate analysis. The experimental results have been conducted on diverse farm datasets related to climate change, evaluating the detection accuracy, recall, mean average precision, normalized correlation, and F-1 score. The proposed method achieved a detection accuracy of 98%, a normalised correlation of 95%, a mean average precision of 92%, a recall of 97%, and an F-MEASURE of 94%. Machine learning can assist in monitoring and predicting the impact of climate change on food security, as indicated by the findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change Deep learning model Remote sensing Smart agriculture Stacked encoder model

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DigiFarmer: Precision Agriculture Empowered by Machine Learning for Sustainable Crop Management

DigiFarmer: Precision Agriculture Empowered by Machine Learn...

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Networking and Communications (ICNWC), International Conference on

作者： Karan Devanand Patra Arya Adesh Bhosle Deepali Mukesh Kothari Tatwadarshi P. Nagarhalli Dept. Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In agriculture, the sector grapples with pressing challenges arising from climate change, water shortages, and pest infestations. Particularly affecting farmers in developing nations, these challenges amplify issues of food insecurity and poverty, necessitating innovative solutions for sustainable global agricultural progress. Addressing the complexities of climate variability, water scarcity, and pest outbreaks requires comprehensive tools and information. The absence of predictive insights intensifies crop losses and hampers overall profitability. In response, this research introduces DigiFarmer, an innovative solution employing advanced machine learning models. DigiFarmer adopts a holistic approach, encompassing crop disease detection, weed classification using Convolutional Neural Networks (CNNs), and crop quality analysis with a Light Gradient Boosting Machine (LGBM) classifier. Yield prediction is facilitated by the Random Forest algorithm, with real-time weather data integration enhancing efficacy. DigiFarmer stands out for empowering farmers with tools for sustainable and resilient agriculture. Leveraging cutting-edge technologies, DigiFarmer not only addresses immediate challenges but equips farmers with foresight to mitigate future risks. The ultimate goal is to contribute to the welfare of global farming communities by enabling informed decisions that enhance productivity, profitability, and sustainability in agriculture.

关键词： Productivity Adaptation models Machine learning algorithms Profitability Crops Boosting Prediction algorithms

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Decoding the News: An Integrated Approach for News Text Classification and Expert System Recommendations

Decoding the News: An Integrated Approach for News Text Clas...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Vivek Prajapati Arpit Mishra Prathmesh Shinde Neha Raut Dept. of Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India

In the era of information overload on online platforms, efficient access to news content has become crucial for individuals leading busy lives. This paper addresses the need for categorizing vast amounts of online news data into specific sections like sports, entertainment, business, finance, etc., catering to users’ personalized interests. Leveraging Machine Learning and Deep Learning algorithms, the study utilized a BBC News dataset from Kaggle and supplemented it with an additional dataset generated through web scraping using BeautifulSoup. With up to 20,000 rows covering sports, business, entertainment, politics, and technology categories, the research applied Random Forest, Support Vector Machine (SVM), Gaussian Naive Bayes (NB), and Multi-Head Attention Transformer algorithms. The findings and recommendations extend to technologies suitable for both news text classification and user-specific content recommendation in this evolving digital landscape.

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