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Computational Intelligence Approaches and applications (ICCIAA), International Conference on

作者： Bilal Ibrahim Sowan Ahmad Altarawneh Mohammad Fasha Dept. of Bus. Intell. & Data Analytics University of Petra Amman Jordan Dept. of Computer Science Brunel University of London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Agility and real-time responsiveness are critical for achieving competitiveness in Industry 4.0. This study introduces an innovative framework that integrates simulation-driven business intelligence and real-time analytics to enhance decision-making processes in dynamic industrial environments. The proposed framework employs advanced multi-agent systems, operational data stores, and machine learning models to process high-velocity data streams, optimize operational workflows, and provide actionable insights. By adopting a layered architecture for data integration, simulation, and decision support, the framework ensures adaptability and scalability. Experimental results demonstrate significant improvements in processing speed, predictive accuracy, and decision latency when compared to traditional approaches. applications across sectors such as manufacturing and logistics validate its effectiveness, showcasing its ability to transform raw data into valuable insights for strategic and operational decisions. This work establishes a solid foundation for future research, including blockchain integration for secure decision-making and real-time analytics in smart city ecosystems.

关键词： Smart cities Scalability Decision making Transforms Solids Real-time systems Fourth Industrial Revolution Business intelligence Streams Multi-agent systems

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Utilizing Machine Learning Techniques for Crime Classification Under the Indian Penal Code (IPC) Based on Crime Reporting data

Utilizing Machine Learning Techniques for Crime Classificati...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ponnusamy Veeramalai Sankaradass HarshivKrishna M Sre Sandhya K Bharath Sampath Aashika B Dept. of Artificial Intelligence and Data Science Madha Engineering College Chennai Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Crime classification using the Indian Penal Code (IPC) is a common task at each police station. It involves complex decision-making related to crimes and respective IPC sections for different cases. The IPC is the foundational criminal code in India, containing a list of all substantive penal laws with definitions and punishments for each offense. Automating the crime classification is helpful due to the complexity and sheer volume of crimes reported daily. This work uses machine learning techniques to automate crime classification based on IPC. To serve this purpose, this paper utilizes the K-Nearest Neighbors algorithm. The proposed system accepts an input crime report and textually corroborates the report to an IPC section. For this intent, the system relies on the bag-of-words technique for identifying sections under IPC by scrutinizing repetition and availability of key terms in the crime to 98% accuracy in classifying crimes. Hence, the automated method is quite effective for mapping crime reports into the corresponding sections of the IPC. Also, because the system can easily be deployed online, it will be easy to integrate with existing workflows at the police stations. So, case filing and management would become more effective. This reduces the amount of manual effort and also brings consistency in classification; for this reason, the tool is of immense value to law enforcement agencies nationwide. The innovation in the paper is the development of a machine learning-based system using the K-Nearest Neighbors (KNN) algorithm to automate the classification of crimes under the Indian Penal Code (IPC) based on textual crime reports.

关键词： Technological innovation Codes Accuracy Machine learning algorithms Automation Law enforcement Machine learning Nearest neighbor methods Classification algorithms System implementation

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Explainable Adversarial Attacks on Coarse-to-Fine Classifiers

arXiv

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arXiv 2025年

作者： Heidarizadeh, Akram Hatfield, Connor Lazzarotto, Lorenzo Cai, HanQin Atia, George Dept. of Electrical and Computer Engineering University of Central Florida OrlandoFL United States School of Technology Pontifícia Universidade Católica do Rio Grande Sul Porto Alegre Brazil Dept. of Statistics and Data Science Dept. of Computer Science University of Central Florida OrlandoFL United States Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida OrlandoFL United States

Traditional adversarial attacks typically aim to alter the predicted labels of input images by generating perturbations that are imperceptible to the human eye. However, these approaches often lack explainability. Moreover, most existing work on adversarial attacks focuses on single-stage classifiers, but multi-stage classifiers are largely unexplored. In this paper, we introduce instance-based adversarial attacks for multi-stage classifiers, leveraging Layer-wise Relevance Propagation (LRP), which assigns relevance scores to pixels based on their influence on classification outcomes. Our approach generates explainable adversarial perturbations by utilizing LRP to identify and target key features critical for both coarse and fine-grained classifications. Unlike conventional attacks, our method not only induces misclassification but also enhances the interpretability of the model’s behavior across classification stages, as demonstrated by experimental results. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Auto Text Correction Using NLP Techniques

Auto Text Correction Using NLP Techniques

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Nalla Srivarsha Gummadi Nithin Shanmugasundaram Hariharan Bibhuti Bhusan Dash Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Auto text correction is a fundamental tool in modern Natural Language Processing (NLP) that upgrades user involvement by decreasing typographical and linguistic errors in composed content. This extends leverages progressed NLP procedures to create a strong and proficient auto-correction framework that precisely predicts and corrects content input errors in real-time. The framework utilizes a combination of deep learning models, such as sequence-to-sequence models and transformers, in conjunction with conventional strategies like n-grams and edit distance algorithms. By analyzing the context and structure of input sentences, the model is able to offer relevant rectifications, making it versatile to different dialects and user composing styles. Moreover, we investigate diverse assessment measurements and compare different calculations to optimize the precision and productivity of the correction system. The arrangement has potential applications in word processors, mobile keyboards, and other content input platforms where high accuracy and low latency are crucial.

关键词： Productivity Program processors Keyboards Linguistics Transformers Prediction algorithms Natural language processing Real-time systems Low latency communication Mobile computing

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Explainable Adversarial Attacks on Coarse-to-Fine Classifiers

Explainable Adversarial Attacks on Coarse-to-Fine Classifier...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Akram Heidarizadeh Connor Hatfield Lorenzo Lazzarotto HanQin Cai George Atia Dept. of Electrical and Computer Engineering University of Central Florida Orlando FL USA School of Technology Pontifícia Universidade Católica do Rio Grande do Sul Porto Alegre Brazil Dept. of Statistics and Data Science Dept. of Computer Science University of Central Florida Orlando FL USA Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida Orlando FL USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Perturbation methods Decision making Signal processing Acoustics Speech processing

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AI for Smart Farming: Machine Learning Models for Precision Crop Yield Prediction

AI for Smart Farming: Machine Learning Models for Precision ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Bhavana, Munugapati Nijhawan, Ginni Raju, Hemanth Bansal, Saloni LTIMindtree Limited Digital Engineering and Assurance Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Dept. of Comp. Sc. & Engg. Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331512965

AI and other advanced technologies are gradually beginning to invade the agriculture sector to serve the increasing demand of the consumers for better methods of food production. A key component of precision agriculture, this work focuses on how to use machine learning models to predict crop yields with high accuracy. Specifically, the nature and volatility of the environmental context - such as the nature and quality of soils, as well as the climatic and water conditions - present a major challenge to the application of the conventional modeling approaches aimed at forecasting productive capacity in agriculture. Still, when comparing, the AI-driven models will allow sorting through much information gathered from different sources, including sensors, satellite imagery, and yield history. This work examines the differences of several machine learning models. Such models are the Random Forests, SVMs, and CNNs and other Deep Learning methodologies respectively. A quality model is evaluated based on scalability, computational performance, and prediction accuracy. Also in scope of the study, there is an opportunity to employ IoT together with data collected with remote sensing to make and provide realtime forecasts and evaluations. By the utilization of the results attained, it is established that the yields expected by the machine learning models are much more precise, which aids in enhancing agricultural decision-making concerning resource utilizing, crop handling, and risk minimization. The purpose of this study is focused on the promotion of the agricultural sustainability and production through the development of the innovative data driven farming practices. © 2025 IEEE.

关键词： Smart agriculture

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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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Predictive Modeling of Fire Hazard: Integrating Environmental Factors with Machine Learning

Predictive Modeling of Fire Hazard: Integrating Environmenta...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Tanjim Taharat Aurpa Samiha Maisha Jeba Khaza Minhaz Bin Monib Maherun Nessa Ela Md Shoaib Ahmed Md Musfique Anwar Dept. of Data Science and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur Bangladesh Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Boise State University Boise Idaho USA Dept. of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s fast-paced world, where technology is advancing rapidly, the tragic loss of lives due to fire accidents is not only heart-wrenching but also poses a significant risk to public safety. Early prediction of such risks can play a crucial role in educating people about fire hazards, enabling them to be better prepared in case they find themselves in such unfortunate circumstances. Our study employed various machine learning (ML) techniques and evaluated their effectiveness by analyzing different factors. These factors are derived from the diverse living conditions in Bangladesh and were gathered through surveys. To refine and identify the critical features for classification, we utilized feature selection methods like ANOVA-f and the chi-square test. Our approaches have achieved a success rate of up to 97.98%.

关键词： Surveys Accuracy Feature extraction Hazards Encoding Vectors Public security Bayes methods Analysis of variance Random forests

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Spatial variability of soil properties using geostatistical approach: a case study in alluvial soils of Samastipur district of Bihar, India

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Arabian Journal of Geosciences 2025年第5期18卷 1-13页

作者： Nidhi Katiyar, Mohit Laik, Ranjan Dept. of Statistics & Computer Applications CBS&H Dr. Rajendra Prasad Central Agricultural University Pusa Samastipur India Dept. of Soil Science PGCA Dr. Rajendra Prasad Central Agricultural University Pusa Samastipur India

In a developing country like India, the research on precision agriculture is concentrated on precision nutrient management. However, in intensive agriculture, crop production is hugely affected by micronutrient deficiency. In the intensively cultivated regions of India, the spatial variability of key micronutrients has not been adequately captured, and there is a dearth of such information. Since the availability of micronutrients is directly connected to plant uptake, it is. crucial to develop an understanding of the variations of soil micronutrients across the fields of any region to establish management zones. The present study is an attempt to evaluate the spatial variability of major soil fertility parameters, namely, pH, electrical conductivity (EC), organic carbon (OC), soil available zinc (Zn), and soil available copper (Cu) in Samastipur district of Bihar, India. The spatial variability of micronutrient availability is expected to be high in the district due to small farms and varied management. The coefficient of variation value was highest for EC (77.7%) and lowest for soil pH (6.3%). The use of geostatistical analyses has been made to determine the spatial variation structure of all the parameters, followed by the generation of surface maps through kriging. The nugget/sill ratio obtained from the experimental semivariogram indicated a moderate degree of spatial dependence (25–75%) for all the studied soil fertility parameters. The maps developed by kriging were observed to be superior to assuming the mean of the observed value for any unsampled location. These maps may help farmers to adopt need-based variable rates of fertilizer application, thus optimizing resource utilization.

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Machine Learning for Medical Image Feature Extraction

Machine Learning for Medical Image Feature Extraction

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

作者： Saurabh Pratap Singh Rathore Ali Guma Sakshi Chamoli Rayappan Lotus Yogendra Kumar Shailendra Singh Sikarwar Dept. of Management & Research ICAPSR New Delhi India Dept. of Computer & Information Science Muni University Kampala Uganda Dept. of Management Uttaranchal University Dehradun India Dept. of Computer Sci. & Engineering Vel Tech Rangarajan University Chennai India Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of CS Jiwaji University Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Feature extraction from medical images is crucial for harnessing the vast information they contain, aiding in diagnosis, treatment planning, and disease monitoring. Traditional feature extraction methods often struggle to capture the complex patterns and subtle variations in medical images. Recently, machine learning techniques have become powerful tools for automatically extracting discriminative features, enabling more accurate and efficient analysis. This paper provides a comprehensive review of advanced machine learning approaches for medical image feature extraction. It covers various methods, including deep learning architectures, convolutional neural networks (CNNs), and feature learning techniques, highlighting their applications across different medical imaging modalities such as MRI, CT, and X-ray. Our CNN model achieves an average classification accuracy of approximately 94%, outperforming the pneumonia detection accuracy of KNN (91%) and SVM (92%).

关键词： Support vector machines Pneumonia Accuracy Tuberculosis Nearest neighbor methods Feature extraction Planning Convolutional neural networks X-ray imaging Medical diagnostic imaging

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