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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Shuvo, Mahfuzur Rahman Rahman, Ashifur Akuthota, Vishwanath Paul, Tanay Islam, Mahfujul Ashraf, Md Sadi Roy, Prosenjit Reza, Md Tanzim International American University Department of Management Information System United States Chittagong University of Engineering and Technology Department of Computer Science and Engineering Bangladesh TechOptima Hyderabad India Brac University Department of Computer Science and Engineering Bangladesh Prairie View A & M University Department of Computer Science TX United States

ISBN: (纸本)9798331513894

The automobile service industry's explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance system, integrating Classical Machine Learning (ML) techniques with Generative AI to optimize garage service discovery and analysis. The system employs sophisticated data processing methods, including Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and regex-based service detection, to extract actionable insights from unstructured garage *** to the system are machine learning models Random Forest (RF) and XGBoost (XGB) which achieve high precision and recall in classifying garage services. A hybrid search mechanism, combining cosine similarity with ML-driven predictions, ensures the delivery of highly personalized search results. To further refine decision-making, the system incorporates Generative AI models such as Perplexity for web-based research, Gemini for location-specific analysis, Mistral for email sending and GPT-4 for detailed service recommendations and dall-e for creating user specific parts images. These advanced tools provide users with comprehensive information that enables them to make well-informed decisions about garage *** evaluation of the system is conducted using robust metrics, including precision, recall, F1-score, and system latency. Experimental results reveal a precision of 85%, recall of 70.8%, and an F1-score of 77.2%, demonstrating the efficacy of integrating classical ML with generative AI. The system's average latency of 5.9 seconds ensures a seamless and responsive user *** hybrid framework highlights the potential of blending classical ML and Large Language Models (LLMs) to enhance search and recommendation functionalities, offering a scalable and robust blueprint for future advancements in the automotive service sector. The system's Propose a Multi-Agent system With high accuracy,

关键词： Inverse problems

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Customized IoT based Smart Irrigation for Terrace Plants 6

Customized IoT based Smart Irrigation for Terrace Plants

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

作者： Priya, S. Padma Pavalarajan, S. Abarna, P.S. Manjula devi, C. Abinaya, B. Sukaina, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India Department of Information Technology Velammal college of Engineering and Technology Madurai India

ISBN: (纸本)9798331522667

An Internet of Things based smart irrigation system for terrace plants is a cutting-edge solution designed to automate and optimize the watering process, ensuring efficient use of water and promoting healthier plant growthSmart irrigation system may vary from field with crops of same variety to terrace plantation with plants of different varieties. As the water requirement may vary based on plant type, environmental condition, growth stage, it is intelligent to provide varying water supply. This Smart irrigation system employs soil moisture to monitor the water content of the terrace garden in real time. Some variety of plants may require minimum quantity of water. The sensors continuously collect data and send it to a central IoT platform or a cloud-based server, where intelligent algorithms analyse the data and determine the precise amount of water needed by the plants. Based on this analysis, the system automatically activates the irrigation mechanism whether it's a drip system, sprinklers, or any other appropriate watering method only when required, thus preventing water wastage and avoiding overwatering or underwatering. © 2025 IEEE.

关键词： Sprinkler systems (irrigation)

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An effective object detection via BS2ResNet and LTK-Bi-LSTM

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Multimedia Tools and Applications 2025年 1-20页

作者： V, Arulalan V, Premanand Kumar, Dhananjay Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering Vellore Institute Technology Chennai Campus Tamil Nadu Chennai India Department of Information Technology Anna University MIT Campus Tamil Nadu Chennai India

Video analytics faces complex challenges in object detection and classification. Deep learning based approaches have achieved remarkable success in past decade. However, existing object identification models that utilize backbone’s core features still present challenges due to their lack of semantic information. To address these issues, a novel object detection and classification framework utilizing Batch normalization and Softswish activation adapted ResNet (BS2ResNet) and Logistic Tanh Kaiming Bi-directional Long Short Term Memory (LTK-Bi-LSTM) techniques was proposed. The framework employs frame conversion, noise removal, and contrast elevation during frame pre-processing, followed by background subtraction using the Supreme Distance-centered Fuzzy C-Means (SD-FCM) clustering algorithm, and edge detection using the Hyperbolic Tangent Kernel Canny Edge Detector (HTKCED). BS2ResNet is then used for object detection, and features are extracted and passed to the LTK-Bi-LSTM neural network for object classification. The proposed system was found to improve object detection and classification accuracy, outperforming existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Multimodal Evidential Learning for Open-World Weakly-Supervised Video Anomaly Detection

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IEEE Transactions on Multimedia 2025年 27卷 3132-3143页

作者： Huang, Chao Huang, Weiliang Jiang, Qiuping Wang, Wei Wen, Jie Zhang, Bob Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518000 China University of Macau PAMI Research Group Department of Computer and Information Science China Ningbo University Faculty of Information Science and Engineering Ningbo China Harbin Institute of Technology Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen518055 China

Efforts in weakly-supervised video anomaly detection center on detecting abnormal events within videos by coarse-grained labels, which has been successfully applied to many real-world applications. However, a significant limitation of most existing methods is that they are only effective for specific objects in specific scenarios, which makes them prone to misclassification or omission when confronted with previously unseen anomalies. Relative to conventional anomaly detection tasks, Open-world Weakly-supervised Video Anomaly Detection (OWVAD) poses greater challenges due to the absence of labels and fine-grained annotations for unknown anomalies. To address the above problem, we propose a multi-scale evidential vision-language model to achieve open-world video anomaly detection. Specifically, we leverage generalized visual-language associations derived from CLIP to harness the full potential of large pre-trained models in addressing the OWVAD task. Subsequently, we integrate a multi-scale temporal modeling module with a multimodal evidence collector to achieve precise frame-level detection of both seen and unseen anomalies. Extensive experiments on two widely-utilized benchmarks have conclusively validated the effectiveness of our method. The code will be made publicly available. © 1999-2012 IEEE.

关键词： Self-supervised learning

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Secure-by-Construction Synthesis for Control systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Document Summarizer: A Machine Learning approach to PDF summarization 4

Document Summarizer: A Machine Learning approach to PDF summ...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Peddarapu, Rama Krishna Anogna, Sudagoni Harshini, M.N.S. Sri Sireesha, J. Rathna, K. Guna Department of Computer Science and Engineering Faculty of Computer Sciences and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India

To address the need for summarizing and extracting information efficiently, this paper highlights the growing challenge posed by the increasing number of PDF files. Reading lengthy documents is a tedious and time-consuming task. To save time and quickly comprehend the key points of a PDF, a PDF summarizer tool has been developed to tackle these issues. In today's professional environments, gathering and managing data from documents is critical. This article introduces an innovative solution called DocSum, which automates the process of summarizing extracted data. Powered by *** Core, the DocSum system allows users to upload PDF documents for processing and receive concise summaries in return. The system features a user-friendly interface that encourages interaction and engagement, utilizing AI and machine learning techniques to streamline document handling. Users can request specific summaries, enabling efficient document management workflows. By empowering users to seamlessly interact with vast amounts of information, DocSum enhances productivity and explores new ways to optimize document management. This solution is ideal for professionals seeking to stay informed and manage data effectively, while also keeping track of advancements in document handling technologies across multiple industries. It is not only helpful for professionals but also helpful for students who need quick revision or examinations, teachers to gain brief knowledge on the concepts. © 2025 The Authors. Published by Elsevier B.V.

关键词： Data handling

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An optimal feature selection-based deep learning approach for wheat disease identification

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Multimedia Tools and Applications 2025年第14期84卷 12855-12876页

作者： Raja, S. M. Naveen Nargunam, A. Shajin Department of Information Technology Noorul Islam Centre for Higher Education Thuckalay Kumaracoil India Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Thuckalay Kumaracoil India

Globally, wheat is a staple food crop, which plays a vital role in food security and sustenance for millions of people. However, the diseases in wheat crops affect their growth, productivity, and crop quality. Hence, early and timely disease detection is significant to prevent the loss of productivity and the economy. This article introduces an innovative hybrid model combining Moth Flame Optimization (MFO) and Radial Basis Functional Neural Network (RBFNN) for the prediction of disease wheat leaves from healthy ones. Initially, the wheat leaf image containing the normal and diseased leaves is collected and utilized as input for the system. Then, the dataset was filtered and related leaf attributes are tracked using the Histogram of Oriented Gradients. Subsequently, MFO fitness was employed to select the most informative leaf images from the extracted feature set. By utilizing these selected features, the RBFNN was trained to classify the normal and healthy leaves accurately. This model was evaluated with the wheat leaf dataset and the performances are evaluated. The presented model achieved greater accuracy 99.32%, recall of 99.33%, f-measure of 99.33%, and precision of 99.33%. The implementation outcomes exhibit that the designed framework has great potential for practical application in agriculture. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Machine Learning-Based Approach to Predict Heart Diseases Using Fused Dataset 3rd

Machine Learning-Based Approach to Predict Heart Diseases Us...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Uddin, Khandaker Mohammad Mohi Mohamed, Abdirahman Biswas, Nitish Mostafiz, Rafid Department of Computer Science and Engineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1205 Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh

ISBN: (纸本)9789819739363

Cardiovascular disease is currently a prominent sickness that kills the majority of sufferers. The medical evaluation of cardiac disease presents significant problems. This diagnostic method is complex, requiring precision and efficiency. Early detection of heart disease can significantly lower the chance of death. In light of the high occurrence of cardiac issues in contemporary times, the prediction of heart disease has emerged as one of the most challenging endeavors within the medical field in recent years. Scientists examined a plethora of closely related characteristics to identify the most trustworthy predictors of these diseases. In this work, we use Machine Learning (ML) approaches to detect the existence of cardiac problems. The suggested method predicts the likelihood of heart disease and classifies people into risk categories. This is performed by utilizing several machine learning algorithms such as Support Vector Machine (SVM), Gradient Boosting, Random Forest (RF), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Logistic Regression (LR). The developed system is trained and evaluated using a composite dataset derived from two separate sources. The results of the experiments show that, when compared to other machine learning algorithms, the Random Forest approach achieves the greatest accuracy rate, achieving an amazing 99.99%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Developing a Translation Model to Bridge ASL and Indian Regional Languages Using Neural Networks

Developing a Translation Model to Bridge ASL and Indian Regi...

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

作者： Swetha, M.S. Muneshwara, M.S. Venkatesh, Varnitha Kudremane, Shreyaa S. M. Nair, Ritika S Vismitha, H. Bms Institute of Technology & Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology & Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

In this resarch work, a real-time hand gesture detection system for Ame rican Sign Language (ASL) that enables the deaf-dumb co mmun ity to interact with others by translating sign language into text. The current methods for translating ASL to English primarily use computer vision tools like Media Pipe and OpenPose to recognise hand landma rks and motions. To tackle this issue, we provide a novel convolutional neural network (CNN) architecture that removes the need for manual feature engineering by automatically obtaining discriminative spatial-temporal characteristics from raw video streams. CNNs are more suited to handle the dynamic and highly changeable nature of hand movements because they are very good in simu Itaneously recording spatial and temporal informat ion, unlike LSTM (Long Short-Term Memory) networks, which are frequently emp loyed for sequential data. CNNs are perfect for rea l-t ime applications because they provide more efficient processing and faster inference time. By using multilingual language translators and advanced CNNbased deep learning models, we aim to reduce the communication gaps across India's diverse linguistic minorities. To ensure accurate performance evaluation and successful model learning, the dataset was divided into 70 % for tra inin g and 30 % for testing. Compared to tradit ional methods, the proposed approach has a 95% accuracy rate. With potential uses in public services, healthcare, and education, this program encourages assistive technology by offering a practical tool to improve communicat ion between sign language users and the general public. Through encouraging interlanguage contact, the system helps create a society that is more inclusive. © 2025 IEEE.

关键词： Convolutional neural networks

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