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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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ResNet-CPDS: Colonoscopy Polyp Detection and Segmentation Using Modified ResNet101V2 1

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10th International Conference on Mathematics and Computing, ICMC 2024

作者： Islam, S. K. Hafizul Pradhan, Jitesh Vashistha, Purnendu Singh, Aman P. Kishore, Aman Department of Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani741235 India Department of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India

ISBN: (数字)9789819720699

ISBN: (纸本)9789819720682

Colorectal cancer (CRC) is a global public health concern, and early detection through screening reduces mortality rates. It is one of the common types of cancer with a high mortality rate. Traditionally, colonoscopy is used to detect CRC, which is inefficient. Therefore, an automated Colonoscopy Polyp Detection and Segmentation (CPDS) system can significantly increase the efficiency of colonoscopy. We propose an automated model, ResNet-CPDS, using the modified ResNet101V2 model. We evaluate the performance of ResNet-CPDS and other CPDS models, and compare their accuracy. We also demonstrate that the ResNet-CPDS model outperforms other models for the CVC-ClinicDB dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

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computers, Materials & Continua 2022年第11期73卷 2591-2618页

作者： Fawaz Alsolami Abdullah Saad Al-Malaise Alghamdi Asif Irshad Khan Yoosef B.Abushark Abdulmohsen Almalawi Farrukh Saleem Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028India

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen *** experts,on the other hand,are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of ***,this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and *** research analysis is based on vast data gathered from professionals and research journals,making this study a comprehensive *** solve this challenging task,the researchers used the HF AHPTOPSIS Methodology,which is a well-known and highly effective MultiCriteria Decision Making(MCDM)*** technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries,based on the recommendations of medical practitioners and *** review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as *** also perform robustness analysis to validate the conclusions of our *** a result,we obtained highly corroborative results that can be used as a *** results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand,implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our *** also has the lowest priority in the estimation.

关键词： AHP-TOPSIS hesitant fuzzy sets SARS-CoV-2 healthcare sector preventive drugs

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Implementation of Blockchain in Automotive Industry to Secure Connected Vehicle Data: Study and Analysis 3rd

Implementation of Blockchain in Automotive Industry to Secur...

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3rd International Conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Viswanadham, Yedida Venkata Rama Subramanya Jayavel, Kayalvizhi Department of Computer Science SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Information Technology SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819966899

The automotive industry is undergoing rapid transformation because of the collaboration between technology companies and automakers. To support the present trend of connected cars, several stakeholders like insurance firms, car buyers and sellers and government agencies need different kinds of vehicle data. Data gathering is currently either manual or unverified, which raises trust, validity, and exactness concerns such as tampered safety checks and false or duplicate vehicle data records, among others. A robust instrument that is capable of safeguarding vehicle data is essential. It is necessary to record the changes for the purpose of auditing and ultimately establishing faith in the system. To meet these requirements, we propose utilizing blockchain technology. Healthcare, transportation, finance, cybersecurity, and supply chain management are just a few of the fields in which blockchain technology has recently gained popularity and been implemented. The determination of this survey is to deliver a comprehensive investigation of the advantages and uses of the Blockchain application in the automotive segment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automotive industry

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Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

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Intelligent Automation & Soft Computing 2023年第4期36卷 911-927页

作者： Kisaezehra Muhammad Umer Farooq Muhammad Aslam Bhutto Abdul Karim Kazi Department of Computer Science and Information Technology NED University of Engineering&TechnologyKarachi75270Pakistan Department of Civil Engineering NED University of Engineering&TechnologyKarachi75270Pakistan

The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime *** in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every *** of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are *** most cases,this leads to serious brain injuries in people present at the site in general and the workers in *** is one of the leading causes of human fatalities at construction *** the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-deﬁned personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not ***,such monitoring through technological aids or automated tools is very much *** present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in *** can indicate whether a worker is wearing a hat or *** proposed system usesﬁve different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 *** results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

关键词： Object detection computer-vision personal protective equipment(PPE) deep learning industry revolution(IR)4.0 safety helmet detection

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computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

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computer systems science & Engineering 2023年第3期44卷 2759-2774页

作者： R.Punithavathi A.Delphin Carolina Rani K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan Department of Information Technology M.Kumarasamy College of EngineeringKarur6391 I3India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112India Department of Electronics and Instrumentation Easwani Engineering CollegeTamil Nadu600089India Pondichery University Puducherry605014India Department of computer science and engineering The Oxford college of EngineeringBangalore560068India Department of Computer Science Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Computer and Information Science Faculty of ScienceAnnamalai University Chidambaram608002India

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

关键词： Precision agriculture smart farming weed detection computer vision deep learning

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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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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer 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 Automated system Pre-Trained to Identify Cardiovascular Disorders from ECG Pictures 2

An Automated System Pre-Trained to Identify Cardiovascular D...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Bhaskar, Nuthanakanti Aelgani, Vivekanand Kumar, Sheo Devi, Bigul Sunitha Kumar, Voruganti Naresh Divya, G. CMR Technical Campus Department of Computer Science and Engineering Hyderabad India CMR College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India CMR Engineering College Department of Computer Science and Engineering Hyderabad India CMR Institute of Technology Department of Computer Science and Engineering Hyderabad India CMR Technical Campus Department of Information Technology Hyderabad India

ISBN: (纸本)9798331519056

Globally, heart disorders, often recognized as cardiovascular diseases, are among the major causes of death. Additional lives might be protected the earlier they are identified and anticipated. Cardiovascular disease can be identified with an electrocardiogram (ECG), a simple, low-cost, non-invasive method of detecting the electrical functioning of the heart. Employing a public dataset of ECG images from heart patients, deep learning algorithms were utilized in this study to calculate the four main cardiac abnormalities: abnormal heartbeat, myocardial infarction history, coronary artery disease, and typical individual categories. Initially, the transfer method for learning was studied using the previously learned deep neural networks, VGG16, RESNET50, and InceptionV3. Classical methods of machine learning such as Support Vector Machine, K-nearest relatives, Random Forest, and Naïve Bayes were able to identify features employing the models that were already trained previously stated. For the study, ECG images from people who had or did not have a diagnosis of CVD were collected. Using these preprocessed pictures, relevant features including time-domain, frequency-domain, including nonlinear features were extracted. The collected features were then used to train various machine learning computations, which helped identify the prevalence of CVD. The efficiency of each algorithm was evaluated using metrics such as precision, specificity, and consciousness. In addition, the research employed Flask technology in Python to create an online user experience for the model, facilitating patients' and healthcare practitioners' easy access. ECG pictures can be uploaded through the interface, and the model analyzes them to determine whether CVD is present. These algorithms can also offer a reliable diagnosis, which lowers the possibility of human error. © 2024 IEEE.

关键词： Deep neural networks

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