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5th International Conference on Smart Electronics and communication, ICOSEC 2024

作者： Indumathi, N. Meera, J. Palani Upadhyay, Shrikant Pithamber, Khobragade Kumar, Manoj Gupta, Jaina Sumith Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sathyabama Instittue of Science and Technology Department of Computer Science and Engineering Chennai India Mlr Institue of Technology Department of Electronics and Communiction Engineering Hyderabad India Mlr Institute of Technology Department of Computer Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798331504403

Caring of newborn babies is one of major concern for the doctors and to take care of his/her life is very challenging and sometime critical. Cardiac attack is one the severe issue that require immediate medical support to the new born baby. Providing some shocks using some device is one the immediate treatment to save the life of babies. The computation and prediction of cardiovascular status is one of the challenging tasks. In this paper we try to analyze the cardia prior heart failure chances with the different levels using machine learning methodology. The target rate of cardiac arrest detected is 58.78% and level of no cardiac found is 46.67% is achieved and this prediction level which help to recognize the level of cardiac attack for new born child. Here, we also analyze the different parameters like false rate, false observation, stability rate etc., that will help for the proper analysis of the collected data with a stability rate of 1.67% and precision rate of 60.039%. This result may provide the healthcare professional to handle the different level of heart failure for new born child. © 2024 IEEE.

关键词： Machine learning

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A Light-Weight Deep Learning-Based Architecture for Sign Language Classification

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Intelligent Automation & Soft Computing 2023年第3期35卷 3501-3515页

作者： M.Daniel Nareshkumar B.Jaison Department of Electronics and Communication Engineering R.M.K.Engineering CollegeKavaraipettai601206India Department of Computer Science and Engineering R.M.K.Engineering CollegeKavaraipettai601206India

With advancements in computing powers and the overall quality of images captured on everyday cameras,a much wider range of possibilities has opened in various *** fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much *** than ever before,there is a plethora of info about sign language usage in the real *** languages,and by extension the datasets available,are of two forms,isolated sign language and continuous sign *** main difference between the two types is that in isolated sign language,the hand signs cover individual letters of the *** continuous sign language,entire words’hand signs are *** paper will explore a novel deep learning architecture that will use recently published large pre-trained image models to quickly and accurately recognize the alphabets in the American Sign Language(ASL).The study will focus on isolated sign language to demonstrate that it is possible to achieve a high level of classiﬁcation accuracy on the data,thereby showing that interpreters can be implemented in the real *** newly proposed Mobile-NetV2 architecture serves as the backbone of this *** is designed to run on end devices like mobile phones and infer signals(what does it infer)from images in a relatively short amount of *** the proposed architecture in this paper,the classiﬁcation accuracy of 98.77%in the Indian Sign Language(ISL)and American Sign Language(ASL)is achieved,outperforming the existing state-of-the-art systems.

关键词： Deep learning machine learning classiﬁcation ﬁlters american sign language

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RETRACTED: Identifying LCF-DLT approach and educating engineering students to learn foreground perception in video surveillance

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International Journal of Electrical engineering and Education 2023年第1_SUPPL期60卷 3069-3079页

作者： Sivabalasundari, M. Banumathi, A. Ulaganathan, G. Venkatarajan, T.S. Department of Computer Science and Engineering SACS MAVMM Engineering College Madurai India Department of Electronics and Communication and Engineering Thiagarajar College of Engineering Madurai India Best Dental Science College Madurai India Department of Mathematics GMS MAVMM Polytechnic Madurai India

Real world actions occur often in crowded and dynamic environments. In this present world, surveillance plays a vital role in many applications. Here this paper proposed an intelligent surveillance for detecting moving objects extraction in indoor and outdoor environment using Likelihood Chain Follow algorithm. This signifies that the proposed method is easy to be scrutinized and can recognize the shadow inclination adaptively, which extracts shadow from moving object accurately. This method can be adapted to a more challenging domain like understanding pupil behaviour becomes essential in ambient assist living field especially for monitoring college learner’s especially young students community where motion significantly shows more assorted occurrences in much wider area. It also helps to recognise that campus security cameras are capable of monitoring campus activities, to ensure that policies are properly followed in engineering colleges especially in electrical, communication and computer departments. © The Author(s) 2019.

关键词： Feature extraction

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

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Noise-Aware-Based Texture Descriptor, Evaluation Adjacent Distance Local Ternary Pattern EAdLTP for Image Classification 2nd

Noise-Aware-Based Texture Descriptor, Evaluation Adjacent Di...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Misty, Most Marria Akter Abir, Md. Anwarul Islam Mondal, Sajal Islam, Md. Zahidul Islam, Md. Monirul Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Sheikh Hasina University Netrokona Bangladesh Department of Information and Communication Technology Islamic University Kushtia Bangladesh

ISBN: (纸本)9789819989362

This study introduces a new local feature descriptor called evaluation window-based adjacent distance local ternary pattern EAdLTP for image classification. It is created by combining evaluation window EwLBP and adjacent distance local ternary pattern (AdLTP) to achieve robustness by encoding adjacent information. EwLBP produces an evaluation window to reduce noise in the neighbor’s values, and AdLTP captures the relationships between sequential neighbors. The adjacent sub-image window and the adjacent neighbor window are used to calculate the neighbors and extract the binary code that is modified to improve the information of the adjacent neighbors. The final EAdLTP pattern is divided into two parts (EAdLTPU and EAdLTPL), and the feature descriptor vector is obtained by concatenating their histograms. The proposed EAdLTP descriptor is tested on the KTH-TIPS and KTH-TIPS2b datasets and consistently outperforms other fundamental methods by being more robust against noise. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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IoT Enabled Smart Mess with Nutrition Benefits and Waste Management 4

IoT Enabled Smart Mess with Nutrition Benefits and Waste Man...

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4th International Conference on Advancement in Electronics and communication engineering, AECE 2024

作者： Utsav, Ankur Kumari, Annu Awasthi, Shivam Kumar, Aniket Rahman, Md Atiqur Galgotias College of Engineering Department of Electronics and Communication Engineering Greater Noida India Iit Patna Department of Computer Science and Engineering Patna India Iilm University Department of Computer Science and Engineering Greater Noida India Gec Vaishali Department of Electronics and Communication Engineering Vaishali India Gec Vaishali Department of Electrical Engg. Vaishali India

ISBN: (纸本)9798350364729

This research explores the implementation of a smart mess system tailored to meet fitness enthusiast dietary needs through the use of RFID technology integrated with a mobile application. The system begins with people inputting basic information such as height, weight, and any food allergies into the app, which calculates their BMI and stores this data on an RFID card. At the mess gate, an RFID receiver grants access to the automated door, allowing students to enter and receive meal recommendations based on their BMI, including what to eat and in what quantities. In the second scenario, the app provides a solution for people who fall ill by enabling them to order food suitable for their symptoms, thus ensuring they receive proper nutrition even when they cannot physically visit the mess. The third case introduces a smart dishwasher system, where leftover waste is efficiently disposed of, and plates are automatically transported via a conveyor belt to the washing section, streamlining the cleaning process. This smart mess system aims to enhance student health, convenience, and operational efficiency within educational institutions. © 2024 IEEE.

关键词： Belt conveyors

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Utilizing Blockchain Technology for Health Insurance in Healthcare Sector 1

Utilizing Blockchain Technology for Health Insurance in Heal...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Buvana, J. Gayathri, R. Sri Venkateswara College of Engineering Department of Computer Science & Engineering Sriperumbudur India Sri Venkateswara College of Engineering Department of Electronics and Communication Engineering Sriperumbudur India

ISBN: (纸本)9798350377002

This project delves into the integration of blockchain technology within the healthcare sector, with a focus on managing health insurance processes. Blockchain, renowned for its decentralized and immutable characteristics, emerges as a promising solution to tackle diverse challenges in health insurance, ranging from fraud prevention to ensuring data security and streamlining claims processing. Through harnessing blockchain's distributed ledger technology, stakeholders in the healthcare ecosystem can securely store and exchange sensitive health insurance data while upholding transparency and integrity throughout the entire process. This research endeavour will explore the practical implementation of blockchain-based solutions in healthcare insurance, examining their potential advantages, obstacles, and ramifications for enhancing efficiency and trust in the healthcare insurance domain. By conducting thorough research and analysis, this project aims to shed light on the real-world applications of blockchain technology in reshaping health insurance systems, thereby contributing to the advancement of accessibility, affordability, and reliability of healthcare services for both individuals and organizations. © 2024 IEEE.

关键词： Blockchain Technology Byzantine Fault Tolerance (BFT) Data Security Decentralized Systems Fraud Prevention Public Key Infrastructure (PKI)

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Food Classification using Machine Learning 1

Food Classification using Machine Learning

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1st International Conference on Computational Intelligence for Security, communication and Sustainable Development, CISCSD 2024

作者： Santhosh Krishna, B.V. Mai, P. Kiran Muthumanickam, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350365405

The use of machine learning techniques in food categorization has received a lot of interest since it has the potential to transform a variety of industries. Food categorization has developed as a critical application of machine learning techniques in recent years, providing potential answers to a variety of problems in the food business, health monitoring, and nutritional evaluation. This paper presents a comprehensive review of methodologies, datasets, and challenges associated with food classification using machine learning. We will have an overview of linear regression. The publicly available datasets are commonly used for training and evaluation purposes, along with challenges such as data imbalance, handling missing values, and limited annotated data. We will perform EDA, finding products by categories and checking missing values and duplicates from the dataset. Finally, we identify future research directions aimed at improving the accuracy, scalability, and real-world applicability of machine learning-based food classification systems. © 2024 IEEE.

关键词： Adversarial machine learning

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