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4th IEEE International Conference on Data engineering and communication systems, ICDECS 2024

作者： Anbazhagan, K. Kumar, Rotash Thilagavathy, R. Anuradha, D. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Government Engineering College Department of Electronics and Communication Engineering Rajasthan Jhalawar India College of Engineering and Technology Srm Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350393354

In recent years, fog computing has extended cloud services to the network edge, lowering network latency and congestion and overcoming cloud computing restrictions. Many hurdles, however, must be overcome before the benefits of the fog-IoT paradigm for low-latency applications can be completely realized. The most challenging difficulty in IoT applications is developing efficient resource management strategies that maximize throughput while minimizing delay. IoT and fog computing, rather than simply utilizing cloud computing technology, have brought intriguing new data management solutions. Fog computing makes real-time analytics applications conceivable. After combining fog computing technologies with Internet of Things (IoT) applications, the system can reply in milliseconds. Existing fog resource managers focus on a subset of fog resource management parameters such as system response time, network bandwidth, energy consumption, and latency. No Fog resource management takes multiple factors into account at the same time for decision-making, which will become increasingly crucial in intelligent homes. Gateway-based Fog Computing pioneered the introduction of the shortest job (SJFGFC) to address these difficulties. The technique is validated in an IoT-based imaginative house automation scenario before being evaluated in a small-scale bright home experiment using the iFogSim toolbox and empirical models. © 2024 IEEE.

关键词： Fog computing

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Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena Optimizer Combined with Transfer Learning Approaches 15th

Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena O...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Balakrishnan, Roshni M. Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Patil, Swetha Rajaguru, Harikumar Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidhyapeetham Bengaluru India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering PES University EC Campus Bengaluru India

ISBN: (纸本)9783031810794

Histopathological oral images are one of the popular methodologies to diagnose oral squamous cell carcinoma. computerized diagnostic systems need to be developed in order to process huge number of histopathological oral images for each subject and helping the clinician in classifying the oral images as either normal class or oral squamous cell carcinoma class. This research work uses deep transfer learning approaches namely MobileNetV3, InceptionV2, and EfficientNetB3 for extracting the features from histopathological oral images. Spotted Hyena Optimizer is used as feature transform for improving classification accuracy. Two dense layers are used for classifying the transformed features. 2784 & 3632 number of histopathological oral images are considered in this study under the two classes as normal and oral squamous cell carcinoma respectively. With the proposed spotted Hyena Optimizer, the maximum accuracy attained is 93% for the features extracted through InceptionV2 model. Relatively, the highest accuracy attained is 89% if the optimizer is not used for the features extracted through MobileNetV3 model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transfer learning

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Degradation Modelling and Prognostics of Rotating Equipment with Automated Machine Learning

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Procedia computer Science 2025年 253卷 1640-1648页

作者： Stefanos Kontos Alexandros Bousdekis Katerina Lepenioti Gregoris Mentzas Information Management Unit (IMU) Institute of Communication and Computer Systems (ICCS) School of Electrical and Computer Engineering National Technical University of Athens (NTUA) Athens Greece

Evaluating and predicting equipment degradation is essential to the reliability and safety of mechanical equipment systems. Condition monitoring based on sensors measuring vibration signals has emerged as the most well-established method for enabling detection and prediction of the health state of rotating equipment. One of the most well-established methods for degradation modelling based on vibration signals is Weibull distribution. Most recent approaches take advantage of Machine Learning (ML) and deep learning to generate prognostics insights. On the other hand, AutoML automates the configuration of ML pipelines to achieve a higher level of automation. Therefore, it has the credentials to enable predictive maintenance applications by optimizing the adopted predictive models; however, the exploitation of AutoML in rotating machine prognostics is an underexplored area. In this paper, we model the degradation procedure according to the Weibull distribution, we extract, combine, and compare various time-domain features, and we apply the H2O AutoML framework for providing predictions about the future health state of the rotating machinery. Our proposed approach is validated in a real-life scenario of cold rolling in the steel industry. Then, we implement and compare various pipelines with different combinations of features in terms of R-squared, MAE, and execution time.

关键词： AutoML predictive maintenance health state prediction rotating machinery

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Classification of Alzheimer’s Disease Subjects from MRI Using Deep Convolutional Neural Networks 3rd

Classification of Alzheimer’s Disease Subjects from MRI Usi...

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3rd International Conference on Novel and Intelligent Digital systems, NiDS 2023

作者： Papadimitriou, Orestis Kanavos, Athanasios Mylonas, Phivos Maragoudakis, Manolis Department of Information and Communication Systems Engineering University of the Aegean Samos Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9783031441455

The classification of Alzheimer’s disease (AD) using deep learning techniques has shown promising results. However, achieving successful application in medical settings requires a combination of high precision, short processing time, and generalizability to different populations. In this study, we propose a convolutional semantic network (CNN)-based classification algorithm that utilizes magnetic resonance imaging (MRI) scans from individuals with AD. Our models achieved average area under the curve (AUC) values of 0.91−0.94 for within- dataset recognition and 0.88−0.89 for between-dataset recognition. The proposed convolutional framework can be potentially applied to any image dataset, offering the flexibility to design a computer-aided diagnosis system targeting the prediction of various clinical conditions and neuropsychiatric disorders using multimodal imaging and tabular clinical data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Advancing Weather Image Classification Using Deep Convolutional Neural Networks 18

Advancing Weather Image Classification Using Deep Convolutio...

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18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Papadimitriou, Orestis Kanavos, Athanasios Mylonas, Phivos Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350327717

Automatic classification of weather images is a crucial task in the field of meteorology. Recent advancements in Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in image classification. In this paper, we propose an innovative CNN-based approach for weather image classification. Building upon prior research, our model capitalizes on the exceptional feature extraction capabilities of CNNs to accurately classify weather images across various categories. To enhance the generalization performance, we train the model using a combination of data augmentation techniques, including rotation, scaling and flipping. This paper introduces a CNN model specifically made for weather image classification, encompassing categories such as cloudy, sunny, rainy and snowy conditions. Through extensive training and evaluation on a substantial weather image dataset, our proposed model achieves an impressive accuracy rate of 98%. Our experimental results highlight the superior performance of our CNN model when compared to various state-of-The-Art methods in weather image classification. © 2023 IEEE.

关键词： Image classification

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Enhancing Sign Language Recognition Using Deep Convolutional Neural Networks 14

Enhancing Sign Language Recognition Using Deep Convolutional...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Kanavos, Athanasios Papadimitriou, Orestis Mylonas, Phivos Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350318067

The advancements in sensing technologies and AI algorithms have opened up a wide range of possibilities for developing applications to meet the needs of individuals who are deaf or hard of hearing. Sign language plays a vital role in the lives of people with hearing and speaking disabilities. This research aims to explore digital image processing and machine learning methods for efficiently building a sign language dataset and creating a sign language interface system. The proposed system utilizes a Convolutional Neural Network (CNN) to analyze and interpret hand gestures and poses, converting them into natural language. The developed CNN model specifically focuses on improving the accuracy of predicting the American Sign Language alphabet. Despite variations in dataset conditions and size, the model achieved an exceptional accuracy rate of 98.73%. Additionally, it demonstrated a low loss value of 0.0539, indicating its robust performance. © 2023 IEEE.

关键词： Image classification

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Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

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computer Modeling in engineering & Sciences 2024年第6期139卷 3513-3534页

作者： Nazik Alturki Abdulaziz Altamimi Muhammad Umer Oumaima Saidani Amal Alshardan Shtwai Alsubai Marwan Omar Imran Ashraf Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department College of Computer Science and Engineering University of Hafr Al-BatinHafarAl-Batin39524Saudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurP.O.Box 63100BahawalpurPakistan Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Information Technology and Management Illinois Institute of TechnologyChicagoIL 60616-3793USA Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate *** learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD *** study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning *** research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for ***,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance *** ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning *** proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for *** in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

关键词： Precisionmedicine chronic kidney disease detection SMOTE missing values healthcare KNNimputer ensemble learning

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education 7

The Emergence of Artificial Intelligence in Education and it...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes. © 2024 IEEE.

关键词： Students

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Decentralized Security in IoT: Lightweight Blockchain with Optimized CA-LSTM for Improved Performance and Privacy 5

Decentralized Security in IoT: Lightweight Blockchain with O...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Selvakumarasamy, K. Kumar, T. Rajesh Meenambika, A. Balamanigandan, R. Mahaveerakannan, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Computer Science and Business Systems India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

An existing infrastructure can be linked to billions of devices, or "things,"through the Internet of Things (IoT). This allows for machine-to-machine communication as well as human-to-human communication. Massive volumes of data are being generated due to the proliferation of devices globally, which permeates every facet of everyday life. Thus, new problems are emerging as a result of the development and use of existing technologies, and these problems pertain to new applications, regulations, cloud computing, security, and privacy. With its decentralized nature, the blockchain has the potential to protect users' and data's privacy. This study introduces a novel method that makes use of lightweight blockchain technology to significantly lessen the computing load usually seen in traditional blockchain systems. Implementation time and computational complexity can be significantly reduced by connecting this lightweight blockchain with IoT devices. Improved safety is achieved by the use of CA-LSTM (channel attention long short-term memory) technology for attack detection. In order to improve the CA-LSTM algorithm's solution, the suggested system uses the RMSSO algorithm as a hyperparameter tuning technique. Similarly, to found very little power consumption and physical memory utilization;for example, Raspberry Pi devices used 0.2 GB of memory and NVIDIA Jetson devices used 0.42 GB. With the deployed model, power consumption increased by an average of 15% per device. This technology makes it easier to build certain types of business models and gives decentralized apps the ability to securely compute on encrypted data without compromising user privacy. The findings confirm that the proposed system is very secure, on par with conventional blockchain implementations. © 2024 IEEE.

关键词： Differential privacy

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