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Iraqi Journal for computer science and Mathematics 2023年第2期4卷 56-71页

作者： Selvaganesh, N. Shanthi, D. Rajesh Pandian, N. Department of Information Technology Sri Venkateswara College of Engineering Tamil Nadu Sriperumbudur602117 India Department of Computer Science and Engineering PSNA College of Engineering and Technology Kothandaraman Nagar Tamil Nadu Dindigul624622 India Faculty of Engineering and Technology Karnataka Bengaluru560069 India

Hearing loss or hearing impairment is one of the most leading cause highly affecting the people around the world in present days. Additionally, this disease primarily affects children and adults, which has an impact on their daily lives, careers, education, and other aspects of their lives. Therefore, it has to be correctly identified and diagnosed so that early therapies can be given to save people's lives. The many kinds of automated hearing loss prediction/detection systems are created for this aim in conventional works. The majority of the current research focuses on applying prediction techniques based on machine learning and deep learning to diagnose diseases. Its shortcomings include challenging computational procedures, longer training and testing times, greater mis-prediction outcomes, and incorrect outputs. Therefore, the proposed work objects to develop a Human Age - Hearing Impairment & Level (HAHIL) prediction system by using the machine learning methodologies. In this study, we describe a computer-aided strategy to forecast hearing loss and then prevent it. Here, three distinct prediction models are deployed for age prediction, hearing loss detection, and its severity level prediction. The Biased Probability Neural Network (BPNN) technique is utilized to predict the age based on simulated human acoustical signals. Then, the Regularized Extreme Learning Machine (RELM) mechanism is deployed for predicting the hearing impairment by constructing the weight and target matrices. During evaluation, the performance of the proposed HAHIL prediction system is validated and tested by using various evaluation indicators. The effectiveness of our computer-aided hearing loss prediction methods has been proven to be very high and they can be applied to the actual application system. Also, it provides an increased accuracy up to 99%, which is highly superior than the existing prediction models. © 2023 Multidiscip. Sci. J. All rights reserved.

关键词： Forecasting

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

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computer Systems science & engineering 2023年第9期46卷 2933-2945页

作者： K.S.Bhuvaneshwari Ahmed Najat Ahmed Mehedi Masud Samah H.Alajmani Mohamed Abouhawwash Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Engineering Lebanese French UniversityErbil44001Iraq Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityPO Box 11099Taif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

The immediate and quick spread of the coronavirus has become a life-threatening disease around the *** widespread illness has dramatically changed almost all sectors,moving from offline to online,resulting in a new normal lifestyle for *** impact of coronavirus is tremendous in the healthcare sector,which has experienced a decline in the first quarter of *** pandemic has created an urge to use computer-aided diagnosis techniques for classifying the Covid-19 dataset to reduce the burden of clinical *** current situation motivated me to choose correlationbased development called correlation-based grey wolf optimizer to perform accurate classification.A proposed multistage model helps to identify Covid from Computed Tomography(CT)scan *** first process uses a convolutional neural network(CNN)for extracting the feature from the CT *** Pearson coefficient filter method is applied to remove redundant and irrelevant ***,theGrey wolf optimizer is used to choose optimal *** analysis proves that this determines the optimal characteristics to detect the deadly *** proposed model’s accuracy is 14%higher than the krill herd and bacterial foraging optimization for severe accurate respiratory syndrome image(SARS-CoV-2 CT)*** COVID CT image dataset is 22%higher than the existing krill herd and bacterial foraging optimization *** proposed techniques help to increase the classification accuracy of the algorithm in most cases,which marks the stability of the stated *** analysis reveals that the proposed classification technique to predict COVID-19 withmaximumaccuracy of 98%outperforms other competitive approaches.

关键词： COVID-19 feature selection CT image classification features extraction

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective 24

A Deep Learning Approach Towards Student Performance Predict...

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24th International Arab Conference on information technology, ACIT 2023

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Western University Electrical and Computer Engineering Department LondonON Canada Zarqa University Faculty of Information Technology Data Science and Artificial Intelligence Department Zarqa Jordan Jadara University Faculty of Science and Information Technology Irbid Jordan Zarqa University Faculty of Information Technology Computer Science Department Zarqa Jordan Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan

ISBN: (纸本)9798350384307

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. © 2023 IEEE.

关键词： Deep Learning e-Learning Online Courses Student Performance Prediction

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Deep Learning to Classify Bacterial Species in the same Genus

Deep Learning to Classify Bacterial Species in the same Genu...

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2024 International Conference on Electronics, information, and Communication, ICEIC 2024

作者： Sheela, Sherin Piang, May Phu Sakorntanant, Sakda Poomrittigul, Suvit Treebupachatsakul, Treesukon School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Biomedical Engineering Thailand Pathumwan Institute of Technology Faculty of Science and Technology Department of Software Engineering and Information System Bangkok Thailand School of Information Technology King Mongkut's Institute of Technology Ladkrabang Thailand

ISBN: (纸本)9798350371888

Bacterial strains in the same genus share highly similar morphology, gram-staining characteristics, colony sizes, and spatial arrangements. Therefore, identifying them by deep learning can be quite challenging. This study aimed to assess the classification of 7 species of bacteria from 2 genera of Bacillus and Vibrio by using 8 Convolutional Neural Network (CNN) models. We implemented Python programming along with Keras API within the Jupyter Notebook. The models were constructed and evaluated under unbalanced and balanced datasets by augmentation (rotation, flip, etc.). Transfer learning with fine-tuning, and pre-processing of mixup and label smoothing were also applied to reduce overfitting and enhance generalization. Based on the experimental results on private dataset, the results of InceptionResNetV2 emerged as the top-performing model with a notable accuracy of 82.8%, 88.6% precision, 78.4% recall, and 78.0% F1-score when label smoothing was applied at 0.5 on balanced dataset. © 2024 IEEE.

关键词： Neural network models

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Comparison of *** and Spring Boot in Web Development 15

Comparison of *** and Spring Boot in Web Development

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Novac, Ovidiu Constantin Ghiurau, David Novac, Mihaela Cornelia Gordan, Cornelia Emilia Oproescu, Mihai Bujdoso, Gyongyi Mihalca, Vlad Ovidiu University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania University of Oradea Management in Information Technology Electrical Engineering and Information Technology Faculty Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electronics and Telecommunications Oradea Romania University of Pitesti Faculty of Electronics Communication and Computer Science Department of Electronics Computers and Electrical Engineering Pitesti Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary School of Engineering Sciences University of Oradea Electrical Engineering and Information Technology Faculty Oradea Romania

ISBN: (纸本)9798350321388

In this paper we present two types of server implementation, we will look at the advantages and disadvantages of each technology. *** server application, built with ***, a single threaded server which will treat the requests in an asynchronous manner, with a non-blocking principle, whilst Spring Boot is a more robust and more common approach of a server technology with the benefits of having a strongly typed architecture and multiple threads available. These types of application will behave the same, making almost impossible to tell the difference between a *** and a Spring Boot web server. © 2023 IEEE.

关键词： Node.Js non-blocking single-multi threaded Spring Boot web server

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An software Defined Networking (SDN) Enhanced Edge Computing Framework for Internet of Healthcare Things (IoHT)

An Software Defined Networking (SDN) Enhanced Edge Computing...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Anh, Dang Van Chehri, Abdellah Quy, Nguyen Minh Hue, Chu Thi Minh Nguyen, Dinh C. Quy, Vu Khanh Hung Yen University of Technology and Education Faculty of Information Technology Hungyen Viet Nam Royal Military College of Canada Department of Mathematics and Computer Science Kingston Canada Fpt University Faculty of Software Engineering Hanoi Viet Nam The University of Alabama Department of Electrical and Computer Engineering HuntsvilleAL United States

ISBN: (纸本)9798350351255

The rapid proliferation of intelligent Internet of Health Things (IoHT) applications within the context of the COVID-19 pandemic has exerted significant strain on the backhaul network infrastructure. This paper aims to introduce a novel framework that leverages software-defined networking (SDN) to enhance edge computing capabilities. This framework will expect to facilitate dynamic and adaptable communication between edge and cloud servers, specifically designed to support real-time Internet of Health Things applications. Through the establishment of a connection between servers and the software-Defined Networking controller, the system is expected to facilitate load balancing, network optimization, and the utilization of resources in an efficient manner. This, in turn, enables the provision of real-time healthcare services. Ultimately, the efficacy of the suggested framework is assessed by analyzing its impact on service response time. The research results indicate that the proposed framework significantly benefits IoHT systems' service response times across various workloads and traffic. © 2024 IEEE.

关键词： Electronic health record

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Learning Age of information-Oriented Energy-Efficient Scheduling with Non-Orthogonal Multiple Access 13

Learning Age of Information-Oriented Energy-Efficient Schedu...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Li, Jinhua Cao, Yi Chan, Tse-Tin Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong

ISBN: (纸本)9798350355079

We propose a deep Q-network (DQN)-based user scheduling scheme aimed at low network-wide age of information (AoI) in a non-orthogonal multiple access (NOMA) uplink status update system. In our system, end users can harvest energy from wireless signals received on the radio-frequency band to extend the lifespan of their batteries. Given the battery replacement cost in many practical scenarios, our objective is to design a scheduling scheme to maintain a low AoI while efficiently utilizing energy harvesting and reducing the number of battery replacements. Considering the instantaneous AoI, remaining energy, and channel condition of end users, we model the user scheduling problem as a Markov decision process (MDP), which is solved by DQN. In particular, the choice of transmission power to save energy while enabling a high NOMA decoding rate is incorporated in the design of DQN. Experiments indicate that our DQN-based scheduling scheme achieves a low average AoI of the system with significantly fewer battery replacements compared to various benchmarking schemes. © 2024 IEEE.

关键词： Markov processes

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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