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23rd International Conference on Cyberworlds, CW 2024

作者： Arfi, Asif Mohammed Toyoura, Masahiro Kashiwagi, Kenji Nishiguchi, Satoshi Go, Kentaro Zhu, Zhenyang Mao, Xiaoyang University of Yamanashi Department of Computer Science and Engineering Yamanashi Kofu Japan University of Yamanashi Faculty of Medicine Department of Ophthalmology Yamanashi Chuo Japan Osaka Institute of Technology Faculty of Information Science and Technology Osaka Hirakata Japan

ISBN: (纸本)9798331527174

The precision of medical diagnoses based on images is inextricably linked to the quality and clarity of the images. Poor image quality can impede accurate diagnosis and pose challenges for both physicians and machine learning algorithms in interpreting images. By automating the process of fundus image quality assessment during capture, we can ensure that only high-quality images are used, improving diagnostic accuracy. Accordingly, we propose a novel approach for automatically assessing the quality of fundus images using deep learning techniques. Our method incorporates retinal vessel segmentation into RGB images to create four-channel images and then trains a deep learning model on these images to identify the image focus and quality of the images. It can evaluate the quality of fundus images and request image recapture with the adjustment of specific parameters that are classified as poor quality. Our proposed method has the potential to improve the diagnostic accuracy and efficiency of retinal disease diagnosis, particularly in telemedicine settings. By automating the process of fundus image quality assessment, we can ensure that only high-quality images are used for diagnosis, thus improving diagnostic precision. It can serve as an efficient screening tool in the initial stage of acquiring high-quality fundus images. © 2024 IEEE.

关键词： Image quality

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Audiovisual speech recognition based on a deep convolutional neural network

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Data Science and Management 2024年第1期7卷 25-34页

作者： Shashidhar Rudregowda Sudarshan Patilkulkarni Vinayakumar Ravi Gururaj H.L. Moez Krichen Department of Electronics and Communication Engineering JSS Science and Technology UniversityMysuru570006India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobar34754Saudi Arabia Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal560064India Department of Information Technology Faculty of Computer Science and Information Technology(FCSIT)Al-Baha UniversityAlaqiq65779-7738Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia

Audiovisual speech recognition is an emerging research *** is the recognition of what someone is saying using visual information,primarily lip *** this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual *** features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural *** feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural ***,integration was performed using a deep convolutional *** audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 *** training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 *** integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

关键词： Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)

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Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

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computer Modeling in engineering & Sciences 2024年第10期141卷 689-711页

作者： Khushal Das Fazeel Abid Jawad Rasheed Kamlish Tunc Asuroglu Shtwai Alsubai Safeeullah Soomro Department of Computer Engineering Modeling Electronics and Systems EngineeringUniversity of CalabriaRende Cosenza87036Italy Department of Information Systems University of Management and TechnologyLahore54770Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Istanbul Nisantasi UniversityIstanbul34398Turkey Department of Computer Science COMSATS University IslamabadLahore CampusLahore54700Pakistan Faculty of Medicine and Health Technology Tampere UniversityTampere33720Finland Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Second Department of Computer Science College of Engineering and ComputingGeorge Mason UniversityFairfaxVA 4418USA

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual *** works based on rich source language have been proposed;however,the work using poor resource language is still *** other SLs,the visuals of the Urdu Language are *** study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this *** existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited *** conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and *** enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise *** analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of ***,our model exhibited superior performance in Precision,Recall,and F1-score *** work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

关键词： Convolutional neural networks Pakistan sign language visual language

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An Assessment of the Security Benefits of Secure Shell (SSH) in Wireless Networks 3

An Assessment of the Security Benefits of Secure Shell (SSH)...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Preetha, M. Dhabliya, Dharmesh Lone, Zeeshan Ahmad Pandey, Sakshi Acharjya, Kalyan Gowrishankar, J. Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Karnataka Ramnagar India

ISBN: (纸本)9798350319125

Comfortable Shell (SSH) is a community protocol that provides relaxed communique over an unsecured community, such as the internet. This protocol is generally used for far flung login, file switch, and remote command execution, making it a vital device for managing and securing networks. Wi-Fi networks, which rely upon radio frequency indicators for verbal exchange, are prone to various safety threats such as eavesdropping, spoofing, and man-in-themiddle assaults. This makes using relaxed protocols like SSH critical for protective sensitive statistics and keeping the integrity of Wi-Fi networks. This technical assessment evaluates the safety advantages of utilizing SSH in wireless networks. First of all, SSH makes use of strong encryption algorithms consisting of AES and RSA to encrypt the whole communique session, ensuring that records is transmitted securely between devices. This prevents attackers from intercepting and reading touchy records exchanged among gadgets. Secondly, SSH makes use of a public-key infrastructure for authentication, that's more at ease than conventional password-based totally authentication. This is because the private key, which is used for authentication, is not transmitted all through the login system, making it greater tough for attackers to advantage get entry to the network. © 2023 IEEE.

关键词： Authentication

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Generation of Multimode Orbital Angular Momentum Waves Using Wearable Array Antenna 8

Generation of Multimode Orbital Angular Momentum Waves Using...

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8th IEEE Symposium on Wireless technology and Applications, ISWTA 2024

作者： Noor, Shehab Khan Osman, Mohamed Nasrun Mohd Yasin, Mohd Najib Ismail, Arif Mawardi Islam, Tanvir Ramli, Nurulazlina Centre of Excellence Faculty Electronic Engineering Technology University Malaysia Perlis Perlis Kangar Malaysia Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Faculty of Engineering Built Environment and Information Technology SEGi University Selangor Petaling Jaya Malaysia

ISBN: (纸本)9798350351354

In this paper, a flexible wearable antenna based on uniform circular array of four identical microstrip patch antennas on a felt textile substrate was designed to generate multiple orbital angular momentum (OAM) modes. The proposed antenna was simulated and analyzed to confirm the generation of OAM mode +1 and -1. The concept of reconfigurable method with 16 p-i-n diodes was implemented for the switching purpose. The proposed antenna covered overall bandwidth above 700 MHz for both the modes in free space while the obtained gain by mode 1 was 1.981 dBi and mode -1 was 1.993 dBi. Moreover, the mode purity analysis was carried out to verify the generation of OAM mode +1 and -1 with acceptable SAR value. The purity obtained was 47.78% for mode +1 and 47.91% for mode -1 at free space. This study shows that using textile antennas for modal multiplexing to produce multimode OAM waves is feasible in order to increase channel capacity and spectrum efficiency. © 2024 IEEE.

关键词： Microstrip antennas

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Linear Random Early Detection for Congestion Control at the Router Buffer

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Informatica (Slovenia) 2022年第5期46卷 105-114页

作者： Abu-Shareha, Ahmad Adel Al-Kasasbeh, Basil Shambour, Qusai Y. Abualhaj, Mosleh M. Alsharaiah, Mohmmad Abdalla Al-Khatib, Sumaya N. Department of Computer Science Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Riyadh Saudi Arabia Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Networks and Information Security Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan

Active Queue Management (AQM) methods control the router's buffer to maintain high network performance and control congestion at the router buffer. Random Early Detection (RED) method is the most well-known and the most utilized AQM. RED suffers from a high dropping rate, which motivates the later AQM methods to use more complex processes, which reach the limits of using fuzzy systems as a processing technique. Yet, high computational cost affects the router's performance specifically and the network as a whole, with so-called processing delay. In this paper, a linear version of RED (LRED) is presented to reduce the computational cost of the original RED and maintain the network performance in terms of throughput, delay, dropping, and loss. LRED is built based on two distinctive features, simplifying the congestion indicator calculation and reducing the operations in calculating the dropping probability. The experimental results showed that the proposed method reduces the delay and the processing time while maintaining the throughput and loss of the RED method. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Network performance

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Text Extraction with Optimal Bi-LSTM

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computers, Materials & Continua 2023年第9期76卷 3549-3567页

作者： Bahera H.Nayef Siti Norul Huda Sheikh Abdullah Rossilawati Sulaiman Ashwaq Mukred Saeed Computer Techniques Engineering Department Ibn Khaldun University CollegeBaghdad10011Iraq Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia School of Electrical Engineering and Artificial Intelligence Xiamen University MalaysiaSepang43900Malaysia

Text extraction from images using the traditional techniques of image collecting,and pattern recognition using machine learning consume time due to the amount of extracted features from the *** Neural Networks introduce effective solutions to extract text features from images using a few techniques and the ability to train large datasets of images with significant *** study proposes using Dual Maxpooling and concatenating convolution Neural Networks(CNN)layers with the activation functions Relu and the Optimized Leaky Relu(OLRelu).The proposed method works by dividing the word image into slices that contain *** pass them to deep learning layers to extract feature maps and reform the predicted *** Short Memory(BiLSTM)layers extractmore compelling features and link the time sequence fromforward and backward directions during the training *** Connectionist Temporal Classification(CTC)function calcifies the training and validation loss *** addition to decoding the extracted feature to reform characters again and linking them according to their time *** proposed model performance is evaluated using training and validation loss errors on the Mjsynth and Integrated Argument Mining Tasks(IAM)*** result of IAM was 2.09%for the average loss errors with the proposed dualMaxpooling and *** the Mjsynth dataset,the best validation loss rate shrunk to 2.2%by applying concatenating CNN layers,and Relu.

关键词： Deep neural network text features dual max-pooling concatenating convolution neural networks bidirectional long short memory text connector characteristics

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Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

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computer Systems Science & engineering 2023年第1期44卷 945-959页

作者： G.Ravikumar K.Venkatachalam Mohammed A.AlZain Mehedi Masud Mohamed Abouhawwash Department of Computer Science and Engineering Coimbatore Institute of Engineering and TechnologyCoimbatore641109India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Sleep apnea syndrome(SAS)is a breathing disorder while a person is *** traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a *** typically provides accurate results,but it is expensive and time ***,for people with Sleep apnea(SA),available beds and laboratories are ***,it may produce inaccurate ***,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)*** paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen *** diagnosis information is securely stored in the IoMT fog computing *** can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing *** proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

关键词： Sleep apnea polysomnography IOMT fog node security neural network KNN signature encryption sensor

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SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding (Extended Abstract) 40

SoulMate: Short-Text Author Linking Through Multi-Aspect Tem...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Najafipour, Saeed Hosseini, Saeid Hua, Wen Kangavari, Mohammad Reza Zhou, Xiaofang School of Information Technology and Electrical Engineering Iran University of Science and Technology Tehran Iran Sohar University Faculty of Computing and Information Technology Sohar Oman Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350317152

We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors. © 2024 IEEE.

关键词： Semantics

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