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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Chaudhary, Varun Sharma, Pradeep Kumar Kumar, Pushpendra Varshney, Neeraj Singh, Teekam Graphic Era Hill University School of Computing Dehradun India Ims Engineering College Ghaziabad India Hcl Technologies UP Noida India Scanno System Greater Noida India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

The creation of a safety system that is based on GPS and is developed exclusively for the health and safety of women via the incorporation of novel elements in order to meet the rising concerns about crimes committed against women. The gadget is capable of contacting authorities, conveying the user's position via SMS, and generating a loud noise to attract attention. It may be triggered in anticipation of potential danger and can be activated in the event that danger is imminent. In order to guarantee that only authorized users are able to activate the device, the system makes use of fingerprint recognition in a very unique way. Using technologies such as global positioning system (GPS), global system for mobile communications (GSM), and microcontrollers, this study makes a contribution to the continuing efforts to improve the safety of women via technological innovation. It provides a proactive approach to prevent violence against women and save lives. The research emphasizes the significance of combining hardware and software in order to provide a complete safety tool. It also illustrates the vital role that quick support plays and the potential of such gadgets to promote confidence among women in public places. © 2024 IEEE.

关键词： Microcontrollers

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Hand Gesture Classification for the Deaf and Mute Using the DenseNet169 Model

Hand Gesture Classification for the Deaf and Mute Using the ...

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International Conference on Communication and Electronics systems (ICCES)

作者： Basel A. Dabwan Mazen Gazzan Omar A. Ismil Enaam A. Farah Soad M. Almula Yahya A. Ali Department of Information System AlBaydha University AlBaydha Yemen Department of Information System College of Computer Science and Information Systems Najran University Kingdom of Saudi Arabia Department of Information System Preparatory in Najran University Najran University Kingdom of Saudi Arabia Department of Computer Science Preparatory in Najran University Najran University Kingdom of Saudi Arabia Department of Computer Science College of Computer Science and Information Systems Najran University Kingdom of Saudi Arabia Department of Information System Collage of Computer Science and Information Systems Najran University Kingdom of Saudi Arabia

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Sign language is a crucial means of communication for people who are deaf or hard of hearing, relying on specific signs, hand shapes, gestures, and facial expressions to express ideas and information. Since many deaf individuals face challenges with verbal communication, reading, and writing, developing a Sign Language Recognition (SLR) system is crucial for enhancing their ability to interact with hearing individuals. This paper presents a solution using the DenseNet169 model to recognize American Sign Language (ASL) gestures. We utilized a dataset representing the ASL alphabet, consisting of 24 classes (excluding the letters J and Z, which involve movement), with a The dataset contains 27,455 samples for training and 7,172 samples for testing. It was divided such that 80% of the data was used for training and 20% for testing. The proposed model has achieved impressive results, with a validation accuracy of 98%, highlighting its potential to significantly enhance communication for the hearing-impaired community. This work contributes to the advancement of human-computer interaction by providing an effective tool for recognizing sign language gestures and facilitating communication.

关键词： Training Hands Sign language Accuracy Translation Shape Face recognition Auditory system Writing Testing

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ConTNet: cross attention convolution and transformer for aneurysm image segmentation

ConTNet: cross attention convolution and transformer for ane...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Yawu Wang, Shudong Zhang, Yulin Ren, Yande Zhai, Xue Wu, Wenhao Pang, Shanchen China University of Petroleum School of Computer Science and Technology Qingdao China Shandong University of Science and Technology College of Mathematics and System Science Qingdao China Affiliated Hospital of Qingdao University Department of Medical Imaging Center Qingdao China

ISBN: (纸本)9798350337488

In recent years, Convolutional Neural Neural networks (CNNs) and Transformer architectures have significantly advanced the field of medical image segmentation. Since CNNs can only obtain effective local feature representations, there is difficulty in establishing long-range dependencies. However, Transformer has gained extensive attention from researchers due to its powerful global context modeling capability. Therefore, to integrate the advantages of the two architectures, we propose a network of ConTNet that can combine local and global information, consisting of two parallel encoders, namely, the Transformer and the CNN encoder. The CNN encoder is a stack of deep convolution and Criss-cross attention module (CCAM), which aims to acquire local features while strengthening the connection with the surrounding pixel points. In addition, two different forms of features are fused and fed into the encoder to ensure semantic consistency. Extensive experiments on the aneurysm and polyp segmentation datasets demonstrate that ConTNet performs better due to other state-of-the-art methods. © 2023 IEEE.

关键词： CNN Criss-cross attention module Medical image segmentation Transformer

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Graph Transformer for Communities Detection in Social networks

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computers, Materials & Continua 2022年第3期70卷 5707-5720页

作者： G.Naga Chandrika Khalid Alnowibet K.Sandeep Kautish E.Sreenivasa Reddy Adel F.Alrasheedi Ali Wagdy Mohamed Department of Computer Science and Engineering ANU College of Engineering and TechnologyGuntur522510India Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia LBEF Campus Kathmandu44600Nepal Department of Computer Science and Engineering ANUGuntur522510India Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12588Egypt

Graphs are used in various disciplines such as telecommunication,biological networks,as well as social *** large-scale networks,it is challenging to detect the communities by learning the distinct properties of the *** deep learning hasmade contributions in a variety of domains,we try to use deep learning techniques to mine the knowledge from large-scale graph *** this paper,we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to *** advantages of neural attention are widely seen in the field of NLP and computer vision,which has low computational complexity for large-scale *** contributions of the paper are summarized as ***,a transformer is utilized to downsample the first-order proximities of the graph into a latent space,which can result in the structural properties and eventually assist in detecting the ***,the fine-tuning task is conducted by tuning variant hyperparameters cautiously,which is applied to multiple social networks(Facebook and Twitch).Furthermore,the objective function(crossentropy)is tuned by L0 ***,the reconstructed model forms communities that present the relationship between the *** proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node *** proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67±0.04,0.198±0.02,0.228±0.02,and 0.68±0.03 on Wikipedia crocodiles,Github Developers,Twitch England,and Facebook Page-Page networks,respectively.

关键词： Social networks graph transformer community detection graph classification

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Metaheuristic with Deep Learning Enabled Biomedical Bone Age Assessment and Classification Model

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computers, Materials & Continua 2022年第12期73卷 5473-5489页

作者： Mesfer Al Duhayyim Areej A.Malibari Marwa Obayya Mohamed K.Nour Ahmed S.Salama Mohamed I.Eldesouki Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The skeletal bone age assessment(BAA)was extremely implemented in development prediction and auxiliary analysis of medicinal issues.X-ray images of hands were detected from the estimation of bone age,whereas the ossification centers of epiphysis and carpal bones are important *** typical skeletal BAA approaches remove these regions for predicting the bone age,however,few of them attain suitable efficacy or *** BAA techniques with deep learning(DL)methods are reached the leading efficiency on manual and typical ***,this study introduces an intellectual skeletal bone age assessment and classification with the use of metaheuristic with deep learning(ISBAAC-MDL)*** presented ISBAAC-MDL technique majorly focuses on the identification of bone age prediction and classification *** attain this,the presented ISBAAC-MDL model derives a mask Region-related Convolutional Neural Network(Mask-RCNN)with MobileNet as baseline model to extract *** by,the whale optimization algorithm(WOA)is implemented for hyperparameter tuning of the MobileNet *** last,Deep Feed-Forward Module(DFFM)based age prediction and Radial Basis Function Neural Network(RBFNN)based stage classification approach is *** experimental evaluation of the ISBAAC-MDL model is tested using benchmark dataset and the outcomes are assessed over distinct *** experimental outcomes reported the better performances of the ISBAACMDL model over recent approaches with maximum accuracy of 0.9920.

关键词： Biomedical images bone age assessment age prediction computer vision deep learning image classification

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Machine Learning-Powered Design and Implementation for Classification of Missing Data in IoT Applications 1

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3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Deepan, S. Suchitra, B. Arya, Namita Ghori, Md. Atheeq Sultan Bora, Ashim Mahender, Nagurla School of CS & AI SR University Telangana Warangal India IT & Cognitive System Tamilnadu Coimbatore India Galgotias University UP Greater Noida India Department of Computer Science and Engineering Telangana University Nizamabad Telangana State Nizamabad India Kampur College Assam Kampur India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

Missing values are a common issue in machine learning, which has formed the foundation for data analysis and extraction. Several reasons can lead to missing values, such as missing entirely, missing at random, and not missing. However, before conducting any data analysis, it is imperative to tackle missing values since this might result in biased or inaccurate results. One common technique to increase the precision of IoT data classification is to combine many sensors with different data techniques. But all modalities are seldom available even for the evaluation results, and this lack of data creates serious obstacles to multimodal education. Inspired by recent developments in deep learning, we present a cross-neural network for the segmentation of the IoT Data Classification, which is created on data modalities not all obtainable during trials. We educate the framework in IoT Data Classification using a cost function specifically designed for imbalanced classes. We are giving the device an insufficiently full baseline dataset. Our technique extends beyond CNN training and the gathering of two CNNs trained in the missing modality by using temporal data, considering that they are not involved in the research process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cost functions

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Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

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computers, Materials & Continua 2022年第11期73卷 3039-3055页

作者： Eatedal Alabdulkreem Jaber S.Alzahrani Majdy M.Eltahir Abdullah Mohamed Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha 62529Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many *** cloud-based IoT health providers have been described in the literature ***,there are a number of issues related to time consumed and overall network performance when it comes to big data *** the existing method,less performed optimization algorithms were used for optimizing the *** the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was *** research presents a method for analyzing healthcare information that uses in future *** major goal is to take a variety of data while improving efficiency and minimizing process *** suggested method employs a hybrid method that is divided into two *** the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor *** the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and *** CSVM modifies CNN’s convolution product to learn hidden deep inside data *** improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog *** results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.

关键词： Healthcare convolutional support vector machine feature selection chaotic cuckoo optimization accuracy processing time convolutional neural network

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Enhanced GAN-Assisted Intrusion Detection Model for Addressing Data Imbalance Issues

Enhanced GAN-Assisted Intrusion Detection Model for Addressi...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Raghu Dhumpati Nirmala Reddy Sankati Ramakrishna Posimsetti Naveen Kumar Sheo Kumar Homera Durani Department of Computer Science and Engineering Bahrain Polytechnic Bahrain Department of Computer Science and Engineering IES College of Technology Bhopal India Department of Computer Science and Engineering-Data Science KKR & KSR Institute of Technology and Sciences Guntur India VLSI System Design Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of CSE School of Engineering RK University Rajkot India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

With the introduction of IIoT systems, industry breakthroughs and linked smart objects revolutionize the world. The enemies are finding a lot of room to launch a number of devastating cyber assaults thanks to all this connection and dependability. However, implementing IDS systems may be tough due to the absence of dataset for training as well as testing. This makes it difficult to detect likely cyberattacks with the required accuracy. The method of detection might not be able to learn perfectly and detect cyberattacks significantly higher precision if there aren't enough records related to every threat category in the dataset. In addition, the detection of breaches procedure is becoming more complicated due to unbalanced datasets. In this light, our research suggests a more effective GAN-based IDS model for creating simulated data samples that can fool and evade security systems before directing the real-life network assault. To be more specific, it creates fictitious datasets and trains IDS on these in conjunction with the actual data in order to boost the system's efficiency. The specifics of the Improved Generative Adversarial Network-based Intrusion Detection system (IGAN-IDS) model that is suggested in this study are described in this section. With an accuracy of 99.6 percent, the suggested IGAN-IDS model surpasses all of its competitors, including BSA-IDS, GAN, MLP-GAN, Synthetic Minority Over-sampling Technique (SMOTE), and ADASYN. This proves that it outperforms all other approaches when it comes to classification.

关键词： Training Accuracy Intrusion detection Generative adversarial networks Rendering (computer graphics) Data models Security computer crime Industrial Internet of Things Testing

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Classification of Coverage Algorithms in Multimedia Wireless Sensor networks 5

Classification of Coverage Algorithms in Multimedia Wireless...

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5th IEEE International Conference on Networking and Advanced systems, ICNAS 2021

作者： Bouhali, Manel Bendjeddou, Amira Ghoualmi-Zine, Nacira Badji Mokhtar-Annaba University Networks and System Laboratory Computer Science Department Annaba Algeria

ISBN: (纸本)9781665428217

In recent years, with the technological development that we are witnessing, the multimedia wireless sensor networks (WMSNs) are attracting more and more interest from researchers, these later are used in various applications for their advantages and reliability. However, the WMSNs have different problems that become the focus of attention of researchers who strived to make various proposals to address these problems. The best-known and popular problem for this type of network is improving and optimizing their coverage. In this paper, several approaches for the coverage optimization problem in the multimedia wireless sensor networks are presented and the advantages and limitations of each one of them are highlighted. Then, a classification of these approaches is made. © 2021 IEEE.

关键词： Heuristic algorithms

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Metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor networks

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computers, Materials & Continua 2022年第12期73卷 5491-5507页

作者： Mashael M.Asiri Saud S.Alotaibi Dalia H.Elkamchouchi Amira Sayed A.Aziz Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Ishfaq Yaseen Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained *** offers independent access to the target region and performs data collection in an effective *** energy constraints remain a challenging issue in WSN since it operates on in-built *** studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of *** organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic *** and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of *** this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based Multi-Hop Routing(IDTOMHR)technique for ***,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of ***,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the *** clustering and routing approaches derive a fitness function with the inclusion of multiple input *** proposed IDTOMHR model was experimentally validated for its performance under different *** extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.

关键词： Wireless sensor network clustering routing metaheuristics energy efficiency lifetime

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