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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Devi, D. Muruga Radha Jancy, P. Leela Tamilselvi, P. Logeswaran, S. Vigneshwaran, E. Jeyachandran, A. Sri Sai Ram Institute of Technology Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer and Business System Chennai India

ISBN: (纸本)9798350397840

Prevalence of Lung cancer is one of the most important categories among the various types of cancers. The basic step in the prediction of Lung Cancer is sampling of CT/MRI scan images of Lungs. An expert in that field must do thorough analysis in the detection of carcinoma in patients. Hence, there is a need for an efficient system that is capable of identifying lung cancer from multiple datasets. This ensures the condition of Cancer by observing the size of tumor and location of tumor. In our method detection of Lung cancer is performed using ZF NET Convolutional Neural Network (CNN) and in the process of machine learning we have used the pre-processed dataset from Kaggle and training, validation, and testing are done appropriately to obtain accuracy of 80% and above. © 2022 IEEE.

关键词： Convolutional neural networks

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Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

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computer systems Science & Engineering 2023年第5期45卷 1759-1773页

作者： Manar Ahmed Hamza Haya Mesfer Alshahrani Sami Dhahbi Mohamed K Nour Mesfer Al Duhayyim ElSayed M.Tag El Din Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt

Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of *** spite of this,it is challenging to design energy-efficient *** routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of *** order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network *** this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for *** aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in *** accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution ***,the inclusion of DE in traditional AOA helps in overcoming local optima *** addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and *** order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches.

关键词： Wireless sensor network routing multihop communication arithmetic optimization algorithm fitness function

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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine systems and Ocean technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第11期73卷 3905-3920页

作者： Radwa Marzouk Eatedal Alabdulkreem Sami Dhahbi Mohamed K.Nour Mesfer Al Duhayyim Mahmoud Othman Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Oral cancer is the most commonly occurring‘head and neck cancers’across the *** of the oral cancer cases are diagnosed at later stages due to absence of awareness among *** earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this *** this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing *** proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data *** by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep ***,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and ***,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several *** extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.

关键词： Oral cancer detection image classification artificial intelligence machine learning deep learning

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Watchtower Selection in Off-Blockchain PCN Using Peterson Leader-Election Algorithm 21

Watchtower Selection in Off-Blockchain PCN Using Peterson Le...

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Amin, Md Faizul Ibne Watanobe, Yutaka Rahman, Md. Mostafizer Kabir, Raihan Graduate Department of Computer and Information System The University of Aizu Fukushima AizuWakamatsu Japan Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh

ISBN: (纸本)9781643683164

Despite the incredible adoption of cryptocurrencies, blockchain-based cryptocurrencies have likewise raised some concerns. The scalability problem is the major one among them. An off-blockchain payment channel network (PCN) has been introduced to solve this issue. PCN can fundamentally reduce blockchain scalability by constructing a number of payment channels between the nodes and without committing every single transaction to the blockchain. But as a matter of fact, there has an unwanted assumption in PCN that channel participants must remain online and follow blockchain updates, for the synchronization with blockchain to protect the channel against deception. To mitigate this issue 'Watchtower' concept has been proposed. Watchtower is a watching service and always stays online that a channel participant can hire it by offering incentives for monitoring the channel and checking blockchain updates consistently to prevent fraud on behalf of the hiring party. However, watchtower may be more beneficial by cooperating with the cheating counterparty and neglecting to perform the watching service properly. The efficiency drawback can occur for that. In this work, we have been motivated by this issue and tried to find out an effective and reliable watchtower for the channel watching service from multiple watchtower nodes or candidates in the PCN. In particular, we have been approached by using the distributed Peterson Leader-Election Algorithm to find the best watchtower among multiple of them where the more successfully performed work node or candidate will be selected for the channel monitoring job. We also have provided a detailed step-by-step process of the algorithm including experiments and illustrations for employing watchtower among multiple of them. © 2022 The authors and IOS Press. All rights reserved.

关键词： Blockchain

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Rice Leaves Disease Diagnose Empowered with Transfer Learning

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computer systems Science & Engineering 2022年第9期42卷 1001-1014页

作者： Nouh Sabri Elmitwally Maria Tariq Muhammad Adnan Khan Munir Ahmad Sagheer Abbas Fahad Mazaed Alotaibi College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

In the agricultural industry,rice infections have resulted in significant productivity and economic *** infections must be recognized early on to regulate and mitigate the effects of the *** diagnosis of disease severity effects or incidence can preserve production from quantitative and qualitative losses,reduce pesticide use,and boost ta country’s *** the health of a rice plant through its leaves is usually done as a manual ocular *** this manuscript,three rice plant diseases:Bacterial leaf blight,Brown spot,and Leaf smut,were identified using the Alexnet *** research shows that any reduction in rice plants will have a significant beneficial impact on alleviating global food hunger by increasing supply,lowering prices,and reducing production's environmental impact that affects the economy of any *** would be able to get more exact and faster results with this technology,allowing them to administer the most acceptable treatment *** Using Alex Net,the proposed approach achieved a 99.0%accuracy rate for diagnosing rice leaves disease.

关键词： Rice bacterial leaf blight brown spot leaf smut machine learning alexnet

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Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

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computer systems Science & Engineering 2023年第5期45卷 2033-2047页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Lubna A.Alharbi Mohamed K.Nour Abdullah Mohamed Ahmed S.Almasoud Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk47512Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMecca24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

Recent developments on Internet and social networking have led to the growth of aggressive language and hate *** provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize *** advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social *** this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for *** presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the *** achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature *** addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of ***,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier *** experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

关键词： Cybersecurity cyberbullying online social network deep learning spotted hyena optimizer

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BiLSTM Models with and Without Pretrained Embeddings and BERT on German Patient Reviews

BiLSTM Models with and Without Pretrained Embeddings and BER...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Roy, Prosenjit Riad, Md Jahid Alam Akter, Lija Hasan, Nobanul Shuvo, Mahfuzur Rahman Quader, Montaser Abdul Quader, Mozdaher Abdul Anwar, Ahmed Selim Prairie View A & M University Department Of Computer Science TX United States Washington University Of Science And Technology Vienna United States Uttara University Department Of Computer Science Bangladesh International American University Department Of Management Information System Los AngelesCA United States Green University Of Bangladesh Department Of Computer Science And Engineering Bangladesh Department Of Eee Bangladesh

ISBN: (纸本)9798350371567

This research searches into the use of Deep learning in sentiment analysis, focusing on the analysis of German patient reviews with two neural network architectures: a Bidirectional Long Short-Term Memory (BiLSTM) model with pretrained embeddings and a BERT (Bidirectional Encoder Representations from Transformers) model. The primary goal is to determine how effective these models are at categorising patient sentiments into 'poor' and 'excellent' categories. We used a dataset of approximately 500,000 reviews, each with a numerical rating and a textual comment. To facilitate model input, the text data was preprocessed. Our findings show a significant difference in performance between the two models, with the BERT model performing better in terms of accuracy and balanced classification across sentiment categories, emphasising the importance of model selection and application. The BERT model demonstrated superior performance, achieving a remarkable accuracy of 98.4%. It exhibited high precision, recall, and F1-scores, particularly in classifying 'poor' sentiments (0.98, 0.99, and 0.99, respectively). BiLSTM model with pretrained embeddings demonstrated a pronounced dip in performance for the 'excellent' category, with zero scores across precision, recall, and F1-score metrics, while retaining high performance for the 'poor' category. LSTM Without Pretrained Embeddings model's accuracy of the model is 91%,LSTM With Pretrained Embeddings result 97%.The study underscores the critical influence of model selection and the incorporation of pretrained embeddings in achieving high accuracy and balanced classification across sentiment categories. This research contributes to the ongoing conversation about the application of advanced machine learning techniques in the analysis of patient feedback and sentiment classification. © 2024 IEEE.

关键词： Long short-term memory

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Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of Engineering & technology 2023.

关键词： Machine learning

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From Laboratory to Field:Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild

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Plant Phenomics 2023年第2期5卷 195-208页

作者： Xinlu Wu Xijian Fan Peng Luo Sruti Das Choudhury Tardi Tjahjadi Chunhua Hu College of Information Science and Technology Nanjing Forestry UniversityNanjing 210037China Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijing 100091China Key Laboratory of Forestry Remote Sensing and Information System National Forestry and Grassland AdministrationBeijing 100091China Department of Computer Science and Engineering University of Nebraska-LincolnLincolnNE 68588USA School of Engineering University of WarwickCoventry CV47ALUK

Plant disease recognition is of vital importance to monitor plant development and predicting crop ***,due to data degradation caused by different conditions of image acquisition,e.g.,laboratory *** environment,machine learning-based recognition models generated within a specific dataset(source domain)tend to lose their validity when generalized to a novel dataset(target domain).To this end,domain adaptation methods can be leveraged for the recognition by learning invariant representations across *** this paper,we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization,namely,Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification(MSUN).Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial ***,MSUN comprises multirepresentation,subdomain adaptation modules and auxiliary uncertainty *** multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source *** effectively alleviates the problem of large interdomain *** adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass ***,the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain *** was experimentally validated to achieve optimal results on the PlantDoc,Plant-Pathology,Corn-Leaf-Diseases,and Tomato-Leaf-Diseases datasets,with accuracies of 56.06%,72.31%,96.78%,and 50.58%,respectively,surpassing other state-of-the-art domain adaptation techniques considerably.

关键词： Plant breakthrough details

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