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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Kanwal, Anika Chandrasekaran, Siva Akhunzada, Adnan Swinburne University of Technology Department of Computer Science and Software Engineering Melbourne Australia University of Doha for Science and Technology Department of Computing and Information Technology Doha Qatar

ISBN: (纸本)9798350322347

Academic and financial sectors are interested in research areas that focus on understanding the patterns of financial activities and predicting their future changes. The daily movement of financial data involves complex, insufficient, and ambiguous information, making its prediction an extremely difficult endeavour. The issues of forecasting and analysing financial data is therefore complex and time-dependent. Deep neural networks (DNNs) can be used to solve nonlinear problems more effectively than conventional machine learning techniques. This study proposes an intelligent and optimal model CuDNNLSTM-Multi(1dCNN) for stock market price prediction using a hybrid of CUDA based Long Short Term Memory (LSTM) and Multiple One Dimensional Convolution Neural Networks (CNNs). A variety of models, including Multiple CuDNNLSTM-CNN, Multi-CuDNNLSTM, Multi (1dCNN), are used to compare the prediction performance of the proposed model. In terms of RMSE, MAE, and MAPE, the results show that the suggested model outperforms all hybrid and individual models. © 2023 IEEE.

关键词： Forecasting

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Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

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computer Systems science & engineering 2022年第5期41卷 479-492页

作者： A.S.Anakath R.Kannadasan Niju P.Joseph P.Boominathan G.R.Sreekanth School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science CHRIST(Deemed to be University)Bengaluru560029India Department of Information Security School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information *** use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing *** cloud is nowadays highly affected by internal threats of the *** applications such as banking,hospital,and business are more likely affected by real user *** intruder is presented as a user and set as a member of the *** becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or *** major issue in today's technological development is identifying the insider threat in the cloud *** data are lost,compromising cloud users is *** and security are not ensured,and then,the usage of the cloud is not *** solutions are available for the external security of the cloud ***,insider or internal threats need to be *** this research work,we focus on a solution for identifying an insider attack using the artificial intelligence *** insider attack is possible by using nodes of weak users’*** will log in using a weak user id,connect to a network,and pretend to be a trusted ***,they can easily attack and hack information as an insider,and identifying them is very *** types of attacks need intelligent solutions.A machine learning approach is widely used for security *** date,the existing lags can classify the attackers *** information hijacking process is very absurd,which motivates young researchers to provide a solution for internal *** our proposed work,we track the attackers using a user interaction behavior pattern and deep learning *** usage of mouse movements and clicks and keystrokes of the real user is stored in a *** deep belief neural

关键词： Cloud computing security insider attack network security privacy user interaction behavior deep belief neural network

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Network Coverage Improvement during Natural Disaster using Self-Organizing Maps

Network Coverage Improvement during Natural Disaster using S...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Hoang, Nam Devabhakthini, Prathyusha Shukla, Raj Mani Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH United States Anglia Ruskin University Computing and Information Science Cambridge United Kingdom

ISBN: (纸本)9798350322972

This paper presents an emergency response management system to tackle the problem of the absence of network connectivity during the time of a natural disaster. Network connectivity is often enabled by the base stations on the ground. However, during the time of the disaster, the connectivity is disrupted due to the base station being damaged. During such scenarios, the Unmanned Aerial Vehicles (UAV) based stations could help in partially providing the network connectivity and help in the rescue operations. But, the UAVs need to be quickly deployed and placed at a suitable location based on the population coverage and base stations being impacted due to the disruptions. In this paper, we propose the Self Organizing Map (SOM) based optimal UAV deployment to enhance the network coverage, and increase the percentage of people having network access. In contrast to other Artificial Intelligence-based approaches, like Deep Neural Networks, our method does not require to be heavily trained using train and the test dataset. © 2023 IEEE.

关键词： Self organizing maps

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Attention Enhanced Feature Fusion for Mental Stress Level Classification 21

Attention Enhanced Feature Fusion for Mental Stress Level Cl...

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21st International computer Conference on Wavelet Active Media Technology and information Processing, ICCWAMTIP 2024

作者： Sahlu, Ameneshewa Abush Yohannes, Gobena Mulugeta Dawit, Bekalu Nigus Teshome, Molla Woretaw School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331519254

Mental stress poses significant health risks, manifesting in various psychological and physical issues such as depression, anxiety, and cardiovascular complications. Establishing a reliable method for swiftly and accurately classifying mental stress levels is crucial due to its widespread impact. Existing classification approaches predominantly rely on either data-intensive traditional machine learning algorithms or opaque deep learning models, hindering real-world use and interpretability. This study addresses these limitations by proposing a novel approach for stress level classification using the WESAD dataset. Our method combines an attention-based ResN et with CBAM. This innovative approach synergistically integrates shallow and deep features, harnessing both low-level details and high-level semantic information to effectively classify stress levels into four distinct states: baseline, stress, amusement, and meditation, or three states excluding the meditation state. The experimental result shows 92.74% accuracy and 91.19% F1 score for the 4-class setup, and 97.58% accuracy and 97.51 % F1 score for the 3-class setup. Our approach offers a more robust and interpretable framework for mental stress classification, with potential applications in health care and stress management. © 2024 IEEE.

关键词： Deep learning

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Digital Assistant for Visually Impaired People 5

Digital Assistant for Visually Impaired People

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Thayaparan, Thuvaraka Jayakumar, Sanjay Fernando, B.A.I. Ananthanadarajan, Gobisan Abeywardana, Lakmini Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper presents a comprehensive exploration of various components to enhance the capabilities of a digital assistant tailored for visually impaired individuals. The first component explores how various image captioning architectures and Vision Encoder Decoder models can be used for better environmental interaction. The second component concerns with facial expression recognition for better social interaction and the third component is a currency note identification component that aims to aid visually impaired individuals with cash transaction whilst the final component that explores a voice bot aims to improve a voice bot and how to manage accents, speech accuracy and naturality using modern deep learning techniques. By investigating these components, this research advances assistive technology, empowering visually impaired individuals with a conversational digital assistant that enhances environmental interaction, supports social interactions, and aids in currency identification. © 2023 IEEE.

关键词： Deep learning

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Container-based data-intensive application scheduling in hybrid cloud-edge collaborative environment

Container-based data-intensive application scheduling in hyb...

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作者： Tang, Bing Luo, Jincheng Zhang, Jiaming School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangdong Guangzhou China School of Information Engineering Wuhan University of Technology Hubei Wuhan China

Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. It can bring convenience to system deployment, operation, and maintenance. This article considers the implementation of AI-based IoT applications based on Docker and container technology in a cloud-edge collaborative environment. Three typical data-intensive applications, preprocessing, training, and inference in machine learning, are deployed using Docker containers. For these three types of tasks, this article proposes a container-based data-intensive application scheduling framework. Using an attribute-based data-driven method, a new scheduling approach considering multiple weighting factors is proposed, which is called DICS-OPT. The cloud nodes and edge nodes are scored and sorted uniformly. The container is scheduled to the node with the highest score, and then the task data is transmitted to the node to execute the task. NSGA-III-based genetic algorithm has been proposed to search for the optimal weighting factors. This article introduces the implementation of the prototype system and builds a cloud-edge collaborative testbed consisting of Raspberry Pis and PCs. The performance evaluation results indicates that the proposed scheduling approach outperforms existing approaches. Compared with existing approaches, DICS-OPT improves the average edge resource utilization by 10.05% to 69.04%, and saves the cloud resource cost by 16.02% to 36.68%. © 2023 John Wiley & Sons Ltd.

关键词： Edge computing

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Innovating Project Management: AI Applications for Success Prediction and Resource Optimization 2nd

Innovating Project Management: AI Applications for Success P...

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2nd International Conference on Advances in Computing Research, ACR 2024

作者： Tarawneh, Monther AbdAlwahed, Huda AlZyoud, Faisal Computer Science Department Isra University Amman Jordan Software Engineering Department Isra University Amman Jordan Information Technology Department Tafilah Technical University Tafila Jordan

ISBN: (纸本)9783031569494

The revolution of Artificial Intelligence (AI) has made transformative changes in all industries. The field of software engineering has great benefit from AI by Automat all process. Applying Artificial intelligence (AI) technologies, particularly Deep Neural Network (DNN) models, to project management will transform traditional methodologies to new ways. This work investigates how AI can be used within project management to better allocate resources, estimate time, and predict costs. Data from many recourses used to improve accuracy in project planning, refine how risks are managed, and develop new methods. Data includes real projects portfolio, open-source data, and simulated data. The integration of AI in project management examines project success and client satisfaction. This work shows 99% accuracy for learning and 78% for testing of predicting project success and highlights the need to constantly update methods to effectively use the big potential changes AI can bring to project management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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Design Of A Framework For Optimizing Bandwidth Allocation Of Video In Wireless Networks Using Hybrid Meta Heuristic Algorithm 5

Design Of A Framework For Optimizing Bandwidth Allocation Of...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Akinyokun, Oluwole Adegoke, Abejide Ashade, Bidemi Achas, Joy Anuoluwapo, Olagunju Ogunbiyi, Temitope Federal University of Technology Department of Software Engineering Ondo State Akure Nigeria Bells University of Technology Department of Computer Science and Information Technology Ogun State Ota Nigeria

ISBN: (纸本)9798331542559

Video transmission in wireless network poses several challenges such as latency, delayed throughput, slow convergence rate, high error rates, packet losses and many others which make it impossible for users to enjoy Quality of Service (QoS). Video in wireless network presents additional challenges due to unavailability of needed bandwidth. Bandwidth in any organization is a limited resource that is being competed for by the different users. Efficient and effective bandwidth allocation is a vital role in satisfying the QoS requirements for multimedia applications such as video streaming. This research work examined conventional and current techniques for bandwidth optimization. For better bandwidth use, conventional bandwidth allocation techniques are gradually being replaced with optimization algorithms using Genetic Algorithm (GA), Bat Algorithm (BA), Whale Optimization Algorithm (WOA), Tabu Search (TS), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and so on. This work specifically addressed bandwidth challenges using hybrid algorithms of PSO+TS algorithms in optimizing the bandwidth allocation of Bells University of Technology, Ota, Ogun State, Nigeria. The PSO algorithm explores bandwidth by giving high priority to video traffic and low priority to other types of traffic (data traffic) while the TS algorithm exploits the bandwidth. The results were evaluated using CPU time and Worst Case Time Complexity of the algorithms. Results showed that with the proposed system, PSO+TS algorithm outperformed other individual algorithms of PSO and TS. Video traffics depending on resolution had higher priority levels and were assigned higher bandwidth while data traffic were assigned low bandwidth and as such video users will be able to stream conveniently with good QoS. © 2024 IEEE.

关键词： Ant colony optimization

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A Deep Learning Approach for Prediction of Protein Secondary Structure

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computers, Materials & Continua 2022年第8期72卷 3705-3718页

作者： Muhammad Zubair Muhammad Kashif Hanif Eatedal Alabdulkreem Yazeed Ghadi Muhammad Irfan Khan Muhammad Umer Sarwar Ayesha Hanif Department of Computer Science Government College UniversityFaisalabadPakistan Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Software Engineering/Computer Science Al Ain UniversityAbu DhabiUnited Arab Emirates

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary *** this reason,it is important to design methods for accurate protein secondary structure *** of the existing computational techniques for protein structural and functional prediction are based onmachine learning with *** deep learning architectures have already been applied to tackle protein secondary structure prediction *** this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were *** input to proposed models is amino acid sequences which were derived from CulledPDB *** tuning with cross validation was employed to attain best parameters for the proposed *** proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

关键词： Convolutional neural network machine learning protein secondary structure deep learning long short-term memory protein secondary structure prediction

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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