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4th IEEE National Computing Colleges Conference, NCCC 2021

作者： Alahmari, Asmaa Jamal, Amani Elazhary, Hanan King Abdulaziz University Faculty of Computing Information Technology Computer Science Department Jeddah Saudi Arabia University of Jeddah College of Computer Science Engineering Jeddah Saudi Arabia

ISBN: (纸本)9781728167190

Clustering is one of the most important data analysis tasks. It is used to organize data points into groups or clusters. Each cluster has similar instances, which are dissimilar to instances belonging to other clusters. Clustering is used in multiple disciplines and has an integral role in a wide variety of applications. This paper presents a comparative study of three common density-based clustering algorithms namely, DBSCAN, OPTICS and Mean-shift. The results are supported by an experimental evaluation using twelve datasets. © 2021 IEEE.

关键词： Clustering algorithms

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Evolving Mobile Cloud: Enhancement Technologies and Future Approaches

Evolving Mobile Cloud: Enhancement Technologies and Future A...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Mahesh TR Ritesh Kumar Mrs. Kehkeshan Jallal S Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

There are still limitations in terms of life of the battery and processing power that make it difficult to fulfill the expanding needs of mobile users, despite the fast progress in hardware capacity or the widespread usage of mobile devices. Both conventional and cutting-edge approaches have been investigated to close the distance between customer demands and the constraints of mobile devices. In the context of the mobile cloud, current studies have concentrated on boosting the performance for mobile devices using augmentation approaches. Our emphasis is on the methods themselves, as opposed to current taxonomies in this area, with the aim of establishing a whole mobile computing cloud system. With the goal of ensuring dependable mobile cloud services, this survey may be used as a reference for implementing the augmentation approaches that are now accessible in portable cloud computing systems. We also talk about the unresolved issues and prospective directions for further study in this area.

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Game-Theoretic Power Allocation and Client Selection for Privacy-Preserving Federated Learning in IoMT

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IEEE Transactions on Communications 2025年

作者： Liu, Jingyuan Chang, Zheng Ye, Chaoxiong Mumtaz, Shahid Hamalainen, Timo University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Jyväskylä Department of Psychology 35 JyväskyläFIN-40014 Finland Nottingham Trent University Department of Computer Science United Kingdom University of Jyväskylä Faculty of Information Technology 35 JyväskyläFIN-40014 Finland

In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. Federated learning (FL) can enhance the utilization of patient data while protecting privacy. Despite the great potential of FL to enhance the architecture of IoMT, the need for effective interference management and the limited energy resources of IoMT devices make the integration of FL into IoMT environments particularly challenging. This study proposes an innovative framework to address these challenges by optimizing power allocation and client selection across participating IoMT devices in the FL process. By employing a Stackelberg game model, our approach orchestrates power allocation among IoMT devices to enhance communication efficiency while adhering to strict differential privacy (DP) standards. Regarding the availability of network state information, we propose non-uniform pricing and uniform pricing strategies, respectively. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. To tackle the issue of energy constraints in IoMT devices, we adopt Lyapunov optimization for adaptive client selection, ensuring sustainable device participation in the FL process over time. In addition, our approach integrates DP to protect patient data, carefully balancing between privacy and the accuracy of the learning model. Our extensive simulations demonstrate marked improvements in privacy preservation, communication efficiency, and energy management efficiency, highlighting the effectiveness of our proposed method over existing solutions. © 1972-2012 IEEE.

关键词： Nonlinear programming

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A deep convolutional neural network model for multi-class fruits classification 19th

A deep convolutional neural network model for multi-class fr...

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19th International Conference on Intelligent Systems Design and Applications, ISDA 2019

作者： Alzubaidi, Laith Al-Shamma, Omran Fadhel, Mohammed A. Arkah, Zinah Mohsin Awad, Fouad H. University of Information Technology and Communications Baghdad Iraq Faculty of Science and Engineering Queensland University of Technology Brisbane Australia College of Computer Science and Information Technology University of Anbar Anbar Iraq University of Sumer Thi Qar Iraq

ISBN: (纸本)9783030493417

Fruits classification is a challenging task due to the several types of fruits. To classify fruits more effectively, we propose a new deep convolutional neural network model to classify 118 fruits classes. The proposed model combines two aspects of convolutional neural networks, which are traditional and parallel convolutional layers. The parallel convolutional layers have been employed with different filter sizes to have better feature extraction. It also helps with backpropagation since the error can backpropagate from multiple paths. To avoid gradient vanishing problem and to have better feature representation, we have used residual connections. We have trained and tested our model on Fruits-360 dataset. Our model achieved an accuracy of 100% on a divided image set from the training set and achieved 99.6% on the test set, which outperformed previous methods. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Fruits

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URLLC in 5G & Beyond: Specifications and Technologies

URLLC in 5G & Beyond: Specifications and Technologies

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Gadug Sudhamsu Awakash Mishra Kiran Kale Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

With the ability to communicate between machines and people, the Tactile Internet (TI) represents a substantial advancement over the Internet of Things (IoT). TI wants to make it easier to develop real-time interactive applications in a variety of fields, including as engineering, social engagement, and business. The future 5th Generation (5G) technology is focused on offering Ultra-Reliable Low Latency Communication (URLLC) services to satisfy the demanding needs of TI applications. According to 3GPP specifications, URLLC may transmit a single 32-byte packet with a latency of less than one millisecond and achieve 99.99% dependability. In order to accomplish these objectives, the 3GPP presents 5G New Radio (5G NR), a flexible orthogonal frequency division multiplexing (OFDM) approach, as a new radio access technology (RAT). With the introduction of this new RAT, it is clear that we need next-generation solutions, especially ones that include network intelligence. To fulfil the URLLC needs of 5G NR, machine learning (ML) methods are anticipated to be crucial in building intelligent network resource distribution algorithms. This review analyses the possible use of Federated reinforcement learned, an ML approach, to satisfy the needs of 5G NR URLLC and lists the pertinent URLLC accomplishments. Plane channel entry techniques that support URLLC in 5G NR for TI workloads are covered in great detail in this study. It also pinpoints seven crucial upcoming situations where FRL might function as a possible facilitator for URLLC in 5G NR.

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN computer Science 2023年第4期4卷 363页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Development of Two Dimension (2D) Game Engine with Finite State Machine (FSM) Based Artificial Intelligence (AI) Subsystem

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Procedia computer Science 2024年 235卷 2996-3006页

作者： Abidemi Emmanuel Adeniyi Biswajit Brahma Marion Olubunmi Adebiyi Joseph Bamidele Awotunde Rasheed Gbenga Jimoh Enoch Olasinde Anjan Bandyopadhyay Department of Computer Science Bowen University Iwo Osun State Nigeria McKesson Corporation USA 32559 Lake Bridgeport St Fremont CA 94555 USA Department of Computer Science Landmark University Omu-Aran Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Nigeria School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

With Al becoming more and more relevant in today’s world, this project aims to develop a 2D game engine with an Al subsystem for state-driven agents, which is rarely implemented by a lot of 2D engines out there. In this study, a 2D game engine was designed with an FSM (Finite State Machine)--based AL subsystem using state-driven game agents. The engine was implemented using the Javascript programming language and the WebGL 2.0 graphics library/API. It is targeted at web-based games/simulations. Components and subsystems include physics, audio, math, rendering, and AI (based on finite-state machines). The FSM-based AI subsystem is a solution aimed at reducing the ambiguity and performance hits associated with creating 2D game AI in the naive approach. The AI subsystem creates an interface for 2D games to be created with a common paradigm, and simulated with a great level of realism. The state machine used in this study is used to represent a variety of behaviours, such as wandering, attacking, and fleeing. The following conclusions were drawn as regards the impact of the AI approach used on rendering performance; the naive approach to implementing game AI is also compared with the FSM approach in terms of rendering (frames per second/ FPS, or frame rate). With the naive approach (vector math) being used to implement AI, there was a drop in the rendering frame rate to 50 FPS. The FSM approach didn’t affect the frame rate, which is usually at 60FPS. With a well-developed FSM (Finite State Machine), game agents can transition between states easily as a response to user input or stimulus from the game environment. The proposed method was tested by creating a prototype game with the 2D game engine. The prototype game was a straightforward side-scrolling platformer featuring a cast of non-player characters (NPCs). This approach to implementing 2D game AI goes a long way toward improving performance and mitigating ambiguity in the code.

关键词： Artificial intelligence 2D game engine Finite state machine software

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis

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Procedia computer Science 2023年 225卷 4930-4942页

作者： Joanna Kołodziejczyk Yuliia Tarasenko Piotr Sulikowski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw 04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment.

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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GazeFed: Privacy-Aware Personalized Gaze Prediction for Virtual Reality

GazeFed: Privacy-Aware Personalized Gaze Prediction for Virt...

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International Workshop on Quality of Service

作者： Jiang Wu Xuezheng Liu Miao Hu Hongxu Lin Min Chen Yipeng Zhou Di Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Gaze prediction is essential for enhancing user experiences of virtual reality (VR) applications. However, existing methods seldom considered the privacy nature of gaze data, which may reveal both psychological and physiological characteristics of VR users. Moreover, the commonly adopted one-sizefits-all prediction model cannot well capture behavioral patterns of different VR users. In this paper, we propose a privacyaware personalized gaze prediction framework called GazeFed, which can train a personalized gaze prediction model for each user in a collaborative manner. In GazeFed, only intermediate computations are exchanged between users and the server. The raw gaze data samples are locally preserved to protect user privacy. The global model is shared among all users, which can be further trained with local gaze data to generate a personalized prediction model for each individual user. We also propose a deep neural network tailored for VR gaze prediction called GazeNet, which can effectively extract features from VR contents, gaze data and other user behaviors, and improve the accuracy of gaze prediction. Moreover, the technique of differential privacy (DP) is also integrated to provide more privacy protection, and we theoretically prove that GazeFed can well converge and satisfy the requirement of differential privacy in the meanwhile. Last, we conduct extensive experiments to evaluate the effectiveness of our proposed GazeFed on real datasets and various VR scenarios. The experimental results demonstrate that GazeFed outperforms the state-of-the-art approaches.

关键词： Solid modeling Privacy Differential privacy Accuracy Computational modeling Virtual reality Predictive models

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Analysis of Network Technologies and Cyber security Assessment for Enhancing Machine Learning, Grid Computing and Cyber-Physical Connectivity Internetwork Effectiveness 2

Analysis of Network Technologies and Cyber security Assessme...

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2nd International Conference on Innovative Practices in technology and Management, ICIPTM 2022

作者： Dhanalakshmi, B. Singh, Chaitanya Surya, S. Hedage, Atharav Kuchhal, Shikha Tongkachok, Korakod B.S. Abdur Rahman Crescent Institute Of Science And Technology Computer Science And Engineering Tamilnadu Vandalur India Swarrnim Startup Innovation University Computer Engineering Department Gujarat Gandhinagar India School Of Computing Kalasalingam University Computer Science And Engineering Tamilnadu Sirivelliputhur India Vishwakarma Institute Of Information Technology Electronics And Telecommunications Pune Belgaum Karnataka India Spitm Dcrust Department Of Ece Murthal India Thaksin University Faculty Of Law Thailand

ISBN: (纸本)9781665466431

The fast improvement of the Internet has incredibly worked with individuals spreading information worldwide, prompting the Internet turning into a pivotal apparatus in many fields like science, trade, schooling, etc. With the Internet common in such countless parts of day to day existence, network security has become basic and requests consistent consideration. For this situation, an IDS's motivation is to recognize between real organization associations and possibly hurtful ones. The recommended method in this examination centers around an interruption identification and avoidance system that comprises of Cloudlet Controller (CC), Trust Authority (TA), and Virtual Machine Manager to recognize questionable practices in a cloud climate. Cloud clients are migrated to different regions in the climate, as indicated by our proposed approach. Whenever information bundles from different clients are gathered, network traffic happens. To resolve this issue, we assembled the Cloudlet Controller, which gets bundles from different clients through switch. CC gets parcels until they arrive at their edge level;if the cloudlet is over-burden, the bundles are shipped off inactive cloudlets. In this philosophy, network traffic is decreased, and it is significantly helpful in identifying interruptions in a straightforward way. In this review, a cross breed arrangement based interruption location and counteraction framework on the cloud is worked to distinguish gatecrasher exercises in the framework. A cross breed characterization based interruption location framework is created with a few cloudlets, a cloudlet regulator, a confided in power, and a virtual machine supervisor to distinguish interloper assaults in the framework, as well as a deterrent instrument to safeguard bundles from gatecrashers. © 2022 IEEE.

关键词： Machine learning

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