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Medical Feature Selection Approach Based on Generalized Normal Distribution Algorithm

computers, Materials & Continua 2021年第12期69卷 2883-2901页

作者： Mohamed Abdel-Basset Reda Mohamed Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam Mohamed Abouhawwash Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Capability Systems Centre School of Engineering and ITUNSWCanberraAustralia Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance,redundancy,or less information;this pre-processing process is often known as feature *** technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization(GNDO)supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary ***,a novel restarting strategy(RS)is proposed to preserve the diversity among the solutions within the population by identifying the solutions that exceed a specific distance from the best-so-far and replace them with the others created using an effective updating *** strategy is integrated with GNDO to propose another binary variant having a high ability to preserve the diversity of the solutions for avoiding becoming stuck in local minima and accelerating convergence,namely improved GNDO(IGNDO).The proposed GNDO and IGNDO algorithms are extensively compared with seven state-of-the-art algorithms to verify their performance on thirteen medical instances taken from the UCI *** is shown to be superior in terms of fitness value and classification accuracy and competitive with the others in terms of the selected *** the principal goal in solving the FS problem is to find the appropriate subset of features that maximize classification accuracy,IGNDO is considered the best.

关键词： Generalized normal distribution optimization feature selection transfer function novel restarting strategy UCI repository

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Clustering Approach for Modeling Course Difficulty Level in Adaptive Learning

Clustering Approach for Modeling Course Difficulty Level in ...

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2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement systems and Applications, CIVEMSA 2024

作者： Imamah Yuhana, Umi Laili Djunaidy, Arif Pardede, Eric Purnomo, Mauridhi Hery Departement of Electrical Engineering Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Informatics Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Information Systems Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Computer Science Information Technology La Trobe University BundooraVIC3086 Australia

ISBN: (纸本)9798350322996

The variations in students' abilities present challenges in achieving desired learning outcomes. Students exhibit different levels of comprehension regarding the complexity of learning materials. In this study, we aim to model the difficulty levels of learning materials using a clustering approach to offer personalized learning material recommendations within an adaptive learning framework. Data were collected from student scores of Data Structure course obtained between 2020 and 2022 at the University of Trunojoyo Madura. The dataset modeling process adheres to the CRISP-DM Methodology, employing the K-Means clustering method and Euclidean Distance. We employed the Within-Cluster Sum of Squares (WCSS) metric and silhouette score to identify the optimal K-value, which resulted in a value of 5, accompanied by a silhouette score of 0.629. Our analysis pinpointed Stack and Deque as the easiest, while Linked List emerged as the most challenging course material. Accordingly, we will assign a label of 1 to the easiest materials, 2 to those of medium difficulty, and 3 to the most challenging ones. These labels will serve as criteria for recommending suitable learning materials to students based on their knowledge level. We will assess the ratio between the overall knowledge level score and the difficulty level of the material. The ratio value will serve as a reference for determining material recommendations for students. Materials categorized with the highest difficulty levels are exclusively suggested to students demonstrating faster learning abilities with a high value of ratio, and vice versa. This approach is feasible for implementation in adaptive learning. © 2024 IEEE.

关键词： Contrastive Learning

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Designing Avatar System and Integrate to the Metaverse

Designing Avatar System and Integrate to the Metaverse

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International Technical Conference o Circuits/systems, computers and Communications (ITC-CSCC)

作者： Suebphong Noisri Pisut Wisessing Kunanont Srisupakwong Halim Budi Santoso Lunchakorn Wittisittikulkij Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Computer Science Faculty of Science Kasetsart University Bangkok Thailand Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Department of Information Systems Faculty of Information Technology Universitas Kristen Duta Wacana Yogyakarta Indonesia

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Digital representations of users in virtual environments are known as avatars in the metaverse. They serve as the main vehicle for self-expression and identification in these digital places, enabling people to connect, socialize, and travel throughout the metaverse. Avatars' physical characteristics can be altered, such as their body type, skin tone, facial features, hairstyle, attire, and accessories. Design, development, and integration activities are usually combined for the avatar system to provide a smooth and interesting user experience. However, prior studies lack on how we should be able to customize the Avatar systems and integrate them into the Metaverse systems. This research tries to highlight and propose a method that might be able to deploy the avatar creation with a more straightforward user interface and apply it to the Metaverse environment. Our study provides a practical contribution to how to create a more straightforward avatar customization system and integrate it into the Metaverse Environment.

关键词： Metaverse Avatars Scalability Clothing systems architecture User interfaces User experience

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Beyond Traditional Motion Planning: A Proximal Policy Optimization Reinforcement Learning Approach for Robotics 5th

Beyond Traditional Motion Planning: A Proximal Policy Optimi...

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5th Joint International Conference on AI, Big Data and Blockchain, ABB 2024

作者： Rjoub, Gaith Drawel, Nagat Dssouli, Rachida Bentahar, Jamal Kassaymeh, Sofian Alweshah, Mohammed Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science 6G Research Center Khalifa University Abu Dhabi United Arab Emirates Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9783031731501

In today’s technologically-driven era, robotic systems have been integrated into various aspects of our daily lives, handling tasks from household chores to complex industrial applications. With these robotic systems becoming increasingly commonplace in homes, offices, and public spaces, the demand for effective and adaptable motion planning becomes paramount. Traditional motion planning methods, although having made noteworthy progress, still have difficulties with the challenges posed by dynamic environments characterized by unpredictable elements and rapid changes. Recognizing this gap, this paper introduces a novel reinforcement learning-based path planning algorithm specifically designed for dynamic scenarios. Through the integration of Proximal Policy Optimization (PPO) reinforcement algorithm, our approach empowers robots to actively chart their paths, adapting in real-time to unforeseen obstacles and shifts in the environment. To validate our methodology, we undertook extensive experiments comparing our approach to established benchmark methods, notably SARSA, A∗, and Regular Reinforcement Learning (RL) (i.e., non PPO-based). The results not only highlight the effectiveness of our proposed solution but also emphasize its potential to reshape how robotic systems navigate complex, ever-evolving environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Robot programming

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Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

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computer systems science & engineering 2023年第8期46卷 1901-1916页

作者： Mohammad Yamin Sarah Basahel Saleh Bajaba Mona Abusurrah ELaxmi Lydia Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Business Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

Recently,there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy(DR).DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed *** detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus *** article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy(DLLSHDM-DR)on Retinal Fundus *** proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making *** the DLLSHDM-DR technique,image preprocessing is initially performed to improve the quality of the fundus ***,the DLLSHDM-DR applies HybridNet for producing a collection of feature *** retinal image classification,the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer(EPO)with a Deep Recurrent Neural Network(DRNN).The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our *** assuring the improved performance of the DLLSHDMDR model,a wide range of experiments was tested on the EyePACS *** comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.

关键词： Decision making healthcare sector deep learning diabetic retinopathy emperor penguin optimizer

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Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

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computers, Materials & Continua 2022年第12期73卷 5441-5458页

作者： Heba G.Mohamed Saud S.Alotaibi Majdy M.Eltahir Heba Mohsen Manar Ahmed Hamza Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid 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

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time *** several applications comprise IoD in real time environment,significant interest has been received by research *** IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD *** article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD *** major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD *** do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible *** addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate ***,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of *** performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

关键词： Internet of drones unmanned aerial vehicles security intrusion detection machine learning

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Drone Gesture Control using OpenCV and Tello

Drone Gesture Control using OpenCV and Tello

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Conference on Information Communication Technology and Society (ICTAS)

作者： Nokuthula Khoza Pius Owolawi Vusumuzi Malele Department of Computer Systems Engineering Faculty of Information and Communication Technology Soshanguve Campus Tshwane University of Technology Pretoria South Africa Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus North-West University Vanderbijlpark South Africa

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography.

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Performance Analysis of Tractors Traveling on Rough Terrain

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ASME Letters in Dynamic systems and Control 2022年第4期2卷 041001页

作者： Izumi, Shinsaku Hayashida, Riku Xin, Xin Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama 719-1197 Japan Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Okayama 719-1197 Japan

This article considers tractors traveling on rough terrain and studies the effects of the road surfaces on their behavior. Clarifying these effects is useful for their stable traveling on rough terrain, which contributes to the automation of tractors. We select the H2norm as a performance index to quantify the behavior of tractors and attempt to analyze the norm of a system corresponding to a tractor traveling on rough terrain. To improve the accuracy of the analysis, we modify the system by focusing on the relationship between the H2norm, white noise, and the characteristics of the road surface. Subsequently, the H2norm of the resulting system is derived as a function of the parameters, including the road roughness. Numerical examples demonstrate that the analysis result based on the modified system accurately captures the behavior of the tractor compared with the result based on the original system.

关键词： linear systems vibrations vehicle dynamics and control Design Pavement Surface roughness Roads Vibration White noise

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An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

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computers, Materials & Continua 2021年第9期68卷 2961-2977页

作者： Mohamed Abdel-Basset Reda Mohamed Mohamed Abouhawwash Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Capability Systems Centre School of Engineering and ITUNSW CanberraAustralia Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the ***,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels ***,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal *** improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive ***-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed *** develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local *** two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR *** compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and *** was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with

关键词： Magnetic resonance imaging brain image segmentation artificial jellyfish search algorithm ranking method local minima Otsu method

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H2 Performance Analysis of Tractors Traveling on Rough Terrain

ASME Letters in Dynamic Systems and Control

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ASME Letters in Dynamic systems and Control 2022年第4期2卷

作者： Izumi, Shinsaku Hayashida, Riku Xin, Xin Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama719-1197 Japan Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Okayama719-1197 Japan

This article considers tractors traveling on rough terrain and studies the effects of the road surfaces on their behavior. Clarifying these effects is useful for their stable traveling on rough terrain, which contributes to the automation of tractors. We select the H2 norm as a performance index to quantify the behavior of tractors and attempt to analyze the norm of a system corresponding to a tractor traveling on rough terrain. To improve the accuracy of the analysis, we modify the system by focusing on the relationship between the H2 norm, white noise, and the characteristics of the road surface. Subsequently, the H2 norm of the resulting system is derived as a function of the parameters, including the road roughness. Numerical examples demonstrate that the analysis result based on the modified system accurately captures the behavior of the tractor compared with the result based on the original system. Copyright © 2022 by ASME.

关键词： Linear systems

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