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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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From Data to Diagnosis: Investigating Approaches in Mental Illness Detection

From Data to Diagnosis: Investigating Approaches in Mental I...

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Intelligent Methods, systems, and Applications (IMSA)

作者： Reem Kadry Montasser Sherif A. Mazen Iman M. A. Helal Software Engineering Department Faculty of Computer Science October University for Modern Sciences and Arts Giza Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Mental illness is a considerable global public health problem, impacting both individual well-being and society's health. The growing popularity of social media and the increase of other data sources led to more research interest in detecting mental illness with a particular focus on utilizing and fusing emotional information. Artificial intelligence techniques like natural language processing and others, have shown encouraging improvements in recognizing complex relationships in a wide range of textual data, including clinical notes, social media posts, and interviews. In this paper, we assess the different branches of artificial intelligence based on previous research in mental illness detection, discussing methods, trends, challenges, and future research directions.

关键词： Deep learning Machine learning algorithms Accuracy Social networking (online) Soft sensors Mental health Feature extraction

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Optimizing Random Forest Classifier for Detection of Alzheimer's Using OASIS

Optimizing Random Forest Classifier for Detection of Alzheim...

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Silchar Subsection Conference (SILCON), IEEE

作者： Karri Sai Sanjana Reddy Kishor Kumar Reddy C. Srinath Doss Kari Lippert Dept. of Computer Science and Engineering Stanley College of Engineering and Technology for Women Hyderabad India Faculty of Engineering and Technology Botho University Botswana Dept. of Systems Engineering University of South Alabama Alabama USA

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

Alzheimer's disease (AD) is a progressive brain disorder impacting behavior, memory, and cognition, with over a million cases reported annually in India. The risk significantly increases beyond age 65. Early diagnosis and treatment can result in better recovery. We propose a predictive model using the Random Forest algorithm and the OASIS dataset for early AD diagnosis, leveraging MRI data, clinical notes, genetic markers, and cognitive test results. Our model was evaluated against several others, including Decision Tree, AdaBoost, SVM, and Logistic Regression. With a 97.3% accuracy and a 2.7% error rate, our Random Forest Classifier o utperformed t he others, demonstrating superior predictive power for early AD diagnosis and potentially improving patient care.

关键词： Training Accuracy Transfer learning Predictive models Prediction algorithms Genetics Classification algorithms Alzheimer's disease Random forests Medical diagnostic imaging

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Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

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computers, Materials & Continua 2022年第9期72卷 5233-5250页

作者： Nawaf Alhebaishi Abdulrhman M.Alshareef Tawfiq Hasanin Raed Alsini Gyanendra Prasad Joshi Seongsoo Cho Doo Ill Chul Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Sejong UniversitySeoulKorea School of Software Soongsil UniversitySeoul06978Korea Artificial Intelligence Education Hankuk University of Foreign StudiesDongdaemun-guSeoul02450Korea

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end *** computing is considered a method of forwarding the processing and communication resources in the cloud towards the *** of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved *** this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing *** SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource *** goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy *** problem is formulated based on the availability of VMs,task characteristics,and queue *** integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data *** experimental validation,a comprehensive set of simulations were performed using the CloudSim *** experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

关键词： Resource scheduling edge computing soft computing fitness function virtual machines

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Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization

arXiv

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arXiv 2023年

作者： Chadi, Mohamed-Amine Mousannif, Hajar Laboratory of Informatics Systems Engineering Department of Computer Science Faculty of Sciences Semlalia University of Cadi Ayyad Marrakech40000 Morocco

This paper presents a review of the field of reinforcement learning (RL), with a focus on providing a comprehensive overview of the key concepts, techniques, and algorithms for beginners. RL has a unique setting, jargon, and mathematics that can be intimidating for those new to the field or artificial intelligence more broadly. While many papers review RL in the context of specific applications, such as games, healthcare, finance, or robotics, these papers can be difficult for beginners to follow due to the inclusion of non-RL-related work and the use of algorithms customized to those specific applications. To address these challenges, this paper provides a clear and concise overview of the fundamental principles of RL and covers the different types of RL algorithms. For each algorithm/method, we outline the main motivation behind its development, its inner workings, and its limitations. The presentation of the paper is aligned with the historical progress of the field, from the early 1980s Q-learning algorithm to the current state-of-the-art algorithms such as TD3, PPO, and offline RL. Overall, this paper aims to serve as a valuable resource for beginners looking to construct a solid understanding of the fundamentals of RL and be aware of the historical progress of the field. It is intended to be a go -to reference for those interested in learning about RL without being distracted by the details of specific applications. © 2023, CC BY.

关键词： Reinforcement learning

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Cybersecurity Study Programs: What’s in a Name?

arXiv

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arXiv 2024年

作者： Vykopal, Jan Švábenský, Valdemar Lopez, Michael Tuscano Čeleda, Pavel Masaryk University Faculty of Informatics Brno Czech Republic Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka Japan Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines

Improving cybersecurity education has become a priority for many countries and organizations worldwide. Computing societies and professional associations have recognized cybersecurity as a distinctive computing discipline and created specialized cybersecurity curricular guidelines. Higher education institutions are introducing new cybersecurity programs, attracting students to this expanding field. In this paper, we examined 101 study programs across 24 countries. Based on their analysis, we argue that top-ranked universities have not yet fully implemented the guidelines and offer programs that have "cyber" in their name but lack some essential elements of a cybersecurity program. In particular, most programs do not sufficiently cover non-technical components, such as law, policies, or risk management. Also, most programs teach knowledge and skills but do not expose students to experiential learning outside the traditional classroom (such as internships) to develop their competencies. As a result, graduates of these programs may not meet employer expectations and may require additional training. To help program directors and educators improve their programs and courses, this paper offers examples of effective practices from cybersecurity programs around the world and our teaching practice. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk management

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Lyapunov Characterization for ISS of Impulsive Switched systems

arXiv

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arXiv 2024年

作者： Ahmed, Saeed Bachmann, Patrick Trenn, Stephan Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands The Institute of Mathematics University of Würzburg Germany The Jan C. Willems Center for Systems and Control The Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Faculty of Science and Engineering University of Groningen Netherlands

In this study, we investigate the ISS of impulsive switched systems that have modes with both stable and unstable flows. We assume that the switching signal satisfies mode-dependent average dwell and leave time conditions. To establish ISS conditions, we propose two types of time-varying ISS-Lyapunov functions: one that is non-decreasing and another one that is decreasing. Our research proves that the existence of either of these ISS-Lyapunov functions is a necessary and sufficient condition for ISS. We also present a technique for constructing a decreasing ISS-Lyapunov function from a non-decreasing one, which is useful for its own sake. Our findings also have added value to previous research that only studied sufficient conditions for ISS, as our results apply to a broader class of systems. This is because we impose less restrictive dwell and leave time constraints on the switching signal and our ISS-Lyapunov functions are time-varying with general nonlinear conditions imposed on them. Moreover, we provide a method to guarantee the ISS of a particular class of impulsive switched systems when the switching signal is unknown. Copyright © 2024, The Authors. All rights reserved.

关键词： Lyapunov functions

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Predictive HR Analytics: Using Machine Learning to Forecast Workforce Trends and Needs 7

Predictive HR Analytics: Using Machine Learning to Forecast ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Angulakshmi, M. Madhumithaa, N. Dokku, Srinivasa Rao Pachar, Sunita Sneha, K. Sahaya Lenin, D. Vellore Institute of Technology School of Computer Science Engineering and Information systems Vellore India Faculty of Management Srmist Katangulatthur Tamilnadu Chennai India Pvp Siddhartha Institute of Technology Department of Business Administration Kanuru Andhra Pradesh Vijayawada520007 India Gla University Ibm Department India Saveetha School of Engineering Simats Department of Ece TN Chennai602105 India

ISBN: (纸本)9798350350067

The present research paper aims to analyze the use of predictive HR analytics integrated with machine learning algorithms for the anticipation of workforce demand and requirements. Thus, based on Kaggle's IBM HR Analytics and Employee Satisfaction Index datasets, the study uses Decision Trees and Random Forest approaches to develop the model and predict essential HR parameters, including attrition rates, job satisfaction, and performance levels. This paper illustrates that with the use of machine learning, it is possible to extract valuable information from the data concerning employees and apply them to the field of Human Resource Management to improve the efficiency of decision-making. The results identify the future opportunities of predictive analytics by illustrating its application for HR functions to reduce the efforts toward proactive management, enhance the methods for recruitment, and enhance the approaches to retention of employees. Thus, the findings of this study enrich the body of knowledge regarding the application of machine learning in improving the field of human resource management, specifically in identifying the best approaches for connecting HR efforts with the strategic objectives of organizations. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

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Electronic systems and Intelligent Computing (ICESIC), International Conference on

作者： S. Muruganandam S.Gnanavel K.E.Narayana M.Jaeyalakshmi K Antony Kumar Department of Computer Science and Business Systems Panimalar Engineering College Chennai India Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu India Department of Information Technology Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%.

关键词： Wireless sensor networks Wireless networks Prevention and mitigation Passwords Routing Mobile nodes Cryptography Communication system security Protection Mobile ad hoc networks

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Super resolution and demosaicing based self learning adaptive dictionary image denoising framework 5

Super resolution and demosaicing based self learning adaptiv...

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5th International Conference on Intelligent Computing and Control systems, ICICCS 2021

作者： Arulkumar, V. Aruna, M. Lakshmi, M. Anantha Rao, B. Hanumantha Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Ravindra College of Engineering for Women Department of Computer Science and Engineering Kurnool India PSCMR College of Engineering and Technology Vijayawada India

ISBN: (纸本)9781665412728

It is an effective technique that can be applied to visual diversity such as editing, visual criticism and the so-called fragmentation of painting images. Creativity, image-based optimization and high-resolution frame work were showcased based on the record-breaking success of a minority representation process. The proposed structure for reconstructing the image is learned from a complete dictionary and much of the image input used for two important problems. It was first used by creating a Gaussian noise figure and then blaming the rain. Special structures using conventional methods suggest that the production environment is grappling with artistic solutions and problems. The proposed method was implemented in rich color using ultra-resolution and demo techniques with removing sound and craftsmanship. © 2021 IEEE.

关键词： Image denoising

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