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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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SimV: A Self-extension Similarity Viterbi Algorithm for Service Function Chain Deployment for Multi-access Edge Computing 22

SimV: A Self-extension Similarity Viterbi Algorithm for Serv...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Deng, Wenqiang Guangzhou College of Applied Science and Technology School of Computer Science Zhaoqing China

ISBN: (纸本)9798331509712

Advancements in Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) offer a lot of potential for providing flexible and low-latency network services at the edge. In NFV, traditional middleboxes are transformed into Virtual Network Functions (VNFs), which are then combined into Service Function Chains (SFCs) to deliver complex network services. However, the features of MEC hosts and VNFs, along with the heterogeneity of their resources, make it challenging to develop low-latency deployment solutions. In this paper, we formulate the problem of Service Function Chain Deployments in Edge networks to minimize Latency under Heterogeneous conditions (SDELH). To address this problem, we designed a self-extension Similarity Viterbi algorithm (SimV), which contains three sub-algorithms. Firstly, a basic Viterbi algorithm is employed to find the delay-optimized SFC deployment path. Secondly, a similarity algorithm is designed to assess suitable hosts for each VNF to streamline the matching process and reduce the time complexity of the Viterbi algorithm. Lastly, a self-extension method is devised to broaden the solution space for enhancing the SFC acceptance rate. Extensive simulations are conducted. Based on the comparison simulation results, SimV significantly reduces decision time for deployment, and outperforms the compared algorithms in terms of latency, number of accepted requests, and scalability, with average improvements of 20.37%, 6.04%, and 14.49%, respectively. © 2024 IEEE.

关键词： Viterbi algorithm

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Employing Artificial Intelligence Methods for the Diagnosis of Autism Spectrum Disorder in Children 5

Employing Artificial Intelligence Methods for the Diagnosis ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sridevi, R. Joy, Helen K Karthikeyan, K.J. Gopika, S.S. Biju, Neha Seirah Shriniha, P.A. Department of Computer Science Bangalore India PSG Institute of Technology and Applied Research Department of Computer Science Coimbatore India

ISBN: (纸本)9798331518097

Accurate and timely diagnosis of disorder known as autism spectrum disorder (ASD) is not an easy task due to the complicated neurodevelopmental condition's high clinical presentation variation. In order to improve the diagnostic procedure for ASD in pediatric patients, machine learning (ML) techniques have come to light as potential approaches. The previous surveys about the practice of ML algorithms for diagnosing ASD in children has been thoroughly reviewed and summarized. The supervised and unsupervised learning, feature selection, and ensemble methods used in ASD research are among the many ML techniques that is methodically examined. The necessity of large-scale, diverse datasets, cross-validation methods, and interpretability are emphasized over the advantages, disadvantages, and potential future directions of ML-based ASD diagnostic models. This study attempts to offer insights for researchers, clinicians, and other stakeholders in the field of ASD diagnosis by critically analyzing the current status of ML in ASD Diagnosis. © 2024 IEEE.

关键词： Self-supervised learning

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A Beam-Steering Broadband Microstrip Antenna with High Isolation

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Chinese Journal of Electronics 2023年第2期32卷 325-333页

作者： JIANG Zhaoneng SHA Yongxin NIE Liying XUAN Xiaofeng School of Computer and Information Hefei University of Technology School of Electronic Science and Applied Physics Hefei University of Technology

In this paper, a 4.2–7.2 GHz(52.6%)beam-steering microstrip antenna was proposed. The proposed antenna consists of three tapered slots and *** three radiation directions of the antenna on the plane are independent of each other, and the three feeds correspond to the three radiation structures. Symmetry isolation trenches are introduced to improve isolation between different ports. Radiation pattern simulation and measurement show horizontal beam steering at the sampled frequencies of 4.2, 5, 6, and 7.2 GHz. The results shows that the overlapped beam of the three ports in the Eplane and H-plane can cover more than 200 degrees and 60degrees, respectively. Apart from the capability of beamsteering, high isolation(more than 28 d B) of the proposed antenna in the operating band is obtained.

关键词： Antenna measurements Mutual coupling Slot antennas Microstrip antennas Antenna feeds Frequency measurement Broadband antennas

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Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete EVent system specification

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International Journal of Vehicle Systems Modelling and Testing 2024年第1期18卷 33-61页

作者： Maatoug, Abdelfettah Kharoubi, Sahraoui Maaskri, Moustafa Belalem, Ghalem Mahmoudi, Said Faculty of Applied Sciences Science and Technology Department University of Tiaret Tiaret14000 Algeria Faculty of Mathematics and Computer Science Computer Science Department University of Tiaret Tiaret14000 Algeria Faculty of Applied Sciences Electrical Engineering Department University of Tiaret Tiaret14000 Algeria Faculty of Exact and Applied Sciences Computer Science Department University of Oran 1 Ahmed Ben Bella Oran31000 Algeria Faculty of Engineering Computer Science Department University of Mons Mons7000 Belgium

This work provides a basis for studying energy management optimisation in power-split hybrid electric vehicles (PSHEVs) to reduce fuel consumption and increase powertrain efficiency by enforcing a strategy related to battery level and vehicle speed. Three modes were used to operate the vehicle to meet different situations related to available energy and desired speed. These are particularly abundance mode, ascetic mode and optimal mode. For this, a system was designed based on several sub-models written in the discrete EVent system (DEVS) specification. The vehicle's power train and control strategy models were created in the DEVSimPy environment, and a joint simulation was carried out from the composite sub models until the overall model is reached. Simulation results under several driving cycles showed that the proposed system improves the energy consumption of the vehicle in different operating modes of the engines and gives more opportunities to adopt electric motors in vehicle transmission. © 2024 Inderscience Enterprises Ltd.

关键词： Energy management

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Fog Computing - Desktop Review 4

Fog Computing - Desktop Review

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4th International Multidisciplinary Information technology and Engineering Conference, IMITEC 2024

作者： Sithole, Sicelo Prince Sonhera, Naume Zuva, Tranos Vaal University of Technology Faculty of Applied and Computer Sciences Department of Computer Science Vanderbijlpark South Africa

ISBN: (纸本)9798350387988

Fog computing is an innovative approach that brings cloud computing capabilities to the edge of the network, and it has great potential for enterprises looking to improve real-time data processing and lower latency. However, the adoption of fog computing in organizations remains relatively unexplored. This review paper aims to investigate the factors influencing fog computing adoption in organizations. Through a systematic review of existing literature, case studies, and expert opinions, this paper identifies and analyses the unique adoption factors. The review examines challenges, characteristics, benefits, and Open Fog Computing Issues with the Internet of Things (IoT), shaping fog computing adoption decisions in organizations across various industries. This analysis highlights fog computing advantages over traditional cloud computing approaches, such as low latency, improved data privacy, and enhanced scalability. Furthermore, this review investigates the challenges hindering the adoption of fog computing, including security concerns, implementation complexity, connectivity, and lack of awareness, as well as open fog computing issues with IoT. Key findings indicate that while fog computing can offer low-latency solutions, organizations face considerable challenges, including security risks, implementation complexity, and a lack of skilled personnel. By synthesizing and analysing the findings, this paper provides valuable insights for decision-makers, researchers, and policymakers seeking to promote and facilitate fog computing adoption. © 2024 IEEE.

关键词： Fog computing

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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

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computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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A location-based fog computing optimization of energy management in smart buildings:DEVS modeling and design of connected objects

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Frontiers of computer science 2023年第2期17卷 179-195页

作者： Abdelfettah MAATOUG Ghalem BELALEM Saïd MAHMOUDI Computer Science Department Faculty of Exact and Applied SciencesUniversity of Oran 1 Ahmed Ben BellaOran 31000Algeria Science and Technology Department Faculty of Science and TechnologyUniversity of TIARETTiaret 14000Algeria Computer Science Department Faculty of EngineeringUniversity of MonsMons 7000Belgium

Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected *** is characterized by low latency with a wider spread and geographically distributed nodes to support mobility,real-time interaction,and location-based *** provide optimum quality of user life in moderm buildings,we rely on a holistic Framework,designed in a way that decreases latency and improves energy saving and services efficiency with different *** EVent system Specification(DEVS)is a formalism used to describe simulation models in a modular *** this work,the sub-models of connected objects in the building are accurately and independently designed,and after installing them together,we easily get an integrated model which is subject to the fog computing *** results show that this new approach significantly,improves energy efficiency of buildings and reduces ***,with DEVS,we can easily add or remove sub-models to or from the overall model,allowing us to continually improve our designs.

关键词： smart building energy consumption IoT fog computing Framework DEVS simulation models

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Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

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Computational Visual Media 2023年第3期9卷 513-529页

作者： Yang Yang Kecheng Lu Yu Wu Yunhai Wang Yi Cao Institute of Applied Physics and Computational Mathematics Beijing 100094China School of Computer Science and Technology Shandong UniversityQingdao 266237China

In this paper,we propose a correlationaware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific *** core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information,spatial location,and correlation distribution using Bayes’*** effectively preserves statistical properties without merging data blocks in different parallel computing nodes and repartitioning them,thus significantly reducing the computational ***,this enables reconstruction of the original data more accurately than existing *** demonstrate the effectiveness of our technique using six datasets,with the largest having one billion grid *** experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-art methods while providing a higher reconstruction accuracy at a lower computational cost.

关键词： correlation-awareness large-scale data multi-block methods probabilistic data summarization

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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