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Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People

Computer Systems Science & Engineering 2023年第8期46卷 1929-1945页

作者： Anwer Mustafa Hilal Fadwa Alrowais Fahd N.Al-Wesabi Radwa Marzouk Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMahayilSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing(NLP)and computer vision(CV).It can be driven by applications like image retrieval or indexing,virtual assistants,image understanding,and support of visually impaired people(VIP).Though the VIP uses other senses,touch and hearing,for recognizing objects and events,the quality of life of those persons is lower than the standard *** Image captioning generates captions that will be read loudly to the VIP,thereby realizing matters happening around *** article introduces a Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System(RDOAI-ICS)for Visually Impaired *** presented RDOAI-ICS technique aids in generating image captions for *** presented RDOAIICS technique utilizes a neural architectural search network(NASNet)model to produce image ***,the RDOAI-ICS technique uses the radial basis function neural network(RBFNN)method to generate a textual *** enhance the performance of the RDOAI-ICS method,the parameter optimization process takes place using the RDO algorithm for NasNet and the butterfly optimization algorithm(BOA)for the RBFNN model,showing the novelty of the *** experimental evaluation of the RDOAI-ICS method can be tested using a benchmark *** outcomes show the enhancements of the RDOAI-ICS method over other recent Image captioning approaches.

关键词： Machine learning image captioning visually impaired people parameter tuning artificial intelligence metaheuristics

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Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

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Computer Systems Science & Engineering 2023年第8期46卷 1371-1383页

作者： Manar Ahmed Hamza Shaha Al-Otaibi Sami Althahabi Jaber S.Alzahrani Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMecaa24382Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,*** recent developments of IoT technology have led to an increase in large amounts of data from various *** IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource *** load scheduling problem was regarded as an optimization problem that is solved by *** this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling(C3SOA-LS)technique for IoT enabled cloud *** proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be ***,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the *** proposed C3SOA-LS model computes an objective with the minimization of energy consumption and *** experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.

关键词： Cloud environment load scheduling energy consumption internet of things metaheuristics

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Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

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Intelligent Automation & Soft Computing 2023年第6期36卷 2879-2895页

作者： Manar Ahmed Hamza Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a ***-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial ***,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in *** learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass *** advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly *** paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital *** presented HTDHDAE-BCC model examines the mammogram images for the identification of *** the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median *** addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature *** parameter tuning process uses the binary spider monkey opti-mization(BSMO)*** HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC *** experimental analysis of the HTDHDAE-BCC model is performed using the MIAS *** experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.

关键词： Digital mammograms breast cancer classification computer-aided diagnosis deep learning metaheuristics

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Determination of AVR System PID Controller Parameters Using Improved Variants of Reptile Search Algorithm and a Novel Objective Function

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Energy Engineering 2023年第7期120卷 1515-1540页

作者： Baran Hekimoglu Department of Electrical&Electronics Engineering Faculty of Engineering and ArchitectureBatman UniversityBatman72100Turkey

Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)controller parameters of an automatic voltage regulator(AVR)system using a novel objective function with augmented *** the proposed algorithms,the opposition-based learning technique improves the global search abilities of the original RSA algorithm,while the hybridization with the pattern search(PS)algorithm improves the local search *** algorithms are compared with the original RSA algorithm and have shown to be highly effective algorithms for tuning the PID controller parameters of an AVR system by getting superior *** analyses such as transient,stability,robustness,disturbance rejection,and trajectory tracking are conducted to test the performance of the proposed algorithms,which have validated the good promise of the proposed methods for controller *** performances of the proposed design approaches are also compared with the previously reported PID controller parameter tuning approaches to assess their *** is shown that both proposed approaches obtain excellent and robust results among all compared *** is,with the adjustment of the weight factorα,which is introduced by the proposed objective function,for a system with high bandwitdh(α=1),the proposed ORSAPS-PID system has 2.08%more bandwidth than the proposed ORSA-PID system and 5.1%faster than the fastest algorithm from the *** the other hand,for a system where high phase and gain margins are desired(α=10),the proposed ORSA-PID system has 0.53%more phase margin and 2.18%more gain margin than the proposed ORSAPS-PID system and has 0.71%more phase margin and 2.25%more gain margin than the best performing algorithm from the literature.

关键词： Reptile search algorithm pattern search multidirectional search metaheuristics automatic voltage regulator optimal PID controller

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A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 2101-2116页

作者： R.Rajakumar S.Sathiya Devi Anna University Chennai600025India University College of Engineering BIT CampusAnna UniversityTiruchirappalli620024India

Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor *** investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classiﬁcation problems have received consid-erable *** the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes *** study develops an outlier detec-tion with feature selection technique for streaming data classiﬁcation,named ODFST-SDC ***,streaming data is pre-processed in two ways namely categorical encoding and null value *** addition,Local Correla-tion Integral(LOCI)is used which is signiﬁcant in the detection and removal of ***,red deer algorithm(RDA)based FS approach is employed to derive an optimal subset of ***,kernel extreme learning machine(KELM)classiﬁer is used for streaming data classiﬁ*** design of LOCI based outlier detection and RDA based FS shows the novelty of the *** order to assess the classiﬁcation outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark *** experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches.

关键词： Streaming data classiﬁcation outlier removal feature selection machine learning metaheuristics

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Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets

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Computer Systems Science & Engineering 2023年第8期46卷 2555-2570页

作者： Aisha M.Mashraqi Hanan T.Halawani College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

Sentiment Analysis(SA)is one of the Machine Learning(ML)techniques that has been investigated by several researchers in recent years,especially due to the evolution of novel data collection methods focused on social *** literature,it has been reported that SA data is created for English language in excess of any other *** is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic *** earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the *** word embedding has been employed in literature,since it is less labor-intensive than automatic feature *** ignoring the context of sentiment,most of the word-embedding models follow syntactic data of *** current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets(DFODLSAAT)*** aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic *** first,data cleaning and pre-processing steps are performed to convert the input tweets into a useful *** addition,TF-IDF model is exploited as a feature extractor to generate the feature ***,Attention-based Bidirectional Long Short Term Memory(ABLSTM)technique is applied for identification and classification of *** last,the hyperparameters of ABLSTM model are optimized using DFO *** performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different *** experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.

关键词： Natural language processing sentiment analysis arabic tweets deep learning metaheuristics lexicon approach

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Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第3期35卷 3199-3212页

作者： K.Arutchelvan R.Sathiya Priya C.Bhuvaneswari Department of Computer and Information Science Annamalai UniversityChidambaramTamilnaduIndia Department of Computer Science Government Arts and Science CollegeThiruvennainallurTamil NaduIndia

Wireless sensor network(WSN)includes a set of self-organizing and homogenous nodes employed for data collection and tracking *** comprises a massive set of nodes with restricted energy and processing *** dissipation is a major concern involved in the design of *** and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic *** order to design an energy aware cluster-based route planning scheme,this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing(HBAC-AVOR)protocol for *** presented HBAC-AVOR model mainly aims to cluster the nodes in WSN effectually and organize the routes in an energy-efﬁcient *** presented HBAC-AVOR model follows a two stage *** the initial stage,the HBAC technique is exploited to choose an opti-mal set of cluster heads(CHs)utilizing aﬁtness function involving many input ***,the AVOR approach was executed for determining the optimal routes to BS and thereby lengthens the lifetime of WSN.A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR *** comparing with existing techniques,the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

关键词： Cluster based routing wireless sensor networks objective function lifetime metaheuristics

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Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

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Intelligent Automation & Soft Computing 2023年第2期35卷 2189-2203页

作者： T.S.Balaji S.Srinivasan Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India Department of Electronics and Communication Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversitySaveetha NagarThandalamChennai602105India

The vehicular ad hoc network(VANET)is an emerging network tech-nology that has gained popularity because to its low cost,ﬂexibility,and seamless *** deﬁned networking(SDN)technology plays a critical role in network administration in the future generation of VANET withﬁfth generation(5G)*** of the beneﬁts of VANET,energy economy and trafﬁc control are signiﬁcant architectural *** and real-time trafﬁcﬂow prediction(TFP)becomes critical for managing trafﬁc effectively in the *** controllers are a critical issue in VANET,which has garnered much interest in recent *** this objective,this study develops the SDNTFP-C technique,a revolutionary SDN controller-based real-time trafﬁcﬂow forecasting technique for clustered *** proposed SDNTFP-C technique combines the SDN controller’s scalability,ﬂexibility,and adaptability with deep learning(DL)***,a novel arithmetic optimization-based clustering technique(AOCA)is developed to cluster automobiles in a *** TFP procedure is then performed using a hybrid convolutional neural network model with atten-tion-based bidirectional long short-term memory(HCNN-ABLSTM).To optimise the performance of the HCNN-ABLSTM model,the dingo optimization techni-que was used to tune the hyperparameters(DOA).The experimental results ana-lysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.

关键词： VANET trafﬁcﬂow prediction clustering metaheuristics SDN controller deep learning

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Deep Learning Enabled Financial Crisis Prediction Model for Small-Medium Sized Industries

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Intelligent Automation & Soft Computing 2023年第1期35卷 521-536页

作者： Kavitha Muthukumaran K.Hariharanath SSN School of Management KalavakkamChennai603110India

Recently,data science techniques utilize artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to take an influence and grow their *** SMEs,owing to the inexistence of consistent data and other features,evaluating credit risks is difficult and *** the other hand,it becomes necessary to design efficient models for predicting business failures orfinancial crises of *** data classification approaches forfinancial crisis prediction(FCP)have been presented for predicting thefinancial status of the organization by the use of past data.A major process involved in the design of FCP is the choice of required features for enhanced classifier *** this motivation,this paper focuses on the design of an optimal deep learning-basedfinancial crisis prediction(ODL-FCP)model for *** proposed ODL-FCP technique incorporates two phases:Archimedes optimization algorithm based feature selection(AOA-FS)algorithm and optimal deep convo-lution neural network with long short term memory(CNN-LSTM)based data *** ODL-FCP technique involves a sailfish optimization(SFO)algorithm for the hyperparameter optimization of the CNN-LSTM *** performance validation of the ODL-FCP technique takes place using a benchmarkfinancial dataset and the outcomes are inspected in terms of various *** experimental results highlighted that the proposed ODL-FCP technique has out-performed the other techniques.

关键词： Small medium-sized enterprises deep learning FCP financial sector prediction metaheuristics sailfish optimization

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Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

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Computer Systems Science & Engineering 2023年第3期44卷 2195-2207页

作者： Ashit Kumar Dutta Nazik M.A.Zakari Yasser Albagory Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Nursing College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly *** affect their quality of life drastically and millions of seniors are diagnosed with PD every year *** models have been presented earlier to detect the PD using various types of measurement data like speech,gait patterns,*** identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of *** recently-emerging Deep Learning(DL)models can leverage the past data to detect and classify *** this motivation,the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine(CBO-OKELM)for diagnosis and classification of *** goal of the proposed CBO-OKELM technique is to identify whether PD exists or ***-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection(CBO-FS)technique for optimal subset of *** addition,Water Strider Algorithm(WSA)with Kernel Extreme Learning Machine(KELM)model is also developed for the classification of *** algorithm is used to elect the optimal set of fea-tures whereas WSA is utilized for parameter tuning of KELM model which alto-gether helps in accomplishing the maximum PD diagnostic *** experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%,96.34%,92.49%,and 92.36%on Speech PD,Voice PD,Hand PD Mean-der,and Hand PD Spiral datasets respectively.

关键词： Parkinson’s disease colliding bodies optimization algorithm feature selection metaheuristics classification kelm model

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