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| 1 | An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations显示文摘Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,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 increases.Consequently,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 solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We 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 minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To 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 30.IJSA 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 the others. | Mohamed Abdel-Basset Reda Mohamed Mohamed Abouhawwash Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，9： | 2 |
| 2 | Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services显示文摘Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures. | E.Dhiravidachelvi M.Suresh Kumar L.D.Vijay Anand D.Pritima Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam | 2023 | Computer Systems Science & Engineering2023，44，2： | 2 |
| 3 | Classification of Fundus Images Based on Deep Learning for Detecting Eye Diseases显示文摘Various techniques to diagnose eye diseases such as diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD),are possible through deep learning algorithms.A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects.However,multiple major eye diseases,such as DR,GLC,and AMD,could not be detected simultaneously by computer-aided systems to date.There were just high-performance-outcome researches on a pair of healthy and eye-diseased group,besides of four categories of fundus image classification.To have a better knowledge of multi-categorical classification of fundus photographs,we used optimal residual deep neural networks and effective image preprocessing techniques,such as shrinking the region of interest,iso-luminance plane contrast-limited adaptive histogram equalization,and data augmentation.Applying these to the classification of three eye diseases from currently available public datasets,we achieved peak and average accuracies of 91.16%and 85.79%,respectively.The specificities for images from the eyes of healthy,GLC,AMD,and DR patients were 90.06%,99.63%,99.82%,and 91.90%,respectively.The better specificity performances may alert patient in an early stage of eye diseases to prevent vision loss.This study presents a possible occurrence of a multi-categorical deep neural network technique that can be deemed as a successful pilot study of classification for the three most-common eye diseases and can be used for future assistive devices in computer-aided clinical applications. | Nakhim Chea Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，4： | 1 |
| 4 | Integrated CWT-CNN for Epilepsy Detection Using Multiclass EEG Dataset显示文摘Electroencephalography is a common clinical procedure to record brain signals generated by human activity.EEGs are useful in Brain controlled interfaces and other intelligent Neuroscience applications,but manual analysis of these brainwaves is complicated and time-consuming even for the experts of neuroscience.Various EEG analysis and classification techniques have been proposed to address this problem however,the conventional classification methods require identification and learning of specific EEG characteristics beforehand.Deep learning models can learn features from data without having in depth knowledge of data and prior feature identification.One of the great implementations of deep learning is Convolutional Neural Network(CNN)which has outperformed traditional neural networks in pattern recognition and image classification.Continuous Wavelet Transform(CWT)is an efficient signal analysis technique that presents the magnitude of EEG signals as timerelated Frequency components.Existing deep learning architectures suffer from poor performance when classifying EEG signals in the Time-frequency domain.To improve classification accuracy,we propose an integrated CWT and CNN technique which classifies five types of EEG signals using.We compared the results of proposed integrated CWT and CNN method with existing deep learning models e.g.,GoogleNet,VGG16,AlexNet.Furthermore,the accuracy and loss of the proposed integrated CWT and CNN method have been cross validated using Kfold cross validation.The average accuracy and loss of Kfold cross-validation for proposed integrated CWT and CNN method are,76.12%and 56.02%respectively.This model produces results on a publicly available dataset:Epilepsy dataset by UCI(Machine Learning Repository). | Sidra Naseem Kashif Javed Muhammad Jawad Khan Saddaf Rubab Muhammad Attique Khan Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，10： | 1 |
| 5 | Chronic Administration of Mood Stabilizers Upregulates BDNF and Bcl-2 Expression Levels in Rat Frontal Cortex显示文摘 | Yunyoung C. Chang Stanley I. Rapoport Jagadeesh S. Rao | 2009 | Neurochemical Research2009，，3： | 1 |
| 6 | Expression of organophosphorns hydrolase in Escherichia call for use as whole - cell bioeatalyst显示文摘 | Yunyoung Kwak Sung - Eun Lee Jae - Ha Shin | 2014 | Journal of Molecular Catalysis B: Enzymatic2014，99，： | 1 |
| 7 | A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification显示文摘Diabetes or Diabetes Mellitus(DM)is the upset that happens due to high glucose level within the body.With the passage of time,this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy(DR)which can cause a major loss of vision.The symptoms typically originate within the retinal space square in the form of enlarged veins,liquid dribble,exudates,haemorrhages and small scale aneurysms.In current therapeutic science,pictures are the key device for an exact finding of patients’illness.Meanwhile,an assessment of new medicinal symbolisms stays complex.Recently,Computer Vision(CV)with deep neural networks can train models with high accuracy.The thought behind this paper is to propose a computerized learning model to distinguish the key precursors of Dimensionality Reduction(DR).The proposed deep learning framework utilizes the strength of selected models(VGG and Inception V3)by fusing the extracated features.To select the most discriminant features from a pool of features,an entropy concept is employed before the classification step.The deep learning models are fit for measuring the highlights as veins,liquid dribble,exudates,haemorrhages and miniaturized scale aneurysms into various classes.The model will ascertain the loads,which give the seriousness level of the patient’s eye.The model will be useful to distinguish the correct class of seriousness of diabetic retinopathy pictures. | Farrukh Zia Isma Irum Nadia Nawaz Qadri Yunyoung Nam Kiran Khurshid Muhammad Ali Imran Ashraf Muhammad Attique Khan | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 8 | An Integrated Deep Learning Framework for Fruits Diseases Classification显示文摘:Agriculture has been an important research area in the field of image processing for the last five years.Diseases affect the quality and quantity of fruits,thereby disrupting the economy of a country.Many computerized techniques have been introduced for detecting and recognizing fruit diseases.However,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the system.Herein,we propose an integrated deep learning framework for classifying fruit diseases.We consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and strawberry.The proposed method comprises several important steps.Initially,data increase is applied,and then two different types of features are extracted.In the first feature type,texture and color features,i.e.,classical features,are extracted.In the second type,deep learning characteristics are extracted using a pretrained model.The pretrained model is reused through transfer learning.Subsequently,both types of features are merged using the maximum mean value of the serial approach.Next,the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm.Finally,the selected features are classified using multiple classifiers.An evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method. | Abdul Majid Muhammad Attique Khan Majed Alhaisoni Muhammad Asfand Eyar Usman Tariq Nazar Hussain Yunyoung Nam Seifedine Kadry | 2022 | Computers, Materials & Continua2022，，4： | 1 |
| 9 | Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images显示文摘While the usage of digital ocular fundus image has been widespread in ophthalmology practice,the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly.We explored a robust deep learning system that detects three major ocular diseases:diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD).The proposed method is composed of two steps.First,an initial quality evaluation in the classification system is proposed to filter out poorquality images to enhance its performance,a technique that has not been explored previously.Second,the transfer learning technique is used with various convolutional neural networks(CNN)models that automatically learn a thousand features in the digital retinal image,and are based on those features for diagnosing eye diseases.Comparison performance of many models is conducted to find the optimal model which fits with fundus classification.Among the different CNN models,DenseNet-201 outperforms others with an area under the receiver operating characteristic curve of 0.99.Furthermore,the corresponding specificities for healthy,DR,GLC,andAMDpatients are found to be 89.52%,96.69%,89.58%,and 100%,respectively.These results demonstrate that the proposed method can reduce the time-consumption by automatically diagnosing multiple eye diseases using computer-aided assistance tools. | Kuntha Pin Jee Ho Chang Yunyoung Nam | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 10 | Gastrointestinal Tract Infections Classification Using Deep Learning显示文摘Automatic gastrointestinal(GI)tract disease recognition is an important application of biomedical image processing.Conventionally,microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract.The procedure is subjective and results in significant inter-/intraobserver variations in disease detection.Moreover,a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination.Consequently,there is a huge demand for a reliable computer-aided diagnostic system(CADx)for diagnosing GI tract diseases.In this work,a CADx was proposed for the diagnosis and classification of GI tract diseases.A novel framework is presented where preprocessing(LAB color space)is performed first;then local binary patterns(LBP)or texture and deep learning(inceptionNet,ResNet50,and VGG-16)features are fused serially to improve the prediction of the abnormalities in the GI tract.Additionally,principal component analysis(PCA),entropy,and minimum redundancy and maximum relevance(mRMR)feature selection methods were analyzed to acquire the optimized characteristics,and various classifiers were trained using the fused features.Open-source color image datasets(KVASIR,NERTHUS,and stomach ULCER)were used for performance evaluation.The study revealed that the subspace discriminant classifier provided an efficient result with 95.02%accuracy on the KVASIR dataset,which proved to be better than the existing state-of-the-art approaches. | Muhammad Ramzan Mudassar Raza Muhammad Sharif Muhammad Attique Khan Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，12： | 1 |
| 11 | An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment显示文摘Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily returns.However,major events’news also contains significant information regarding market drivers.An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market.This research proposes an efficient model for stock market prediction.The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and hospitality.We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock prediction.The LSTM has the advantage of analyzing relationship between time-series data through memory functions.The performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model. | Ayesha Jabeen Sitara Afzal Muazzam Maqsood Irfan Mehmood Sadaf Yasmin Muhammad Tabish Niaz Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，4： | 1 |
| 12 | Automatic Heart Disease Detection by Classification of Ventricular Arrhythmias on ECG Using Machine Learning显示文摘This paper focuses on detecting diseased signals and arrhythmias classification into two classes:ventricular tachycardia and premature ventricular contraction.The sole purpose of the signal detection is used to determine if a signal has been collected from a healthy or sick person.The proposed research approach presents a mathematical model for the signal detector based on calculating the instantaneous frequency(IF).Once a signal taken from a patient is detected,then the classifier takes that signal as input and classifies the target disease by predicting the class label.While applying the classifier,templates are designed separately for ventricular tachycardia and premature ventricular contraction.Similarities of a given signal with both the templates are computed in the spectral domain.The empirical analysis reveals precisions for the detector and the applied classifier are 100%and 77.27%,respectively.Moreover,instantaneous frequency analysis provides a benchmark that IF of a normal signal ranges from 0.8 to 1.1 Hz whereas IF range for ventricular tachycardia and premature ventricular contraction is 0.08–0.6 Hz.This indicates a serious loss of high-frequency contents in the spectrum,implying that the heart’s overall activity is slowed down.This study may help medical practitioners in detecting the heart disease type based on signal analysis. | Khalid Mahmood Aamir Muhammad Ramzan Saima Skinadar Hikmat Ullah Khan Usman Tariq Hyunsoo Lee Yunyoung Nam Muhammad Attique Khan | 2022 | Computers, Materials & Continua2022，，4： | 1 |
| 13 | Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks显示文摘As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy supply.Plant pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling price.In the agriculture industry,the identification of fungal diseases plays a vital role.However,it requires immense labor,greater planning time,and extensive knowledge of plant pathogens.Computerized approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several times.Therefore,the proposed study presents a new framework for the recognition of fruits and vegetable diseases.This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)model.The localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model.The localized images passed as input to classify the different types of plant diseases.The classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 fully-connected.The proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain. | Javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam | 2022 | Computers, Materials & Continua2022，，1： | 1 |
| 14 | Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework显示文摘Background:In medical image analysis,the diagnosis of skin lesions remains a challenging task.Skin lesion is a common type of skin cancer that exists worldwide.Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer.Challenges:Many computerized methods have been introduced in the literature to classify skin cancers.However,challenges remain such as imbalanced datasets,low contrast lesions,and the extraction of irrelevant or redundant features.Proposed Work:In this study,a new technique is proposed based on the conventional and deep learning framework.The proposed framework consists of two major tasks:lesion segmentation and classification.In the lesion segmentation task,contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination.Subsequently,the best channel is selected and the lesion map is computed,which is further converted into a binary form using a thresholding function.In the lesion classification task,two pre-trained CNN models were modified and trained using transfer learning.Deep features were extracted from both models and fused using canonical correlation analysis.During the fusion process,a few redundant features were also added,lowering classification accuracy.A new technique called maximum entropy score-based selection(MESbS)is proposed as a solution to this issue.The features selected through this approach are fed into a cubic support vector machine(C-SVM)for the final classification.Results:The experimental process was conducted on two datasets:ISIC 2017 and HAM10000.The ISIC 2017 dataset was used for the lesion segmentation task,whereas the HAM10000 dataset was used for the classification task.The achieved accuracy for both datasets was 95.6% and 96.7%, respectively, which was higher thanthe existing techniques. | Amina Bibi Muhamamd Attique Khan Muhammad Younus Javed Usman Tariq Byeong-Gwon Kang Yunyoung Nam Reham R.Mostafa Rasha H.Sakr | 2022 | Computers, Materials & Continua2022，，5： | 1 |
| 15 | A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation显示文摘The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency.Recently,researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it;this is called the stego image.This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response(QR)code and maze game image generation.This system consists of two components.The first component contains two processes,encryption process,and hiding process.The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image.On the other hand,the second component contains two processes,extraction and decryption,which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image,respectively.The results were obtained using the bit error rate(BER)metric.These results confirmed that the system achieved high hiding capacity,good performance,and a high level of robustness against attackers compared with other coverless steganography methods. | Al-Hussien Seddik Mohammed Salah Gamal Behery Ahmed El-harby Ahmed Ismail Ebada Sokea Teng Yunyoung Nam Mohamed Abouhawwash | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 16 | Instagram Mobile Application Digital Forensics显示文摘In this research,we developed a plugin for our automated digital forensics framework to extract and preserve the evidence from the Android and the IOS-based mobile phone application,Instagram.This plugin extracts personal details from Instagram users,e.g.,name,user name,mobile number,ID,direct text or audio,video,and picture messages exchanged between different Instagram users.While developing the plugin,we identified resources available in both Android and IOS-based devices holding key forensics artifacts.We highlighted the poor privacy scheme employed by Instagram.This work,has shown how the sensitive data posted in the Instagram mobile application can easily be reconstructed,and how the traces,as well as the URL links of visual messages,can be used to access the privacy of any Instagram user without any critical credential verification.We also employed the anti-forensics method on the Instagram Android’s application and were able to restore the application from the altered or corrupted database file,which any criminal mind can use to set up or trap someone else.The outcome of this research is a plugin for our digital forensics ready framework software which could be used by law enforcement and regulatory agencies to reconstruct the digital evidence available in the Instagram mobile application directories on both Android and IOS-based mobile phones. | Muhammad Asim Mubarik Zhijian Wang Yunyoung Nam Seifedine Kadry Muhammad Azam waqar | 2021 | Computer Systems Science & Engineering2021，37，5： | 0 |
| 17 | A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression显示文摘Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN). | Amr Ismail WalidHamdy Aya MAl-Zoghby Wael AAwad Ahmed Ismail Ebada Yunyoung Nam Byeong-Gwon Kang Mohamed Abouhawwash | 2024 | Computer Systems Science & Engineering2024，48，2： | 0 |
| 18 | Citrus Diseases Recognition Using Deep Improved Genetic Algorithm显示文摘Agriculture is the backbone of each country,and almost 50%of the population is directly involved in farming.In Pakistan,several kinds of fruits are produced and exported the other countries.Citrus is an important fruit,and its production in Pakistan is higher than the other fruits.However,the diseases of citrus fruits such as canker,citrus scab,blight,and a few more impact the quality and quantity of this Fruit.The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure.In the agriculture sector,deep learning showing significant success in the last five years.This research work proposes an automated framework using deep learning and best feature selection for citrus diseases classification.In the proposed framework,the augmentation technique is applied initially by creating more training data from existing samples.They were then modifying the two pre-trained models named Resnet18 and Inception V3.The modified models are trained using an augmented dataset through transfer learning.Features are extracted for each model,which is further selected using Improved Genetic Algorithm(ImGA).The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine(SVM)and name a few more.The experimental process is conducted on three different datasets-Citrus Hybrid,Citrus Leaf,and Citrus Fruits.On these datasets,the best-achieved accuracy is 99.5%,94%,and 97.7%,respectively.The proposed framework is evaluated on each step and compared with some recent techniques,showing that the proposed method shows improved performance. | Usra Yasmeen Muhammad Attique Khan Usman Tariq Junaid Ali Khan Muhammad Asfand EYar ChAvais Hanif Senghour Mey Yunyoung Nam | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 19 | Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks显示文摘Wireless Sensor Networks(WSN)play a vital role in several real-time applications ranging from military to civilian.Despite the benefits of WSN,energy efficiency becomes a major part of the challenging issue in WSN,which necessitate proper load balancing amongst the clusters and serves a wider monitoring region.The clustering technique for WSN has several benefits:lower delay,higher energy efficiency,and collision avoidance.But clustering protocol has several challenges.In a large-scale network,cluster-based protocols mainly adapt multi-hop routing to save energy,leading to hot spot problems.A hot spot problem becomes a problem where a cluster node nearer to the base station(BS)tends to drain the energy much quicker than other nodes because of the need to implement more transmission.This article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems(JSOUCP-MHP)in WSN.The JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search,persecution,and jumping skills to attack prey.The presented JSOUCPMHP technique mainly resolves the hot spot issue for maximizing the network lifespan.The JSOUCP-MHP technique elects a proper set of cluster heads(CHs)using average residual energy(RE)to attain this.In addition,the JSOUCP-MHP technique determines the cluster sizes based on two measures,i.e.,RE and distance to BS(DBS),showing the novelty of the work.The proposed JSOUCP-MHP technique is examined under several experiments to ensure its supremacy.The comparison study shows the significance of the JSOUCPMHP technique over other models. | Mohammed Altaf Ahmed T.Satyanarayana Murthy Fayadh Alenezi E.Laxmi Lydia Seifedine Kadry Yena Kim Yunyoung Nam | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 20 | Video Analytics Framework for Human Action Recognition显示文摘Human action recognition(HAR)is an essential but challenging task for observing human movements.This problem encompasses the observations of variations in human movement and activity identification by machine learning algorithms.This article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation,features reduction and selection framework.A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are extracted.An entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion.A custom made genetic algorithm is implemented on the constructed features codebook in order to select the strong and wellknown features.The features are exploited by a multi-class SVM for action identification.Comprehensive experimental results are undertaken on four action datasets,namely,Weizmann,KTH,Muhavi,and WVU multi-view.We achieved the recognition rate of 96.80%,100%,100%,and 100%respectively.Analysis reveals that the proposed action recognition approach is efficient and well accurate as compare to existing approaches. | Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Fayadh Alenezi Usman Tariq Yunyoung Nam Tallha Akram | 2021 | Computers, Materials & Continua2021，，9： | 0 |
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