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Data Fusion-Based Machine Learning Architecture for Intrusion Detection

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Computers, Materials & Continua 2022年第2期70卷 3399-3413页

作者： Muhammad Adnan Khan taher M.Ghazal Sang-Woong Lee Abdur Rehman Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)43600BangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan

In recent years,the infrastructure of Wireless Internet of Sensor Networks(WIoSNs)has been more complicated owing to developments in the internet and devices’*** effectively prepare,control,hold and optimize wireless sensor networks,a better assessment needs to be *** field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data *** study investigates the methodology of Real Time Sequential Deep Extreme LearningMachine(RTS-DELM)implemented to wireless Internet of Things(IoT)enabled sensor networks for the detection of any intrusion *** fusion is awell-knownmethodology that can be beneficial for the improvement of data accuracy,as well as for the maximizing of wireless sensor networks *** also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more *** using the Real Time Sequential Deep Extreme Learning Machine(RTSDELM)methodology,an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is *** results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed ***,threats and a more general outlook are explored.

关键词： Wireless internet of sensor networks machine learning deep extreme learning machine artificial intelligence data fusion

Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

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Computers, Materials & Continua 2023年第7期76卷 1-17页

作者： Mohammed Salahat Liaqat Ali taher M.Ghazal Haitham M.Alzoubi College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600Malaysia School of Business Skyline University CollegeUniversity City Sharjah1797SharjahUAE

Knowing each other is obligatory in a multi-agent collaborative *** may develop the desired know-how of each other in various aspects such as habits,job roles,status,and *** different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral *** has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become *** knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about *** writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,*** of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the ***-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding *** article proposes an empirical approach to assessing personality by applying convolutional networks to text *** Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.

关键词： Personality traits convolutional neural network deep learning word embedding

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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Computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

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Computer Modeling in Engineering & Sciences 2020年第11期125卷 459-494页

作者： Soukaina Mjahed Khadija Bouzaachane Ahmad taher Azar Salah El Hadaj Said Raghay Faculty of Sciences and Technology Department of Applied Mathematics and Computer SciencesCadi Ayyad UniversityMarrakech40000Morocco Robotics and Internet-of-Things Lab(RIOTU) Prince Sultan UniversityRiyadh12435Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13511Egypt

This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data *** unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow *** experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed *** if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered *** first contribution lies in the method used for Step(1)to select the most informative features and *** used the t-Statistic technique to rank ***,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between ***,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set *** second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.

关键词： Ant lion optimization binary clustering clustering algorithms Higgs boson feature extraction dimensionality reduction elbow criterion genetic algorithm particle swarm optimization

Development of cork oak (Quercus suber L.) seedlings in response to tree shelters and mulching in northwestern Tunisia

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Journal of Forestry Research 2013年第2期24卷 193-204页

作者： taher Mechergui Marta Pardos Naceur Boussaidi Brahim Hasnaoui Douglass F. Jacobs Faculté des Sciences de Bizerte (FSB) Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria (INIA). Centro de Investigacion Forestal. Ctra. de La Coruňa Institut Sylvo-Pastoral de Tabarka Department of Forestry and Natural Resources Purdue University

The need for reforestation in cork oak （Quercus suber L.） areas is challenged by difficulties. Principal among these is herbivory of young plants, vegetative competition, and slow growth rates of cork oak seedlings. We evaluated the early development of cork oak seedlings treated using tree shelters and mulching in northwestern Tunisia. We tested three tree shelter treatments （non-vented, vented, and control） to shield seedlings from animal damage and five mulch types to control competing vegetation （Italian Stone Pine, Lentisk, combination of Italian Stone Pine and Lentisk （organic mulches）, gravel （inorganic mulch） and no mulch）. At the end of the two-year experiment, sheltered seedlings were 89-99% taller than unshelteredseedlings and had higher numbers and lengths of shoot growth flushes. In contrast, both stem diameter growth and dry weight biomass （from samples extracted after two years） were significantly reduced inside tree shelters. Root-to-shoot ratio was not significantly different in sheltered vs. unsheltered seedlings, suggesting that tree shelters do not adversely affect this parameter. Mulching alone did not favour growth, but could be beneficial when combined with tree shelters. The combination of vented tree shelters and gravel mulch was the most effective treatment for promoting diameter, height and stem volume growth.

关键词： Quercus suber L. afforestation tree shelter mulching seedling growth polycyclism

Machine Learning-Enabled Communication Approach for the Internet of Medical Things

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Computers, Materials & Continua 2023年第8期76卷 1569-1584页

作者： Rahim Khan Abdullah Ghani samia Allaoua Chelloug Mohammed Amin Aamir Saeed Jason Teo Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh11671Saudi Arabia Department of Computer Science Abdul Wali Khan UniversityMardan23200Pakistan College of Computer Sciences and Information Technology King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science and IT UET PeshawarJalozai CampusJalozai24240Pakistan

The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed ***,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing *** this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT *** this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable ***,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested *** from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy ***,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is *** proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT *** results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,***,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes.

关键词： Machine learning Internet of Medical Things healthcare load balancing communication

Hybrid Global Optimization Algorithm for Feature Selection

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Computers, Materials & Continua 2023年第1期74卷 2021-2037页

作者： Ahmad taher Azar Zafar Iqbal Khan Syed Umar Amin Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13511Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Technology and Computer Science Nile UniversitySheikh ZaidEgypt

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization *** experimental study is performed in two stages to assess the proposed *** I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO *** based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)*** phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT *** phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical *** experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable *** from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

关键词： Particle swarm optimization(PSO) time-variant acceleration coefficients(TVAC) genetic algorithms differential evolution feature selection medical data

Profile of hepatocyte apoptosis and bile lakes before and after bile duct decompression in severe obstructive jaundice patients

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Hepatobiliary & Pancreatic Diseases International 2010年第5期9卷 520-523页

作者： Toar JM Lalisang Raden Sjamsuhidajat Nurjati C Siregar Akmal taher Department of Surgery Department of Anatomical Pathology Faculty of Medicine University of Indonesia Department of Urology Cipto Mangunkusumo Hospital

BACKGROUND:Excessive hepatocyte apoptosis and bile lakes in severe obstructive jaundice might impair liver *** decompression of the bile duct has been reported to improve liver functions in animal studies,the mechanism of obstruction differs from that in *** study aimed to determine the profiles of hepatocyte apoptosis and bile lakes following bile duct decompression in patients with severe obstructive jaundice in the clinical ***:We conducted a 'before and after study' on severe obstructive jaundice patients as a model of inhibition of the excessive process by bile duct *** of liver biopsies were taken before and after decompression of the bile duct and then stained by terminal deoxynucleotide transferase-mediated dUTP nick end-labeling(TUNEL) to identify hepatocyte apoptosis and by hematoxilin-eosin(HE) to identify bile *** measurements were independently done by 2 ***:Twenty-one severe obstructive jaundice patients were *** all patients,excessive hepatocyte apoptosis and bile lakes were *** decompression,the hepatocyte apoptosis index decreased from 53.1(SD 105) to 11.7(SD 13.6)(P<0.05),and the bile lakes from 23.6(SD 14.8) to 10.9(SD 6.9)(P<0.05).CONCLUSION:Bile duct decompression improves hepatocyte apoptosis and bile lakes in cases of severe obstructive jaundice,similar to the findings in animal studies.

关键词： hepatocyte apoptosis bile lakes TUNEL obstructive jaundice bile duct decompression

Rough Sets Hybridization with Mayfly Optimization for Dimensionality Reduction

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Computers, Materials & Continua 2022年第10期73卷 1087-1108页

作者： Ahmad taher Azar Mustafa Samy Elgendy Mustafa Abdul Salam Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computer Studies Arab Open UniversityCairoEgypt Faculty of Information Technology and Computer Science Nile UniversityShikh ZaidEgypt

Big data is a vast amount of structured and unstructured data that must be dealt with on a regular *** reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed *** tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning *** achieve dimensionality reduction for huge data sets,this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS.A novel hybrid strategy based on the Mayfly algorithm(MA)and the rough set(RS)is proposed in *** performance of the novel hybrid algorithm MA-RS is evaluated by solving six different data sets from the *** simulation results and comparison with common reduction methods demonstrate the proposed MARS algorithm’s capacity to handle a wide range of data ***,the rough set approach,as well as the hybrid optimization techniques PSO-RS and MARS,were applied to deal with the massive data ***-hybrid RS’s method beats other classic dimensionality reduction techniques,according to the experimental results and statistical testing studies.

关键词： Dimensionality reduction metaheuristics optimization algorithm mayfly particle swarm optimizer feature selection