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Subspace Clustering in High-Dimensional Data Streams:A Systematic Literature Review

Computers, Materials & Continua 2023年第5期75卷 4649-4668页

作者： Nur Laila Ab Ghani Izzatdin Abdul Aziz said jadid abdulkadir Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Centre for Research in Data Science(CeRDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia

Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering *** clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data *** clustering methods extend traditional clustering to account for the constraints imposed by data *** streams are not only high-dimensional,but also unbounded and *** necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data *** many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data ***,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming *** review follows a systematic methodological approach and includes 18 articles for the final *** analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data *** main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation *** algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering *** density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected *** algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to *** work can focus on the clustering framework,parameter optimiza

关键词： Clustering subspace clustering projected clustering data stream stream clustering high dimensionality evolving data stream concept drift

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A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

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Computers, Materials & Continua 2023年第3期74卷 5799-5820页

作者： Noureen Talpur said jadid abdulkadir Mohd Hilmi Hasan Hitham Alhussian Ayed Alwadain Computer and Information Sciences Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Computer Science Department Community CollegeKing Saud UniversityRiyadh145111Saudi Arabia

Machine learning(ML)practices such as classification have played a very important role in classifying diseases in medical *** medical science is a sensitive field,the pre-processing of medical data requires careful handling to make quality clinical ***,medical data is considered high-dimensional and complex data that contains many irrelevant and redundant *** factors indirectly upset the disease prediction and classification accuracy of any ML *** address this issue,various data pre-processing methods called Feature Selection(FS)techniques have been presented in the ***,the majority of such techniques frequently suffer from local minima issues due to large solution ***,this study has proposed a novel wrapper-based Sand Cat SwarmOptimization(SCSO)technique as an FS approach to find optimum features from ten benchmark medical *** SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control ***,K-Nearest Neighbor(KNN)classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO *** performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy,optimum feature size,and computational cost in *** simulation results on the benchmark medical datasets revealed that the proposed SCSO-KNN approach has outperformed comparative algorithms with an average classification accuracy of 93.96%by selecting 14.2 features within 1.91 ***,the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSOKNN method and six other algorithms for a p-value less than *** findings revealed that the proposed algorithm produces better outcomes with an average p-value o

关键词： Machine learning optimization feature selection classification medical data

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Hyper-Parameter Optimization of Semi-Supervised GANs Based-Sine Cosine Algorithm for Multimedia Datasets

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Computers, Materials & Continua 2022年第10期73卷 2169-2186页

作者： Anas Al-Ragehi said jadid abdulkadir Amgad Muneer Safwan Sadeq Qasem Al-Tashi Computer and Information Sciences Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Centre for Research in Data Science(CERDAS) Universiti Teknologi PETRONASSeri Iskandar32610Malaysia Mechanical Engineering Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Department of Imaging Physics The University of Texas MD Anderson Cancer CenterHoustonTXUSA University of Albaydha AlbaydhaYemen

Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training ***-Supervised GAN Classifiers are a recent innovation in GANs,where GANs are used to classify generated images into real and fake and multiple classes,similar to a general multi-class ***,GANs have a sophisticated design that can be challenging to *** is because obtaining the proper set of parameters for all models-generator,discriminator,and classifier is *** a result,training a single GAN model for different datasets may not produce satisfactory ***,this study proposes an SGAN model(Semi-Supervised GAN Classifier).First,a baseline model was *** model was then enhanced by leveraging the Sine-Cosine Algorithm and Synthetic Minority Oversampling Technique(SMOTE).SMOTE was used to address class imbalances in the dataset,while Sine Cosine Algorithm(SCA)was used to optimize the weights of the classifier *** optimal set of hyperparameters(learning rate and batch size)were obtained using grid manual *** well-known benchmark datasets and a set of evaluation measures were used to validate the proposed *** proposed method was then compared against existing models,and the results on each dataset were recorded and demonstrated the effectiveness of the proposed *** proposed model successfully showed improved test accuracy scores of 1%,2%,15%,and 5%on benchmarking multimedia datasets;Modified National Institute of Standards and Technology(MNIST)digits,Fashion MNIST,Pneumonia Chest X-ray,and Facial Emotion Detection Dataset,respectively.

关键词： Generative adversarial networks semi-supervised generative adversarial network sine-cosine algorithm SMOTE principal component analysis grid search

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Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions

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Computers, Materials & Continua 2022年第10期73卷 1799-1815页

作者： Muhammad Hamza Azam Mohd Hilmi Hasan Azlinda A Malik Saima Hassan said jadid abdulkadir Centre of Research in Data Science Computerand Information Sciences DepartmentUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Petroleum Engineering Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Institute of Computing Kohat University of Science and TechnologyKohat26000K.P.KPakistan

Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future *** price forecasting have been a critical input to energy corporations’strategic decision-making systems over the last 15 *** strategies have been utilized for price forecasting in the past,however Artificial Intelligence Techniques(Fuzzy Logic and ANN)have proven to be more efficient than traditional techniques(Regression and Time Series).Fuzzy logic is an approach that uses membership functions(MF)and fuzzy inference model to forecast future electricity *** c-means(FCM)is one of the popular clustering approach for generating fuzzy membership ***,the fuzzy c-means algorithm is limited to producing only one type of MFs,Gaussian *** generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem *** a result,for the best and most improved results for electricity price forecasting,an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is ***,the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM *** approach is used to compute and improve forecasting accuracy for electricity prices,where Australian Energy Market Operator(AEMO)data is *** results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted.

关键词： Fuzzy logic fuzzy C-means type-1 fuzzy membership function electricity price forecasting

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Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

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Computers, Materials & Continua 2023年第5期75卷 3223-3238页

作者： Alawi Alqushaibi Mohd Hilmi Hasan said jadid abdulkadir Amgad Muneer Mohammed Gamal Qasem Al-Tashi Shakirah Mohd Taib Hitham Alhussian Computer&Information Sciences Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Centre for Research in Data Science(CERDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Department of Imaging Physics University of Texas MD Anderson CancerHoustonTXUSA

Diabetes mellitus is a long-term condition characterized by *** could lead to plenty of *** to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be *** is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’*** to its rapid development,deep learning(DL)was used to predict numerous ***,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters ***,the selection of hyper-parameters is critical in improving classification *** study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters *** issues have been investigated and solved during the experiment to enhance the *** first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)*** second issue is the model’s poor performance,which has been solved using the Bayesian optimization *** findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.

关键词： Type 2 diabetes diabetes mellitus convolutional neural network Bayesian optimization SMOTE

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Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection

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Computer Systems Science & Engineering 2023年第11期47卷 1937-1966页

作者： Qasem Al-Tashi Tareq M Shami said jadid abdulkadir Emelia Akashah Patah Akhir Ayed Alwadain Hitham Alhussain Alawi Alqushaibi Helmi MD Rais Amgad Muneer Maliazurina B.Saad Jia Wu Seyedali Mirjalili Department of Imaging Physics The University of Texas MD Anderson Cancer CenterHoustonTXUSA Department of Electronic Engineering University of YorkYorkYO105DDUK Computer and Information Sciences Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Computer Science Department Community CollegeKing Saud UniversityRiyadhP.O.Box 145111Saudi Arabia Torrens University Australia BrisbaneQLDAustralia Yonsei Frontier Lab Yonsei UniversitySeoulKorea University Research and Innovation Center Obuda UniversityBudapest1034Hungary

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be *** it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization *** paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection *** proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation *** Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected *** mutation operator is integrated to add more informative features that can assist in enhancing classification *** feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid *** evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization ***,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.

关键词： Feature selection multi-objective optimization grey wolf optimizer Lévy flight mutation classification

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Optimal Day-ahead Dynamic Pricing of Grid-connected Residential Renewable Energy Resources Under Different Metering Mechanisms

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 168-178页

作者： Kimia Parandeh Abed Bagheri Shahram jadid Center of Excellence for Power System Automation and Operation School of Electrical EngineeringIran University of Science and Technology(IUST)TehranIran

Nowadays,grid-connected renewable energy resources have widespread applications in the electricity ***,providing household consumers with photovoltaic(PV)systems requires bilateral interfaces to exchange energy and *** addition,residential consumers’contribution requires guaranteed privacy and secured data *** dynamic pricing is one of the incentive-based demand response methods that has substantial effects on the integration of renewable energy resources with smart grids and social *** metering mechanisms of renewable energy resources such as feed-in tariffs,net metering,and net purchase and sale are important issues in power grid operation *** this paper,optimal condition decomposition method is used for dayahead dynamic pricing of grid-connected residential renewable energy resources under different metering mechanisms:feed-intariffs,net metering,and net purchase and sale in conjunction with carbon emission *** to the stochastic nature of consumers’load and PV system products,uncertainties are considered in a two-stage decision-making *** results demonstrate that the net metering with the satisfaction average of 68%for consumers and 32%for the investigated electric company leads to 28%total load *** the case of net purchase and sale mechanism,a satisfaction average of 15%for consumers and 85%for the electric company results in 11%total load *** feed-in-tariff mechanism,in spite of increased social welfare,load reduction does not take place.

关键词： Dynamic pricing renewable energy resources feed-in-tariffs net metering net purchase and sale stochastic decision-making

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Internal Combustion Engine as a New Source for Enhancing Distribution System Resilience

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Journal of Modern Power Systems and Clean Energy 2021年第5期9卷 1130-1136页

作者： Ahad Abessi Shahram jadid Department of Electrical Engineering Iran University of Science and TechnologyTehranIran

Enhancing distribution system resilience is a new challenge for *** distribution loads,especially the residential customers and high-priority loads after disasters,is vital for this *** this paper,the internal combustion engine(ICE)vehicles are firstly introduced as valuable backup energy sources in the aftermath of disasters and the use of this technology is ***,the improvement of distribution system resilience is investigated through supplying smart residential customers and injecting extra power to the main *** this method,it is assumed that the infrastructure of distribution system is partially damaged(common cases)and it can be restored in less than one *** extra power of residential customer can be delivered to other loads.A novel formulation for increasing the injected power of the smart home to the main grid using ICE vehicles is ***,the maximum backup duration in case of extensive damages in the distribution system is calculated for some commercial ICE *** this case,the smart home cannot deliver extra energy to the main grid because of its *** results demonstrate the effectiveness of the proposed method for increasing backup power during power *** is also shown that ICE vehicles can supply residential customers for a reasonable amount of time during a power outage.

关键词： Internal combustion engine(ICE) smart home building energy management system(BEMS) distribution system resilience

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Supplementing Conventional Treatment with Pycnogenol(R) May Improve Hepatitis C Virus-Associated Type 2 Diabetes:A Mini Review

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Journal of Clinical and Translational Hepatology 2016年第3期4卷 228-233页

作者： Sayeh Ezzikouri Fatima Zahra jadid Salsabil Hamdi Lahcen Wakrim Kyoko Tsukiyama-Kohara Soumaya Benjelloun Virology Unit Viral Hepatitis Laboratory Institut Pasteur du Maroc Casablanca Morocco Virology Unit Immunovirology Laboratory Institut Pasteur du Maroc Casablanca Morocco Laboratory of Animal Hygiene Joint Faculty of Veterinary MedicineKagoshima University Kagoshima Japan Transboundary Animal Diseases Centre Joint Faculty of Veterinary MedicineKagoshima University Kagoshima Japan

Hepatitis C virus (HCV) infection and type 2 diabetes mellitus (T2DM) present a significant health burden,with increasing complications and mortality rates ***(R) (PYC),a natural product,possesses antidiabetic and antiviral properties that may improve HCV-associated *** this review,we present previously published data on the effectiveness of PYC against HCV replication and *** believe that supplementing conventional treatment with PYC may improve the current HCV therapy,attenuate HCV-associated T2DM,and reduce the risk of complications such as cirrhosis or hepatocellular carcinoma and cardiovascular disease.

关键词： T2DM Hepatitis C Pycnogenol(R) Complementary medicine

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Proximate composition, nutritional values and phytochemical screening of Piper retrofractum vahl. fruits

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Asian Pacific Journal of Tropical Biomedicine 2018年第1期8卷 37-43页

作者： Nurul jadid Byan Arasyi Arraniry Dewi Hidayati Kristanti Indah Purwani Wiwi Wikanta Sylviana Rosyda Hartanti Rizka Yuanita Rachman Department of Biology Institut Teknologi Sepuluh Nopember Department of Biology Muhammadiyah University

Objective: To investigate the proximate and mineral composition of the Piper retrofractum(P. retrofractum) vahl. Fruit and to evaluate its total alkaloids, phenol and flavonoid. Methods: The proximate composition of P. retrofractum fruit was ananlyzed using standard protocols according to Indonesian Standard and Association of Official Analytical Chemist. Meanwhile, mineral composition of the fruit was analyzed using inductively coupled plasma-mass spectrometry. Phytochemical screening and quantification were performed using standard protocols according to Harborn and spectrophotometric methods. Results: The results showed that P. retrofractum fruit contained carbohydrate(63.4%), crude protein(11.4%), total ash(4.29%), dietary fiber(28.8%) and total fat(2.97%). The fruit also contained calcium, copper, iron, magnesium, phosphor, potassium, sodium and zinc in different concentrations. Additionally, quinone, sterol, glycosides and alkaloid were detected in both n-hexane and ethyl acetate extracts. Moreover, tannin was presented also in ethylacetate and methanol extracts. Meanwhile, methanol extract contained sterol, glycosides, flavones, tannin and alkaloid. The results also revealed that methanol extract of the fruit contained highest phenol compared to other extract. Finally, small quantity of flavonoid(0.060 0%±0.000 2%) was observed. Conclusions: The overall results show that P. retrofractum contains potential nutritional and phytochemicals values, which support their function for pharmaceutical purposes.

关键词： Mineral composition Piper retrofractum Vahl. Phytochemical analysis Proximate composition

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