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A Survey of Data Partitioning and Sampling Methods to Support Big Data Analysis

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Big Data Mining and Analytics 2020年第2期3卷 85-101页

作者： mohammad sultan mahmud Joshua Zhexue Huang Salman Salloum Tamer Z.Emara Kuanishbay Sadatdiynov National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityUniversityShenzhen 518060China Big Data Institute College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China.

Computer clusters with the shared-nothing architecture are the major computing platforms for big data processing and analysis.In cluster computing,data partitioning and sampling are two fundamental strategies to speed up the computation of big data and increase scalability.In this paper,we present a comprehensive survey of the methods and techniques of data partitioning and sampling with respect to big data processing and analysis.We start with an overview of the mainstream big data frameworks on Hadoop clusters.The basic methods of data partitioning are then discussed including three classical horizontal partitioning schemes:range,hash,and random partitioning.Data partitioning on Hadoop clusters is also discussed with a summary of new strategies for big data partitioning,including the new Random Sample Partition(RSP)distributed model.The classical methods of data sampling are then investigated,including simple random sampling,stratified sampling,and reservoir sampling.Two common methods of big data sampling on computing clusters are also discussed:record-level sampling and blocklevel sampling.Record-level sampling is not as efficient as block-level sampling on big distributed data.On the other hand,block-level sampling on data blocks generated with the classical data partitioning methods does not necessarily produce good representative samples for approximate computing of big data.In this survey,we also summarize the prevailing strategies and related work on sampling-based approximation on Hadoop clusters.We believe that data partitioning and sampling should be considered together to build approximate cluster computing frameworks that are reliable in both the computational and statistical respects.

关键词： big data analysis data partitioning data sampling distributed and parallel computing approximate computing

Surface Morphology and Thermo-Electrical Energy Analysis of Polyaniline (PANI) Incorporated Cotton Fabric

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Energy Engineering 2024年第1期121卷 1-12页

作者： Md.Shohan Parvez Md.Mustafizur Rahman Mahendran Samykano mohammad Yeakub Ali Automotive Engineering Research Group Faculty of Mechanical and Automotive Engineering TechnologyUniversiti Malaysia Pahang Al-Sultan AbdullahPekanMalaysia Department of Textile Engineering Khulna University of Engineering&Technology(KUET)KhulnaBangladesh Centre for Research in Advanced Fluid and Processes(CARIFF) Universiti Malaysia Pahang Al-Sultan AbdullahLebuhraya Tun RazakGambangMalaysia Mechanical Engineering Programme Area Faculty of EngineeringUniversiti Teknologi BruneiBruneiDarussalam

With the exponential development in wearable electronics,a significant paradigm shift is observed from rigid electronics to flexible wearable devices.Polyaniline(PANI)is considered as a dominant material in this sector,as it is endowed with the optical properties of both metal and semiconductors.However,its widespread application got delineated because of its irregular rigid form,level of conductivity,and precise choice of solvents.Incorporating PANI in textile materials can generate promising functionality for wearable applications.This research work employed a straightforward in-situ chemical oxidative polymerization to synthesize PANI on Cotton fabric surfaces with varying dopant(HCl)concentrations.Pre-treatment using NaOH is implemented to improve the conductivity of the fabric surface by increasing the monomer absorption.This research explores the morphological and structural analysis employing SEM,FTIR and EDX.The surface resistivity was measured using a digital multimeter,and thermal stability is measured using TGA.Upon successful polymerization,a homogenous coating layer is observed.It is revealed that the simple pre-treatment technique significantly reduces the surface resistivity of Cotton fabric to 1.27 kΩ/cm with increasing acid concentration and thermal stability.The electro-thermal energy can also reach up to 38.2°C within 50 s with a deployed voltage of 15 V.The modified fabric is anticipated to be used in thermal regulation,supercapacitor,sensor,UV shielding,antimicrobial and other prospective functional applications.

关键词： Polyaniline cotton in-situ polymerization SEM TGA heating fabric

Double-E-Triple-H-Shaped NRI-Metamaterial for Dual-Band Microwave Sensing Applications

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Computers, Materials & Continua 2022年第6期71卷 5817-5836页

作者： Shafayat Hossain Md.Iquebal Hossain Patwary Sikder Sunbeam Islam sultan mahmud Norbahiah Binti Misran Ali F.Almutairi mohammad Tariqul Islam Department of Electrical&Electronics Engineering International Islamic University Chittagong4318ChittagongBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Electrical Engineering Department Kuwait UniversityKuwait City13060Kuwait

This paper presents a new Double-E-Triple-H-Shaped NRI(negative refractive index)metamaterial(MM)for dual-band microwave sensing applications.Here,a horizontal H-shaped metal structure is enclosed by two face-to-face E-shaped metal structures.This double-E-H-shaped design is also encased by two vertical H-shaped structures along with some copper links.Thus,the Double-E-Triple-H-Shaped configuration is developed.Two popular substrate materials of Rogers RO 3010 and FR-4 were adopted for analyzing the characteristics of the unit cell.The proposed structure exhibits transmission resonance inside the S-band with NRI and ENG(Epsilon Negative)metamaterial properties,and inside the C-band with ENG and MNG(Mu Negative)metamaterial properties.A good effective medium ratio(EMR)of 8.06 indicates the compactness and effectiveness of the proposed design.Further analysis has been done by changing the thickness of the substrate material as well and a significant change in the effective medium ratio is found.The validity of the proposed structure is confirmed by an equivalent circuit model.The simulated result agrees well with the calculated result.For exploring microwave sensing applications of the proposed unit cell,permittivity and pressure sensitivity performance were investigated in different simulation arrangements.The compact size,effective parameters,high sensitivity and a good EMR represent the proposed metamaterial as a promising solution for S-band and C-band microwave sensing applications.

关键词： Metamaterial NRI MNG ENG dual-band sensing S-band C-band

Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

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Computers, Materials & Continua 2024年第6期79卷 3725-3756页

作者： Widad Elbakri Maheyzah Md.Siraj Bander Ali Saleh Al-rimy sultan Noman Qasem Tawfik Al-Hadhrami Faculty of Computing Universiti Teknologi MalaysiaJohor BahruJohor81310Malaysia Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Science and Technology Nottingham Trent UniversityNottinghamNG118NSUK

Cloud computing environments,characterized by dynamic scaling,distributed architectures,and complex work-loads,are increasingly targeted by malicious actors.These threats encompass unauthorized access,data breaches,denial-of-service attacks,and evolving malware variants.Traditional security solutions often struggle with the dynamic nature of cloud environments,highlighting the need for robust Adaptive Cloud Intrusion Detection Systems(CIDS).Existing adaptive CIDS solutions,while offering improved detection capabilities,often face limitations such as reliance on approximations for change point detection,hindering their precision in identifying anomalies.This can lead to missed attacks or an abundance of false alarms,impacting overall security effectiveness.To address these challenges,we propose ACIDS(Adaptive Cloud Intrusion Detection System)-PELT.This novel Adaptive CIDS framework leverages the Pruned Exact Linear Time(PELT)algorithm and a Support Vector Machine(SVM)for enhanced accuracy and efficiency.ACIDS-PELT comprises four key components:(1)Feature Selection:Utilizing a hybrid harmony search algorithm and the symmetrical uncertainty filter(HSO-SU)to identify the most relevant features that effectively differentiate between normal and anomalous network traffic in the cloud environment.(2)Surveillance:Employing the PELT algorithm to detect change points within the network traffic data,enabling the identification of anomalies and potential security threats with improved precision compared to existing approaches.(3)Training Set:Labeled network traffic data forms the training set used to train the SVM classifier to distinguish between normal and anomalous behaviour patterns.(4)Testing Set:The testing set evaluates ACIDS-PELT’s performance by measuring its accuracy,precision,and recall in detecting security threats within the cloud environment.We evaluate the performance of ACIDS-PELT using the NSL-KDD benchmark dataset.The results demonstrate that ACIDS-PELT outperforms existing cloud intrusion detection techniqu

关键词： Adaptive cloud IDS harmony search distributed denial of service(DDoS) PELT machine learning SVM ISOTCID NSL-KDD

Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN

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Computer Systems Science & Engineering 2022年第6期41卷 1223-1240页

作者： Anika Tahsin Meem mohammad Monirujjaman Khan Mehedi Masud sultan Aljahdali Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

The COVID-19 pandemic has caused trouble in people’s daily lives andruined several economies around the world, killing millions of people thus far. Itis essential to screen the affected patients in a timely and cost-effective manner inorder to fight this disease. This paper presents the prediction of COVID-19 withChest X-Ray images, and the implementation of an image processing systemoperated using deep learning and neural networks. In this paper, a Deep Learning,Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used forbuilding and training neural nets. Scikit-learn was used for machine learning fromend to end. Various deep learning features are used, such as Conv2D, Dense Net,Dropout, Maxpooling2D for creating the model. The proposed approach had aclassification accuracy of 96.43 percent and a validation accuracy of 98.33 percentafter training and testing the X-Ray pictures. Finally, a web application has beendeveloped for general users, which will detect chest x-ray images either as covidor normal. A GUI application for the Covid prediction framework was run. Achest X-ray image can be browsed and fed into the program by medical personnelor the general public.

关键词： Covid-19 prediction covid-19 coronavirus normal deep learning convolutional neural network image processing chest x-ray

Simultaneous biportal endoscopic management of pineal region tumors in patients with obstructive hydrocephalus:technical notes

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Chinese Neurosurgical Journal 2023年第2期9卷 51-57页

作者： sultan Al-Saiari Khalid Al Orabi mohammad Ghazi Abdoh Abdulaziz A.Basurrah sultan Faez Albalawi Ahmed A.Farag Neurosurgery Department King Abdullah Medical CityP.O 24246MakkahSaudi Arabia

Background The goal of this study is to show the feasibility and benefits of using the simultaneous biportal endoscopic procedure to treat pineal tumors in patients with obstructive hydrocephalus.Methods We retrospectively reviewed three patients with pineal tumors and acute obstructive hydrocephalus who were treated in one session with a frameless stereotactic guided simultaneous biportal endoscopic third ventriculostomy and endoscopic tumor biopsy performed through two separate ports using one rigid ventriculoscope.Results In the three patients,ventriculostomy and endoscopic biopsies were conducted.There was no death or morbidity throughout the 45-min procedure.All of the patients’histological findings were confirmed.Germinoma was diagnosed in two patients who recieved postoperative radiotherapy,and the third patient diagnosed with a pineocytoma.Magnetic resonance imaging with flow-sensitive sequences was used to confirm ventriculostomy patency in all patients 6 months after the surgery.Conclusion Biportal endoscopic approach enables better visual control of both procedures.Furthermore,it allows the surgeon to safely pass the ventriculoscope via the foramen of monro,even if it is narrow.Moreover,during endoscopic tumor biopsy and third ventriculostomy,the intracranial pressure can be smoothly managed using the outlet tubes accessible.This treatment may be an alternative to traditional uniportal endoscopic operations in certain patients.

关键词： Pineal tumors Germinoma Hydrocephalus Endoscopic third ventriculostomy Endoscopic biopsy

Deep Learning Control for Autonomous Robot

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Computers, Materials & Continua 2022年第8期72卷 2811-2824页

作者： Rihem Farkh Saad Alhuwaimel sultan Alzahrani Khaled Al Jaloud mohammad Tabrez Quasim College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical Engineering National Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these approaches.Autonomous cars are one such application.This is expected to have a significant and revolutionary influence on society.Integration with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles.The autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement.Cars are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic implications.However,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action.The majority of self-driving car technologies are based on computer systems that automate vehicle control parts.From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and cameras.Recent researches on computer vision and deep learning are used to control autonomous driving systems.For self-driving automobiles,lane-keeping is crucial.This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane.We propose an advanced control for a selfdriving robot by using two controllers simultaneously.Convolutional neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering control.This study uses a Raspberry PI based camera to control t

关键词： Autonomous car cascade PID control deep learning convolutional neural network differential drive system raspberry PI road lane detector

Numerical Study and Optimization of CZTS-Based Thin-Film Solar Cell Structure with Different Novel Buffer-Layer Materials Using SCAPS-1D Software

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Energy and Power Engineering 2024年第4期16卷 179-195页

作者： Md. Zamil sultan Arman Shahriar Rony Tota Md. Nuralam Howlader Hasibul Haque Rodro Mahfuja Jannat Akhy Md. Abir Al Rashik Department of Electrical and Electronic Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

This study explored the performances of CZTS-based thin-film solar cell with three novel buffer layer materials ZnS, CdS, and CdZnS, as well as with variation in thickness of buffer and absorber-layer, doping concentrations of absorber-layer material and operating temperature. Our aims focused to identify the most optimal thin-film solar cell structure that offers high efficiency and lower toxicity which are desirable for sustainable and eco-friendly energy sources globally. SCAPS-1D, widely used software for modeling and simulating solar cells, has been used and solar cell fundamental performance parameters such as open-circuited voltage (), short-circuited current density (), fill-factor() and efficiency() have been optimized in this study. Based on our simulation results, it was found that CZTS solar cell with Cd0.4Zn0.6S as buffer-layer offers the most optimal combination of high efficiency and lower toxicity in comparison to other structure investigated in our study. Although the efficiency of Cd0.4Zn0.6S, ZnS and CdS are comparable, Cd0.4Zn0.6S is preferable to use as buffer-layer for its non-toxic property. In addition, evaluation of performance as a function of buffer-layer thickness for Cd0.4Zn0.6S, ZnS and CdS showed that optimum buffer-layer thickness for Cd0.4Zn0.6S was in the range from 50 to 150nm while ZnS offered only 50 – 75 nm. Furthermore, the temperature dependence performance parameters evaluation revealed that it is better to operate solar cell at temperature 290K for stable operation with optimum performances. This study would provide valuable insights into design and optimization of nanotechnology-based solar energy technology for minimizing global energy crisis and developing eco-friendly energy sources sustainable and simultaneously.

关键词： Thin-Film Solar Cell CZTS Buffer-Layer Renewable Energy Green-House Gases Efficiency

Overview of Jute Fibre as Thermoplastic Matrix Polymer Reinforcement

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Journal of Renewable Materials 2024年第3期12卷 457-483页

作者： Tezara Cionita mohammad Hazim Mohamad Hamdan Januar Parlaungan Siregar Deni Fajar Fitriyana Ramli Junid Wong Ling Shing Jamiluddin Jaafar Agustinus Purna Irawan Teuku Rihayat Rifky Ismail Athanasius Priharyoto Bayuseno Emilianus Jehadus Faculty of Engineering and Quantity Surveying INTI International UniversityNilaiNegeri Sembilan71800Malaysia Technical Department Dyna Forming Engineering Technology(DFETECH)SDN.BHD.SerembanNegeri Sembilan70300Malaysia Faculty of Mechanical and Automotive Engineering Technology UniversitiMalaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Automotive Engineering Center(AEC) UniversitiMalaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Mechanical Engineering Universitas Negeri SemarangKampus SekaranGunungpatiSemarang50229Indonesia Faculty of Manufacturing and Mechatronic Engineering Technology Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Faculty of Health and Life Sciences INTI International UniversityNilai71800Malaysia Faculty of Mechanical and Manufacturing Engineering Universiti Tun Hussein Onn MalaysiaParit RajaBatu PahatJohor86400Malaysia Faculty of Engineering Universitas TarumanagaraJakarta Barat11440Indonesia Department of Chemical Engineering Politeknik Negeri LhokseumaweLhokseumawe24301Indonesia Department of Mechanical Engineering Faculty of EngineeringDiponegoro UniversitySemarang50275Indonesia Department of Mathematics Education Universitas Katolik Indonesia Santu Paulus RutengNusa Tenggara Timur8651Indonesia

Recent decades have seen a substantial increase in interest in research on natural fibres that is aligned with sustainable development goals(SDGs).Due to their renewable resources and biodegradability,natural fiberreinforced composites have been investigated as a sustainable alternative to synthetic materials to reduce the usage of hazardous waste and environmental pollution.Among the natural fibre,jute fibre obtained from a bast plant has an increasing trend in the application,especially as a reinforcement material.Numerous research works have been performed on jute fibre with regard to reinforced thermoset and thermoplastic composites.Nevertheless,current demands on sustainable materials have required new developments in thermoplastic composites.In this paper,the author reviews jute plants as reinforcement materials for thermoplastic matrix polymers.This review provides an overview of the sustainability of jute plants as reinforcement material for thermoplastic matrix polymers.The overview on jute based thermoplastic composites focused on the thermal behavior and mechanical properties.Apart from physical,chemical,and mechanical properties,the study also covers the current and perspectives for future research challenges faced by the researchers on jute fibre reinforced thermoplastic composites.

关键词： Jute fibre sustainable development goals(SDGs) natural fibre thermoplastic composite sustainable manufacturing