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4th International Conference on Emerging Electrical Energy, Electronics and Computing, ICE4CT 2022

作者： Hamzah, Husam Talib Veluru, Sridevi Hasan, Hikmat Mohammed Abed, Hayder Yousif Mahdi, Husam Salah Hamdullah, Noor Abdulqader Tukarambai, M. Poiba, Venkata Rao Srikanth, R. Department of Chemical Engineering College of Engineering Andhra University Visakhapatnam India Department of Electrical Engineering Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Electrical Engineering University of Technology Baghdad Iraq Department of Computer Science and System Engineering College of Engineering Andhra University Visakhapatnam India Electrical and Computer Engineering School of Engineering and Architecture Altinbas University Istanbul Turkey Department of Chemical Engineering Anil Neerukonda Institute of Technology and Sciences Sangivalasa India

Globally, biomass usage as a supply of non-depletable resources materials used in the production of energy at their rawest state is an issue. Pyrolysis is a method of thermally treating biomass that as a consequence in the formation of liquid, products, both solid and gaseous. Elevated heating is required to transform the complicated composition of the biomass's underlying structure matrix into usable products. Heating with a microwave oven has the potential to become a competitive option. to more traditional methods of heating. Owing to its ease of operation and rapid heating rate, it has recently been widely employed in pyrolysis. The goals of this research are to found the principles of MW-assisted pyrolysis action and to examine some critical issues characteristics of operation that have an influence on the yield of a product. This procedure appears to be governed by a number of operational factors for instance, microwave power and temperature degree, the addition of microwave absorbers and their concentration, initial relative humidity, primary sweep gas flow rate/time spent in residence. © Published under licence by IOP Publishing Ltd.

关键词： Biomass

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Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

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computers, Materials & Continua 2022年第12期73卷 6579-6594页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Amal S.Mehanna Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and *** recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected *** the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful *** and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection system(IMVO-DLIDS)for IoT *** presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT *** proposed IMVO-DLIDS model follows a three-stage *** first,data pre-processing is performed to convert the actual data into useful *** addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature ***,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for ***,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU *** experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct *** extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

关键词： Internet of everything deep learning feature selection classification intrusion detection cybersecurity

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Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm 2

Impact of Image Enhancement for Osteoporosis Detection Based...

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2nd International Conference on computer system, Information Technology, and Electrical engineering, COSITE 2023

作者： Iman, Riza Dianil Roslidar, Roslidar Muchtar, Kahlil Amalia, Amalia Syaryadhi, Mohammad Mufti, Alfatirta Maulina, Novi Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (纸本)9798350343069

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an F1-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance. © 2023 IEEE.

关键词： Image enhancement

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Performance Analysis of Higher Order Tensor Storages for Highly Sparse Multidimensional Data 5

Performance Analysis of Higher Order Tensor Storages for Hig...

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5th International Conference on Electrical Information and Communication Technology, EICT 2021

作者： Safayet Hossain, Md. Azharul Hasan, K.M. Tsuji, Tatsuo Department of Computer Science and Engineering Khulna Bangladesh University of Fukui Faculty of System Design Engineering Fukui Japan

ISBN: (纸本)9781665409063

To compute multidimensional data, tensor algebra is a powerful tool and it has many applications in machine learning and data analytics. This multidimensional data which is represented with higher-order tensors for multi-Aspect data analysis is very sparse for an actual dataset. The degree of data sparsity increases with the increase of dimensionality. This is because the volume of the tensor increases very fast with the increase of dimensionality but the actual data to the tensor does not increase at such a rate for high-performance scientific computing. Storing and applying operations on this highly sparse multidimensional data is a challenge for data scientists. Therefore, an efficient storage scheme for such sparse tensor is very important. There are many storage schemes in the literature for storing sparse tensors. In this paper, we analyze and compare different tensor storage formats using synthetic and actual datasets. We found that no single storage scheme is efficient for all the operations or applications of tensors. We calculated the memory required for per-nonzero elements among these tensor formats and compared them for different parameters. © 2021 IEEE.

关键词： Tensors

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Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

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computers, Materials & Continua 2022年第12期73卷 5011-5024页

作者： Mesfer Al Duhayyim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of *** recently developed deep learning(DL)models pave the way to design CB classifier models with maximum *** the same time,optimal hyperparameter tuning process plays a vital role to enhance overall *** study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social *** proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media ***,the input data is cleaned and pre-processed to make it compatible for further *** by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of ***,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the *** assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several *** simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

关键词： Social media deep learning cyberbullying cybersecurity hyperparameter optimization

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A Big Data, Bigger Impact: A Comprehensive Review of Machine Learning Advancements

A Big Data, Bigger Impact: A Comprehensive Review of Machine...

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International Conference on Electrical engineering and computer science (ICECOS)

作者： Didar Dlshad Hamad Ameen Shahab Wahhab Kareem Sarhad Baez Hasan Computer Science Department Soran University Faculty of Science Soran Kurdistan Iraq Department of Information System Engineering Erbil Polytechnic University Erbil Kurdistan Iraq

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

The exponential growth of data driven by the internet has necessitated effective extraction of insights, with big data and machine learning standing as pivotal tools. This paper aims to provide insights into the evolving landscape of machine learning and big data analytics through a comprehensive literature review. It seeks to underscore the key advancements and intersections between these domains and their transformative applications across industries. Specifically, the study will delve into the foundational theories that underpin machine learning algorithms, data collection and preprocessing techniques involved in big data analysis, and the prominent challenges associated with extracting insights from large and complex datasets. It will further explore major studies that exemplify the integration of machine learning methodologies and big data technologies for addressing real-world problems. The study will also examine visualization methods that facilitate comprehending insights gleaned from big data. Finally, it will discuss future research directions and highlight best practices for leveraging machine learning and big data analytics in decision-making processes.

关键词： Industries computer science Electrical engineering Machine learning algorithms Reviews Bibliographies Decision making Machine learning Transforms Big Data

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Combination of CRITIC and PROMETHEE II Methods as An Early Detection of Potential Dropout Students

Combination of CRITIC and PROMETHEE II Methods as An Early D...

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International Conference on Information and Communications Technology (ICOIACT)

作者： Mochammad Yusa Funny Farady Coastera Deni Rahmadani Julia Purnama Sari Kusrini Kusrini Anggit Dwi Hartanto Department of Informatics Faculty of Engineering University of Bengkulu Bengkulu Indonesia Department of Information System Faculty of Engineering University of Bengkulu Bengkulu Indonesia Magister of Informatics Engineering Faculty of Computer Science Universitas AMIKOM Yogyakarta Yogyakarta Indonesia Department of Information System Faculty of Computer Science Universitas AMIKOM Yogyakarta Yogyakarta Indonesia

Dropout (DO) has negative implications for individuals and educational institutions. The field of education data mining (EDM) offers valuable contributions to prevent dropout cases and improve student retention. This study focuses on implementing EDM to identify students at risk of dropping out by integrating academic activity criteria with real-time data sources. Previous studies have encountered limitations in classification and clustering techniques, with classification relying on labeled training data (supervised learning) and clustering constrained by attribute or criteria weight (unsupervised learning). This study aims to address these limitations by combining the PROMETHEE II ranking approach and the CRITIC weighting method, producing a list of students with dropout potential. The results can aid stakeholders in implementing preventive measures. The study involves several steps: 1) Collecting data through integration with academic portals; 2) Filtering and selecting criteria for student academic activities; 3) Transforming and normalizing the data; 4) Applying automatic weighting using CRITIC; 5) Calculating criteria rankings using the PROMETHEE II technique; and 6) Validating the ranking outcomes.

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An intelligent cylindrical reflector for airborne virus detection 20

An intelligent cylindrical reflector for airborne virus dete...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Hadded, Ahmed Ayed, Mossaad Ben Alshaya, Shaya A. Sfax University Computer and Embedded System Laboratory National Engineering School of Sfax Sfax3038 Tunisia Sousse University Electronic Industrial ENISo Tunisia Sfax University Tunisia Computer and Embedded System Laboratory ENIS Sfax3038 Tunisia Majmaah University Digital Transformation and E-Transactions Faculty of Sciences and Humanities Sciences Prince Sultan University Department of Computer Science Majmaah11952 Saudi Arabia

ISBN: (纸本)9798350332568

In 2020, the world suffered from the COVID-19 pandemic. This situation highlighted the considerable need for systems to detect viruses quickly. Providing an accurate e-health system that detects, monitors, and controls virus spread became necessary. Countries attempt to reduce the virus's spread using basic processes such as quarantine and lockdown. Early virus detection is requested to reduce the threat and decrease the propagation of the pandemic. This paper proposes an attempt to avoid speed-up spreading based on airborne virus detection. The suggested detection based on the SiNW-FET biosensor is not approved as a standardized diagnostic test. To support the airborne detection methodology, a metasurface technology based on Reflector cylindrical antenna with a sensing unit is proposed to detect viruses. The proposal considered the propagation delays and concentration of a virus. Conducted results prove that the receiver's volume is considered a primary key to an accurate detection rather than the virus's concentration. © 2023 IEEE.

关键词： Reflection

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Deep Learning Enabled Intelligent Healthcare Management system in Smart Cities Environment

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computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support system for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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A Review of Fintech Management and Security Development

A Review of Fintech Management and Security Development

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Information Technology International Seminar (ITIS)

作者： Ali Akbar Ritonga Syaiful Zuhri Harahap Adri Gabriel Sooai Aryo Nugroho Samsir Puput Dani Prasetyo Adi Department of Informatics Management Faculty of Science and Technology Universitas Labuhanbatu Labuhanbatu North Sumatera Indonesia Department of Information System Faculty of Science and Technology Universitas Labuhanbatu Labuhanbatu North Sumatera Indonesia Department of Computer Engineering Faculty of Computer Science Universitas Katolik Widya Mandira Kupang NTT Indonesia Department of Information System Faculty of Computer Science Universitas Narotama Surabaya Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Labuhanbatu Labuhanbatu North Sumatera Indonesia Department of Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026.

关键词： Seminars Reviews computer hacking Fintech Bitcoin Blockchains Internet of Things Security Servers Protection

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