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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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Renewable energy EV charging station

Renewable energy EV charging station

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2021 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2021 and 2021 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2021

作者： Dorin, Petreus Toma, Patarau Radu, Etz Marcian, Cirstea Technical University of Cluj-Napoca Faculty of Electronics Telecommunications and Information Technology Department of Applied Electronics Cluj-Napoca Romania Anglia Ruskin University School of Computing and Information Science Faculty of Science and Engineering Cambridge United Kingdom

ISBN: (纸本)9781665402989

Due to the increased interest in electric vehicle (EV) technologies and the cost reduction of photovoltaic systems, industrial development of battery charging stations for electric vehicles based on solar energy has started. Although it is convenient to charge EVs at night, when there is less electricity consumption, there will always be consumers who prefer or need to charge their EV during daytime and even during peak hours. In this paper, an EV charging station integrating renewable energy in the form of solar energy is proposed and analyzed. Using a local battery pack, the charging station allows semi-fast and fast charging and can be installed in individual homes and apartment buildings, where the maximum power delivered by the grid is limited to 3.6kW. The charging station uses converters widely available on the market. A simulation model for this charging station is developed and various distinct operation modes are presented, validating the correct operation of the entire system. © 2021 IEEE.

关键词： Solar energy

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Sports Events Recognition Using Multi Features and Deep Belief Network

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Computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Priority Based Fair Scheduling: Enhancing Efficiency in Cloud Job Distribution

Priority Based Fair Scheduling: Enhancing Efficiency in Clou...

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International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Saydul Akbar Murad Zafril Rizal M Azmi Faria Jerin Brishti Md Saib Anupam Kumar Bairagi Faculty of Computing College of Computing & Applied Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Malaysia Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh South China University of Technology Guangzhou China Khulna University Khulna Bangladesh

In recent years, there has been a growing interest in cloud computing as a means to enhance user access to shared computing resources, including software and hardware, through the internet. However, the efficient utilization of these cloud resources has been a challenge, often resulting in wastage or degraded service performance due to inadequate scheduling. To overcome this challenge, numerous researchers have focused on improving existing Priority Rule (PR) cloud schedulers by developing dynamic scheduling algorithms, but they have fallen short of meeting user satisfaction. In this study, we introduce a new PR scheduler called Priority Based Fair Scheduling (PBFS), which takes into account key parameters such as CPU Time, Job Arrival Time, and Job Length. We evaluate the performance of PBFS by comparing it with five existing algorithms, and the results demonstrate that PBFS surpasses the performance of the other algorithms. The experiment was conducted using the CloudSim simulator, utilizing a dataset of 300 and 400 jobs. In order to assess the performance, three key metrics were employed: flow time, makespan time, and total tardiness. These metrics were chosen to evaluate and analyze the effectiveness of the proposed scheduling algorithm.

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Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators

arXiv

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arXiv 2023年

作者： Eskandari, Mahdi Puiman, Lars Zeitler, Jakob Division of Computing Engineering and Mathematical Sciences University of Kent Canterbury United Kingdom Faculty of Applied Sciences Department of Biotechnology Delft University of Technology Delft Netherlands Matterhorn Studio Oxford United Kingdom

A Bayesian optimization approach for maximizing the gas conversion rate in an industrial-scale bioreactor for syngas fermentation is presented. We have access to a high-fidelity, computational fluid dynamic (CFD) reactor model and a low-fidelity ideal-mixing-based reactor model. The goal is to maximize the gas conversion rate, with respect to the input variables (e.g., pressure, biomass concentration, gas flow rate). Due to the high cost of the CFD reactor model, a multi-fidelity Bayesian optimization algorithm is adopted to solve the optimization problem using both high and low fidelities. We first describe the problem in the context of syngas fermentation followed by our approach to solving simulator optimization using multiple fidelities. We discuss concerns regarding significant differences in fidelity cost and their impact on fidelity sampling and conclude with a discussion on the integration of real-world fermentation data. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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Navigating the Newscape: Long Short-Term Memory Models for Misinformation Detection

Navigating the Newscape: Long Short-Term Memory Models for M...

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International Conference on Software Defined Systems (SDS)

作者： Dina Tahat Raghad Alfaisal Khalaf Tahat Said Salloum Applied Sociology Department Al Ain University UAE Faculty of Computing and Meta-Technology Universiti Pendidikan Tanjung Malim Malaysia Media & Creative Industries United Arab Emirates University and Yarmouk University Jordan University of Sharjah Sharjah UAE

ISBN: (数字)9798331518325

ISBN: (纸本)9798331518332

In an era where digital misinformation poses a severe challenge to societal trust, identifying fake news is imperative. This study employs a Long Short-Term Memory (LSTM) neural network to discern misinformation within a dataset of approximately 79,000 labeled news texts. Our LSTM model benefits from its aptitude for processing sequential data, crucial for understanding textual context. We evaluate our model's proficiency through accuracy, precision, recall, and F1 score, key metrics reflecting its classification capability. The model exhibits robust performance with a test accuracy of 94.09%, precision of 91.57%, recall of 95.45%, and an F1 score of 93.47%. These results underscore the potential of LSTM networks in automating fake news detection, which could significantly support the integrity of information consumption in the digital realm.

关键词： Measurement Analytical models Accuracy Navigation Neural networks Software Data models Fake news Long short term memory Context modeling

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Advanced Energy Management in Smart Grids using Lyrebird Optimization Algorithm

Advanced Energy Management in Smart Grids using Lyrebird Opt...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Mahmoud Z. Mistarihi Putchakayala Yanna Reddy A.Senthilnathan R. Bagavathi Subbi Naidu Bora Prasad Yadav Kurikyala Mechanical and Industrial Engineering Department Faculty of Engineering and Computing Liwa College Abu Dhabi UAE Hijjawi Faculty for Engineering Technology Yarmouk University Jordan Department of EEE Bharat Institute of Engineering and Technology Department of EEE Dr.N.G.P Institute of Technology Coimbatore India Department of Mathematics Rathinam Technical Campus Coimbatore Rajiv Gandhi University of Knowledge Technologies Nuzvid Andhra Pradesh Department of Engineering and Technology University of Technology and Applied Sciences Ibri Oman

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Integration of a hybrid wind turbine (HWT) and a battery energy system (BES) is expected to take place under the framework of a Smart Grid (SG) to optimize energy generation, storage, and distribution and enhance stability and efficiency. One aspect of such systems is a challenge concerning the effective management of Renewable Energy (RE) fluctuation to achieve balanced operation among renewable generation and energy demand. This paper proposes a Lyrebird Optimization Algorithm (LOA) to optimize energy demand forecasting and management within the hybrid system. The LOA is used to minimize Mean Absolute Percentage Error (MAPE) in energy demand forecasting within the SG system. By optimizing energy demand forecasting, the LOA effectively improves accuracy in energy management and forecasting within the SG. Its adaptive optimization ensures that forecasting errors are minimized across various operating conditions, enhancing overall efficiency of the energy distribution system. By then the proposed LOA method is implemented on the MATLAB platform, and its performance is evaluated by comparing it with existing optimization techniques such as Improved Grey Wolf Optimization (IGWO), Multi-Objective Optimization (MOO), Multi-Objective Wind Driven Optimization (MOWDO), and Coati Optimization Algorithm (COA). The results show that the proposed LOA method provides superior performance in minimizing MAPE, achieving the lowest MAPE value of 2.90%, indicating its enhanced accuracy in energy demand forecasting and optimization.

关键词： Energy consumption Renewable energy sources Accuracy Scalability Real-time systems Smart grids Wind turbines Batteries Forecasting Optimization

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Effects of natural-based SiO2 nanocoolant on car radiator: Thermal profile

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Materials Today: Proceedings 2022年 66卷 2734-2737页

作者： Ruey Ong, Huei Mohd Eqhwan Iskandar, Wan Yapp Joo, Ming Rahman Khan, Md. Maksudur Khairul Anuar Mohamed, Muhammad Faculty of Engineering & Technology DRB-HICOM University of Automotive Malaysia Peramu Jaya Industrial Area Pekan Pahang 26607 Malaysia Department of Chemical Engineering College of Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Kuantan Pahang 26300 Malaysia Centre for Mathematical Sciences College of Computing & Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang 26300 Malaysia

Nanofluid as a transport medium has a great deal of potential in heat transfer engineering applications. In this work, nanosilica was derived from natural sources, rice husk through incineration and chemical treatment process. Various SiO2 nanoparticle volume concentrations (0–1 vol%) were dispersed in EG/water binary mixture assisted by ultrasound technique to produce SiO2 nanocoolant. Scanning Electron Microscopy (SEM) was used to identify the surface morphology and size of the nanosilica. Furhermore, the study was further conducted in a car radiator to determine the thermal profile consists of the effect of nanoparticle concentration and the longevity testing of the nanocoolant. Findings show that nanosilica particles were in range of 15–25 nm with irregular shape. Moreover, the heat transfer performance was enhanced as the SiO2 concentration was increased. Apart from that, SiO2 1 vol% shows a great thermal behaviour as it takes a shorter time to achieve and maintain at optimal working temperature compared to other samples. Apart from that, it can be observed that SiO2 nanocoolant possesses slightly higher properties than the base fluid and conventional coolant. The results also revealed the average heat transfer coefficient is directly proportional to the volume concentration of nanofluids and the heat transfer performance of the radiator increased with the inclusion of nanoparticles to the base fluid. © 2022

关键词： Scanning electron microscopy

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Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

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Computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

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Classification of Retroviruses Based on Genomic Data Using RVGC

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Computers, Materials & Continua 2021年第12期69卷 3829-3844页

作者： Khalid Mahmood Aamir Muhammad Bilal Muhammad Ramzan Muhammad Attique Khan Yunyoung Nam Seifedine Kadry Department of CS&IT University of SargodhaSargodha40100Pakistan Department of CS&IT University of MianwaliMianwali42200Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Retroviruses are a large group of infectious agents with similar virion structures and replication ***,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus *** of retroviruses by genome sequence is a biological problem that benefits from computational *** National Center for Biotechnology Information(NCBI)promotes science and health by making biomedical and genomic data available to the *** research aims to classify the different types of rotavirus genome sequences available at the ***,nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing *** on some significant results,the number of features used for classification is reduced to *** classification shall be carried out in two *** first phase of classification shall select only two *** data in the first phase is transferred to the next phase,where the final decision is taken with the remaining three *** data sets of animals and human retroviruses are selected;the training data set is used to minimize the classifier’s number and training;the validation data set is used to validate the *** performance of the classifier is analyzed using the test data ***,we use decision tree,naive Bayes,knearest neighbors,and vector support machines to compare *** results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset.

关键词： Retroviruses machine learning bioinformatics classification

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