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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Sallam, Amer Amery, Hadeel Al Al-Qudasi, Somaia Al-Ghorbani, Safaa Rassem, Taha H. Makbol, Nasrin M. Taiz University Alsaeed Faculty for Engineering IT Yemen College of Computing and Applied Sciences Faculty of Computing Pekan Malaysia University of Hodeidah College of Computer Science and Engineering Yemen

ISBN: (纸本)9781665414074

Identification system is one of the important parts in security domains of the present time. The traditional protection methods considered to be inefficient and unreliable as they are subjected to the theft, imitation or forgetfulness. In contrast, biometrics such as facial recognition, fingerprints and the retina have emerged as modern protection methods, but still also suffer from some defects and violations. However, Iris recognition is an automated method that considered as a promising biometric identification due to the stability and the uniqueness of its patterns. In this paper, an iris recognition system based on Convolutional Neural Network (CNN) model was proposed. CNN is used to perform the required processes of feature extraction and classification. The proposed system was evaluated through CASIA-V1 and ATVS datasets, after the required pre-processing steps taken place, and achieved 98% and 97.83% as a result, respectively. © 2021 IEEE.

关键词： Biometrics

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Epileptic Spike Detection Based on Deep Learning 23

Epileptic Spike Detection Based on Deep Learning

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9th International Conference on computing and Artificial Intelligence, ICCAI 2023

作者： Duan, Lijuan Cui, Yuanqi Wang, Yao Wang, Zeyu Cao, Min Qiao, Yuanhua Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China College of Applied Science Beijing University of Technology University Beijing China

ISBN: (纸本)9781450399029

Epilepsy is a chronic disease with high prevalence and high disease burden. Electroencephalogram (EEG) is one of the most important tools for diagnosing epilepsy. Detection of epileptic spikes from EEG can effectively aid the diagnosis of epilepsy but it is also a time-consuming and laborious process for neurologists, which can only be addressed by automatic spike detection methods. However, existing approaches are limited by the simplistic nature of conventional machine learning algorithms. The work researched about how human experts identify spikes and over-parameterized the process into a deep neural network named temporal-frequential and multi-order difference fusion (TeFreDiA) model which can accurately detect epileptic *** model was evaluated on data collected from 7 subjects with ten-fold cross-validation. Given an average accuracy and sensitivity of 95.64% and 96.89%, the model can be utilized to aid the diagnosis of epilepsy. © 2023 Copyright held by the owner/author(s).

关键词： Electroencephalography

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Effectiveness of Machine Learning Algorithms on Battling Counterfeit Items in E-commerce Marketplaces

Effectiveness of Machine Learning Algorithms on Battling Cou...

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International Research Conference on Smart computing and Systems Engineering (SCSE)

作者： Hmkt Gunawardhana Btgs Kumara Kapila Rathnayake Prasad M. Jayaweera Department of Computing and Information Systems Faculty of Computing Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Department of Computer Science Faculty of Applied Sciences University of Sri Jayawardanapura Sri Lanka

For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study’s findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces.

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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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A decentralized and privacy-preserving framework for electronic health records using blockchain

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Alexandria Engineering Journal 2025年 126卷 196-203页

作者： Alahmari, Saad Alshardan, Amal Al-Wesabi, Fahd N. Sorour, Shaymaa Alghushairy, Omar Alsini, Raed Khadidos, Alaa O. Duhayyim, Mesfer Al Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj16273 Saudi Arabia

As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted. © 2025 The Authors

关键词： Electronic health record

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Fake Twitter Followers Detection using Machine Learning Approach 2

Fake Twitter Followers Detection using Machine Learning Appr...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Zeshan Shabbir, Muhammad Naseer, Iftikhar Akhter, Shamim Abubakar, Muhammad Issa, Ghassan F. Hassaan Mehmood, Muhammad Comsats University Faculty of Information and Technology Islamabad Pakistan The Superior University Faculty of Computer Science & Information Technology Lahore Pakistan Minhaj University School of Library & Information Sciences Lahore Pakistan Fast School of Computing National University of Computer and Emerging Sciences Lahore Pakistan Skyline University College School of Information Technology University City Sharjah Sharjah1797 United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private Universit Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

Fake user accounts on social media are a serious threat. Artificial intelligence can play an important role in getting rid of fake user accounts. Twitter has a problem with fake accounts that are run by automated bots. These bots are used for bad things like spamming, sabotaging trends, and getting more followers. In this research, publicly available information about Twitter users, such as their activity patterns, profile information, and tweets, to figure out how real each one is. Twitter's API and other ways of getting information will be used to get user data. The proposed model consists of a machine learning technique, and it detects whether the user accounts are real or fake. Various algorithms such as logistic regression, long short-term memory, K-mean, and random forest are applied to evaluate the proposed model. The experimental results show that the random forest algorithm has obtained the highest accuracy 0.7557, a precision of 0.7277, and F1 of 0.7943 among all other algorithms. © 2023 IEEE.

关键词： Logistic regression

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An Exemplifying Study of the E-Learning Adoption during Covid-19 Pandemic in Oman

An Exemplifying Study of the E-Learning Adoption during Covi...

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2022 applied Informatics International Conference, AiIC 2022

作者： Al Maashri, Issaa Mohamed Hassan Ghosh, Shounak Bukar, Umar Ali University of Technology and Applied Sciences Academic Electronic Systems Center Muscat Oman School of Computing and Technology Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Malaysia

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

As a consequence of the Covid-19 outbreak, students are required to stay at home to prevent the virus from spreading. This has resulted in a quick acceptance of online learning, as students and faculty cannot participate in in-class activities. This study examines the determinants affecting E-Learning uptake in Oman during the Covid-19 outbreak, using 223 student responses and structural equation modeling (SEM). Four hypotheses were supported, while two were rejected. Specifically, perceived ease of use, perceived usefulness, and institutional support affect behavioral intention to adopt E-Learning. However, infrastructure support and privacy risk, on the other hand, were not. Thus, this research serves as a springboard for higher education officials to increase the use of E-Learning and adjust to student hurdles for students to comprehend and effectively use the system. © 2022 IEEE.

关键词： E-learning

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The Rise of Ransomware: A Review of Attacks, Detection Techniques, and Future Challenges

The Rise of Ransomware: A Review of Attacks, Detection Techn...

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2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

作者： Kamil, Samar Siti Norul, Huda Sheikh Abdullah Firdaus, Ahmad Usman, Opeyemi Lateef Al-Mansour University College Department of Computer Science and Information Systems Baghdad Iraq Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia University Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pahang Darul Pekan Malaysia Tai Solarin University of Education Department of Computer Science Ogun State Nigeria

ISBN: (纸本)9781665409209

Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are on the rise, such as Ransomware. Various governments and businesses take a variety of measures to combat cybercrime. People are still concerned about ransomware, despite numerous cybersecurity precautions. In ransomware, the attacker encrypts the victim's files/data and demands payment to unlock the data. Cybersecurity is a collection of tools, regulations, security guards, security ideas, guidelines, risk management, activities, training, insurance, best practices, and technology used to secure the cyber environment, organization, and user assets. This paper analyses ransomware attacks, techniques for dealing with these attacks, and future challenges. © 2022 IEEE.

关键词： Cybersecurity

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An Integrated Deep Learning Framework for Fruits Diseases Classification

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Computers, Materials & Continua 2022年第4期71卷 1387-1402页

作者： Abdul Majid Muhammad Attique Khan Majed Alhaisoni Muhammad Asfand Eyar Usman Tariq Nazar Hussain Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science Bahria UniversityIslamabad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

:Agriculture has been an important research area in the field of image processing for the last five *** affect the quality and quantity of fruits,thereby disrupting the economy of a *** computerized techniques have been introduced for detecting and recognizing fruit ***,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the ***,we propose an integrated deep learning framework for classifying fruit *** consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and *** proposed method comprises several important ***,data increase is applied,and then two different types of features are *** the first feature type,texture and color features,i.e.,classical features,are *** the second type,deep learning characteristics are extracted using a pretrained *** pretrained model is reused through transfer ***,both types of features are merged using the maximum mean value of the serial ***,the resulting fused vector is optimized using a harmonic threshold-based genetic ***,the selected features are classified using multiple *** evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.

关键词： Fruit diseases data augmentation deep learning classical features features fusion features selection

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