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15th International Conference on Developments in esystems Engineering, DeSE 2023

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Jasim, Omar Hammad Al-Dosary, Duaa Ahmed, Hamsa M. Al-Ani, Muzhir Shaban University of Anbar Computer Networking Systems Department Ramadi Iraq Al Baha University Faculty of Computer Science and Information Technology Al Baha Saudi Arabia Human Resources Department Ramadi Iraq College of Computer Sciences and Information Technology University of Anbar Anbar Iraq University of Human Development College of Science and Technology Department of Information Technology Sulaymaniyah Iraq

ISBN: (纸本)9798350335149

Cyber-attacks involve stifling processes and activities, conciliating data, or restricting data access by carefully modifying computer systems and networks with malware. There has been a significant increase in these types of attacks over time. Due to the rise in complexity and structure, advanced defensive methods are needed. In the face of growing security threats, traditional methods of identifying cyber-attacks are ineffective. In this paper, the intelligent of intrusion a detection system is suggested. Moreover, the suggested system attempts to evaluate the capability of the k-nearest neighbour's algorithm (KNN) in terms of distinguishing between authentic and tampered data. A reliable dataset named the Multi-Step Cyber-Attack Dataset (MSCAD) is utilized to determine the behavior Among the new sorts of attacks. Moreover, 60% of the dataset was utilized for training the model, and a remaining 40% was used for testing. Evaluation metrics like accuracy, precision, recall, and F1 score are used. Experiments suggest that the proposed system-based KNN could enhance detection performance. Moreover, the suggested approach increases detection accuracy while minimizing false alarms. © 2023 IEEE.

关键词： Network security

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Research and Development of Enterprise Data Warehouse Based on SAP BW Modeling

Research and Development of Enterprise Data Warehouse Based ...

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2023 IEEE International Conference on Smart information systems and Technologies, SIST 2023

作者： Turken, Gulzat Naizabayeva, Lyazat Satymbekov, Maxatbek Abdiakhmetova, Zukhra Al-Farabi Kazakh National University Department of Computer Science Almaty Kazakhstan International Information Technology University Department of Information System Almaty Kazakhstan

ISBN: (纸本)9798350335040

The data warehouse is based on the development of relational databases, parallel processing and distributed processing technologies, as well as online analytical processing technologies. SAP BW is widely used in many enterprise data warehouses. It can issue reports of enterprises at the group level, manage the group's business operations, help find the laws and trends behind the performance of corporations and ultimately provide decision-making support for decision makers of enterprises. Therefore, how to use the SAP BW model architecture to better realize enterprise data warehouses is one of the hot topics in the data warehouse industry today. For a large corporation, its branches and subsidiaries have deployed multiple sets of enterprise data warehouses. At the same time, because there are multiple sets of data warehouse systems, when the data model needs to be modified at the group level, the data model of the branch also needs to be modified accordingly, and the flexibility of maintenance is poor. Finally, every time the corporation needs to upgrade the version of the data warehouse, it needs to maintain and upgrade the version of the data warehouse of each branch and the maintenance cost is high. Therefore, how to integrate multiple sets of data warehouses distributed in various subsidiaries into a corporation enterprise data warehouse by designing the model hierarchy, so as to ensure the reliability of data, enhance the flexibility of model modification and reduce the cost of data warehouse. This paper completes the design of an enterprise data warehouse of a large corporation, which can be used for the summary and collation of data from multiple branches such as finance and sales of a corporation, effectively improving the efficiency of data utilization and ensuring the authenticity of data. At the same time, through hierarchical design, the problems of poor flexibility in model modification and high system maintenance costs are solved. © 2023 IEEE.

关键词： Data warehouses

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Intelligent Eczema management and awareness system for Saudi Arabia system Architecture 3

Intelligent Eczema management and awareness system for Saudi...

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3rd International Conference on Computing and information technology, ICCIT 2023

作者： Alotaibi, Mohammed University of Tabuk Faculty of Computer Science and Information Technology Tabuk47191 Saudi Arabia

ISBN: (纸本)9798350321487

Eczema disease diagnosis and treatment are frequently challenging procedures. Recent development in artificial intelligence (AI) or digital healthcare technology have improved the self-management of severe chronic diseases. for example, skin conditions, insulin, pressure, etc. Numerous research has validated its effectiveness. Additionally, studies have also shown that greater knowledge about any illness enables people to properly control their chronic health conditions, which in turn lowers the problems brought on by disorders such as diabetes, eczema, and hypertension. In this research, a novel dermatitis illness monitoring and treatment system is presented for Saudi Arabian patients. The technology being shown uses AI in conjunction with smart healthcare technology. Employing digital healthcare technologies and AI, it also hopes to arm eczema patients with numerous features about the condition. Comprehensive testing and evaluation of the method that this research proposes will take place in a randomized scientific experiment in Saudi Arabic. © 2023 IEEE.

关键词： mHealth

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Model-Driven Engineering for Embedded systems with Non-Functional Properties Analysis Integration: A Review 16

Model-Driven Engineering for Embedded Systems with Non-Funct...

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16th International Conference on Developments in esystems Engineering, DeSE 2023

作者： Kadhim, Omar Jawad Nafea, Ahmed Adil Al-Thiabi, Mohamed Kareem Sallibi, Anas D. Modern Uuniversity for Business & Science Dept. Computer Science Beirut Lebanon College of Computer Science and Information Technology University of Anbar Dept. Artificail Inteligence Ramadi Iraq College of Computer Science and Information Technology University of Anbar Dept. Computer Science Ramadi Iraq College of Computer and Information Technology University of Anbar Department of Computer Sciences Ramadi Iraq

ISBN: (纸本)9798350381344

Model-Driven Engineering (MDE) has revolutionized the realm of embedded system development, providing an avenue for systematic, high-level system design, and automatic code generation. This paper delves into an enhanced approach to MDE, emphasizing the integration of nonfunctional properties analysis. Such an integration ensures that embedded systems not only perform their primary functions, but also adhere to crucial parameters like security, reliability, and efficiency. Through a comprehensive exploration of current tools and technologies, we identify gaps and opportunities in the current landscape. We further discuss the challenges inherent in marrying non-functional attributes with traditional system design and propose innovative solutions to navigate these complexities. Real-world case studies punctuate the discussion, offering tangible insights into the tangible benefits and practical challenges of this integrated approach. In conclusion, the seamless integration of nonfunctional properties into MDE emerges as a pivotal progression, heralding a new era of robust, efficient, and holistic embedded system development. © 2023 IEEE.

关键词： Embedded systems

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A Bi-directional Gated Recurrent Unit and Long Short-Term Memory based Fake Profile Identification system 1

A Bi-directional Gated Recurrent Unit and Long Short-Term Me...

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1st International Conference on Software, systems and information technology, SSITCON 2024

作者： Geetha, B. Sushmitha, Boggarapu Ilanchezhian, P. Alabdeli, Haider Ahila, R. Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai India Avn Institute of Engineering and Technology Hyderabad Department of Computer Science and Engineering India Sona College of Technology Salem Department of Information Technology India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, fake profiles are programmatically created online individualities that mislead and influence others, which are done by frequently using stolen photos, fraudulent information, or fictional particulars. Hence, these fake profiles are used for malicious activities such as phishing, social engineering, catfishing, and scattering misinformation. The expansion of fake profiles is increased with triggering more damage than any other cyber-attacks. The solution has been recommended in various ways to detect fake profiles, but the existing models have more overfitting problems. To overcome these issues, a combined model of Bi-directional Gated Recurrent Unit (BiGRU) and Long Short-Term Memory (LSTM) algorithms is proposed. Initially, data preprocessing is done on TwiBot-20 dataset with Natural Language Processing (NLP) and tokenization to remove unnecessary characters. Next, a word embedding technique name called GloVe is used to extract the optimal features, Finally, from the extracted features, the proposed BiGRU-LSTM model gives a precise rate in detecting and classifying fake profiles as human and bot generated. The proposed BiGRU-LSTM gives better results than the other compared existing methods Convolutional Neural Network (CNN) in terms of accuracy, precision, recall, and f1 score of 99.73 %, 98.23 %, 99.56 %, and 99.63 %respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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computer aided detection of mammographic mass using exact Gaussian–Hermite moments

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1139-1147页

作者： Eltoukhy, Mohamed Meselhy Elhoseny, Mohamed Hosny, Khalid M. Singh, Amit Kumar Computer Science Department Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Faculty of Computers and Information Mansoura University El Mansûra Egypt Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig Egypt Waknaghat Himachal Pradesh Solan India

Breast cancer is one of the common cancer deaths in women worldwide. Early detection is the key to reduce the mortality rate. Clinical trials have shown that computer aided systems (CAD) have improved the accuracy of breast cancer detection. This paper proposed a highly accurate CAD system based on extracting highly significant features using exact Gaussian–Hermite moments. The obtained feature vector is presented to K-NN, random forests and AdaBoost classifiers. The proposed system is evaluated using two different datasets namely IRMA and MIAS. The evaluation metrics of accuracy, TP, FP and area under ROC curve using 10-fold cross-validation are calculated. The results indicate the usefulness of the proposed exact Gaussian–Hermite moments features for distinguishing between normal and abnormal lesions and the superiority of the moments features compared with the conventional methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Medical informatics

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A Comprehensive Framework for Electronic Medical Records (EMRs) Leveraging Blockchain technology 1st

A Comprehensive Framework for Electronic Medical Records (EM...

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1st International Conference on Intelligent systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Pandey, Shraiyash Alazzawi, Esraa Mohammed Pandey, Surendra Bhushan, Bharat Jamil, Akhtar Baniya, Pashupati Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering University of Baghdad Al-Jadriya Baghdad10071 Iraq Computer and Information Technology Tata Consultancy Services MariettaGA United States Department of Computer Science National University of Computer and Engineering Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031823763

In today’s healthcare landscape, where participation is widespread, the need for secure and efficient record management is evident. However, many healthcare organizations, particularly in regions like India, still rely on outdated paper-based methods for handling medical records. Unfortunately, these traditional approaches fall short in meeting the rigorous requirements for security, confidentiality, and efficiency in today’s digital age. This work presents a comprehensive framework that integrates modern technologies such as Smart Contracts, Ethereum Blockchain, and the Interplanetary File system (IPFS) to ensure secure storage, and seamless management of Electronic Medical Records (EMRs). By leveraging these emerging technologies, our goal is to lead a shift in electronic medical record management. Additionally, we explore recent advancements in the healthcare sector, with a focus on enhancing security protocols within Electronic Medical Records management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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A Survey of Data Mining Techniques for Digital Forensic Analysis 16

A Survey of Data Mining Techniques for Digital Forensic Anal...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Abdallah, Emad E. Elsoud, Esraa Abu Abdallah, Alaa E. Department of Information Technology Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan

Analyzing digital devices to generate digital evidence relevant to incidents is essential in modern digital investigations. Machine learning models and pattern recognition capabilities can be used in forensic analysis to uncover hidden evidence in digital objects that would have been missed if performed manually, especially given the exponential growth in textual sources and digital devices used in the forensic investigation process over the years. Text mining, a subset of data mining, has played an important role in developing effective digital forensic techniques. This survey provides a comprehensive overview of digital forensic analysis using data mining techniques. It presents the critical role of data mining in developing the digital forensics process and addressing the challenges posed by increasing data complexity. © 2025 Elsevier B.V.. All rights reserved.

关键词： Feature Selection

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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2023 International Research Conference on Smart Computing and systems Engineering, SCSE 2023

作者： Arunoda, D.P.H. Walpola, S.R. Piumira, S.M.I. Athukorala, A.D.M.P. Thilakarathna, Thusithanjana Chandrasiri, Sanjeevi Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students' interactions (attention, emotions) with the system to identify students' ability to use the learning tool, identifying gaps in students' knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students' knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy. © 2023 IEEE.

关键词： Students

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