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Applied Set-Valued Analysis and Optimization 2024年第3期6卷 309-317页

作者： Trinh, Ngoc Hai Tran, Trung Duc Faculty of Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam College of engineering and computer science VinUniversity Hanoi Viet Nam

In this paper, we propose two self-adaptive extragradient-like algorithms for solving pseudomonotone variational inequalities. We consider two cases: the mapping is Lipschitz continuous (with unknown modulus) and is not Lipschitz continuous. The step size in our algorithms can either increase or decrease at each step. This feature makes our algorithms more efficient. We also give some numerical examples to compare the performance of our algorithm with the existing one. ©2024 Applied Set-Valued Analysis and Optimization.

关键词： Variational techniques

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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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Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset

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IEEE Access 2024年 12卷 9369-9381页

作者： Ainan, Ummey Hany Por, Lip Yee Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces the innovative hybrid model XGBoost+ANN, designed to leverage the strengths of both ensemble learning and artificial neural networks. This model integrates a comprehensive set of features with parameters optimized through genetic algorithms, eschewing traditional feature selection approaches. Our research focuses on an unbalanced dataset of Polish companies and reveals that the XGBoost+ANN model, in particular, exhibits outstanding performance. Optimized using genetic algorithms and without feature selection, this model achieved the highest AUC (0.958), sensitivity (0.752), and accuracy (0.983) scores, surpassing other models in our study. This remarkable outperformance, along with the robust results, marks a substantial advancement in the field of bankruptcy prediction. It underscores the efficacy of our approach in addressing the persistent challenge of data imbalance, offering a more reliable and accurate solution for financial risk assessment. © 2013 IEEE.

关键词： Feature extraction

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural Computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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Replication Strategy with Comprehensive Data Center Selection Method in Cloud Environments

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computers, Materials & Continua 2023年第1期74卷 415-433页

作者： M.A.Fazlina Rohaya Latip Hamidah Ibrahim Azizol Abdullah Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSerdang43400SelangorMalaysia

As the amount of data continues to grow rapidly,the variety of data produced by applications is becoming more affluent than *** computing is the best technology evolving today to provide multi-services for the mass and variety of *** cloud computing features are capable of processing,managing,and storing all sorts of *** data is stored in many high-end nodes,either in the same data centers or across many data centers in cloud,performance issues are still *** cloud replication strategy is one of best solutions to address risk of performance degradation in the cloud *** real challenge here is developing the right data replication strategy with minimal data movement that guarantees efficient network usage,low fault tolerance,and minimal replication *** key problem discussed in this research is inefficient network usage discovered during selecting a suitable data center to store replica copies induced by inadequate data center selection ***,to mitigate the issue,we proposed Replication Strategy with a comprehensive Data Center Selection Method(RS-DCSM),which can determine the appropriate data center to place replicas by considering three key factors:Popularity,space availability,and *** proposed RS-DCSM was simulated using CloudSim and the results proved that data movement between data centers is significantly reduced by 14%reduction in overall replication frequency and 20%decrement in network usage,which outperformed the current replication strategy,known as Dynamic Popularity aware Replication Strategy(DPRS)algorithm.

关键词： Cloud computing data replication replica placement data center merits replication algorithm

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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An Improved Structure for Academic information Services through AI Chatbots

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Journal of Engineering science and technology Review 2023年第5期16卷 164-173页

作者： Ula, Mutammimul Hardi, Richki Hipiny, Irwandi Department of Information Systems Faculty of Engineering Universitas Malikussaleh Aceh Indonesia Department of informatics Universitas Mulia Balikpapan Indonesia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia

Academic information service is the most critical factor that must be considered in a university. Service quality is an important indicator affecting all academics’ satisfaction and loyalty. Improvement of information services on campus has been a lot of research focusing on academics. Meanwhile, the software aspect has received less attention. Researchers have previously discussed information service frameworks but have not accommodated future needs. Software that must be considered can quickly respond to the user, one of which is a chatbot. Chatbots can be used to solve problems related to question-and-answer services, especially for academic institutions that do not have specific resources to handle this work. With chatbots, redundant user questions can be handled automatically. Chatbots are increasingly being offered as an alternative source of customer service. For a user to use a chatbot for this purpose, the user must trust the chatbot to provide the necessary support. To overcome this knowledge gap, a framework can integrate software, stakeholders, and academics in higher education. Therefore it is necessary to conduct new research to help answer future needs. © 2023 School of science, IHU. All rights reserved.

关键词： information services

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Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

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computer Systems science & Engineering 2023年第2期44卷 1679-1689页

作者： Abdullah Alshehri Nayeem Khan Ali Alowayr Mohammed Yahya Alghamdi Information Technology Department Faculty of Computer Science and Information TechnologyAl Baha UniversityAl Baha65799Saudi Arabia Computer Science Department Faculty of Science and Arts at BuljurashiAl Baha UniversityAl Baha65799Saudi Arabia

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior *** framework models the user behavior as sequences of events representing the user activities at such a *** represented sequences are thenﬁtted into a recurrent neural network model to extract features that draw distinctive behavior for individual ***,the model can recognize frequencies of regular behavior to proﬁle the user manner in the *** subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular *** importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the *** detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including ***,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reﬂect a normal network workﬂ*** contrast,the irregular patterns can trigger an alert for a potential *** framework has been fully described where the evaluation metrics have also been *** experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM *** paper has been concluded with pro-viding the potential directions for future improvements.

关键词： Cybersecurity deep learning machine learning user behavior analytics

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RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

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Journal of High Speed Networks 2025年第1期31卷 71-89页

作者： Elhoseny, Mohamed Riad, Khaled College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Computer Science Department College of Computer Sciences & Information Technology King Faisal University Al-Ahsa 31982 Saudi Arabia Mathematics Department Faculty of Science Zagazig University Zagazig44519 Egypt

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key g

关键词： Ciphertext

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