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Adaptive Density-Based Spatial Clustering of Applications with Noise(ADBSCAN)for Clusters of Different Densities

computers, Materials & Continua 2023年第5期75卷 3695-3712页

作者： Ahmed Fahim Computer Science Department Prince Sattam bin Abdulaziz UniversityAflajSaudi Arabia Computer Science Department Faculty of Computers and InformationSuez UniversitySuezEgypt

Finding clusters based on density represents a significant class of clustering *** methods can discover clusters of various shapes and *** most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise *** requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for *** article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only *** user input in the proposed method is the *** on the other hand,is computed automatically based on statistical information of the *** proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density *** remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered *** process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s *** proposed method requires MinPts only as an input parameter because epsilon is computed from *** datasets were used to evaluate the effectiveness of the proposed method that produced promising *** experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between *** accuracy of the method ranges from 92%to 100%for the experimented datasets.

关键词： Adaptive DBSCAN(ADBSCAN) Density-based clustering Data clustering Varied density clusters

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Super-Resolution Based on Curvelet Transform and Sparse Representation

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computer Systems science & Engineering 2023年第4期45卷 167-181页

作者： Israa Ismail Mohamed Meselhy Eltoukhy Ghada Eltaweel Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology University of JeddahCollege of Computing and Information Technology at KhulaisJeddahSaudi Arabia

Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution *** uses the nonlocal mean filter as a prior step to produce a denoised *** proposed algorithm is based on curvelet *** converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both *** parallel,we applied sparse representation with over complete dictionary for the denoised *** proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher *** experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced *** comparison study shows that the proposed super-resolution algorithm outperforms the *** mean absolute error is 0.021±0.008 and the structural similarity index measure is 0.89±0.08.

关键词： Super-resolution Curvelet transform non-local means filter lancozos interpolation sparse representation

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Novel Steganalysis Method for Stego-Images Directly Constructed from Color Images Based on Their Quantum Noise

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Journal of Shanghai Jiaotong University (science) 2024年 1-11页

作者： Abdeltawwab, Mohammed R. Taie, Shereen A. El-Bahnasawy, Hany H. Eissa, Amir Department of Basic Science Faculty of Computers and Artificial Intelligence Fayoum University El Fayoum63514 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Fayoum University El Fayoum63514 Egypt Department of Physics Faculty of Science Al-Azhar University Nasr City Cairo11884 Egypt

Steganography and steganalysis are two different sides of the same coin. Both are just as important as the other. Image steganography is considered one of the most promising secure data transmission methods because it hides the data in an image file. In contrast, steganalysis tries to attack steganography and retrieve the hidden data that can be a secret message. Many robust and powerful image steganography methods have been presented in the literature. One of these methods is referred to as steganography without embedding (SWEM). It is considered more secure because it adopts the concept of data transmission without embedding or concealing it in the file. Like any new technology, image steganography may have a negative impact and can be misused. There is no steganalysis method for distinguishing and attacking the image file that has been constructed by the SWEM method. To overcome the aforementioned issues, the main contribution of this paper is to propose a new image steganalysis method based on the fast Fourier transform (FFT). The proposed method starts by examining a suspected image using FFT. Then, by calculating the ratio of different parts of the resulting FFT spectrum and comparing the result with a threshold value, we can decide whether the suspected image has been constructed by the SWEM or not. The proposed method overcomes the other existing methods that fail to attack this strong image steganography method. To check the generalizability of the proposed method as a method for distinguishing and attacking the image file that is created by SWEM, we constructed a data set of images, used the SWEM algorithm to construct images with concealed secret messages without embedding, and applied the proposed method and other two common existing steganalysis methods. The results show that the proposed method is able to distinguish these images with a high recognition rate compared to the results of the two methods. (Figure presented.) © Shanghai Jiao Tong University

关键词： Quantum noise

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Dog behaviors identification model using ensemble convolutional neural long short-term memory networks

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3425-3439页

作者： Abd El-Latif, Eman I. El-dosuky, Mohamed Darwish, Ashraf Hassanien, Aboul Ella Faculty of Science Benha University Benha Egypt Computer Science Department Arab East Colleges Riyadh Saudi Arabia Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt Faculty of Science Helwan University Cairo Egypt Cairo Egypt Kuwait University Kuwait City Kuwait Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt *** Cairo Egypt

This paper presents a new model based on Convolutional Neural Networks (CNN) with a long short-term memory network (LSTM) and ensemble technique for identifying seven different dogs’ behaviors. The proposed model uses data collected from two sensors attached to the dog’s back and neck. In the initial step in the model, the undefined tasks are removed, and the synthetic minority oversampling technique (SMOTE) is performed to address the imbalanced data problem. Then, CNN_LSTM and ensemble classifier are adapted to identify various dog behaviors. Finally, two experiments are performed to evaluate the model. The first experiment is performed utilizing the original data (imbalanced datasets) while the second uses a balanced dataset. Experimental results can identify seven dog behaviors with a potential accuracy of 96.73%, 96.76% sensitivity, 96.73% specificity, and 96.73% F1 score. Therefore, the SMOTE method, a data balancing strategy, not only overcomes the unbalanced data problem but also significantly improves minority class accuracy. Additionally, the suggested model is tested against cutting-edge algorithms, and the outcomes demonstrate its superior performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Long short-term memory

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Efficient Computation Offloading of IoT-Based Workflows Using Discrete Teaching Learning-Based Optimization

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computers, Materials & Continua 2022年第11期73卷 3685-3703页

作者： Mohamed K.Hussein Mohamed H.Mousa Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailiaEgypt Department of Information Technology College of Computer Science at AlKamilUniversity of JeddahJeddahSaudi Arabia

As the Internet of Things(IoT)and mobile devices have rapidly proliferated,their computationally intensive applications have developed into complex,concurrent IoT-based workflows involving multiple interdependent *** exploiting its low latency and high bandwidth,mobile edge computing(MEC)has emerged to achieve the high-performance computation offloading of these applications to satisfy the quality-of-service requirements of workflows and *** this study,we propose an offloading strategy for IoT-based workflows in a high-performance MEC *** proposed task-based offloading strategy consists of an optimization problem that includes task dependency,communication costs,workflow constraints,device energy consumption,and the heterogeneous characteristics of the edge *** addition,the optimal placement of workflow tasks is optimized using a discrete teaching learning-based optimization(DTLBO)*** experimental evaluations demonstrate that the proposed offloading strategy is effective at minimizing the energy consumption of mobile devices and reducing the execution times of workflows compared to offloading strategies using different metaheuristics,including particle swarm optimization and ant colony optimization.

关键词： High-performance computing internet of things(IoT) mobile edge computing(MEC) workflows computation offloading teaching learning-based optimization

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An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors

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Neural Computing and Applications 2025年第6期37卷 4881-4894页

作者： Saber, Abeer Elbedwehy, Samar Awad, Wael A. Hassan, Esraa Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Department of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively. © The Author(s) 2024.

关键词： Adversarial machine learning

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Data Quality Frameworks: A Systematic Review 5

Data Quality Frameworks: A Systematic Review

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： El Gayar, Ismail Hassan, Hesham Wassif, Khaled T. Faculty of Computers and Artificial Intelligence Department of Computer Science Cairo Egypt

ISBN: (纸本)9798331528553

Nowadays, digital transformation, automation, and decision-making are critical needs. The importance of data science and improving data quality is increasing day by day due to this need. Currently, a lot of research discusses the improvement of algorithms, AI techniques, and decision-making, as depending on the quality of data becomes necessary and critical. Therefore, in this article, different frameworks, techniques, and methodologies for data quality improvement are introduced. © 2024 IEEE.

关键词： Data accuracy

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UNRAVELING THE EFFECTIVENESS OF THE K-NN ALGORITHM FOR IMBALANCED DATA IN RESEARCH TOPIC RECOMMENDATIONS

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Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2024年第1期59卷 271-282页

作者： Muludi, Kurnia Aisyah, Nonik Nama, Gigih Forda Lumbanraja, Favorisen R. Informatics & Business Institute Darmajaya Bandar Lampung35142 Indonesia Computer Science Department Faculty of Science The University of Lampung Bandar Lampung Indonesia Informatics Engineering Department Faculty of Science The University of Lampung Bandar Lampung Indonesia

This study addresses the challenge of selecting research topics for undergraduate students, focusing on computer science, by evaluating a recommendation model based on the k-Nearest Neighbor algorithm (kNN). The objective is to enhance the accuracy of research topic recommendations in the presence of imbalanced data. The methodology involves data cleaning, transformation, and correlation-based feature selection, with a particular focus on addressing missing values and optimizing the feature set. The Synthetic Minority Over-Sampling Technique (SMOTE) is employed to balance the dataset. The model development includes a comprehensive analysis of various k values, leading to the identification of k=3 with Manhattan distance as the optimal configuration, achieving an accuracy of 82%. The experiment explores different training data proportions, revealing that a 90:10 ratio yields the highest accuracy. This study incorporates a Grid Search technique for hyperparameter tuning, highlighting the importance of selecting appropriate distance metrics and nearest neighbor values. The system implementation is presented with user and administrator interfaces. Findings indicate varying model performance across research categories, emphasizing the need for category-specific evaluation metrics. The study concludes with a discussion on the significance of the results, which contribute to the improvement of research topic recommendation systems for students through the integration of advanced machine learning techniques. © 2024 by the authors.

关键词： Recommender systems

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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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Development Generative AI for Cybersecurity: Evaluating Script Generation and Attack Classification in Penetration Testing 1

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International Workshop on Advanced in Information Security Management and Applications, AISMA 2023

作者： Elashmawi, Walaa H. Osman, Heba Osama, Mahmoud Nader, Nour Faculty of Computers and Informatics Suez Canal University Ismailia Egypt Faculty of Computer Science Misr International University Cairo Egypt

ISBN: (数字)9783031772290

ISBN: (纸本)9783031772283

The application of Artificial Intelligence (AI) in cybersecurity is designed to address the growing complexity and volume of cyber threats, which create significant challenges for traditional penetration testing methods. Utilizing advanced Natural Language Processing (NLP) models can boost automation, consistency, and efficiency in detecting and mitigating vulnerabilities. This paper focuses on generating payloads for penetration testers using tuned pre-trained models such as ChatGPT-2, CodeBERT, and T5 models on a customized dataset. The effectiveness of the outperforming model is compared with Support Vector Machines (SVM) and Logistic Regression for dataset classification. By achieving these objectives, this study aims to enhance penetration testing processes, contributing to stronger cybersecurity defenses. The results of this study prove the effectiveness of using CodeBERT for the payload generation and as a classifier. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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