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An Integrated Framework for Cloud Service Selection Based on BOM and TOPSIS

computers, Materials & Continua 2022年第8期72卷 4125-4142页

作者： Ahmed M.Mostafa Computer and Systems Engineering Department Faculty of Engineering at HelwanHelwan UniversityCairo11795Egypt

Many businesses have experienced difficulties in selecting a cloud service provider(CSP)due to the rapid advancement of cloud computing services and the proliferation of *** independent criteria should be considered when evaluating the services provided by different *** is a case of multi-criteria decision-making(MCDM).This paper presents an integrated MCDM cloud service selection framework for determining the most appropriate service provider based on the best only method(BOM)and technique for order of preference by similarity to ideal solution(TOPSIS).To obtain the weights of criteria and the relative importance of CSPs based on each criterion,BOM performs pairwise comparisons of criteria and also for alternatives on each criterion,and TOPSIS uses these weights to rank cloud *** evaluation and validation of the proposed framework have been carried out through a use-case model to prove its efficiency and ***,the developed framework was compared with the analytical hierarchical process(AHP),a popular MCDM approach,based on two perspectives:efficiency and *** to the research results,the proposed framework only requires 25%of the comparisons needed for the AHP ***,the proposed framework has a CR of 0%,whereas AHP has 38%.Thus,the proposed framework performs better than AHPwhen it comes to computation complexity and consistency,implying that it is more efficient and trustworthy.

关键词： Cloud computing(CC) multiple-criteria decision-making(MCDM) cloud service providers(CSPs) analytical hierarchical process(AHP) the best only method(BOM) technique for order of preference by similarity to ideal solution(TOPSIS)

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Anomalous Detection in Noisy Image Frames using Cooperative Median Filtering and KNN

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2022年第1期49卷 1-10页

作者： Esan, Dorcas. O. Owolawi, Pius. A. Tu, C. Master Student at Tshwane University of Technology Department of Computer Systems Engineering Soshanguve Campus South Africa Head of Department of Computer Systems Engineering Tshwane University of technology Soshanguve Campus South Africa Head of Research of Computer Systems Engineering Department and a Senior Lecturer in Computer Systems Engineering Department Tshwane University of Technology Soshanguve Campus South Africa

The widespread of surveillance cameras has consequently led to a significant increment in the global surveillance video market over the years. However, as the number of surveillance systems is increasing, the rate of crime threats is also increasing. Despite the advancements in surveillance systems, there are still challenges in the detection of anomalous behavioural patterns from noisy image frames, which significantly affect the accuracy of intelligent surveillance systems. This paper employs the median filtering technique and the k-nearest neighbor (KNN) classifier for the detection of behavioural patterns. Median filtering is used because of its ability to preserve the image edges while removing the noise;the statistical property of KNN is used to obtain vector distribution from images during the detection stage. Different analyses were conducted using the publicly available dataset repository that has been used by many researchers in the field of computer vision in the detection of anomalous behaviour. The results are compared with existing -state-of-anomalous detection models and the results obtained show that the proposed system outperforms the others mentioned by 85.15% and an F1-score of 0.54, equal error rate of 0.15, precision value of 0.90 and recall value of 0.40. © 2022. All Rights Reserved.

关键词： Nearest neighbor search

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Reliability-aware swarm based multi-objective optimization for controller placement in distributed SDN architecture

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Digital Communications and Networks 2024年第5期10卷 1245-1257页

作者： Abeer A.Z.Ibrahim Fazirulhisyam Hashim Aduwati Sali Nor K.Noordin Keivan Navaie Saber M.E.Fadul Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Wireless and Photonics Networks Research Centre(WiPNET) Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia Department of Communication and Computer Engineering College of Engineering and Medical SciencesKhartoum11111Sudan School of Computing and Communications Lancaster UniversityLancasterLA14YWUnited Kingdom Department of Electrical and Electronics Engineering Faculty of EngineeringUniversiti Putra MalaysiaSerdang43400Malaysia

The deployment of distributed multi-controllers for Software-Defined Networking(SDN)architecture is an emerging solution to improve network scalability and ***,the network control failure affects the dynamic resource allocation in distributed networks resulting in network disruption and low ***,we consider the control plane fault tolerance for cost-effective and accurate controller location models during control plane *** fault-tolerance strategy has been applied to distributed SDN control architecture,which allows each switch to migrate to next controller to enhance network *** this paper,the Reliable and Dynamic Mapping-based Controller Placement(RDMCP)problem in distributed architecture is framed as an optimization problem to improve the system reliability,quality,and *** considering the bound constraints,a heuristic state-of-the-art Controller Placement Problem(CPP)algorithm is used to address the optimal assignment and reassignment of switches to nearby controllers other than their regular *** algorithm identifies the optimal controller location,minimum number of controllers,and the expected assignment costs after failure at the lowest effective cost.A metaheuristic Particle Swarm Optimization(PSO)algorithm was combined with RDMCP to form a hybrid approach that improves objective function optimization in terms of reliability and *** effectiveness of our hybrid RDMCP-PSO was then evaluated using extensive experiments and compared with other baseline *** findings demonstrate that the proposed hybrid technique significantly increases the network performance regarding the controller number and load balancing of the standalone heuristic CPP algorithm.

关键词： Software defined networking Dynamic mapping Particle swarm optimization Reliability Multi-objective optimization Evolutionary

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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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computer Modeling in engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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Supervised learning techniques for blood product prediction in patients with hematologic diseases: a multi-centre study in Western Algeria

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Ykhlef, Amel Labri, Nedjla Selma Brahami, Menaouer LABAB Laboratory Computer Systems Engineering Department National Polytechnic School of Oran Oran Algeria

Blood transfusion is a medical procedure that involves transfusing blood or one of its components from one or more donors into a patient. Digital technology and machine learning have played a crucial role in the blood field and have provided real prospects for the production and distribution of blood products. In this study, we propose supervised machine learning techniques for the multi-label classification of blood products in patients with hematologic diseases. We used three multi-label approaches from the problem transformation category to create a decision support system for blood products label power set (LP), binary relevance (BR), and classifier chain (CC). Multi-label classification using the problem transformation approach is a flexible approach. In this study, we used data from different hospitals in hematology departments and blood transfusion centers to explore the application of contemporary supervised learning algorithms to blood product prediction. The experiment was performed by calculating the Hamming loss and accuracy to facilitate the classification of blood products. As a result, the prediction model achieved an area under the ROC curve of 99.80%, a Hamming loss of 0.30, and an accuracy of 98.8%. The proposed model has been developed to provide accurate and fast results that can save patients’ lives. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blood product Blood transfusion Hematological diseases Knowledge management Machine learning Multi-label classification Problem transformation methods

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Computationally efficient reductions between some statistical models 36

Computationally efficient reductions between some statistica...

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Lou, Mengqi Bresler, Guy Pananjady, Ashwin School of Industrial and Systems Engineering Georgia Tech United States Department of Electrical Engineering and Computer Science MIT United States Schools of Industrial and Systems Engineering and Electrical and Computer Engineering Georgia Tech United States

We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computationally efficient such reductions between canonical statistical experiments. In particular, we provide computationally efficient procedures that approximately reduce uniform, Erlang, and Laplace location models to general target families. We illustrate our methodology by establishing nonasymptotic reductions between some canonical high-dimensional problems, spanning mixtures of experts, phase retrieval, and signal denoising. Notably, the reductions are structure-preserving and can accommodate missing data. We also point to a possible application in transforming one differentially private mechanism to another. © 2025 M. Lou, G. Bresler & A. Pananjady.

关键词： Differential privacy

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DRL-based Trajectory Planning and Sensor Task Scheduling for Edge Robotics 10

DRL-based Trajectory Planning and Sensor Task Scheduling for...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Bouhoula, Sirine Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Department of Software and IT Engineering Montréal Canada Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350373011

Mobile Edge Computing (MEC) and Edge Robotics have recently emerged as transformative technologies, revolutionizing industries by enabling real-time processing, decision-making, and automation at the network edge. However, the dynamicity induced by the system's conditions and specifically the mobility poses a challenge for optimally deciding where to execute a given computational task. As a response, we develop an intelligent algorithm for dynamic sensor task offloading tailored to the unique requirements of MEC-enabled robotic environments. Specifically, we first introduce the environmental dynamics including a sensor task's end-to-end delay and the robots' mobility and energy consumption and provide mathematical formulations to model these dynamics. Then, we mathematically formulate the optimization problem and its MDP counterpart and we propose a Deep Reinforcement Learning (DRL)-based computational offloading strategy to jointly optimize Quality of Service (QoS) and energy consumption through robot trajectory planning and fine-grained task allocation. Through hand-picked representative simulation scenarios, we demonstrate the superiority of our proposed mechanism in enhancing the overall system performance, specifically in optimizing task execution, reducing energy consumption, and mitigating transmission delays, compared to various baseline approaches. © 2024 IEEE.

关键词： Computation offloading

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Performance Evaluation Framework for Insider Threat Detection Using Machine Learning 2

Performance Evaluation Framework for Insider Threat Detectio...

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2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Abdallah, Habiba Emad-Eldin Abd-Elkader, Habiba Hamed Mohamed, Kholoud Khaled Abd-Elmoniem, Mohamed El-Assal, Nizar Walid Mohamed, Salma Mohsen Said, Samar A. Salem, Sameh A. Helwan University Faculty of Engineering Egypt Helwan University Department of Computer and Systems Engineering Egypt

ISBN: (纸本)9798350362633

Nowadays, malicious behavior identification is considered a significant and challenging issue in cybersecurity. To mitigate this problem, an effective detection system can be a promising candidate to facilitate precise and rapid detection of a malicious insider. In this paper, a performance evaluation framework for insider threat detection using machine learning is proposed. This framework employs the effects of several machine learning models to assist in identifying insider threats. The proposed framework applied XGboost, SVM, RF, KNN, MLP, and LR to detect insider threats. The machine learning framework is evaluated across the CERT r5.2 dataset. After that, the results of these models are compared with evaluation metrics to determine which model is the most effective for insider detection at different granularity levels. Furthermore, we have empirically shown that XGBoost outperforms competitive algorithms and can achieve a range precision of 95.24%-97.85%, recall of 97.34%-99.30%, F1 score of 96.79%-98.81 %, and an AUC of 97.04%-98.63% for insider detection. © 2024 IEEE.

关键词： Adversarial machine learning

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