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Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

computer Modeling in Engineering & sciences 2024年第2期138卷 1469-1483页

作者： Laila M.Halman Mohammed J.F.Alenazi Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11451Saudi Arabia

The healthcare sector holds valuable and sensitive *** amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast *** to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and *** this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe *** attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human *** can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or *** this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various *** propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS *** then evaluate the accuracy and performance of the proposed TBDC *** technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

关键词： Network resilience network management attack prediction software defined networking(SDN) distributed denial of service(DDoS) healthcare

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Enhancing Plant Leaf Disease Prediction Through Advanced Deep Feature Representations: A Transfer Learning Approach

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Journal of The Institution of Engineers (India): Series B 2024年第3期105卷 469-482页

作者： Naralasetti, Veeranjaneyulu Bodapati, Jyostna Devi Department of Information Technology Vignan’s Foundation for Science Technology and Research Guntur India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Guntur India

Plant diseases present a considerable threat to the farming industry, causing significant economic losses by reducing crop yields. The emergence of deep neural network models in the realm of computer vision has brought about a groundbreaking transformation in the swift recognition and surveillance of plant diseases. In this study, we introduce an innovative approach for plant disease prediction, leveraging transfer learning techniques to enable accurate and early detection of plant diseases. The proposed leaf disease prediction framework encompasses multiple key stages, including preprocessing, feature extraction, classification, and postprocessing. Notably, our model excels at feature extraction, utilizing the pre-trained convolutional architecture of VGG16 to extract valuable information from leaf images. These features are then fed into a deep neural network model designed for disease prediction. The feature extraction method employed in our approach excels at capturing intricate disease-related details from plant images. When the deep features extracted are utilized for classification using our proposed classifier, the outcome demonstrates a substantial enhancement in disease prediction performance. Based on extensive experimentation with the benchmark plant village dataset, it is evident that our transfer learning-based approach outperforms existing methods, achieving an impressive accuracy rate of 96.56%. Besides accuracy, the proposed approach surpasses F1_score and Kappa scores for the benchmark plant village dataset. © The Institution of Engineers (India) 2024.

关键词： Forecasting

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Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

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computer Systems science & Engineering 2023年第1期44卷 501-517页

作者： K.Nagalakshmi Dr.S.Suriya Department of Computer Science and Engineering Sethu Institute of TechnologyVirudhunagar626115Tamil NaduIndia Department of Computer Science and Engineering PSG College of TechnologyCoimbatore641004Tamil NaduIndia

Breast cancer is one of the deadly diseases prevailing in *** detection and diagnosis might prevent the death *** diagnosis of breast cancer remains a significant challenge,and early diagnosis is essential to avoid the most severe manifestations of the *** existing systems have computational complexity and classification accuracy problems over various breast cancer *** order to overcome the above-mentioned issues,this work introduces an efficient classification and segmentation ***,there is a requirement for developing a fully automatic methodology for screening the cancer *** paper develops a fully automated method for breast cancer detection and segmenta-tion utilizing Adaptive Neuro Fuzzy Inference System(ANFIS)classification *** proposed technique comprises preprocessing,feature extraction,classifications,and segmentation ***,the wavelet-based enhancement method has been employed as the preprocessing *** texture and statistical features have been extracted from the enhanced ***,the ANFIS classification algorithm is used to classify the mammogram image into normal,benign,and malignant ***,morphological processing is performed on malignant mam-mogram images to segment cancer *** analysis and comparisons are made with conventional *** experimental result proves that the pro-posed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.

关键词： Breast cancer detection segmentation classification malignant

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Resource scheduling in cloud environment using particle swarm search algorithm

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International Journal of Cloud Computing 2024年第4期13卷 330-352页

作者： Majhi, Malay Kumar Kabat, Manas Ranjan Sahoo, Satya Prakash Department of Computer Science and Engineering Veer Surendra Sai University of Technology Sambalpur India

Cloud computing has gained significant popularity as a platform for processing large-scale data analytics, offering benefits such as high availability, robustness, and cost-effectiveness. However, job scheduling in cloud systems presents a major challenge, as it directly impacts execution time and operational costs. To address these issues, this paper presents a novel multi-adaptive convergent particle swarm optimisation (MAC-PSO) algorithm designed to decrease the failure rate, minimise makespan values, and enhance resource utilisation. The round Robin scheduling method aids in task execution by determining the appropriate time-space allocation. The proposed algorithm’s performance is compared to that of the TLBO algorithm, demonstrating that MAC-PSO outperforms both TLBO and the original PSO. Moreover, a comprehensive analysis is proposed to evaluate the performance metrics within the MAC-PSO algorithm. Notably, MAC-PSO effectively increases the ratio of solutions that dominate previous algorithmic approaches and identifies a greater number of solutions that cater to user preferences. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Cloud platforms

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Exploring ensemble optimized voting and stacking classifiers through Cross-validation for early detection of suicidal ideation

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 335-349页

作者： Shukla, Shiv Shankar Prasad Singh, Maheshwari Prasad Department of Computer Science Engineering National Institute of Technology Patna Bihar Patna India

Detecting behavioral changes associated with suicidal ideation on social media is essential yet complex. While machine learning and deep learning hold promise in this regard, current studies often lack generalizability due to single dataset reliance. Traditional embedding techniques struggle with semantic analysis,leading to challenges in achieving high accuracy models and conventional validation methods have data drift limitations. To address these challenges, this study proposes a novel evaluation approach using natural language processing across diverse platforms like Twitter and Reddit. By integrating BERT embedding, adept at handling semantic nuances, with an optimized Stacked Classifier combining different base classifiers and XGBoost as the meta-classifier, the model excels in swiftly detecting signs of suicidal ideation compared to the Voting Classifier, i.e., the combination of Decision Tree, Random Forest, Gradient Boost and XGBoost and several machine learning models. Additionally, the study explores advanced embedding techniques like MUSE and LLM, and deep learning models including Bi-LSTM, Bi-GRU, and Text-CNN for *** ensemble approach aims to create a model that is not only interpretable but also robust, reducing computational complexity and enhancing resilience against noisy data - common challenges faced in text classification tasks. Through K-fold validation, which involves partitioning the dataset into k equal-sized subsets or "folds"and training the model k times, using k-1 folds for training and one-fold for testing each time, the proposed model achieves impressive accuracy rates of 97% on Reddit and 96% on Twitter datasets, underscoring its effectiveness in identifying suicidal ideation across social media platforms. © 2024 - IOS Press. All rights reserved.

关键词： Semantics

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iSSL-AL: a deep active learning framework based on self-supervised learning for image classification

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Neural Computing and Applications 2024年第28期36卷 17699-17713页

作者： Agha, Rand Mustafa, Ahmad M. Abuein, Qusai Department of Computer Information Systems Jordan University of Science and Technology Irbid22110 Jordan

Deep neural networks have demonstrated exceptional performance across numerous applications. However, DNNs require large amounts of labeled data to avoid overfitting. Unfortunately, the labeled data may not be available;annotating large amounts of data is time-consuming, laborious, and requires human expertise, making it unfeasible to rely on manpower for annotation. One of the solutions to address this limitation is active learning (AL), a technique that utilizes unlabeled data while maintaining high performance. AL reduces the annotation cost of large datasets and enhances the training of models with fewer annotations. Uncertainty sampling has been proven to be one of the most effective strategies in AL;however, it lacks diversity. This research proposes iSSL-AL, a novel active learning framework that utilizes self-supervised learning (SSL) to ensure informative yet diverse samples. Three main aspects categorize the novelty of our work. The first is extending the margin uncertainty sampling by incorporating SSL to select informative and diverse points. The second is employing incremental learning for efficient training of the AL base classifier, where the model is trained incrementally in each AL cycle. The third is addressing the cold start problem, as our framework achieved high results in the early stages of training. Experiments show that iSSL-AL outperforms other state-of-the-art algorithms on the MNIST, FashionMNIST, and CIFAR-10 datasets, with accuracy scores of 99%, 98.9%, and 93.5%, respectively, effectively selecting diverse and informative samples. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Supervised learning

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Integrated Swarm Intelligence: ACO-PSO Fusion for Enhanced Satellite Image Feature Selection

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第5期52卷 1545-1557页

作者： Rana, Samriti Singh, Lovejit Department of Computer Science Engineering Chandigarh University Punjab Mohali India Department of Computer Science Engineering located Chandigarh University Punjab Mohali India

Satellite image classification is vital for land cover mapping, environmental monitoring, and urban planning. This paper proposes a method for classifying satellite images using Deep Neural Networks (DNNs) combined with Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The Firefly Algorithm (FFA) is used for systematic tuning of DNN hyperparameters, enhancing classification accuracy. Evaluations using precision, recall, and F-measure metrics demonstrate high values for the 16 land cover classes in the Indian Pines dataset, with precision at 0.889, recall at 0.93, and F-measure at 0.8964. Comparative analysis shows that the proposed ACO+PSO+DNN method outperforms state-of-the-art methods. Incorporating Histogram of Oriented Gradients (HoG) features with statistical features further improves performance, achieving a precision of 0.914, recall of 0.927, and F-measure of 0.920 for the Indian Pines dataset. For the Alwar dataset, significant enhancements are shown using ACO+PSO+FFBPNN, ACO+PSO+Naïve Bayes, and ACO+PSO+Random Forest algorithms. Overall, comparative analysis of the proposed ACO+PSO+DNN exhibits 9.72%, 8.75%, and 9.23% improvement over the existing research work for precision, recall, and f-measure metrics, respectively. These findings present a novel approach that achieves high precision and robustness, paving the way for advancements in satellite image classification. © (2025), (International Association of Engineers). All rights reserved.

关键词： Ant colony optimization

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DIST: Distributed Learning-based Energy-Efficient and Reliable Task Scheduling and Resource Allocation in Fog Computing

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IEEE Transactions on Services Computing 2025年第3期18卷 1336-1351页

作者： Oustad, Elyas Younesi, Abolfazl Ansari, Mohsen Safari, Sepideh Soleimani, Mohammad Arman Henkel, Jorg Ejlali, Alireza Sharif University of Technology Department of Computer Engineering Tehran14588 Iran School of Computer Science Tehran Iran Karlsruhe Institute of Technology Karlsruhe76131 Germany

This paper presents DIST, a novel distributed reinforcement learning-based (DRL) framework for energyefficient and reliable task scheduling and resource allocation in fog computing, low-latency computing solutions driven by the rapid deployment of IoT devices, and time-sensitive applications. DIST is built based on a novel distributed Q-learning to enable fog nodes to learn an optimal strategy to balance energy consumption, task execution time, and system reliability. The main novelty includes a cooperative Dynamic Voltage and Frequency Scaling-enabled task scheduling policy that dynamically adjusts node energy level to ensure power consumption reduction without sacrificing deadline adherence or reliability. The results demonstrate that DIST reduces energy consumption by up to 52.26%, realizes 38% higher success rates, and reduces task wait times by up to 46.77%, compared with state-of-the-art algorithms. © 2008-2012 IEEE.

关键词： Reinforcement learning

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FMCSSE: fuzzy modified cuckoo search with spatial exploration for biomedical image segmentation

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Soft Computing 2024年第19期28卷 11565-11585页

作者： Chakraborty, Shouvik Department of Computer Science and Technology Women’s Polytechnic West Bengal Chandernagore India

Biomedical image segmentation is considered an important and challenging task. Automated biomedical image analysis plays a major role in the early and quick diagnosis of diseases. Accurate and precise segmentation can lead to early treatment planning and it demands sophisticated approaches. Inspired by this, a novel approach is proposed. This approach will be known as the Fuzzy modified cuckoo search with spatial exploration (FMCSSE). High correlation among pixels is an important property of image data and pixels surrounding a particular pixel possess similar feature information. Therefore, it is extremely essential to consider the spatial information to generate a meaningful segmented image. The traditional fuzzy clustering approach is not suitable for exploiting spatial information. Therefore, this work is designed to explore spatial information and find the optimal clusters from biomedical images with the help of the fuzzy-modified cuckoo search approach. This approach is applied to different biomedical images and compared with various state-of-the-art unsupervised approaches like FEMO, FMCS, MCS, and CS. The proposed approach does not suffer from the choice of the initial assignment of the cluster centers. The proposed approach uses the type-2 fuzzy system blended with the modified cuckoo search (McCulloch approach) and spatial exploration procedure. Both qualitative and quantitative results show the superiority of the FMCSSE approach in terms of performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Pixels

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A novel method for indian vehicle registration number plate detection and recognition using CNN

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8563-8585页

作者： Pandey, Vibha Choubey, Siddhartha Patra, Jyotiprakash Mall, Shachi Choubey, Abha Department of Computer Science Engineering Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science Engineering Shri Shankaracharya Institute of Professional Management and Technology Chhattisgarh Raipur India School of Computer Science and Engineering Galgotias University Greater Noida India

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "preprocessing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. © 2024 – IOS Press. All rights re

关键词： Image classification

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