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A Color Image Encryption Scheme Based on Singular Values and Chaos

computer Modeling in engineering & sciences 2023年第10期137卷 965-999页

作者： Adnan Malik Muhammad Ali Faisal S.Alsubaei Nisar Ahmed Harish Kumar Electrical Engineering Department University of Engineering and TechnologyLahore54000Pakistan Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Computer Engineering Department University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The security of digital images transmitted via the Internet or other public media is of the utmost *** encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized *** study provides an approach to color image encryption that could find practical use in various *** proposed method,which combines four chaotic systems,employs singular value decomposition and a chaotic sequence,making it both secure and *** unified average change intensity,the number of pixels’change rate,information entropy analysis,correlation coefficient analysis,compression friendliness,and security against brute force,statistical analysis and differential attacks are all used to evaluate the algorithm’s *** a thorough investigation of the experimental data,it is concluded that the proposed image encryption approach is secure against a wide range of attacks and provides superior compression friendliness when compared to chaos-based alternatives.

关键词： Encryption image encryption chaos theory color image encryption singular value decomposition compression friendliness

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Enhancing Human Activity Detection and Classification Using Fine Tuned Attention-Based Transformer Models

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SN computer science 2024年第8期5卷 1142页

作者： Yadav, Ram Kumar Daniel, A. Semwal, Vijay Bhaskar Department of Computer Science and Engineering Amity University Gwalior India Department of Computer Science and Engineering MANIT Bhopal India

Recognition of human activity is an active research area. It uses the Internet of Things, Sensory methods, Machine Learning, and Deep Learning techniques to assist various application fields like home monitoring, robotics, surveillance, and healthcare. However, researchers face problems such as time complexity, more execution time of the model, and classification accuracy. This paper introduces a novel approach to overcome the issue as mentioned earlier by using the deep learning transformer model such as ViT(Vision Transformer), DieT(data-efficient image Transformers), and SwinV2 transformer, which are used for image-based datasets (i.e., Standard40, MPII human pose) and VideoMAE transformer is used for video-based UCF101 and HMDB51 datasets. The approaches achieved remarkable accuracy in classifying human activities. Evaluations using the ViT, DeiT, and Swin transformer V2 with Stanford40 are 90.8%, 90.7%, and 88%;similarly, MPII Human Pose datasets show 87%, 85.6%, and 87.1%. In addition, this paper's method has achieved remarkable accuracies of 94.15% and 78.44%, respectively, when applying the VideoMAE transformer to video-based activity recognition on the UCF101 and HMDB51 datasets. These findings emphasize the efficacy of the attention-based transformer (i.e., ViT, DeiT, SwinV2, and VideoMAE) model and the novelty of earlier no-result evaluation on these various datasets with attention-based transformers. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention-based transformers data augmentation Deep learning HAR Machine learning

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Joint Energy Predication and Gathering data in Wireless Rechargeable Sensor Network

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computer Systems science & engineering 2023年第3期44卷 2349-2360页

作者： I.Vallirathi S.Ebenezer Juliet Department of Computer Science and Engineering PET Engineering CollegeVallioor627117India Department of Computer Science and Engineering VV College of EngineeringThisayanvilai627657India

Wireless Sensor Network(WSNs)is an infrastructure-less wireless net-work deployed in an increasing number of wireless sensors in an ad-hoc *** the sensor nodes could be powered using batteries,the development of WSN energy constraints is considered to be a key *** wireless sensor networks(WSNs),wireless mobile chargers(MCs)conquer such issues mainly,energy *** proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network(WRSN),which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the *** this algorithm,each node gets sorted using the K-means technique,in which the data gets distributed into various *** mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster’s anchor *** position of the anchor points is calculated based on the energy distribution using the base *** this case,the network will act as a spare MC,so that one of the two MCs will run out of energy before reaching the *** the current tours of the two MCs terminate,regression analysis for energy prediction initiates,enabling the updating of anchor points in the upcoming *** on thefindings of the regression-based energy prediction model,the recommended algorithm could effectively refill network energy.

关键词： WSNs MCs WRSN K-means algorithm shortest hamiltonian cycle regression analysis

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Efficient Saliency Map Detection for Low-Light Images Based on Image Gradient

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第2期34卷 852-865页

作者： Lin, Chun-Yi Haq, Muhamad Amirul Chen, Jiun-Han Ruan, Shanq-Jang Naroska, Edwin National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Hochschule Niederrhein University of Applied Science Faculty of Electrical Engineering and Computer Science Krefeld47805 Germany

Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as face recognition, vehicle recognition, and license plate recognition. However, conventional methods for object recognition may not be appropriate for low-light image recognition due to information loss in the dark regions and unexpected noise that can impair object quality. Therefore, the development of specialized techniques for low-light image enhancement has become a major research focus for object detection. This paper proposed a gradient-based saliency map detection method with an improved ResNet architecture that outperforms previous works in detecting multiple or large objects. Additionally, the proposed method enhances images with the object as the center and emphasizes foreground-background differences. Compared with previous works, this paper achieves1.28× improvements in the parameters and 1.32× faster inference speed than the original ResNet architecture. © 1991-2012 IEEE.

关键词： Neural networks

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Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

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computer Systems science & engineering 2023年第7期46卷 1215-1231页

作者： G.Selvakumar L.S.Jayashree S.Arumugam Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Computer Science and Engineering PSG College of TechnologyCoimbatore641004India Department of Computer Science and Engineering Nandha College of EngineeringErode638052India

Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex *** Architecture is one of the recent techniques in which Enterprise Systems can be developed as fine-grained smaller components and deployed *** methodology brings numerous benefits like scalability,resilience,flexibility in development,faster time to market,*** the advantages;Microservices bring some challenges *** microservices need to be invoked one by one as a *** most applications,more than one chain of microservices runs in parallel to complete a particular requirement To complete a user’s *** results in competition for resources and the need for more inter-service communication among the services,which increases the overall latency of the application.A new approach has been proposed in this paper to handle a complex chain of microservices and reduce the latency of user requests.A machine learning technique is followed to predict the weighting time of different types of *** communication time among services distributed among different physical machines are estimated based on that and obtained insights are applied to an algorithm to calculate their priorities dynamically and select suitable service instances to minimize the latency based on the shortest queue waiting *** were done for both interactive as well as non interactive workloads to test the effectiveness of the *** approach has been proved to be very effective in reducing latency in the case of long service chains.

关键词： Microservices load balancing cloud computing latency optimization netflix

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Comparing HAProxy Scheduling Algorithms during the DDoS Attacks

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 139-142页

作者： Kumar, Anmol Somani, Gaurav Agarwal, Mayank Indian Institute of Technology Patna Department of Computer Science and Engineering Patna800013 India Central University of Rajasthan Department of Computer Science and Engineering Ajmer305817 India

Web-services have become most common IT enablers today. Cyber attacks such as the distributed denial of service (DDoS) attacks pose availability concerns which may result into service outages and consequently financial and reputation losses. Load balancing software such as HAProxy (high availability proxy) is an important building block of the Web service delivery today. Load balancing software provides incoming request distribution among Web-servers and admission control which may even help with combating DDoS attacks. In this letter, we study and explore the performance of different load distribution or scheduling strategies in the presence of DDoS attacks. With the help of attack experiments, we find that First, HDR, Source, and URI scheduling algorithms performs best for both static and Poisson arrivals. © 2019 IEEE.

关键词： Scheduling algorithms

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Triplet encoded sequence based membrane protein classification using BiLSTM

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Multimedia Tools and Applications 2024年第36期83卷 84251-84273页

作者： Gomathi, S. Ram, K. Nithish Mary, N. Ani Brown Department of Computer Science and Engineering Francis Xavier Engineering College Tamilnadu Tirunelveli India ValueLabs Hyderabad India Department of Computer Science Sarah Tucker College Tamilnadu Tirunelveli India

Membrane proteins provide a significant part in cellular activities. The role of membrane proteins is inevitable in drug interactions and in all living organisms. Membrane protein classification is used to identify the relationships between proteins. With the help of amino acid composition, proteins get classified. A novel protein classification scheme is proposed using Tri-code Embedding vector. This proposed method forms triplet subgroups which are assigned with unique code words. Then a triplet subgroup is formed from the amino acid subgroup which is provided as input to the Bidirectional Long Short-Term Memory (BiLSTM) and SoftMax layer for classification. Two data sets are utilized and classified, with 7582 membrane proteins and 4684 membrane proteins. The results are investigated applying the self-consistency test, the Mathew’s correlation coefficient and the independent data set. Moreover, the proposed method shows its improvement in protein classification process in terms of accuracy, specificity, sensitivity, precision, recall and fmeasure. Thus, the proposed scheme provides an effective protein classification scheme that incorporates the optimistic features of deep learning. The results depict that overall accuracy obtained for data set1 is 99.48% and for data set2 is 99.87%. The proposed method achieves the highest overall classification accuracy with minimum execution time when compared to the other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Proteins

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Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques

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Multimedia Tools and Applications 2024年第16期84卷 1-32页

作者： Dalal, Surjeet Lilhore, Umesh Kumar Faujdar, Neetu Simaiya, Sarita Agrawal, Akshat Rani, Uma Mohan, Anand Department of Computer Science and Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Computer Science & amp Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering World College of Technology & ampManagement Haryana Gurugram India Department of Physics Kunwar Singh College Bihar Darbhanga India

The thyroid gland, a pivotal regulator of essential physiological functions, orchestrates the production and release of thyroid hormones, playing a vital role in metabolism, growth, development, and overall bodily functions. While thyroid nodules are predominantly benign, certain cases may manifest malignancy, leading to thyroid cancer. Despite the generally favorable prognosis, early detection and intervention remain paramount for optimal outcomes. Given the limitations in clinical data, this paper introduces an innovative approach employing a modified XGBoost model that integrates both clinical and molecular properties into a unified predictive model. Utilizing fine-needle aspiration biopsies and gene expression analysis, our model aims to enhance cancer prediction in thyroid nodules. We explore the influence of incorporating high-quality clinical data on the prioritized gene set, employing a nature-inspired method for hyperparameter optimization. The modified XGBoost model emerges as highly recommended for non-parametric feature selection, extraction, and prediction due to its remarkable accuracy. Our proposed model attains an impressive 94.6% accuracy, surpassing other machine learning models such as C5.0 (92.5%), CART (88.7%), CHAID (83.4%), Quest (81.5%), LSVM (77%), and random tree (24.1%). The real-time efficiency of our model is demonstrated in the results section. Experiments showcase the superior performance of combined molecular and clinical data-based prediction models. Emphasis is placed on the importance of scrutinizing the origins of clinical data and maintaining vigilance over variable quality. This study signifies a substantial advancement in thyroid cancer prediction, offering a robust and accurate model that integrates diverse data sources, ultimately contributing to improved patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Generalized Learning of Coefficients in Spectral Graph Convolutional Networks 15

Generalized Learning of Coefficients in Spectral Graph Convo...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Coskun, Mustafa Grama, Ananth Koyuturk, Mehmet Ankara University Department of Ai and Data Engineering Ankara Turkey Purdue University Department of Computer Science IN United States Case Western University Department of Data and Computer Science OH United States

ISBN: (纸本)9798331508821

Spectral Graph Convolutional Networks (GCNs) have gained popularity in graph machine learning applications due, in part, to their flexibility in specification of network propagation rules. These propagation rules are often constructed as polynomial filters whose coefficients are learned using label information during training. In contrast to learned polynomial filters, explicit filter functions are useful in capturing relationships between network topology and distribution of labels across the network. A number of algorithms incorporating either approach have been proposed;however the relationship between filter functions and polynomial approximations is not fully resolved. This is largely due to the ill-conditioned nature of the linear systems that must be solved to derive polynomial approximations of filter functions. To address this challenge, we propose a novel Arnoldi orthonormalization-based algorithm, along with a unifying approach, called G-Arnoldi-GCN that can efficiently and effectively approximate a given filter function with a polynomial. We evaluate G-Arnoldi-GCN in the context of multi-class node classification across ten datasets with diverse topological characteristics. Our experiments show that G-Arnoldi-GCN consistently outperforms state-of-the-art methods when suitable filter functions are employed. Overall, G-Arnoldi-GCN opens important new directions in graph machine learning by enabling the explicit design and application of diverse filter functions. Code link: https://***/mustafaCoskunAgu/GArnoldi-GCN © 2024 IEEE.

关键词： Convolutional neural networks

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Conditional-GAN-Based Face Inpainting Approaches With Symmetry and View-Degree Utilization

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IEEE Access 2024年 12卷 87467-87478页

作者： Hong, Tzung-Pei Wu, Jin-Hang Su, Ja-Hwung Yin, Tang-Kai National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung811726 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

Recently, image inpainting has been proposed as a solution for restoring the polluted image in the field of computer vision. Further, face inpainting is a subfield of image inpainting, which refers to a set of image editing algorithms re-conducting the missing regions in face smoothly. Actually, face inpainting is more challenging than general image inpainting because it needs more face structure information. Although a number of past studies were proposed for face inpainting by using face segmentation, face edge and face topology, there is some important information ignored, such as geometric and symmetric properties. Based on such concepts, in this paper, we propose a two-stage face inpainting method called CGAN (Conditional Generative Adversarial Network) which integrates face landmarks and Generative Adversarial Network (called GAN). In the first stage, the face landmark is predicted as the condition, providing GAN with important information of geometry and symmetry. The main idea in this stage is to dynamically adjust the loss by the proposed view degree. Accordingly, the masked face image and the corresponding face landmark are used as conditions input to the GAN in the second stage. Finally, the missing-regions are inpainted by the proposed CGAN. To reveal the effectiveness of proposed method, a number of evaluations were conducted on real datasets. The experimental results show that, the proposed method predicts a better face landmark by information of geometric structures and symmetric outlooks, and thereupon the proposed CGAN reconstructs the missing regions superior to the compared methods. © 2013 IEEE.

关键词： Image reconstruction

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