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Deep learning-based decision-making system for cooperative routing in wireless multimedia sensor network

International Journal of Networking and Virtual Organisations 2024年第3期30卷 257-281页

作者： Nagalingayya, M. Mathpati, Basavaraj S. Department of Computer Science and Engineering Sharnbasva University Kalaburgi Vidya Nagar Karnataka Gulbarga India

This research aims to present a deep belief network (DBN) based a technique for choosing the most suitable cooperative nodes. Additionally, constraints such as: 1) tri-level energy consumption of nodes (text at level 1 has a lower energy level than information;at level 2, which has a medium energy level for text, image and multimedia data;level 3: higher energy levels for high-definition pictures);2) reliability;3) delay are examined while sending multimedia data across the network. Making the right decision, a novel hybrid dragon integrated firefly (DIFF) schemes that integrate the ideas of firefly optimisation (FF) and dragonfly optimisation (DA) method is intended to adjust the optimal weight of DBN. The chosen scheme’s efficiency is then compared to other traditional methods in terms of alive nodes, delay, residual energy, network lifespan and analysis method. © 2024 Inderscience Publishers. All rights reserved.

关键词： Energy utilization

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Enriched cloud computing data security through a triple level encryption model for healthcare systems

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International Journal of Cloud Computing 2024年第6期13卷 566-580页

作者： Kanagaraj, Hari Priya Christo, Brintha Nalsingh Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankovil India

Cloud computing (CC) stores and accesses data over the internet, posing security risks from within the cloud service provider (CSP) or outsiders, especially in medical systems. Cryptography is crucial for security, but performance must not be compromised. This research proposes enhancing security with minimal performance impact by refining Blowfish cipher and using an elliptic curve-based algorithm for key coding. Serpent encryption divides data into blocks and combines them for triple-level encryption. Hash functions reduce processing time, even with multiple users accessing the same document. The approach aims to bolster confidentiality in medical cloud data while ensuring data integrity with digital signatures. Software evaluation shows improvements in throughput, execution time, and memory consumption. © 2024 Inderscience Enterprises Ltd.

关键词： Cloud security

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

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Digital Communications and Networks 2023年第2期9卷 411-421页

作者： Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of the University of DhakaSavarDhaka1350Bangladesh Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Future Technology Research Center College of FutureNational Yunlin University of Science and TechnologyDouliouTaiwanChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Information and Communication Engineering Hankuk University of Foreign StudiesSouth Korea Department of Computer Science Aalto UniversityFinland

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

关键词： Smart IIoT Blockchain SDN IoT Security Privacy OpenFlow SDN-Controller Data security Cloud computing Cloud management

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Saliency and Power Aware Contrast Enhancement for Low OLED Power Consumption

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-17页

作者： Nugroho, Kuntoro Adi Ruan, Shanq-Jang National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan

The mass adoption of display devices calls for advanced power-reduction techniques. Power-constrained contrast enhancement (PCCE) gains many improvements in recent years, yet several important issues were neglected. Computation complexity increases significantly when processing high-resolution content, which is commonplace nowadays. Moreover, many works focus on improving a finite set of quality metrics while abandoning the salient information of image content. We propose to develop an efficient, saliency aware, on-demand PCCE method that is end-to-end optimized on image quality and saliency criterion. Our method starts by extracting multilevel features from a low-resolution luminance input using an efficient feature encoder. A lightweight power-attention mechanism realizes the on-demand power reduction via input image statistics. The last stage mitigates the artifacts introduced by the low-resolution saliency information using fast-guided filtering (GF) and local enhancement (LEN) to restore the high-frequency component. To bridge the unsupervised PCCE and supervised saliency task, we develop a local quality measure that captures a quality ratio given a desired power level. Experiments on multiple datasets with up to 4K resolution demonstrate the effectiveness of our method to produce high-quality and saliency scores. With a 20% power reduction on RAISE dataset, our method achieves structural similarity (SSIM) of 0.99 with backbone network computation of fewer than 0.1 giga multiply-accumulate operations per second (GMACs). Measurement on an organic light-emitting diode (OLED) panel indicates that our method can achieve 0.83 SSIM with a 61% reduction rate. The implementation is available at https://***/kuntoro-adi/SPACE. © 1963-2012 IEEE.

关键词： Luminance

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A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 1-18页

作者： Kumar, Ankit Shukla, Santosh Kumar Prakash, Navin Yadav, Rakesh Kumar Department of Information Technology Babu Banarasi Das Institute of Technology and Management Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering MSOET Maharishi University of Information Technology Lucknow India

Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing. As a result, the mean grey-level approach was used to segment the MRI images using a threshold. In the second stage of statistical feature analysis, the picture features were extracted using the spatial gray-level dependency matrix using Hara lick's feature equations. As a consequence, the best features were picked and the tumor was placed in the appropriate location. In the third stage, an automated tool for classifying the photographs under assessment as having a tumor or not was created utilizing supervised learning and an artificially intelligent methodology. An efficient test of the network's performance yielded 97% of the desired results. © 2023, The Institution of Engineers (India).

关键词： Tumors

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Efficient Approximation Algorithms for Scheduling Coflows with Total Weighted Completion Time in Identical Parallel Networks

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IEEE Transactions on Cloud Computing 2024年第1期12卷 116-129页

作者： Chen, Chi-Yeh National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

This article addresses the scheduling problem of coflows in identical parallel networks, a well-known NPNP-hard problem. We consider both flow-level scheduling and coflow-level scheduling problems. In the flow-level scheduling problem, flows within a coflow can be transmitted through different network cores, while in the coflow-level scheduling problem, flows within a coflow must be transmitted through the same network core. The key difference between these two problems lies in their scheduling granularity. Previous approaches relied on linear programming to solve the scheduling order. In this article, we enhance the efficiency of solving by utilizing the primal-dual method. For the flow-level scheduling problem, we propose an approximation algorithm that achieves approximation ratios of 6-2m and m5-2m for arbitrary and zero release times, respectively, where m represents the number of network cores. Additionally, for the coflow-level scheduling problem, we introduce an approximation algorithm that achieves approximation ratios of4m+1 and 4m for arbitrary and zero release times, respectively. The algorithm presented in this article has practical applications in data centers, such as those operated by Google or Facebook. The simulated results demonstrate the superior performance of our algorithms compared to previous approach, emphasizing their practical utility. © 2013 IEEE.

关键词： Approximation algorithms

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PODBoost: an explainable AI model for polycystic ovarian syndrome detection using grey wolf-based feature selection approach

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Neural Computing and Applications 2024年第30期36卷 18627-18644页

作者： Moral, Poonam Mustafi, Debjani Sahana, Sudip Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi835215 India

Polycystic Ovary Syndrome (PCOS) is a recurring endocrine disorder that primarily affects women of reproductive age. It is difficult to diagnose due to its heterogeneous characteristics and overlapping symptoms with other illnesses. As a result, accurate and trustworthy prediction models are required to detect PCOS early. This research work aims to develop ML methods that predict the risk of PCOS among women based on demographic and clinical features. The entire framework is divided into four phases: in Phase I of the study, the SMOTE-Tomek Links (SMTL) technique balances the data set by combining oversampling and undersampling approaches. A novel meta-heuristic-based feature selection approach, the Grey Wolf Optimization (GWO) method, has been employed to select the most crucial features from the dataset, explained in Phase II. Subsequently, in Phase III, a hybridized classifier PODBoost algorithm (Polycystic Ovarian Disorder Boosting algorithm) is devised for faithful early prediction of PCOS using the concepts of different classical supervised learning algorithms. Finally, Explainable AI (XAI) such as the Local Interpretable Model-Agnostic Explanations (LIME) tool has been implemented to interpret relevant predictions made by the proposed classifier. The proposed algorithm is examined utilizing numerous metrics such as Accuracy, Error-Rate, ROC-AUC Score, Recall, Precision, and F1-Score. Among all the evaluated models, the proposed hybridized model has shown an impressive performance with an exceptional accuracy of 97.42%, indicating its superiority by delivering outstanding results. Based on the findings, the novel meta-heuristic-based feature selection method significantly impacts the outcomes of the proposed hybridized PODBoost algorithm. This algorithm may be recommended to predict PCOS or other relevant diseases having datasets which are multimodal in nature. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 202

关键词： Feature Selection

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Pre-Routing Timing Prediction Across Different Technology Nodes

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第7期44卷 2697-2710页

作者： Zhang, Xinyun Zhu, Binwu Liu, Fangzhou Jiang, Jiaxi Wang, Ziyi Xu, Peng Xu, Hong Yu, Bei The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

In the domain of Very-Large-Scale Integration (VLSI) design, the accuracy of pre-routing timing prediction is of paramount importance for ensuring the performance and reliability of integrated circuits. Traditional methods based on machine learning necessitate the availability of extensive and high-quality datasets. However, this requirement poses significant challenges for advanced technology nodes due to the laborious and time-intensive nature of data preparation. To address this critical issue, we introduce a novel transfer learning framework that leverages data from preceding technology nodes to facilitate learning and prediction on the target node. Our methodology commences with the disentanglement and alignment of timing path features across different nodes, ensuring the preservation and effective translation of intrinsic timing path properties. Subsequently, we employ a Bayesian-based model to predict the arrival times of individual timing paths. This model is particularly adept at managing the high variability inherent in arrival times and exhibits strong generalization capabilities to novel design scenarios. Moreover, we propose a new algorithm to reweight the preceding node data during training by estimating their transferability through the cell type distribution. We validate the efficacy of our proposed framework through comprehensive experimental evaluations, demonstrating successful transfer learning from 130nm or 45nm to 7nm technology nodes. The results underscore the potential of our approach to significantly mitigate the dependency on extensive data preparation while maintaining high accuracy in timing prediction for cutting-edge VLSI designs. © 1982-2012 IEEE.

关键词： Prediction models

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Regenerating Arbitrary Video Sequences with Distillation Path-Finding

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IEEE Transactions on Visualization and computer Graphics 2024年第7期30卷 3622-3635页

作者： Le, Thi-Ngoc-Hanh Yao, Sheng-Yi Wu, Chun-Te Lee, Tong-Yee National Cheng-Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

If the video has long been mentioned as a widespread visualization form, the animation sequence in the video is mentioned as storytelling for people. Producing an animation requires intensive human labor from skilled professional artists to obtain plausible animation in both content and motion direction, incredibly for animations with complex content, multiple moving objects, and dense movement. This article presents an interactive framework to generate new sequences according to the users' preference on the starting frame. The critical contrast of our approach versus prior work and existing commercial applications is that novel sequences with arbitrary starting frame are produced by our system with a consistent degree in both content and motion direction. To achieve this effectively, we first learn the feature correlation on the frameset of the given video through a proposed network called RSFNet. Then, we develop a novel path-finding algorithm, SDPF, which formulates the knowledge of motion directions of the source video to estimate the smooth and plausible sequences. The extensive experiments show that our framework can produce new animations on the cartoon and natural scenes and advance prior works and commercial applications to enable users to obtain more predictable results. © 1995-2012 IEEE.

关键词： Animation

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