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Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

computer Systems science & engineering 2023年第7期46卷 181-193页

作者： V.S.Vaisakhi D.Surendran Department of Electronics and Communication Engineering Nehru Institute of Engineering and TechnologyCoimbatore641105India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641105India

In signal processing and communication systems,digital filters are widely *** some circumstances,the reliability of those systems is crucial,necessitating the use of fault tolerant filter *** strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the *** technology advances,more complicated systems with several filters become *** of the filters in those complicated systems frequently function in parallel,for example,by applying the same filter to various input ***,a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was *** fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the *** primary idea is to use structured authentication scan chains to study the internal states of finite impulse response(FIR)components in order to detect and recover the exact state of faulty modules through the state of non-faulty ***,a simple solution of Double modular redundancy(DMR)based fault tolerance was developed that takes advantage of the availability of parallel filters for image *** approach is expanded in this short to display how parallel filters can be protected using error correction codes(ECCs)in which each filter is comparable to a bit in a standard ECC.“Advanced error recovery for parallel systems,”the suggested technique,can find and eliminate hidden defects in FIR modules,and also restore the system from multiple failures impacting two FIR *** the implementation,Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of *** were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

关键词： Fault tolerance filters efficiency redundancy image denoising error correction codes double modular redundancy

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Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

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computers, Materials & Continua 2024年第5期79卷 3067-3087页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Department of Computer Science and Technology Indian Institute of Engineering Science and TechnologyShibpurHowrah711103India Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas

关键词： Workout action recognition video stream action recognition residual network GRU attention

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Multilevel characterization of unknown protein sequences using hierarchical long short term memory model

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Multimedia Tools and Applications 2025年第9期84卷 6593-6614页

作者： Agrawal, Saurabh Sisodia, Dilip Singh Nagwani, Naresh Kumar Department of Computer Science & Engineering National Institute of Technology Raipur GE Road Chhattisgarh Raipur492010 India

Multilevel characterization of the recently developed Unknown Protein Sequence (UPS) is significant for the drug-designing, disease-diagnosis, and treatment plans. UPS can demonstrate harmful as well as useful characters. Long Short Term Memory (LSTM) based Multilevel Protein Characterization (ML_PC) model using Neuro-Fuzzy Classifier (NFC) and transfer learning has been proposed in this paper for characteristics, species, and function prediction of the UPS. Gram Negative (GN) dataset with known 2286 protein sequences, is used for formation and validation of the ML_PC model. Thereafter NFC is applied at initial stage of the validated ML_PC model to filter the 518 independent protein sequences of the UPS dataset as GN or Non-GN in context with known GN dataset. However, NFC is competent to handle likelihood classification complications of supervised learning. Characteristics, species, and functions of the filtered GN UPS have been predicted in succeeding levels of the ML_PC framework through Protein Characterization (PC) model. LSTM and transfer learning based PC model has been trained and validated using augmented feature of the known GN protein sequence samples, while tested with UPS and filtered GN protein sequences. Transfer learning has been applied as requisite postprocessing to overcome overfitting in the prediction of bacterial protein species with insufficient protein sequence samples. Performance of the multilevel model is improved by 7.52% after combining with transfer learning and NFC. ML_PC model has been compatible to extract relevant biological information of the UPS in context to various categories of bacterial proteins. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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An efficient botnet detection approach based on feature learning and classification

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Journal of Control and Decision 2023年第1期10卷 40-53页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Computer Science and Engineering Easwari Engineering CollegeChennaiIndia DMI College of Engineering ChennaiIndia

Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced by the existing system. This research concentrates on adopting a graphbasedfeature learning process to reduce feature dimensionality. The incoming samples arecorrectly classified and optimised using an Adaboost classifier with an improved grey wolfoptimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multiconstraintissues related to bot detection and provide better local and global solutions (to satisfyexploration and exploitation). The extensive results show that the proposed g-AGWO model outperformsexisting approaches to reduce feature dimensionality, under-fitting/over-fitting andexecution time. The error rate prediction shows the feasibility of the given model to work over thechallenging environment. This model also works efficiently towards the unseen data to achievebetter generalization.

关键词： Botnet machine learning AdaBoost grey wolf optimisation exploration exploitation

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Deep learning based RAGAE-SVM for Chronic kidney disease diagnosis on internet of health things platform

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Multimedia Tools and Applications 2024年 1-39页

作者： Kandukuri, Prabhakar Abdul, Ashu Kumar, Kuchipudi Prasanth Sreenivas, Velagapudi Ramesh, G. Gundu, Venkateswarlu Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering SRM University Andhra PradeshNeerukonda Mangalagiri 522503 India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering SRK Institute of Technology Andhra Pradesh Vijayawada521108 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology TelanganaBachupally Hyderabad500090 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India

Chronic kidney disease (CKD) is a prominent disease that causes loss of functionality in the kidney. Doctors can now more easily gather patient health status data due to the growth of the Internet of Health Things (IoHT). The IoHT data contains a huge number of redundant data, making it challenging to predict CKD disease quickly and accurately. In healthcare applications like feature-based classification, a variety of disease diagnosis systems were used to address this problem. Current disease detection algorithms suffer from imbalanced dataset processing, low-accuracy feature learning, and high computational power requirements. Thus, deep learning-based clinical decision support systems have been developed to solve these complexities. To remove outliers from medical data, data collected with IoHT devices is first pre-processed using an enhanced K-means clustering technique. The Synthetic Minority over Sampling Technique is used to balance data because the IoHT dataset is highly imbalanced. The classifier detects anomalies in less time because of the implemented processing step. The balanced CKD dataset is then presented for use with a novel classifier called Residual Attention-Gated Autoencoder with Support Vector Machine. In order to improve accurate detection, the adopted classifier can learn and extract features. The proposed method results in an increased accuracy of 99.43% within 70.1 s of computation time. The mean intersection of the union and kappa coefficient of the proposed method is 98.15% and 98.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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Unique hash value based identical video sharing traffic flow optimization across social networks

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Multimedia Tools and Applications 2025年第13期84卷 11983-12011页

作者： Konyak, Chenlep Yakha Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima Dimapur797 103 India

Identical video sharing across social networks results in redundant video data traffic, consuming additional bandwidth, storage resources, and data usage. A novel method is proposed to optimise redundant video-sharing services across social media applications exclusively on the push model, which is based on creating a unique hash value identity (UHVID) to identify the video distinctly. The UHVID is generated based on the centroid of gradient orientation of the key frames. The key frames are extracted from the video based on colour histogram and structural differences. Using UHVID and video metadata, the server application validates and optimizes identical video content flow before actual video sharing. The end users’ privacy is not affected in the proposed framework, as the actual video is not used for comparison, only the hash values of the video. The statistical analysis has shown that in 14 test cases of 2700 traffic initiations and 39,180 transactions, 83.2% of redundant data of identical videos were optimised, resulting in 63.61% data savings, amounting to 19.75 GB. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Digital storage

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DeepVNE: Deep Reinforcement and Graph Convolution Fusion for Virtual Network Embedding 16

DeepVNE: Deep Reinforcement and Graph Convolution Fusion for...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Keerthan Kumar, T.G. Kb, Aneesh Siddheshwar, Anagha Marali, Amulya Kamath, Anupama Koolagudi, S.G. Addya, Sourav Kanti National Institute of Technology Department of Computer Science and Engineering Karnataka India Siddaganga Institute of Technology Department of Computer Science and Engineering India Pes University Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Network virtualization (NV) plays a crucial role in modern network management. One of the fundamental challenges in NV is allocating physical network (PN) resources to the demands of the virtual network requests (VNRs). This process is known as a virtual network embedding (VNE) and is NP-hard. Most of the existing approaches for VNE are based on heuristic, meta-heuristic, and exact strategies with limited flexibility and the risk of being stuck in local optimal solutions. In this concern, we provide a deep reinforcement learning (DRL) and graph convolution network (GCN) fusion for VNE (DeepVNE) for maximizing the revenue-to-cost ratio. The DeepVNE takes advantage of the power of actor-critic models within the DRL framework to detect network states and provide optimal solutions matched to current conditions. DeepVNE effectively captures the structural dependencies of both VNRs and PN resources by GCNs, allowing better decision-making during the embedding. Considering several features in the agents throughout the training phase improves resource utilization. The experiments show that DeepVNE outperforms the baselines by gaining a 51% acceptance ratio and 28% revenue-to-cost ratio. © 2024 IEEE.

关键词： Graph Convolutional Network Network Virtualization Reinforcement Learning Virtual Network Embedding

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Pattern Analysis and Regressive Linear Measure for Botnet Detection

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computer Systems science & engineering 2022年第10期43卷 119-139页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： BotNet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

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Advancing automotive high-performance computing: integrating direction API with LORA technology in unified machine vision for future smart cars

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Multimedia Tools and Applications 2025年第18期84卷 19315-19342页

作者： Chaudhary, Kapil Department of Computer Science & Engineering BipinTripathi Kumaon Institute of Technology Gaucher Dwarahat Road Uttarakhand263653 India

This manuscript addresses the challenges in developing Automotive High-Performance Computing (HPC) systems for Future Smart Cars and Unmanned Mobile Vehicles (UMVs). We introduce a novel Unified Machine Vision (UMV) narrative methodology for operators overseeing UMVs. The integration of diverse sensors, Direction API software, and LoRa communication technology presents significant technical hurdles. To tackle these, we focus on integrating Direction API with LoRa in UMVs, incorporating open-source natural interaction OpenNI, and haptic driver inputs into the Master Control System (MCS). The MCS, using the Comp-GAP algorithm, serves as the central interface between the driver and machinery sensors, effectively managing commands to achieve multifaceted objectives. Additionally, we present the CCHA, CROMM-MSV parallel algorithm to mitigate power consumption issues in autonomous vehicles via centralized HPC. This new centralized Advanced Driver Assistance System (ADAS) methodology optimizes UMV energy consumption in air and enhances operational efficiency through parallel HPC techniques. Simulation results demonstrate stable behavior in parallel processing, validating the proposed methodologies. In summary, this manuscript offers a robust framework for HPC systems in Future Smart Cars and UMVs, paving the way for improved navigation and vehicular control. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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