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Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 299-327页

作者： A.Robert Singh Suganya Athisayamani gyanendra prasad joshi Bhanu Shrestha Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203Tamil NaduIndia School of Computing Sastra Deemed to be UniversityThanjavur613401Tamil NaduIndia Department of Artificial Intelligence&Software Kangwon National UniversitySamcheok25913Republic of Korea Department of Information Convergence System Graduate School of Smart ConvergenceKwangwoon UniversitySeoul01897Republic of Korea

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

关键词： SPECT-MPI CAD MSDC denoising attenuation correction classification

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh gyanendra prasad joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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Improving the Detection Rate of Rarely Appearing Intrusions in Network-Based Intrusion Detection Systems

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Computers, Materials & Continua 2021年第2期66卷 1647-1663页

作者： Eunmok Yang gyanendra prasad joshi Changho Seo Department of Financial Information Security Kookmin UniversitySeoul02707Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Convergence Science Kongju National UniversityGongju32588Korea

In network-based intrusion detection practices,there are more regular instances than intrusion *** there is always a statistical imbalance in the instances,it is difficult to train the intrusion detection system *** this work,we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate *** technique mitigates the statistical imbalance in these *** also carried out an experiment on the training model by increasing the instances,thereby increasing the attack instances step by step up to 13 *** experiments included not only known attacks,but also unknown new *** results are compared with the existing studies from the literature,and show an improvement in accuracy,sensitivity,and specificity over previous *** detection rates for the remote-to-user(R2L)and user-to-root(U2L)categories are improved significantly by adding fewer *** detection of many intrusions is increased from a very low to a very high detection *** detection of newer attacks that had not been used in training improved from 9%to 12%.This study has practical applications in network administration to protect from known and unknown *** network administrators are running out of instances for some attacks,they can increase the number of instances with rarely appearing instances,thereby improving the detection of both known and unknown new attacks.

关键词： Intrusion detection statistical imbalance SMO machine learning network security

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Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

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Computer Systems Science & Engineering 2023年第7期46卷 687-699页

作者： VDinesh SSrinivasan gyanendra prasad joshi Woong Cho Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha University602105ChennaiIndia Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time *** the same time,vehicles exhibit high velocity,leading to more vehicle *** of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection *** is considered one of the possible solutions for attaining effectual interaction in *** one such difficulty was reducing the cluster number under increasing transmitting *** article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for *** major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping *** addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given *** DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean *** experimental assessment of the DHOGO-EAC technique was carried out under distinct *** comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

关键词： Vehicular networks clustering evolutionary algorithm fitness function distance metric

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Mitigating and Monitoring Smart City Using Internet of Things

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Computers, Materials & Continua 2020年第11期65卷 1059-1079页

作者： Sudan Jha Lewis Nkenyereye gyanendra prasad joshi Eunmok Yang School of Computer Science and Engineering Lovely Professional UniversityPunjab144411India Department of Computer and Information Security Sejong UniversitySeoul05006Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Computer Engineering Kongju National UniversityGongju32588Korea

The present trends in smart world reflects the extensive use of limited resources through information and communication *** limited resources like space,mobility,energy,etc.,have been consumed rigorously towards creating optimized but smart ***,a new concept of IoT integrated smart city vision is yet to be proposed which includes a combination of systems like noise and air loss monitoring,web monitoring and fire detection systems,smart waste bin systems,etc.,that have not been clearly addressed in the previous *** paper focuses on developing an effective system for possible monitoring of losses,traffic management,thus innovating smart city at large with digitalized and integrated systems and software for fast and effective *** our proposed system,a real time data analysis is *** data are collected by various sensors to analyze different factors that are responsible for such *** proposed work is validated on a real case study.

关键词： Internet of Things smart city environmental impairments pollution temperature

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Optimized Load Balancing Technique for Software Defined Network

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Computers, Materials & Continua 2022年第7期72卷 1409-1426页

作者： Aashish Kumar Darpan Anand Sudan Jha gyanendra prasad joshi Woong Cho Department of Computer Science and Engineering Chandigarh UniversityMohaliPunjab140413India School of Computational Sciences CHRIST(Deemed to be University)Ghaziabad201003India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Automotive ICT Convergence Engineering Daegu Catholic UniversityGyeongsan38430Korea

Software-defined networking is one of the progressive and prominent innovations in Information and Communications *** mitigates the issues that our conventional network was ***,traffic data generated by various applications is increasing day by *** addition,as an organization’s digital transformation is accelerated,the amount of information to be processed inside the organization has increased *** might be possible that a Software-Defined Network becomes a bottleneck and *** models have been proposed in the literature to balance the ***,most of the works consider only limited parameters and do not consider controller and transmission media *** loads also contribute to decreasing the performance of Software-Defined *** work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the *** proposed a deep learning-dependent convolutional neural networkbased load balancing technique to handle a software-defined network *** simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques.

关键词： SDN software-defined networks load balancing performance enhancement

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Three-Dimensional Distance-Error-Correction-Based Hop Localization Algorithm for IoT Devices

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Computers, Materials & Continua 2021年第2期66卷 1529-1549页

作者： Deepak Prashar gyanendra prasad joshi Sudan Jha Eunmok Yang Kwang Chul Son School of Computer Science and Engineering Lovely Professional UniversityPunjab144001India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Financial Information Security Kookmin UniversitySeoul02707Korea Department of Information Contents Kwangwoon UniversitySeoul01897Korea

The Internet of Things(IoT)is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time *** networks of wireless sensors monitor the physical environment and report the collected data to the base station,allowing for smarter *** in wireless sensor networks is to localize a sensor node in a two-dimensional ***,in some application areas,such as various surveillances,underwater monitoring systems,and various environmental monitoring applications,wireless sensors are deployed in a three-dimensional ***,localization-based applications have emerged as one of the most promising services related to *** this paper,we propose a novel distributed range-free algorithm for node localization in wireless sensor *** proposed three-dimensional hop localization algorithm is based on the distance error correction *** this algorithm,the error decreases with the localization *** distance correction factor is used at various stages of the localization process,which ultimately mitigates the *** simulated the proposed algorithm using MATLAB and verified the accuracy of the *** simulation results are compared with some of the well-known existing algorithms in the *** results show that the proposed three-dimensional error-correctionbased algorithm performs better than existing algorithms.

关键词： 3D localization DV-hop algorithm IoT PSO wireless sensor networks

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Intelligent Tunicate Swarm-Optimization-Algorithm-Based Lightweight Security Mechanism in Internet of Health Things

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Computers, Materials & Continua 2021年第1期66卷 551-562页

作者： Gia Nhu Nguyen Nin Ho Le Viet gyanendra prasad joshi Bhanu Shrestha Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Graduate School Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science and Engineering Sejong UniversitySeoul05006South Korea Department of Electronic Engineering Kwangwoon UniversitySeoul01897South Korea

Fog computing in the Internet of Health Things(IoHT)is promising owing to the increasing need for energy-and latency-optimized health sector ***,clinical data(particularly,medical image data)are a delicate,highly protected resource that should be utilized in an effective and responsible manner to fulfil consumer ***,we propose an energy-efficient fog-based IoHT with a tunicate swarm-optimization-(TSO)-based lightweight Simon cipher to enhance the energy efficiency at the fog layer and the security of data stored at the cloud *** proposed Simon cipher uses the TSO algorithm to select the optimal keys that will minimize the deterioration of quality between the original and reconstructed(decrypted)*** this study,the decrypted image quality is preserved by the peak signal-to-noise ratio(PSNR)such that consumers can generate precise medical reports from IoHT devices at the application ***,a lightweight encryption step is implemented in the fog to improve energy efficiency and reduce additional computations at the cloud *** results indicate that the TSO-Simon model achieved a high PSNR of 61.37 dB and a pixel change rate of 95.31.

关键词： Internet of Health Things healthcare Simon cipher tunicate swarm optimization

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A Direct Data-Cluster Analysis Method Based on Neutrosophic Set Implication

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Computers, Materials & Continua 2020年第11期65卷 1203-1220页

作者： Sudan Jha gyanendra prasad joshi Lewis Nkenyereya Dae Wan Kim Florentin Smarandache School of Computer Science and Engineering Lovely Professional UniversityPhagwaraPunjab144411India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Computer and Information Security Sejong UniversitySeoul05006Korea Department of Business Administration Yeungnam UniversityGyeongsan38541Korea University of New Mexico New Mexico87301USA

Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of *** paper focuses on cluster analysis based on neutrosophic set implication,i.e.,a k-means algorithm with a threshold-based clustering *** algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering *** evaluate the validity of the proposed method,several validity measures and validity indices are applied to the Iris dataset(from the University of California,Irvine,Machine Learning Repository)along with k-means and threshold-based clustering *** proposed method results in more segregated datasets with compacted clusters,thus achieving higher validity *** method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and threshold-based clustering algorithms.

关键词： Data clustering data mining neutrosophic set k-means validity measures cluster-based classification hierarchical clustering

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Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

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Computers, Materials & Continua 2023年第1期74卷 2179-2194页

作者： R.Poonguzhali Sultan Ahmad P.Thiruvannamalai Sivasankar S.Anantha Babu Pranav joshi gyanendra prasad joshi Sung Won Kim Department of Computer Science and Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science and Engineering Jain Deemed to-be UniversityBangalore560069India Department of Neurology Annapurna Neuro HospitalKathmandu44600Nepal Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival *** location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and *** automatic recognition,place,and classifier process was desired and *** study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor *** presentedADRUSCM model majorly focuses on the segmentation and classification of *** accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in *** addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain ***,VGG-19 model is exploited as a feature ***,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes *** performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

关键词： Brain tumor diagnosis image classification biomedical images image segmentation deep learning

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