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Robust multifocus deep neural network for progression prediction on patient trajectory data

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International Journal of Intelligent Computing and Cybernetics 2022年第4期15卷 589-598页

作者： K.Arunkumar S.Vasundra department of cse JNTUAAnanthapuramIndia department of cse JNTUACEAAnanthapuramIndia

Purpose-Patient treatment trajectory data are used to predict the outcome of the treatment to particular disease that has been carried out in the *** order to determine the evolving disease on the patient and changes in the health due to treatment has not considered existing *** deep learning models to trajectory data mining can be employed to identify disease prediction with high accuracy and less computation ***/methodology/approach-Multifocus deep neural network classifiers has been utilized to detect the novel disease class and comorbidity class to the changes in the genome pattern of the patient trajectory data can be identified on the layers of the *** is employed to learn extracted feature set with activation and weight function and then merged on many aspects to classify the undetermined sequence of diseases as a new *** performance of disease progression learning progress utilizes the precision of the constituent classifiers,which usually has larger generalization benefits than those optimized ***-Deep learning architecture uses weight function,bias function on input layers and max *** of the input layer has applied to hidden layer to generate the multifocus characteristics of the disease,and multifocus characterized disease is processed in activation function using ReLu function along hyper parameter tuning which produces the effective outcome in the output layer of a fully connected *** results have proved using cross validation that proposed model outperforms methodologies in terms of computation time and ***/value-Proposed evolving classifier represented as a robust architecture on using objective function to map the data sequence into a class distribution of the evolving disease class to the patient ***,the generative output layer of the proposed model produces the progression outcome of the disease of the particular pa

关键词： Patient trajectory data Deep learning representatives Disease evolution Unstructured hierarchical data Cluster reconstruction error

Enhancing the energy efficiency for prolonging the network life time in multi-conditional multi-sensor based wireless sensor network

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Journal of Control and Decision 2023年第1期10卷 72-81页

作者： Raviteja Kocherla M.Chandra Sekhar Ramesh Vatambeti Research scholar Department of CSEPresidency UniversityBangaloreIndia department of cse Presidency UniversityBangaloreIndia department of cse CHRIST(Deemed to be University)BangaloreIndia

A wireless sensor network is one of the networks that is highly demanding by various real-timenetworking applications nowadays. A huge amount of sensor nodes is deployed in the networkrandomly and distributed. Most of the applications using wireless sensor network (WSN) aresurveillance monitoring applications like a forest, home, healthcare, environment, and remotemonitoring systems. Based on the application usage, the type of sensor, a number of sensornodes are deployed in such a manner where the sensors can be used effectively. But the sensornodes are restricted in the battery and sensing region. Thus, the battery of the sensor nodesis decreased based on the nodes’ function. The energy level of the sensor nodes highly affectsthe network lifetime. Improving the energy efficiency in WSN is one of the most important challengingtasks. Most of the earlier research works have proposed various methods, techniques,and routing protocols, but they are application dependent and as a common method. So, thispaper is motivated to propose a Multi-Conditional Network Analysis (MCNA) framework for savingthe energy level of the sensor nodes by reducing energy consumption. TheMCNAframeworkinvolves two different clustering processes with cluster head selection, choosing the best nodesbased on the signal strength, and the best route for data transmission. The data transmissionis done by cluster based on source-destination based. The simulation results proved that theproposed MCNA framework outperforms the other existing methods.

关键词： Wireless sensor networks energy efficiency clustering signal strength verification

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of cse V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

Chaotic Social Group Optimization for Structural Engineering Design Problems

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Journal of Bionic Engineering 2023年第4期20卷 1852-1877页

作者： Anima Naik department of cse VIITVisakhapatnamAndhra Pradesh530049India

Till now,several novel metaheuristic algorithms are proposed for global *** only specific algorithms have become popular or attracted researchers,who are efficient in solving global optimization problems as well as real-world application *** Social Group Optimization(SGO)algorithm is a new metaheuristic bioinspired algorithm inspired by human social behavior that attracted researchers due to its simplicity and problem-solving *** this study,to deal with the problems of low accuracy and local convergence in SGO,the chaos theory is introduced into the evolutionary process of *** chaotic mapping has certainty,ergodicity,and stochastic property,by replacing the constant value of the self-introspection parameter with chaotic maps,the proposed chaotic social group optimization algorithm increases its convergence rate and resulting *** proposal chaotic SGO is validated through 13 benchmark functions and after that 9 structural engineering design problems have been *** simulated results have been noticed as competent with that of state-of-art algorithms regarding convergence quality and accuracy,which certifies that improved SGO with chaos is valid and feasible.

关键词： Chaos Bionic algorithm Constrained optimization SGO Design problem

Fuzzy least brain storm optimization and entropy-based Euclidean distance for multimodal vein-based recognition system

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Journal of Central South University 2017年第10期24卷 2360-2371页

作者： Dipti Verma Sipi Dubey department of cse Vishwavidyalaya Engineering CollegeLakhanpurSarguja UniversityChhattisgarh 497001India department of cse Rungta College of Engineering and Technology(RCET)BhilaiChhattisgarh 490024India

Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.

关键词： multimodality brain storm optimization (BSO) least mean square (LMS) score level fusion recognition

Logistic Regression Trust–A Trust Model for Internet-of-Things Using Regression Analysis

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Computer Systems Science & Engineering 2023年第2期44卷 1125-1142页

作者： Feslin Anish Mon Solomon Godfrey Winster Sathianesan R.Ramesh department of cse Saveetha Engineering CollegeChennaiIndia department of cse SRM Institute of Science and TechnologyChennaiIndia department of ECE Saveetha Engineering CollegeChennaiIndia

Internet of Things(IoT)is a popular social network in which devices are virtually connected for communicating and sharing *** is applied greatly in business enterprises and government sectors for delivering the services to their customers,clients and ***,the interaction is success-ful only based on the trust that each device has on *** trust is very much essential for a social *** Internet of Things have access over sen-sitive information,it urges to many threats that lead data management to *** issue is addressed by trust management that help to take decision about trust-worthiness of requestor and provider before communication and *** trust-based systems are existing for different domain using Dynamic weight meth-od,Fuzzy classiﬁcation,Bayes inference and very few Regression analysis for *** proposed algorithm is based on Logistic Regression,which provide strong statistical background to trust *** make our stand strong on regression support to trust,we have compared the performance with equivalent sound Bayes analysis using Beta *** performance is studied in simu-lated IoT setup with Quality of Service(QoS)and Social parameters for the *** proposed model performs better in terms of various *** IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application *** most salient features of IoT system is to design it with scalability,extendibility,compatibility and resiliency against *** existing worksﬁnds a way to integrate direct and indirect trust to con-verge quickly and estimate the bias due to attacks in addition to the above features.

关键词： LRTrust logistic regression trust management internet of things

Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model

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Big Data Mining and Analytics 2021年第2期4卷 116-123页

作者： Vishan Kumar Gupta Avdhesh Gupta Dinesh Kumar Anjali Sardana department of Computer Science and Engineering(cse) Graphic Era Deemed to be UniversityDehradun 248002India department of cse IMS Engineering CollegeGhaziabad 201009India department of cse KIET Group of InstitutionsGhaziabad 201206India

A novel coronavirus(SARS-CoV-2) is an unusual viral pneumonia in patients, first found in late December 2019, latter it declared a pandemic by World Health Organizations because of its fatal effects on public health. In this present, cases of COVID-19 pandemic are exponentially increasing day by day in the whole world. Here, we are detecting the COVID-19 cases, i.e., confirmed, death, and cured cases in India only. We are performing this analysis based on the cases occurring in different states of India in chronological dates. Our dataset contains multiple classes so we are performing multi-class classification. On this dataset, first, we performed data cleansing and feature selection, then performed forecasting of all classes using random forest, linear model, support vector machine,decision tree, and neural network, where random forest model outperformed the others, therefore, the random forest is used for prediction and analysis of all the results. The K-fold cross-validation is performed to measure the consistency of the model.

关键词： coronavirus COVID-19 respiratory tract multi-class classification random forest

Recognizing Ancient South Indian Language Using Opposition Based Grey Wolf Optimization

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Intelligent Automation & Soft Computing 2023年第3期35卷 2619-2637页

作者： A.Naresh Kumar G.Geetha department of cse Anna UniversityChennai600025TamilnaduIndia department of cse Agni College of TechnologyChennai600130TamilnaduIndia

Recognizing signs and fonts of prehistoric language is a fairly difﬁcult job that requires special *** stipulation make the dispensation period over-riding,difﬁcult and tiresome to *** paper present a technique for recognizing ancient south Indian languages by applying Artiﬁcial Neural Network(ANN)associated with Opposition based Grey Wolf Optimization Algorithm(OGWA).It identiﬁes the prehistoric language,signs and *** is an apparent from the ANN system that arbitrarily produced weights or neurons linking various layers play a signiﬁcant role in its *** adaptively determining these weights,this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization,Particle Swarm Optimization and Grey Wolf Opti-mization to the ANN *** results are illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other *** test case 1,the accuracy value of OGWO is 94.89%and in test case 2,the accu-racy value of OGWO is 92.34%,on average,the accuracy of OGWO achieves 5.8%greater accuracy than ANN-GWO,10.1%greater accuracy than ANN-PSO and 22.1%greater accuracy over conventional ANN technique.

关键词： Ancient language symbols characters artiﬁcial neural network opposition based grey wolf optimization

Image Captioning Using Detectors and Swarm Based Learning Approach for Word Embedding Vectors

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Computer Systems Science & Engineering 2023年第1期44卷 173-189页

作者： B.Lalitha V.Gomathi cse department Sethu Institute of TechnologyPulloorKariapatti626115India cse department National Engineering CollegeK.R.NagarKovilpatti628503India

IC(Image Captioning)is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important ***,in existing works,because of the complexity in images,neglecting major relation between the object in an image,poor quality image,labelling it remains a big problem for ***,the main objective of this work attempts to overcome these challenges by proposing a novel framework for *** in this research work the main contribution deals with the framework consists of three phases that is image understanding,textual understanding and ***,the image understanding phase is initiated with image pre-pro-cessing to enhance image ***,object has been detected using IYV3MMDs(Improved YoloV3 Multishot Multibox Detectors)in order to relate the interrelation between the image and the object,and then it is followed by MBFOCNNs(Modified Bacterial Foraging Optimization in Convolution Neural Networks),which encodes and providesfinal feature ***,the tex-tual understanding phase is performed based on an image which is initiated with preprocessing of text where unwanted words,phrases,punctuations are removed in order to provide a healthy *** is then followed by MGloVEs(Modified Glo-bal Vectors for Word Representation),which provides a word embedding of fea-tures with the highest priority towards the object present in an ***,the decoding phase has been performed,which decodes the image whether it may be a normal or complex scene image and provides an accurate text by its learning ability using MDAA(Modified Deliberate Adaptive Attention).The experimental outcome of this work shows better accuracy of shows 96.24%when compared to existing and similar methods while generating captions for images.

关键词： Denoising improved YoloV3 multishot multibox detector