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Optimal feature with modified bi-directional long short-term memory for big data classification in healthcare application

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4441-4450页

作者： Kamble, Subhash Arunalatha, J.S. Venugopal, K.R. Computer Science and Engineering Bangalore University Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore University

Artificial intelligence together with its applications are advancing in all fields, particularly medical science. A considerable quantity of clinical data is available, yet the vast majority of it is wasted. It will be valuable in diagnosing human life early if it is applied effectively. An excellent classification system assists medical practitioners in providing a suitable diagnosis at a younger stage. Medical data typically contain a large number of features, and including all of them in decision making may result in over fitting of the classification model, lowering accuracy. As a result, an effective dimensionality reduction strategy that minimizes the amount of features while also improving classification accuracy is required. Therefore in this study, efficient dimension reduction-based medical data classification is proposed. The proposed approach is made up of three stages namely, pre-processing, feature selection, and classification. Initially, the medical data are collected and pre-processed. Then, to minimize computational complexity and time consumption, optimal features are selected using the adaptive rat swarm optimization (ARWO) algorithm. Then, the selected features are given to the modified bi-directional long short-term memory (MBi-LSTM) classifier to classify data as normal or abnormal. Then, the performance of the model is evaluated using different datasets and the effectiveness of the proposed model is analyzed in terms of different metrics. The proposed model implemented in python. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ARWO Big data MBi-LSTM Medical data Optimal features

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Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

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Journal of Bionic engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering Color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

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Polyp Detection Using U-Net Neural Network Based Algorithm 2

Polyp Detection Using U-Net Neural Network Based Algorithm

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Bipin Kumar Singh, Devansh Shukla, Archita Dayananda Sagar University Department of Computer Science & Engineering Bengaluru India Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Polyp is an earlier stage of cancer development in gastro-intestinal tract. Despite the fact that numerous techniques for automatic segmentation and detection of polyps have been developed, it still remains an open problem. Examining color images captured by the board camera in endoscopy is contemplated the most reliable method for detectingdeveloping polyps. To automate this process, in this paper we present a modified U-Net Neural Network based algorithm. This algorithm is a binary classifier which labels the pixels as part of the polyp or not. This model can analyze the video of GItract frame by frame and produce the collection of frames with possibility of presence of polyp thus reducing the time required to process the GI-tract. Detecting, localizing, and segmenting polyps using Kvasir-SEG, an open-access dataset of colonoscopy images, we benchmark various modern day approaches in this work, assessing bothmethod speed and accuracy. This is attributed to outstanding performance of image classification compared to preceding techniques. The automatically identifying polyps serves to aid gastroenterologists during colonoscopies. While existing literature contains publications addressing the challenge of polyp detection, many of these systems remain confined toresearch settings and lack implementation for clinical use. Hence, we present the inaugural fully open-source automated polyp detection system, which not only outperforms the best- performing system documented in the literature but is also prepared for immediate clinical application. © 2024 IEEE.

关键词： Colonoscopy ColonSegNet Colorectal cancer Kvasir-SEG Medical image segmentation Polyp Detection U-net

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An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

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computer Systems science & engineering 2023年第1期44卷 143-156页

作者： M.Hema S.Kanaga Suba Raja Department of Information Technology Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated *** phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their *** is to be noted that the identification of an extensively used host or underloaded host is challenging to ***-old values were proposed in the literature to detect this *** current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other *** researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud *** mechanism presented in this study is contrasted with existing mechanisms *** results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be *** proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

关键词： Workload consolidation energy consumption under load server resource utilization

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Hashing In Blockchain Using Merkle Tree POW Cosensus Algorithms 2

Hashing In Blockchain Using Merkle Tree POW Cosensus Algorit...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Ashok, Kumar K. Anathajothi, K. Kcg College of Engineering Department of Computer Science and Engineering Karapakkam India Rajalakshmil Engineering College Department of Computer Science and Engineering Thandalam India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384277

The preface of Privacy based decentralized application and massive information Analytics has been illustrate the consequence of block chain tools to the industry. Blockchain skill as a policy allows creating a scattered and mock ledger of transactions. Blockchain provide a promise of tamper conflict used for the data generate from various process to assemble enormous datasets. In view of the fact that the data is colossal in mass and grows exponentially by means of point in time, the data management should be proficient to stock up or course of action it. We survey the unusual standards of Merkle Tree Generation works in numerous research instructions for bring the blockchain implementation more rapidly to the variety of domain of database and its operation. in addition, the projected research work also discusses regarding the privacy, challenges, and future research direction by applying Merkle Tree Generation involves Data Slicing and Encryption, Generating Hash value. © 2024 IEEE.

关键词： Differential privacy

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Behavior-Based Machine Learning Approaches for Malware Detection

Behavior-Based Machine Learning Approaches for Malware Detec...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Vivek Srivastava Manya Khare Manasvi Kansal Harsh Department of Computer Science and Engineering ABES Engineering College Ghaziabad

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The present research investigates the implementation of various methods of machine learning to analyze fluid information from executable files for the purpose identify contamination. We extracted feature vectors from a dataset that contained both malicious and benign samples. Standard metrics like F1-score, recall, and precision were used to train and evaluate a range of classifiers, including Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression. Our test findings show that the models successfully distinguish between harmful and benign files, with Random Forest providing the highest level of consistency in all evaluation measures. These Results demonstrate the promise of machine learning methods to improve systems for detecting malware.

关键词： Logistic regression Accuracy Computational modeling Static VAr compensators Feature extraction Malware Vectors Decision trees Kernel Random forests

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A Novel Fuzzy Marine White Shark Optimization Based Efficient Routing and Enhancing Network Lifetime in MANET

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期132卷 2363-2385页

作者： Devi, K. Lalitha Bhat, C. Rohith Devi, K. Lalitha KSR College of Engineering Tiruchengode Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu 602105 India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.

关键词： MANET Cluster head Fuzzy Marine White Shark Cluster generation Network lifetime Energy consumption

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Weed Classification Using Particle Swarm Optimization and Deep Learning Models

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computer Systems science & engineering 2023年第1期44卷 913-927页

作者： M.Manikandakumar P.Karthikeyan Department of Computer Science and Engineering Thiagarajar College of EngineeringTamil NaduIndia Department of Information Technology Thiagarajar College of EngineeringTamil NaduIndia

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and *** and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of *** address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image *** this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed *** Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed *** effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification *** proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed *** work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

关键词： Deep learning convolutional neural network weed classification transfer learning particle swarm optimization evolutionary computing Algorithm 1:Metrics Evaluation

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A Hybrid Approach for Diabetic Retinopathy Classification Using EfficientNetB5 and ResNet50 Models 2

A Hybrid Approach for Diabetic Retinopathy Classification Us...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Abirami, A. Lakshmi Priya, S. Lekshmi Prriya, T. Madhanraj, J. Computer Science and Engineering Easwari Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Diabetic retinopathy (DR), a severe diabetes-related eye complication that can result in blindness if left untreated, progressively damages retinal blood vessels with varying degrees of severity. Traditional screening methods predominantly rely on manual expert evaluation, which can be time-intensive, prone to human error, and subject to interpretation variability due to inconsistent image quality. To address these challenges, our research presents an innovative hybrid deep learning solution that seamlessly combines EfficientNetB5 and ResNet50 architectures for automated DR classification. This advanced model strategically harnesses EfficientNetB5 superior feature extraction capabilities alongside ResNet50 sophisticated deep layer processing power to accurately classify DR into mild, moderate, severe, and proliferative stages. Through extensive training on a comprehensive dataset of retinal fundus images, our hybrid approach demonstrates significant improvements in early detection accuracy, reduced diagnostic inconsistency, and enhanced reliability compared to conventional methods. The results validate that this integrated approach provides healthcare professionals with a robust, efficient, and reliable automated screening tool for clinical applications, ultimately addressing the current limitations in DR diagnosis and management while potentially improving patient outcomes through earlier intervention. © 2025 IEEE.

关键词： Electrotherapeutics

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Leveraging Satellite Imagery to Analyze Large-Scale Economic Trends

Leveraging Satellite Imagery to Analyze Large-Scale Economic...

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2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Lavanya, Raja Indira, K. Santhiya, C. Chakkaravarthy, G. Vinoth Abhinav, S. Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India Department of Computer Science and Engineering Velammal College of Engineering Madurai India

ISBN: (纸本)9798350376913

Poverty is still one of the factors that have been affecting the lives of humans over hundreds of decades in the world but still, it is not entirely eradicated. One of the main reasons is the lack of identification of the economical pattern in a particular area that has been affected by poverty over a long period which has not been noted by any NGOs or government. So, that is the main reason why poverty eradication is still the first goal of UN sustainable development goals.[1] Understanding economical patterns over a region can lead us in providing informed policy-making, targeted NGO, and government-aided efforts. If we could track them down in an easy method at the earlier stage or in the advanced method and predict this fatal enigma on the region, the poverty can surely prevent or at least save lives. The above dilemma leads us to the essential method of tracking the reliable and timely measurements of economic activities, which are key for understanding economic development and designing government policies. But still many countries, especially developing countries, lack reliable data. Though data is available, the lack of quality in the data remains a huge challenge. In this paper, we propose a low-cost yet efficient approach to predict the economical pattern in a rural region from satellite imagery, which is globally available, in the meantime, satellite images are continuously updating to the changeable environmental conditions. Since the satellite image is way more advantageous than traditional methods, they play a huge role in the model. Traditional methods like surveys are expensive to conduct and include a lot of manpower. [2]This contributes to infer socioeconomic indicators from large-scale, remotely-sensed data. The available dataset is used to extract the luminous intensity from the night-time satellite images and added along with the other features of the particular region that determines the socio- economic conditions. Then with machine learning al

关键词： Forecasting

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