International Journal of Next-Generation Computing最新文献_第9页

Improved League Championship Algorithm (ILCA) for Load Balancing in Cloud Computing 基于改进联赛冠军算法(ILCA)的云计算负载均衡

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.930

A. Gaikwad, Kavita Singh

{"title":"Improved League Championship Algorithm (ILCA) for Load Balancing in Cloud Computing","authors":"A. Gaikwad, Kavita Singh","doi":"10.47164/ijngc.v13i5.930","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.930","url":null,"abstract":"You can’t obtain the outcomes you need without planning, thus it’s at the heart of cloud computing. This article’s major goal is to decrease value-added time, increase resource utilisation, and make cloud services viable for a single activity. In recent years, metaheuristic algorithms have drew attention to the correct functioning of work scheduling algorithms among the many job scheduling techniques. With sports leagues, the algorithm based on the League Championship (LCA) is fascinating because it can be used to identify the best team/task for programming.This article uses the Improved League Championship Algorithm (ILCA) to schedule tasks, reducing deployment time, cloud usage, and cost. The ILCA is implemented through the Cloudsim simulator and the Java programming language with a nonpreventive planning strategy. ILCA also enhances economies of scale and minimises the value of using the cloud. As it has proven to be versatile in terms of time to manufacture, resource usage and economics, ILCA could be a good candidate for a cloud broker as it has proven to be versatile in termsof time to manufacture, resource usage and economics usage.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"50 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90593897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lost + Found: The Lost Angel Investigator 失物招领:失落的天使调查员

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.906

Harsh Shrirame, Bhavesh Kewalramani, Daksh Kothari, Darshan Jawandhiya, Rina Damdoo

{"title":"Lost + Found: The Lost Angel Investigator","authors":"Harsh Shrirame, Bhavesh Kewalramani, Daksh Kothari, Darshan Jawandhiya, Rina Damdoo","doi":"10.47164/ijngc.v13i5.906","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.906","url":null,"abstract":"Each year, a large number of youngsters are found missing in India. Among them, a large number of cases are never solved due to various difficulties faced by the police ranging from heavy paperwork to lacking technology. Therefore, one of this work’s key goals is to provide an application that may assist people whose children have been missing and rescued by the public. This will also reduce the time required to find the missing child to reunite the child with their loved ones as soon as possible. The pictures of child victims can be uploaded by the citizens along with landmarks, to our web app. The photographs will be matched to the missing child’s registered photographs if existing in the database. A deep neural network model is trained to locate the lost youngster using a facial picture uploaded by the citizens. Multi-Tasking CNN (MTCNN), the most efficient DNN technique for image-based apps, is used for facial Identification. The images were passed through an augmentation layer to get images of different orientations, brightness, and contrast, which were used ahead to train the EfficientNetB0 model. This model is then used to recognize faces in photographs. Using the MTCNN model for facial recognition with EfficientNetB0 and developing it yields a deep learning model that is free from all types of distortion. The model’s training accuracy is 96.66 percent and its testing accuracy is 76.81 percent, implying that there is approximately 77 percent possibility of finding a match for the missing kid. It was evaluated using 25 Child classes. Each Child class has around 15 to 20 images. These images are taken with different backgrounds and real-time settings so that model will work even when noise is present in the image.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"51 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90850833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart College Campus Recruitment System 智慧高校校园招聘系统

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.970

Dr. Purshottam J. Assudani, Dr. Rakesh K. Kadu, Rizwan Sheikh, Tushar Khanna

引用次数: 1

Novel approach to Create Human Faces with DCGAN for Face Recognition 基于DCGAN的人脸识别新方法

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.936

Roshni Khedgaonkar, Kavita Singh, Sunny Mate

引用次数: 0

Energy Aware Job Scheduling and Simulation in a Cloud Datacenter 云数据中心中能源感知作业调度与仿真

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.950

Purushottam Assudani, Mehvash Khan, Mukesh Kumar, Tejas V. Bhutada

引用次数: 0

PLAGIARISM DETECTION IN PROGRAMMING USING PERFORMANCE ANALYZING FEATURES 基于性能分析特征的编程剽窃检测

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.964

D.S. Adane, Abhishek Angale, Ayush Singh, Rituj Aryan, Sumeet Yadav

引用次数: 1

Object Detection using Speech Recognition 使用语音识别的目标检测

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.974

Chetana B. Thaokar, Gayatri Ladsawangikar, Tanaya Wadibhasme, Sandeep Sureka

引用次数: 0

Voice Based Authentication System for Web Applications using Machine Learning 使用机器学习的基于语音的Web应用程序认证系统

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.966

Rakesh K Kadu, Purshottam J Assudani, Sahil Bhojane, Tanish Agrawal, Vidhi Siddhawar, Yash Kale

引用次数: 0

Detection of Diseases in Tomato Plant using Machine Learning 基于机器学习的番茄病害检测

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International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.941

Anshul Sharma, Ashish Chandak, Aryan Khandelwal, Raunak Gandhi

{"title":"Detection of Diseases in Tomato Plant using Machine Learning","authors":"Anshul Sharma, Ashish Chandak, Aryan Khandelwal, Raunak Gandhi","doi":"10.47164/ijngc.v13i5.941","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.941","url":null,"abstract":"A major part of the Indian economy relies on agriculture, thus identification of any diseased crop in the initial phase is very important as these diseases cause a significant drop in agricultural production and also affect the economy of the country. Tomato crops are susceptible to various diseases which may be caused due to transmission of diseases through Air or Soil. We have tried to automate the procedure of detection of diseases in the Tomato Plant by studying several attributes related to the leaf of the plant. Using various machine learning algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), ResNet, and InceptionV3 we have trained the model, and based on the results obtained we have evaluated and compared the performance of these algorithms on different features set. For the dataset we had 10 classes (healthy and other unhealthy classes) having a total of 18,450 images for the training of the models. After implementing all of the algorithms and comparing their results we found that the ResNet was most appropriate for extracting distinct attributes from images. The trained models can be used to detect diseases in Tomato Plant timely and automatically.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"51 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90777345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Salp Swarm Optimization-based Fuzzy Centroid Region Growing for Liver Tumor Segmentation and Deep Learning Oriented Classification 基于改进Salp群优化的模糊质心区域生长的肝脏肿瘤分割和面向深度学习的分类

IF 0.3

International Journal of Next-Generation Computing Pub Date : 2022-11-26 DOI: 10.47164/ijngc.v13i5.902

Ramchand Hablani, Suraj Patil, Dnyaneshwar Kirange

{"title":"Improved Salp Swarm Optimization-based Fuzzy Centroid Region Growing for Liver Tumor Segmentation and Deep Learning Oriented Classification","authors":"Ramchand Hablani, Suraj Patil, Dnyaneshwar Kirange","doi":"10.47164/ijngc.v13i5.902","DOIUrl":"https://doi.org/10.47164/ijngc.v13i5.902","url":null,"abstract":"Due to heterogenous shape of liver, the segmentation and classification of liver is challenging task. Therefore, Computer-Aided Diagnosis (CAD) is employed for predictive decision making for liver diagnosis. The major intuition of this paper is to detect liver cancer in a precise manner by automatic approach. The developed model initially collects the standard benchmark LiTS dataset, and image preprocessing is done by three techniques like Histogram equalization for contrast enhancement, and median filtering and Anisotropic diffusion filtering for noise removal. Further, the Adaptive thresholding is adopted to perform the liver segmentation. As a novelty, optimized Fuzzy centroid-based region growing model is proposed for tumor segmentation in liver. The main objective of thistumor segmentation model is to maximize the entropy by optimizing the fuzzy centroid and threshold of region growing using Mean Fitness-based Salp Swarm Optimization Algorithm (MF-SSA). From segmented tumor, the features like Local Directional Pattern (LDP) and Gray Level Co-occurrence Matrix (GLCM) are extracted. The extracted features are given as input to NN, and segmented tumor is given to Convolutional Neural Network (CNN). The AND bit operation to both of the outputs obtained from NN and CNN confirms the healthy and unhealthy CT images. Since the number of hidden neurons makes an effect on final classification output, the optimization of neurons is done using MF-SSA. From the experimental analysis, it is confirmed that the proposed model is better as compared with state of art results of previous study can assist radiologists in tumor diagnosis from CT scan images.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"49 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80950957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0