International Journal of Next-Generation Computing最新文献_第3页

Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm 基于改进企鹅搜索优化算法的云任务调度

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International Journal of Next-Generation Computing Pub Date : 2023-03-31 DOI: 10.47164/ijngc.v14i2.831

Tarun Kumar Ghosh, Krishna Gopal Dhal, Sanjoy Das

{"title":"Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm","authors":"Tarun Kumar Ghosh, Krishna Gopal Dhal, Sanjoy Das","doi":"10.47164/ijngc.v14i2.831","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.831","url":null,"abstract":"The cloud computing has emerged as a novel distributed computing system in past few years. It provides computation and resources over the Internet via dynamic provisioning of services. There are quite a few challenges and issues connected with implementation of cloud computing. This paper considers one of its major problems, i.e. task scheduling. The function of an efficient task scheduling algorithm is that it concentrates not only on attaining the requirements of the user but also in enhancing the efficiency of the cloud computing system. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. This paper proposes a modified Penguins Search Optimization Algorithm (MPeSOA) for efficient cloud task scheduling. The main contribution of our work is to schedule all tasks to available virtual machines so that the makespan is minimized, resource utilization is increased and the degree of imbalance is reduced. The proposed scheduling algorithm was simulated using the CloudSim 4.0 simulator. Experimental results showed that the proposed MPeSOA outperformed three existing meta-heuristics, namely Penguins Search Optimization Algorithm (PeSOA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"104 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80470251","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

Comparative Analysis of Deep Learning based Vehicle Detection Approaches 基于深度学习的车辆检测方法比较分析

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International Journal of Next-Generation Computing Pub Date : 2023-03-31 DOI: 10.47164/ijngc.v14i2.976

Nikita Singhal, Lalji Prasad

引用次数: 0

Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN 基于递归神经网络和KNN的脑电信号情感分类

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International Journal of Next-Generation Computing Pub Date : 2023-03-31 DOI: 10.47164/ijngc.v14i2.691

Shashank Joshi, Falak Joshi

{"title":"Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN","authors":"Shashank Joshi, Falak Joshi","doi":"10.47164/ijngc.v14i2.691","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.691","url":null,"abstract":"In human contact, emotion is very crucial. Attributes like words, voice intonation, facial expressions, and kinesics can all be used to portray one's feelings. However, brain-computer interface (BCI) devices have not yet reached the level required for emotion interpretation. With the rapid development of machine learning algorithms, dry electrode techniques, and different real-world applications of the brain-computer interface for normal individuals, emotion categorization from EEG data has recently gotten a lot of attention. Electroencephalogram (EEG) signals are a critical resource for these systems. The primary benefit of employing EEG signals is that they reflect true emotion and are easily resolved by computer systems. In this work, EEG signals associated with good, neutral, and negative emotions were identified using channel selection preprocessing. However, researchers had a limited grasp of the specifics of the link between various emotional states until now. To identify EEG signals, we used discrete wavelet transform and machine learning techniques such as recurrent neural network (RNN) and k-nearest neighbor (kNN) algorithm. Initially, the classifier methods were utilized for channel selection. As a result, final feature vectors were created by integrating the features of EEG segments from these channels. Using the RNN and kNN algorithms, the final feature vectors with connected positive, neutral, and negative emotions were categorized independently. The classification performance of both techniques is computed and compared. Using RNN and kNN, the average overall accuracies were 94.844 % and 93.438 %, respectively.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"15 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85976806","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: 1

Enhancing Object Mapping in SLAM using CNN 利用CNN增强SLAM中的对象映射

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International Journal of Next-Generation Computing Pub Date : 2023-03-31 DOI: 10.47164/ijngc.v14i2.566

Rakesh Singh, Radhika Kotecha, Karan Shethia

{"title":"Enhancing Object Mapping in SLAM using CNN","authors":"Rakesh Singh, Radhika Kotecha, Karan Shethia","doi":"10.47164/ijngc.v14i2.566","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.566","url":null,"abstract":"Automation is becoming more prevalent among manufacturing and eCommerce companies as a way to better serve their customers. One of the key problems in warehouse management is controlling the internal delivery/movement of goods/objects. It is labor-intensive, time-consuming, and needs additional care based on delicacy goods. Automated guided vehicles (AGVs) that are small in size can serve as a solution to the aforementioned problem of locomotion. For any robot to move autonomously, the initial and critical requirement is to understand the surrounding environment precisely. Simultaneous Localisation and Mapping (SLAM) is the preferred method to build an environment map at runtime. SLAM is designed to work in a static environment and faces a few challenges once it involves dynamic objects. This research proposes Deep Learning to enhance the SLAM technique. It aids the identification of static and dynamic objects and consequently updates the occupancy grid map. The proposed approach has been validated through a simulated environment and a Convolution Neural Network (CNN) for the classification of static and dynamic objects. The simulation results demonstrate the promising nature of the proposed approach.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"22 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87451332","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

Remote Sensing Based Land Cover Classification Using Machine Learning and Deep Learning: A Comprehensive Survey 基于机器学习和深度学习的遥感土地覆盖分类综述

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International Journal of Next-Generation Computing Pub Date : 2023-03-31 DOI: 10.47164/ijngc.v14i2.1137

Soma Mitra, Dr. Saikat Basu

{"title":"Remote Sensing Based Land Cover Classification Using Machine Learning and Deep Learning: A Comprehensive Survey","authors":"Soma Mitra, Dr. Saikat Basu","doi":"10.47164/ijngc.v14i2.1137","DOIUrl":"https://doi.org/10.47164/ijngc.v14i2.1137","url":null,"abstract":"Since the 1990s, remote sensing images have been used for land cover classification combined with MachineLearning algorithms. The traditional land surveying method only works well in places that are hard to get to, likehigh mountain regions, arid and semi-arid land, and densely forested areas. As the satellites and airborne sensorspass over a specific point of land surface periodically, it is possible to assess the change in land cover over a longtime. With the advent of ML methods, automated land cover classification has been at the center of researchfor the last few decades. From 2015 forward, a technical shift has been noticed with the emergence of severalbranches of Neural Networks (NN) and Deep Learning (DL). This paper examines current practices, problems,and trends in satellite image processing. This formal review focused on the summarization of major classificationapproaches from 1995. Two dominant research trends have been noticed in automated land cover classification,e.g., per pixel and subpixel analysis. Classical machine learning algorithms and deep learning methods are mainlyused for per-pixel analysis, whereas Fuzzy algorithms are used for sub-pixel analysis. The current article includesthe research gap in automated land cover classification to provide comprehensive guidance for subsequent researchdirection.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"32 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81186119","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

Sentence Generator for English Language using Formal Semantics 基于形式语义的英语句子生成器

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International Journal of Next-Generation Computing Pub Date : 2023-02-15 DOI: 10.47164/ijngc.v14i1.1090

Ankita Gore, Vanshika Bajaj, Preeti Yadav, Vaishnavi Chouhan, Madhuri A. Tayal, M. S. Kumar

{"title":"Sentence Generator for English Language using Formal Semantics","authors":"Ankita Gore, Vanshika Bajaj, Preeti Yadav, Vaishnavi Chouhan, Madhuri A. Tayal, M. S. Kumar","doi":"10.47164/ijngc.v14i1.1090","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1090","url":null,"abstract":"Natural Language Processing (NLP), is more specifically the branch of ”artificial intelligence” (AI) concerned with providing computers the ability to comprehend spoken and written language in a manner similar to that of humans. It is used for practical purposes to help connects us with everyday activities such as texting, emailing, and cross-language communication. The requirement for intelligent systems that can read a text and listen to voice memos and can converse with people in a natural language like English has substantially increased in recent years. In this paper, the random clausal sentence generator which is simple, compound, and complex sentences are described. This random sentence generation is beneficial for students studying on online platforms to learn clauses as they will get a variety of exercises to practice. Initially, simple sentences get generated and subsequently moved on to compound sentence and complex sentence generation. In this method, roughly hundredverbs are used to get varied randomness along with 3-4 conjunctions and objects which nearly fit with the verbs and give a syntactically and semantically meaningful sentence as the outcome.","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"68 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90365244","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

Optical Cup and Disc Segmentation using Deep Learning Technique for Glaucoma Detection 基于深度学习技术的青光眼光学杯盘分割

IF 0.3

International Journal of Next-Generation Computing Pub Date : 2023-02-15 DOI: 10.47164/ijngc.v14i1.1017

P. Parkhi, Bhagyashree Hambarde Hambarde

{"title":"Optical Cup and Disc Segmentation using Deep Learning Technique for Glaucoma Detection","authors":"P. Parkhi, Bhagyashree Hambarde Hambarde","doi":"10.47164/ijngc.v14i1.1017","DOIUrl":"https://doi.org/10.47164/ijngc.v14i1.1017","url":null,"abstract":"The optic nerve damaging condition called Glaucoma. This disease is increment at an alarming rate. By the end of the 2044 there is possibility that across 111.8 million populations will be influenced by glaucoma. It is a neurodegenerative disease. If intravascular pressure is increases, optic nerve of the eye gets damage. This damage may cause permanent or total blindness in person. The Glaucoma is examined by an experienced ophthalmologist on the retinal part of the eye. This process required excessive equipment, experienced medical practitioners and also it take more time to work out manually. After considering this problem there is an extreme requirement of developing an automatic system which will effectively and automatically work properly in lack of any professional doctor and it should also take less time. Lots of different parameters are available to detect glaucoma but thebest parameter is to find out optical cup-to-disc-ratio. To increase or to enhance the precision and accuracy of the result, cup to disc value is needed to find CDR value. In order to detect glaucoma, automatic separation of the OC and DC is very essential to avoid any error. We use deeplabv3 architecture to perform segmentation of optic disc and cup and classification is done using ensemble machine learning. This proposes research achieve intersection over union (IOU) scores, 0.9423 for optic disc and 0.9310 for optic cup. We perform testing on globally accessible data-sets i.e. DRISHTI, ORIGA, and RIMONE with accuracy of 93%, 91% and 92% respectively","PeriodicalId":42021,"journal":{"name":"International Journal of Next-Generation Computing","volume":"50 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74781781","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: 1

Diet Recommendation Model Using Multi Constraint Metaheuristic and Knapsack Optimization Algorithm. 基于多约束元启发式和背包优化算法的饮食推荐模型。

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International Journal of Next-Generation Computing Pub Date : 2023-02-15 DOI: 10.47164/ijngc.v14i1.1000

Leena K. Gautam, V. Gulhane

引用次数: 1

Sign Language Gesture to Speech Conversion Using Convolutional Neural Network 用卷积神经网络进行手语手势到语音的转换

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International Journal of Next-Generation Computing Pub Date : 2023-02-15 DOI: 10.47164/ijngc.v14i1.999

Shreya Tope, Sadnyani Gomkar, Pukhraj Rathkanthiwar, Aayushi Ganguli, P. Selokar

引用次数: 0

Denoising Of Digital Images Using Cyclespinning Algorithm With Shifted DWT 基于移位DWT的循环旋转算法的数字图像去噪

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International Journal of Next-Generation Computing Pub Date : 2023-02-15 DOI: 10.47164/ijngc.v14i1.1098

Bhumika Neole

引用次数: 0