ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal最新文献_第7页

Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques 基于CovNet-DualLSTM技术的情感分类混合模型

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-02-08 DOI: 10.14201/adcaij202110401418

Roopesh Ranjan, A. Daniel

{"title":"Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques","authors":"Roopesh Ranjan, A. Daniel","doi":"10.14201/adcaij202110401418","DOIUrl":"https://doi.org/10.14201/adcaij202110401418","url":null,"abstract":"The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user’s review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user’s. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"5 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76386760","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

Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity 基于Jaccard系数和Dice系数相似度的k -均值乌尔都语新闻聚类

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-02-08 DOI: 10.14201/adcaij2021104381399

Zahida Rahman, Altaf Hussain, Hussain Shah, M. Arshad

{"title":"Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity","authors":"Zahida Rahman, Altaf Hussain, Hussain Shah, M. Arshad","doi":"10.14201/adcaij2021104381399","DOIUrl":"https://doi.org/10.14201/adcaij2021104381399","url":null,"abstract":"Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"39 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80410171","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

Review on recent Computer Vision Methods for Human Action Recognition 人体动作识别的计算机视觉方法综述

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-02-08 DOI: 10.14201/adcaij2021104361379

Azhee Wria Muhamada, A. Mohammed

{"title":"Review on recent Computer Vision Methods for Human Action Recognition","authors":"Azhee Wria Muhamada, A. Mohammed","doi":"10.14201/adcaij2021104361379","DOIUrl":"https://doi.org/10.14201/adcaij2021104361379","url":null,"abstract":"\u0000 \u0000 \u0000The subject of human activity recognition is considered an important goal in the domain of computer vision from the beginning of its development and has reached new levels. It is also thought of as a simple procedure. Problems arise in fast-moving and advanced scenes, and the numerical analysis of artificial intelligence (AI) through activity prediction mistreatment increased the attention of researchers to study. Having decent methodological and content related variations, several datasets were created to address the evaluation of these ways. Human activities play an important role but with challenging characteristic in various fields. Many applications exist in this field, such as smart home, helpful AI, HCI (Human-Computer Interaction), advancements in protection in applications such as transportation, education, security, and medication management, including falling or helping elderly in medical drug consumption. The positive impact of deep learning techniques on many vision applications leads to deploying these ways in video processing. Analysis of human behavior activities involves major challenges when human presence is concerned. One individual can be represented in multiple video sequences through skeleton, motion and/or abstract characteristics. This work aims to address human presence by combining many options and utilizing a new RNN structure for activities. The paper focuses on recent advances in machine learning-assisted action recognition. \u0000Existing modern techniques for the recognition of actions and prediction similarly because the future scope for the analysis is mentioned accuracy within the review paper. \u0000 \u0000 \u0000","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"7 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89748915","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: 2

Taking FANET to Next Level 让FANET更上一层楼

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-02-08 DOI: 10.14201/adcaij2021104321337

Altaf Hussain, Habib Ullah Khan, S. Nazir, Ijaz Ullah, T. Hussain

{"title":"Taking FANET to Next Level","authors":"Altaf Hussain, Habib Ullah Khan, S. Nazir, Ijaz Ullah, T. Hussain","doi":"10.14201/adcaij2021104321337","DOIUrl":"https://doi.org/10.14201/adcaij2021104321337","url":null,"abstract":"Flying Ad-hoc Network (FANET) is a special member/class of Mobile Ad-hoc Network (MANET) in which the movable nodes are known as by the name of Unmanned Aerial Vehicles (UAVs) that are operated from a long remote distance in which there is no human personnel involved. It is an ad-hoc network in which the UAVs can more in 3D ways simultaneously in the air without any onboard pilot. In other words, this is a pilot free ad-hoc network also known as Unmanned Aerial System (UAS) and the component introduced for such a system is known as UAV. There are many single UAV applications but using multiple UAVs system cooperating can be helpful in many ways in the field of wireless communication. Deployments of these small UAVs are quick and flexible which overcome the limitation of traditional ad hoc networks. FANETs differ from other kinds of ad hoc networks and envisioned to play an important role where infrastructure operations are not available and assigned tasks are too dull, dirty, or dangerous for humans. Moreover, setting up to bolster the range and performance of small UAV in ad hoc network lead to emergent evolution with its high stability, quick deployment, and ease-of-use for the formation of the network. Routing and task allocation are the challenging research areas of the network with ad hoc nodes. The paper overview based on the study of biological inspired routing protocols (Moth-and-Ant and Bee Ad-Hoc) routing protocols.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"44 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76839582","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

Distributed Artificial Intelligence: Third International Conference, DAI 2021, Shanghai, China, December 17–18, 2021, Proceedings 分布式人工智能:第三届国际会议，DAI 2021，中国上海，2021年12月17-18日，会议录

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2022-01-01 DOI: 10.1007/978-3-030-94662-3

引用次数: 0

Learning in AI Processor AI处理器中的学习

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2021-11-02 DOI: 10.30564/aia.v3i2.3878

Xinhua Wang, Weikang Wu

引用次数: 0

Connected and Autonomous Vehicles (CAVs) Challenges with Nonmotorized Amenities Environments 网联和自动驾驶汽车(cav)在非机动便利环境中的挑战

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2021-10-09 DOI: 10.30564/aia.v3i2.3651

Febronie Nambajemariya, Yongshun Wang, Twizerane Jean D’Amour, Kwizera Niyigena Vincent DePaul, Yao Hu

引用次数: 0

Projects Distribution Algorithms for Regional Development 区域发展的项目分配算法

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2021-10-05 DOI: 10.14201/adcaij2021103293305

M. Jemmali

引用次数: 13

Crime Detection Using Sentiment Analysis 基于情感分析的犯罪侦查

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2021-10-05 DOI: 10.14201/adcaij2021103281291

R. Khan, Shadab Siddiqui, Abhishek Rastogi

{"title":"Crime Detection Using Sentiment Analysis","authors":"R. Khan, Shadab Siddiqui, Abhishek Rastogi","doi":"10.14201/adcaij2021103281291","DOIUrl":"https://doi.org/10.14201/adcaij2021103281291","url":null,"abstract":"Women and girls have been subjected to a great deal of violence and harassment in public locations around the country, ranging from stalking to abuse harassment and assault. This research paper examines the role of social media in improving women's safety in Indian cities, with a focus on the use of social media websites and apps such as Twitter, Facebook, and Instagram. This research also looks at how ordinary Indians can develop a sense of responsibility in Indian society so that we can focus on the protection of women in their surroundings. Tweets on the safety of women in Indian cities, which often include images and text as well as written phrases and quotations, can be used to send a message to the Indian youth culture and encourage them to take harsh action and punish those who harass women. Twitter and other Twitter handles that feature hash tag messages are extensively used throughout the world as a channel for women to share their feelings about how they feel when going to work or travelling by public transportation and what is their mental condition when they are surrounded by unknown males, and do they feel safe or not?","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"196 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79875469","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

An Agent-Based Simulation to Explore Communication in a System to Control Urban Traffic with Smart Traffic Lights 基于agent的城市交通智能信号灯控制系统通信仿真研究

IF 1.4

ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2021-10-05 DOI: 10.14201/adcaij2021103209225

Marcos Antonio de Oliveira, R. Teixeira, R. Sousa, E. Gonçalves

引用次数: 2