Pub Date : 1900-01-01DOI: 10.26634/jcom.10.2.19145
M. Shiny
Sentiment Analysis is a field of study that focuses on figuring out how to extract, identify, or otherwise describe emotions in units of written text. One of the most common tasks in sentiment analysis is finding the polarity of a person's feelings. There are many blog posts, tweets, and comments in Indian languages online these days. Sentiment analysis in Indian languages is a relatively new field, and research in this area is just beginning. There is a lot of offensive content on social media, which is a worry for businesses and government agencies. This paper presents the methodology of sentiment analysis and offensive language detection in social media.
{"title":"Sentiment analysis and offensive language detection in social media","authors":"M. Shiny","doi":"10.26634/jcom.10.2.19145","DOIUrl":"https://doi.org/10.26634/jcom.10.2.19145","url":null,"abstract":"Sentiment Analysis is a field of study that focuses on figuring out how to extract, identify, or otherwise describe emotions in units of written text. One of the most common tasks in sentiment analysis is finding the polarity of a person's feelings. There are many blog posts, tweets, and comments in Indian languages online these days. Sentiment analysis in Indian languages is a relatively new field, and research in this area is just beginning. There is a lot of offensive content on social media, which is a worry for businesses and government agencies. This paper presents the methodology of sentiment analysis and offensive language detection in social media.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124605076","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}
Pub Date : 1900-01-01DOI: 10.26634/jcom.10.2.19046
Y. B. Genetu, Reddy P. V. G. D. Prasad
In a cloud environment, handling user service requests and providing the requested resources fairly is critical. Load balancing is important to distribute service requests fairly to an unloaded server and to dynamically maintain load balancing across server farms. In conventional internet protocol (IP) networks, maintaining load balancing is a stubborn and non-adaptable task due to the forwarders' lack of global topology representation. Software-Defined Networking (SDN) provides centralized decision-making for any topological changes to manage changes dynamically. To solve the above problem, we propose a new server-side load balancing strategy that provides an efficient and effective server management scheme for SDN Open Flow networks. Experiments were done on the Ryu controller and the Mininet emulator showed that the performance was better than what was already available.
在云环境中，处理用户服务请求并公平地提供所请求的资源至关重要。负载平衡对于将服务请求公平地分发到未加载的服务器以及动态地维护服务器群之间的负载平衡非常重要。在传统的互联网协议(IP)网络中，由于转发器缺乏全局拓扑表示，维持负载平衡是一项顽固的、不可适应的任务。软件定义网络(SDN)为任何拓扑更改提供集中决策，以动态管理更改。为了解决上述问题，我们提出了一种新的服务器端负载均衡策略，为SDN Open Flow网络提供了一种高效的服务器管理方案。在Ryu控制器和Mininet仿真器上进行了实验，结果表明该控制器的性能优于现有的控制器。
{"title":"Optimized load balancing using software-defined networking (SDN)","authors":"Y. B. Genetu, Reddy P. V. G. D. Prasad","doi":"10.26634/jcom.10.2.19046","DOIUrl":"https://doi.org/10.26634/jcom.10.2.19046","url":null,"abstract":"In a cloud environment, handling user service requests and providing the requested resources fairly is critical. Load balancing is important to distribute service requests fairly to an unloaded server and to dynamically maintain load balancing across server farms. In conventional internet protocol (IP) networks, maintaining load balancing is a stubborn and non-adaptable task due to the forwarders' lack of global topology representation. Software-Defined Networking (SDN) provides centralized decision-making for any topological changes to manage changes dynamically. To solve the above problem, we propose a new server-side load balancing strategy that provides an efficient and effective server management scheme for SDN Open Flow networks. Experiments were done on the Ryu controller and the Mininet emulator showed that the performance was better than what was already available.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"36 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128230347","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}
{"title":"SIGNIFICANCE OF R IN RESEARCH","authors":"G. Sateesh, B. Padmaja, M. Bhuvaneswari","doi":"10.26634/jcom.7.2.15639","DOIUrl":"https://doi.org/10.26634/jcom.7.2.15639","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127191246","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}
K. Jyothsna, Teja D. RAVI, L. R. S. SAI, K. E. G. SURYA
{"title":"A REVIEW ON STEGANOGRAPHY WITH LSB ALGORITHM","authors":"K. Jyothsna, Teja D. RAVI, L. R. S. SAI, K. E. G. SURYA","doi":"10.26634/jcom.9.3.17199","DOIUrl":"https://doi.org/10.26634/jcom.9.3.17199","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132765455","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}
{"title":"YOGA POSE CLASSIFICATION USING RESNET\u0000OF DEEP LEARNING MODELS","authors":"J. Lakshmi, K. Chetan","doi":"10.26634/jcom.9.2.18464","DOIUrl":"https://doi.org/10.26634/jcom.9.2.18464","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132360176","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}
Pub Date : 1900-01-01DOI: 10.26634/jcom.10.1.18507
S. Manoj, Mall Hitik, Choudhary Rahul, Khandelwal Abhijeet, Verma Saket
This is a web application designed to manage all police stations in the area. The system consists of four modules, such as the admin module, investigator module, user module, and visitor module. The user is the one who registers the complaint after logging in. The visitor is a user without registration. Visitors can view live news, helpline numbers, and a list of missing persons. In the area, all police stations are managed and administrated by the Superintendent of Police (SP) in this application. Complaints will be taken by the investigator (Police Sub Inspector (PSI)) of the police station entered by the user. If the investigator cannot resolve this complaint, they may forward this complaint to the administrator. The Administrator manages the complaints sent by the investigator and assigns the cases to a higher investigator such as a Circle Inspector (CI), Deputy Superintendent of Police (DSP), etc., and the admin will manage the police stations working under its control. The investigator processes the cases assigned by the administrator and resolves it. Conversational interfaces, also called chatbots, are the best and newest way to get people involved in working with computer systems. Optimizing the use of chatbots between services and people enhances the customer experience. At the same time, companies are being given new opportunities to improve operational efficiency and the customer experience process by reducing typical customer service costs. To be successful, a chatbot must perform these tasks effectively. Human support is needed when approaching. Human intervention is critical to optimizing, customizing, and training a chatbot system.
{"title":"Efficient chatbot for complaint registration","authors":"S. Manoj, Mall Hitik, Choudhary Rahul, Khandelwal Abhijeet, Verma Saket","doi":"10.26634/jcom.10.1.18507","DOIUrl":"https://doi.org/10.26634/jcom.10.1.18507","url":null,"abstract":"This is a web application designed to manage all police stations in the area. The system consists of four modules, such as the admin module, investigator module, user module, and visitor module. The user is the one who registers the complaint after logging in. The visitor is a user without registration. Visitors can view live news, helpline numbers, and a list of missing persons. In the area, all police stations are managed and administrated by the Superintendent of Police (SP) in this application. Complaints will be taken by the investigator (Police Sub Inspector (PSI)) of the police station entered by the user. If the investigator cannot resolve this complaint, they may forward this complaint to the administrator. The Administrator manages the complaints sent by the investigator and assigns the cases to a higher investigator such as a Circle Inspector (CI), Deputy Superintendent of Police (DSP), etc., and the admin will manage the police stations working under its control. The investigator processes the cases assigned by the administrator and resolves it. Conversational interfaces, also called chatbots, are the best and newest way to get people involved in working with computer systems. Optimizing the use of chatbots between services and people enhances the customer experience. At the same time, companies are being given new opportunities to improve operational efficiency and the customer experience process by reducing typical customer service costs. To be successful, a chatbot must perform these tasks effectively. Human support is needed when approaching. Human intervention is critical to optimizing, customizing, and training a chatbot system.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115000702","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}
Femi Aminu Enesi, A. Qasim, O. O. Ishaq, B. Muhammad, Tajudeen Salaudeen Muhammadu
{"title":"ENHANCING WORDNET AGAINST OVERLAPPING RETURNS OF\u0000 SENSES FOR EFFICIENT POLYSEMY REPRESENTATION IN\u0000 ONTOLOGY DEVELOPMENT","authors":"Femi Aminu Enesi, A. Qasim, O. O. Ishaq, B. Muhammad, Tajudeen Salaudeen Muhammadu","doi":"10.26634/JCOM.7.1.15720","DOIUrl":"https://doi.org/10.26634/JCOM.7.1.15720","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115488187","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}
Pub Date : 1900-01-01DOI: 10.26634/jcom.10.4.19334
Prakalya P. Alagu, Gaud Nirmal
The utilization of online platforms for spreading hate speech has become a major concern. The conventional techniques used to identify hate speech, such as relying on keywords and manual moderation, frequently fall short and can lead to either missed detections or incorrect identifications. In response, researchers have developed various deeplearning strategies for locating hate speech in text. This paper covers a wide range of Deep Learning approaches, encompassing Convolutional Neural Networks and especially transformer-based models. It also discusses the key factors that influence the performance of these methods, such as the choice of datasets, the use of pre-processing strategies, and the design of the model architecture. In conjunction with summarizing existing research, it also identifies a selection of key hurdles and limitations of Deep Learning for discovering hate speech and has proposed a novel method to overcome them. In Bidirectional Long Short-Term Memory and BERT for Hate Speech Detection (BiDETECT), which involves adding a Bidirectional Long Short-Term Memory (BiLSTM) layer to Bidirectional Encoder Representations from Transformers (BERT) for classification, the hurdles include the difficulties in defining hate speech, the limitations of current datasets, and the challenges of generalizing models to new domains. It also discusses the ethical implications of employing Deep Learning to pinpoint hate speech and the need for responsible and transparent research in this area.
{"title":"BiDETECT: BiLSTM with BERT for hate speech detection in tweets","authors":"Prakalya P. Alagu, Gaud Nirmal","doi":"10.26634/jcom.10.4.19334","DOIUrl":"https://doi.org/10.26634/jcom.10.4.19334","url":null,"abstract":"The utilization of online platforms for spreading hate speech has become a major concern. The conventional techniques used to identify hate speech, such as relying on keywords and manual moderation, frequently fall short and can lead to either missed detections or incorrect identifications. In response, researchers have developed various deeplearning strategies for locating hate speech in text. This paper covers a wide range of Deep Learning approaches, encompassing Convolutional Neural Networks and especially transformer-based models. It also discusses the key factors that influence the performance of these methods, such as the choice of datasets, the use of pre-processing strategies, and the design of the model architecture. In conjunction with summarizing existing research, it also identifies a selection of key hurdles and limitations of Deep Learning for discovering hate speech and has proposed a novel method to overcome them. In Bidirectional Long Short-Term Memory and BERT for Hate Speech Detection (BiDETECT), which involves adding a Bidirectional Long Short-Term Memory (BiLSTM) layer to Bidirectional Encoder Representations from Transformers (BERT) for classification, the hurdles include the difficulties in defining hate speech, the limitations of current datasets, and the challenges of generalizing models to new domains. It also discusses the ethical implications of employing Deep Learning to pinpoint hate speech and the need for responsible and transparent research in this area.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120958974","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}
{"title":"ANALYSIS ON MACHINE LEARNING TECHNIQUES","authors":"R. Karthiga, B. Keerthiga, S. Preethi","doi":"10.26634/jcom.7.3.16739","DOIUrl":"https://doi.org/10.26634/jcom.7.3.16739","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115844347","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}
Mahadevaswamy UDIGALA BASAVARAJU, Manjunath Chandini, N. Shivani, P. Swathi
The novel coronavirus disease (COVID-19) has currently affected millions of people, claiming more than 4,000,000 lives all over the world. Several dashboards have been created to analyze the present situation and get a better grasp of the current status of COVID-19. As the situation unfolded, infections caused by species of fungi, Mucormycosis (commonly called black fungus), have affected patients treated for COVID-19. Therefore, to facilitate information and to create awareness, it would be better to have a dashboard that display trends and data on COVID-19 and associated related diseases. In the proposed work, a dashboard has been created to visualize how COVID-19 epidemic has an impact in the global scenario. With the present work, the spread of diseases associated with COVID-19 (fungi variants) can be visualized. The data visualization is performed using Python. The tool kits and packages used for this purpose is Dash by Plotly. The acquired data is classified and filtered with interesting criteria in the ranging process stage. Using specific tools, data representations like line chart, bubble map, heat map, choropleths, tree map and, folium map are plotted to visualize the data.
新型冠状病毒病(COVID-19)目前已影响数百万人，夺去了全世界400多万人的生命。已经创建了几个仪表板来分析目前的情况，并更好地掌握COVID-19的现状。随着形势的发展，由毛霉病(通常称为黑菌)真菌引起的感染已影响到接受COVID-19治疗的患者。因此，为了方便信息和提高认识，最好有一个仪表板，显示COVID-19和相关疾病的趋势和数据。在拟议的工作中，创建了一个仪表板，以可视化COVID-19流行病如何对全球情景产生影响。通过目前的工作，可以可视化与COVID-19(真菌变体)相关的疾病的传播。数据可视化是使用Python执行的。用于此目的的工具包和包是Dash by Plotly。在测距处理阶段，对采集到的数据进行分类和过滤。使用特定的工具，绘制数据表示，如折线图、气泡图、热图、枝叶图、树图和叶图，以使数据可视化。
{"title":"DATA VISUALIZATION OF COVID-19 AND ASSOCIATED DISEASES","authors":"Mahadevaswamy UDIGALA BASAVARAJU, Manjunath Chandini, N. Shivani, P. Swathi","doi":"10.26634/jcom.9.1.18406","DOIUrl":"https://doi.org/10.26634/jcom.9.1.18406","url":null,"abstract":"The novel coronavirus disease (COVID-19) has currently affected millions of people, claiming more than 4,000,000 lives all over the world. Several dashboards have been created to analyze the present situation and get a better grasp of the current status of COVID-19. As the situation unfolded, infections caused by species of fungi, Mucormycosis (commonly called black fungus), have affected patients treated for COVID-19. Therefore, to facilitate information and to create awareness, it would be better to have a dashboard that display trends and data on COVID-19 and associated related diseases. In the proposed work, a dashboard has been created to visualize how COVID-19 epidemic has an impact in the global scenario. With the present work, the spread of diseases associated with COVID-19 (fungi variants) can be visualized. The data visualization is performed using Python. The tool kits and packages used for this purpose is Dash by Plotly. The acquired data is classified and filtered with interesting criteria in the ranging process stage. Using specific tools, data representations like line chart, bubble map, heat map, choropleths, tree map and, folium map are plotted to visualize the data.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116464279","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}
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