i-manager's Journal on Computer Science最新文献_第5页

Sentiment analysis and offensive language detection in social media 社交媒体中的情感分析和攻击性语言检测

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/jcom.10.2.19145

M. Shiny

引用次数: 0

Optimized load balancing using software-defined networking (SDN) 使用软件定义网络(SDN)优化负载均衡

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/jcom.10.2.19046

Y. B. Genetu, Reddy P. V. G. D. Prasad

引用次数: 0

SIGNIFICANCE OF R IN RESEARCH r在研究中的意义

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/jcom.7.2.15639

G. Sateesh, B. Padmaja, M. Bhuvaneswari

引用次数: 0

A REVIEW ON STEGANOGRAPHY WITH LSB ALGORITHM LSB算法隐写技术研究进展

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/jcom.9.3.17199

K. Jyothsna, Teja D. RAVI, L. R. S. SAI, K. E. G. SURYA

引用次数: 0

YOGA POSE CLASSIFICATION USING RESNETOF DEEP LEARNING MODELS 使用resnetof深度学习模型的瑜伽姿势分类

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/jcom.9.2.18464

J. Lakshmi, K. Chetan

引用次数: 2

Efficient chatbot for complaint registration 高效的投诉注册聊天机器人

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/jcom.10.1.18507

S. Manoj, Mall Hitik, Choudhary Rahul, Khandelwal Abhijeet, Verma Saket

{"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}

引用次数: 0

ENHANCING WORDNET AGAINST OVERLAPPING RETURNS OF SENSES FOR EFFICIENT POLYSEMY REPRESENTATION IN ONTOLOGY DEVELOPMENT 增强词网，防止语义的重叠返回，在本体开发中实现高效的多义表示

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/JCOM.7.1.15720

Femi Aminu Enesi, A. Qasim, O. O. Ishaq, B. Muhammad, Tajudeen Salaudeen Muhammadu

引用次数: 0

BiDETECT: BiLSTM with BERT for hate speech detection in tweets BiDETECT:基于BERT的推文仇恨言论检测的BiLSTM

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/jcom.10.4.19334

Prakalya P. Alagu, Gaud Nirmal

{"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}

引用次数: 1

ANALYSIS ON MACHINE LEARNING TECHNIQUES 机器学习技术分析

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/jcom.7.3.16739

R. Karthiga, B. Keerthiga, S. Preethi

引用次数: 3

DATA VISUALIZATION OF COVID-19 AND ASSOCIATED DISEASES COVID-19和相关疾病的数据可视化

i-manager's Journal on Computer Science Pub Date : 1900-01-01 DOI: 10.26634/jcom.9.1.18406

Mahadevaswamy UDIGALA BASAVARAJU, Manjunath Chandini, N. Shivani, P. Swathi

{"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}

引用次数: 0