2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)最新文献_第4页

Discriminative Deep Association Learning based on the Optimized Feature Analysis Adaptive Spider Foraging Model for twitter sentiment analysis 基于优化特征分析自适应蜘蛛觅食模型的判别深度关联学习微博情感分析

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10151412

Lalit Khanna

{"title":"Discriminative Deep Association Learning based on the Optimized Feature Analysis Adaptive Spider Foraging Model for twitter sentiment analysis","authors":"Lalit Khanna","doi":"10.1109/ICDCECE57866.2023.10151412","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10151412","url":null,"abstract":"The sentiment aspect is an important discussion in social media to discuss various forums like the product, social events, monuments, etc. All over, social media is the most dominant factor in discussion forums user comments as tweets be problematic analyses. Due to increasing sarcasm in social media terms contain sentiment terms and behaviors of users, the importance of features in data analyses needs more deep evaluation to improve the accuracy. To propose a Discriminative Deep Association Learning based on the Optimized Feature analysis Adaptive Spider Foraging Model (ASFM) to predict the occurrence of the event in social media terms. The method utilizes the tweets and messages generated from a social network with Tweet term features. Initially, the progress begins with the preprocessing of the social media terms and Tweet term facts to identify the features. Because of the sentimental side of sarcasm, the Semantic Entropy Vector Transformation model detects both sarcasm and non-sarcasm weights as features. Social foraging models identify optimal features based on fitness weights. The tweets and the structure of tweet words are analyzed and grouped into classes based on a semantic ontology process.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131780158","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

Congestion Aware Traffic Prediction System Based on Pipelined Time Variant Feature Selection for Improving Transportation of Real Time Service 基于流水线时变特征选择的拥堵感知交通预测系统改进实时服务交通

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10150903

Pooja Sharma

引用次数: 0

Mining Twitter for Insights into ChatGPT Sentiment: A Machine Learning Approach 挖掘Twitter洞察ChatGPT情绪:一种机器学习方法

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10150620

Shivam Sharma, Rahul Aggarwal, M. Kumar

{"title":"Mining Twitter for Insights into ChatGPT Sentiment: A Machine Learning Approach","authors":"Shivam Sharma, Rahul Aggarwal, M. Kumar","doi":"10.1109/ICDCECE57866.2023.10150620","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150620","url":null,"abstract":"In the past few years, ChatGPT has evolved into a powerful N.L.P. technology, with applications ranging from text generation to question resolution. However, there is still relatively little research on how the public perceives this technology. In this research, we use sentiment analysis techniques to assess the sentiment of tweets regarding ChatGPT. Users manually categorized a dataset of tweets mentioning ChatGPT as positive, negative, or indifferent based on their attitude. The overall sentiment of the tweets was therefore directly determined utilizing machine learning models including logistic regression and support vector machines. Our results show that the majority of tweets related to ChatGPT are neutral, while a smaller proportion are positive or negative. We also found that certain words and phrases, such as \"AI\" and \"language model\", are strongly associated with positive sentiment, while others, such as \"bias\" and \"privacy\", are associated with negative sentiment. These findings have important implications for the development and deployment of ChatGPT and other NLP technologies, as they suggest that public perception is influenced by factors such as trust, transparency, and ethical considerations. Overall, this paper highlights the importance of understanding public sentiment towards emerging technologies like ChatGPT, and the potential of sentiment analysis techniques to shed light on these issues","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133081835","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

A Prediction of Water Quality Analysis Using Machine Learning 利用机器学习预测水质分析

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10150940

Suma S, Rohit Moon, Mohammed Umer, K. S. Raju, Nuthanakanti Bhaskar, Rakshita Okali

引用次数: 0

An Innovation Development of Routing for Smart Data Traffic Environment in Data Mining 数据挖掘中智能数据流量环境路由的创新发展

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10151402

T. Ramachandran, K. S. Arvind

{"title":"An Innovation Development of Routing for Smart Data Traffic Environment in Data Mining","authors":"T. Ramachandran, K. S. Arvind","doi":"10.1109/ICDCECE57866.2023.10151402","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10151402","url":null,"abstract":"The routing for smart data traffic environment in data mining is a complex process that requires advanced algorithms to allow for efficient data collection and analysis. The process starts with the initial data collection, which involves gathering data from various sources, such as sensors, web applications, and databases. Once the data is collected, it is processed and stored in a data warehouse. This data warehouse is then analyzed to identify patterns, correlations, and anomalies in the data. From there, algorithms are used to determine the optimal route for data traffic, based on factors such as distance, time, cost, and the likelihood of successful data transfer. The routing process involves both traditional and newer methods. Traditional methods include the use of routing tables, which allow for the efficient routing of data traffic to specific nodes in a network. Other methods include the use of route optimization algorithms, which take into account the traffic loads of different nodes in a network and prioritize the routing of data traffic in an optimal manner. Newer methods of routing data traffic include Software-Defined Networking (SDN), which is a software-based approach to routing data traffic. This approach allows for the use of automated routing algorithms that can quickly adjust the routing of data traffic based on the current traffic load.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132714850","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

Rheumatoid Arthritis Predictor Using ML Techniques and Explainable AI 使用ML技术和可解释的AI预测类风湿关节炎

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10150759

Soham Sakaria, Srajan Jain, M. Rana

{"title":"Rheumatoid Arthritis Predictor Using ML Techniques and Explainable AI","authors":"Soham Sakaria, Srajan Jain, M. Rana","doi":"10.1109/ICDCECE57866.2023.10150759","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150759","url":null,"abstract":"Rheumatoid Arthritis (RA) is a chronic autoimmune disease that occurs in multiple organs and joints in the body. It is characterized by inflammation in the lining of joints, known as the synovium, which leads to pain, stiffness, and eventually, loss of function. RA can also cause fatigue, fever, and weight loss, and in severe cases, it can lead to permanent joint damage and disability. Diagnosis is essential during the early stages of affection, but the current process is costly and inefficient, disadvantaging those with limited finances. To address this, a study was conducted using five different machine learning (ML)models (Convolutional Neural Networks (CNN), K-Nearest Neighbor (KNN), Xg-boost (XB), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM)) to find the most efficient way to diagnose RA. The study compares the accuracy of these algorithms and determines the most effective one for predicting RA in a patient. The process involves two main steps: image processing and algorithm-based prediction. During the image processing phase, the uploaded image undergoes optimization techniques to remove false negatives and enhance the image quality for a more ideal input to the following step. The image is processed using the most effective ML model in the second step, which results in 98% prediction accuracy, a significant improvement over and above the state-of-the-art literature.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133117634","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

Prediction of Heart Disease Using Naive Bayes and Particle Swarm Optimization (PSO) Method 基于朴素贝叶斯和粒子群优化(PSO)方法的心脏病预测

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10150626

Kiran, S. D S, Bharathesh Patel N, H. R, S. K. V.

{"title":"Prediction of Heart Disease Using Naive Bayes and Particle Swarm Optimization (PSO) Method","authors":"Kiran, S. D S, Bharathesh Patel N, H. R, S. K. V.","doi":"10.1109/ICDCECE57866.2023.10150626","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10150626","url":null,"abstract":"Health conditions are becoming more prevalent today as a result of hereditary and societal factors. Particularly, heart disease has been increasingly prevalent recently, putting people's lives in danger. Each person's blood pressure, cholesterol, and pulse rate are unique to them. However, medically validated results show that the normal ranges for blood pressure, cholesterol, pulse rate, and heart rate are 120/90, 100-129, 100, 60-100, and 60-100 bpm, respectively. Major vessels range in width from the aortas 25 mm (1 inch) to the capillaries 8 m. The risk level of each individual is estimated in this study using a variety of classification techniques based on variables like age, gender, blood pressure, cholesterol, and pulse rate. The user's disease is predicted via a \"Disease Prediction\" method based on predictive modelling using the symptoms they offer as input. The system evaluates the user's symptoms as input and outputs the likelihood that the disease will occur. Naive Bayes and particle Swarm optimization (PSO) method used to predict diseases. These methods determine the likelihood of the condition. As a result, 90% of predictions are accurate on average.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133154655","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

Teaching Quality Evaluation of Higher Education based on Intuitionistic Fuzzy Information 基于直觉模糊信息的高等教育教学质量评价

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10151109

Zheng-yang Zhou, Yan Li

{"title":"Teaching Quality Evaluation of Higher Education based on Intuitionistic Fuzzy Information","authors":"Zheng-yang Zhou, Yan Li","doi":"10.1109/ICDCECE57866.2023.10151109","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10151109","url":null,"abstract":"The quality evaluation of higher education is a complex problem. It involves many factors such as teaching quality, learning environment, student motivation, etc. This study aims to explore the relationship between higher education quality evaluation and fuzzy information. Quality assessment can be defined as “the process of assessing whether a product has achieved its intended purpose” (Bryman, 2011). Quality assessment has been widely used in different sectors such as healthcare, banking and other industries. In addition to evaluating products or services, it also includes evaluating people’s classroom teaching as a teaching method that has been adopted by Chinese universities. The quality of classroom teaching directly affects the quality of national talent cultivation. Therefore, how to scientifically evaluate the teaching quality of teachers has become a major issue for the current scientific and standardized teaching management in colleges and universities. This paper starts with the teaching quality evaluation based on intuitionistic fuzzy sets, and verifies the feasibility and scientificity of this method through an example.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128883967","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

Deep Learning & Computer Vision Integrated Smart Voting System 深度学习和计算机视觉集成智能投票系统

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10151439

Sanskruti Dube, Mysore Venkata Siva Sandeep, Hamsitha Challagundla, Pulaparthi Nikhilesh Chand

{"title":"Deep Learning & Computer Vision Integrated Smart Voting System","authors":"Sanskruti Dube, Mysore Venkata Siva Sandeep, Hamsitha Challagundla, Pulaparthi Nikhilesh Chand","doi":"10.1109/ICDCECE57866.2023.10151439","DOIUrl":"https://doi.org/10.1109/ICDCECE57866.2023.10151439","url":null,"abstract":"In this paper, an online voting method for elections in India is initially suggested. The suggested model has higher security as the voter’s raised secure password must be validated prior to the recording of the vote in the major database owned by our nation’s Election Commission. The model’s additional feature allows the voter to verify that the right candidate or party received their vote. In this arrangement, a voter has the option to cast a ballot from a place other than the one designated for them or from their favorite site. Vote counting will be made in an automated fashion under the proposed approach, saving a significant amount of period and allowing our nation’s Commission set for Election to declare the results in a much fast succession. Alongside the password-based authentication, we have also utilized the face-based authentication along with the successful implementation of the OpenCV in addition to the password validation. We describe a model for a web voting entity for India in this application using the above said constituents. When contrasted with the conventional voting system, this version of voting systems seems to be considerably much secure and efficient. The delays and frauds occurring during counting of votes can be easily prevented.","PeriodicalId":221860,"journal":{"name":"2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124363339","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

Modeling and Simulation of Electric Vehicle Powertrain for Dynamic Performance Evaluation 面向电动汽车动力总成动态性能评价的建模与仿真

2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) Pub Date : 2023-04-29 DOI: 10.1109/ICDCECE57866.2023.10150956

B. Bairwa, S. B, Chidre Pratiksha, S. A

引用次数: 1