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Sentiment Analysis using deep learning for use in recommendation systems of various public media applications 3

Sentiment Analysis using deep learning for use in recommenda...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Arava, Karuna Chaitanya, Rudraraju Sri Krishna Sikindar, Shaik Praveen, S Phani Swapna, D. Jntu Kakinada Department of Cse Kakinada India Sir C R Reddy College of Engineering Department of C.S.E Eluru India Vignan's Foundation for Science Technology & Research Department of C.S.E Guntur India Pvp Siddhartha Institute of Technology Department of C.S.E Vijayawada India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Sentiment Analysis is a method of analyzing text and extracting opinions from it. It's also known as emotion or opinion extraction, and it's part of the machine learning as well as data mining categories. There are numerous ways to convey one's sentiments. It can be articulated in a variety of ways, such as through feelings, making judgments, or expressing one's vision or insight. Sentiment investigation is the act of detecting, recognizing, and categorizing a user's emotion or view for any service, such as movies, product issues, events, or any other attribute that can be good, negative, or neutral. This analysis is based on social communication channels such as websites that included ratings, forum conversations, blogs, micro blogs, Twitter, and other social media platforms. The important goal of suggested system is to improvise accuracy and to generate recommendation system using deep learning algorithms. © 2022 IEEE.

关键词： Sentiment analysis

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3rd international conference on Big data and Security, ICBDS 2021

3rd International Conference on Big Data and Security, ICBDS...

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3rd international conference on Big data and Security, ICBDS 2021

ISBN: (纸本)9789811908514

The proceedings contain 59 papers. The special focus in this conference is on Big data and Security. The topics include: Research on Active Sampling with Self-supervised Model;research on Civil Aviation Economic Security Situation Assessment Model Based on Ant Colony Algorithm;a Supervised learning Model for Detecting Ponzi Contracts in Ethereum Blockchain;community Detection Based on Deep Network Embedding with Dual Self-supervised Training;detecting Hybrid Anomalies Using an Unsupervised Approach in Online Social Networks;vehicle Road Privacy Recommendation System Equipped with Markov Chain;data-Driven Distribution Network Energy-Saving Planning Based on Cluster Division and High-Penetration Renewable Energy Access;state machine Inference Method of Unknown Binary Protocol Based on Recurrent Neural Network;research and Judgment of Enterprise Energy Consumption Anomaly Based on Massive data Clustering Algorithm;based on data mining New Energy Medium - and Long - Term Electricity Market Trading Power Decomposition Strategy;application of machine learning Algorithm in Art Field – Taking Oil Painting as an Example;using Multi-criteria Evaluation of E-learning System: A Methodology Based on learning Outcomes;optimization on Service Function Chain Deployment for Cloud Network Collaborative Operation Platform;Research on Defect Intelligent Identification System Based on Cloud-Fog-Edge Cooperation for UAV Electric Power Inspection;anomaly Detection of Cloud Network Resource State Based on Deep learning;Spinal Posture Recognition Device Using Cloud Storage and BP Neural Network Approach Based on Surface Electromyographic Signal;research on Identification Method of Sensitive data in Power System;research on Civil Aviation Economic Pricing Based on Equilibrium Game;design Guidance for Lightweight Object Detection Models.

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Recognizing Sentiments In Regional Language Text From Social Media Using machine learning Approach: A comparative Study to understand the state of the art tools and associated functionalities available 3

Recognizing Sentiments In Regional Language Text From Social...

引用

3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Sirsat, Sudarshan Zulpe, Nitish K J Somaiya Institute of Management Somaiaya Vidyavihar University Department. of Data Science and Technology Mumbai India Swami Ramanand Teerth Marathwada Univesity College of Computer Science and Information Technology Latur India

ISBN: (纸本)9798350348347

Regional language contents gain the highest traction in today's social media network marketing and from users' perspectives since nearly 90% of users consume contents in regional language. Those who are only focusing on English as the major language of social media or website contents are clearly missing the majority. Social media currently is touching the untouched digital population of the world through creative contents, easy to use convincing features and infinite possibilities of enhancing personal social networks using regional language like Hindi, Marathi, and Gujarati and many other state and regional languages. This has created immense opportunities in the area of text mining, text analysis and text generation too many scholars and researchers to visualize and analyses the contents to gain insights into what are the multidimensional perspectives of the authors of any such contents. This research work is going to understand the current work done in the area of regional language sentiment analysis in India and all across the globe to understand the status and research gap in the domain to work further in the Maharashtra state regional language contents available on social media pages. This research work will try to explore all possible tools and technologies available and used by the recent research scholars and industry leaders to decide the way ahead to work in the Marathi regional language domain in near future. © 2023 IEEE.

关键词： Sentiment analysis

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Traditional Work Station System with AI based Crypto mining

Traditional Work Station System with AI based Crypto Mining

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： M. Vanitha Harivanth A Govardhan K Electronics and Communication Engineering Saveetha Engineering College India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research explores the feasibility of AI-powered cryptocurrency mining on traditional workstations. Traditional workstations, while widely available, face challenges in terms of computational efficiency and energy consumption compared to specialized hardware like ASICs and GPUs. This project aims to address these challenges by leveraging machine learning techniques to optimize mining performance. By analyzing real-time data such as hash rates, power usage, and network difficulty, the system will dynamically adjust mining parameters to maximize profitability while minimizing resource consumption. Furthermore, the project will investigate the use of reinforcement learning to enable the system to continuously adapt to changing mining conditions and optimize its performance over time. This approach aims to democratize cryptocurrency mining by making it accessible to users with traditional workstations, enabling them to participate in a sustainable and efficient manner.

关键词： Renewable energy sources Energy consumption Profitability Reinforcement learning Hardware Regulation Real-time systems Cryptocurrency Workstations data mining

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machine learning Technique based Trip Planning System - TripMa 3

Machine Learning Technique based Trip Planning System - Trip...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Vishwajith, Lochana Attanayake, Hasitha Rangana, Savith Ushara, Thimal Bandara, Pradeepa Rupasinghe, Shalini Sri Lanka Institue of Information Technology Department of Software Engineering Malabe Sri Lanka

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Travel planning is a tedious task to engage from the perspective of travelers since it involves crucial and critical decision-making due to a lack of foreign geography and cultural knowledge. Initially, the concept of traveling through travel agencies was used and it was slowly diminishing due to the fact of freedom and exploration are too constant and bounded according to the organizer's perspective. The proposed model overcomes almost all existing challenges and removes the traveler's local knowledge gap by using the dynamic prediction and recommendation features. The proposed system has identified the importance of grouping or the sharing expectation of a traveler through mapping out the best matching companions along with identification of tourist attractions and the best time to visit those places, which also included safety precaution methodologies to the travelers, which provide them the capabilities of preparing and mitigating the potential risks along with the traveling areas. The inclusion of the user feedback analysis model will impact identifying the user's perspective of the system and ensures users' trust towards the system, The system has iteratively used location-based knowledge through third-party APIs such as GOOGLE MAP API. The below sections will describe and illustrate the modes of machine learning techniques used, weighted models, and the NLP data mining methodologies used to acquire the systems architecture. © 2022 IEEE.

关键词： Decision making

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Risk Prediction of Earthquakes using machine learning 3

Risk Prediction of Earthquakes using Machine Learning

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Arunadevi, B. Madiajagan, M. Hussain, Mohammed Ihtesham Lakshmi, R. Ramakrishna, M.M. Sengupta Das, Kakali Dr.N.G.P Institute of Technology Department of Ece Tamil Nadu Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India University of Technology and Applied Sciences Department of Engineering Al-Khuwair Oman M. Kumarasamy College of Engineering Thalavapalayam Department of Mca Tamil Nadu Karur India Wolaita Sodo University Snnpr Department of Ece Ethiopia Budge Budge Institute of Technology Ece Department Kolkata India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

As the population of earthquake-prone areas continues to grow, the number of people killed and injured by earthquakes is also increasing. There are an increasing number of populated places that are at risk from earthquakes, but there is only a limited number of earthquake engineering professionals to deal with the problem. As a result of this, automatic intelligence-monitored warning and mitigation systems are required. According to the findings, in this paper, an artificial intelligence model based on machine learning is being built to enable an autonomous approach for analysing and identifying risk potential, then alerting stakeholders and initiating risk reduction steps. It is possible to monitor slope movement and deformations remotely and autonomously in a variety of ways, including through the use of automated seismograph connections and collaboration with local, state, and national disaster relief organisations. Damage models typical of India current urban infrastructure can be used in modelling and simulation scenarios, and nonlinear seismic analysis scenarios can be used for these models. People who work with machine learning in earth quake prediction applications can use data mining techniques like decision trees and geographic information systems to make their applications smarter by making them smarter. © 2022 IEEE.

关键词： Earthquakes

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Generating Recommendations for Various Problems Using data mining and machine learning Algorithms 3rd

Generating Recommendations for Various Problems Using Data M...

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3rd international conference on Communication, Computing and Electronics Systems, ICCCES 2021

作者： Lobo, L.M.R.J. Birbal, Kale Manoj Department of Computer Science & Engineering Walchand Institute of Technology Solapur India

ISBN: (纸本)9789811688614

The system available today which generates recommendations is supported by the user’s information collected within the past. It does not include intention at a specific time. The utilization of data mining and machine learning algorithms offers real-time recommendations employing a fitness function and models that estimate the suitability of recommended lists. The utilization of a social network provides user-generated content during a much more convenient scenario. It also uses a user experience. Recommendation systems play an outsized role in providing quality to those. In this paper, we present how recommendations are often produced using data processing and machine learning algorithms. Three systems with varied applications are addressed during this paper. First, a recommendation is used for better crop cultivation using certain parameters of concern with the assistance of association rule mining and genetic algorithms. Second, a way to recommend videos for advertisements from those available with titles, descriptions, and hashtags as extracted features is presented. These use machine learning-associated multi-label classification algorithm. Finally, an anti-vice recommendation system that uses neural networks to get recommendations is brought forward. Altogether the three cases, it is observed that the accuracy and efficiency of recommendations are upgraded. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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machine learning Approaches for Marketing Campaign in Portuguese Banks 3

Machine Learning Approaches for Marketing Campaign in Portug...

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3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Alexandra, Jennifer Sinaga, Kristina Pestaria Bina Nusantara Univ Dept Master Informat Syst Management Jakarta Indonesia

ISBN: (纸本)9781665425803

Bank is a type of business that deals with saving, circulation of money, deposits and others. The number of services provided by banks is very diverse, this depends on the capabilities of each bank. The more capable and better the bank is, the more services it will offer. Introducing the product directly has been commonly used for various industries, one of them is the banking industry. In directly introducing products, banks can conduct market analysis by utilizing the information technology space that can assist in making decisions. By analyzing bank marketing data, it can be used to select the type of marketing to do. Marketing campaigns can be carried out via email, telephone, and direct email to prospective customers that allow potential customers to decide whether to take the product offered or not. With increasing time, the amount of incoming data continues to grow. With this increasing data, one of the bank institutions found it difficult to predict whether their clients would subscribe to a term deposit or not. Therefore, in this paper, the data mining process will be carried out using classification (Decision Tree, Wye Bayes, and Random Forest) and clustering (K-Means, K-Medoids, and DBSCAN) methods to predict if the client will subscribe a term deposit.

关键词： component machine learning data mining Classification Clustering

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3rd international conference on Soft Computing and its Engineering Applications, icSoftComp 2021

3rd International Conference on Soft Computing and its Engin...

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3rd international conference on Soft Computing and its Engineering Applications, icSoftComp 2021

ISBN: (纸本)9783031057663

The proceedings contain 33 papers. The special focus in this conference is on Soft Computing and its Engineering Applications. The topics include: Sentiment Analysis of Twitter data Using machine learning Approaches;mining Spatio-Temporal Sequential Patterns Using MapReduce Approach;a Split-Then-Join Lightweight Hybrid Majority Vote Classifier;identification of Barriers in Adoption of IoT: Commercial Complexes in India;a Dynamically Adapting Framework for Stock Price Prediction;evaluating Binary Classifiers with Word Embedding Techniques for Public Grievances;database Concentration Method for Efficient Image Retrieval Using Clustering and Image Tag Comparison;microstructure Image Classification of Metals Using Texture Features and machine learning;segregation of Areca Nuts Using Three Band Photometry and Deep Neural Network;Early Diagnosis of Alzheimer’s Disease from MRI Images Using Scattering Wavelet Transforms (SWT);constraint Pushing Multi-threshold Framework for High Utility Time Interval Sequential Pattern mining;KTSVidRec: A Knowledge-Based Topic Centric Semantically Compliant Approach for Video Recommendation on the Web;intelligent Facial Expression Evaluation to Assess Mental Health Through Deep learning;intelligent Mobility: A Proposal for Modeling Traffic Lights Using Fuzzy Logic and IoT for Smart Cities;crop Disease Prediction Using Multiple Linear Regression Modelling;radial Basis Function Network Based Intelligent Scheme for Software Quality Prediction;generating the Base Map of Regions Using an Efficient Object Segmentation Technique in Satellite Images;KCEPS: Knowledge Centric Entity Population Scheme for Research Document Recommendation;weighted Hybrid Recommendation System Using Singular Value Decomposition and Cosine Similarity;CT/MRI 3D Fusion for Cerebral System Analysis;text Analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems;arabic Cyberbullying Detection from Imbalanced dataset Using machine learning;in

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Implementation of Different machine learning Approaches for Heart Disease Diagnosis

Implementation of Different Machine Learning Approaches for ...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Ragav Sharma Anuj Kumar Goel ECE Department Chandigarh University Punjab

Cardiovascular disease is a deadly ailment that can strike unexpectedly and without warning. Despite generating copious amounts of patient and illness data, the healthcare sector often fails to effectively harness this information for precise diagnosis and decision-making. To unlock the wealth of information available, researchers and practitioners can utilize data mining and machine learning approaches. This paper implements a proposed system consisting of data collection, preprocessing, feature selection, model selection, model training, and model evaluation. The study compares different machine learning models for predicting heart disease such as KNN classifier, Support vector machine learning classifier (SVM), Random Forest and Decision tree, where KNN is giving best output of 87.05% with 8 neighbor. An analytical review of existing research on the prediction of heart disease is given in this article and drawing insightful conclusions for future study.

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