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Opinion Mining of Customer Reviews Using Supervised Learning algorithms

Opinion Mining of Customer Reviews Using Supervised Learning...

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international conference on Electrical Information and Communication Technology (EICT)

作者： Shibbir Ahmed Arif Taslima Binte Hossain Port City International University Chittagong Bangladesh

ISBN: (纸本)9781665409070

Nowadays, a lot of people express their opinions on various topics using social networking sites. Twitter has become a famous social networking site where people can express their opinions to the point and so it has become a great source for opinion mining. In this research, the goal was to train and build a model that can automatically and accurately categorize the opinion of customer tweet reviews about popular cell phone brands. We have used python TextBlob library for getting the polarity values of all the tweet reviews of the dataset. We have also used Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree and Random Forest algorithms along with Bag of Words and TF-IDF vectorizers separately to train and build the model that will categorize the customer tweet reviews into five opinion categories: Strongly Negative, Weakly Negative, Neutral, Weakly Positive and Strongly Positive. We have observed that SVM and Logistic Regression algorithms have outperformed other algorithms with 88% accuracy using Bag of Words vectorizer while SVM algorithm has outperformed other algorithms with 87% accuracy using TF-IDF vectorizer.

关键词： Support vector machines Sentiment analysis Social networking (online) Supervised learning Predictive models Prediction algorithms Planning

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Proceedings of 2020 5th international conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020

Proceedings of 2020 5th International Conference on Cloud Co...

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5th international conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020

ISBN: (纸本)9781728161754

the proceedings contain 53 papers. the topics discussed include: block sizes control for an efficient real time record linkage;run time optimization using a novel implementation of parallel-PSO for real-world applications;deployment of containerized deep learning applications in the cloud;big data storage and analysis for smart farming;a cross-layered interference in multichannel MAC of VANET;an overall statistical analysis of AI tools deployed in cloud computing and networking systems;a distributed large graph coloring algorithm on giraph;a new recurrent neural network fuzzy mean square clustering method;a recommendation approach based on correlation and co-occurrence within social learning network;a re-engineering approach for extension of the tourist guide knowledge base;and bloom filter and its variants for the optimization of MapReduce’s algorithms: a review.

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Confronting Power and Corporate Capture at the FAccT conference 22

Confronting Power and Corporate Capture at the FAccT Confere...

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5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Young, Meg Katell, Michael Krafft, P.M. Cornell Tech New YorkNY United States The Alan Turing Institute London United Kingdom University of the Arts London London United Kingdom

ISBN: (纸本)9781450393522

Fields such as medicine and public health attest to deep conflict of interest concerns present when private companies fund evaluation of their own products and services. We draw on these lessons to consider corporate capture of the ACM Fairness, Accountability, and Transparency (FAccT) conference. We situate our analysis within scholarship on the entanglement of industry and academia and focus on the silences it produces in the research record. Our analysis of the institutional design at FAccT indicates the conference's neglect of those people most negatively impacted by algorithmic systems. We focus on a 2021 paper by Wilson et al., "Building and auditing fair algorithms: A case study in candidate screening"as a key example of conflicted research accepted via peer review at FAccT. We call on the conference to (1) lead on models for how to manage conflicts of interest in the field of computing beyond individual disclosure of funding sources, (2) hold space for advocates and activists able to speak directly to questions of algorithmic harm, and (3) reconstitute the conference with attention to fostering agonistic dissensus - un-making the present manufactured consensus and nurturing challenges to power. these changes will position our community to contend with the political dimensions of research on AI harms. © 2022 Owner/Author.

关键词： Diagnosis

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AIpowered Automation of Fraud Detection in Financial Services 23

AIpowered Automation of Fraud Detection in Financial Service...

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Proceedings of the 5th international conference on Information Management & Machine Intelligence

作者： Rahul Sharma Dattatraya Babanrao Nalawade Pushpa Negi Ritika Dhabliya Saurabh Bhattacharya Vinit Khetani Auricle Global Society of Education and Research Sudarshan Nagar Bikaner Rajasthan Auricle Global Society of Education and Research Sudarshan Nagar Bikaner Rajasthan India Assistant Professor Mech Engg VIIT Pune Assistant Professor Mech Engg VIIT Pune India Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India Director Yashika Journal Publications Pvt. Limited Wardha Maharashtra Director Yashika Journal Publications Pvt. Limited Wardha Maharashtra India Research Scholar Department of Computer Applications NIT Raipur C.G Research Scholar Department of Computer Applications NIT Raipur C.G India Cybrix Technologies Nagpur Maharashtra Cybrix Technologies Nagpur Maharashtra India

ISBN: (纸本)9798400709418

Due to the rise in credit card fraud, robust and efficient fraud detection systems are needed. this study introduces an AI-based banking fraud detection method. this paper introduces a new method that combines Support Vector Machines (SVM) with Random Forest algorithms to improve reliability. Credit card fraud detection is vital to protecting financial institutions and consumers. the study uses data preprocessing, feature engineering, and model selection to find the best fraud detection solution. through wide experimentation and evaluation using real-world credit card transaction data, proposed hybrid model of SVM and Random Forest model achieved 99.42% accuracy. this performance shows the power of AI-driven automation and is a major improvement over current fraud detection method. Proposed method is highly precise and adaptable to new fraud patterns, making it ideal for the ever-changing financial services industry. Its real-time functionality makes it ideal for the dynamic industry. the hybrid model combines SVM and Random Forest benefits. this combination uses SVM classification accuracy and Random Forest's ensemble learning to enhance the model's overfitting resistance. AI-based solutions for financial services fraud detection have advanced significantly with this study. these solutions aim to increase security, reduce financial losses, and make the financial landscape safer for everyone. this enhances security and operational efficiency in digital finance's dynamic world.

关键词： RF

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Artificial Intelligence Based Rural E-Commerce Boosting Using Big Data

Artificial Intelligence Based Rural E-Commerce Boosting Usin...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Saumi Roy Archana Ravindra Salve Jaymin Arvind Shah Sanjeev Kadam Iskandar Muda Manoranjan Dash Department of Management Studies New Horizon College of Engineering Bengaluru MBA faculty Indira College of Engineering and Management Pune Maharashtra Amity Business School Amity University Mumbai Symbiosis Institute of Business Management Pune Symbiosis International (Deemed University) Pune Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara Medan Indonesia 20222 Jl. Prof TM Hanafiah 12 USU Campus Padang bulan Medan Indonesia Siksha O Anusandhan Deemed to be University Ghatikia Kalinga Nagar Bhubaneswar Odisha

Following 40 years of reform and opening up, India's economy has achieved a new peak, with rural ecommerce emerging as a key driver of this expansion. As the state's policy continues to develop to encourage rural ecommerce, the sector has opened up a big historical opportunity, with the size of the market growing and the sector's social awareness rising rapidly. With the widespread use of big data networks, big data-driven, AI-powered e-commerce has also grown rapidly in recent years. the use of electronic commerce is more common in urban regions than in rural ones. As part of the plan for "revitalizing the countryside," widespread use of ecommerce in rural regions will be a major step toward bolstering the rural economy. the growth of this route has the potential to strongly encourage the modernization of antiquated production techniques in rural regions while also improving the accessibility and efficiency of the flow of agricultural goods inside India and so facilitating their eventual export. the third-party distribution model was used as the logistics approach, and agricultural items from Mysore City, such as paddy and vegetables, served as the primary research objects to illustrate the utility of the Novel Colony Algorithm in the context of rural e-commerce. the issue of the third-party distribution model is examined through the process of model building. In light of this, PyCharm is used to determine the shortest path and total cost using a novel colony algorithm. In this research, we evaluate the models and conduct analyses of the efficiency and cost issues that arise when changing the parameters.

关键词： Analytical models Costs Urban areas Software algorithms Big Data Data models Software

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Proceedings of the 2020 5th international conference on Cloud Computing and Internet of things, CCIOT 2020

Proceedings of the 2020 5th International Conference on Clou...

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5th international conference on Cloud Computing and Internet of things, CCIOT 2020

ISBN: (纸本)9781450375276

the proceedings contain 12 papers. the topics discussed include: survey on evaluation of IoT services leveraging virtualization technology;EE-Navi: energy-efficient navigation service for electric vehicle;design and development of IoT based accident detection and emergency response system;the many faces of patterns in the Internet of things;a fault tolerant and secured network design for file and application sharing in a mid-sized business environment;toward awareness locality algorithms of peer-to-peer file sharing network;acoustic attacks in IoT era: risks and mitigations;value creation framework based on designable evaluation network;development conception and promotion strategy of bus system of the future;and balanced dominating top-k queries over uncertain data.

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Proceedings - 2020 5th international conference on Smart Grid and Electrical Automation, ICSGEA 2020

Proceedings - 2020 5th International Conference on Smart Gri...

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5th international conference on Smart Grid and Electrical Automation, ICSGEA 2020

ISBN: (纸本)9781728182780

the proceedings contain 136 papers. the topics discussed include: research on intelligent firefighting robot in transformer substation based on water mist technology;economic load distribution strategy of power system based on artificial intelligence;hierarchical fault location method for active distribution network with high penetration of distributed generation;key technologies for ultra-wideband PDS detection in transformer under AC/DC withstand voltage tests, part 1: application status of conventional pulse current method;key technologies for ultra-wideband PDS detection in transformer under AC/DC withstand voltage tests, part 2: overview of on-site pd test of converter transformer;optimal configuration of distributed generation and energy storage in microgrid;research on electric power business terminal classification technology based on unsupervised algorithms;research on novel power supply mode for elevators based on inductively coupled power transfer technology;and research on the calibration influence factors of UHF partial discharge detector.

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A Review on Predicting Autism Spectrum Disorder(ASD) meltdown using Machine Learning algorithms

A Review on Predicting Autism Spectrum Disorder(ASD) meltdow...

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international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Sara Karim Nazina Akter Muhammed J. A. Patwary Md. Rashedul Islam International Islamic University Chittagong

ISBN: (纸本)9781665495233

Autism Spectrum Disorder (ASD) is a well-known mental disorders that prevails in the ability of a person’s social communication. the significance of early diagnosing drew the attention of researchers to use different machine learning-based procedures. Many analyses are done with the help of machine learning (ML) techniques to foresee meltdowns of autism together with Support Vector Machines, Random Forests, Naive Bayes, K-nearest Neighbors and many more. this paper gives a wide-spread review of papers applying machine learning in predicting ASD, along with algorithms for data analysis and classification. More than 80 research papers are considered, and the articles are assembled from the internet. Finally 48 research articles are coped up with the prerequisites in this study. the main goal of this review is to distinguish and mark out the machine learning trends in ASD literature and show the way to researchers curious in expanding the core of predicting ASD data and observe momentous research patterns in the field of ML. this paper will be a guideline to future researchers who are willing to work in the field of predicting ASD meltdown.

关键词： Support vector machines Autism Silver Machine learning algorithms Mental disorders Machine learning Predictive models

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Evaluation of DBSCAN algorithm on different programming languages: An exploratory study

Evaluation of DBSCAN algorithm on different programming lang...

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international conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Md Amiruzzaman Rashik Rahman Md. Rajibul Islam Rizal Mohd Nor West Chester University West Chester PA USA University of Asia Pacific Dhaka Bangladesh International Islamic University Kuala Lumpur Malaysia

ISBN: (纸本)9781665495233

DBSCAN is a well-known clustering algorithm that is often used to find associations and structures in large spatial data. Due to its popularity, built-in functions for DBSCAN have been implemented on top of many different programming languages. Researchers and practitioners (i.e., data scientists) have been using these built-in functions to cluster and analyze a prolific area of research in data science. Due to the many implementations of DBSCAN and its utilization in many different languages, the output of each built-in DBSCAN function is assumed to be identical. In this paper, we present a systematic approach to evaluate the built-in functions of DBSCAN algorithms and to identify discrepancies in their output. the evidence from the study shows that there are some discrepancies and recommends caution in dealing with built-in functions.

关键词： Systematics Clustering algorithms Data science Benchmark testing Spatial databases Libraries Communications technology

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Scientific Measurement and Visualization Analysis of international Omni-channel Retailing Research (2011-2020)-Research on Knowledge Graph-Based on Co-word Analysis 5

Scientific Measurement and Visualization Analysis of Interna...

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2021 5th international Workshop on Advanced algorithms and Control Engineering, IWAACE 2021

作者： Chen, Xiaoxia Li, Zhongbin College of Management Fujian Agriculture and Forestry University Fuzhou350002 China Jinshan College Fujian Agriculture and Forestry University Fuzhou350002 China

With the development of digital technologies such as cloud computing, big data, and 5G, omni-channel retailing has become the direction of the retail industry reform. To understand the research context, hot spots, and future development trend of global omni-channel retailing in the past decade and provide a reference for relevant research, this paper collects relevant articles in the WOS database from 2011 to 2020, uses the co-word analysis method to make statistics and analysis on keywords, and uses Bicomb, Spss and Excel software to conduct cluster analysis and strategic coordinate analysis on omni-channel retailing research. the results show that the research on omni-channel retailing logistics and supply chain, the research on omni-channel integration and management, and the research on omni-channel digital marketing and consumer behavior are the three hot areas of omni-channel retailing research. In the future, the mixed methods combining quantitative researches (such as machine learning, artificial neural network, and quantitative model) with qualitative research (such as case research and interviews) can be used from a mixed perspective of consumers and retailers to conduct in-depth research on the operation mechanism and the decision-making mechanism of omni-channel retailing and the interdisciplinary research combining omni-channel and organizational management. © Published under licence by IOP Publishing Ltd.

关键词： Cluster analysis

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