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Sentiment Analysis on Amazon Product Review

Sentiment Analysis on Amazon Product Review

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： B Kamalesh S. Vijayalakshmi Data Science Christ (Deemed to be University) Pune India

Users throughout the world may now access massive amounts of data thanks to the internet and social media platforms. [5] In every facet of human existence, electronic commerce (e-commerce) plays a crucial role. E-commerce is a marketing approach that enables businesses and consumers to buy and sell things via the internet. When buyers look for product information and compare alternatives online, they generally have access to dozens or hundreds of product reviews from alternative shoppers. Machine learning is the most appropriate approach to training a neural network in today’s age of practical artificial intelligence. So implementing a model to polarize those reviews and learn from them would make passing hundreds of comments a lot easier. [24] The interpretation will be a very basic product with positive, neutral, and negative polarization. The product is checked. This study suggests a sentiment evaluation model for shopper reviews based on the object and emotive word mining for emotional level analysis using machine learning approaches.

关键词： Training Sentiment analysis Analytical models Social networking (online) Computational modeling Neural networks Process control

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Review on Common IoT Communication technologies for Both Long-Range Network (LPWAN) and Short-Range Network 1st

Review on Common IoT Communication Technologies for Both Lon...

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1st international conference of Advanced computing and Informatics, ICACIN 2020

作者： Bahashwan, Abdullah Ahmed Anbar, Mohammed Abdullah, Nibras Al-Hadhrami, Tawfik Hanshi, Sabri M. GelugorPenang11800 Malaysia School of Science and Technology Nottingham Trent University Nottingham United Kingdom Seiyun Community College Hadhramaut Yemen

ISBN: (纸本)9789811560477

The Internet of Things refers to network of physical objects that use IP address for Internet connectivity and to communicate with other Internet-enabled devices and systems. The Internet of Things comes with a number of top-class applications and has also developed many things into smart devices. The enhanced objects would ultimately need to have better connectivity and wireless communication protocols with low power consumption. Hence, this chapter presents the survey which gives a picture of the current state of the art on conventional wireless technologies used in most IoT devices, as well as to select the most suitable common IoT communication technologies that can ensure uninterrupted connection and support real-time data transmission in energy efficient form. Also, this chapter contributed a comprehensive comparison for both long-range and short-range common IoT communication technologies that are used in different applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Hybrid ensemble learning approaches for cancer classification from gene expression data

Hybrid ensemble learning approaches for cancer classificatio...

引用

IEEE international conference on Research, Innovation and Vision for the Future

作者： Cao Truong Tran Data Science Research Group Le Quy Don Technical University Hanoi Viet Nam

ISBN: (纸本)9781665461672

The expression levels of genes is well-recognised to hold the keys to address many fundamental biological problems. A major application of such datasets is cancer diagnosis which is essentially a classification task. Ensemble learning, which is a powerful machine learning approach, has been widely used to improve the performance of many real-world classification problems. Ensemble learning has been also applied for cancer classification from gene expression data. This paper proposed two hybrid ensemble machine learning approaches for classifying cancer gene expression data. The first approach is the integration of random subspace ensemble with bagging, and the second one is the integration of random subspace ensemble with boosting. Experimental results show that the proposed methods can improve classification accuracy for cancer classification from gene expression data.

关键词： Boosting Communications technology Biology Gene expression Ensemble learning Task analysis Bagging

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Contrasting Parallelized with Sequential Sorting

Contrasting Parallelized with Sequential Sorting

引用

Recent advances and Innovations in Engineering (ICRAIE)

作者： Anurag Dutta Manjari Saha Department of Computer Science Government College of Engineering and Textile Technology Serampore Calcutta India

Sorting is one of the important data Management Techniques that is quite commonly operated on data Structures. It has been proved that best, any sorting algorithm can do, in asymptotic versions is φlog 2 φ, when the iterations are made to run in sequential manner. But what if we parallelize them? Architectural advancements, like Amdahl’s Law have made use of the concept of Parallelism, and have successfully revolutionized the computing power of the microprocessors. In this paper, we will try to push the limit of computing, by making use of Graphical Processing unit and will briefly compare the efficiency of the algorithms acquainted with both the techniques, sequential as well and parallel in terms of running time. In the first section, we have included some of the preexisting works on this topic, and have mentioned about scopes of further improvement. From the next section onwards, we have slowly put together all the pillars required to support our work.

关键词： Technological innovation Microprocessors Graphics processing units Parallel processing data structures Sorting

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data Analysis in Shopping Mall data using K - Means Clustering

Data Analysis in Shopping Mall data using K - Means Clusteri...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Meghna Patel Shital Patel Sonal Patel A.M.Patel Institute of Computer Studies Ganpat University Mehsana India Department of Computer Science Ganpat University Mehsana India

Now a days in modern era, a shopping mall takes up large part of people's lives. For the mall operator, store design is of paramount importance as it increases customer satisfaction and business profit. Finding a sensible layout for a shopping center, however, remains a challenging and long-term challenge. Based on this scrutiny, a machine learning approach is proposed to evaluate preferred arrangement of a shopping mall. With this scrutiny, information about transactions is collected from banks as input. Many shopping malls have basic analysis system for analyzing data of customer, data of each and every transaction including demo-graphic data, gender, age and income. They include additional features to shop-ping malls to maintain loyalty of customer. In this paper, we have a shopping center analyze customer buying behavior. We rank customers based on how often they shop, how much they buy, and what the value of the item purchased is. Applied the K-Means algorithm to group based on the customer's annual income and spending money, the customer's age, and the money spent at the mall, spending money, customer age and spending money in shopping mall.

关键词： Measurement Machine learning algorithms data analysis IEEE merchandise Layout Clustering algorithms Machine learning

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Customer Segmentation and Future Purchase Prediction using RFM measures

Customer Segmentation and Future Purchase Prediction using R...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Akash Patra Ramkrishna Khan S. Vijayalakshmi Department of Data Science M.Sc. Data Science Christ University Pune Lavasa Department of Data Science Christ University Pune Lavasa Lavasa India

Winning in the E-Commerce business race at a competitive age like this requires proper usage of Customer data. Using that database and grouping it in similar segments in terms of spending expenditure, observation time, sex, and location so that every customer falls in a segment of characteristics. This mechanism is called Customer Segmentation. In the modern era of highly compatible technological advancements, Machine Learning Algorithms are being vastly used to bring solutions to these difficult yet essential services. In the field of research methods like simple clustering based on purchase behaviour, buyer targeting or automated customer promotion mechanism by dividing into two major categories, have been worked on. However, ensemble algorithms have come handy where different clustering algorithms are combined to deliver best segmentation. Lately combination techniques like clustering and classification mechanism have also delivered good results where, not only segmentation is done but also classification of existing and new customers are possible into the clusters. Depending on that an effective customer relationship management can really benefit the company to a huge extent. Unlike other studies where clustering was performed directly on RFM table, a different approach was taken in this study where, one dimensional clustering was done individually on Recency, Frequency, Monetary columns, then an overall score was calculated and customers were classified into three segments. However, for a new customer depending on his purchase behaviour he/she also can be classified into any of the categories.

关键词： Time-frequency analysis Machine learning algorithms databases Customer relationship management Clustering algorithms Companies Frequency measurement

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An Efficient Storage Solution for Healthcare Management Systems Using Cloud

An Efficient Storage Solution for Healthcare Management Syst...

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Smart technologies in computing, Electrical and Electronics (ICSTCEE), international conference on

作者： Judy Flavia B Balika J Chelliah Department of Computer Science and Engineering SRM IST Chennai India Department of Computer Science and Engineering SRM IST Chennai Chennai India

E –Healthcare is a blooming field in the intersection of communal health and business, relating to health amenity and information. This has been used to store medical records and other personal information on the patient's medical history and recovery. This has been a reason for the development of a new relationship between the patients and the doctors, enabling information exchange through online portals. Though E – healthcare and Healthcare records have been very useful in maintaining patient history, there are a few things that are still under developed. Over the past several years, there has been a dramatic trend toward the use of electronic health records (EHRs) in cloud environments, where many devices are synced with cloud computing to improve the sharing of patient information. However, concerns concerning data privacy and security have been raised in light of this recent shift in the environment. We came up with an EHR sharing framework that combines block chain and decentralized interplanetary system (IFPS) with an application that can be used in any application. We present a model that provides a practical solution for reliable data exchange being turned on cloud with protecting the sensitive information provided by the user The patients can also upload their PHIs (Private health information statistics) to the cloud for later reviews. This paper deals with an application that is very useful for maintaining the healthcare records of patients and their doctors and other necessary information using cloud. Today, most the data are stored in cloud. The data may vary from normal data to very personal and sensitive data like healthcare records of a patient. In the proposed system, a sanitizer is used to summarize blocks of data corresponding to sensitive file details. The proposed system is based on Identity based cryptography, which simplifies complex certificate management. The proposed system is based on the pseudorandom function and BLS signature.

关键词： Access control Cloud computing Costs Medical services Software data models Blockchains

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A Novel Approach to Predict Success of Online Games Using Random Forest Regressor for Time Series data 1

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international conference on advances in Electrical and Computer technologies, ICAECT 2021

作者： Varghese, Rohit Renne Aiswarya, D.R. Roy, Athulya Muraly, Vighnesh Renjith, Shini Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Kerala Thiruvananthapuram695015 India

ISBN: (数字)9789811911118

ISBN: (纸本)9789811911101

Online games are an important and frequently used mode of entertainment by people nowadays due to the pandemic situation. Though several games are released each year, only a few of them become successful. The success of a game is determined by multiple factors, among which the most significant one is the poor marketing strategies adopted for its promotion. If the developers are given an insight on how their games would perform after its release, they could modify their gaming strategies and/or the game itself to garner more players/fans in its subsequent releases. There have been previous attempts to achieve this goal, achieve this goal, however, those are limited to the games that were already released. Furthermore, they do not deal with modern games like online first-Person shooter (FPS) games/personal computer (PC) games. This work aims to introduce a novel system to predict the success of online games after getting the initial data from their release. This data are collected by the developers over a few months after the game is released. The data contain various attributes like Twitch viewers, viewer ratio, etc. After evaluating multiple time series and machine learning models, random forest regressor is selected as the core of the proposed system. The system is capable of predicting the percentage increase in the number of players in upcoming months. Based on this prediction, the developers can get an idea on how successful their game will be and they can adopt the best possible strategies to attract more followers and thereby increase the revenue. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels

A Temporal Anomaly Detection System for Vehicles utilizing F...

引用

international conference on Collaborative computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Subash Neupane Ivan A. Fernandez Wilson Patterson Sudip Mittal Shahram Rahimi Department of Computer Science & Engineering Mississippi State University Mississippi USA

A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs). Examples of these FWGs include the engine system, transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels that gauge vehicular operating conditions. This data rich environment is conducive to the development of Predictive Maintenance (PdM) technologies. Undercutting various PdM technologies is the need for robust anomaly detection models that can identify events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal vehicular operational behavior. In this paper, we introduce the Vehicle Performance, Reliability, and Operations (VePRO) dataset and use it to create a multi-phased approach to anomaly detection. Utilizing Temporal Convolution Networks (TCN), our anomaly detection system can achieve 96% detection accuracy and accurately predicts 91% of true anomalies. The performance of our anomaly detection system improves when sensor channels from multiple FWGs are utilized.

关键词： Convolution Sensor systems data models Sensors Internet Reliability Fuels

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Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anxiety Notification System 9th

Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anx...

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9th international conference on Computational science and Technology, ICCST 2022

作者： Abdulmanab, Muhammad Rahiman Nor’Azman, Nur Faidz Hazirah Meon, Hasni Razak, Chempaka Seri Abdul Ab Hamid, Siti Hafizah Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Communication Faculty of Communication Visual Art and Computing Universiti Selangor Bestari Jaya Malaysia School of Pre-University Studies Taylor’s College Lakeside Campus Subang Jaya Malaysia

ISBN: (纸本)9789811984051

As anxiety becomes increasingly prevalent among youths especially university students, early prevention via anxiety disorder profiling is crucial. Nevertheless, most screening tools to date are not automated, labour-intensive and time-consuming. Mental health detection in online social networks (OSNs) using artificial intelligence (AI) can provide means for capturing overlooked behavioural attributes, but few are centred around anxiety detection. Internet of Things (IoT)’s increasing popularity enables bio-signals monitoring to aid in mental health care. However, minimum research has been devoted to IoT-based assistive technologies for anxiety, which currently are only used by individuals already diagnosed and actively monitored. This study proposes a novel approach to anticipatory anxiety detection for Malaysian university students aged between 18 and 25 inclusive as an early notification system using AI and IoT for bilingual Malay and English tweets and vital signs analysis respectively. Our result shows that Logistic Regression with Bag-of-Words (BoW) is the overall best performing model with 81.06% accuracy. The Arduino smartwatch sensor readings from the MAX30102 pulse oximeter sensor are used to determine different body conditions using a rule-based approach. The comparison test result between the prototype and conventional measuring devices shows the prototype’s ability to accurately distinguish vital signs with a relatively low Mean Absolute Percent Error (MAPE) rate of 10.92% and 1.51% for pulse rate and temperature respectively. Together, our proof-of-concept results indicate the potential of AI and IoT in deriving new measures of anxiety, by analysing social data combined with bio-signals monitoring to alert users regarding their anxiety state. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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