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Marketing decision making using sentiment score with co-extracting opinion target words from reviews

IOP Conference Series: Materials Science and engineering 2021年第1期1055卷

作者： T M Saravanan T Kavitha S Hemalatha Gourav Kumar Associate Professor Department of Computer Applications Assistant Professor (Sr.G) Department of Computer Applications PG Scholar Department of Computer Applications Kongu Engineering College Perundurai – 638 060 Tamil Nadu India

This paper proposes a broad-spectrum outline that utilizes natural language processing approaches counting sentiment analysis, web mining that uses clustering schemes to acquire fresh outcome elicited from reviews in support of diverse features of products. An ultimate part in the scheme is finding positive and negative sentiment scope for all features of a product in the given reviews. Massive figures of product reviews are crawling on the Web with the rapid development of Internet. Commencing these reviews, customers can get hold ofactualexamination of product rank and unswerving supervision of their acquisition actions. Withdrawal feelings from web reviews have developed into a gradually more fiery activity and haveengrossed a vastpact of considerationcommencingthe researchers. The key task for opinion mining is obtaining opinion emotions from reviews in online, the novelfactor involves in identifying relations between words. The project proposes a new scheme that contains graph based co-ranking scheme that is employed in finding poise of every word. Then, words with superiorpoise are withdrawn as opinion objects. Based on the nearest neighbor rule we compare with previous schemes, the scheme more preciselycaptures opinion relations, particularly for elongated cover relations. The investigationaloutcome shows that the schemesuccessfully outperforms all other methods and techniques.

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Multi stratagem analysis of sentiments on twitter data using partial phrase harmonizing

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IOP Conference Series: Materials Science and engineering 2021年第1期1055卷

作者： T M Saravanan T Kavitha S Hemalatha Ankit Kumar Associate Professor Department of Computer Applications Assistant Professor (Sr.G) Department of Computer Applications PG Scholar Department of Computer Applications Kongu Engineering College Perundurai – 638 060 Tamil Nadu India

Sentiment analysis is constructive in the application environment for business intelligence and suggests systems because it is a very easy medium for the two ends of the availability to communicate. Numerous strategies and schemes have been worn inside the sentiment analysis, such as language processing, polarity lexicons, machine learning, and psychometric scales which establish diverse types of analyzing sentiments as assumptions ended, scheme reveals, and corroboration data set. Since the internet has to turn into a commanding resource of retrospect the sphere of sentiment is moreover referred to as Sentiment Analysis or Opinion Mining. It has seen an enormous boost in academia over the decades. Analyzing sentiment to extract sentiments in different levels like word, sentence, and document provides articles' feeling polarities. While well identified consumers' sentiments articulated in sentences by opinion. Customary machine learning schemes cannot virtuously mirror the views of writers. This paper proposes a scheme called multi-strategy sentiments with semantic resemblance to disentangle the topic with partial phrase matching. Additionally, the Naïve Bayes classification is applied to search for the probability of the distribution of knowledge in different categories of knowledge set.

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Secure data sharing for mobile cloud computing using RSA

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IOP Conference Series: Materials Science and engineering 2021年第1期1055卷

作者： M Arumugam S Deepa G Arun P Sathishkumar K Jeevanantham Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Information Technology MP Nachimuthu M Jaganathan Engineering College Chennimalai India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai Erode India

Mobile cloud computing (MCC) refers to an infrastructure in which data processing and storage can take place far from mobile devices. The convergence of compact registration and network distributed computing has created scalable distributed computing. This technology provides consumers a number of points of interest, similar to storage limits, reliability, scalability and access to real-time information. As a result, it is expanding steadily and is undoubtedly organised into a daily day-to-day life. The Cloud servers can be used for the preparation and storage of ***, in the current conditions, the secrecy of photos and information is generally important. In this paper, we concentrate mainly on the safe re-appropriation of photographs.

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Skin Disease Prediction and Provision of Medical Advice Using Deep Learning

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Journal of Physics: Conference Series 2021年第1期1724卷

作者： S. Hemavathi K. Jayasakthi Velmurugan Assistant Professor Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai-44 Tamil Nadu India Associate Professor Department of Computer Science and Engineering Jeppiaar Engineering College Chennai-119 Tamil Nadu India

Skin diseases are hazardous and often contagious, especially melanoma, eczema, and impetigo. These skin diseases can be cured if detected early. The fundamental problem with it is, only an expert dermatologist is able to detect and classify such disease. Sometimes, the doctors also fail to correctly classify the disease and hence provide inappropriate medications to the patient. Our project proposes a skin disease detection method based on Image Processing and Deep Learning Techniques. Our system is Personal computer based so can be used even in remote areas. The patient needs to provide the image of the infected area and it is given as an input to the application. Image Processing and Deep Learning techniques process it and deliver the accurate output. The output is used to get the best medical cure for the disease with nearest hospital details. In this project, we present a comparison of two different approaches for real-time skin disease detection algorithm based on accuracy.

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A Comparative Analysis of using Various Machine learning Techniques based on Drowsy Driver Detection

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IOP Conference Series: Materials Science and engineering 2021年第1期1119卷

作者： A Bano A Saxena G K Das Department of Computer Science & Engineering Compucom Institute of Technology and Management Jaipur India Professor Department of Computer Science & Engineering Compucom Institute of Technology and Management Jaipur India Assistant Professor Department of Computer Science & Engineering Compucom Institute of Technology and Management Jaipur India

In image processing or computer vision, image segmentation is a vital issue for applications such as scene understanding, medical image evaluation, robotic perception, video surveillance, increased reality or compression, etc. Every year in road accidents caused because of human mistake, the numbers of dead and injured are rising. Drowsiness and driving are particularly risky and difficult to recognize. The second leading cause of road crashes in drowsiness after alcohol. Detecting driver drowsiness is a technology of safety for vehicles that helps placed an end to driver injuries that are dozy. One of the main causes of road accidents is driver drowsiness. It is a very serious issue for road safety. We have presented various methods for detecting the drowsiness of the driverin this paper and the comparisons among such methods are extremely challenging. For this purpose, we have compared machine learning methods based on facial expression, especially on eye state. Apart from eye detection, it performed experiments on mouth detection and face detection as well. This paper explores several methods for machine learning, like SVM, CNN, or HMM. From the analysis, we have found that the HMM model achieved more accurate results in comparisonto others.

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IMPROVED RESOURCE MANAGEMENT AND UTILIZATION BASED ON A FOG-CLOUD COMPUTING SYSTEM WITH IOT INCORPORATED WITH CLASSIFIER SYSTEMS

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Microprocessors and Microsystems 2021年

作者： M. Buvana K. Loheswaran Karanam Madhavi Sivakumar Ponnusamy Aradhana Behura R. Jayavadivel Assistant Professor Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Associate Professor Department of Computer Science and Engineering CMR College of Engineering &Technology Kandlakoya Medchal Road Hyderabad - 501 401 Telangana India Professor Computer Science and Engineering Department Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad 500090 Telangana state India Associate Professor Department of Computer Science and Engineering SRM Institute of Science &Technology Delhi NCR Campus Modinagar Ghaziabad Uttar Pradesh-201204 India Assistant Professor Department of Computer Science and Engineering GITA Engineering College Road Badaraghunathpur Janla Bhubaneswar Odisha-752054 India Associate Professor Department Computer Science and Engineering Vivekanandha College of Engineering for Women Tiruchengode Namakkal Tamilnadu-– 637205 India

With the rapid rise of computing devices-linked networks, the Internet of Things (IoT) has increased proportionally and speedily. After that, the size of the data generated and significantly increased computational effort. A solution for a very large workload is cloud computing significantly slowed and continues to be a problem in a distributed computer network. Addressing the workload at the network edge can enchase time feedback, and delaying the data load management task imposes performance constraints from the power source to the edge of the network, which does not support only limited battery power. Therefore, the workload must be distributed equally to the clouds and across the network. This document presents the XCS Classification System (LCS), by XCS and BCM-XCS, to ensure energy consumption at the end of the project which decreases the workloads of the processing time. Our test outcome showed the dominance of BCM-XCS XCS according to standard methods. A distributed method of allocating responsibility for reducing the processing time between node cloud and fog. In addition to the significant benefits of handling fluctuating turnaround times, the proposed procedures can simultaneously decrease 42% response times utilizing an average consumption of energy in the grid edge. With the propounded method, rechargeable batteries are used on the edge of the grid also can load approximately 18% more than the best method of the prior art.

关键词： Learning classification systems (LCS) Extended Classification System (XCS) Internet of Things (IoT) fog computing best classifier memory (BCM)

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An Automated Optimize Utilization of Water and Crop Monitoring in Agriculture Using IoT

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IOP Conference Series: Materials Science and engineering 2021年第1期1131卷

作者： Sanwta Ram Dogiwal Pankaj Dadheech Ankit Kumar Linesh Raja Abhishek Kumar Mahender Kumar Beniwal Associate Professor Department of Information Technology Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Associate Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India Assistant Professor Department of Computer Applications Manipal University Jaipur Rajasthan India Assistant Professor Department of Computer Science & IT Jain (Deemed to be University) Bangalore India

AI Solution for Farmers is an agricultural based project, created to help farmers and to help them in increasing their productivity. Since the technologies are running the world, why agriculture must be deprived of it. Agriculture is one of the most important areas that have major impacts on the economy and society of a country. Technological developments serve as tools to share knowledge and practices of agricultural products and make more satisfactory lives for farmers, traders, policymakers, and the overall society. It is evident that knowledge has become a crucial component in production, society, food security, health, poverty, and other millennium development goals. The use of technology may provide a better approach to solve the problems arising from sowing the seed to harvest the crop. The new technologies like Machine Learning and Data Science may be great help to have an eye on the deciding factors to the growth of crop.

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Consumer product preference prediction towards online shopping

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IOP Conference Series: Materials Science and engineering 2021年第1期1055卷

作者： S Deepa R Suguna P Sathishkumar C Jamunadevi E N Vidhya Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai India

Nowadays E-commerce plays a major role in a business organization. People prefer online shopping rather than offline shopping which helps them to purchase their product from anywhere around the world through mobile phones and laptop. Online shopping websites help people by saving time and product order can be done easily by clicking the product. Online shopping websites are built using the Single Page Application (SPA) framework and the objective of this research is to find the customer preference product prediction by tracking the frequent clicks of the product by the customer. By tracking the clicks of customer we can find their product choice and helps retailer to add the products according to user preference.

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Frequent Pattern Mining Using Db-Scan Algorithm.

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Journal of Physics: Conference Series 2021年第1期1916卷

作者： M Kavitha Margret A Ponni A Priyanka Assistant Professor Department of Computer Science and Engineering Sri Krishna College Of Technology Coimbatore. Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore.

Continuous item set mining is a generally exploratory procedure that centers on finding intermittent relationships among information. The unflinching advancement of business sectors and business conditions prompts the need of information mining calculations to find huge relationship changes to responsively suit item and administration arrangement to client needs. Change mining, with regards to visit item sets, centers around recognizing and revealing critical changes in the arrangement of mined item sets starting with one time span then onto the next. The revelation of continuous summed up item sets, i.e., item sets that regularly happen in the source information, and give an undeniable level reflection of the mined information, gives new difficulties in the investigation of item sets that become uncommon, and accordingly are not, at this point removed, from a specific point. This task proposes a novel sort of powerful example, to be specific the A DB-Scan Dynamic Sequential Combinatorial Analysis (DSCA-SCANNING), that addresses the development of an item set in continuous time-frames, by revealing the data about its successive speculations described by insignificant excess (i.e., least degree of reflection) on the off chance that it gets rare in a specific time-frame. To address DSCA mining, it proposes DSCA, a calculation that centers around evading item set mining followed by post handling by abusing a help driven item set speculation approach. To concentrate on the insignificantly repetitive incessant speculations and hence decrease the measure of the created designs, the revelation of a savvy subset, specifically the, is tended to also in this work.

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Churn Prediction in Telecom sector using Deep Neural Network with Flask Application

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IOP Conference Series: Materials Science and engineering 2021年第1期1166卷

作者： J.K. Kiruthika B.S. Neshamoney L. Madhan Kumar V. Mugunthan Assistant Professor KPR Institute of Engineering and Technology Coimbatore India Department of computer Science KPR Institute of Engineering and Technology Coimbatore India

Agitate Analysis is one of the overall utilized examination on Subscription Oriented Industries to break down client practices to anticipate the clients which are going to leave the help understanding from an organization. It depends on Deep Learning techniques and calculations and become so significant for organizations in the present business conditions as acquiring another client's expense is more than holding the current ones. The paper audits the significant examinations on Customer Churn Analysis on Telecommunication Industry in writing to introduce general data to perusers about the regularly utilized information mining techniques utilized, results and execution of the strategies and revealing an insight to additional studies. To stay up with the latest, contemplates distributed in most recent five years and basically most recent two years have been incorporated.

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