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

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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Detection and prevention from different attacks in VANET: A Survey

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Journal of Physics: Conference Series 2021年第1期2040卷

作者： Arun Singh Kaurav Sushama Rani Dutta Research Scholar Department of Computer Science & Engineering KL Deemed to be University Hyderabad India Assistant Professor Department of Computer Science & Engineering KL Deemed to be University Hyderabad India

Routing attacks will have distressing effects over the network and bequest a significant challenge once planning strong security mechanisms for vehicular communication. In this paper, we examine the effect and malicious activities of a number of the foremost common attacks and also mention some security schemes against some major attacks in VANET. The attacker's aim is only to modify the actual route or provides the false data about the route to the sender and also some attackers are only flooding unwanted packets to consume resources in available network. Various routing approaches are also mentioned in the paper because the routing of data is very important to deliver the traffic information to leading vehicles. It's advised that a number of the ways that to approach this made field of analysis issues in VANET might be to fastidiously design new secure routing protocols in which attacks are often rendered meaningless and because of the inherent constraints found in the network, there's a desire for light-weight and sturdy security mechanisms.

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A Study on Tree Rings: Dendrochronology using Image Processing

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IOP Conference Series: Materials Science and engineering 2021年第1期1022卷

作者： K Divya Sukhvir Kaur Research Scholar Department of Computer Science and Engineering Lovely Professional University Phagwara India Assistant Professor Department of Computer Science and Engineering Lovely Professional University Phagwara India

This paper outlines an introduction to dendrochronology. The tree rings are being identified through image processing and statistical simulations. The study of the tree rings is known as dendrochronology but the techniques do need to be modified for better performance. Image analyses convert the tree ring into digital data using imaging tools. This method involves the scaling, piling, width calculation. Technology is required to evaluate the factors of different tree ring pattern. In this review paper, literature survey has been provided which deals with the contribution of different researchers, which methodologies they preferred and what were their limitations. And two attributes are being calculated named MSE and PSNR, as these obtainable values indicates to work further with the enhanced image. Also, researcher can get idea what all the attributes are important to implement this technique.

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A deep learning approach for Indian sign language gestures classification with different backgrounds

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Journal of Physics: Conference Series 2021年第1期1950卷

作者： R. Dhiman G. Joshi C. Rama Krishna PG Scholar Department of Computer Science & Engineering NITTTR Chandigarh INDIA. Assistant Professor Department of Electronics & Communication Engineering UIET Panjab University Chandigarh INDIA Professor & Head Department of Computer Science & Engineering NITTTR Chandigarh INDIA.

The sign language recognition system recently has drawn the attention of various researchers as there is no universal sign language, moreover, it consists of many patterns and postures. Many methods for extracting features and classifying sign language have been proposed in the literature, most of them are based on machine learning techniques. In this article, a deep learning method has been adopted by designing a Convolution Neural Network (CNN) model to extract the sign language features where for classification softmax layer is used. All alphabets in simple as well as complex backgrounds have been considered, where data is collected from 100 subjects in different lighting conditions. The effect of various optimization techniques (Adam, Sgdm, RMSProp), activation functions (ReLU and Leaky ReLU) for generalization ability is also observed. The proposed approach has succeeded in attaining the testing accuracy of 99.10%, 92.69%, and 95.95% on the Indian sign language dataset with simple, complex backgrounds and on a mixed background scenarios, respectively. The model is also tested on NUS dataset-I, NUS dataset-II, their combination, and achieved the accuracy of 100%, 95.95%, and 97.22% respectively.

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Performance Evaluation of Novel Convolution Neural Network Architecture for Melanoma Skin Cancer Diagnosis on Different Hardware Processing Units

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Journal of Physics: Conference Series 2021年第1期1950卷

作者： Mohammad Naved Qureshi Mohammad Sarosh Umar Assistant Professor EES University Polytechnic Faculty of Engineering & Technology AMU Aligarh India Professor Department of Computer Engineering ZHCET Faculty of Engineering & Technology AMU Aligarh India.

Skin cancer is one of the major public health concerns among the white population with more than a hundred thousand cases every year. Melanoma is one of the deadliest forms of skin cancer which is responsible for thousands of deaths in US alone in recent years and, therefore, early diagnosis is very important to increase the survival rate of melanoma patients. In last few years' Deep neural networks have been utilized by researchers to build best models for classifying or diagnosing skin cancer. In this paper Deep neural network-based CNN architectures to classify Melanoma is proposed. The CNN architecture proposed in this work is implemented on CPU, GPU and TPU and the performance of the model is shown on all these platforms. The proposed model is compared to other works done so far for melanoma diagnosis in terms of various performance metrics like prediction accuracy, specifity, sensitivity and it is observed that the proposed models outperformed them. The dataset utilized in training and testing the proposed models is ISIC archive dataset which contains 4750 skin images for two classes i.e. melanoma and benign. The results of our study have proved that utilizing GPU and TPU speeds up the training 38 times faster than CPU and can accelerate the performance of CNN for features extraction, optimization and classification of skin cancer images and the proposed model has outperformed the other models compared with it.

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Performance analysis on automating the PDF download process using Robotics Process Automation

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IOP Conference Series: Materials Science and engineering 2021年第1期1059卷

作者： T Malathi P L Navaneethakrishnan E Arun V N Devakipriyadharshini Assistant Professor Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India.

Many people download one or two files from the internet and it does not take ' much time to download them. What if there is a need for more number of files. For that problem RPA could be the solution. Robotic process automation (or RPA) is a form of business process automation technology based on metaphorical software robots (bots) or on artificial intelligence (AI)/digital workers. The main objective of this paper is to download 100 random pdf files from the internet using excel, csv and datatable automations in Uipath and compare them with the manual way of downloading and finding the most efficient way of downloading the large number of files.

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Data dimensional reduction and principal components analysis

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Procedia computer Science 2019年 163卷 292-299页

作者： Nema Salem Sahar Hussein Assistant Professor Electrical and Computer Engineering Department Effat University Jeddah 21478 KSA Assistant Professor National Science Mathematics and Technology Unit Effat University Jeddah 21478 KSA

Research in the fields of machine learning and intelligent systems addresses essential problem of developing computer algorithms that can deal with huge amounts of data and then utilize this data in an intellectual way to solve a variety of real-world problems. In many applications, to interpret data with a large number of variables in a meaningful way, it is essential to reduce the number of variables and interpret linear combinations of the data. Principal Component Analysis (PCA) is an unsupervised learning technique that uses sophisticated mathematical principles to reduce the dimensionality of large datasets. The goal of this paper is to provide a complete understanding of the sophisticated PCA in the fields of machine learning and data dimensional reduction. It explains its mathematical aspect and describes its relationship with Singular Value Decomposition (SVD) when PCA is calculated using the covariance matrix. In addition, with the use of MATLAB, the paper shows the usefulness of PCA in representing and visualizing Iris dataset using a smaller number of variables.

关键词： PCA Data dimension reduction Iris dataset SVD

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