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

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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Critical Comparative Study of Robot Path Planning in Grid-Based Environment

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Journal of Physics: Conference Series 2021年第1期1804卷

作者： R D Dhaniya K M Umamaheswari Research Scholar School of Computing Department of Computer science and Engineering SRM Institute of Science and Technology Kattankulathur-603203 Assistant Professor School of Computing Department of Computer science and Engineering SRM Institute of Science and Technology Kattankulathur-603203

The path planning problem under two-dimensional maps is a fundamental issue in mobile robotics and other applications (unmanned vehicles, and so forth). The traditional algorithms (graph searching, artificial potential field and so forth) are used to find the shortest path that rely on grid-by-grid searching. The main disadvantage of the traditional methods is robot size is not considered and has much redundant in sharp curves which is difficult to guide a robot movement. In this paper, we compared the five baseline approaches of heuristic methods to find the optimal path which reduces the number of turns and time costs to attain a target. The experimental analysis shows that the second-order x derivative has an advantage over other baseline approaches that the search time and the number of turns are reduced.

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Fuzzy modeling and particle swarm optimization of Al2O3/SiO2 nanofluid

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International Journal of Thermofluids 2021年 10卷

作者： Salameh, Tareq Kumar, Polamarasetty P Sayed, Enas Taha Abdelkareem, Mohammad Ali Rezk, Hegazy Olabi, A.G. Department of Sustainable and Renewable Energy Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates Mechanical Engineering and Design Aston University School of Engineering and Applied Science Aston Triangle BirminghamB4 7ET United Kingdom GMR institute of technology EEE department Assistant professor Andhra Pradesh India Center for Advanced Materials Research University of Sharjah PO Box 27272 Sharjah United Arab Emirates Chemical Engineering Department Faculty of Engineering Minia University Egypt College of Engineering at Wadi Addawaser Prince Sattam Bin Abdulaziz University Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Egypt

This study aims to model and optimize the performance of a shell and helically coiled tube heat exchanger application using Aluminum oxide (Al2O3) and Silicon dioxide (SiO2) nanofluids. An adaptive network-based fuzzy inference system (ANFIS) in MATLAB was used to build the fuzzy logic model for density, viscosity, specific heat, and thermal conductivity properties of nanofluid. The accuracy of the model was evaluated by checking the mean square error (MSE) for training data, testing data, and all data. The particle swarm optimization (PSO) based on the effectiveness of heat exchanger and friction factor as objective functions were used to find the optimum nanofluid properties. The optimum properties can be achieved by minimizing the viscosity and specific heat and maximizing the thermal conductivity;while keeping the density at optimum value for this application. The optimal properties of nanofluids were found at 60 °C using a hybrid nanofluid consisting of 0.3 Al2O3 and 0.1 SiO2. The optimum values of the density, viscosity, specific heat, and thermal conductivity were 985.77 kg/m3, 0.000471 Pa s, 4129.36 J/kg.°K and 0.71069 W/m. °K, respectively. The coupling between the fuzzy model and the PSO method was effective to find the optimum properties of nanofluids under operation. © 2021 The Author(s)

关键词： Silica

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Detection and Classification of Breast Cancer Using improved Grey Wolf Algorithm

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IOP Conference Series: Materials science and engineering 2021年第1期1084卷

作者： T Senthil J Deepika R. Nithya Assistant Professor Deparment of Electronics and Communication Engineering EngineeringBannari Amman institute of TechnologySathyamangalam ErodeTamilnadu India Assistant professor.Department of Information Technology Engineering Bannari Amman institute of TechnologySathyamangalam ErodeTamilnadu India Assistant professor.Department of Computer Science Engineering Engineering Bannari Amman institute of TechnologySathyamangala ErodeTamilnadu India

Breast Cancer is the hazardous infection among young ladies and most significant reason for developing destruction rate. Physically anticipation of this affliction takes longer hours and structures are to be had in significantly less number, there's motivation to expand a mechanized forecast machine for early expectation and visualization of malignancy. The sort of considerate and threatening tumor are accomplished the use of type methodologies of device becoming acquainted with wherein the device is found from the past records and predicts the sorts of late sources of info. This paper is an overall report on the execution of models utilizing Logistic Regression, Support Vector Machine (SVM) and Gray wolf algorithm(GWO). The outcomes are assessed with the exactness, accuracy, affectability, explicitness and False Positive Rate boundaries for every calculation and are thought about. These procedures are coded in Java and executed in MATLAB, the Image handling Environment. Our investigations have demonstrated that Gray wolf is the best for prescient examination with an exactness of 92.7%.We surmise from our test results that SVM is the appropriate calculation for expectation and in general Gray wolf has performed well close to SVM.

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Attendance Management System Using Face Recognition Method

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Journal of Physics: Conference Series 2021年第1期2089卷

作者： Syed Mansoora Giribabu Sadineni Shaik Heena Kauser P.G Scholar Department of Computer Science & Engineering PACE Institute of Technology and Sciences A.P India Associate Professor Department of Computer Science & Engineering PACE Institute of Technology and Sciences A.P India Assistant Professor Department of Computer Science & Engineering PACE Institute of Technology and Sciences A.P India

When it comes to classroom management, the attendance check is a critical component. Time-consuming, particularly when it comes to open meetings, is checking attendance by calling names or by handing around a sign-in sheet to make it easier to commit fraud. An implementation of a real-time attendance check is described in this article in great detail facial recognition system and its outcomes. The system must be able to identify a student's face in order for it to work first snap a photograph of the pupil and save it in a database as a reference for future use. During the event, there were students may be identified by using the webcam, which captures photos of their faces auto-detects faces and selects students with names that are most likely to match, and lastly, depending on the facial recognition findings, an excel file will be updated to reflect attendance. To identify faces in webcam footage, the system uses a pre-trained Haar Cascade model. As a result, a 128-bit FaceNet has been generated by training it to minimise the triplet loss. The dimensions of the facial picture. When two facial pictures have similar encodings If the two facial pictures are from the same student or different. Use of the system as part of a class, and the outcomes have been extremely positive. There has been a poll done to find out more about There are both advantages and disadvantages to using a college attendance system.

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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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Classification of Malignant Melanoma using Convolutional Neural Networks

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

作者： V Vijeya Kaveri V Meenakshi K A Saran Karthik S Shri Raam Professor Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Assistant Professor Department of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology India UG Student Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Human cancer is one of the world's most deadly diseases caused due to genetic disruption of skin cells and several molecular mutations. Skin cancer remains the most predominant form of cancer in human beings. The major goal is to detect skin cancer in early stages by research and analyse it using various techniques such as segmentation and feature extraction. The diagnosis of malignant melanoma skin cancer is done by dermatologist by examining skin and physical biopsy to determine the accurate stage of melanoma. It is developed because of high accumulation of melanin in the dermis layer of the skin. ABCD law is used in along with dermoscopy technology to detect malignant melanoma skin cancer. Image Acquisition Technique, pre-processing, segmentation, distinguishing function for skin Feature Selection, which specifies lesion characterization and classification methods are all conducted in this project for melanoma skin lesion characterization. We used symmetry detection, border detection, colour and diameter detection, as well as feature extraction to remove texture based features using a digital image processing technique. The deep Neural Network was proposed here to characterize the benign or malignant stage.

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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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Predicting biochar yield from biomass pyrolysis: A comprehensive data-driven approach using machine learning and SHAP analysis

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Results in engineering 2025年 26卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Krunal Vaghela Gowrishankar J Prabhat Kumar Sahu Kamred Udham Singh R. Sivaranjani Rohit Chauhan Siya Singla Samim Sherzod Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of Pharmacy The Islamic University Najaf Iraq College of Pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi University Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Information Technology Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India School of Computing Graphic Era Hill University Dehradun India Adjunct Professor Centre for Promotion of Research Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India Department of CSE Raghu Engineering College Visakhapatnam Andhra Pradesh 531162 India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali Punjab 140307 India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India Faculty of Engineering Nangarhar University Nangarhar Afghanistan

Biochar, a pivotal product of biomass pyrolysis, is crucial in sustainable agriculture, carbon sequestration, and renewable energy systems. This study employs a comprehensive data-driven approach to predict biochar yield using machine learning algorithms. The dataset, comprising 14 chemical, physical, and reaction parameters collected from reputable studies, was processed using outlier detection via the Monte Carlo Outlier Detection (MCOD) algorithm and hyperparameter tuning to optimize model performance. Among the evaluated models, the Decision Tree algorithm emerged as the most robust predictor, achieving an R² value of 0.771, a mean square error (MSE) of 16.182, and an average absolute relative error percentage (AARE%) of 11.848 % in testing. The SHAP analysis identified residence time, pyrolysis temperature, and ash content as the most influential predictors of biochar yield. Simplified explanations and interpretability offered by these models support their practical application. These findings underline the potential of machine learning to optimize biochar production, enhance agricultural systems, and create scalable methods for sustainable waste management, directly contributing to renewable energy and environmental remediation industries.

关键词： Biochar yield Machine learning Biomass pyrolysis Decision tree SHAP analysis

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Analysis of hiring a freelance coder website

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AIP Conference Proceedings 2021年第1期2387卷 140039页

作者： C. Jamunadevi Malathi Eswaran B. Renukadevi S. Priya B. Varniha Assistant Professor (SrG) Department of Computer Technology-PG Kongu Engineering College Perundurai Erode Tamil Nadu India Assistant Professor Department of Computer Technology-PG Kongu Engineering College Perundurai Erode Tamil Nadu India Assistant Professor Department of Information Technology Sri Sai Ram Engineering College Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamil Nadu India Student Department of Computer Technology-PG Perundurai Kongu Engineering College Erode Tamil Nadu India

The 2019 novel Corona-virus infection spreads very quickly all over the world, many employers have decided to secure themselves as freelancers and to shift to remote work. The current system offers project outsourcing in a manual work. Hiring a Freelance Coder is designed to develop an interface for the organization and the freelancing professionals using responsive web design. This article highlights the project outsourcing through virtual environment there is no need of real physical meet up. The working flow of this website, The Company posts its project requirement to this application by the given category. The freelancer can search and view any project posted on this application and the freelancer can bid the project to outsource from the company. The entire bid details can be verified by the company and can approve the bid. The company can monitor the work status given by the freelancer and ask queries. The internship candidate can view the internship list and they can apply and send a resume. The present study was conducted during the month of December 2020, where questionnaires are formed and data collected using Google forms. Analysis of the data is made through Simple percentage method, Chi square test and ranking method. After analysis it found that the majority holding 31-40 age group of freelancers prefer online freelancing.

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