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Estimation of Temporal R-factor Based on Monthly Precipitation Data

Journal of Physics: Conference Series 2021年第1期2070卷

作者： Narendra Kumar Maurya Prakash Singh Tanwar Research Scholar Department of Civil Engineering Madhav University Pindwara (Sirohi) India Associate Professor Department of Computer Science & Engineering Lovely Professional University Phagwara Jalandhar Punjab India

This study assesses temporal variation in rainfall erosivity of Gurushikhar, Rajasthan, (India) on a monthly precipitation basis in the form of the USLE/RUSLE R-factor. The objective of the paper is to theoretically calculate rainfall erosivity when the unavailability of high temporal resolution pluviographic rainfall data such as Indian condition. In the study, the rainfall erosivity has been calculated using the Modified Fourier Index. The results show that the annual rainfall erosivity factor (R) value highest in the year 2017 and lowest in 1974. Conferring to an examination through NASA, earth's global superficial temperatures in 2017 ranked as second warmest since 1880. Therefore, the rainfall amount was more in 2017 compared to past years, and also rainfall erosivity value suddenly increased in 2017, achieved the highest value. They concluded that the heavy precipitation events in the year are lead to an increase in rainfall erosivity value and risk of soil erosion.

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LRFM model for customer purchase behaviour using K-Means algorithm

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

作者： C Jamunadevi S Tamil Selvan M Govindarajan C Saravanan B R Janaki Raman Assistant Professor Department of CT-PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India Associate Professor Department of Computer Applications Velalar College of Engineering and Technology Erode India Assistant Professor Department of Information Technology Velalar College of Engineering and Technology Erode India UG Scholar Department of Computer Technology-PG Kongu Engineering College Perundurai Erode India

The COVID-19 pandemics have a major collision on every aspect of life, including how people shop for their requirements. As the pandemic has reshaped life as we know, it's also initiated many trends – but the biggest of these trends may be online shopping. The shift toward online shopping was happening before the pandemic, but according to new statistics from IBM, the COIVD-19 has accelerated consumers shift toward online shopping by 5 years. The chief idea of the article is to inspect if the situation is approaching people to purchase things online and the continuation of shopping things online even after the end of pandemic. The information for the article has been gathered by circulating the survey on social networks. The questionnaire is comprised of 12 different questions, and 615 people responded to it. This work is based on LRFM (Length, Recency, Frequency, and Monetary) replica and separation of data based on the questionnaire using K-Means algorithm. Silhouette analysis helps to decide the extent of division among clusters. The results of the survey has a termination that people are fond of purchasing products online through the lockdown and people too agreed that the rate of online shopping will increase in the future when this pandemic is over.

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Contactless Attendance Management System using Artificial Intelligence

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Journal of Physics: Conference Series 2021年第1期1714卷

作者： M Rajamanogaran S Subha S Baghavathi Priya Jeevitha Sivasamy Data Science Consultant Chennai Tamil Nadu India Associate Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Professor Department of Information Technology Rajalakshmi Engineering College Chennai Tamil Nadu India Research Scholar Department of Computer and Information Science Annamalai University Chidambaram Tamil Nadu India

Attendance monitoring is one of the vital administrative processes in all educational institutions and organizations. A well-structured system will enable the institutions to have an increasing growth. It helps the students and teachers in all ways to have a good progress in the attendance, thereby reducing the teachers' time and effort. In physical classrooms, traditional method of calling the students names and marking their presence/absence is the routine process followed regularly. Attendance monitoring system is proposed using artificial intelligence. Firstly, a database containing the facial images of the students in a particular class is constructed. Knowledge gained using Convolutional Neural Network (CNN) is reused in a perfect manner using transfer learning. This system is designed to improve the students' engagement time inside the classroom, to communicate to the parents frequently, to avoid proxy attendance and to generate detailed reports for future reference.

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Advancing 3D dental scanning: The use of photogrammetry with light detection and ranging for edentulous arches

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Journal of Prosthetic Dentistry 2024年

作者： Saghiri, Mohammad Ali Saghiri, Ali Mohammad Samadi, Elham Vakhnovetsky, Julia Kowalczyk, Azam Farhadi, Maziar Shahid, Omar Memariani, Amin Morgano, Steven M. Biomaterial and Prosthodontic Laboratory Department of Restorative Dentistry Rutgers School of Dental Medicine Rutgers Biomedical and Health Sciences Rutgers University Newark NJ and Adjunct Associate Professor Department of Endodontics Arthur A. Dugoni School of Dentistry University of the Pacific San Francisco Calif United States Department of Computer Science William Paterson University Wayne NJ United States Researcher AfsarTech Rutherford NJ United States Dental student University of Michigan School of Dentistry Ann Arbor Mich United States Researcher Department of Computer Engineering Oakland University Rochester Mich United States Researcher AfsarTech Rutherford NJ United States Researcher Biomaterial and Prosthodontic Laboratory Department of Restorative Dentistry Rutgers School of Dental Medicine Rutgers Biomedical and Health Sciences Rutgers University Newark NJ United States and Chair Department of Restorative Dentistry Rutgers School of Dental Medicine Rutgers Biomedical and Health Sciences Rutgers University Newark NJ United States

Statement of problem: The advent of computer-aided design and computer-aided manufacturing (CAD-CAM) has necessitated the acquisition of digital scans. However, there are limitations and problems with acquiring accurate 3-dimensional (3D) casts from edentulous patients, especially in the presence of saliva. Purpose: The purpose of this in vitro study was to develop a novel approach for obtaining 3D casts of edentulous arches by using 2-dimensional (2D) images as an alternative to traditional 3D scanners with and without light detection and ranging (LiDAR). Material and methods: This study comprised 6 groups, each consisting of 10 specimens. For the control group, 3D casts were generated by scanning edentulous mandibular molds using a dental laboratory scanner. Experimental groups included photogrammetry with and without LiDAR under various conditions (Groups PG360, PG120, LPG120, PG360S, LPG120S). For Groups PG120, LPG120, and LPG120S, a custom-made manikin was used. In all photogrammetry groups, images of each mold were captured with a mobile phone (iPhone 14 Pro Max). The casts from the experimental groups were superimposed onto those from the control group using the Blender Foundation software program (Version 3.6.1). The mean distances were calculated and statistically analyzed using 1-way ANOVA followed by the post hoc Tukey test (α=.05). Results: The mean distances between the experimental groups and the control group varied significantly. The PG360 and PG120 groups showed a statistically significant difference from the control group (P<.001, 95% CI), with mean distances of 1.54 ±0.31 mm and 4.54 ±1.65 mm, respectively. The LPG120S group, which combined photogrammetry with LiDAR in the presence of artificial saliva, achieved a mean distance of 2.03 ±0.46 mm, which was not significantly different from the control group (P=.501, 95% CI). Conclusions: The successful scanning of edentulous mandibular molds using a mobile phone was achieved through a combination of

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Agriculture Cloud System based Emphatic Data Analysis and Crop Yield Prediction Using Hybrid Artificial Intelligence

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

作者： D. Naga Swetha Savadam Balaji Research Scholar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Deemed to be University Hyderabad Telangana-500075. Professor Associate Dean Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Deemed to be University Hyderabad Telangana-500075.

In agricultural nations, such as India, where agriculture leads more to India's Economic growth, it plays a significant part. The prediction of the crop is one of the main tasks in agriculture. Crop prediction methods are employed by detecting different soil parameters and factors connected to the atmosphere for predicting the appropriate crop. The unstable climate exposes farmers to danger in the environment. Therefore the correct history data must be maintained is essential. The data stored may be evaluated to predict agricultural production. In a cloud server, experts analyze sensed data, land type, land, climate, and farmers' economies with a prediction effect. The method forecasts the use of artificial intelligence algorithms for appropriate crops and fertilizers. A crucial strategy for handling numerous challenges connected to agriculture is the domain of artificial intelligence with its high-quality learning capacity. Technologies to help farmers find better solutions around the world are being created. To benefit from the parallel computational and storage management of huge data sets, the agricultural community must establish an architectural design that would enable the identification of new statistical structures to extract valuable information from data structures. These processes assist to explore the field and different challenges and effectively respond to certain challenges. In the improved integration of diverse data collection types from multiple sources, artificial intelligence offers attractive computing and analytical methods. The main principle of AI and systemic approaches to understanding its use in agriculture are presented in this paper. It also addresses several algorithms for artificial intelligence which may be used to create models to deal with various agricultural problems.

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Artificial Intelligence Driven Resiliency with Machine Learning and Deep Learning Components

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通讯和计算机（中英文版） 2019年第1期15卷 1-13页

作者： Bahman Zohuri Farhang Mossavar Rahmani Research Associate Professor University of New MexicoElectrical Engineering and Computer Science DepartmentAlbuquerqueNew Mexico USA Professor of Finance and Director of MBA School of Business and Management San DiegoCaliforniaUSA

The future of any business from banking,e-commerce,real estate,homeland security,healthcare,marketing,the stock market,manufacturing,education,retail to government organizations depends on the data and analytics capabilities that are built and *** speed of change in technology in recent years has been a real challenge for all *** manage that,a significant number of organizations are exploring the Big Data(BD)infrastructure that helps them to take advantage of new opportunities while saving *** transformation of information is also critical for the survivability of an *** the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right *** is no longer enough to rely on a sampling of information about the organizations'*** decision-makers need to get vital insights into the customers'actual behavior,which requires enormous volumes of data to be *** believe that Big Data infrastructure is the key to successful Artificial Intelligence(AI)deployments and accurate,unbiased real-time *** data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and *** this article,we discuss these topics.

关键词： Artificial intelligence resilience system machine learning deep learning big data.

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Fast Hybrid Adaboost Binary Classifier For Brain Tumor Classification

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： S. Jayaprada G. JayaLakshmi L. KanyaKumari Associate Professor Department of Computer Science & Engineering Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India Assistant Professor Department of Information Technology VR Siddhartha Engineering College Vijayawada Andhra Pradesh India Assistant Professor Department of Information Technology Andhra Loyola Institute of Engineering and Technology Vijayawada Andhra Pradesh India

The brain tumor is a dangerous disease and its characterization is a difficult undertaking for radiologists in light of the heterogeneous idea of the tumor cells. computer-Aided tasks and its implementation of the current models with their frameworks would suffice the design metrics to recognize and relate the different tumors including brain though the process with scanning of the brain would emphasize on MRI. The models with support vector machine-nearest neighbor, naïve bayes analysis are obtained aren't enough to produce the different scenario at each set of layers and its correlation values of the performance observed. We propose a design model with fast Adaboost binary classifier, ensemble approach with fast boosting algorithm with the pre-trained model's dataset to analyze the different problem which proposes a strategy for multiple application scenarios of feature extraction to provide a classification model for different tumors in the brain to improve different performance parametric values for each trained and test sets with the prediction algorithm with each design formulation.

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Question Answering Systems for Covid-19

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Journal of Physics: Conference Series 2021年第1期2062卷

作者： Poonam Gupta Ruchi Garg Amandeep Kaur Assistant Professor Computer Science and Engineering Department MMEC M.M.(Deemed to be) University Mullana Ambala Haryana India guptapoonam 231 Assistant Professor Electronics and Communication Engineering Department MMEC M.M. (Deemed to be) University Mullana Ambala Haryana India Associate Professor Computer Science and Engineering DepartmentMMEC M.M. (Deemed to be) University Mullana Ambala Haryana India

In the present scenario COVID-19 pandemic has ruined the entire world. This situation motivates the researchers to resolve the query raised by the people around the world in an efficient manner. However, less number of resources available in order to gain the information and knowledge about COVID-19 arises a need to evaluate the existing Question Answering (QA) systems on COVID-19. In this paper, we compare the various QA systems available in order to answer the questions raised by the people like doctors, medical researchers etc. related to corona virus. QA systems process the queries submitted in natural language to find the best relevant answer among all the candidate answers for the COVID-19 related questions. These systems utilize the text mining and information retrieval on COVID-19 literature. This paper describes the survey of QA systems-CovidQA, CAiRE (Center for Artificial Intelligence Research)-COVID system, CO-search semantic search engine, COVIDASK, RECORD (Research Engine for COVID Open Research Dataset) available for COVID-19. All these QA systems are also compared in terms of their significant parameters-like Precision at rank 1 (P@1), Recall at rank 3(R@3), Mean Reciprocal Rank(MRR), F1-Score, Exact Match(EM), Mean Average Precision, Score metric etc.; on which efficiency of these systems relies.

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Preface

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Journal of computer science & Technology 2020年第3期35卷 491-492页

作者： Shi-Min Hu Ying He Belen Masia Professor Department of Computer Science and TechnologyTsinghua UniversityBeijing Associate Professor School of Computer Science and EngineeringNanyang Technological University Singapore Assistant Professor Department of Computer ScienceUniversity of ZaragozaZaragoza

The Computational Visual Media(CVM)conference series is intended to provide a major international forum for exchanging novel research ideas and significant computational methods that either underpin or apply visual *** primary goal is to promote cross-disciplinary research to amalgamate aspects of computer graphics,computer vision,machine learning,image and video processing,visualization and geometric *** main topics of interest to CVM include classification,composition,retrieval,synthesis,cognition and understanding of visual media(e.g.,images,videos,3D geometry).

关键词： computer graphics visual

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Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules

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Computational Intelligence and Neuroscience 2023年第1期2023卷

作者： Vinay Kumar Baraa Riyadh Altahan Tariq Rasheed Prabhdeep Singh Devpriya Soni Hashem O. Alsaab Fardin Ahmadi Department of Computer Science Dyal Singh Evening College (University of Delhi) Delhi 110003 India Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University College Hilla Babylon Iraquomus.edu.iq Department of English College of Science and Humanities Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabiapsau.edu.sa Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun 248002 Uttarakhand Indiageu.ac.in Associate professor Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida Indiajiit.ac.in Department of Pharmaceutics and Pharmaceutical Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabiatu.edu.sa Addiction and Neuroscience Research Unit Taif University Taif 21944 Saudi Arabiatu.edu.sa Computer Science Faculty University Rana University Kabul Afghanistan

Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning, and metaheuristics to identify cancer in its early stages. At this point, a new convolutional neural network is constructed. The predator technique has the potential to increase network architecture and accuracy. Deep learning identified lung cancer spinal metastases in as energy consumption increased CT readings for lung cancer bone metastases decreased. Qualified physicians, on the other hand, discovered 71.14 and 74.60 percent of targets with energies of 140 and 60 keV, respectively, whereas the proposed model gives 76.51 and 81.58 percent, respectively. Expert physicians’ detection rate was 74.60 percent lower than deep learning’s detection rate of 81.58 percent. The proposed method has the highest accuracy, sensitivity, and specificity (93.4, 98.4, and 97.1 percent, respectively), as well as the lowest error rate (1.6 percent). Finally, in lung segmentation, the proposed model outperforms the CNN model. High-intensity energy-spectral CT images are more difficult to segment than low-intensity energy-spectral CT images.

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