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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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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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Scheduling of preventive maintenance of an power equipment of the agricultural enterprises

Scheduling of preventive maintenance of an power equipment o...

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2020 International Conference on Applied sciences, ICAS 2020

作者： Trunova, I. Miroshnik, O. Savchenko, O. Moroz, O. Pazyi, V. Shchur, T. Kasner, R. Baldowska-Witos, P. Kharkiv Petro Vasylenko National Technical University of Agriculture Scientific Educational Institute of Power and Computer Technologies Electrical Power Supply and Energy Management Department Rizdvjana str. no. 19 Kharkiv61052 Ukraine Lviv National Agrarian University Department of Operation and Technical Service of Machines Named after Professor Semkovych O.D. Volodymyr the Great str 1. Dublyany Zhovkva district Lviv region80381 Ukraine University of Science and Technology in Bydgoszcz Faculty of Mechanical Engineering Al. Prof. S. Kaliskiego no. 7 Bydgoszcz85-796 Poland

Presented paper deals with the topic of preventive maintenance of an power equipment of typical agricultural enterprises. A decision support method was designed, incorporating the analysis of annual schedules of load diagrams of the typical agricultural enterprises and the planning of preventive maintenance of the power equipment with month priority during which maximal consumption of electric energy take place. The correction of typical load diagrams on the basis of the fact sheet about electric loading of the consumer is possible. The designed method optimizes maintenance costs without supplementary investment and running costs. An algorithm of the designed method is offered and a case study of its implementation is described in the paper. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Preventive maintenance

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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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Comprehensive Analysis of Lightweight Cryptographic Algorithms for Battery-Limited Internet of Things Devices

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International Journal of Distributed Sensor Networks 2025年第1期2025卷

作者： Nahom Gebeyehu Zinabu Yihenew Wondie Marye Kula Kekeba Tune Samuel Asferaw Demilew Department of Software Engineering College of Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia aastu.edu.et Associate Professor in School of Electrical and Computer Engineering (SECE) Addis Ababa Institute of Technology (AAiT) Addis Ababa University (AAU) Addis Ababa Ethiopia aau.edu.et Department of Information Technology College of Computing Debre Berhan University Debre Berhan Ethiopia dbu.edu.et

The growth of billions of devices functioning in resource-constrained situations in the Internet of Things (IoT) era poses serious security issues. As a result of their limited processing power efficiency, memory, and battery life, traditional cryptographic algorithms like elliptic curve cryptography (ECC), Rivest–Shamir–Adleman (RSA), and Advanced Encryption Standard (AES) are sometimes too resource-intensive for these devices. Lightweight cryptography has become a crucial field as a remedy, created especially to maintain security while maximizing energy efficiency and reducing resource usage. With an emphasis on contrasting well-known lightweight block ciphers and pointing out areas for future research, this paper examines the most recent developments in lightweight cryptographic methods. In order to synthesize insights into present issues and future goals, it explores cutting-edge methods that improve performance, security, and energy efficiency. Among the main trends that were covered were the trade-offs between resource limitations and security strength, hardware–software co-optimization, block and stream cipher optimization, and hybrid encryption techniques. The study’s conclusion emphasizes how urgently more research is needed to create cryptographic solutions that can sufficiently safeguard IoT devices.

关键词： battery-limited devices block cipher algorithm embedded system security energy-efficient cryptography IoT security lightweight cryptography low-power cryptographic algorithms and performance analysis

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PIEMAP: Personalized inverse eikonal model from cardiac electro-anatomical maps

arXiv

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arXiv 2020年

作者： Grandits, Thomas Pezzuto, Simone Lubrecht, Jolijn M. Pock, Thomas Plank, Gernot Krause, Rolf Institute of Computer Graphics and Vision Graz University of Technology Austria Center for Computational Medicine in Cardiology Institute of Computational Science Università della Svizzera italiana Lugano Switzerland Institute of Biophysics Medical University of Graz Austria BioTechMed-Graz Austria

Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflows of personalized therapies. Copyright © 2020, The Authors. All rights reserved.

关键词： Heart

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Supplemental educational program to heighten the impact of research - an opportunity for OA imaging

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Osteoarthritis imaging 2023年第2期3卷 100155页

作者： Deborah Burstein Rudolph Mitchell Emery Brown Martha L Gray Associate Professor Department of Radiology Beth Israel Deaconess Medical Center Harvard Medical School 330 Brookline Avenue Boston MA USA. Associate Director for Research and Evaluation in the Teaching + Learning Lab Massachusetts Institute of Technology Cambridge MA USA. Edward Hood Taplin Professor of Computational Neuroscience and Health Sciences and Technology Department of Brain and Cognitive Sciences and Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA. J.W. Kieckefer Professor of Medical and Electrical Engineering Department of Electrical Engineering and Computer Science and Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA USA.

Objective:To develop and evaluate a supplementary educational program ("IMPACT") centered on enabling participants to consider specifically and articulate explicitly the best path for and potential impact of their research. Design:Participants (trainees) and faculty mentors were from all areas of biomedical research. The group worked longitudinally in small, rotating groups, through a process of developing a written statement ("Impact Statement"), an overview ("Impact Storyline") and an oral presentation ("Impact Case") of their work. Results:One hundred and eighty-seven Fellows enrolled in the program. Of the 179 (96%) Fellows who completed the program, 159 (89%) responded to a post-program survey; 94% indicated that IMPACT was a significant learning experience, 89% indicated that they were more able to identify the long-term potential of their research, 95% felt more able to talk about their work to diverse audiences. Conclusion:This voluntary educational program was appreciated by the participants and led to increased confidence in their ability to drive their science towards a clear impact and communicating that potential to others. This type of program may aid in redirecting some of the efforts and resources of imaging in OA from the large focus on technical developments to more direct biological and clinical questions which might be resolved with current technology.

关键词： Communication Diversity Impact Research

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Extracting Facial Features in Filter Modified Images using Neural Networks

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

作者： M Sumithra M Shyamala Devi A Poornima Shatadru Sarkar Harish Patel Preethi Mahankali Boggula Navya Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Facial identification plays a vital role in this digital era. Recent advancements in technology provide various filtering effects while capturing the images. Extracting the facial features from the filter modified image becomes challenging. To identify the important facial attribute values from those images, special features extraction techniques needs to be applied. Facial points are the key factors to recognize the face. When these factors are altered, facial detection becomes crucial. Initially apply the image pre-processing techniques to reduce the noise level in the images then perform feature selection. To overcome the curse of dimensionality, Occam's razor approach is followed to limit the selection of attributes. In different stages the features are going to be filtered by replacing the weaker attributes. Later Pearson Correlation, a filter based correlation technique is applied to evaluate the deviated numerical value from the target. The deviated features can be treated with Lasso effect to suppress the poor weighted features. Finally through recursive feature elimination, highest influencing attribute factor can be identified. In this approach some of the facial feature which is used in training the neural network can be discarded based on the deviation rates. In terms of accuracy the proposed system can recognize the dataset with 70% of accuracy with filter effected images.

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Advancing primary care with Artificial Intelligence and Machine Learning

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HEALTHCARE 2022年第1期10卷 100594-100594页

作者： Yang, Zhou Silcox, Christina Sendak, Mark Rose, Sherri Rehkopf, David Phillips, Robert Peterson, Lars Marino, Miguel Maier, John Lin, Steven Liaw, Winston Kakadiaris, Ioannis A. Heintzman, John Chu, Isabella Bazemore, Andrew The Center for Professionalism & Value in Health Care USA American Board of Family Medicine USA Duke Margolis Center for Health Policy USA Duke Institute for Health Innovation USA Department of Health Policy Stanford University USA Stanford Center for Population Health Sciences USA Department of Family Medicine Oregon Health & Science University USA Director of Research & Development Department of Family Medicine University of Pittsburgh USA Stanford Healthcare AI Applied Research Team Vice Chief for Technology Innovation Stanford Division of Primary Care and Population Health USA Department of Health System and Population Health Science University of Houston College of Medicine USA Hugh Roy and Lillie Cranz Cullen Distinguished University Professor Department of Computer Science University of Houston USA OCHIN Inc USA Oregon Health and Science University USA

Primary care is the largest healthcare delivery platform in the US. Facing the Artificial Intelligence and Machine Learning technology (AI/ML) revolution, the primary care community would benefit from a roadmap revealing priority areas and opportunities for developing and integrating AI/ML-driven clinical tools. This article presents a framework that identifies five domains for AI/ML integration in primary care to support care delivery transformation and achieve the Quintuple Aims of the healthcare system. We concluded that primary care plays a critical role in developing, introducing, implementing, and monitoring AI/ML tools in healthcare and must not be overlooked as AI/ML transforms healthcare.

关键词： Primary care AI ML PCP Population health

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Improved Upper Bounds on the Growth Constants of Polyominoes and Polycubes

arXiv

引用

arXiv 2019年

作者： Barequet, Gill Shalah, Mira Center for Graphics Geometric Computing Computer Science Department Technion—Israel Institute of Technology Haifa3200003 Israel Computer Science Department Stanford University CA United States

A d-dimensional polycube is a facet-connected set of cells (cubes) on the d-dimensional cubical lattice Zd. Let Ad(n) denote the number of d-dimensional polycubes (distinct up to translations) with n cubes, and λd denote the limit of the ratio Ad(n+1)/Ad(n) as n → ∞. The exact value of λd is still unknown rigorously for any dimension d ≥ 2;the asymptotics of λd, as d → ∞, also remained elusive as of today. In this paper, we revisit and extend the approach presented by Klarner and Rivest in 1973 to bound A2(n) from above. Our contributions are: • Using available computing power, we prove that λ2 ≤ 4.5252. This is the first improvement of the upper bound on λ2 in almost half a century;• We prove that λd ≤ (2d−2)e+o(1) for any value of d ≥ 2, using a novel construction of a rational generating function which dominates that of the sequence (Ad(n));• For d = 3, this provides a subtantial improvement of the upper bound on λ3 from 12.2071 to 9.8073;• However, we implement an iterative process in three dimensions, which improves further the upper bound on λ3 to 9.3835;Copyright © 2019, The Authors. All rights reserved.

关键词： Rational functions

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