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Multi Path Routing Technique Based RRP for P2P Network

IOP Conference Series: Materials Science and engineering 2020年第1期994卷

作者： Mary Subaja Christo R. M. Bommi K. Mariyappan Associate Professor Department of Information Science and Engineering Faculty of Engineering Technology Jain University Bangalore India Associate Professor Mechatronics Engineering Department Chennai Institute of Technology Kundratur India Assistant Professor Department of Computer Science and Engineering Jain University Bangalore India

All articles must contain an abstract. Peer to Peer (P2P) network is useful for developing applications and downloading files from the internet. In the existing P2P networks, communication is initiated and supported by peers. Security issues constitute the main challenge in the design of reliable communication techniques for P2P networks. In the past, Rumor Riding Protocol was used in distributed systems for performing coordination and communication. However, the existing Rumor Riding Protocol is used flooding technique to forward the response to the initiator it may leads high network traffic to avid this I have Enhanced Multi path forwarding technique to Rumor Riding Protocol and also enhancement with security features to make them suitable for downloading applications in P2P networks. In this method, it enhances the Rumor Riding Protocol with security features by including a Cryptographic Puzzle, a Challenge Question scheme using trust modeling and an intelligent beta reputation model for secured communication in P2P networks. For this purpose, five new techniques are proposed in this research work, all of which aim at enhancing the security of communication using Rumor Riding Protocol in the P2P networks.

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Machine Learning Models for Prediction of Cardiovascular Diseases

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

作者： K. Sivaraman V. Khanna Research Scholar Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai-73 Tamil Nadu India. Professor Department of Information Technology Bharath Institute of Higher Education and Research Chennai-73 Tamil Nadu India.

Support Vector Machines (SVM) [9], Ada Boost (AB) [10], and Gradient Boosting (GB). Maximum Relevance, Minimum Redundancy (mRMR), Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) are examples of fast correlation-based filters. The authors tested all attributes as well as the selected attributes generated by the above feature selection methods on the Cleveland heart disease dataset (CHDD) and the Hungarian heart disease dataset (HHDD). For their suggested framework, the authors were able to attain the greatest feasible model accuracy of 92.09 percent (all features) and 94.41 percent (selected features). The early detection of cardiac disease was demonstrated by several additional researchers.

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Design of an Infological Model for Prediction of Problems during Drilling Using Machine Learning and Systems Analysis

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IOP Conference Series: Earth and Environmental Science 2022年第4期988卷

作者： A L Zolkin V D Munister R V Faizullin N N Smoktal Ph.D. in Engineering Science Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Department of Mathematical Methods and Computer-Aided Design Private educational institution 'Donetsk Academy of Transport' Department of Enterprise Economics State educational institution of higher professional education 'Donetsk National Technical University' Associate Professor of the Department of Information Technologies in Public Administration Institute of Management Technologies MIREA — Russian technological university Teacher at the Department of 'Renewable Energy Sources and Electrical Systems and Networks' Sevastopol State University

The article deals with the actual problem of identification of probabilistic processes as a result of the operation of drilling rigs in the oil industry. The world experience of finding ways to solve optimal forecasting tools using machine learning is summarized. Mnemonic rule for the implementation of classification and ranking systems in the detection of feedbacks as probable indicators of complications of ongoing technological processes is implemented through the description of the formal model of the drilling process in form of a hidden Markov model. The results of evaluation of the developed mathematical apparatus in the form of predictive analytics and a cut of basic complications in the drilling process are presented. An infological diagram of the developed architectural solution of the analysis project is proposed. The results of the control algorithms formalization are given in conclusion. These results allow to ensure the effective procees modes of equipment operation and make it possible to save electricity and water.

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Fetal Brain Tissue Annotation and Segmentation Challenge Results

arXiv

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arXiv 2022年

作者： Payette, Kelly Li, Hongwei De Dumast, Priscille Licandro, Roxane Ji, Hui Siddiquee, Md Mahfuzur Rahman Xu, Daguang Myronenko, Andriy Liu, Hao Pei, Yuchen Wang, Lisheng Peng, Ying Xie, Juanying Zhang, Huiquan Dong, Guiming Fu, Hao Wang, Guotai Rieu, ZunHyan Kim, Donghyeon Kim, Hyun Gi Karimi, Davood Gholipour, Ali Torres, Helena R. Oliveira, Bruno Vilaça, João L. Lin, Yang Avisdris, Netanell Ben-Zvi, Ori Bashat, Dafna Ben Fidon, Lucas Aertsen, Michael Vercauteren, Tom Sobotka, Daniel Langs, Georg Alenyà, Mireia Villanueva, Maria Inmaculada Camara, Oscar Fadida, Bella Specktor Joskowicz, Leo Weibin, Liao Yi, Lv Xuesong, Li Mazher, Moona Qayyum, Abdul Puig, Domenec Kebiri, Hamza Zhang, Zelin Xu, Xinyi Wu, Dan Liao, KuanLun Wu, YiXuan Chen, JinTai Xu, Yunzhi Zhao, Li Vasung, Lana Menze, Bjoern Cuadra, Meritxell Bach Jakab, Andras Center for MR Research University Children's Hospital Zurich University of Zurich Zurich Switzerland Neuroscience Center Zurich University of Zurich Zurich Switzerland Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University of Munich Munich Germany Medical Image Analysis Laboratory Department of Diagnostic and Interventional Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Lausanne Switzerland Laboratory for Computational Neuroimaging Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School CharlestownMA United States Medical University of Vienna Vienna Austria Arizona State University United States NVIDIA United States Shanghai Jiaotong University China School of Computer Science Shaanxi Normal University Xi'An710119 China Research Institute NEUROPHET Inc. Seoul06247 Korea Republic of Department of Radiology The Catholic University of Korea Eunpyeong St. Mary's Hospital Seoul06247 Korea Republic of Boston Children's Hospital Harvard Medical School BostonMA United States 2Ai - School of Technology IPCA Barcelos Portugal Algoritmi Center School of Engineering University of Minho Guimarães Portugal School of Medicine University of Minho Braga Portugal ICVS 3B's - PT Government Associate Laboratory Guimarães Braga Portugal School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Sagol Brain Institute Tel Aviv Sourasky Medical Center Israel Sagol School of Neuroscience Tel Aviv University Israel Sackler Faculty of Medicine Tel Aviv University Israel School of Biomedical Engineering & Imaging Sciences King's College London LondonSE1 7EU United Kingdom Department of Radiology University Hospitals Leuven Leuven3000 Belgium Computational Imaging Research Lab Department of Biomedical Imaging and Image-guided Therapy Medical

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero. © 2022, CC BY.

关键词： Magnetic resonance imaging

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IoT as a Health Guide Tool

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IOP Conference Series: Materials Science and engineering 2020年第4期981卷

作者： Dr.S Hrushikesava Raju Dr Lakshmi Ramani Burra Saiyed Faiayaz Waris S Kavitha Associate Professor Dept. of Computer Science and Engineering Koneru Lakshmaiah Educational Education Vaddeswaram Guntur Assitant Professor Dept. of CSE PVP Siddhartha Institute of Technology Kanuru Vijayawada Assistant Professor Dept. of CSE Vignan's foundation for Science Technology and Research Vadlamudi Guntur

Now-a-days, food is very important in order to feed to the live people or animals. It is required to know the statistics about the food in terms of proteins, fat, and factors that increase immunity power. Hence, the proposed methodology called food IoT is developed that will scan the food and displays the statistics that guide details to the customer or client. The details describe about calories that are helpful, the details that will increase fat, the details like food is poisoned, and from the provided statistics, any one could judge the food is useful to feed. This proposed application guarantees the good health because health depends on feeding of food. This is more proactive because it guides which items to be added in case there is less kind of positive minerals and less proteins identified. Hence, a sensor to be required that will display the statistics about the food and is more helpful to the people in order to know category of factors involved in the food. The timed out and mineral less food is identified and could come to be known in this proposed approach. Also, this proposed approach helpful in terms of alerting as a guide based on past experience and reports future predictions about the health as like future weather predictions.

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Development of a module for broadband detection of digital transmissions for a radio broadcast monitoring system using parallel computing means

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IOP Conference Series: Materials Science and engineering 2021年第1期1047卷

作者： A N Sokolova E V Soboleva M M Nimatulaev M V Petukhova Department of Applied Mathematics and Computer Science Vyatka State University Kirov Russia Department of Digital Technologies in Education Vyatka State University Kirov Russia Department of Business Informatics Financial University under the Government of the Russian Federation Moscow Russia Associate Professor of the Department of Information Technology in the Agro-Industrial Complex Russian State Agrarian University–Moscow Timiryazev Agricultural Academy Moscow Russia

Short-wave communication is a means of trunk, zone, mobile and industrial communication for general and departmental use. Taking into account its technical potential, economic efficiency, strategic role as a necessary reserve, the task of monitoring the use of radio frequency resources in the short-wave range remains relevant. The main problem is the search for digital modes of transmission in the entire shortwave band. The purpose of the study is to develop a module for broadband signal detection operating in real time and integrate it into an operating radio monitoring system. The methodology of the work includes an analysis of the literature of foreign and domestic authors on solving monitoring problems, methods for detecting signals in the short-wave range; method of systematization and generalization of facts about the problems of choosing software and hardware for broadband signal detection. The features of short-wave communication have been studied, and the choice of comparison of signals has been made in this paper. The choice was made taking into account the computational complexity of the algorithms. Modules of broadband and narrowband signal detectors have been developed, the places of their integration into the existing radio monitoring system, as well as interfaces for their interaction, have been determined. During the pilot industrial operation the developed modules effectively coped with the solution of the problems of radio monitoring of the short-wave range in real time, so appropriate conclusions are formulated.

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Visuo-haptic Simulations to Improve Students’ Understanding of Friction Concepts

Visuo-haptic Simulations to Improve Students’ Understanding...

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Frontiers in education (FIE) Conference

作者： Luis Neri Alejandra J. Magana Julieta Noguez Yoselyn Walsh Andres Gonzalez-Nucamendi Victor Robledo-Rella Bedrich Benes Professor of the School of Engineering and Sciences Tecnologico de Monterrey Mexico City Mexico Associate Professor of Computer and Information Technology and Engineering Education Purdue University West Lafayette Indiana Graduate Student of Computer and Information Technology & Costa Rica Institute of Technology Purdue University West Lafayette Indiana Professor of Computer Graphics Technology and Computer Science Purdue University West Lafayette Indiana

ISBN: (纸本)9781538611753;9781538611746

Statics is a backbone course for several engineering disciplines and also a pre-requisite for dynamics and mechanics of materials. Researchers have identified a lack of understanding of statics as a significant source of difficulties in terms of both conceptual understanding, representation of free body diagrams (FBD) and problem-solving ability. Our approach to improve the learning of the concept of friction focuses on students' understanding of acting forces and specific components of such forces of a system. The presented quasi-experimental study investigates how the use of visuo-haptic simulations can improve students understanding and use of FBD. Specifically, we compared two visuo-haptic simulations; one that explicitly visually depicts FBD of multiple objects interacting with different surfaces, while the other only provides haptic feedback while students engage in the same forms of interaction. Our results suggest that using the visuo-haptic simulator with FBD leads to better learning results. These findings support the hypothesis that appropriately sequenced visuo-haptic simulators with well-designed visual cues can help students to better understand and use FBD.

关键词： Friction Force Haptic interfaces Visualization Rough surfaces Surface roughness Kinetic theory

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Mathematical Modelling of Material Removal Rate During Electric Discharge Machining Using Buckingham's Pi-Theorem

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IOP Conference Series: Materials Science and engineering 2020年第1期810卷

作者： Vaibhav S Gaikwad Vijaykumar S Jatti Satish S Chinchanikar Nikhil S Kakuste Keshav N Nandurkar Assistant Professor Department of Production Engineering K.K.Wagh Institute of Engineering Education and Research Centre Nashik India Associate Professor Department of Mechanical Engineering D.Y. Patil College of Engineering India Professor Department of Mechanical Engineering Vishwakarma Institure of Information Technology India U.G. Student Department of Production EngineeringK.K.Wagh Institute of Engineering Education and Research Centre Nashik India Professor Department of Production EngineeringK.K.Wagh Institute of Engineering Education and Research Centre Nashik India

In the present work an attempt has been made to model material removal rate (MRR) by using Buckingham's Pi-theorem during electric discharge machining (EDM) of shape memory NiTi alloy. Machining of Ni-Ti alloyis mostly attempted by non- conventional methods of machining, and, especially by EDM, as this advance material imposes machinability issues when cut by conventional processes. However, EDM process has low material removal *** present investigation EDM of NiTi alloy is carried out using electrolytic copper toolon die sink type of electric discharge machine. The experimental-based mathematical model to predict MRR is developed varying process parameters such as gap current, pulse on time, pulse off time, voltage, workpiece electrical conductivity and tool electrode electrical conductivity during the experiments. The model developed using dimensional analysis and Buckingham pie theorempredicts the value of MRR close to the experimental observations with the accuracy of 91 % shows that the developed model could be used to predict MRR during EDM of NiTi alloy within the domain of the parameters selected in the present study.

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Coordinate Access System for Live Video Acquisition

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Journal of Physics: Conference Series 2020年第1期1712卷

作者： B. Annapurna T Rama Reddy Ch. V. Raghavendran Raushan Kumar Singh Vedurai Veera Prasad Associate Professor Department of Computer Science and Engineering Aditya College of Engineering Surampalem East Godavari Dt. AP India. Professor Department of Computer Science and Engineering Aditya Engineering College (A) Surampalem East Godavari Dt. AP India Professor Department of Information Technology Aditya College of Engineering and Technology Surampalem East Godavari Dt. AP India Technical Director Spectrum Solutions Pondicherry India Assistant Professor Department of Computer Science and Engineering Aditya College of Engineering Surampalem East Godavari Dt. AP India.

Biometric systems are the most advanced access technology developed so far in the 21st century. It does not even require to carry key cards or passwords in mind. Today most of the commercial and private entries are protected by biometric recognition systems like fingerprint scans facial recognition, retina scans, voice matching, etc. Even our phones, laptops, and daily access devices are equipped with biometric systems. In banks, the PCs are secured by the combination of passwords and fingerprint scans. Biometric scans are considered the most secure access technology so far. Our paper is to examine whether they are secure? Should we rely on them with our hard-earned money and social identity? Is there any way we can use these services without actually compromising our data and security? Our observation is on our digital identity. Promoting digitization in every department brings our topic in the picture. All our information is saved in our phones, our daily routine, whom we talk, what we purchase, whom we chat, where we travel, etc. Almost every smartphone has biometric fingerprint locks which means our phones have our fingerprint scans in database and with internet blend it's tethered worldwide. Our fingerprints are connected to our bank accounts, PAN Cards, Passport, and SIM Cards using Aadhar Cards. If someone has our fingerprint they can easily reach our Aadhar Card and through that to all our personal information. Most of the phone companies are Chinese, Korean, German, and American. As per their country policies, they must share their data with the governing authorities. We aim to create a security system without actually using the biometric scans. The system is an advancement of the biometric system but with better accuracy and intelligence. We interface image acquisition tools to live track the red color things. The web camera or inbuilt system lens can be used as the acquisition system. When the red color object is moved in front of the lens it shows the co

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The impact of generative artificial intelligence on socioeconomic inequalities and policy making

arXiv

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arXiv 2023年

作者： Capraro, Valerio Lentsch, Austin Acemoglu, Daron Akgun, Selin Akhmedova, Aisel Bilancini, Ennio Bonnefon, Jean-François Brañas-Garza, Pablo Butera, Luigi Douglas, Karen M. Everett, Jim A.C. Gigerenzer, Gerd Greenhow, Christine Hashimoto, Daniel A. Holt-Lunstad, Julianne Jetten, Jolanda Johnson, Simon Kunz, Werner H. Longoni, Chiara Lunn, Pete Natale, Simone Paluch, Stefanie Rahwan, Iyad Selwyn, Neil Singh, Vivek Suri, Siddharth Sutcliffe, Jennifer Tomlinson, Joe van der Linden, Sander Van Lange, Paul A.M. Wall, Friederike Van Bavel, Jay J. Viale, Riccardo Department of Psychology University of Milan-Bicocca Italy Department of Economics MIT United States Institute Professor and Department of Economics MIT United States College of Education Michigan State University United States IMT School of Advanced Studies Lucca Italy Toulouse School of Economics France Department of Economics Loyola Andalucia University Spain Department of Economics Copenhagen Business School Denmark School of Psychology University of Kent United Kingdom Max Planck Institute for Human Development Berlin Germany Penn Computer Assisted Surgery and Outcomes Laboratory Department of Surgery Perelman School of Medicine University of Pennsylvania United States Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania United States Department of Psychology and Neuroscience Brigham Young University United States School of Psychology University of Queensland Australia MIT Sloan School of Management United States Department of Marketing University of Massachusetts Boston United States Department of Marketing Bocconi University Italy Economic & Social Research Institute Dublin Ireland Department of Humanities University of Turin Italy Department of Service and Technology Marketing Aarhus University Denmark Center for Humans and Machines Max Planck Institute for Human Development Berlin Germany Faculty of Education Monash University Australia Microsoft Research United States York Law School United Kingdom Department of Psychology University of Cambridge United Kingdom Department of Experimental and Applied Psychology Vrije Universiteit Amsterdam Netherlands Department of Management Control and Strategic Management University of Klagenfurt Austria Department of Psychology Center for Neural Science New York University United States Norwegian School of Economics Bergen Norway Department of Economics University of Milan-Bicocca Italy

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI. © 2023, CC BY.

关键词： Artificial intelligence

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