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Cloud computing based renewable energy demand management system

AIP Conference Proceedings 2023年第1期2603卷

作者： shiva Johri balaji Ramkumar Rajagopal shahanawaj Ahamad b. Kannadasan Chandra Kumar Dixit Prabhdeep singh Department of MBA Oriental College of Management Bhopal 462 022 Madhya Pradesh India Data Architect Department of Artificial Intelligence Cognizant Technology Solutions Chennai 600107 Tamilnadu India Department of Information and Computer Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Department of Civil Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology GST Road Vandalur Chennai 600048 Tamilnadu India Department of Physics Dr. Shakuntala Misra National Rehabilitation University Lucknow 226017 Uttar Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun 248002 Uttarakhand India

In the area of information technology, cloud technology is the current operating *** cloud based infrastructures have benefits, their electricity consumption is becoming a big issue. Data centres consume a lot of energy since they supply the infrastructure and resource pool for cloud computing. Energy consumption forecasts for these data centres in the future are much more concerning. Energy-efficient ways of operation must be explored and used to minimize cloud computing’s energy usage, and hence its carbon footprint and greenhouse gas emissions, as well as information technology in general. Furthermore, using renewable energy instead of non-renewable energy can help to minimize the carbon emissions.

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Teaching style Recommender using Machine Learning

Teaching Style Recommender using Machine Learning

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International Conference on Advances in Information Technology (ICAIT)

作者： M Hariharan Kavitha sooda Nandhini vineeth G s Rekha Department of Computer Science and Engineering B.M.S College of Engineering Bengaluru Karnataka India

This paper aims to bridge the gap between teaching and learning in engineering education. Different students learn using different techniques or styles. There can be different kinds of learners within a single class. The teacher needs to understand this difference in order to deliver their contents in the classroom effectively. The proposed system aids the faculty in understanding the classroom composition in terms of the learning styles of students and suggests specific teaching methods that can be helpful for the entire class. The system uses a survey called the Index of Learning styles (ILs) to understand the learning style of the class. A machine learning model is used to suggest the possible teaching methods based on the learning styles to ensure effective classroom delivery by the faculty. The model is trained on a custom made dataset to understand the styles. In future, this model can be extended for subjects other than engineering and this could be used by all faculties regardless of their experience in the field.

关键词： Recommender systems Neural networks Information technology Machine learning engineering education social networking (online)

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RETRACTED ARTICLE: Integrated CNN- and LsTM-DNN-based sentiment analysis over big social data for opinion mining

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behaviour & Information Technology 2021年第9期40卷 XI-XIX页

作者： P. Kaladevi K. Thyagarajah a Department of Computer Science and Engineering K.S. Rangasamy College of Technology Tiruchengode India b Department of Electronics and Communication Engineering K.S. Rangasamy College of Technology Tiruchengode India

We, the Editors and Publisher of the journalbehaviour & Information Technology, have retracted the following article which was part of the special Issue on behaviour Monitoring and Management of Customers, People and Organizations using Deep Learning:P. Kaladevi & K. Thyagarajah (2019) Integrated CNN- and LsTM-DNN-based sentiment analysis over big social data for opinion mining, behaviour & Information Technology, DOI:10.1080/0144929X.2019.1699960After publication it came to our attention that the person named as the Guest Editor of the special Issue was impersonated by a fraudulent entity and the articles were not reviewed fully in line with the journal’s peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract the articles. The authors have been informed of this *** have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

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Optical Characterization of Chalcone-Doped PMMA Thin Films for Photonic Applications Using spectroscopic Technique of Drop Casting Method

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Macromolecular symposia 2020年第1期392卷 2000165-2000165页

作者： sharma, Kalpana Melavanki, Raveendra Yallur, basappa C. Kusanur, Raviraj Patil, N.R. shelar, vikas M. singh, Diksha Rosario, Jones Hussaini, Qurban Mohan., A. Thampan, Arun varma Department of Physics M S Ramaiah Institute of Technology BangaluruKarnataka560054 India Department of Chemistry M S Ramaiah Institute of Technology BangaluruKarnataka560054 India Department of Chemistry R.V. College of Engineering BangaloreKarnataka India Department of Physics B.V.B. College of Engineering & Technology HubliKarnataka580031 India Department of Physics Faculty of Mathematical and Physical Sciences M S Ramaiah University of Applied Science BengaluruKarnataka560058 India BengaluruKarnataka560027 India

Thin films of PMMA (polymethyl methacrylate) and chalcone prepared via drop casting method is reported here. The newly synthesized chalcone derivative namely 1-(3-methoxy-phenyl)-3-(4-nitro-phenyl)-propanone (C2) is used to dope the films with different concentrations. The prepared films are transparent so they are studied for applications in photonics. Optical characterization of the samples is done through different spectroscopy techniques. Absorption spectra of the samples is obtained using Uv–vis spectrophotometer. Other significant optical parameters such as refractive index, extinction coefficient, and band gap energies are calculated from the absorption spectra. The doping effect is observed on emission spectra that is obtained using fluorescence spectrophotometer. Also, FTIR spectra of the doped films is obtained and compared with the pure compound to notice changes in the peak values and intensity of the peaks. The present work is done to study the effect of doping on optical properties and figure out the application of samples in photonics. © 2020 Wiley-vCH GmbH

关键词： Fourier transform infrared spectroscopy

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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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Raspberry pi based secured cloud data

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Journal of Physics: Conference series 2021年第4期1964卷

作者： s Prasath kumar P Rayavel N Anbarasi b Renukadevi D Maalini Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai-600044 Tamilnadu India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai-600044 Tamilnadu India Department of Information Technology Anna University CEG Campus Tamilnadu India Department of Information Technology Sri Sai Ram Engineering College Chennai-600044 Tamilnadu India Department of Information Technology V.S.B Engineering College Karur Tamilnadu India

In the growing digital world, Cloud storage plays a major role. Cloud storage is preferred for storing enormous amounts of data that our system cannot hold. but the cost of using private Cloud storage services will be costly and our data is under the control of others. The main objective of our idea is that our data will be within ourselves rather than sending the data to unknown Cloud storage providers and which is also cost effective. We are using the hard disks as our own Cloud storage which will be controlled by raspberry pi and it can be accessed from anywhere. It is also more secure of using the material cloud with ourselves rather than depending on a private Cloud storage service which may or may not be secure but here our data is only with ourselves as we will implement our suitable security measures. The design of our project is also much compact and portable as it gives a major advantage, we can carry it anywhere. The main motto of our project is to give a user personal Cloud storage of his own in a cost-efficient manner which will be much more reliable than any other material cloud services.

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Evaluation of semi-Automated versus Fully Automated Technologies for Computed Tomography scalable body Composition Analyses in Patients with severe Acute Respiratory syndrome Coronavirus-2

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Clinical nutrition EsPEN 2025年 68卷 638-644页

作者： Amy Wozniak Paula O'Connor Jared seigal vasilios vasilopoulos Mirza Faisal beg Karteek Popuri Cara Joyce Patricia sheean Loyola University Chicago 2160 South First Avenue Building 115 Maywood IL 60153. Electronic address: awozniak@luc.edu. Loyola University Chicago Parkinson School of Health Science and Public Health 2160 South First Avenue Cuneo 4(th) Floor Suite Maywood IL 60153. Electronic address: poconnor1@luc.edu. Loyola University Chicago Parkinson School of Health Science and Public Health 2160 South First Avenue Maywood IL 60153. Electronic address: jseigal@luc.edu. Loyola University Medical Center Department of Radiology 2160 South First Avenue Maywood IL 60153. Electronic address: vvasilopoulos@lumc.edu. School of Engineering Science Simon Fraser University 8888 University Drive Burnaby BC V5A 0S6. Electronic address: mfbeg@sfu.ca. Department of Computer Science Memorial University of Newfoundland (MUN) EN-2014 S. J. Carew Building St. John's NL A1B 3X5 Canada. Electronic address: kpopuri@mun.ca. Loyola University Chicago 2160 South First Avenue Building 115 Room 254 Maywood IL 60153. Electronic address: cjoyce6@luc.edu. Loyola University Chicago Parkinson School of Health Science and Public Health 2160 South First Avenue Cuneo 439 Maywood IL 60153. Electronic address: psheean1@luc.edu.

RATIONALE AND ObJECTIvEs:Fully automated, artificial intelligence (AI) -based software has recently become available for scalable body composition analysis. Prior to broad application in the clinical arena, validation studies are needed. Our goal was to compare the results of a fully automated, AI-based software with a semi-automatic software in a sample of hospitalized patients. MATERIALs AND METHODs:A diverse group of patients with Coronovirus-2 (COvID-19) and evaluable computed tomography (CT) images were included in this retrospective cohort. Our goal was to compare multiple aspects of body composition procuring results from fully automated and semi-automated body composition software. bland-Altman analyses and correlation coefficients were used to calculate average bias and trend of bias for skeletal muscle (sM), visceral adipose tissue (vAT), subcutaneous adipose tissue (sAT), intermuscular adipose tissue (IMAT), and total adipose tissue (TAT-the sum of sAT, vAT, and IMAT). REsULTs:A total of 141 patients (average (standard deviation (sD)) age of 58.2 (18.9), 61% male, and 31% White Non-Hispanic, 31% black Non-Hispanic, and 33% Hispanic) contributed to the analysis. Average bias (mean ± sD) was small (in comparison to the sD) and negative for sM (-3.79 cm ± 7.56 cm) and sAT (-7.06 cm ± 19.77 cm), and small and positive for vAT (2.29 cm ± 15.54 cm). A large negative bias was observed for IMAT (-7.77 cm ± 5.09 cm), where fully automated software underestimated intramuscular tissue quantity relative to the semi-automated software. The discrepancy in IMAT calculation was not uniform across its range given a correlation coefficient of -0.625; as average IMAT increased, the bias (underestimation by fully automated software) was greater. CONCLUsIONs:When compared to a semi-automated software, a fully automated, AI-based software provides consistent findings for key CT body composition measures (sM, sAT, vAT, TAT). While our findings support good overall agreement as

关键词： Automated segmentation sARs-Cov-2 infection body composition computed tomography malnutrition nutrition status

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Prediction of Crop Fertilizer Consumption 4

Prediction of Crop Fertilizer Consumption

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4th International Conference on Computing, Communication Control and Automation, ICCUbEA 2018

作者： Hampannavar, Krutika bhajantri, vijay Totad, shashikumar G. B v Bhoomaraddi College of Engineering and Technology Dept. of Computer Science and Engineering Hubli India

ISBN: (纸本)9781538652572

Agriculture is the major factor contributing to Indian Economy. According to the statistics its GDP sector composition is 17.9%. India is the second larger producer of agricultural products. In order to produce more agricultural products without any wastage, technical advancements are required in this domain. In research of identification of nitrogen deficiency and prediction of fertilizers consumption by chilly, the images of chilly was captured at two stages. Leaf part of plant is prime factor in identification of nitrogen deficiency. During identification of deficiency various features like texture feature, area of the leaf etc of leaf image are extracted. Feature Extraction is the main key for the identification of deficiency of nitrogen. The farmers usually have little control over the usage of fertilizers. There is need for proper guidance for optimal usage of these fertilizers and is required by farmers in order to get more yields and prevent wastage. Research works carried out so far only identify the nutrient deficiency and disease detection. The proposed work identifies the nitrogen deficiency in chilly, determines the area of region of deficient region and predicts the amount of fertilizer consumption by these deficient plants. Thus the identification of nutrient deficiency is helpful to farmer for proper monitoring of crop growth and optimum usage of fertilizers. © 2018 IEEE.

关键词： Crops

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An IoT based Framework For Activity Recognition Using Deep Learning Technique

arXiv

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arXiv 2019年

作者： Geet D’sa, Ashwin Prasad, b.G. Department of Computer Science B.M.S. College of Engineering Bengaluru India

Activity recognition is the ability to identify and recognize the action or goals of the agent. The agent can be any object or entity that performs action that has end goals. The agents can be a single agent performing the action or group of agents performing the actions or having some interaction. Human activity recognition has gained popularity due to its demands in many practical applications such as entertainment, healthcare, simulations and surveillance systems. vision based activity recognition is gaining advantage as it does not require any human intervention or physical contact with humans. Moreover, there are set of cameras that are networked with the intention to track and recognize the activities of the agent. Traditional applications that were required to track or recognize human activities made use of wearable devices. However, such applications require physical contact of the person. To overcome such challenges, vision based activity recognition system can be used, which uses a camera to record the video and a processor that performs the task of recognition. The work is implemented in two stages. In the first stage, an approach for the Implementation of Activity recognition is proposed using background subtraction of images, followed by 3D-Convolutional Neural Networks. The impact of using background subtraction prior to 3D-Convolutional Neural Networks has been reported. In the second stage, the work is further extended and implemented on Raspberry Pi, that can be used to record a stream of video, followed by recognizing the activity that was involved in the video. Thus, a proof-of-concept for activity recognition using small, IoT based device, is provided, which can enhance the system and extend its applications in various forms like, increase in portability, networking, and other capabilities of the device. The source-code for this work has been made available3. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

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Cover Picture: Development of a Diethylcarbamazine Citrate-based Electrochemical sensor for Quick and Affordable Detection of sulfadiazine and Uric Acid in Environmental Monitoring (Chemistryselect 22/2024)

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Chemistryselect 2024年第22期9卷

作者： Amit b. Teradale shrishila N. Unki Pattan s. Ganesh Kusal K. Das Dr. swastika N. Das PG Department of Chemistry BLDEA's S.B. Arts and K.C.P. Science College Vijayapur Karnataka 586103 India Interaction Laboratory Future Convergence Engineering Advanced Technology Research Center Korea University of Technology and Education Cheonan-si Chungcheongnam-do 31253 Republic of Korea. Laboratory of Vascular Physiology & Medicine Department of Physiology Shri B.M.Patil Medical College Hospital & Research Centre Director - Center for Advanced Medical Research BLDE (Deemed to be University) Vijayapura 586103 Karnataka India. Department of Chemistry BLDEA's V. P. Dr. P. G. Halakatti College of Engineering and Technology Vijayapur 586103 Karnataka India

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