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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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Multi-disciplinary Conference in Emerging Research Trends (IMCERT), International

作者： Muhammad Shahbaz Muneer Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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Survey on LED lamps for residential applications: Characterization of performance 23

Survey on LED lamps for residential applications: Characteri...

引用

23rd International Scientific Conference on Electric Power Engineering, EPE 2023

作者： Hudač, Tomáš Drapela, Jiri Kukačka, Leoš Meyer, Jan Stiegler, Robert Brno University of Technology Faculty of Elect. Eng. and Communication Dept. of Electrical Power Engineering Brno Czech Republic Institute of Mechatronics and Computer Engineering Technical University of Liberec Liberec Czech Republic Institute of Electrical Power Systems and High Voltage Engineering Technische Universität Dresden Dresden Germany

ISBN: (纸本)9798350335934

Aim of the paper is to conduct a survey of the present market, focusing on the best-selling retrofit LED lamps and to analyze experimentally their fundamental EMC performance for subsequent classification of the LED drivers. Two stage experimental characterization testing approach is adopted. First, input current and output luminous flux in steady-state under nominal supply conditions is acquired. Next, transfer of input supply voltage interharmonics to output visible flicker components in terms of gain factor curves is obtained. Based on the achieved and discussed testing results, classification of the LED lamps is developed. © 2023 IEEE.

关键词： Light emitting diodes

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Condition monitoring of industrial exhaust motor-fan assembly using statistical feature optimization with nature-inspired optimizers

Condition monitoring of industrial exhaust motor-fan assembl...

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International Conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Abhrodeep Das Ayushman Agrawal Nishkarsh Kundoliya Ananta Dutta Surjya K Pal Rishi Kumar Dept. of Computer Science and Engineering Jalpaiguri Government Engineering College WB India Dept. of Mechanical Engineering Indian Institute of Technology Kharagpur WB India Dept of Ocean Engg & Naval Architecture Indian Institute of Technology Kharagpur WB India Research & Development Centre for Iron & Steel Steel Authority of India Limited (SAIL) Ranchi Jharkhand India

Exhausters are very crucial components in sinter plant of steel manufacturing industries. Problems in fan or motor side of the exhausters lead to machine stoppages or reduced speed, resulting in stoppage of the system and decreased sinter production. Common issues include imbalance, misalignments, looseness, eccentricity, and motor bearing failure. The proposed IoT-based system aims to continuously analyze vibration signals, predict issues, and prevent sudden stoppages and exhauster tripping, thereby enhancing overall plant efficiency. This study focuses on online monitoring and health prediction of industrial exhausters by selection of statistical features extracted from triaxial time-series vibration data by using multiple optimizers, followed by implementation of machine learning classifiers. By incorporating a novel feature selection process based on nature-inspired optimizers, this methodology aims to enhance the overall generalization performance of classification. The retrained models with cross-validation were evaluated with real-time industrial data, which shows 99.5% cross-validation accuracy and 95.83% real-time prediction accuracy. The proposed methodology holds great potential for detecting machine faults in industrial applications.

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Cancer in Jordan: Post-Hoc Comparative Analysis

Cancer in Jordan: Post-Hoc Comparative Analysis

引用

International Conference on Information and Communication systems (ICICS)

作者： Sara Shatnawi Esra’ a Odat Maram Thiabat Aya A. Al-Badarneh Computer Information Systems Dept. Jordan University of Science and Technology Irbid Jordan Dept. of Anatomy and Histology The University of Jordan Amman Jordan

Cancer disease results from abnormal cell growth with the ability to spread to other parts of the body. It is a serious illness in which malignant cells form in the body and kill the normal body cells, which are shaped like a tumor. The human body is affected by 10 types of cancer. This paper aims to study the distribution of cancer in Jordan among different factors, (age, gender, and regions). Also, studying some cases of non-Jordanians, presenting the numbers of deaths is caused by cancer to raise awareness among the people and to analyze the results using statistical analysis.

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Dynamic Image Deblurring based on Crosshatch Attention Adversarial Network and Hybrid Loss

Dynamic Image Deblurring based on Crosshatch Attention Adver...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Ummama Aslam Arslan Shaukat Basim Azam Dept. of Computer and Software Engineering College of Electrical and Mechanical Engineering (CEME) National University of Sciences and Technology (NUST) Islamabad Pakistan Institute of Integrated and Intelligent Systems Griffith University Brisbane Australia

Image deblurring techniques that uses deep learning have shown great potential but due to low generalizability, noise immunity and the correlation among different pixels is not addressed in detail that results in unwanted artifact that appears in the deblurred image. To tackle this problem an end-to-end approach is proposed for the recovery of sharp image from blurred one without the estimation of blur kernel. A special type of attention module known as crosshatch attention is used after Residual Block of Generator model for removing noise and for the collection of correlation of different pixels in an image. Hybrid Loss function is defined which focus on different part of image and improve edges and texture details. The performance of the model for deblurring is measured on GoPro dataset. Our proposed model has slightly higher objective and subjective evaluation i-e PSNR, SSIM value and the visual results.

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A Neural Network-based System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

A Neural Network-based System Identification Model to Predic...

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Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Emmanuel Adetiba Olumide O. Oluleye Ayodele H. Ifijeh Victoria Oguntosin Olatayo M. Olaniyan Olubunmi Adewale Akinola Gbenga Afolabi Jacob O. Odetola. Abdultaofeek Abayomi Dept. of Electrical & Information Engineering Covenant University Ota Ogun State Nigeria HRA Institute for -Systems Science Durban University of Technology Durban South Africa Dept. of Computer Engineering Federal University Oye-Ekiti Ekiti State Nigereia Dept. of Information and Communication Technology Mangosuthu University of Technology Durban South Africa

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers.

关键词： Temperature measurement Renewable energy sources Voltage measurement Current measurement Weather forecasting Solar energy Predictive models

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Token Based Energy-efficient Offloading Schemes for IoV Networks

Token Based Energy-efficient Offloading Schemes for IoV Netw...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Pranshu Srivastava Kaustubh Ijardar Anuj Joshi Nikumani Choudhury Anakhi Hazarika Dept. of Computer Science and Information Systems Birla Institute of Technology &#x0026 Science Pilani Hyderabad India Dept. of ECE Indian Institute of Information Technology Guwahati India

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The emergence of applications in vehicles requires computational capability which poses a major challenge in mo-bile edge computing. In this paper, we have proposed the token-based predictive offloading scheme with the aim of providing the optimal cost of offloading and reducing the average delay. Specifically, the proposed scheme uses a token-based approach for offloading the data from vehicles to MEC(Mobile Edge Computing) server. We have designed a separate table for MEC servers to show the status of consumed tokens and available tokens and another table for vehicles to store the status for another vehicle for vehicle to vehicle (V 2 V) communication. Dedicated Short Range Service (DSRC) technology is used to facilitate the communication between vehicle to vehicle and vehicle to infrastructure(*** gate). Extensive simulations are conducted in highway scenarios and the results demonstrate the superiority of this offloading scheme. The proposed scheme achieves low delay performance and decreased computation cost over other competing schemes in typical urban and highway scenarios.

关键词： Road transportation Costs Logic gates Software Delays Internet Servers

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Foreword to the 6th International Workshop on Engineering and Cybersecurity of Critical systems (EnCyCriS)

Foreword to the 6th International Workshop on Engineering an...

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Engineering and Cybersecurity of Critical systems (EnCyCriS), IEEE/ACM International Workshop on

作者： Coralie Esnoul Eunkyoung Jee John Eidar Simensen Ita Ryan Mary Sánchez-Gordón Nathan Lau Risk and Safety Department Institute for Energy Technology Halden Norway School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea University College Cork Cork Ireland Dept. of Computer Science and Communication Østfold University College Halden Norway Dept. of Industrial and Systems Engineering Virginia Tech Virginia USA

ISBN: (数字)9798331538101

ISBN: (纸本)9798331538118

This marks the 6th edition of the International Workshop on Engineering and Cybersecurity of Critical systems (EnCyCriS) at the ICSE conference. For industrial critical systems, although the previous premises of increasing system interconnectivity, decentralization, and introduction of new, more intelligent technologies still hold true, there is an increased societal awareness with regards to cybersecurity. This has led to clearer regulation, sharper requirements, and higher expectations for industry. At the same time, the availability of readily deployable competence, methods, tools, and solutions is lacking, which should be considered a critical societal risk. In the current international political climate, cyber security, and safety of critical infrastructures across industry, are more important than ever before. The EnCyCriS workshop facilitates discourse and discussion amongst researchers, practitioners, and students who are working on challenges and solutions related to industrial critical infrastructure. It has a particular focus on sharing industry experience and project results pertaining to cyber threats on critical systems, secure systems engineering, and attack detection and response mechanisms.

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Dynamic Optimizations in GPU Using Roofline Model

Dynamic Optimizations in GPU Using Roofline Model

引用

IEEE International Symposium on Circuits and systems

作者： Winnie Thomas Suryakant Toraskar Virendra Singh Computer Architecture and Dependable Systems Laboratory Dept. of Electrical Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781728192017;9781728192024

Massively parallel processors such as graphics processing units (GPUs) often face the challenge of resource underutilization due to varying resource proclivity of workloads. Running multiple applications on a GPU has been an efficient and known alternative to mitigate underutilization. This paper proposes a multi-application oriented framework that carries out dynamic optimizations based on the operational intensities of various applications. Our framework analyzes applications based on operational intensities to identify their bottleneck resources using Roofline model. We demonstrate that the proposed optimizations improve the utilization and system-wide throughput of the GPU co-running applications with irregular resource demands. The dynamic optimizations improve the performance by 14.8% on average and up to 72.4% over a state-of-the-art spatial multitasking technique.

关键词： Heuristic algorithms Graphics processing units Throughput Multitasking Resource management Optimization Faces

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A Demonstrative Study on Prediction of Disease Using Bayesian Machine Learning Techniques

A Demonstrative Study on Prediction of Disease Using Bayesia...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R. Dhanalakshmi N. Vijayaraghavan S. Narasimhan R Rajesh Centre for Cyber Physical Systems School. of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Mathematics KCG College of Technology Chennai India School. of Computer Science and Engineering Vellore Institute of Technology Chennai India

With the rapid advancement of biological information, accurate analysis of treatment data aids in early disease detection. To uncover knowledge for medical research, advanced Machine Learning algorithms are applied. Here in this research paper, to determine if a patient is affected by any Infectious disorders, we use the Naive Bayes Algorithm method.

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