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Dynamic Model to Predict the Flattening of the Covid-19 Outbreak Curve in India

Dynamic Model to Predict the Flattening of the Covid-19 Outb...

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2022 international conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Sharma, Ashutosh Singh, Meeta Manav Rachna International Institute of Research & Studies Department of Computer Science & Engineering Faridabad India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

This paper aims to forecast and visualize the confirmed cases, deaths, and recoveries of COVID-19 in India and also predict the end of the growth of COVID-19 cases in India. The methods used for the prediction of future COVID19 cases are machine learning techniques, improved logistic growth equation with a dynamic rate of infection, and automation of the calculations using Python programming language. The paper discusses the current models being used to predict the flattening of the curve, and the pros and cons of using these techniques. The paper then presents the solution and results achieved using our method. The average accuracy percentage of predictions of total confirmed cases was 85.6%, deaths were 84.5%, and recoveries were 83.8%. According to the predictions, the curve started to flatten in October and the curve will completely flatten in the 2nd week of January which confirms the situation that prevailed in India. © 2022 IEEE.

关键词： COVID-19

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Speech Recognition System for Upper-Limb Impairment: A Text Editor for programming via Voice

Speech Recognition System for Upper-Limb Impairment: A Text ...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： R.S. Rampriya R. Dharshan S. Santhosh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamilnadu India Department of Computer Technology Madras Institute of Technology Chennai Tamilnadu India

programming is one of the most important skills to learn for the present and future generations. However, those with hand disabilities may find it difficult to program with a traditional keyboard and mouse. Disabled people would greatly benefit from assistive technology that allows them to use their voices to control computers. The existing system recognizes user input as voice and translates it into a program. However, the existing system only recognizes a limited number of keywords in a programming language. It would also be difficult to debug errors if the voice was misinterpreted. Our suggested work in speech recognition using natural language processing (NLP) would perform better than the previously available systems. It is possible to build a foundation for programming without a keyboard by creating tools that use a publicly accessible speech recognition APL Voice control of the user interface for program execution and code debugging would also be quite beneficial. This can be done with the help of modules such as Pynput. Keyboard, PyAutoGUI, which can simulate mouse cursor movements and clicks as well as keyboard button presses. The accuracy of our proposed system is 87%. When converting voice commands to text, the Word Error Rate (WER) is 18%.

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Deep Learning for Early Skin Cancer Detection: A Comparative Study on Hybrid CNN Models 2

Deep Learning for Early Skin Cancer Detection: A Comparative...

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2nd international conference on Data, Information and Computing science, CDICS 2024

作者： Matiray, Sakshi Singh, Law Kumar GLA University Mathura India Amity School of Engineering and Technology Amity University Punjab Mohali India

ISBN: (纸本)9781643685762

Globally, skin cancer—especially melanoma—is a major health concern, and better patient outcomes depend on early and precise diagnosis. Manual examination is a common component of traditional diagnostic techniques, but it is labor-intensive and subject to error. Promising approaches to automate skin lesion analysis have been made possible by recent developments in deep learning. In order to overcome the shortcomings of current approaches, this work investigates the creation of a hybrid deep learning model that blends VGG16 and ResNet50 architectures. The suggested approach incorporates sophisticated preprocessing methods, such as picture normalization and data augmentation, to improve feature extraction and classification accuracy using datasets like ISIC 2018 and HAM10000. Important results show that the hybrid model outperforms state-of-the-art benchmarks in performance metrics, achieving 98.75% training accuracy and 97.50% validation accuracy. The model's enhanced precision (97.60%), recall (97.55%), and F1 score (97.58%) highlight how reliable it is at differentiating between benign and malignant tumors. The study also emphasizes how crucial it is to strike a balance between model complexity and computing performance in order to support practical clinical deployment. This study advances computer-aided diagnostic (CAD) systems for the identification of skin cancer by tackling issues including class imbalance and dataset unpredictability. The suggested method has a great deal of promise to improve early diagnosis, lessen the need for invasive treatments, and assist dermatologists in making clinical decisions. This paper thoroughly reviews current methodologies used in skin cancer detection, offering a timely resource for researchers developing automated and precise melanoma detection models. © 2025 The Authors.

关键词： Contrastive Learning

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Optimal Sizing and Placement of Droop-based Converters in DC Microgrids With ZIP Loads 2

Optimal Sizing and Placement of Droop-based Converters in DC...

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2nd international conference on Sustainable Mobility Applications, Renewables and Technology, SMart 2022

作者： Chapaloglou, Spyridon Abdolmaleki, Babak Tedeschi, Elisabetta Ntnu Dep. of Electric Power Eng. O. S. Bragstads plass 2E Trondheim Norway University of Trento Department of Industrial Engineering Trento Italy

ISBN: (数字)9781665471466

ISBN: (纸本)9781665471466

Achieving satisfactory voltage regulation in dc microgrids with distributed generators while using those as efficiently as possible, does not only depend on implementing appropriate control laws but also on making smart decisions in the planning stage. For the droop-based decentralized control framework, the droop design depends on the generator's location, the grid topology, and the load distribution, especially when constant power loads are considered. In this paper, a mixed-integer linear programming methodology is proposed for simultaneously deciding the locations and the size of distributed generators in dc microgrids to achieve optimal current sharing. The problem formulation includes a detailed discussion on the linearization of the various non-linear terms and the integration of the power flow equations. In addition, a set of constraints is proposed to help estimate the least upper bound of the generator's rating depending on the number of available generators. Then, the proposed formulation is used to explore the optimal placement and sizing of droop-based generators as a function of how many are available, revealing preference patterns associated with the microgrid structure. The proposed optimal decisions are also validated with time-domain simulations demonstrating the feasibility of the solution. © 2022 IEEE.

关键词： Distributed power generation

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Research on the Optimization of Wind-Solar Energy Storage Systems in Park Microgrids Based on Mixed Integer Linear programming and Monte Carlo Simulation

Research on the Optimization of Wind-Solar Energy Storage Sy...

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Sensors, Electronics and Computer engineering (ICSECE), IEEE international conference on

作者： Dongbin Wen Fan Zhang Chengyue Li Peizhi Sheng Huaneng Clean Energy Research Institute Beijing China School of Statistics and Information Shanghai University of International Business and Economics Shanghai China College of Mathematics and Computer Shantou University Shantou China School of Computer and Information Science School of Software Southwest University Chongqing China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

This paper delves into the optimization and economic benefits of wind-solar energy storage systems in park microgrids. By constructing and refining multiple mathematical models, the study provides scientific decision support for system configuration, aiming to meet the increasing demand for load and enhance overall economic benefits. Firstly, the paper proposes the Photovoltaic and Energy Storage Coordination Optimization Model (PCSO-Model), which combines Mixed Integer Linear programming (MILP) with Monte Carlo simulation, effectively reducing the total supply cost of the park. Subsequently, considering the aging effect of energy storage systems, load forecasting errors at different time scales, and the impact of electricity price fluctuations on economic viability, the PCSO model is improved and solved using an improved genetic algorithm. Simulation and optimization results demonstrate that the introduction of energy storage systems reduces costs by approximately 10 % on average, decreases the curtailment of wind and solar power by about 15%, and increases the utilization rate of wind and solar power generation by 20%.

关键词： Wind energy generation Photovoltaic systems Costs Monte Carlo methods Optimization models Microgrids Wind power generation Mixed integer linear programming Optimization Energy storage

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Research on rehabilitation training system for autistic children based on virtual reality 2

Research on rehabilitation training system for autistic chil...

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2nd international conference on Digital Signal and Computer Communications, DSCC 2022

作者： Shi, Lijuan Cui, Yuqing Peng, Xiong Liu, Feng Wang, Runmin Zhao, Jian College of Electronic Information Engineering Changchun University Changchun China College of Computer Science and Technology Changchun University Changchun China Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement Changchun China

ISBN: (纸本)9781510656734

Aiming at the problems of insufficient therapists in traditional rehabilitation training for autistic children, difficult to persist in training for a long time and limited training venues, a rehabilitation training system for autistic children based on virtual reality technology was designed. The system uses the Unity3D engine combined with VR equipment to build a rehabilitation training scene, and controls the interaction between the VR equipment and the virtual scene through the C# programming language. Children with autism only need to wear 3D glasses and a tracking locator to perform rehabilitation training in the virtual scene. The results show that the training method in the system can effectively improve the autism degree of autistic children, and has a positive effect on their rehabilitation. © 2022 SPIE.

关键词： Human computer interaction

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Green Time Extension Method of Transit Signal Priority Control for Phase Overlapping Intersection

Green Time Extension Method of Transit Signal Priority Contr...

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Automation, Robotics and Computer engineering (ICARCE), international conference on

作者： Yuting Yan Haiqing Liu Xue Cui Chunyan Yu Yue Zhao College of Transportation Shandong University of Science and Technology Shandong China School of Transportation and Logistics Engineering Shandong Jiaotong University Shandong China Jinan Labor Information Technology Co. Ltd Shandong China

Aiming at the transit priority control problem under the existence of phase overlapping constraints at signalized intersection, this paper proposes a green time extension method based on non-linear programming. On the basis of the traditional ring-barrier, an improved phase signal timing structure is proposed to address the issue of overlapping. Further, a non-linear programming model for green time extension is presented, in which the objective function aims to optimize the transit priority effect while minimize the adjustments required for non-transit phases, and the constrains include minimum green time, maximum green time, overlapping rules and migration of coordinated phase. The numerical analysis results indicate that under the constraint of a fixed overlapping duration of 4 seconds and the existence of phase coordination, the proposed method can extend the green time for transit priority phases by 166.7% more than the traditional timing scheme. Moreover, the standard deviation of green time adjustments for non-transit phases is reduced by 43.1% compared to the traditional timing scheme under the same extension effect.

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Simplifying code translation: Custom syntax language to C language transpiler

Simplifying code translation: Custom syntax language to C la...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Pecheti Charan Lalith Sujal Goel Misha Kakkar Seema Sharma Department of Computer Science and Engineering Amity University Noida Uttar Pradesh India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In today’s world, programming has become a very essential skill as it is used everywhere, from creating software’s for operating systems to analysis of patients’ health care data in biomedical engineering. Programmers usually face a lot of difficulties to understand beginner level coding languages having low-level syntax. The proposed study "Simplifying Code Translation: Custom Syntax Language to C Language Transpiler", targets the specified problem and provides the solution in a form of transpiler that converts the newly developed custom language (made up of high-level syntax) to C language. The developed tool minimizes the overall cost incurred during the code designing process. This transpiler consists of 4 main parts: preprocessor, lexer, parser and emitter. It first starts by creating preprocessor which handles preprocessor directives such as 'define' and 'include'. This preprocessed code is then passed onto lexer which tokenizes the code. The parser is responsible for creating Abstract Syntax Tree (AST) using the tokens provided. The final AST is then transformed to C program by the emitter. Then output C code maintains proper syntactic and semantic structure of the original custom syntax language code.

关键词： Program processors Codes Translation Semantics C languages Syntactics programming Libraries Software tools Standards

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Long-Term Reversible Hydropower System Operation Optimization Based on Fast Unit Commitment

Long-Term Reversible Hydropower System Operation Optimizatio...

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Smart Grid and artificial Intelligence (SGAI), international conference on

作者： Chengyuan Li Luyu Ma Xiaowei Yu School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Electric Power Research Institute State Grid Hubei Electric Power Co. Ltd Wuhan China Centrual China Branch State Grid Corporation of China Wuhan China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

To address the urgent demand for large-scale energy storage in renewable energy transition of China, this study proposes a schematic of reversible hydropower system and its long-term operation optimization model. By employing Fast Unit Commitment (FUC) algorithm, the model effectively reduces computational complexity while maintaining accuracy. Case studies demonstrate that the proposed method takes 1,000 times less time compared to nonlinear programming methods. With cascade reservoirs retrofitted into reversible hydropower stations, wind and solar curtailment rates decrease by 93.83% and 88.72 % , respectively, leading an annual profit increase of 0.47 billion CNY. These results show the algorithm's efficiency and the retrofit's economic benefit, providing critical insights for enhancing renewable integration in high-penetration grids.

关键词： Vibrations Systems operation Optimization models Hydroelectric power generation programming Reservoirs Smart grids Power systems Resource management Surges

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Automated Moisture Monitoring and Irrigation Vehicle for Smart Agriculture

Automated Moisture Monitoring and Irrigation Vehicle for Sma...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： D. Femi K T Habeeb Rahman Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

The population of the world is around 8 billion and it is projected to increase with average growth rate of 8.5 percent each year. This means that we need to produce more food for the survival, but the decrease in the availability of freshwater has been a barrier to it. Traditional way of irrigation requires farmers to intervene. In order to reduce the usage of water in the sector of agriculture, this paper talks about a way to monitor the moisture level of the soil in each crop and providing the required water to the crops using an automated vehicle. The vehicle is automated using Arduino programming Board. It follows a pre-drawn line to navigate through the agriculture sector.

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