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IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model

Measurement: Sensors 2023年 29卷

作者： Nithyashri, J. Poluru, Ravi Kumar Balakrishnan, S. Ashok Kumar, M. Prabu, P. Nandhini, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpet District Kattankulathur603203 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria Bengaluru560029 India Department of Computer Science and Engineering Karpagam Academy of Higher Education Tamilnadu Coimbatore India

This research proposes an IoT based technique for predicting rainfall forecast in coastal regions using a deep reinforcement learning model. The proposed technique utilizes Long Short-Term Memory (LSTM) networks to capture the temporal dependencies between the rainfall data collected from the coastal regions and the prediction model parameters. The proposed technique is evaluated on a dataset of rainfall data collected from the coastal regions of India and compared to traditional methods of rainfall forecasting. The accuracy and reliability of these models are evaluated by comparing them to prior models. Precipitation in coastal locations may be predicted with an average accuracy of 89% using the suggested model, as shown by the results. The suggested framework is computationally efficient and can be trained with little input. The results of this research give strong evidence that the proposed model is an effective tool for coastal precipitation forecasting. © 2023 The Authors

关键词： Internet of things

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Estimation Analysis of Edge and Line Detection Methods in Digital Image Processing

Estimation Analysis of Edge and Line Detection Methods in Di...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Prateek Garg Rakesh Kumar Yadav Devaraj Verma C D. Nirmala Nilesh P. Sable Krishn Murari Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Civil Engineering Vivekananda Global University Jaipur India

This paper provides an estimation evaluation of the area and line detection methods in virtual photo processing. This analysis evaluates the performance of numerous facet and line detection algorithms in phrases of facet/line first-rate, accuracy, computational complexity, and robustness to noise in the context of artificial and actualinternational snapshots. Mainly, it discusses the overall performance of 4 edge detection methods: Roberts, Prewitt, Sobel, and Canny; and 3 line detection techniques: Hough remodel, Linear Estimation, and Probabilistic Hough remodel. as compared to the classical algorithms, the Probabilistic Hough rework algorithm is determined to have the excellent accuracy and robustness to noise. Furthermore, a contrast of computational complexities shows that the Hough transform has the bottom complexity and computational time, even as the Linear Estimation algorithm has the best complexity and computational time. The primary outcome of this study is that the brink/line first-rate, accuracy, computational complexity, and robustness are very dependent on the sort of entered snapshots and the respective selected parameters.

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A Color-Based Re-Ranking Process for People Re-Identification : Paper ID 21 9

A Color-Based Re-Ranking Process for People Re-Identificatio...

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9th European Workshop on Visual information Processing, EUVIP 2021

作者： Mortezaie, Zahra Hassanpour, Hamid Beghdadi, Azeddine Shahrood University of Technology Faculty of Computer Engineering Shahrood Iran Institut Galilee Universite Paris Laboratory Information Processing and Transmission Cite Villetaneuse France

ISBN: (纸本)9781665432306

People re-identification is a fundamental stage in video surveillance systems. This stage is used for determining the labels of the people's images usually based on their visual similarities. The appearance salience caused by wearing the clothes and carrying objects, with specific colors can be used for improving the performance of re-identification systems. To address the mentioned point, in this paper, we propose a color-based re-ranking process. In the proposed approach first, the input images are semantically segmented as: person's body, possible carried objects, and background. Then the most frequent colors of person's body and possible carried objects are used to re-order the ranked results obtained from two commonly used re-identification approaches i.e., Gaussian of Gaussian (GOG) and Hierarchical Gaussian Descriptors (HGDs). Experimental results on various people re-identification datasets show that our proposed re-ranking approach can improve the performance of re-identification systems. © 2021 IEEE.

关键词： Color

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Expression of Concern for: Design Strategies for Machine Learning Algorithms for Text Classification

Expression of Concern for: Design Strategies for Machine Lea...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Aparna A. Junnarkar Sunil Kumar Jakhar Manish Nagpal Rakesh Kumar Yadav Vinod Moger M. Sindhu Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Department of Mechanical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

The text type is a vital undertaking within the realm of gadget-gaining knowledge of algorithms and can contain classifying text files into predefined classes. Text category is used in many herbal language processing obligations, sentiment evaluation, topic modeling, and record summarization. Design techniques for device mastering algorithms for textual content categories contain information preprocessing, feature engineering, model choice, and evaluation. Records preprocessing requires expertise in text information and includes the stairs of tokenizing the text records, removing stopwords, acting stemming and lemmatization, part-of-speech tagging, and vectorization. Feature engineering, which is a step that requires domain expertise, applies strategies consisting of feature selection, function extraction, and feature transformation. Version choice and optimization techniques, including hyperparameter tuning and version choice, are used to choose the most appropriate studying algorithm for the task. Lastly, assessment metrics evaluate the model's effectiveness, including accuracy, precision, remembering, and f-ratings. On average, these layout techniques are critical components while growing a successful text-type model.

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Choosing a Suitable Requirement Prioritization Method: A Survey

SSRN

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SSRN 2023年

作者： Mahmoud Alhenawi, Esra'a Awawdeh, Shatha Khurma, Ruba Abu Castillo, Pedro A. Department of Computer Science King Abdullah II School of Information Technology The University of Jordan Amman Jordan Applied Science Private University Jordan Faculty of Information Technology Computer Science department Al-Ahliyya Amman University Amman Jordan Department of Computer Engineering Automation and Robotics University of Granada Granada Spain

Software requirements prioritization plays a crucial role in software development. It can be viewed as the process of ordering requirements by determining which requirements must be done first and which can be done later. Powerful requirements prioritization techniques are of paramount importance to finish the implementation on time and budget. Many factors affect requirement prioritization such as stakeholder expectations, complexity, dependency, scalability, risk and cost. Therefore, finding the proper order of requirements is a challenging process. Hence, different types of requirements prioritization techniques have been developed to support this task. In this survey we propose a novel classification that can classify the prioritization techniques under two major classes: relative and exact prioritization techniques class where each class is divided into two subclasses. We also provide an overview about fifteen different requirements prioritization techniques that are classified under our proposed classification. Moreover, we make a comparison between methods that are related to the same subclass to analyze their strengths and weakness. Based on the comparison results, the properties for each proposed subclass of techniques are identified. Depending on these properties, we present some recommendations to help project managers in the process of selection the most suitable technique to prioritize requirements based on their projects characteristics. © 2023, The Authors. All rights reserved.

关键词： Requirements engineering

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An Analysis of Encapsulating Security Payload in Wireless Network Security

An Analysis of Encapsulating Security Payload in Wireless Ne...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Devika A. Verma Vaishali Singh Beemkumar. N Ritu Shree Deepak Minhas S. Yuvaraj Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

The Encapsulating Safety Payload (ESP) is a necessary part of the IPsec suite of protocols, imparting a critical layer of security within a Wi-Fi community. This paper aims to evaluate ESP and its software in wireless community protection comprehensively. ESP capabilities as a secured payload encapsulating data via imparting integrity, authentication, and confidentiality. It commonly accomplishes this by using a mixture of cryptography and tunneling to shield the contents of the packets from alteration, eavesdropping, and unauthorized get entry. Moreover, this paper outlines the distinctive ESP modes and cipher suites, presenting how they may be used to defend data being despatched over a Wi-Fi community. ESP additionally affords numerous safety enhancements, such as protection from replay assaults, packet length control, and critical negotiation. Furthermore, this paper explores the challenges associated with implementing and deploying ESP in wireless networks. Subsequently, the paper will pay attention to the numerous overall performance issues for ESP utilization by analyzing the effect of latency, throughput, and power intake on ESP-enabled Wi-Fi networks.

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Expression of Concern for: Adaptive Modeling for Real-Time Data Analysis with Machine Learning

Expression of Concern for: Adaptive Modeling for Real-Time D...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Mahesh TR Shweta Singh Vaibhav Srivastav Hitesh Kalra T. Surulivel rajan Gitanjali B. Yadav Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

Adaptive Modeling for actual-Time information evaluation with system studying is a critical studies area in the modern-day technology of information-in depth evaluation. Conventional gadget studying strategies are static, meaning that the models do no longer evolve as information scientist interactions occur or because the data changes over time. As a result, models can grow to be obsolete and further downstream machine overall performance will become constrained. Consequently, the want for adaptive modeling algorithms has arisen with the intention to permit dynamic, system-stage getting to know from statistics. Such strategies look to provide affordable predictions for real-time inputs, while also looking forward to enough destiny reaction behavior to make more accurate predictions for destiny, unseen facts. The purpose of this approach is to create a solution which optimally balances accuracy and flexibility for an ever converting environment. One of these system could be carried out for predictive upkeep, diagnostic analysis, cyber security detection or self-sustaining vehicle decision making.

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Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems

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IEEE Transactions on Evolutionary Computation 2025年

作者： Shiraishi, Hiroki Hayamizu, Yohei Hashiyama, Tomonori Takadama, Keiki Ishibuchi, Hisao Nakata, Masaya Yokohama National University Faculty of Engineering Yokohama240-8501 Japan State University of New York at Binghamton Department of Computer Science BinghamtonNY13902 United States University of Electro-Communications Department of Informatics Tokyo182-8585 Japan University of Tokyo Information Technology Center Chiba277-0882 Japan Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

Rule representations significantly influence the search capabilities and decision boundaries within the search space of Learning Classifier Systems (LCSs). However, it is very difficult to choose an appropriate rule representation for each problem. Additionally, some problems benefit from using different representations for different subspaces within the input space. Thus, an adaptive mechanism is needed to choose an appropriate rule representation for each rule in LCSs. This article introduces a flexible rule representation using a four-parameter beta distribution and integrates it into a fuzzy-style LCS. The four-parameter beta distribution can form various function shapes, and this flexibility enables our LCS to automatically select appropriate representations for different subspaces. Our rule representation can represent crisp/fuzzy decision boundaries in various boundary shapes, such as rectangles and bells, by controlling four parameters, compared to the standard representations such as trapezoidal ones. Leveraging this flexibility, our LCS is designed to adapt the appropriate rule representation for each subspace. Moreover, our LCS has a generalization bias to produce as many crisp rules as possible. Experimental results on real-world classification tasks show that our LCS significantly outperformed LCSs with popular rule representations in test classification accuracy on up to 17 of the 25 datasets tested. © 1997-2012 IEEE.

关键词： Adversarial machine learning

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Optimization of Bit-Rate for Underwater Fibre Optic Networks Using Bandwidth Analysis

Optimization of Bit-Rate for Underwater Fibre Optic Networks...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Shivam Khurana Latha B Manisha P. Mali Mala Mathur Preeti Naval Naveen Kumar Rajendran Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Department of Civil Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Aerospace Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

This study investigates the optimization of bitrate for underwater optical fiber networks using bandwidth evaluation. The research seeks to discover viable strategies to obtain higher bit-price throughputs with increased reliability and lower latency in such networks. The community performance parameters, including latency, throughput, and jitter, might be analyzed and appropriate answers will be proposed. Bandwidth optimization models, together with the SPF version, could be used for the analysis. An empirical approach will also be taken to analyze the consequences of varying transmission characteristics in those networks. The effects of this look at will offer insights to set up high-pace and dependable transmission that may be applied in underwater optical fiber networks. The intention is to provide an extra comprehensive understanding of those networks and so as to optimize the bit-charge for green use.

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Smartphone Application As An Air Quality Monitor Using Raspberry Pi for Reducing Air Pollution 2

Smartphone Application As An Air Quality Monitor Using Raspb...

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2nd International Conference on Innovative and Creative information technology, ICITech 2021

作者： Siagian, Pandapotan Fernando, Erick Computer Engineering Study Program Del Institute of Technology Faculty of Informatics and Electrical Engineering North Sumatera22381 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781728197463

Good air quality is very important in human health. However, air pollution has been polluted everywhere, whether in the house, in cities. This is caused by a growing industry that produces air pollution. Room air pollution and various cities will contribute to air pollution, which will cause problems for human health. This work measures the carbon monoxide level using the MQ-7 sensor and measures the air's carbon dioxide level with the CCS811 sensor and MH-Z19 sensor. All three sensors are connected to the NodeMCU ESPS266, and we call it a NodeMCU module. Tests are carried out in three rooms in the house, and then three NodeMCU modules are used so that the three modules will send data alternately. Raspberry Pi3 (RPi3) functions as a server that collects data and acquires information data from sensors. Data from the CCS811 (TCOV) sensor, MH-Z19 (co2), will be processed first with fuzzy logic rules to make decisions about CO2, which are right, moderate, nasty conditions. The process is carried out on the Raspberry Pi 3 server with MQTT using publication, customer, fuzzy logic, and broker services. The value of the sensor's measurement results can be accessed via a smartphone and provides good air control values. © 2021 IEEE.

关键词： Air quality

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