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IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

computers, Materials & Continua 2022年第4期71卷 1713-1728页

作者： P.Suresh R.H.Aswathy Sridevi Arumugam Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Alamgeer Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical Sciences Adithya Institute of TechnologyCoimbatore641107India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation *** the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971.

关键词： Smart irrigation precision agriculture internet of things deep learning machine learning

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Have Java Production Methods Co-Evolved With Test Methods Properly?: A Fine-Grained Repository-Based Co-Evolution Analysis 48

Have Java Production Methods Co-Evolved With Test Methods Pr...

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48th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2022

作者： Kita, Tenma Aman, Hirohisa Amasaki, Sousuke Yokogawa, Tomoyuki Kawahara, Minoru Department of Computer Science Faculty of Engineering Ehime University Matsuyama Ehime790–8577 Japan Center for Information Technology Ehime University Matsuyama Ehime790–8577 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja Okayama719–1197 Japan

ISBN: (纸本)9781665461528

Any source code of a software product (production code) is expected to be tested to ensure its correct behavior. Whenever a developer updates production code, the developer should also update or create the corresponding test code to check if the updated parts still work correctly. Such a desirable co-evolution relationship between production and test code forms a logical coupling. Although the logical coupling is detectable through an association analysis on the code repository such as Git, the detection granularity is coarse because the conventional repository is at the file level. For observing those logical couplings as precisely as possible, this paper utilizes the finer-grained, Java method-level repository (FinerGit). Then the paper proposes a metric measuring the extent to which a production method has co-evolved with test methods and conducts a case study using ten open-source projects. The results show that most Java methods (98% on average) have co-evolved with test methods, but some have not;The proposed metric helps detect those methods having the potential risk that the developers might not test adequately. © 2022 IEEE.

关键词： Software testing

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COVID-19 assessment using HMM cough recognition system

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International Journal of Information Technology (Singapore) 2023年第1期15卷 193-201页

作者： Hamidi, Mohamed Zealouk, Ouissam Satori, Hassan Laaidi, Naouar Salek, Amine Advanced Systems Engineering Laboratory ENSA-UIT Kenitra Morocco Multimedia and Arts Department FLLA UIT Kenitra Morocco LISAC Department of Mathematics and Computer Science FSDM USMBA Fez Morocco Faculty of Medicine and Pharmacy UMP Oujda Morocco

This paper is a part of our contributions to research on the ongoing COVID-19 pandemic around the world. This research aims to use Hidden Markov Model (HMM) based automatic speech recognition system to analyze the cough signal and determine whether the signal belongs to a sick or healthy speaker. We built a configurable model by using HMMs, Gaussian Mixture Models (GMMs), Mel frequency spectral coefficients (MFCCs) and a cough corpus collected from healthy and sick voluntary speakers. Our proposed method is able to classify dry cough with sensitivity from 85.86% to 91.57%, differentiate the dry cough, and cough COVID-19 symptom with specificity from 5 to 10%. The obtained results are very encouraging to enrich our corpus with more data and increase the performance of our diagnostic system. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： ASR system COVID-19 Diagnostic GMM HMMs

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Clustering graph data: the roadmap to spectral techniques

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Discover Artificial Intelligence 2024年第1期4卷 7页

作者： Mondal, Rahul Ignatova, Evelina Walke, Daniel Broneske, David Saake, Gunter Heyer, Robert Faculty of Computer Science Otto-von-Guericke-University Universitätsplatz 2 Saxony-Anhalt Magdeburg39106 Germany Lange Laube 12 Lower Saxony Hannover30159 Germany Faculty of Technology Bielefeld University Universitätsstraße 25 North Rhine-Westphalia Bielefeld33615 Germany Faculty of Process and Systems Engineering Otto-von-Guericke-University Universitätsplatz 2 Saxony-Anhalt Magdeburg39106 Germany Multidimensional Omics Analyses Group Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Bunsen-Kirchhoff-Straße 11 North Rhine-Westphalia Dortmund44139 Germany Faculty of Computer Science Technische Hochschule Ingolstadt Esplanade 10 Bavaria Ingolstadt85049 Germany

Graph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance. Clustering techniques, such as K-means, hierarchical clustering, are highly beneficial tools in data mining and machine learning to find meaningful similarities and differences between data points. Recent developments in graph data models, as well as clustering algorithms for graph data, have shown promising results in image segmentation, gene data analysis, etc. This has been primarily achieved through research and development of algorithms in the field of spectral theory, leading to the conception of spectral clustering algorithms. Spectral clustering algorithms have been one of the most effective in grouping similar data points in graph data models. In this paper, we have compiled 16 spectral clustering algorithms and compared their computational complexities, after an overview of graph data models and graph database models. Furthermore, we provided a broad taxonomy to classify most existing clustering algorithms and discussed the taxonomy in detail. © The Author(s) 2024.

关键词： Digital storage

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Advanced Traffic Framework for Managing Congestion 9

Advanced Traffic Framework for Managing Congestion

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Alharbi, Awad Saleh Sen, Adnan Ahmed Abi Yamin, Mohammad Alhudhaif, Abdullah Alaboudi, Abdulellah A. School of Mathematics Computer Science and Engineering Department of Electrical Engineering London United Kingdom Islamic University Faculty of Computer and Information Systems Al-Madinah Saudi Arabia College of Engineering and Computer Science Australian National University Canberra Australia King Abdulaziz University Faculty of Economics and Administration Jeddah Saudi Arabia Shaqra University Department of Computer Sciences Shaqra Saudi Arabia

ISBN: (纸本)9789380544441

The most important challenge for any traffic system is to address traffic congestion and reduce it as much as possible. This process is complicated even for smart systems, as it requires the integration between many solutions, technologies, and services because a single solution cannot effectively address this issue. In this paper, we present a framework to support cooperation of many solutions, technologies, tools, and services in the traffic domain. To achieve this, the different service providers must be enabled to contribute to providing solutions, support services, and cooperate to obtain better quality, performance, availability, and greater flexibility in the available options. Therefore, we have added a new module that is responsible for supporting interoperability between devices and different heterogeneous services. We also added a special privacy protection component which is also a challenge that limits the ability to collaborate and share data between different apps and services. Finally, we discuss the benefits of the advanced traffic framework, with a case study that proves its importance. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Interoperability

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Quaternion Tensor Completion Via Qr Decomposition and Nuclear Norm Minimization

SSRN

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SSRN 2024年

作者： Sun, Jian Liu, Xin Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Department of Mathematics University of Manitoba Winnipeg Canada

The task of tensor (matrix) completion has been widely used in the fields of computer vision and image processing, etc. To achieve the completion, the existing methods are mostly based on singular value decomposition of the real tensor and nuclear norm minimization. However, the real tensor completion methods cannot simultaneously maintain color channel correlation and evolution robustness of color video frames, they need high computational cost to handle the high-dimensional data. Hence they have some limitations in model generalization ability and computational efficiency. In this paper, a new completion method for the quaternion tensor (matrix) is explored via the QR decomposition and the definition of novel quaternion tensor norm, which can well balance the model generalization ability and efficiency, and the performance of the completion method has been substantially improved. Numerical experiments on color images and videos prove the effectiveness of our proposed method. © 2024, The Authors. All rights reserved.

关键词： Tensors

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AutoSW: A new automated sliding window-based change point detection method for sensor data

AutoSW: A new automated sliding window-based change point de...

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2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2022

作者： Nejad, Ebrahim Behrouzian Silva, Carla Rodrigues, Arlete Jorge, Alipio Dutra, Ines University of Porto Faculty of Sciences Department of Computer Science Porto Portugal Bosch Security Systems - Sistemas de Segurança S.A FIR Software Engineering Ovar Ovar Portugal University of Porto Faculty of Sciences LIAAD and Department of Computer Science Porto Portugal University of Porto Faculty of Sciences Department of Computer Science and CINTESIS Porto Portugal

ISBN: (数字)9781665451260

ISBN: (纸本)9781665451260

Change point detection methods try to find any sudden changes in the patterns and features of a given time series. In this paper a new change point detection method is presented, where the window width is automatically calculated. The proposed algorithm, AutoSW, is based on a Sliding Window search method of the Python ruptures package and uses a subset of statistical concepts to compute a possibly optimal window width. The proposed algorithm is compared with some other popular methods such as PELT using different real-world and synthetic time series. Results show that AutoSW can perform better than PELT producing a better set of change points in the time series tested. © 2022 IEEE.

关键词： Time series

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Online Reduction of Exploration Space for Automated Underwater Modem Optimization

Online Reduction of Exploration Space for Automated Underwat...

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MILCOM, Military Communications Conference

作者： Marcel Rieß Steffen Moser Frank Slomka Institute of Embedded Systems/Real-Time Systems Faculty of Engineering Computer Science and Psychology Ulm University Germany

Developing efficient underwater communication systems is complex, as multiple factors impact communication quality. While rapid prototyping has been a common approach, it carries a high risk of failure. To address this challenge, model-based development and implementation have gained popularity. However, selecting the optimal combination of modulation type, correction properties, synchronization properties, and other parameters remains time-consuming and intricate. This paper presents an innovative approach to streamline the development process by reducing the exploration space. The proposed method evaluates potential solutions based on scenario-specific, application-specific, and performance-related upper and lower bounds for the selectable communication system. These bounds consider common underwater challenges, such as SNR degradation and multiple bounce loss, and distinguish between dynamic and static system analyses. By adopting this approach, existing analysis and development workflow achieve more optimized communication systems and afford more efficient variant selection using narrowing down the solution space and considering the specific requirements of each scenario. The approach’s effectiveness is demonstrated through case studies and evaluations, highlighting the benefits of reducing the exploration space and utilizing scenario-, application-, and performance-based bounds. This enables the development of robust and tailored underwater communication systems, improving communication quality and overall system performance.

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LIMIT THEOREMS FOR β-LAGUERRE AND β-JACOBI ENSEMBLES

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Acta Mathematica Scientia 2022年第5期42卷 2025-2039页

作者： Naqi HUANG Yutao MA Faculty of Electrical Engineering Mathematics and Computer ScienceDelft University of TechnologyMekelweg 42628 CD DelftThe Netherlands School of Mathematical Sciences&Laboratory of Mathematics and Complea Systems of Ministry of Education Beijing Normal UniversityBeijing 100875China

We use tridiagonal models to study the limiting behavior of β-Laguerre and β-Jacobi ensembles,focusing on the limiting behavior of the extremal eigenvalues and the central limit theorem for the two *** the central limit theorem of β-Laguerre ensembles,we follow the idea in[1]while giving a modified version for the generalized *** we use the total variation distance between the two sorts of ensembles to obtain the limiting behavior of β-Jacobi ensembles.

关键词： beta-ensembles largest and smallest eigenvalues central limit theorem total variationdistance

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Exponential Moving Average for Time-Series Smoothing in Wind Speed Forecasting

Exponential Moving Average for Time-Series Smoothing in Wind...

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International Conference on Informatics and Computing (ICIC)

作者： Rudi Sutomo Risma Yulistiani Melissa Indah Fianty Information Systems Faculty of Engineering & Informatics Multimedia Nusamtara University Banten Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The need for sustainable energy sources is of utmost importance, and wind power is becoming increasingly prominent. Ensuring accurate and dependable wind speed predictions is crucial for tackling energy sustainability issues. This experiment involved using Exponential Moving Average (EMA) to reduce temporal irregularities in a wind dataset time series. The categorization method Long Short-Term Memory (LSTM) was also applied. The investigation concluded with the calculation of a statistically significant R2 coefficient, which particularly reached a value of 0.935. This demonstrates how effectively the recommended method predicts wind speed to maximize renewable energy.

关键词： Renewable energy sources Smoothing methods Accuracy Wind speed Time series analysis Wind power generation Sustainable development Informatics Forecasting Long short term memory

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