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Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

computer systems science & engineering 2023年第9期46卷 3847-3864页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana murthy Ravuri Daniel E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenangMalaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science University of Central AsiaNarynKyrgyzstan Faculty of Computers and Information South Valley UniversityQenaEgypt

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is *** are the major reason behind crop quality degradation and diminished crop ***,accurate pest detection is essential to guarantee safety and crop *** identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological ***,some progress has been made in agriculture by employing machine learning(ML)to classify and detect *** study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)*** presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image *** neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the *** last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification *** simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

关键词： Sustainable agriculture crop monitoring pest management insect classification computer vision

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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Decision Aid sciences and Application (DASA), International Conference on

作者： Lamya Benaddi Charaf Ouaddi Lahbib Naimi Adnane Souha Abdeslam Jakimi Brahim Ouchao Department of Computer Sciences Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Moulay Ismail University Morocco

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction.

关键词： Decision support systems Sentiment analysis Social networking (online) Reviews Soft sensors Transportation User interfaces User experience Driver behavior Usability

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An Ant-colony Based Model for Load Balancing in Fog Environments

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2023年第1期10卷 4-20页

作者： Mirtaheri, Seyedeh Leili Azari, Mahya Greco, Sergio Arianian, Ehsan Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Faculty of Mathematics and Computer Science Kharazmi University Tehran Iran Department of Computer Engineering Modeling Electronics and Systems University of Calabria Italy Tehran Iran

Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will be overlooked in cloud environments due to the communication delay between the processing center and the client’s request. The ‘fog-based computing paradigm’, a novel processing model, can be added to cloud computing to help with the aforementioned issues. The performance of computing systems is always influenced by latency. In this paper, we focus on balancing the load on the fog nodes to lower the latency. It is also a crucial component of fog computing devices. It necessitates the use of load-balancing algorithms to select the optimal hosts, resulting in an even distribution of the load on the available *** provide a load-balancing approach based on the Ant-colony optimization algorithm’s latency rate for responding to tasks. A random data set evaluation of this model reveals shorter response times than those of earlier strategies suggested in this field. © The Authors 2023. This paper is published with open access at ***

关键词： Fog

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VCOACH: A Virtual Coaching System Based on Visual Streaming

VCOACH: A Virtual Coaching System Based on Visual Streaming

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International Neural Network Society Workshop on Deep Learning Innovations and Applications, INNS DLIA 2023

作者： Youssef, Fatma Parque, Victor Gomaa, Walid Department of Computer Science and Engineering Egypt-Japan University of Science and Technology Alexandria21934 Egypt Department of Modern Mechanical Engineering Waseda University Tokyo169-8050 Japan Cyber-Physical Systems Lab. Egypt-Japan University of Science and Technology Alexandria21934 Egypt Faculty of Engineering Alexandria University Alexandria21544 Egypt

A virtual coaching system is key for monitoring the performance during exercise regimes and rendering timely feedback to avoid potential physical harm. Ideally, the core technologies behind such a system imply exercise recognition/assessment for performance evaluation and subject identification for retrieval/update of personal profiles. In this work, we develop techniques based on learning models for inferences regarding exercise recognition, prediction of the subject's biometrics, exercise evaluation, and simultaneous recognition and evaluation. We address four indoor exercises: squats, push-ups, shoulder-press, and lunges. Different sorts of exercise evaluation are provided: (1) the overall-binary assessment, with a detailed assessment rendering the exact mistakes performed, and (2) the score-based evaluation giving a metric in the range of 0-10 for each aspect of the exercise. Thermal and RGB imagery are examined with various descriptors, such as raw images, Gait Energy Image (GEI), and skeletal pose, with a comparison between their performance. The GEI descriptor extracted from RGB imagery is introduced for exercise recognition and evaluation tasks achieving remarkable results. The GEI descriptor is further utilized for subject recognition and verification from the performed exercise showing the ability of subject discrimination from the way of exercising. Consequently, we introduce new kinds of biometrics based on indoor exercising inspired by the notion that individuals are recognizable from the fingerprint motion dynamics in exercise regimes. To the best of our knowledge, our work is the first to use GEI in such directions and show its remarkable effectiveness. © 2023 The Authors. Published by Elsevier B.V.

关键词： Biometrics

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Evaluation of the Economic Feasibility of Price Arbitrage Operations in the Iberian Electricity Market

Evaluation of the Economic Feasibility of Price Arbitrage Op...

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International Conference on European Electricity Market, EEM

作者： Francisco Lobo João Tomé Saraiva FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

This paper describes a study developed to analyse the interest in investing in Li-ion batteries to perform price arbitrage in the power system of Portugal. In this context, it was developed a methodology to identify the most suitable hours for charging and discharging the energy, and the new market prices were estimated for these hours. It was concluded that at current investment costs in this storage technology, and current market prices, this investment would not be viable in the lifetime of the batteries despite the recent rise of electricity market prices and also the larger price spread. This spread is now larger given the depression of prices at sunny hours that is getting typical in the Iberian electricity market.

关键词： Lithium-ion batteries Visualization Costs Europe Aging Electricity supply industry Depression

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IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

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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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