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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Investigation of Status of Bananas During Ripening With Wavelengths In 405-940 nm 5

Investigation of Status of Bananas During Ripening With Wave...

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5th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2023

作者： Duong, Huu Tai Tran, Anh Tu Trinh Tran, Hong Duyen Tran, Trung Nghia Laboratory of Laser Technology Faculty of Applied Science Ho Chi Minh City Viet Nam Laboratory of General Physics Faculty of Applied Science Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350320978

Bananas are an essential food in modern society. During the ripening process, the pigment content, starch index, sugar content, and color of the banana change. We investigate the condition of bananas during ripening under normal conditions. The analysis was performed on normal and intentionally damaged subjects. The survey was conducted with a multi-spectral imaging system by reflecting the object with wavelengths 405-940 nm. First, preprocessing and region selection are performed to normalize the resulting image. Then, the light intensity of the pixels was surveyed and investigated. The survey results showed that the wavelength of 940 nm is suitable for analyzing the change, condition, and difference between the damaged and normal conditions during the ripening process. © 2023 IEEE.

关键词： Fruits

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Simplifying road to defining a scientific problem and hypothesis through gamified and storytelling-enhanced teaching of requirements engineering

Simplifying road to defining a scientific problem and hypoth...

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Joint of the BIR 2024 Workshops and Doctoral Consortium, BIR-WS 2024

作者： Lace, Ksenija Kirikova, Marite Institute of Applied Computer Systems Faculty of Computer Science and Information Technology Riga Technical University 6A Kipsalas Street RigaLV-1048 Latvia

The transition from structured coursework to independent research poses significant challenges for students, particularly in mastering the design science method required for thesis work. To address this issue, the paper proposes implementing gamified learning strategies within a Requirements Engineering course to enhance student readiness for thesis projects. A detailed course outline is provided, demonstrating how gamification, storytelling, and the design science method can be effectively embedded in the curriculum. By integrating these multifaceted teaching methods, the course aims to manage the complexity of preparing students for independent research and thesis writing. The course design refers to Bloom's taxonomy to ensure comprehensive learning outcomes across multiple cognitive levels, thereby fostering a deeper understanding and application of the design science method in engineering education. © 2024 Copyright for this paper by its authors.

关键词： Requirements engineering

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Machine learning models for modeling the biosorption of Fe(III) ions by activated carbon from olive stone

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Neural Computing and Applications 2024年第21期36卷 13357-13372页

作者： Massaoudi, Ayman Echouchene, Fraj Ben Ayed, Mossaad Berguiga, Abdelwahed Harchay, Ahlem Al-Ghamdi, Sara Belmabrouk, Hafedh Department of Computer Science College of Science and Arts in Gurayat Jouf University Sakakah Saudi Arabia Olive Research Center Jouf University Sakakah Saudi Arabia Laboratory of Electronics and Microelectronics Faculty of Science of Monastir University of Monastir Environment Boulevard Monastir5019 Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia Department of Industrial Electronics ENISo University of Sousse Sousse Tunisia Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Physics College of Science Majmaah University Al Majma’ah11952 Saudi Arabia

Using experimental results related to the biosorption of Fe(III) by activated carbon derived from olive pit waste, we developed and evaluated four artificial neural network (ANN) models in this study, namely MLP-ANN, RBF-ANN, GR-ANN, and PSO-ANN, to predict the removal efficiency of Fe(III) during the adsorption process. The purpose of these models was to forecast the effect of the following five important operational variables: the initial concentration, time, stirring speed, temperature, and biosorbent dose. We conducted a thorough assessment of the performance of these models and compared their ability to predict the removal capacity. Several statistical metrics have been used to quantify the quality of the different models. The results calculated by the machine learning models were analyzed and compared with the experimental results. The obtained values of the coefficient of determination were 0.9997 for the PSO-ANN model, 0.991 for the GR-ANN model, 0.983 for the RBF-ANN model, and 0.837 for the MLP-ANN model concerning the removal efficiency. All the studied models are able to accurately predict the adsorbed quantity. ANOVA analysis was used to evaluate the effect of the parameters inherent to the PSO-ANN model. The PSO-ANN model proves to be a powerful tool for estimating the efficiency of Fe(III) ion removal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. corrected publication 2024.

关键词： Biosorption

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Automatic Speech Recognition Techniques for Transcription of Thai Traditional Medicine Texts 21

Automatic Speech Recognition Techniques for Transcription of...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Popun, Jettasic Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Currently, Automatic Speech Recognition (ASR) technology is widely used for communication convenience in converting speech to text. Initial surveys reveal that existing studies on ASR, both in Thai and foreign languages, focus on contemporary language communication, lacking support for specialized vocabulary. Traditional Thai Medicine contain ancient medical terminology recorded in various media such as palm leaves, parchment, notebooks, and inscriptions. Centuries of textual preservation have led to text degradation. Given the suitability of ASR study to accommodate medical terminology and ancient languages, it is proposed to apply it to convert original Traditional Thai Medicine texts into contemporary language, aiding experts in reading and interpreting them. ASR involves converting speech signals into text. In this study, Hidden Markov Model techniques using Kaldi are employed. Due to the absence of a Thai Traditional Medicine vocabulary repository, a dataset comprising text and speech is developed, consisting of 900 training sentences and 100 test sentences randomly selected from recordings of 10 Thai Traditional Medicine experts. Offline evaluation methods are employed, resulting in improved accuracy rates. Future efforts will focus on expanding the speech dataset to enhance recognition accuracy further. This study lays the groundwork for efficiently extracting knowledge from Traditional Thai Medicine prescriptions, ensuring the preservation of this valuable heritage for future generations. © 2024 IEEE.

关键词： Hidden Markov models

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Generalized Parabola Chaotic map for Pseudorandom Random Number Generator 26

Generalized Parabola Chaotic map for Pseudorandom Random Num...

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Jiteurtragool, Nattagit King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok10800 Thailand

ISBN: (纸本)9791188428120

In this paper, a generalized form of chaotic map based on nonlinear function with parabolic shape is introduced. The study involves the investigation of chaotic dynamics in terms of apparent in time-domain, and both qualitatively and quantitatively examination using bifurcation diagram and Lyapunov exponents. Furthermore, the practical application of these parabolic chaotic maps is showcased in a pseudorandom number generator, with its performance evaluated using statistical tests from the NIST SP800-22 test suite. © 2024 Global IT Research Institute - GIRI.

关键词： Lyapunov methods

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Enhancing Event Extraction and Impact Analysis with Knowledge Graph Augmented Transformer 21

Enhancing Event Extraction and Impact Analysis with Knowledg...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Songklang, Kallaya Srisomboon, Kanabadee Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Understanding the impact of events reported in news articles plays pivotal roles in various tasks. In this study, we propose a framework for extracting event components from the news and analyzing their multifaceted impact. The framework employs transformers techniques for event extraction and leverages a biography knowledge base to assess the potential impact of events across social, political, economic, and culture (SPEC) dimensions. Our framework achieves an F1-score of 78.12% in event identification as well as significant accuracy score in evaluating the impact levels prediction. By integrating results from event extraction and impact analysis, the level of multifaceted influence of the event along with the event information are effectively visualized. This comprehensive approach enhances understanding of multi-perspective interpretation and supports insights for various applications. © 2024 IEEE.

关键词： Distribution transformers

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The Development of a Question-Answering System for Learning Article using Natural Language Processing 5

The Development of a Question-Answering System for Learning ...

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5th International Conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： Sriharee, Gridaphat Boonyamarn, Kewalee Department of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok 1518 Pracharat 1 Wonsawang Bangsur Bangkok10800 Thailand

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

This paper proposes developing a question-answering (QA) system for Thai learning articles. This system is a closed-domain QA system. Thai text processing was proposed to retrieve answers relevant to the natural language-based questions specified by users. The methodology to generate the keywords/topics used for retrieving answers was introduced. Matching and selecting methods for obtaining the best answer using cosine similarity is the support technique. Creating the corpus database, which contains Thai synset - a set of words with similar meanings or that may relate to each other, was explained. With the proposed corpus database, the question-answering can extract and analyze the meaning of the unbound sentence in Thai. The proposed QA system performed well, with a precision score of 87.67%, a recall score of 73.35%, and an F1 score of 81.91%. © 2024 SPIE.

关键词： Contrastive Learning

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Interaction graph learning of line cascading failure in power networks and its statistical properties

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Energy Informatics 2023年第1期6卷 17页

作者： Ghasemi, Abdorasoul de Meer, Hermann Kantz, Holger Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Faculty of Computer Science and Mathematics Passau University Passau Germany Max Planck Institute for the Physics of Complex Systems Dresden Germany

We consider line failure cascading in power networks where an initial random failure of a few lines leads to consecutive other line overloads and failures before the system settles in a steady state. Such cascades are rooted in non-obvious, long-range, and higher-order couplings among the lines’ flows induced by physical constraints on the network. Failure interaction graph encodes which and to what extent other lines in a networked system are affected after each line failure and can help to predict the final state after an initial disturbance. We perform data analytics on the final lines’ steady states of cascade trajectories to infer a specific line’s state given the states of others. We use a generative model to reconstruct possible steady states, and a predictive model aims to predict the probability of each line’s failures after the initial failure as a regression problem. The generative model uses regularized pseudolikelihood estimator to infer interaction weights by solving the inverse Ising problem and deploys Glauber dynamics to generate steady states. The discriminative model uses boosted trees to efficiently learn over training and predict over test data the state of each line as a target finding an appropriate subset of other lines’ states as explanatory variables. We analyze the degree distribution of the corresponding interaction graphs to study the number of other components affected by each line failure (out-degree) or the number of lines that affect the state of a given line (in-degree). Both models show that the in-degree follows a power-law distribution. Finally, we discuss the possible application of the interaction graph for early link removal to mitigate the failure-cascading consequences. © 2023, The Author(s).

关键词： Forecasting

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FEATURE SELECTIONS AND EXPLORATORY ANALYSIS FOR ML STOCK PREDICTIONS USING SET50 9

FEATURE SELECTIONS AND EXPLORATORY ANALYSIS FOR ML STOCK PRE...

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9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on computer science and Information Systems 2024, MCCSIS 2024

作者： Sriharee, Gridaphat Department of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok 1518 Pracharat 1 Wonsawang Bangsur Bangkok10800 Thailand

ISBN: (纸本)9789898704597

Stock prediction using machine learning is an interesting topic for investors. However, the performance of the prediction relies on different techniques and the train data set. Feature selection is an important factor in creating the model. In this paper, feature selection based on correlation was proposed, and exploratory analysis of SET50 from Thailand Stock market was experimented. The fundamental data – OHLC, percent change, and three technical indicators – SMA, RSI, and EMA were applied in the experimentation. The fundamental ML models: linear regression, logistic regression, ANN, decision tree, random forest, and XGBoost were conducted to evaluate the model’s performance based on the proposed features. The result of the experiments showed that multiple-parameter model performed better than single-parameter model. RSI was the most powerful indicator, and different periods can be a critical factor for different ML models. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Adversarial machine learning

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