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arXiv 2024年

作者： Sikder, Niloy Khan, Al-Masrur Bairagi, Anupam Kumar Masud, Mehedi Tiang, Jun Jiat Nahid, Abdullah-Al Radboud University Medical Center Donders Institute for Brain Cognition and Behaviour Nijmegen Netherlands Faculty of Technology and Bionics Rhine-Waal University of Applied Sciences Kleve Germany Department of Intelligent Systems Engineering Indiana University BloomingtonIN United States Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Faculty of Engineering Multimedia University Cyberjaya63100 Malaysia Electronics and Communication Engineering Discipline Khulna University Khulna9208 Bangladesh

Viruses are submicroscopic agents that can infect all kinds of lifeforms and use their hosts’ living cells to replicate themselves. Despite having some of the simplest genetic structures among all living beings, viruses are highly adaptable, resilient, and given the right conditions, are capable of causing unforeseen complications in their hosts’ bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses are the biggest biological threat faced by animal and plant species. It is often challenging to promptly detect the presence of a virus in a possible host’s body and accurately determine its type using manual examination techniques;however, it can be done using computer-based automatic diagnosis methods. Most notably, the analysis of Transmission Electron Microscopy (TEM) images has been proven to be quite successful in instant virus identification. Using TEM images collected from a recently published dataset, this article proposes a deep learning-based classification model to identify the type of virus within those images correctly. The methodology of this study includes two coherent image processing techniques to reduce the noise present in the raw microscopy images. Experimental results show that it can differentiate among the 14 types of viruses present in the dataset with a maximum of 97.44% classification accuracy and F1-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable way of virus identification subsidiary to the thorough diagnostic procedures. © 2024, CC BY.

关键词： computer aided diagnosis

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Big data for sustainable agri‐food supply chains: a review and future research perspectives

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Journal of Data, information and Management 2021年第3期3卷 167-182页

作者： Rejeb, Abderahman Rejeb, Karim Zailani, Suhaiza Széchenyi István University Kautz Gyula Faculty of Economics Győr 9026 Hungary Higher Institute of Computer Science El Manar 2 Rue Abou Raïhan El Bayrouni Ariana 2080 Tunisia Department of Operations Management and Information System Faculty of Business and Accountancy University Malaya Kuala Lumpur 50203 Malaysia

Research on agri-food supply chains (AFSCs) has attracted significant attention in recent years due to the challenges associated with sustainably feeding the global population. The purpose of this study is to review the potentials of big data for sustainable AFSCs. One hundred twenty-eight (128) journal articles were selected to identify how big data can contribute to the sustainable development of AFSCs. As part of our focus, a framework was developed based on the conceptualization of AFSCs in the extant literature to analyse big data research in the context of AFSCs and to provide insights into the potentials of the technology for agri-food businesses. The findings of the review indicate that there is a noticeable growth in the number of studies addressing the applications of big data for AFSCs. The potentials of big data for AFSC sustainability were synthesized in a summary framework, highlighting the primary resources and activities that are ready for improvement with big data. These include soil, water, crop and plant management, animal management, waste management and traceability management. The challenges of big data integration in AFSCs, the study’s implications, contributions, and the future research directions are highlighted in detail. © The Author(s) 2021.

关键词： Agri-food supply chain Big data Environment Soil Sustainability Water

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Opportunities and Conditions in Education on Blockchain Gamification Technology

Opportunities and Conditions in Education on Blockchain Gami...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Budi Tjahjono Muhammad Dede Soleman Aulia Azmi Shofiyul Millah Riri Andriani Elisa Royani Dewi Computer Science Faculty Esa Unggul University Tangerang Indonesia Webcenter Sentra Solusindo Ltd. University of Raharja Tangerang Indonesia Department of Science and Technology University of Raharja Tangerang Indonesia Department of Information System University of Raharja Tangerang Indonesia

In this disruptive 4.0 era, many generations are competing in creating a new innovation, it makes technological growth increase sharply, one of which is blockchain technology. This technology has ledgers that are simultaneously decentralized and distributed. There is a node that functions to record all transaction histories on the Blockchain with all different networks. Blockchain This is certainly a very practical and safe technology for the world of education, one of which is Gamification. Gamification itself means a concept that has layers of features like a game that is connected or integrated with the blockchain. Gamification provides opportunities for the world of education by creating game elements that focus on the orientation of creating new educational spaces. This study uses the analisis Gamifikasi method and SWOT analysis to determine the overall use of the system starting from the strategies, opportunities, strengths and weaknesses. With this research, it is hoped that gamification in the blockchain system can run optimally in improving the implementation of Blockchain technology in the world of education. The novelty of this research is to apply a concept that has a feature layer such as a game that is connected or integrated with a block chain.

关键词： Technological innovation Education Games Software Blockchains History

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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images

Weighted Mask Recurrent-Convolutional Neural Network based P...

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International Conference on ent Computing and Control systems (ICICCS)

作者： R. Sharmila R Kamalitta Moorthy Devesh Pratap Singh Amit Chauhan Purnendu Bikash Acharjee Department of MCA Dhanalakshmi Srinivasan Engineering College Perambalur Tamilnadu India Information Technology K.Ramakrishnan College of Engineering Trichy Tamilnadu India Electronics Engineer Production Dhaksha Unmanned System Pvt ltd Chennai Tamilnadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Department of life Sciences CHRIST (Deemed to be University) Bengaluru Karnataka India Faculty Member Computer Science CHRIST University Pune Maharashtra India

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India’s GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin.

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SSRN

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SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

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Prototype of a Model for the Alignment of Corporate Strategies and information and Communication Technologies

Prototype of a Model for the Alignment of Corporate Strategi...

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2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Toapanta Toapanta, Segundo Moises Lara, Stefania Nefali Guaranda Seis, Joseph Alexander Guaman Gallegos, Luis Enrique Mafla Trejo, Jose Antonio Orizaga Arellano, Ma. Rocio MacIel Information Technology Consultant Quito Ecuador Department of Computer Science Guayaquil Ecuador Quito Ecuador Faculty of Engineering System Quito Ecuador Guadalajara Mexico

ISBN: (纸本)9781728176246

Many companies have opted for the implementation of information and communication technologies (ICT) in their business processes, in order to achieve their corporate goals and obtain excellent results, but this has not always been achieved. The objective of this study was to present the prototype of a model that can align corporate strategies with ICT, in an Ecuadorian company. The quantitative approach was applied and the deductive method and exploration were used to analyze the information of articles and scientific. The result was a model that aligns ICT with corporate strategies in which a table was included with indicators of alignment between ICT and business strategy classified according to the strategic leadership style of the company for decision making, it was applied under the framework of Miles and Snow, in addition to the evaluation of the strategies, was effective using the Saaty scale and the Analytical Hierarchy Process (AHP) method applied for making objective decisions. It was concluded that the strategic alignment was achieved when the Scope of the Planning with the ICT Scope, under the Miles and Snow framework, the Analytical Hierarchy Process method was also applied, where a value of 0.08 for CR was obtained, which indicates that it is within the acceptable and consistent, transmission of cost reduction, standardization of processes, improvement in work flow and communications. © 2020 IEEE.

关键词： Snow

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Two-Layer SVM, Towards Deep Statistical Learning

Two-Layer SVM, Towards Deep Statistical Learning

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Alireza Kazemi Reza Boostani Mahmoud Odeh Mohammad Rasmi AL-Mousa Mathematics Department Faculty of Science Shiraz University Shiraz Fars Iran CSE&IT Dept. Electrical and Computer Engineering School Shiraz University Shiraz Fars Iran Department of Management Information System Zarqa University Jordan Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan

Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K*(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs).

关键词： Support vector machines Electric potential Statistical learning Neural networks Transforms Nonhomogeneous media Robustness

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An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

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Healthcare Analytics 2024年 5卷 100303-100303页

作者： Mehedi Shamrat, F M Javed Shakil, Rashiduzzaman Sharmin Hoque ovy, Nazmul Akter, Bonna Ahmed, Md Zunayed Ahmed, Kawsar Bui, Francis M. Moni, Mohammad Ali Department of Computer System and Technology University of Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science and Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Software Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka 1216 Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia 4072 QLD Australia

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy. © 2024 The Authors

关键词： Classification Convolutional neural network Diabetic retinopathy network Diagnostic analytics Feature map

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