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Mari Papel y Corrugado 2024年第1期2024卷 165-172页

作者： Abdullahi, Akibu Mahmoud Hassan, Isyaku Hossain, Md Shakil Abubakar, Yusuf School of Computing and Information Technology Taylor's University Selangor Subang Jaya47500 Malaysia Faculty of Languages and Communication Universiti Sultan Zainal Abidin Terengganu Malaysia School of Computing and Informatics Albukhary International University Alor Setar Malaysia

Rice cultivation faces significant challenges due to diseases such as bacterial blight, Blast, brown spot, and tungro, which diminish the production and quality of crops. Additionally, traditional methods of detecting these diseases are difficult and prone to mistakes. This situation worsens production losses and risks food security. This research seeks to overcome this issue by examining the effectiveness of deep learning techniques, particularly Convolutional Neural Networks (CNNs), in automating the detection of rice leaf disease. We collected a diverse data set consisting of 6229 pre-processed photos of common rice leaf diseases and healthy images. This dataset was used to train the CNN model, and performance was assessed using evaluation measures including confusion matrices and accuracy assessment. The results showed significant performance improvements, with the CNN model obtaining a final validation accuracy of 98.98%. In detecting rice leaf diseases, our method exceeded other deep learning systems. In places where rice production is the primary source of food security, this work highlights the potential of CNN-based models in automated rice disease detection, providing an effective way to improve crop productivity. © The authors.

关键词： Convolutional neural networks

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Enhanced Authentication Protocol for Securing Internet of Medical Things with Lightweight Post-Quantum Cryptography 6

Enhanced Authentication Protocol for Securing Internet of Me...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Man Lo, Chris Ka Tan, Soo Fun Chung, Gwo Chin Universiti Malaysia Sabah Cybersecurity Research Lab Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Malaysia Sabah Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Multimedia University Faculty of Enginerering Cyberjaya Malaysia

ISBN: (纸本)9798350389692

As the healthcare sector transitions to the Internet of Medical Things (IoMT), it promises significant improvements in productivity by streamlining processes, reducing administrative burdens, and automating routine tasks. However, the current authentication methods are vulnerable due to reliance on simple passwords and a lack of additional protective layers, posing a heightened risk of data breaches. Given the significant value of medical data, security measures for medical devices remain inadequate, especially in safeguarding user and device identities against future quantum computing threats. This paper proposes an enhanced authentication protocol aimed at ensuring the confidentiality and integrity of medical data. The protocol validates user and device identities while incorporating post-quantum cryptography to address upcoming challenges posed by quantum technology. © 2024 IEEE.

关键词： Quantum computers

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Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

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Computers, Materials & Continua 2024年第5期79卷 3193-3209页

作者： Israa Ismail Ghada Eltaweel Mohamed Meselhy Eltoukhy Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution ***-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance ***-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater *** study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution *** shearlet transform is chosen for its excellent sparse approximation ***,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high *** shearlet coefficients are fed into the EDSR *** high-resolution image is subsequently reconstructed using the inverse shearlet *** incorporation of the EDSR network enhances training stability,leading to improved generated *** experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image *** to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

关键词： Super-resolution shearlet transform shearlet coefficients enhanced deep super-resolution network

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A Quantitative Analysis of OpenMP Task Runtime Systems 14th

A Quantitative Analysis of OpenMP Task Runtime Systems

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14th International Symposium on Benchmarking, Measuring, and Optimization, Bench 2022

作者： Hunold, Sascha Kraßnitzer, Klaus Research Group for Parallel Computing Faculty of Informatics TU Wien Vienna Austria

ISBN: (纸本)9783031311796

Although OpenMP is heavily used to parallelize for-loops, it also supports task-parallel programming, which is important for parallelizing irregular applications. In this work, we focus on the performance of OpenMP runtime systems for task-based applications. In particular, we investigate the performance of different OpenMP runtime systems when scheduling a large set independent tasks of different granularity. To that end, we propose a new OpenMP benchmark, which features profiling and tracing options that help developers to reason about the observed performance differences. We compare the execution times measured for a variety of compilers, such as gcc, icc, clang, aocc, and pgcc, for both homogeneous and heterogeneous workloads. Our study shows that there are significant performance differences between the different OpenMP implementations. We also show that the performance attainable with a compiler strongly depends on the machine architecture, the number of threads, the thread-pinning strategy, and the task granularity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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Driver-Drowsiness Detection System Using Deep Learning (CNN) 5

Driver-Drowsiness Detection System Using Deep Learning (CNN)

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5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Ridwan, Firman Hung, Lai Po Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

Drowsiness and fatigue of drivers are amongst the significant causes of road accidents. Every year, this factor increases the amounts of deaths and fatalities globally. When drivers are drowsy or fatigued, the frequency of yawning is different from those in the normal state. This behavior can determine whether the drivers are fatigued or not. It's critical to use technologies to create and build systems that can detect and monitor drivers' levels of attention throughout the driving process, whether they're alert or sleepy. In this study, A high-precision artificial intelligence model was developed using deep learning to detect driver drowsiness during driving to enhance road safety statistics. The model was trained using a dataset containing pictures of people yawning and blinking. The model is then integrated with mobile applications for ease of use with smartphones for drowsiness detection. The application includes authentication, camera stream, trip history, trip statistics, alarm and notification, and emergency modules. Similar systems proposed by prior researchers using hardware devices such as webcam, smartwatch, and many more to predict drowsiness. The results achieved model's accuracy ranges from 62-87%, depending on the frames input from the camera stream and device performance. The mAP and the average recall of the model were 0.55 and 0.662, respectively. The testing and evaluation phase is divided into three parts: unit testing, integration testing, and user acceptance testing. The results of unit testing and integration testing indicate that the application is working as intended, with high accuracy in detecting driver drowsiness. User acceptance testing is done using the System Usability Scale questionnaire, which shows that the application is perceived to be easy to use, effective, and satisfactory. The proposed system is expected to reduce the statistics of road accidents by detecting drowsy drivers during their driving trips, and it is widely and eas

关键词： Roads and streets

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Web-Based Mental Health Predicting System Using K-Nearest Neighbors and XGBoost Algorithms 8th

Web-Based Mental Health Predicting System Using K-Nearest Ne...

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8th International Visual informatics Conference, IVIC 2023

作者： Zulkefli, Nurul Farhanaa Diah, Norizan Mat Ismail, Azlan Hanum, Haslizatul Fairuz Mohamed Ibrahim, Zaidah Arif, Yunifa Miftachul School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia

ISBN: (纸本)9789819973385

Problems with mental health are common presently and have been a worry for a long time. Mental health problems, like anxiety, depression, and panic attacks, can be caused by numerous things. Therefore, recognising the start of mental disease is becoming increasingly crucial to maintaining a good life balance. This study uses machine learning to identify any possible mental health disorders in an individual to attain this goal. The investigation employed supervised machine learning to predict mental health status, namely K-Nearest Neighbors (KNN) and XGBoost, with performance evaluation criteria including accuracy, precision, recall, and F1 score. When these two algorithms were compared, it was discovered that XGBoost produced a more effective prediction model, which was then employed to develop a web-based mental health prediction system. The web-based method creates a questionnaire for mental health issues. Based on the user’s responses to the questions, the system will predict his or her mental health status as normal, depression, anxiety, stress, loneliness, or regularity. Every component of the system, including buttons and forms, has been successfully tested using functionality tests. Moreover, the system’s advantages, weaknesses, and future study directions are identified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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Insider Threat Detection Using Bidirectional Encoder Representations from Transformers and Optimized AdaBoost Classifier

Insider Threat Detection Using Bidirectional Encoder Represe...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Kumpf, Katarina Protic, Mihajlo Jovanovic, Luka Cajic, Miroslav Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Faculty of Informatics and Computing Belgrade Serbia University 'Union Nikola Tesla' Faculty of Information Technology and Engineering Belgrade Serbia

ISBN: (纸本)9798350365306

Cybersecurity plays an increasingly vital role in contemporary times, particularly as large companies face escalating threats and substantial losses, with ransomware attacks standing out as one of the more sophisticated forms of intrusion. In such attacks, hackers infiltrate systems, encrypt crucial company data, and then demand a ransom for data decryption or the restoration of control over the system. The application of natural language processing (NLP) emerges as a promising avenue, offering the potential to analyze attack patterns and recognize malicious intentions before actual attempts occur. Given the inherent specificity of each system, fine-tuning AI algorithms with special settings becomes imperative for optimal efficiency. This research delves into the synergy of NLP and robust classification algorithms to detect cyber attacks through website content analysis. Recognizing that classifier performance hinges on the judicious selection of hyperparameters, we explore a modified version of the recently introduced red fox algorithm (RFO). This adaptation is combined with bidirectional encoder representations from transformers to identify malicious intent in email content. The proposed approach undergoes testing on a real-world dataset, with the best models demonstrating an accuracy of 0.975822 suggesting viability. © 2024 IEEE.

关键词： Natural language processing systems

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Multimodal Contrastive Learning for Protein-Protein Interaction Inhibitor Prediction

Multimodal Contrastive Learning for Protein-Protein Interact...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Zitong Wang, Zhixian Zhao, Lingling Wang, Junjie Wang, Chunyu Harbin Institute of Technology Faculty of Computing Harbin China Nanjing Medical University School of Biomedical Engineering and Informatics Department of Medical Informatics Nanjing China

ISBN: (纸本)9798350386226

Protein-protein interactions (PPIs) are crucial for various cellular activities and disease development, and modulating PPIs using small molecule inhibitors (PPIIs) has gradually become a promising therapeutic strategy. Recently, researchers have proposed several machine learning methods to screen PPIIs, but most of the works focused on unimodal representations of molecules or combining multimodal features in a naive splicing manner. Meanwhile, current research progress is being slowed by the lack of large-scale PPII datasets. To address these issues, we propose MCLPPII, a unified multimodal contrastive learning framework for PPII prediction. MCLPPII extracts comprehensive molecular information from four modalities and effectively combines them through an adaptive feature fusion method. Furthermore, we propose a three-stage training strategy to enhance the PPII prediction capability of MCLPPII by leveraging self-supervised pre-training on a large unlabeled dataset. We evaluate MCLPPII on nine PPI targets and two downstream tasks, including the PPI inhibitor identification task and potency prediction task. Experimental results show that MCLPPII achieves competitive performance. The source code and datasets are freely available at https://***/1zzt/MCLPPII. © 2024 IEEE.

关键词： Contrastive Learning

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Secure Image Communication Using Galois Field,Hyper 3D Logistic Map,and B92 Quantum Protocol

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Computers, Materials & Continua 2024年第12期81卷 4435-4463页

作者： De Rosal Ignatius Moses Setiadi Nova Rijati Ahmad Rofiqul Muslikh Bonifacius Vicky Indriyono Aceng Sambas Faculty of Computer Science Universitas Dian NuswantoroSemarang50131Central JavaIndonesia Research Center for Quantum Computing and Materials Informatics Faculty of Computer ScienceUniversitas Dian NuswantoroSemarang50131Central JavaIndonesia Faculty of Information Technology Universitas MerdekaMalang65146East JavaIndonesia Faculty of Economics and Business Universitas STRADA IndonesiaKediri64133East JavaIndonesia Faculty of Informatics and Computing Universiti Sultan Zainal AbidinCampus BesutBesut22200TerengganuMalaysia Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaTamansari GobrasTasikmalaya46196Indonesia

In this paper,we propose a novel secure image communication system that integrates quantum key distribution and hyperchaotic encryption techniques to ensure enhanced security for both key distribution and plaintext ***,we leverage the B92 Quantum Key Distribution(QKD)protocol to secure the distribution of encryption keys,which are further processed through Galois Field(GF(28))operations for increased *** encrypted plaintext is secured using a newly developed Hyper 3D Logistic Map(H3LM),a chaotic system that generates complex and unpredictable sequences,thereby ensuring strong confusion and diffusion in the encryption *** hybrid approach offers a robust defense against quantum and classical cryptographic attacks,combining the advantages of quantum-level key distribution with the unpredictability of hyperchaos-based *** proposed method demonstrates high sensitivity to key changes and resilience to noise,compression,and cropping attacks,ensuring both secure key transmission and robust image encryption.

关键词： Galois field hyper 3D logistic map hyperchaotic map image security improved logistic map secure communication quantum security

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Bi-directional Long Short-term Memory Optimization by Improved Teaching-learning Based Algorithm for Univariate Gold Price Forecasting 6

Bi-directional Long Short-term Memory Optimization by Improv...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Stankovic, Marko Bacanin, Nebojsa Budimirovic, Nebojsa Zivkovic, Miodrag Sarac, Marko Strumberger, Ivana Technical Faculty Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (纸本)9798350398496

Critical operations of the global financial sector are significantly impacted by the volatility of the price of gold. Developing a robust forecasting model capable of recognizing patterns in the gold price dynamics may significantly reduce investment risks and enable considerable profits. This manuscript introduces an inventive deep learning forecasting system rested on the Bi-directional Long Short-term (BiLSTM) neural network that exploits the potential of these networks to apprehend short-term as well as long-term dependencies. This study developed a unique improved Teaching-learning Based (ITLB) method utilized to optimize the hyperparameter selection procedure since the performance of neural networks is heavily dependent upon the combination of adequate control parameters. Variation Mode Decomposition (VMD) was also used to discover trends in the gold price data prior to being submitted as input to the BiLSTM. A set of experiments has been conducted and the suggested model was compared to several cutting-edge metaheuristic algorithms. Overall results illustrate that the introduced BiLSTM-VMD-ITLB approach achieved superior forecasting results in predicting gold price fluctuations. © 2023 IEEE.

关键词： Mode decomposition

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