In cloud computing, a new threat is introduced namely Economic Denial of Sustainability (EDoS) which is similar to Distributed Denial of Services (DDoS) attack that targets the vulnerabilities of the cloud server user...
详细信息
Hoaxes are something that can not be avoided, especially in Indonesia, where the literacy rate in Indonesia is quite low, they are easy to believe in news without doing fact check. The worst thing is that news that is...
详细信息
The increasing threat of ransomware attacks in an interconnected and digital world presents important hurdles for cybersecurity, prompting further examination into how organizational security culture affects their abi...
详细信息
This study presents a comprehensive approach to IT asset security risk management in physical access control systems, integrating NIST SP 800-116, 800-53, 800-30, ISO 31000, and 27002:2022 standards. Using a mixed-met...
详细信息
The research was centered on adapting and assessing a new, unique game concept. The game onKeys presents a new approach and poses as an alternative method for improving typing skills. However, despite offering a promi...
详细信息
Soilborne diseases like Fusarium oxysporum and Rhizoctonia solani significantly impact sugar beet production, causing major yield losses. Accurate disease rating and characterization enhance disease management and bre...
详细信息
Soilborne diseases like Fusarium oxysporum and Rhizoctonia solani significantly impact sugar beet production, causing major yield losses. Accurate disease rating and characterization enhance disease management and breeding by tracking progression, assessing resistance, and guiding control strategies. Existing diagnostic approaches often focus on limited aspects of disease assessment, addressing only one or two ICQP objectives—identification, classification, quantification, and prediction—leaving gaps in comprehensive disease management. This study proposes an all-in-one framework that integrates hyperspectral imaging and machine learning to address all ICQP objectives. Hyperspectral data were collected from 122 plants inoculated with F. oxysporum and R. solani over 30 days using a Specim IQ hyperspectral sensor (400–1000 nm, 204 bands). To ensure accurate spectral data extraction, image segmentation was performed using a trained Deeplabv3+ model. Optimal wavelengths for each ICQP task were identified using the ANOVA algorithm and fed into three machine learning classifiers, including random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM). The study revealed that no single spectral region or machine learning model was universally optimal across all ICQP objectives. Chlorophyll-sensitive wavelengths (670–700 nm) were optimal for both F. oxysporum and R. solani disease identification, while the near-infrared range (830–1000 nm) provided critical insights for disease type classification. RF achieved the highest accuracy (96%) in identifying healthy and infected plants and demonstrated strong performance in disease type classification. For disease quantification, MLP achieved superior results with 94% accuracy and an IoU of 88%, enabling detailed pixel-level mapping of disease severity with high confidence. This study demonstrates the importance of task-specific optimization in spectral analysis and machine learning, linking spectral features t
This paper presents an approach for speeding up the convergence of adaptive intelligent agents using reinforcement learning algorithms. Speeding up the learning of an intelligent agent is a complex task since the choi...
详细信息
ISBN:
(纸本)9789898565105
This paper presents an approach for speeding up the convergence of adaptive intelligent agents using reinforcement learning algorithms. Speeding up the learning of an intelligent agent is a complex task since the choice of inadequate updating techniques may cause delays in the learning process or even induce an unexpected acceleration that causes the agent to converge to a non-satisfactory policy. We have developed a technique for estimating policies which combines instance-based learning and reinforcement learning algorithms in Markovian environments. Experimental results in dynamic environments of different dimensions have shown that the proposed technique is able to speed up the convergence of the agents while achieving optimal action policies, avoiding problems of classical reinforcement learning approaches.
Virtual memory mechanisms allow offering more RAM memory space to processes than the amount of memory physically available in the system, using disk space as a memory extension. When there is not enough RAM memory to ...
详细信息
Sign language is used as a communication medium in the field of trade,defence,and in deaf-mute communities *** the last few decades,research in the domain of translation of sign language has grown and become more *** ...
详细信息
Sign language is used as a communication medium in the field of trade,defence,and in deaf-mute communities *** the last few decades,research in the domain of translation of sign language has grown and become more *** necessitates the development of a Sign Language Translation System(SLTS)to provide effective communication in different research *** this paper,novel Hybrid Adaptive Gaussian Thresholding with Otsu Algorithm(Hybrid-AO)for image segmentation is proposed for the translation of alphabet-level Indian Sign Language(ISLTS)with a 5-layer Convolution Neural Network(CNN).The focus of this paper is to analyze various image segmentation(Canny Edge Detection,Simple Thresholding,and Hybrid-AO),pooling approaches(Max,Average,and Global Average Pooling),and activation functions(ReLU,Leaky ReLU,and ELU).5-layer CNN with Max pooling,Leaky ReLU activation function,and Hybrid-AO(5MXLR-HAO)have outperformed other *** open-access dataset of ISL alphabets with approx.31 K images of 26 classes have been used to train and test the *** proposed framework has been developed for translating alphabet-level Indian Sign Language into *** proposed framework attains 98.95%training accuracy,98.05%validation accuracy,and 0.0721 training loss and 0.1021 validation loss and the perfor-mance of the proposed system outperforms other existing systems.
The selection and prioritization of software requirements represents an area of interest in Search-Based Software Engineering (SBSE) and its main focus is finding and selecting a set of requirements that may be part o...
详细信息
暂无评论