Diabetic Retinopathy (DR) is a prevalent complication of diabetes that affect the retina. If not detected early, it can cause loss of vision. Diabetic Retinopathy is considered to be the cause for vision loss to patie...
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Fusion-based hyperspectral image super-resolution has recently attracted increasing interest due to its superior reconstruction quality. This approach enhances the spatial resolution of low-resolution hyperspectral im...
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In this paper, a new approach for mining image association rules is presented, which involves the fine-tuned CNN model, as well as the proposed FIAR and OFIAR algorithms. Initially, the image transactional database is...
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Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee *** deadly disease is har...
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Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee *** deadly disease is hard to control because wind,rain,and insects carry *** researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest *** the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate *** overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate *** proposed methodology selects CBD image datasets through four different stages for training and *** to train a model on datasets of coffee berries,with each image labeled as healthy or *** themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed *** of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions *** inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of *** evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is *** involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its *** comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.
The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy ***,sensitive information disclosure may also be caused by t...
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The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy ***,sensitive information disclosure may also be caused by these personalised *** address the matter,this article develops a personalised data publishing method for multiple *** to the requirements of individuals,the new method partitions SAs values into two categories:private values and public values,and breaks the association between them for privacy *** the private values,this paper takes the process of anonymisation,while the public values are released without this *** algorithm is designed to achieve the privacy mode,where the selectivity is determined by the sensitive value frequency and undesirable *** experimental results show that the proposed method can provide more information utility when compared with previous *** theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an *** overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.
Semi-supervised learning (SSL) aims to reduce reliance on labeled data. Achieving high performance often requires more complex algorithms, therefore, generic SSL algorithms are less effective when it comes to image cl...
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Autism spectrum disorder (ASD) affects 1 in 100 children globally. Early detection and intervention can enhance life quality for individuals diagnosed with ASD. This research utilizes the support vector machine-recurs...
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Autism spectrum disorder (ASD) affects 1 in 100 children globally. Early detection and intervention can enhance life quality for individuals diagnosed with ASD. This research utilizes the support vector machine-recursive feature elimination (SVM-RFE) method in its approach for ASD classification using the phenotypic and Automated Anatomical Labeling (AAL) Brain Atlas datasets of the Autism Brain Imaging Data Exchange preprocessed dataset. The functional connectivity matrix (FCM) is computed for the AAL data, generating 6670 features representing pair-wise brain region activity. The SVM-RFE feature selection method was applied five times to the FCM data, thus determining the optimal number of features to be 750 for the best performing support vector machine (SVM) model, corresponding to a dimensionality reduction of 88.76%. Pertinent phenotypic data features were manually selected and processed. Subsequently, five experiments were conducted, each representing a different combination of the features used for training and testing the linear SVM, deep neural networks, one-dimensional convolutional neural networks, and random forest machine learning models. These models are fine-tuned using grid search cross-validation (CV). The models are evaluated on various metrics using 5-fold CV. The most relevant brain regions from the optimal feature set are identified by ranking the SVM-RFE feature weights. The SVM-RFE approach achieved a state-of-the-art accuracy of 90.33% on the linear SVM model using the Data Processing Assistant for Resting-State Functional Magnetic Resonance Imaging pipeline. The SVM model’s ability to rank the features used based on their importance provides clarity into the factors contributing to the diagnosis. The thalamus right, rectus right, and temporal middle left AAL brain regions, among others, were identified as having the highest number of connections to other brain regions. These results highlight the importance of using traditional ML models fo
Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected *** is characterized by low latency with a wider sp...
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Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected *** is characterized by low latency with a wider spread and geographically distributed nodes to support mobility,real-time interaction,and location-based *** provide optimum quality of user life in moderm buildings,we rely on a holistic Framework,designed in a way that decreases latency and improves energy saving and services efficiency with different *** EVent system Specification(DEVS)is a formalism used to describe simulation models in a modular *** this work,the sub-models of connected objects in the building are accurately and independently designed,and after installing them together,we easily get an integrated model which is subject to the fog computing *** results show that this new approach significantly,improves energy efficiency of buildings and reduces ***,with DEVS,we can easily add or remove sub-models to or from the overall model,allowing us to continually improve our designs.
In the contemporary landscape, autonomous vehicles (AVs) have emerged as a prominent technological advancement globally. Despite their widespread adoption, significant hurdles remain, with security standing out as a c...
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We study the complexity of infinite-domain constraint satisfaction problems (CSPs): our basic setting is that a complexity classification for the CSPs of first-order expansions of a structure Б can be transferred to ...
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