Object detection in thermal images is vital for diverse applications, utilizing machine learning algorithms to analyze infrared radiation. This study focuses on classifying contrast-enhanced thermal images (CLAHE) usi...
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Globally, skin diseases are emerging as the most common health problem. It initiates depressive disorder, and it also causes physical health distress. It rarely led to skin cancer in extreme cases. Diagnosing skin dis...
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Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over *** interaction analysis aids in defining people’s emotions and aids in a...
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Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over *** interaction analysis aids in defining people’s emotions and aids in assessing public attitudes,towards different issues such as violence against women and *** this paper,we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social *** searched for Arabic posts of many countries through Facebook application programming interface(API).We discovered that the stress state of a battered woman is usually related to her friend’s stress states on *** applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social *** extracted a set of tex-tual,spatial,and interaction attributes from various ***,we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for vio-lence-induced stress *** depict that our proposed hybrid mod-el can enhance the prediction performance by 10%in ***,considering the user interaction information can learn an interesting phenomenon,where,the sparse social interactions of violence-induced stress stressed victims is higher by around 15%percent non-battered users,signifying that the structure of the friends of such victims is less connected than non-stressed users.
In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper...
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Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can l...
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Cardiovascular health is becoming more common over the world. Electrocardiography (ECG) is a test that utilizes an electrical signal in the heart to diagnose and monitor cardiac problems. Utilizing the myDAQ data acqu...
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In the realm of underwater robotics,optical imaging plays a pivotal role in many scientific *** to the effects of absorption and scattering,images captured in turbid water are severely ***,enhancing the quality of und...
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In the realm of underwater robotics,optical imaging plays a pivotal role in many scientific *** to the effects of absorption and scattering,images captured in turbid water are severely ***,enhancing the quality of underwater optical images stands paramount in ensuring the continued advancement and efficacy of underwater robots across its multifarious applications.
Crude oil prices (COP) profoundly influence global economic stability, with fluctuations reverberating across various sectors. Accurate forecasting of COP is indispensable for governments, policymakers, and stakeholde...
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Recently, deep learning neural networks have been widely used in object classification. The process of object classification typically involves extracting features from the point cloud using neural networks and integr...
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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 me...
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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.
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