In the realm of agricultural automation, the precise identification of crop stress holds immense significance for enhancing crop productivity. Existing methods primarily focus on controlled environments, which may not...
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The goal of this study is to build a self-driving robot that can effectively navigate mazes by utilizing sophisticated computer vision algorithms with ROS2. Fusion 360 is used to create the robot model, and ROS2 launc...
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This paper analyses current best practices for using security mechanisms in extract, transform, and load (ETL) tools. The results of the analysis are visually represented in a form of an optimal security model used in...
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Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interf...
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Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research...
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The immense volume of data generated and collected by smart devices has significantly enhanced various aspects of our daily lives. However, safeguarding the sensitive information shared among these devices is crucial....
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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.
In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a subst...
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The architecture of integrating Software Defined Networking (SDN) with Network Function Virtualization (NFV) is excellent because the former virtualizes the control plane, and the latter virtualizes the data plane. As...
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Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system m...
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