In this paper, an approach for the classification of different hardwood species of open access database, using texture feature extraction and supervised machine learning technique has been implemented. Edges of complex cellular structure of microscopic images of hardwood are enhanced with the application of Gabor filter, and Gray Level Co-occurrence Matrix (GLCM) as an effective texture feature extraction technique is being revalidated. About, 44 features have been extracted from GLCM; these features have been further normalized in the range [0.1, 1]. Multilayer Perceptron Backpropagation Artificial Neural Network have been used for classification. Experiments conducted on 25 wood species have resulted in recognition accuracy of about 88.60% and 92.60% using Levenberg-Marquardt backpropagation training function with two different datasets for training, validation and testing ratio (70%, 15%, 15% and 80%, 10%, 10%) respectively. Proposed methodology can be extended with optimized machine learning techniques for online identification of wood.

Kidney illness constitutes a category of many serious persistent diseases that can affect an individual. Early diagnosis of this condition is critical for effective therapy. Kidney tumors are the 2nd most common type of urological tumor. They come in a variety of forms, the majority of which are cancerous. In comparison to the laborious and lengthy conventional evaluation, deep learning’s autonomous detection techniques may reduce diagnostic time, enhance the precision of tests, lower expenses, and minimize the radiologist’s burden. It is difficult for clinicians to distinguish kidney cancers from renal Computerized Tomography (CT) images. During an operation, the precise division of kidney tumors can assist physicians in determining tumor intricacy and severity. However, due to their variety, segmenting renal tumors mechanically might be challenging. Therefore, an intellectual kidney tumor segmentation and classification model is implemented to recognize benign and malignant tumors at an early stage. To execute this procedure, the input CT images are gathered from standard websites. Then these images are given to the proposed 3D-Trans-Residual DenseUnet++ (3D-TR-DUnet++) network for the segmentation process.With the help of the segmentation process, doctors can identify the normal and abnormal regions in the kidney. The segmented images are then preceded by the classification stage. To classify kidney tumors, a deep learning-based method called Adaptive and Attentive Residual Densenet with Gated Recurrent Unit (AA-RD-GRU) is developed. Here, the parameters from this network are optimized via the recommended Modified Crayfish Optimization Algorithm (MCOA). The precise segmentation and classification of tumors in the kidney helps to provide better treatments at the correct time. The segmentation and classification results are contrasted with other deep learning networks as well as various optimization algorithms.

The integration of IoT with the cloud infrastructure is essential for designing smart applications. However, such integration may lead to security issues. Authentication and session key establishment is an essential security requirement for secure communication between IoT devices and cloud servers. For evaluating authentication key agreement schemes, the extended Canetti–Krawczyk (eCK) adversary model is regarded to be a more strict and relevant adversary model. Many schemes for authenticated key exchange between IoT devices and cloud servers have been proposed in the literature but have been assessed under Dolev and Yoa (DY) adversary model. Recently, Rostampour et al. introduced an ECC-based approach for enabling authentication between IoT devices and cloud servers that is secure and robust to various attacks under the Dolev and Yoa adversary model. In this paper, a detailed review and the automated security verification of the Rostampour et al. scheme are carried out under the eCK adversary model using Scyther-Compromise. The validation indicates that the scheme is not secure and is susceptible to various attacks under the eCK adversary model. To overcome the limitation of the Rostampour et al. scheme, a design of an ECC-based scheme for authentication between IoT devices and cloud servers under the eCK adversary model is proposed. The Scyther verification indicates that the scheme is safe under the eCK adversary model. The soundness of the correctness of the proposed scheme has been analyzed using BAN logic. Comparative analysis indicates that the scheme is resilient under the eCK adversary model with an energy overhead of 278.16 mJ for a resource constraint IoT device and a communication overhead of 1,408 bits.