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Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.

The agricultural sector plays a key role in supplying quality food and makes the greatest contribution to growing economies and populations. Plant disease may cause significant losses in food production and eradicate diversity in species. Early diagnosis of plant diseases using accurate or automatic detection techniques can enhance the quality of food production and minimize economic losses. In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems. Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease identification. We focused on fine tuning the hyperparameters of popular pre-trained models, such as DenseNet-121, ResNet-50, VGG-16, and Inception V4. The experiments were carried out using the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 classes. The performance of the model was evaluated through classification accuracy, sensitivity, specificity, and F1 score. A comparative analysis was also performed with similar state-of-the-art studies. The experiments proved that DenseNet-121 achieved 99.81% higher classification accuracy, which was superior to state-of-the-art models.

Various amounts of short fibers (glass and carbon) and particulate fillers like polytetrafluoroethylene (PTFE), silicon carbide (SiC), and alumina (Al 2 O 3 ) were systematically introduced into the thermoplastic copolyester elastomer (TCE) matrix for reinforcement purpose. The mechanical properties such as storage modulus, loss modulus, and Tan by dynamic mechanical analysis (DMA) and three-body abrasive wear performance on a dry sand rubber wheel abrasion tester have been investigated. For abrasive wear study, the experiments were planned according to 27 orthogonal array by considering three factors and three levels. The complex moduli for TCE hybrid composites were pushed to a higher level relative to the TCE filled PTFE composite. At lower temperatures (in the glassy region), the storage modulus increases with increase in wt.% of reinforcement (fiber + fillers) and the value is maximum for the composite with 40 wt.% reinforcement. The loss modulus and damping peaks were also found to be higher by the incorporation of SiC and Al 2 O 3 microfillers. The routine abrasive wear test results indicated that TCE filled PTFE composite exhibited better abrasion resistance. Improvements in the abrasion resistance, however, have not been achieved by short-fiber and particlaute filler reinforcements. From the Taguchi's experimental findings, optimal combination of control factors were obtained for minimum wear volume and also predictive correlations were proposed. Further, the worn surface morphology of the samples was discussed.

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