Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is utilized. These blood cell images are subjected to various data analyzing techniques that count and classify the different types of blood cells. Nowadays, deep learning-based methods are in practice to analyze the data. These methods are less time-consuming and require less sophisticated equipment. This paper implements a deep learning (D.L) model that uses the DenseNet121 model to classify the different types of white blood cells (WBC). The DenseNet121 model is optimized with the preprocessing techniques of normalization and data augmentation. This model yielded an accuracy of 98.84%, a precision of 99.33%, a sensitivity of 98.85%, and a specificity of 99.61%. The proposed model is simulated with four batch sizes (BS) along with the Adam optimizer and 10 epochs. It is concluded from the results that the DenseNet121 model has outperformed with batch size 8 as compared to other batch sizes. The dataset has been taken from the Kaggle having 12,444 images with the images of 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. With such results, these models could be utilized for developing clinically useful solutions that are able to detect WBC in blood cell images.

Alzheimer’s disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brain’s ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Naïve base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective.

A brain tumor (BT) is an unexpected growth or fleshy mass of abnormal cells. Depending upon their cell structure they could either be benign (noncancerous) or malign (cancerous). This causes the pressure inside the cranium to increase that may lead to brain injury or death. This causes excessive exhaustion, hinders cognitive abilities, headaches become more frequent and severe, and develops seizures, nausea, and vomiting. Therefore, in order to diagnose BT computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and blood and urine tests are implemented. However, these techniques are time consuming and sometimes yield inaccurate results. Therefore, to avoid such lengthy and time-consuming techniques, deep learning models are implemented that are less time consuming, require less sophisticated equipment, yield results with greater accuracy, and are easy to implement. This paper proposes a transfer learning-based model with the help of pretrained VGG19 model. This model has been modified by utilizing a modified convolutional neural network (CNN) architecture with preprocessing techniques of normalization and data augmentation. The proposed model achieved the accuracy of 98% and sensitivity of 94.73%. It is concluded from the results that proposed model performs better as compared to other state-of-art models. For training purpose, the dataset has been taken from the Kaggle having 257 images with 157 with brain tumor (BT) images and 100 no tumor (NT) images. With such results, these models could be utilized for developing clinically useful solutions that are able to detect BT in CT images.

Alzheimer's disease (AD) is a neurodegenerative ailment, which gradually deteriorates memory and weakens the cognitive functions and capacities of the body, such as recall and logic. To diagnose this disease, CT, MRI, PET, etc. are used. However, these methods are time-consuming and sometimes yield inaccurate results. Thus, deep learning models are utilized, which are less time-consuming and yield results with better accuracy, and could be used with ease. This article proposes a transfer learning-based modified inception model with pre-processing methods of normalization and data addition. The proposed model achieved an accuracy of 94.92 and a sensitivity of 94.94. It is concluded from the results that the proposed model performs better than other state-of-the-art models. For training purposes, a Kaggle dataset was used comprising 6,200 images, with 896 mild demented (M.D) images, 64 moderate demented (Mod.D) images, and 3,200 non-demented (N.D) images, and 1,966 veritably mild demented (V.M.D) images. These models could be employed for developing clinically useful results that are suitable to descry announcements in MRI images.

The present solution relates to a stackable smart footwear rack and a cabinet housing the stackable smart footwear rack. The present solution provides easy access to organize, store and retrieve footwear in the racks in addition to providing efficient utilization of storage space in a smart and intelligent manner by means of human machine interface and sensors coupled to motor mechanisms. The present solution also helps negate disorganized arrangement of footwear in the racks and is easy to use thereby contributing to a comfortable lifestyle for people of all age groups. Additionally, the present solution also provides a means to minimize any incidences of dust, odor or dirt in the cabinets and racks due to soiled footwear leading to increased levels of hygiene when handling the contents.