More than 60 percent of the global surface is covered by clouds, and they play a vital role in the hydrological circle, climate change, and radiation budgets by modifying shortwaves and longwave. Weather forecast reports are critical to areas such as air and sea transport, energy, agriculture, and the environment. The time has come for artificial intelligence-powered devices to take the place of the current method by which decision-making experts determine cloud types. Convolutional neural network models (CNNs) are starting to be utilized for identifying the types of clouds that are caused by meteorological occurrences. This study uses the publicly available Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which consists of 2543 ground-based cloud images altogether. We propose a model called Cloud-MobiNet for the classification of ground-based clouds. The model is an abridged convolutional neural network based on MobileNet. The architecture of Cloud-MobiNet is divided into two blocks, namely the MobileNet building block and the support MobileNet block (SM block). The MobileNet building block consists of the weights of the depthwise separable convolutions and pointwise separable convolutions of the MobileNet model. The SM block is made up of three dense network layers for feature extraction. This makes the Cloud-MobiNet model very lightweight to be implemented on a smartphone. An overall accuracy success of 97.45% was obtained for the CCSN dataset used for cloud-type classification. Cloud-MobiNet promises to be a significant model in the short term, since automated ground-based cloud classification is anticipated to be a preferred means of cloud observation, not only in meteorological analysis and forecasting but also in the aeronautical and aviation industries.

Scholars have classified soil to understand its complex and diverse characteristics. The current trend of precision agricultural technology demands a change in conventional soil identification methods. For example, soil color observed using Munsell color charts is subjective and lacks consistency among observers. Soil classification is essential for soil management and sustainable land utilization, thereby facilitating communication between different groups, such as farmers and pedologists. Misclassified soil can mislead processes; for example, it can hinder fertilizer delivery, affecting crop yield. On the other hand, deep learning approaches have facilitated computer vision technology, where machine-learning algorithms trained for image recognition, comparison, and pattern identification can classify soil better than or equal to human eyes. Moreover, the learning algorithm can contrast the current observation with previously examined data. In this regard, this study implements a convolutional neural network (CNN) model called Soil-MobiNet to classify soils. The Soil-MobiNet model implements the same pointwise and depthwise convolutions of the MobileNet, except the model uses the weight of the pointwise and depthwise separable convolutions plus an additional three dense layers for feature extraction. The model classified the Vellore Institute of Technology Soil (VITSoil) dataset, which is made up of 4864 soil images belonging to nine categories. The VITSoil dataset samples for Soil-MobiNet classification were collected over the Indian states and it is made up of nine major Indian soil types prepared by experts in soil science. With a training and validation accuracy of 98.47% and an average testing accuracy of 93%, Soil-MobiNet showed outstanding performance in categorizing the VITSoil dataset. In particular, the proposed Soil-MobiNet model can be used for real-time soil classification on mobile phones since the proposed system is small and portable.

On account of the uncontrolled and quick growth of cells, Brain Tumor (BT) occurs. It may bring about death if not treated at an early phase. Brain Tumor Detection (BTD) has turned out to be a propitious research field in the current decennia. Precise segmentation along with classification sustains to be a difficult task in spite of several important efforts and propitious results in this field. The main complexity of BTD emerges from the change in tumor location, shape, along with size. Providing detailed literature on BTD via Magnetic Resonance Imaging (MRI) utilizing Machine Learning (ML) methods to aid the researchers is the goal of this review. Diverse datasets are mentioned which are utilized most often in the surveyed articles as a prime source of Brain Disease (BD) data. Furthermore, a concise epitome of diverse segmentation methods that are utilized in diagnosing BDs has been offered. Lastly, an outline of key outcomes from the surveyed articles is exhibited, and several main problems related to ML-centred BD diagnostic methodologies are elucidated. The most precise method to detect diverse BDs can be engaged for future advancement via this study.

Alzheimer's disease (AD) is the most prevalent kind of dementia illness that can significantly impair a person's capability to carry out everyday tasks. According to findings, AD may be the third provoking reason of mortality among older adults, behind cancer and heart disease. Individuals at risk of acquiring AD must be identified before treatment strategies may be tested. The study's goal is to give a thorough examination of tissue structures using segmented MRI, which will lead to a more accurately labeling of certain brain illnesses. Several complicated segmentation approaches for identify AD have been developed. DL algorithms for brain structure segmentation and AD categorization have gotten a lot of attention since they can deliver accurate findings over a huge amount of data. As a result, DL approaches are increasingly favored over cutting-edge Machine Learning (ML) techniques. This study provides you with an overview of current trend deep learning-based segmentation algorithms for analyzing brain Magnetic Resonance Imaging for the treatment of AD. Finally, a conversation on the approaches' benefits and drawbacks, as well as future directives, was held, which may help researchers better comprehend present algorithms and methods in this field, and eventually design new and more successful algorithms.

Alzheimer's disease (AD), particularly affects adults, is among the most common brain disorders, causing memory loss and losing information to varied degrees. is a progressive neurological condition that gradually impairs cognitive performance and can lead to death. With today's technological advancements, Magnetic Resonance Imaging (MRI) scanning can diagnose AD. As a result, MRI is the most often utilized tool for diagnosing and monitoring the progression of AD. Image identification in the early detection of AD can be accomplished automatically utilizing machine learning (ML) with this technique. Although ML has several advantages, deep learning (DL) is currently more extensively used because it has superior learning ability and is more suited to tackling image identification difficulties. However, there are significant hurdles to implementing DL, such as the requirement for huge datasets, large computational resources, and careful parameter tuning to avoid overfitting or underfitting. In order to solve the problem of classifying AD using DL, this research provides randomised concatenated feature representations obtained by two pre-trained network models that learn feature information in deep from brain functional schemes of MRI scans simultaneously. To execute the task of AD multiclass classification, we experimented using concatenated CNN of DenseNet and MobileNet. To highlight the discriminating regions from scans for the suggested model prediction, a gradient class activation map has been used. In multiclass classification, the suggested model achieved 98.87 percent accuracy, 98.95 percent precision, and 98.99 percent recall, according to the experimental results. The findings suggest that advanced DL techniques combined with MRI data can be utilised to classify and forecast n eurodegenerative brain illnesses such as Alzheimer's.

The most impressive organ that has the ability to produce emotion, flexible enough to enthusiastically modify itself, and is more potent than any computer ever created is the human brain. The well-known form of dementia, which is characterized by progressive loss of memory and cognitive function that affects more than 15 million people globally, is termed Alzheimer’s Disease (AD). Nevertheless, for neurologists, AD classification is a well-known problem. For the classification of various stages of AD, enormous techniques are wielded recently. Grounded on Deep Learning (DL) approaches, a review was done on AD classification. The recent research on the classification of AD grounded on Feature Extraction (FE) and selection approaches and Deep Neural Network (DNN) centered classification along with the dataset-centric classification of AD are emphasized by this research. Finally, regarding performance metrics, the performance of the several approaches wielded for the classification of AD is analogized.