Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. The exact causes of ASD remain elusive and likely involve a combination of genetic, environmental, and neurobiological factors. Doctors often face challenges in accurately identifying ASD early due to its complex and diverse presentation. Early detection and intervention are crucial for improving outcomes for individuals with ASD. Early diagnosis allows for timely access to appropriate interventions, leading to better social and communication skills development. Artificial intelligence techniques, particularly facial feature extraction using machine learning algorithms, display promise in aiding the early detection of ASD. By analyzing facial expressions and subtle cues, AI models identify patterns associated with ASD features. This study developed various hybrid systems to diagnose facial feature images for an ASD dataset by combining convolutional neural network (CNN) features. The first approach utilized pre-trained VGG16, ResNet101, and MobileNet models. The second approach employed a hybrid technique that combined CNN models (VGG16, ResNet101, and MobileNet) with XGBoost and RF algorithms. The third strategy involved diagnosing ASD using XGBoost and an RF based on features of VGG-16-ResNet101, ResNet101-MobileNet, and VGG16-MobileNet models. Notably, the hybrid RF algorithm that utilized features from the VGG16-MobileNet models demonstrated superior performance, reached an AUC of 99.25%, an accuracy of 98.8%, a precision of 98.9%, a sensitivity of 99%, and a specificity of 99.1%.

This study provided insight into the size of the difference between the actual and predicted changes in Landsat 8 satellite imagery for the case study Sana’a of Yemen. The LULC classification was created using data available in 2005, 2010, 2015, and 2020. It used the MOLUSCE tool for predicting land changes for the predicted for 2010, 2015, 2020, 2025, and 2030. The objectives of this study are 1) To compare the actual and predicted land changes in 2010,2015 and 2020. 2) To analyze and verify the tool’s performance (MOLUSCE). 3) To identify the size of effect which evented land changes in 2015 on land changes in 2020,2025 and 2030. The results were: 1/the effects of land changes in 2010 showed the accuracy and reliability of MOLUSCE for predicting land changes due to the low difference between the actual and predicted 2010 before the conflict in the region. 2/the actual changes for 2015 were negative and did not support the logical trend toward progress where it is natural that the human element progresses to the increasing construction. 3/identify prediction changes for (2020,2025,2030) are affected by events conflict, which showed in the results of the 2015 images.

This article presents the comparative analysis of classification techniques to assign land use and land cover classes from different strategies (pixel-based, object-based, rule-based, distance-based, and neural-based) with a Sentinel-2A satellite image for 2016. The study area is the Sanaa city of Yemen which covers about 18,796.88km² land area. This research aims to present the fundamentals of supervised machine learning approaches, including their limitations and strengths and experimentation for twelve classifiers. The outcome of experimentation showed that the Random Forest could be a good choice as a classifier for object-based strategy. In contrast, DTC and SVM were efficient in rule-based and pixel-based strategies. Results also showed that the highest accuracy was with object-based strategy, followed by rule-based and then pixel-based and distance-based strategies.

Lung and colon (LC) cancers are among the most deadly types of cancer, often resulting in death. However, the chances of survival significantly increase with the early detection of these cancers. This study addresses the challenge of early detection of LC tumors, which is crucial for effective treatment. LC cancer is one of the most common tumors that require early detection. However, these tumors appear similar in their early stages, making it challenging for doctors to differentiate between them. To overcome this challenge, we propose an AI-based diagnostic model for detecting LC tumors. Artificial intelligence (AI) techniques have been employed to address this challenge. This study proposes two histopathological image analysis strategies for the early detection of LC cancer. The first strategy involves the diagnosis of LC cancer using decision tree (DT) and random forest (RF) networks with CNN model features, namely ResNet50, DenseNet169, and MobileNet, based on geometric active contour (GAC) and ant colony optimization (ACO) algorithms. The second strategy involves the diagnosis of LC cancer using DT and RF classifiers with the features of combined CNN, namely ResNet50-DenseNet169, DenseNet169-MobileNet, DenseNet169-MobileNet, and ResNet50-DenseNet169-MobileNet, based on the GAC and ACO algorithms. All strategies have demonstrated good results for early stage LC cancer detection. The RF network, which utilized the combined features extracted from the ResNet50- MobileNet-DenseNet169 models, demonstrated notable performance with an AUC of 99.7%, sensitivity of 99.72%, accuracy of 99.8%, precision of 99.76%, and specificity of 99.78%.

Skin cancer, particularly melanoma, is an aggressive form of cancer. The problem with human diagnosis is that it is too subjective and inefficient for detecting early signs. Artificial intelligence (AI) is a solution that can clearly be a very efficient, fast, objective, and accurate pathway to improve early interventions and patient care. A new hybrid model, MobileNetV2-ResNet101-ViTE, was developed, which effectively combines Convolutional Neural Network and the Vision Transformer Encoder (ViTE) using a Spatial Detail Enhancement Block. The proposed model differs from existing models by retaining accurate local histological representations and utilizing the global context necessary for accurate classification of skin lesions. Model performance was assessed on the ISIC2019 dataset, yielding an average Area Under the ROC Curve (AUC) of 96.94% and a classification accuracy of 98%. The model’s high scores in sensitivity to the malignant classes have astounding potential in improving false negatives with scores of 98.5% for Melanoma, and 91.5% for squamous cell carcinoma, giving some confidence in its potential to improve false negatives, and has consistently reported very high specificity (average: 99.55%) and precision (average: 94.44%). The proposed MobileNetV2-ResNet101-ViTE model is ground-breaking for dermatologists using an AI-driven approach for diagnosis and should provide a better outcome for early melanoma detection.