Amharic language is the second most spoken language in the Semitic family after Arabic. In Ethiopia and neighboring countries more than 100 million people speak the Amharic language. There are many historical documents that are written using the Geez script. Digitizing historical handwritten documents and recognizing handwritten characters is essential to preserving valuable documents. Handwritten digit recognition is one of the tasks of digitizing handwritten documents from different sources. Currently, handwritten Geez digit recognition researches are very few, and there is no available organized dataset for the public researchers. Convolutional neural network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. In this work, the proposed model is to recognize Geez digits using CNN. Deep neural networks, which have recently shown exceptional performance in numerous pattern recognition and machine learning applications, are used to recognize handwritten Geez digits, but this has not been attempted for Ethiopic scripts. Our dataset, which contains 51,952 images of handwritten Geez digits collected from 524 individuals, is used to train and evaluate the CNN model. The application of the CNN improves the performance of several machine-learning classification methods significantly. Our proposed CNN model has an accuracy of 96.21% and a loss of 0.2013. In comparison to earlier research works on Geez handwritten digit recognition, the study was able to attain higher recognition accuracy using the developed CNN model.

Disaster prediction systems enable authorities and communities to identify and understand the risks associated with various natural and man‐made disasters. Disaster prediction systems are essential tools for enhancing public safety, reducing the impact of disasters, and enabling more informed and strategic decision‐making across various sectors. Their development and implementation represent a crucial aspect of modern disaster risk management and resilience systems. This novel technique introduces a modern approach to forest fire prediction by integrating a deep learning model with an optimized cluster head selection technique. The major goal is to augment the accuracy and efficiency of forest fire prediction, leveraging the capabilities of advanced machine learning algorithms and optimized sensor network management. The proposed system comprises two core components: a deep learning model for predictive analysis and an optimized selection process for cluster heads in sensor networks. The deep learning model utilizes various environmental data parameters such as humidity, wind speed, temperature and former fire incidents. These parameters are processed through a sophisticated neural network architecture designed to identify patterns and correlations that signify the likelihood of a forest fire. The model is trained on historical data to improve its predictive accuracy, and its performance is continuously evaluated against new data. Simultaneously, the optimized cluster head selection using the cat‐mouse optimization technique plays a crucial role in efficiently managing sensor networks deployed in forests. The integration of these two components results in a robust system capable of predicting forest fires with high precision. The system not only assists in early detection and timely alerts but also contributes to the strategic planning of firefighting and resource allocation efforts. This approach has the prospective to significantly lessen the impact of forest fires, thereby protecting ecosystems and communities.

A hybrid approach employing Ant Colony Optimisation (ACO) and Grey Wolf Optimiser (GWO) is proposed in this paper along with Proximal Support Vector Machine (PSVM) classifier to carry out brain tumour classification for the given 3D MRI brain images. The proposed hybrid ACO-GWO is employed for selecting the optimal features required for performing the classification process. PSVM is employed over the Support Vector Machine (SVM), Back-Propagation Network (BPN) and k-Nearest Neighbour (k-NN) for evaluating the effectiveness of the classifier approach.

Image classification is one of the most multifaceted disciplines in image processing. There are quite a few approaches to categorize images and they offer good classification outcome but they not be up to snuff to provide acceptable classification upshots when the image comprises blurry content. The two chief techniques for image classification are supervised and unsupervised classification. Mutually each possess its own pros and cons. The foremost intent of literature survey is to present a concise outline about some of most widespread image classification schemes and comparison between them. At this point in a survey on diverse classification practices for images and moreover its application for diagnosis of scores of diseases is provided. A few of the unsurpassed processes for classification comprise Artificial Neural Network, Support Vector Machine, and Decision Tree. Their characteristics, upshots and certain vital issues have been judged against each other in order to ascertain the effectual algorithm. To conclude it has been shown that the proposed system Hybrid RGSA and Support Vector Machine Framework is the paramount one to classify images competently.

Over the years, the growth in medical image processing is increasing in a tremendous manner. The rate of increasing diseases with respect to various types of cancer and other related human problems paves the way for the development in biomedical research. Thus processing and analyzing these medical images is of high importance for clinical diagnosis. This work focuses on performing effective classification of brain tumor images and segmentation of live disease images employing the proposed hybrid intelligent techniques. The challenges and objectives on design of feature extraction, image classification and segmentation for medical images are discussed.