Breast Cancer (BC) is still one of the most significant, life-threatening, and prevalent diseases that affects women all around the globe. The early recognition and strategies of effective treatment measures improve the rate of survival among patients significantly, contributing to a critical research area in medical science. This review presents a comprehensive review of recent trends and advancements in the recognition of BC recognition, diagnosis, and treatment. It covers multiple imaging modalities, including Magnetic Resonance Imaging (MRI), ultrasound, mammography, and histopathology, along with various approaches of Machine Learning (ML) and Deep Learning (DL) that enhance the efficiency of diagnosis and improve accuracy. In particular, in some recent decades, the Computer-Aided Diagnosis (CAD) system gained noteworthy attention for the recognition of affected regions, for the minimization of diagnostic rates, and for assisting radiologists. ML algorithms have robust capabilities for feature extraction and classification, while the DL models, specifically Convolutional Neural Networks (CNNs), have shown remarkable performance in image-based diagnostic tasks. Moreover, the presented review also discusses some challenges, such as dense breast tissue, noise, and tumor heterogeneity, along with the limitations of the current studies. The review concludes with a compact summary of current trends, research problems, and future directions for the improvement of BC detection.

Natural disasters are on a rise all over the world, and these have pointed out vulnerabilities in existing Public Warning Systems, which fail during crises when they become choked with information, and it is not very efficient for people using basic mobile technology. The standard Short Message Service is widely accepted and in practice but lacks basic authentication, which makes this service a highly vulnerable target for Denial of Service (DoS) attacks and message spoofing attacks. This research proposes a Disaster Alert System with an advanced Cryptographic Security system over SMS notifications for authenticated and safe transfer of information. The major highlight is a security system capable of functioning under resource-constrained systems. The system utilizes a robust 4096-bit key and RSA/ECDSA encryption methods for providing a high level of integrity. This solution has proved to be resilient under simulated critical disaster situations in terms of latency performance in encryption speed. The system has undergone thorough testing and proved to possess strong reliability and stability. Furthermore, this system can be deployed with relative ease, which confirms scalability capabilities in covering a country-wide warning system. This research successfully tackled the SMS security vulnerability to present a secure and efficient model which can transform the conventional Short Message Service into a reliable public warning service platform.

Early recognition of maize leaf disorders and applying precautionary measures on time may help to increase the yield and quality. This study introduces an architecture for the recognition and categorization of maize leaf diseases based on the deep Inception-v3 and maximum value-based color features. The core steps of the designed framework include data acquisition, feature extraction, fusion, and classification. The maize leaf image dataset is utilized, which is publicly available on Kaggle, comprising four classes. The deep learning features are collected by applying the transfer learning approach to the pre-trained Inception-v3 model. In addition to the deep features, maximum value-based color features are computed from RGB, HSV, and LAB color spaces. After that, both deep and color attributes are merged using a serial-based strategy. Finally, the fused vector is fed to the various machine learning classifiers for the recognition of disorders. The designed approach achieves an accuracy of 99% on the Ensemble Subspace Discriminant (ESD) classifier. The results showed that the proposed approach achieved promising disease recognition performance.