Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC's early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection. We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods.

Understanding where proteins are located within the cells is essential for proteomics research. Knowledge of protein subcellular location aids in early disease detection and drug targeting treatments. Incorrect localization of proteins can interfere with the functioning of cells and leads to illnesses like cancer. Technological advances have enabled computational methods to detect protein's subcellular location in living organisms. The advent of high-quality microscopy has led to the development of image-based prediction algorithms for protein subcellular localization. Confocal microscopy, which is used by the Human Protein Atlas (HPA), is a great tool for locating proteins. HPA database comprises millions of images which have been procured using confocal microscopy and are annotated with single as well as multi-labels. However, the multi-instance nature of the classification task and the low quality of the images make image-based prediction an extremely difficult problem. There are probably just a few algorithms for automatically predicting protein localization, and most of them are limited to single-label classification. Therefore, it is important to develop a satisfactory automatic multi-label HPA recognition system. The aim of this research is to design a model based on deep learning for automatic recognition system for classifying multi-label HPA. Specifically, a novel Convolutional Neural Network design for classifying protein distribution across 28 subcellular compartments has been presented in this paper. Extensive experiments have been done on the proposed model to achieve the best results for multilabel classification. With the proposed CNN framework as F1-score of 0.77 was achieved which outperformed the latest approaches.

Changes in household composition and the residential environment have had a considerable impact on the features of postal delivery regions in recent years, resulting in a large increase in the overall workload of domestic postal delivery services. In this paper, we provide complex analysis results for postal delivery areas using various unsupervised learning approaches. First, we extract highly influential features using several feature-engineering methods. Then, using quantitative and qualitative cluster analyses, we find the distinctive traits and semantics of postal delivery zones. Unsupervised learning approaches are useful for successfully grouping postal service zones, according to our findings. Furthermore, by comparing a postal delivery region to other areas in the same group, workload balancing was achieved.

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The first part of the study refers to the development of a 3D solar concentrator composed of two optical elements; a flat circular Fresnel lens associated with a hyperbolic concentrator. This research is conducted to provide better insight into the concentrator optical performance. The concentrator optical performance system is evaluated for the acceptance angle, achieved concentration, fresnel lens tolerances in secondary element placement, and flux distribution on the secondary optic output. Results show the optical efficiencies of the circular apertures and the square apertures of hyperbole as a function of the heights. We noticed that the circular apertures of hyperbole present high optical efficiency for lengths of 30 to 55mm. Still, beyond this length, the square apertures of the hyperbole exhibit higher optical efficiency. Comparison of these optical elements as secondary optical elements with the elements studied in the previous work (pyramid, compound parabolic concentrator, cone, crossed compound parabolic concentrator). We found that pyramid remains the best secondary optical element for a Fresnel lens as a primary optic. The second part is based on deploying a new cost-effective method using IoT to remotely monitor and assess a photovoltaic plant operation. Using technology to supervise concentrated solar generation can significantly improve plant performance, monitoring, and maintenance. This will make preventive maintenance, defect detection, historical plant analysis, and real-time tracking easier. The follow-up program successfully collected all the data from morning till evening. The mean transmission time is 52.34 seconds, with 30 and 102 seconds the shortest and greatest transmission times.