Coronavirus (COVID-19) disease is an infectious disease caused by the newly and deadly pneumonia type identified Coronavirus2 (SARS-CoV-2). A real-time Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the main method and has been regarded as the gold standard for diagnosing the COVID-19. Strict requirements and the limited supply of RT-PCR kits for the laboratory environment leads to delay in the accurate diagnosis of patients in addition to the test takes 4-6 hours to obtain the results. To tackle this problem, radiological images such as chest X-rays and CT scan could be the answer to test the COVID-19 infection rapidly and more efficiently. In this paper, an efficient proposed Convolution Neural Network (CNN) architecture model for COVID-19 detection based on chest X-ray images is presented. The proposed model is developed to provide accurate detection for binary classification (Normal vs. COVID-19), three class classification (Normal vs. COVID-19 vs. Pneumonia), and four class classification (Normal vs. COVID-19 vs. Pneumonia vs. Tuberculosis (TB)). Our proposed model produced an overall testing accuracy of 99.7%, 95.02%, and 94.53% for binary, three, and four class classifications, respectively. A comparison is made between this work and others shows the superior of this work over the others.

Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

A brain tumor is a problem that threatens life and impedes the normal working of the human body. The brain tumor needs to be identified early for the proper diagnosis and effective treatment planning. Tumor segmentation from an MRI brain image is one of the most focused areas of the medical community, provided that MRI is non-invasive imaging. Brain tumor segmentation involves distinguishing abnormal brain tissue from normal brain tissue. This paper presents a systematic literature review of brain tumor segmentation strategies and the classification of abnormalities and normality in MRI images based on various deep learning techniques, interbreeding. It requires presentation and quantitative analysis, from standard segmentation and classification methods to the best class strategies.

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The concept of a smart building or a home automation system is often characterized by the ability to control and monitor various household appliances to provide improved convenience, energy efficiency and security. In this paper a smart building real-time power monitoring system is proposed. The system targeted at enhanced safety and user awareness about power consumption with reduced cost. The consumed power can be instantaneously measured. The system can be programmed such in the case increasing power consumption, and start to turn off some insignificant appliances. Consequently, power consumption is reduced down and the cost is reduced. The general-purpose microcontroller (Arduino) available in market with low price. An interpolation technique is proposed to reduce the time of data acquisition. The experimental power load measurements achieved by proposed system are compared in the Lab with the precision and commercial power meters. The comparison shows good results, and the typical time required to measure the real power load and power factor is 42.8 ms for one cycle measurement with 192 samples of data acquisition. The maximum error in measurements are 6% W for real power and 4% for PF.