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Electricity theft is a major concern for electric power distribution companies. The data set from the State Grid Corporation of China (SGCC) is preprocessing; first, order dataset by date, second, remove the empty record from the dataset, third, missing values by linear interpolation, and finally, imbalanced data handling technique. Then find feature extraction including monthly average, slope, moment and standard deviation, Variance, Peak to Peak, Energy Entropy, Skewness, Crest Factor, Total harmonic distortion, Log Energy, and Kurtosis for all months with gather. After finding features of the dataset, Deep Convolution Neural Network (DCNN) applied DCNN with A map is classified using a convolution layer to extract features, followed by a softmax layer. DCNN is used for data classification in energy theft or non-theft. Finally, the calculated accuracy achieved 100%, which is quite promising in comparison to other reported categorization schemes. The number and date of the theft were then calculated for each of the records in which the theft occurred.

Osteoporosis is one of the most common diseases that affect the bones of adults, especially women in menopause, and the reason for this is due to the lack of bone mineral density bone mineral density (BMD). BMD can be measured by X-ray and dual energy X-ray absorptiometry (DEXA) images, this article, focused on using DEXA images for Osteoporosis detection. At first, the original image must passed through the preprocessing stage, during which the noisy parts is reduced, and the useless parts are eliminated, and then the contrast between adjacent areas is increased and the area of interest is allocated. After that, the image is passed in a deep learning model in order to extract the unique features on the basis of which each image is classified. The classification result was excellent with 98% accuracy. The used dataset is “Osteoporosis DEXA scans images” of Spine from Pakistan.

International audience

International audience