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Labor safety at workplaces is a critical human rights concern in all industries around the world. Coal mines are considered one of the most dangerous workplaces and every year thousands of miners around the world die or get severe injuries in mining accidents. To make efficient technology-based accident mitigation plans for such work environments, the analysis of reasons which cause these accidents is of great value. This study contributes to the coal mines domain and proposed an approach using machine learning techniques to identify the reasons for the accidents. In our approach, a dataset containing the causes for accidents in text form that occurred in the past in coal mines has been used. We performed preprocessing to clean text data and then extract features to train the machine learning model using the term frequency-inverse document frequency (TF-IDF) technique. This study proposed the voting-based hybrid classifier (VHC) which is a combination of three individual machine learning models random forest, support vector classifier, and logistic regression using soft voting criteria. Evaluation of the model has been done in terms of accuracy, precision, recall, and f1 score. VHC outperforms all other stat of the art models by achieving the highest 0.96 accuracy score.

Skin lesion detection and classification is always observed as a difficult problem to solve. Manual detection of skin lesions via visual image inspection can be time-consuming and tedious. Automatic diagnosis and classification are considered a critical problem to solve because of the involvement of many factors like different image sizes, hairs in the image, bad color schemes, ruler marker, low-contrast, variation in lesion sizes, and gel bubble. Different methodologies were proposed by the researchers in the Dermatology Pigmented lesion classification. Researchers work on the binary class problem for the detection of Melanocytic lesions from the normal one. This study makes use of the MNIST HAM10000 dataset published by International Skin Image Collaboration. The dataset consists of seven classes of skin cancer diseases. Furthermore in this research, our stacked CNN model proves its superiority by achieving 95.2% accuracy along with data augmentation and image preprocessing techniques.

The main objective of the paper is to theoretically as well as empirically analyze the Sharī’ah compliance of running Musharkah (RM) being practiced by Islamic banks. The study employs both theoretical and empirical descriptive analytical approaches. The empirical part of the study uses primary data of 1000 Shariah scholar respondents. The data was collected through questionnaire throughout the four provinces and capital city of Pakistan. The study employs univariate and bivariate analysis. The results reveals that an RM is quite close to running finance. An RM is causing the concentration of wealth in the hands of corporate class who invest less of their capital but get more of profit. It is found that an RM violates obvious principles of Islamic jurisprudence. In RM, fixation of profit at two levels as being practiced is nothing but stratagem to legalize the illegal interest based running finance.