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In both developed and emerging countries, like Bangladesh, uterine cancer is one of the most common cancers for women. It is also the sixth most common cancer among females in the world and the fourteenth most common cancer in total. In recent years, the high prevalence of uterine cancer in women has risen dramatically. This covers several aspects that are linked to the signs and symptoms of this disease that are to be assessed and tested. Typically, these dimensions are expressed in quantitative and qualitative forms and also exist different kinds of uncertainty. Therefore, by using suitable methods to resolve the problem of uncertainty; otherwise, the detecting procedure will become unreliable. There are several systems proposed in this paper to tackle the problem. None of them, however, is worthy of solving the problem of ambiguity. This paper explains the development of a Belief Rule Based Expert System using the RIMER method, which by taking into account signs and symptoms, is able to detect the involvement of malignant tumors and risk factors for this cancer under uncertainty. Specifically, the system will expect a huge employment in diminishing the cost of lab assessments. The structure will support patients in making judicious strides well early. Moreover, this research has been done on MATLAB environment shown that the yields produced using the made system are more reliable than from manual structure, artificial neural network (ANN) and support vector machine (SVM).

Fabric inspection is very significant in textile manufacturing. Quality of fabric defends on vital activities of fabric inspection to detect the defects of fabric. Profits of industrialists have been decreased due to fabric defects and cause disagreeable loses. Traditional defect detection methods are conducted in many industries by professional human inspectors who manually draw defect patterns. However, such detection methods have some shortcomings such as exhaustion, tediousness, negligence, inaccuracy, complication as well as time-consuming which cause to reduce the finding of faults. In order to solve these issues, a framework based on image processing has been implemented to automatically and efficiently detect and identify fabric defects. In three steps, the proposed system works. In the first step, image segmentation has been employed on more than a few fabric images in order to enhance the fabric images and to find the valuable information and eliminate the unusable information of the image by using edge detection techniques. After the first step of the paper, morphological operations have been employed on the fabric image. In the third step, feature extraction has been done through FAST (Features from Accelerated Segment Test) extractor. After feature extraction, If PCA (Principal Component Analysis) is applied as it reduces the dimensions and preserves the useful information and classifies the various fabric defects through a neural network and used to find the classification accuracy. The proposed system provides high accuracy as compared to the other system. The investigation has been done in a MATLAB environment on real images of the TILDA database.

Facial feature detection and recognition are widely used in current world scenarios and technologies. Almost every smartphone is embedding facial recognition & detection in security matters like phone unlocking. Another increasing use of this type of technology is being noticed in cameras and social media like snap chat. In this paper, we developed a system/platform in which facial features containing noise, eyes, mouth, and eyebrows can be detected from an image. The developed system can also recognize a human subject from two different input images. The proposed system works in two steps-in the first step, image segmentation has been employed on more than a few images to enhance the images by using various edge detection techniques and filtering out the noise. In the second part, features are extracted from an idea through histogram of gradient (HOG). The entire facial feature detection and recognition system was developed with a histogram of oriented gradients (HOG), support vector machine (SVM), and K nearest neighbor (KNN). The proposed system gives an accuracy of 96% in the detection and recognition of facial features and human subjects. The projected strategy of procedures offers improved truth once appeared otherwise in relevance the others mixture of ways in which. The assessment has been done in the Python platform on authentic images of the Indian face database.