Object detection is one of the most important sectors in digital forensics science. The object detection technique is valuable for a number of purposes for instance: medical diagnosis scanners, traffic monitoring system, airport security examination, law regulation firms, and for diverse local or international data rescue departments. The purpose of my paper is to deliver an object detection method to detect a weapon in a camera image by relating a detailed analysis of weapon detection techniques such as image enhancement, image segmentation, image feature extraction, and image classification. However, the applicable techniques are created through the computation of different mathematical and algorithms models.

Requirement elicitation is one of the major phases of the software development life cycle. As per authors knowledge, among many reviews, there is no review available on a comparison between Online Collaborative Requirement Engineering (OCRE) and Traditional Collaborative Requirement Engineering (TCRE) techniques. In this review paper, OCRE and TCRE techniques are reviewed in terms of research methods employed in the related research. In addition, the techniques are compared in terms of software tools used in elicitation of the requirements and the types of software developed by using these techniques. The advantages and disadvantages mentioned in the literature are also highlighted in this research. The relevant papers were selected in a systematic way and data is extracted into the excel files for analysis. The results revealed some interesting findings like the most important techniques in both OCRE and TCRE are literature review followed by experimentation.

From last 50 years the work has been conducted on building such systems that can have capabilities by which it can evaluate or check like a human tutor or even better than a human tutor, this is the goal of Automatic Essay Evaluation System. Grading essays is one of the most tedious and time-consuming task, subjectivity of topic, bias nature of human grader are also key points which affect the process of assessing, this becomes initial motivation for advancing the method of assessment resulting human written essays are now assessed by humans and also by computer system Automatic Essay Evaluation System. In this paper a detailed study is conducted on AEE systems and its building approaches such as text mining and text processing for the purpose to bring the exposure to this research field as technology upgrades, it has become more commercialized raising to the most important problem in the development of AEE system, the lack of its exposer. This paper also addresses our approach replicating all possible qualities of existing AEE system for the students and teachers of Pakistan.

This paper is a comparative study about detecting straight road from satellite images. There are multiple applications of road detection. Here, only straight road is considered for use as a landing strip for aircraft emergency landing. To fully guarantee the safe landing of aircraft, multiple criterions are required to be addressed, for example, buildings and traffic. However, the focus of this paper is only to detect straight road from aerial image to ensure it is feasible to land an aircraft or not. If it is feasible, only then the detection process can move forward to the analysis of non-road objects. To find such road, three different image processing methods are used which are (Canny, Sobel and Prewitt), Fuzzy C-Means (FCM) clustering method and Markov Random Field (MRF) classification model. Each method is used to segment the roads from non-road objects. Since, edge detectors and segmentation models may have broken segments morphological operations are applied to join these broken segments, namely dilation and erosion. Then, the Hough transform is applied to detect a straight road. The results obtained were compared and was concluded that canny performed better as compared to other methods used in this comparative study. But practically none of them were found effective enough as straight road detectors. In the end, some issues are addressed and few solutions have been proposed for future work on this paper.

Retailers industry normally involve big investment as their products have many categories with different options. To increase the profit margin, retailers need to identify the right products otherwise cost and stock of their products would increase significantly. The efficient demand forecast system is a useful method to accomplish prior goals, improve customer satisfaction and reduce out of stock conditions for products. The main idea behind this study is to predict the demand of products for future and increase the sales revenue of grocery retailing industry by using two machine learning algorithms namely support vector machine (SVM) and artificial neural network (ANN). In this study, the dataset of a supermarket located in Pakistan is used which comprises of the actual demand of the past year. The results specified that SVM ensured a better-forecasted quality rather than ANN.