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Waste or trash management is receiving increased attention for intelligent and sustainable development, particularly in developed and developing countries. The waste or trash management system comprises several related processes that carry out various complex functions. Recently, interest in deep learning (DL) has increased in providing alternative computational techniques for determining the solution to various waste or trash management problems. Researchers have concentrated on this domain, and as a result, significant research has been published, particularly in recent years. According to the literature, a few comprehensive surveys have been done on waste detection and classification. However, no study has investigated the application of DL to solve waste or trash management problems in various domains and highlight the available datasets for waste detection and classification in different domains. To this end, this survey contributes by reviewing various image classification and object detection models, and their applications in waste detection and classification problems, providing an analysis of waste detection and classification techniques with precise and organized representation and compiling over twenty benchmarked trash datasets. Also, we backed up the study with the challenges of existing methods and the future potential in this field. This will give researchers in this area a solid background and knowledge of the state-of-the-art deep learning models and insight into the research areas that can still be explored.

Requirements negotiation is a centralized process of making a decision in order to resolve conflicts in the requirements of the stakeholder. The negotiation will enable the shared vision of software to be developed among the heterogeneous stakeholder in the software industry to be achieved. Many process models used for the negotiation of stakeholder’s requirements have been proposed for the software industry by the research community, yet the acceptance of these process models is discouraging. This study tends to investigate the inadequate adoption of requirements negotiation process models. Further, it finds the acceptance criteria for the software industry to adopt requirements negotiation models. Finding shows that the software industries do not adopt the process models. The perceived usefulness, perceived ease of use and many more criteria have been identified through the literature review on the general criteria of software systems acceptance.

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This paper presents the outcome of an investigation of the costs and benefits of thinning as part of preprocessing for line detection including specification of end-points, from visual images of indoor rectilinear environments. This is done as part of a bigger process with the goal of detecting lines to enable a small mobile robot self-navigate within the environment based on navigationally important features such as doors and corridors reconstructed from the lines detected. The straight line Hough transform is used to determine parameters which specify gradients and positions of lines, and then the end-points of the lines are determined. To do this images can be preprocessed to the point of edge-detection which typically yields edge lines several pixels thick, or edge-detection followed by thinning yielding edge lines about a single pixel in thickness. Since the Hough transform operates on all pixels in an input image, more work is needed to process the "unthinned" image. However, thinning itself takes time. This paper looks into whether the taking the time to do thinning is justified in terms of overall time taken, and quality of resulting lines found, and concludes that for the purpose described, thinning does appear to improve the quality of line detection, while taking less total time to do it.