Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we present a methodology for visualizing coffee diseases using different visualization approaches. Our goal is to visualize aspects of a coffee disease to obtain insight into what the model “sees” as it learns to classify healthy and non-healthy images. In addition, visualization helped us identify misclassifications and led us to propose a guided approach for coffee disease classification. The guided approach achieved a classification accuracy of 98% compared to the 77% of naïve approach on the Robusta coffee leaf image dataset. The visualization methods considered in this study were Grad-CAM, Grad-CAM++, and Score-CAM. We also provided a visual comparison of the visualization methods.

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The topic of suspicious behavior detection has been one of the most emergent research themes in computer vision, video analysis, and monitoring. Due to the huge number of CCTV (closed-circuit television) systems, it is not easy for people to manually identify CCTV for suspicious motion monitoring. This paper is concerned with an automatic suspicious behavior detection method using a CCTV video stream. Observers generally focus their attention on behaviors that vary in terms of magnitude or gradient of motion and behave differently in rules of motion with other objects. Based on these facts, the proposed method detected suspicious behavior with a temporal saliency map by combining the moving reactivity features of motion magnitude and gradient extracted by optical flow. It has been tested on various video clips that contain suspicious behavior. The experimental results show that the performance of the proposed method is good at detecting the six designated types of suspicious behavior examined: sudden running, colliding, falling, jumping, fighting, and slipping. The proposed method achieved an average accuracy of 93.89%, a precision of 96.21% and a recall of 94.90%.

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

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