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An efficient table detection process offers a solution for enterprises dealing with automated analysis of digital documents. Table detection is a challenging task due to low inter-class and high intra-class dissimilarities in document images. Further, the foreground-background class imbalance problem limits the performance of table detectors (especially single stage table detectors). The existing table detectors rely on a bottom-up scheme that efficiently captures the semantic features but fails in accounting for the resolution enriched features, thus, affecting the overall detection performance. We propose an end to end trainable framework (DeepDoT), which effectively detect the tables (of different sizes) over arbitrary scales in document images. The DeepDoT utilizes a top-down as well as a bottom-up approach, and additionally, it uses focal loss for handling the pervasive class imbalance problem for accurate predictions. We consider multiple benchmark datasets: ICDAR-2013, UNLV, ICDAR-2017 POD, and MARMOT for a thorough evaluation. The proposed approach yields comparatively better performance in terms of F1-score as compared to state-of-the-art table detection approaches.

With the remarkable increase in online transactions, market basket analysis has become more relevant. Identifying the buying patterns and generating more relevant recommendations to users is the prime goal of online shopping portals. However, extracting associations between products from millions of transactions is a big challenge. Association rule mining techniques are commonly used to discover the associations among transactional databases. To further enhance the elicitation of important strong rules between items, this work applies metaheuristic algorithms such as Genetic Algorithm, Particle Swarm Optimization and Firefly algorithm on Apriori algorithm. The performance of proposed approach is evaluated in terms of number of rules generated and the time complexity of the algorithm. Experiments performed over two different transactional datasets of varying size reveals that Apriori with firefly results into least number of rules as compared to GA and PSO.

Drinking alcohol in excess leads to lower self-consciousness, damaging a persons judgment and thus enhances risk of aggressive behavior. It leads to various problems like social abuse, violence, crime, and road accidents. Hence, density of drunk people in a given area is one of the indicators of safety risk. In this work we propose a novel framework to determine density of drunk people in a smart city scenario. Smart cities provide multiple sources of information such as audio, video, and text (online social networks). We detect presence of drunk persons along with time and location by analyzing these information sources individually and then fuse this information to obtain a single drunk index for a given location. We put special focus text analysis and propose a more accurate method to detect drunk event (person) with an accuracy of 84.2%. Experimental results demonstrate the functionality and efficacy of the proposed framework.