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In this paper, we propose and evaluate a selfoptimization strategy for a clustering-based tag recommendation system. For tag recommendation, we use an efficient discriminative clustering approach. To develop our selfoptimization strategy for this tag recommendation approach, we empirically investigate when and how to update the tag recommender with minimum human intervention. We present a nonlinear optimization model whose solution yields the clustering parameters that maximize the recommendation accuracy within an administrator specified time window. Evaluation on "BibSonomy" data produces promising results. For example, by using our self-optimization strategy a 6% increase in average F1 score is achieved when the administrator allows up to 2% drop in average F1 score in the last one thousand recommendations.

Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall (with 2 classes and 3 classes) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets.

Suicide bomb attacks are a high priority concern nowadays for every country in the world. They are a massively destructive criminal activity known as terrorism where one explodes a bomb attached to himself or herself, usually in a public place, taking the lives of many. Terrorist activity in different regions of the world depends and varies according to geopolitical situations and significant regional factors. There has been no significant work performed previously by utilizing the Pakistani suicide attack dataset and no data mining-based solutions have been given related to suicide attacks. This paper aims to contribute to the counterterrorism initiative for the safety of this world against suicide bomb attacks by extracting hidden patterns from suicidal bombing attack data. In order to analyze the psychology of suicide bombers and find a correlation between suicide attacks and the prediction of the next possible venue for terrorist activities, visualization analysis is performed and data mining techniques of classification, clustering and association rule mining are incorporated. For classification, Naïve Bayes, ID3 and J48 algorithms are applied on distinctive selected attributes. The results exhibited by classification show high accuracy against all three algorithms applied, i.e., 73.2%, 73.8% and 75.4%. We adapt the K-means algorithm to perform clustering and, consequently, the risk of blast intensity is identified in a particular location. Frequent patterns are also obtained through the Apriori algorithm for the association rule to extract the factors involved in suicide attacks.

As the motivations and capabilities of threat actors continue to evolve, providing data security has become more important than ever. For this purpose, different ciphers using various techniques are being developed. Currently, chaotic maps are designed and applied in the development of these ciphers. Modern ciphers utilize a substitution box (S-Box) as a core module to provide data security. In this article, an innovative chaotic map is suggested for the design of new and dynamic S-Box. Criteria like Bijectiveness, Nonlinearity (NL), Strict Avalanche Criterion (SAC), Bit Independence Criterion (BIC), Linear Approximation Probability (LP), and Differential Approximation Probability (DP) are used to critically analyze and evaluate the proposed S-Box performance against various attacks. The cryptanalytic strength of the proposed S-Box is equated with freshly designed S-Boxes for its customization in real-life security applications. The comparative analysis gratifies the true potential of the proposed S-Box for its solicitation in data security domain.