Emotion detection (ED) plays a vital role in determining individual interest in any field. Humans use gestures, facial expressions, and voice pitch and choose words to describe their emotions. Significant work has been done to detect emotions from the textual data in English, French, Chinese, and other high-resource languages. However, emotion classification has not been well studied in low-resource languages (i.e., Urdu) due to the lack of labeled corpora. This article presents a publicly available Urdu Nastalique Emotions Dataset (UNED) of sentences and paragraphs annotated with different emotions and proposes a deep learning (DL)-based technique for classifying emotions in theUNEDcorpus. Our annotatedUNEDcorpus has six emotions for both paragraphs and sentences. We perform extensive experimentation to evaluate the quality of the corpus and further classify it using machine learning and DL approaches. Experimental results show that the developed DL-based model performs better than generic machine learning approaches with an F1 score of 85% on the UNED sentence-based corpus and 50% on the UNED paragraph-based corpus.

One of the most significant problems in cluster analysis is to determine the number of clusters in unlabeled data, which is the input for most clustering algorithms. Some methods have been developed to address this problem. However, little attention has been paid on algorithms that are insensitive to the initialization of cluster centers and utilize variable weights to recover the number of clusters. To fill this gap, we extend the standard fuzzy k-means clustering algorithm. It can automatically determine the number of clusters by iteratively calculating the weights of all variables and the membership value of each object in all clusters. Two new steps are added to the fuzzy k-means clustering process. One of them is to introduce a penalty term to make the clustering process insensitive to the initial cluster centers. The other one is to utilize a formula for iterative updating of variable weights in each cluster based on the current partition of data. Experimental results on real-world and synthetic datasets have shown that the proposed algorithm effectively determined the correct number of clusters while initializing the different number of cluster centroids. We also tested the proposed algorithm on gene data to determine a subset of important genes.

Ant-Miner, a rule-based classifier, is a powerful algorithm used to extract classification rules. It has the ability to classify the complex data accurately but it has limitations of high selective pressure, and pre-mature convergence or very slow convergence. One of the big limitations is the correct prediction based evaluation of the rules that has not yet been addressed. In this paper, we propose a new ant-miner (named as AMclr) based on a new quality function, rank based term selection and on rule rejection thresholds. The new quality function is based on rule length, coverage and correct prediction. The rule rejection thresholds are based on coverage and quality of the rule. The proposed algorithm has been tested on various benchmark datasets and compared with other state of the art algorithms. The experimental results showed that the proposed approach achieved better results in terms of accuracy, convergence speed, and comprehensibility.

This article represents the focused performance analysis of the statistical Pattern Recognition methods. There is a variety of emerging pattern recognition problems for different data representations like multimedia, spatial, temporal and textual data. Statistical Pattern Recognition approaches are extensively applied for pattern recognition and classification purposes. There are various statistical pattern-recognition methods proposed in the contemporary literature. For the selection of more appropriate statistical pattern recognition, method demands comprehensive performance evaluation of contemporary methods. However, this empirical study is focused on the performance evaluation of statistical methods for pattern discovery in terms of accuracy and error rate. The studied methods include Naïve-Bayes (NB-C), Linear Discriminant Analysis (LDA-C), Kernel Classifier (Kernel-C), Least Mean Square Linear Classifier (LinearLMS-C), Least Mean Square Quadratic classifier(PolQuadraticLMS-C), Multinomial logistic regression model with a ridge estimator(Logistic-C) and Particle Swarm Optimization - Linear Discriminant Analysis (PSOLDA-C). The implementation of mentioned methods in KEEL, a data mining tool, is applied on public datasets for the comparative analysis. Experimental results reveal that the performance of PSOLDA-C is promising than other methods in terms of accuracy.

Abstract Network security risks grow with increase in the network size. In recent past, the attacks on computer networks have increased tremendously and require efficient network intrusion detection mechanisms. Data mining and machine‐learning techniques have been used for network intrusion detection during the past few years and have gained much popularity. In this paper, we propose an intrusion detection mechanism based on binary particle swarm optimization (PSO) and random forests (RF) algorithms called PSO‐RF and investigate the performance of various dimension reduction techniques along with a set of different classifiers including the proposed approach. Binary PSO is used to find more appropriate set of attributes for classifying network intrusions, and RF is used as a classifier. In the preprocessing step, we reduce the dimensions of the dataset by using different state‐of‐the‐art dimension reduction techniques, and then this reduced dataset is presented to the proposed PSO‐RF approach that further optimizes the dimensions of the data and finds an optimal set of features. PSO is an optimization method that has a strong global search capability and is used here for dimension optimization. We perform extensive experimentation to prove the worth of the proposed approach by using different performance metrics. The standard benchmark, that is, KDD99Cup dataset, is used that contains the information about various kinds of network intrusions. The experimental results indicate that the proposed approach performs better than the other approaches for the detection of all kinds of attacks present in the dataset. Copyright © 2012 John Wiley & Sons, Ltd.

The primary objective of this research is to propose and investigate a novel ant colony optimization-based classification rule discovery algorithm and its variants. The main feature of this algorithm is a new heuristic function based on the correlation between attributes of a dataset. Several aspects and parameters of the proposed algorithm are investigated by experimentation on a number of benchmark datasets. We study the performance of our proposed approach and compare it with several state-of-the art commonly used classification algorithms. Experimental results indicate that the proposed approach builds more accurate models than the compared algorithms. The high accuracy supplemented by the comprehensibility of the discovered rule sets is the main advantage of this method.

During the past few years, huge amount of network attacks have increased the requirement of efficient network intrusion detection techniques. Different classification techniques for identifying various attacks have been proposed in the literature. In this paper we propose and implement a hybrid classifier based on binary particle swarm optimization (BPSO) and random forests (RF) algorithm for the classification of PROBE attacks in a network. PSO is an optimization method which has a strong global search capability and is used for fine-tuning of the features whereas RF, a highly accurate classifier, is used here for classification. We demonstrate the performance of our technique using KDD99Cup dataset. We also compare the performance of our proposed classifier with eight other well-known classifiers and the results show that the performance achieved by the proposed classifier is much better than the other approaches.

This paper presents the challenges and problems faced in dynamic environment optimization problems. Real world problems are mostly dynamic thus such algorithms are required which can track the moving optima. Classical Optimization algorithm searches sequentially for the solution and are based on differential equations. In order to track the moving optima algorithm will have to be restarted and there is no guarantee that algorithm will find the optimum solution. On the other, Evolutionary algorithms are more suitable for such kind of problems as they can find the multiple optima in parallel. Evolutionary algorithms can track the optima and then repeatedly balance the need to exploit known optima with the need to search for new optima. A number of techniques have been proposed to deal with the dynamic environment problems. Different challenges and problems faced in dynamic environment whether it is for a multimodal functions or multi-objective have been discussed here.

A highly efficient Binary PSO based cryptanalysis approach for four-rounded DES is presented. Several optimum keys are generated in different runs of the algorithm on the basis of their fitness value and finally, the real key is found by guessing every individual bit. The robustness of the proposed technique is also checked for eight-rounded DES. Our proposed approach has shown promising results over cryptanalysis of DES performed using other methods such as Genetic Algorithm.