Abstract: Motivated by the concept of fuzzy finite automata and fuzzy pushdown automata, we investigate a novel fuzzy state grammars and fuzzy deep pushdown automata concept. This concept represents a natural extension of contemporary state grammar and deep pushdown automaton, making them more robust in terms of imprecision, errors, and uncertainty. It has been proved that we can construct fuzzy deep pushdown automata from fuzzy state grammars and vice-versa. Furthermore, it has been proved that if fuzzy deep pushdown automaton M fd is constructed from fuzzy state grammar G fs then L (M fd ) = L (G fs ). In other words,…for any string α ∈ Σ * , μ (α ; α ∈ L (G fs )) = μ (α ; α ∈ L (M fd )) where μ denotes the membership of a string.
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Keywords: Regulated grammars, regulated automata, fuzzy state grammars, fuzzy deep pushdown automata
Abstract: Software defect prediction models are used for predicting high risk software components. Feature selection has significant impact on the prediction performance of the software defect prediction models since redundant and unimportant features make the prediction model more difficult to learn. Ensemble feature selection has recently emerged as a new methodology for enhancing feature selection performance. This paper proposes a new multi-criteria-decision-making (MCDM) based ensemble feature selection (EFS) method. This new method is termed as MCDM-EFS. The proposed method, MCDM-EFS, first generates the decision matrix signifying the feature’s importance score with respect to various existing feature selection methods. Next, the decision…matrix is used as the input to well-known MCDM method TOPSIS for assigning a final rank to each feature. The proposed approach is validated by an experimental study for predicting software defects using two classifiers K-nearest neighbor (KNN) and naïve bayes (NB) over five open-source datasets. The predictive performance of the proposed approach is compared with existing feature selection algorithms. Two evaluation metrics – nMCC and G-measure are used to compare predictive performance. The experimental results show that the MCDM-EFS significantly improves the predictive performance of software defect prediction models against other feature selection methods in terms of nMCC as well as G-measure.
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Abstract: Regression via Classification (RvC) is a process to solve a regression problem by using a classifier. An ensemble consists of many models, in which the final result is the combination of the results of these individual models. In this paper, two RvC ensemble methods are proposed. In the first ensemble method, the output of the ensemble method is modified to achieve the final output. A formula is derived in this paper for this purpose. In the second method, a new approach is proposed to compute the output of each model of an ensemble. It is shown that an accurate binary…classifier can be transformed into an accurate regression method with the proposed methods. It is also shown experimentally, by using popular Random Forests as a classifier in the proposed ensemble methods against Random Forests as a regression method, the effectiveness of the proposed RvC ensemble methods.
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Abstract: In the new era of technology with the development of wearable sensors, it is possible to collect data and analyze the same for recognition of different human activities. Activity recognition is used to monitor humans’ activity in various applications like assistance for an elderly and disabled person, Health care, physical activity monitoring, and also to identify a physical attack on a person etc. This paper presents the techniques of classifying the data from normal activity and violent attack on a victim. To solve this problem, the paper emphasis on classifying different activities using machine learning (supervised) techniques. Various experiments have…been conducted using wearable inertial fabric sensors for different activities. These wearable e-textile sensors were woven onto the jacket worn by a healthy subject. The main steps which outline the process of activity recognition: location of sensors, pre-processing of the statistical data and activity. Three supervised algorithmic techniques were used namely Decision tree, k-NN classifier and Support Vector Machine (SVM). Based on the experimental work, the results obtained show that the SVM algorithm offers an overall good performance matched in terms of accuracy i.e. 97.6% and computation time of 0.85 seconds for k-NN and Decision Tree for all activities.
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Abstract: Mining high utility itemsets (HUIs) is a basic task of frequent itemsets mining (FIM). In recent years, a trend in FIM has been to design algorithm for mining HUIs because FIM assumes that each item can not appear more than once in a transaction and all items have the same importance (weight, unit profit, price, etc.). However, in real-world, items appear more than once in a transaction and also have some importance. HUIs mining considers that items appear with some quantity and importance. Traditional HUIs mining algorithms assume that items have only positive unit profit. However, in real-world, items may…appear with negative unit profit also. For example, it is common that a retail store sells items at a loss to stimulate the sale of other related items or simply to attract customers to their retail location. Therefore, items occur with negative unit profit or negative utility. To consider negative unit profit, HUIs with negative utility has been introduced. This paper surveys recent studies on HUIs mining with negative utility and their applications. The main goal is to provide a survey of recent advancements and research opportunities. This paper presents key concepts and terminology related to HUIs mining with negative utility. This presents a taxonomy of all the algorithms consider negative utility. To the best of our knowledge, this is the first survey on the mining task of HUIs with negative utility. The paper also presents research opportunities and the challenges in HUIs mining problems.
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Keywords: High utility itemsets mining, utility mining, negative utility
Abstract: Background: Merosin-deficient congenital muscular dystrophy (MDC1A) is caused by a loss of Laminin-α2. Secondary manifestations include failed regeneration, inflammation, and fibrosis; however, specific pathomechanisms remain unknown. Objectives: Using the LAMA2DyW (DyW) mouse model of MDC1A, we sought to determine if Integrin-αV and -α5, known drivers of pathology in other diseases, are dysregulated in dystrophic muscle. Additionally, we investigated whether Losartan, a drug previously shown to be antifibrotic in dystrophic scenarios, rescues integrin overexpression in DyW mice. Methods: qRT-PCR, ELISA, and immunohistochemistry were utilized to characterize integrin and matricellular protein dysregulation in hind limb muscles from WT and untreated/ Losartan-treated DyW…mice. Results: Integrin-αV and –α5 are significantly upregulated on both gene and protein level in DyW muscle- Losartan treatment attenuates this dysregulation. Immunohistochemistry showed that Integrin-αV is expressed on both infiltrating cells as well as on muscle cells- Losartan attenuates expression in both compartments. In addition, transcriptional overexpression of common matricellular and beta binding partners is rescued close to WT levels with Losartan. Lastly, latent and active TGF-β are upregulated in the serum of DyW mice, but only active TGF-β levels are attenuated by Losartan treatment. Conclusions: Our results suggest that overexpression of Integrin-αV and –α5 are likely contributing to secondary pathologies in MDC1A. We also believe that downregulation of Integrin-αV could be partially responsible for Losartan's antifibrotic effect and therefore could serve as a novel therapeutic target in MDC1A and other degenerative fibrotic diseases.
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Abstract: Due to resource constraints and the diverse nature of the devices involved, energy efficiency and scalability enhancement are important challenges in the Internet of Things (IoT) ecosystem. It is difficult to manage the edge resources in a consistent way that promotes cooperation and sharing of resources across the devices because of the heterogeneity of the Internet of Things devices and the dynamic nature of the surroundings in which edge computing takes place. In this research, we offer Intelligent techniques for resource optimization for Internet of Things devices. This is a full-stack system architecture to support across heterogeneous Internet of Things…devices that have limited resources. The paradigm that is being suggested is made up of several edge servers, and Internet of Things (IoT) devices have the qualities of being heterogeneity-compatible, high performing, and intelligently adaptable. In order to do this, a clustered environment is generated in heterogeneous Internet of Things devices, and a routing method called Search and Rescue Optimization is used to set up connections between the CH nodes. After that, the edge nodes that are closest to the source of the data are chosen for transmission. Overall, what was suggested Multi-Edge-IoT accomplishes superior efficiency in terms of consumption of energy, latency, communication overhead, and packet loss rate than existing approaches to attaining energy efficiency in the Internet of Things.
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