Emotional speaker recognition in real life conditions using multiple descriptors and i-vector speaker modeling technique

Multimedia Tools and Applications - Emotional speaker recognition under real life conditions becomes an urgent need for several applications. This paper proposes a novel approach using multiple...     

Anomaly Detection in ICS Datasets with Machine Learning Algorithms

An Intrusion Detection System (IDS) provides a front-line defense mechanism for the Industrial Control System (ICS) dedicated to keeping the process operations running continuously for 24 hours in a day and 7 days in a week. A well-known ICS is the Supervisory Control and Data Acquisition (SCADA) system. It supervises the physical process from sensor data and performs remote monitoring control and diagnostic functions in critical infrastructures. The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as the first line of defense, have been detailed. The machine learning algorithms have been performed with labeled output for prediction classification. The activity traffic between ICS components is analyzed and packet inspection of the dataset is performed for the ICS network. The features of flow-based network traffic are extracted for behavior analysis with port-wise profiling based on the data baseline, and anomaly detection classification and prediction using machine learning algorithms are performed.     

SNEE: a query processor for wireless sensor networks

A wireless sensor network (WSN) can be construed as an intelligent, large-scale device for observing and measuring properties of the physical world. In recent years, the database research community has championed the view that if we construe a WSN as a database (i.e., if a significant aspect of its intelligent behavior is that it can execute declaratively-expressed queries), then one can achieve a significant reduction in the cost of engineering the software that implements a data collection program for the WSN while still achieving, through query optimization, very favorable cost:benefit ratios. This paper describes a query processing framework for WSNs that meets many desiderata associated with the view of WSN as databases. The framework is presented in the form of compiler/optimizer, called SNEE, for a continuous declarative query language over sensed data streams, called SNEEql. SNEEql can be shown to meet the expressiveness requirements of a large class of applications. SNEE can be shown to generate effective and efficient query evaluation plans. More specifically, the paper describes the following contributions: (1) a user-level syntax and physical algebra for SNEEql, an expressive continuous query language over WSNs; (2) example concrete algorithms for physical algebraic operators defined in such a way that the task of deriving memory, time and energy analytical cost-estimation models (CEMs) for them becomes straightforward by reduction to a structural traversal of the pseudocode; (3) CEMs for the concrete algorithms alluded to; (4) an architecture for the optimization of SNEEql queries, called SNEE, building on well-established distributed query processing components where possible, but making enhancements or refinements where necessary to accommodate the WSN context; (5) algorithms that instantiate the components in the SNEE architecture, thereby supporting integrated query planning that includes routing, placement and timing; and (6) an empirical performance evaluation of the resulting framework.     

N-Body Simulation Inspired by Metaheuristics Optimization

The N-body problem in classical physics, is the calculation of force of gravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists and mathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been the major hurdles in solving this problem for large and complex galaxies. Advent of high performance computational machines have mitigated the problem to much extent, but still for large number of bodies it consumes huge amount of resources and days for computation. Conventional algorithms have been able to reduce the computational complexity from to by splitting the space into a tree or mesh network, researchers are still looking for improvements. In this research work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. The results are compared with that of conventional algorithms. The outcomes show about 50% time saving with almost no loss in accuracy. The proposed approach being a metaheuristics optimization technique, attempts to find optimal solution to the problem, searching the entire space in a unique and efficient manner in a very limited amount of time.     

Chemical composition and antimicrobial activity of the essential oils from four Ruta species growing in Algeria

Antimicrobial properties of plants essential oils have been investigated in order to suggest them as potential tools to overcome the microbial drug resistance and the increasing incidence of food borne diseases problems. The aim of this research is to study the antibacterial and antifungal effects of four traditional plants essential oils, Ruta angustifolia, Ruta chalepensis, Ruta graveolens and Ruta tuberculata, against standard bacterial and fungal strains. The chemical compounds of the oils were examined by GC/MS. Results revealed a powerful antifungal activity against filamentous fungi. Aspergillus fumigatus and Cladosporium herbarum are the most sensitive strains to these oils with MIC values less than 3.5 μg ml⁻¹ for certain oils, reaching 7.8 μg ml⁻¹ for other. GC/MS essay exhibited ketones as the most abundant constituent of these oils except for R. tuberculata essential oil which has a completely different composition, monoterpenes alcohols being the most abundant. These compositions explain their potential antifungal activity.     

Statistical Modelling of 1-min Rain Rate Derived from 1-h Integration Time in Malaysia

Statistical analysis of 1 min rain rate used in estimation of rain attenuation is presented. In rainy condition, the electromagnetic signal tend to experience serious degradation in satellite communication system that operate above 10 GHz. Prediction of 1-min rain rate requires conversion method from longer integration time is important due to its limited 1 min availability in most places. A linear model was proposed in this paper and compared with few existing model in Malaysia. The proposed model achieved good result for Malaysia data which is at par with established models such as Segal, Burgu?no, and Chebil and Rahman.     

Characterization and Properties of Treated Oil Palm Empty Fruit Bunch Regenerated Cellulose Biocomposite Films with Butyl Methacrylate Using Ionic Liquid

Regenerated cellulose biocomposite films from oil palm empty fruit bunch and microcrystalline cellulose were prepared using N,N-dimethylacetamide and lithium chloride. The effects of oil palm empty fruit bunch contents and butyl methacrylate on properties of regenerated cellulose biocomposite films were investigated. At 2?wt% of untreated oil palm empty fruit bunch content showed highest crystallinity index, tensile strength, modulus of elasticity, and thermal stability but lower elongation at break than other oil palm empty fruit bunch content. The treated regenerated cellulose biocomposite films with butyl methacrylate showed better tensile strength, modulus of elasticity, thermal stability, and crystallinity index while Fourier transform infrared spectroscopy study showed interaction between cellulose and butyl methacrylate.     

Two layered Genetic Programming for mixed-attribute data classification

The important problem of data classification spans numerous real life applications. The classification problem has been tackled by using Genetic Programming in many successful ways. Most approaches focus on classification of only one type of data. However, most of the real-world data contain a mixture of categorical and continuous attributes. In this paper, we present an approach to classify mixed attribute data using Two Layered Genetic Programming (L2GP). The presented approach does not transform data into any other type and combines the properties of arithmetic expressions (using numerical data) and logical expressions (using categorical data). The outer layer contains logical functions and some nodes. These nodes contain the inner layer and are either logical or arithmetic expressions. Logical expressions give their Boolean output to the outer tree. The arithmetic expressions give a real value as their output. Positive real value is considered true and a negative value is considered false. These outputs of inner layers are used to evaluate the outer layer which determines the classification decision. The proposed classification technique has been applied on various heterogeneous data classification problems and found successful.