Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Investigating the effect of varying block size on power and energy consumption of GPU kernels

The Journal of Supercomputing - Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have...     

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions. However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD). Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown. Therefore, to tackle the FGAD problem, this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators. Accordingly, this paper proposes a multivariate time series fine-grained anomaly detection (MFGAD) framework. To avoid excessive fusion of features, MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly. Based on this framework, an algorithm based on Graph Attention Neural Network (GAT) and Attention Convolutional Long-Short Term Memory (A-ConvLSTM) is proposed, in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators. Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.     

On the ideal convergence of double sequences in intuitionistic fuzzy normed spaces

Recently, the concept of statistical convergence of double sequences has been studied in intuitionistic fuzzy normed spaces by Mursaleen and Mohiuddine [Statistical convergence of double sequences in intuitionistic fuzzy normed spaces, Chaos, Solitons Fractals, 41 (2009) pp. 2414–2421]. We know that ideal convergence is more general than statistical convergence for single or double sequences. This has motivated us to study the ideal convergence of double sequences in a more general setting. That is, in this paper, we study the concept of ideal convergence and ideal Cauchy for double sequences in intuitionistic fuzzy normed spaces.     

A new expert system for learning management systems evaluation based on neutrosophic sets

There has been a sudden increase in the usage of Learning Management Systems applications to support learner's learning process in higher education. Many studies in learning management system evaluation are implemented under complete information, while the real environment has uncertainty aspects. As these systems were described by development organizations with uncertainty terms such as vague, imprecise, ambiguity and inconsistent, that is why traditional evaluation methods may not be effective. This paper suggests neutrosophic logic as a better option to simulate human thinking than fuzzy logic because unlike fuzzy logic, it is able to handle indeterminacy of information which expresses the percentage of unknown parameters. As previous studies suggested neutrosophic decision maker and neutrosophic expert systems as future work in ecommerce and e‐learning applications, this paper presents neutrosphic expert system for learning management systems evaluation. Information for building and validating the neutrosophic expert system is collected from five experts using surveys, and then analysis is done by using Fuzzytech 5.54d software. Finally, the comparison between fuzzy expert system and neutrosophic expert system results show that the neutrosophic logic is capable of representing uncertainty in human thinking for evaluating Learning Management Systems.     

Optimization Algorithm for Selection and on Site Location of Mobile Cranes

Mobile crane manufacturers provide operators and practitioners with tabulated lift-capacity charts. These charts are structured based on predetermined crane configurations, which consist of boom/jib length, lifting radius, lifting height, main boom angles to ground, and jib angle to ground or its offset to its main boom centerline. Practitioners, however, are often required to lift on a partially extended hydraulic section and/or lifting radius other than those listed in the manufacturers’ lift-capacity charts. This paper presents a newly developed optimization algorithm (referred to in this paper as Algorithm-2) for selecting and locating mobile cranes on construction sites based on their minimum boom length and/or minimum radius, accordingly higher lifting capacities. Algorithm-2 includes an optimization procedure that avoids lifting capacity violations. In addition to the practical use of Algorithm 2, the mathematical representations of the crane’s geometry are also useful for research in the automation and robotic field of operations involving cranes. The algorithm is incorporated into a computer system that integrates a selection module and a database. Data pertinent to crane lift configurations are retrieved from the database and are then processed to determine the optimum, geometrical selection of the crane configuration. A case example is described in order to demonstrate the use of the developed algorithm and to illustrate its essential features.     

Compressibility factor model of sweet,sour, and condensate gases using genetic programming

Gas compressibility factor (z-factor) is necessary in most petroleum engineering calculations. The most common sources of z-factor values are experimental measurements, equations of state (EOS) and empirical correlations. There are more than twenty correlations available with two variables for calculating the z-factor from fitting Standing–Katz chart values in an EOS or just through fitting techniques. However, these correlations are too complex, which require initial value and longer computations, and have significant error. This work presents a new model for estimating z-factors of sweet gases, sour gases and gas condensates using genetic programming (GP). The z-factor model was developed using pseudo-reduced pressure, and pseudo-reduced temperature. Moreover, two new models of pseudo-critical pressure and temperature were built as a function of the gas composition (mol percent of C₁–C₇₊, H₂S, CO₂, and N₂) and the specific gravity of the C₇₊. The developed new GP-based model yields a more accurate prediction of gas z-factor compared to the commonly used correlations and EOS's.     

A multi-variate heart disease optimization and recognition framework

Neural Computing and Applications - Cardiovascular diseases (CVD) are the most widely spread diseases all over the world among the common chronic diseases. CVD represents one of the main causes of...