Android is currently the most popular smartphone platform which occupied 88% of global sale by the end of 2nd quarter 2018. With the popularity of these applications, it is also inviting cybercriminals to develop malw...
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ISBN:
(纸本)9781450368490
Android is currently the most popular smartphone platform which occupied 88% of global sale by the end of 2nd quarter 2018. With the popularity of these applications, it is also inviting cybercriminals to develop malware application for accessing important information from smartphones. The major objective of cybercriminals to develop Malware apps or Malicious apps to threaten the organization privacy data, user privacy data, and device integrity. Early identification of such malware apps can help the android user to save private data and device integrity. In this study, features extracted from intermediate code representations obtained using decompilation of APK file are used for providing requisite input data to develop the models for predicting android malware applications. These models are trained using extreme learning with multiple kernel functions ans also compared with the model trained using most frequently used classifiers like linear regression, decision tree, polynomial regression, and logistic regression. This paper also focuses on the effectiveness of data sampling techniques for balancing data and feature selection methods for selecting right sets of significant uncorrelated metrics. The high-value of accuracy and AUC confirm the predicting capability of data sampling, sets of metrics, and training algorithms to malware and normal applications.
The trajectory planning of redundant manipulator is key areas of research, which require efficient optimization algorithms. This paper presents a new method that combines multiple objectives for trajectory planning an...
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ISBN:
(纸本)9783037850176
The trajectory planning of redundant manipulator is key areas of research, which require efficient optimization algorithms. This paper presents a new method that combines multiple objectives for trajectory planning and generation for redundant manipulators. The algorithm combines collision detection, finding target and optimizing trajectory using Genetic algorithm. In order to optimize the path, an evaluation function is defined based on multiple criteria, including the total displacement of the end-effector, the total angular displacement of all the joints, as well as the uniformity of Cartesian and joint space velocities. These criteria result in minimized, smooth end-effector motions. These algorithm yields solutions instantaneously and generate the path. The proposed algorithm is analyzed and its performance is demonstrated through simulation and the results are compared with the other methods.
作者：
Xia, LMCent S Univ
Informat Engn Coll Changsha 410075 Peoples R China
In this paper, a new method for image restoration is proposed based on particle filters, which provides a simple mid flexible algorithm to compute complex posterior distributions for Bayesian image restoration. We inc...
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ISBN:
(纸本)7121002159
In this paper, a new method for image restoration is proposed based on particle filters, which provides a simple mid flexible algorithm to compute complex posterior distributions for Bayesian image restoration. We incorporate genetics algorithm into particle filtering to introduce diversity on the "population" of particles so that improve the robustness, accuracy, and flexibility of the particle filtering. Several experiments are shown to demonstrate the effectiveness of our method in restoring synthetic and real images.
The paper presents the development of monitoring, system of intelligent water supply network. The main task of this system is water leakage detection and localization. For inputs, this system uses information from flo...
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The paper presents the development of monitoring, system of intelligent water supply network. The main task of this system is water leakage detection and localization. For inputs, this system uses information from flow, sensors, mounted on the pipeline network, while the output is a piece of information about leakage detection and localization. The main advantage of this system is a possibility of approximate leakage localization using only a limited number of installed sensors. The system is based on an artificial neural network which classified the states of network (leakage in defined part of network, no leakage). In the paper, some developments and attempts to improve the sensitivity and accuracy of this system, and develop the method of classifier building were, described.
The paper presents an innovative tool for supporting facility design in manufacturing and assembling developed by the authors; the general architecture integrating Genetic algorithms with discrete-event simulation is ...
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The paper presents an innovative tool for supporting facility design in manufacturing and assembling developed by the authors; the general architecture integrating Genetic algorithms with discrete-event simulation is proposed including experimental results obtained in real industrial case studies.
In this research a closed loop supply chain is designed which incorporates reverse logistics and forward logistic system simultaneously. In the design of reverse logistic system, recovery options are embedded in tradi...
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In this research a closed loop supply chain is designed which incorporates reverse logistics and forward logistic system simultaneously. In the design of reverse logistic system, recovery options are embedded in traditional supply chain for treating returned products. The recovery system includes collection centres, remanufacturing plants and disposal centres. Since the product return is supply driven, there is an uncertainty about it. In the proposed configuration for closed loop supply chain, the optimised configuration for supply chain in terms of locating recovery plants is developed. Accordingly, a fuzzy mixed integer linear programming model develops to deal with the uncertainty of returning products by customers. A general-purpose solver (LINGO 8.0) and a Meta heuristic approach (genetics algorithm) are implemented to solve the proposed model. The answers are compared by defining indexes and then the optimal answer, configuration and variables are identified. This solution will suggest a new design of supply chain network in which waste of materials is minimised and the new raw materials are necessary only when the used products may not be recovered by recovery options.
Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC syste...
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Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm's efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm's superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems.
This paper discusses the design of genetics algorithm based proportional integral derivative-power system stabilizer and Adaptive Neuro Fuzzy Inference System based power system stabilizer for the stability analysis o...
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ISBN:
(纸本)9781479913022
This paper discusses the design of genetics algorithm based proportional integral derivative-power system stabilizer and Adaptive Neuro Fuzzy Inference System based power system stabilizer for the stability analysis of the single machine infinite bus system. The controller has been used to generate the appropriate supplementary control signal for the excitation system of synchronous generator. The signal generated has been used to damp the low frequency oscillations and improves the performance of power system dynamics. The non- linear simulations of the system has been carried out, which results show the efficacy and capability of two schemes for the design of PSS under the various disturbances and faults conditions.
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