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Detection of malicious Android applications using Ontology-based intelligent model in mobile cloud environment

JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021年 58卷

作者： Nisha, Jannath O. S. Bhanu, Mary Saira S. Natl Inst Technol Dept Comp Sci & Engn Thiruchirappalli Tamil Nadu India

Mobile Cloud Computing (MCC) is a computing model that makes mobile devices resourceful by executing mobile applications (apps) in the cloud and storing data in cloud servers. MCC faces several security threats in both the Cloud and Mobile environments. Among several threats, malicious apps are the most threatening ones, because they can perform various malicious activities in both environments. The traditional malware detection methods may not detect new types of malware or rapidly changing malware behavior. So, there is a need to develop an accurate model for detecting malicious apps in the MCC environment. Scalability and Knowledge Reusability are challenging issues in existing detection methods. To overcome these issues, the proposed model uses an effective Ontology-based intelligent model based on app permissions to detect malware apps. This model extracts the relationship between the static features from the apps and builds an Apps Feature Ontology (AFO). A concept vector set for apps is created using the items obtained from the AFO. The most discriminant features are selected using optimization algorithms like Particle Swarm optimization, Social Spider Algorithm (SSA), and Gravitational Search Algorithm to reduce the dimension of the concept vector set. Various classifiers are applied to the reduced set. The efficiency of the proposed approach was evaluated on datasets obtained from the AndroZoo repository and VirusShare. The experimental results reveal that the proposed model can correctly detect malware using the Random Forest (RF) classifier with SSA and achieve higher detection accuracy with the lesser fall-out and less detection speed than existing Android malware detection techniques. Specifically, RF with SSA obtained higher accuracy, F1-score, and reduction in the fall-out of 94.11%, 93%, and 3%, respectively.

关键词： Mobile Cloud Computing Malware and Benign apps Ontology optimization algorithms Machine Learning classifiers

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The open banking era: An optimal model for the emergency fund

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 244卷

作者： Liu, Junxiu Huang, Shaodong Fu, Qiang Luo, Yuling Qin, Sheng Cao, Yi Zhai, Jia Yang, Su Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain inspired Comp & Intelligent Guilin 541004 Peoples R China Univ Edinburgh Business Sch Management Sci & Business Econ Grp Edinburgh Scotland Xian Jiaotong Liverpool Univ Business Sch Suzhou Jiangsu Peoples R China Swansea Univ Dept Comp Sci Swansea Wales

The COVID-19 outbreak has negatively impacted the income of many bank users. Many users without emergency funds had difficulty coping with this unexpected event and had to use credit or apply to the government for bailout funds. Therefore, it is necessary to develop spending plans and deposit plans based on transaction data of users to assist them in saving sufficient emergency funds to cope with unexpected events. In this paper, an emergency fund model is proposed, and two optimization algorithms are applied to solve the optimal solution of the model. Secondly, an early warning mechanism is proposed, i.e. an unexpected prevention index and a consumption index are proposed to measure the ability of users to cope with unexpected events and the reasonableness of their expenditure respectively, which provides early warning to users. Finally, the model is experimented with real bank users and the performance of the model is analysed. The experiments show that compared to the no-planning scenario, the model helps users to save more emergency funds to cope with unexpected events, furthermore, the proposed model is real-time and sensitive.

关键词： Emergency fund optimization algorithms Open Banking

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AN OPTIMAL SCALING FRAMEWORK FOR COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2012年第6期21卷 1250033-1250033页

作者： Vozalis, Manolis G. Markos, Angelos I. Margaritis, Konstantinos G. Univ Macedonia Dept Appl Informat Thessaloniki 54006 Greece Democritus Univ Thrace Dept Primary Educ Alexandroupolis Greece

Collaborative Filtering (CF) is a popular technique employed by Recommender Systems, a term used to describe intelligent methods that generate personalized recommendations. Some of the most efficient approaches to CF are based on latent factor models and nearest neighbor methods, and have received considerable attention in recent literature. Latent factor models can tackle some fundamental challenges of CF, such as data sparsity and scalability. In this work, we present an optimal scaling framework to address these problems using Categorical Principal Component Analysis (CatPCA) for the low-rank approximation of the user-item ratings matrix, followed by a neighborhood formation step. CatPCA is a versatile technique that utilizes an optimal scaling process where original data are transformed so that their overall variance is maximized. We considered both smooth and non-smooth transformations for the observed variables (items), such as numeric, (spline) ordinal, (spline) nominal and multiple nominal. The method was extended to handle missing data and incorporate differential weighting for items. Experiments were executed on three data sets of different sparsity and size, MovieLens 100k, 1M and Jester, aiming to evaluate the aforementioned options in terms of accuracy. A combined approach with a multiple nominal transformation and a "passive" missing data strategy clearly outperformed the other tested options for all three data sets. The results are comparable with those reported for single methods in the CF literature.

关键词： Collaborative filtering recommender systems low-rank approximation optimization algorithms categorical principal component analysis optimal scaling

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The forecast of power consumption and freshwater generation in a solar-assisted seawater greenhouse system using a multi-layer perceptron neural network

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷

作者： Liu, Jing Shao, Ming Sun, Minghui Weifang Univ Sci & Technol Weifang 262700 Peoples R China Shandong Prov Univ Lab Protected Hort Weifang 262700 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

This research is a simulation of the solar seawater greenhouse framework by using a hybrid model of multilayer perceptron neural network and different optimization algorithms. Four key factors, roof transparency, the front evaporator height, greenhouse length, and width, are considered decision-making variables to investigate power consumption and water production. In this regard, six metaheuristic approaches are employed to hyper-tune the learning process of the neural network. The accuracy of the proposed models is estimated via statistical in-dicators. The obtained results show that biogeography-based optimization and genetic algorithm had the min-imum RMSE value for both power consumption and water generation. Based on these criteria, the ACO method has the worst rank. It should be mentioned that some data are training set and some others are employed for the testing procedure. According to the results, less testing error is related to BBO and GA methods. Water con-sumption assessment represents that the least error is related to ACO strategy. It is found that by change of width and transparency, more water production is related to ES and GA methods with almost 120 m3/day and 105 m3/ day, respectively. Moreover, ES, ACO, and GA have less power consumption. Finally, it is found that when transparency was 0.4, the water production value could be 115 m3/day while for 0.6 of transparency this amount was 95.

关键词： Solar seawater greenhouse Water production capacity Multi-layer perceptron optimization algorithms

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Turning Functions in Optimal Synthesis of Mechanisms

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JOURNAL OF MECHANICAL DESIGN 2015年第6期137卷 062302-062302页

作者： Nadal, Fernando Cabrera, Juan A. Bataller, Alex Castillo, Juan J. Ortiz, Antonio Univ Malaga Dept Mech Engn E-29071 Malaga Spain

In this paper, we describe the use of turning functions to compare errors between the coupler and the target paths. The main reason to use turning functions is that the measured error does not depend on the mechanism scale or the position and rotation of the fixed link. Therefore, the searching space for the optimization algorithm is reduced. To carry out mechanism synthesis, we use an evolutionary algorithm. The effectiveness of the proposed method has been demonstrated in five synthesis examples.

关键词： Turning algorithms Errors Shapes Design optimization algorithms

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Contact Stability and Contact Safety of a Magnetic Resonance Imaging-Guided Robotic Catheter Under Heart Surface Motion

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2021年第7期143卷 071010页

作者： Hao, Ran Tuna, E. Erdem Cavusoglu, M. Cenk Case Western Reserve Univ Dept Elect Comp & Syst Engn Cleveland OH 44106 USA

Contact force quality is one of the most critical factors for safe and effective lesion formation during catheter based atrial fibrillation ablation procedures. In this paper, the contact stability and contact safety of a novel magnetic resonance imaging (MRI)-actuated robotic cardiac ablation catheter subject to surface motion disturbances are studied. First, a quasi-static contact force optimization algorithm, which calculates the actuation needed to achieve a desired contact force at an instantaneous tissue surface configuration is introduced. This algorithm is then generalized using a least-squares formulation to optimize the contact stability and safety over a prediction horizon for a given estimated heart motion trajectory. Four contact force control schemes are proposed based on these algorithms. The first proposed force control scheme employs instantaneous heart position feedback. The second control scheme applies a constant actuation level using a quasi-periodic heart motion prediction. The third and the last contact force control schemes employ a generalized adaptive filter-based heart motion prediction, where the former uses the predicted instantaneous position feedback, and the latter is a receding horizon controller. The performance of the proposed control schemes is compared and evaluated in a simulation environment.

关键词： Stability Robotics Simulation Biological tissues Catheters Magnetic resonance imaging Force control Safety optimization algorithms

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OPTIMAL TOLERANCE DESIGN OF A PRINTER ACTUATOR

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JOURNAL OF MECHANISMS TRANSMISSIONS AND AUTOMATION IN DESIGN-TRANSACTIONS OF THE ASME 1984年第4期106卷 510-517页

作者： CHEN, JSC WANG, S ZUG, P J. S.-C. Chen S. Wang P. Zug IBM Thomas J. Watson Research Center Yorktown Heights NY 10598

In this paper we describe an extension of an existing optimization technique to the tolerance design of a printer actuator mechanism. We provide the designer with a method for selecting optimal nominal design values for the parameters of a generalized system which allows maximum tolerance excursions away from the nominal design values and yet still maintains performance standards. The method uses an interactive linear-programming based design optimization algorithm to select optimal nominal parameter values and their associated maximal tolerances. We illustrate the method with a simple two-dimensional example. Finally we show the results from the tolerance optimization of a printer actuator which demonstrates the applicability of the theory to a real design problem.

关键词： Actuators Design optimization Linear programming optimization algorithms Mechanisms

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Generalized Smooth Functions for Modeling Steady-State Response of Controls in Transmission and Distribution

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期213卷

作者： Agarwal, Aayushya Pandey, Amritanshu Bandele, Naeem Turner Pileggi, Larry

The promise of renewables and the consequent fluctuations in the power grid necessitate a robust simulation framework to capture the steady-state behavior of new controls. However, standard non-differentiable models of control mechanisms produce divergence and/or numerical oscillations in large power flow simulations. In this paper, we describe a methodology that introduces two generalized class C1 smooth basis functions to model the steady-state of various controls in power flow for transmission and three-phase distribution as well as optimi-zation settings. These models are accompanied by homotopy methods and limiting techniques in the simulation engine that ensure scalable and robust convergence. We map standard power flow controls, typically modeled as non-differentiable functions in commercial tools, to the proposed class C1 basis functions and demonstrate the benefits for robustness in comparison to commercial tools on test cases as large as the US Eastern Interconnection system. We further extend the approach to model non-standard devices such as STATCOMs and the steady-state effects of inverter-based generators in three-phase distribution and optimization problems.

关键词： Power flow Computational methods Transmission systems Distribution systems optimization algorithms Nonlinear systems

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Metaheuristics and Transmission Expansion Planning: A Comparative Case Study

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ENERGIES 2021年第12期14卷 3618页

作者： Abdi, Hamdi Moradi, Mansour Lumbreras, Sara Razi Univ Engn Fac Elect Engn Dept Kermanshah *** Iran Islamic Azad Univ Kermanshah Branch Young Researchers & Elite Club Kermanshah *** Iran Univ Pontificia Comillas Inst Res Technol Madrid 28015 Spain

Transmission expansion planning (TEP), the determination of new transmission lines to be added to an existing power network, is a key element in power system planning. Using classical optimization to define the most suitable reinforcements is the most desirable alternative. However, the extent of the under-study problems is growing, because of the uncertainties introduced by renewable generation or electric vehicles (EVs) and the larger sizes under consideration given the trends for higher renewable shares and stronger market integration. This means that classical optimization, even using efficient techniques, such as stochastic decomposition, can have issues when solving large-sized problems. This is compounded by the fact that, in many cases, it is necessary to solve a large number of instances of a problem in order to incorporate further considerations. Thus, it can be interesting to resort to metaheuristics, which can offer quick solutions at the expense of an optimality guarantee. Metaheuristics can even be combined with classical optimization to try to extract the best of both worlds. There is a vast literature that tests individual metaheuristics on specific case studies, but wide comparisons are missing. In this paper, a genetic algorithm (GA), orthogonal crossover based differential evolution (OXDE), grey wolf optimizer (GWO), moth-flame optimization (MFO), exchange market algorithm (EMA), sine cosine algorithm (SCA) optimization and imperialistic competitive algorithm (ICA) are tested and compared. The algorithms are applied to the standard test systems of IEEE 24, and 118 buses. Results indicate that, although all metaheuristics are effective, they have diverging profiles in terms of computational time and finding optimal plans for TEP.

关键词： transmission expansion planning (TEP) optimization algorithms uncertainty wind farms electrical vehicles (EVs)

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Prediction of small-scale piles by considering lateral deflection based on Elman Neural Network - Improved Arithmetic Optimizer algorithm

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ISA transactions 2022年 127卷 473-486页

作者： Ming Zhang Jianxun Yang Rongfu Ma Qian Du Dragan Rodriguez Shenzhen Bureau of Geology Shenzhen Guangdong 518023 China. Shenzhen Bureau of Geology Shenzhen Guangdong 518023 China. Electronic address: yangjx_gddz@***. Case Western Reserve University Cleveland OH USA.

Piles (kinds of geotechnical structures) are used for resisting various lateral loads including earthquakes and inclined loads. Hence, these structures' behavior under lateral load should be studied. Therefore, this investigation studies the lateral deflection (LD) of piles under different situations. 192 physical models were carried out by consideration of the most important factor on the lateral deflection amounts in dried sandy soils. Besides, a model of the Elman Neural Network (ENN) - Improved Arithmetic Optimizer (IAO) algorithm was suggested for predicting the piles' lateral deflection. For the intention of comparison, the Elman Neural Network model and Particle Swarm optimization - Artificial Neural Network were utilized in lateral deflection amounts estimation. For evaluating the proposed model validity, some parameters like Variance Account For, determination coefficient, and Root Mean Squared Error were estimated. The results showed the ENN-IAO method is more reliable for lateral deflection prediction in a small-scale pile in comparison to the ENN method and PSO-ANN model.

关键词： Elman Neural Network — Improved Arithmetic Optimizer algorithm Elman Neural Network, Lateral deflection optimization algorithms Particle Swarm optimization — Artificial Neural Network Small-scale pile

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