Yuanmin Shi^1,2, Siran Yin^1,2, Ze Chen^1,2, Leiming Yan^1,2,*

Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 9-16, 2021, DOI:10.32604/jihpp.2021.012193

Abstract Privacy protection is a hot research topic in information security field. An improved XGBoost algorithm is proposed to protect the privacy in classification tasks. By combining with differential privacy protection, the XGBoost can improve the classification accuracy while protecting privacy information. When using CART regression tree to build a single decision tree, noise is added according to Laplace mechanism. Compared with random forest algorithm, this algorithm can reduce computation cost and prevent overfitting to a certain extent. The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data. More >

Lei Zhao^*, Ming Zhao

Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 1-8, 2021, DOI:10.32604/jihpp.2021.010065

Abstract Object detection has been studied for many years. The convolutional neural network has made great progress in the accuracy and speed of object detection. However, due to the low resolution of small objects and the representation of fuzzy features, one of the challenges now is how to effectively detect small objects in images. Existing target detectors for small objects: one is to use high-resolution images as input, the other is to increase the depth of the CNN network, but these two methods will undoubtedly increase the cost of calculation and time-consuming. In this paper, based on the RefineDet network framework,… More >

Mohammed Abaker^1,*, Abdelzahir Abdelmaboud², Magdi Osman³, Mohammed Alghobiri⁴, Ahmed Abdelmotlab⁴

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 843-856, 2021, DOI:10.32604/iasc.2021.017714

Abstract The rock-fall is a natural hazard that results in many economic damages and human losses annually, and thus proactive policies to prevent rock-fall hazard are needed. Such policies require predicting the rock-fall occurrence and deciding to alert the road users at the appropriate time. Thus, this study develops a rock-fall early warning system based on logistic regression model. In this system, the logistic regression model is used to predict the rock-fall occurrence. The decision-making algorithm is used to classify the hazard levels and delivers early warning action. This study adopts two criteria to evaluate the system predictive performance, including overall… More >

Ibrahim B.M. Taha¹, Diaa-Eldin A. Mansour^2,*

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 739-752, 2021, DOI:10.32604/iasc.2021.017703

Abstract Power transformer is one of the more important components of electrical power systems. The early detection of transformer faults increases the power system reliability. Dissolved gas analysis (DGA) is one of the most favorite approaches used for power transformer fault prediction due to its easiness and applicability for online diagnosis. However, the imbalanced, insufficient and overlap of DGA dataset impose a challenge towards powerful and accurate diagnosis. In this work, a novel fault diagnosis for power transformers is introduced based on DGA by using data transformation and six optimized machine learning (OML) methods. Four data transformation techniques are used with… More >

Mohamed I. Mossad^1,*, Fahd A. banakhr¹, Sherif S. M. Ghoneim², Tarek A. AbdulFattah³, Mohamed Mahmoud Samy⁴

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 715-727, 2021, DOI:10.32604/iasc.2021.017534

Abstract This paper introduces voltage self-regulation of a variable speed single-phase induction generator-based wind energy conversion system (WECS) for stand-alone applications. The idea behind the voltage self-regulation technique proposed in this paper is adjusting the fixed capacitor’s effective value for exciting the single-phase induction generator. This adjustment is performed using an inexpensive Sinusoidal PWM (SPWM) switching circuit to short circuit the capacitor during different periods to make a virtual change of the capacitance value extracted from the fixed capacitor. That optimized fixed capacitor size is firstly determined using harmony search (HS) optimization technique. HS is also used to determine the capacitance… More >

Ayesha Maqbool^1,*, Farkhanda Afzal², Tauseef Rana³, Alina Mirza⁴

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 653-667, 2021, DOI:10.32604/iasc.2021.017506

Abstract This paper presents a co-operative, self-organisation method for Multiple Autonomous Vehicles aiming to share surveillance responsibilities. Spatial organization or formation configuration of multiple vehicles/agents’ systems is crucial for a team of agents to achieve their mission objectives. In this paper we present simple yet efficient thermodynamic inspired formation control framework. The proposed method autonomously allocates region of surveillance to each vehicle and also re-adjusts the area of their responsibilities during the mission. It provides framework for heterogeneous UAVs to scatter themselves optimally in order to provide maximum coverage of a given area. The method is inspired from a natural phenomenon… More >

Anshu Parashar^*, Nidhi Kalra, Jaskirat Singh, Raman Kumar Goyal

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 669-682, 2021, DOI:10.32604/iasc.2021.017478

Abstract Electrophysiological (EEG) signals provide good temporal resolution and can be effectively used to assess and diagnose children with Attention Deficit Hyperactivity Disorder (ADHD). This study aims to develop a machine learning model to classify children with ADHD and Healthy Controls. In this study, EEG signals captured under cognitive tasks were obtained from an open-access database of 60 children with ADHD and 60 Healthy Controls children of similar age. The regional contributions towards attaining higher accuracy are identified and further tested using three classifiers: AdaBoost, Random Forest and Support Vector Machine. The EEG data from 19 channels is taken as input… More >

Jinrong Hu¹, Lujin Li¹, Wenyi Yang², Zhe Wang³, Junhui Wang⁴, Yan Zhu^5,*

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 785-795, 2021, DOI:10.32604/iasc.2021.017467

Abstract The meridian theory, as an essential part of Traditional Chinese Medicine (TCM) fundamentals, provides an explanation of the spatial and functional relationship between the superficial part and the internal organs based on empiric observations. Cutaneous regions which are the body superficies on which the functions of the meridians are reflected, and the sites where the qi of the collateral’s spreads, play an important role in TCM clinical diagnosis and treatment of skin diseases. The survey of the literature on anatomical site, pathology in patients with skin disease, particularly in TCM perspective, clearly indicates that a better cutaneous region model and… More >

Tingting Yang^1,*, Shuwen Jia¹, Boxiong Yang¹, Chenxi Kan²

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 683-692, 2021, DOI:10.32604/iasc.2021.017235

Abstract In the augmented reality system, the position and direction of the user’s point of view and line of sight in the real scene is acquired in real-time. The position and direction information will determine the exact position of the virtual object of the real scene. At the same time, various coordinate systems are established according to the user’s line of sight. So registration tracking technology is very important. The paper proposes an accurate, stable, and effective augmented reality registration algorithm. The method adopts the method of ORB (oriented FAST and rotated BRIEF) features matching combined with RANSAC (random sample consensus)… More >

Hongshuo Zhang¹, Bo Zhu^1,*, Kaimin Pang¹, Chunmei Chen¹, Yuwei Wan²

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 797-810, 2021, DOI:10.32604/iasc.2021.017232

Abstract Using machine learning method to recognize abnormal patterns covers the shortage of traditional control charts for autocorrelation processes, which violate the applicable conditions of the control chart, i.e., the independent identically distributed (IID) assumption. In this study, we propose a recognition model based on support vector machine (SVM) for the AR (1) type of autocorrelation process. For achieving a higher recognition performance, the cuckoo search algorithm (CS) is used to optimize the two hyper-parameters of SVM, namely the penalty parameter c and the radial basis kernel parameter g. By using Monte Carlo simulation methods, the data sets containing samples of… More >