International Journal of Innovative Computing and Applications (17 papers in press)
Optimal Placement of Unified Power Flow Controller using Differential Search Algorithm
by Dhiman Banerjee, Sriparna Bhattacharya, Provas Kumar Roy
Abstract: Optimal power flow (OPF) problem plays a crucial role to run an economically efficient and well-planned power system. It is a strenuous and challenging task for the power system researchers to cope with the ever-increasing load-demand while getting the minimum system loss. The development of flexible ac transmission system (FACTS) has added a new dimension both to the system operation and research. Unified power flow controller (UPFC) is the most reliable FACTS controller, having its operational capability as series and shunt compensator. As a matter of fact, UPFC can reliably control the different power system parameters. In this article, UPFC is incorporated into the modified IEEE 5-bus and modified IEEE 30-bus test system. Differential search algorithm (DSA) is proposed and implemented to run the OPF with and without UPFC , and the results are listed, analyzed and compared with the same that is obtained by genetic algorithm (GA) and BAT search algorithm.
Keywords: Differential search algorithm; Optimal power flow; FACTS devices; UPFC.
Development of Fuzzy Logic Controller for Improved Interline Unified Power Quality Conditioner
by Ravindranath Tagore Yadlapalli, Rajanand Patnaik Narasipuram, Anusha Dodda
Abstract: This paper presents the improved interline Unified Power Quality Conditioner (iUPQC) and its controlling aspects for nonlinear loads. The nonlinear loads are the major sources of harmonics and raise the power quality issues. However, the iUPQC compensates the harmonics that are generated by a nonlinear load besides reactive power support. This in turn minimizes the harmonic distortion both in the source current as well as voltage. Furthermore, it also provides the current and voltage imbalance compensations, reactive power, frequency and voltage support at grid. The simulation of entire power system is fulfilled using proposed FUZZY controller and compared with the conventional PI controller. The performance of both the controllers is sifted in terms of %Total Harmonic Distortion (THD) by considering different case studies having the 3-ϕ diode rectifier connected to R, RL & RLE loads. The MATLAB/Simulink version R2012b is used for accomplishing the in depth simulation studies.
Keywords: interline dynamic voltage restorer,IDVR; improved interline Unified Power Quality Conditioner; iUPQC; interline Voltage Controller; IVOLCON; Fuzzy logic Controller (FLC); Proportional Integral (PI) controller; Total Harmonic Distortion; THD%.
New stochastic synchronization condition of neutral-type Markovian chaotic neural networks under impulsive perturbations
by Cheng-De Zheng
Abstract: This paper investigates the globally stochastic synchronization problem for a class of neutral-type chaotic neural networks with Markovian jumping parameters under impulsive perturbations in mean square. By virtue of drive-response concept and time-delay feedback control techniques, by using the Lyapunov functional method, vector Wirtinger-type inequality, a novel reciprocal convex lemma and the free-weight matrix method, a novel sufficient condition is derived to ensure the asymptotic synchronization of two identical Markovian jumping chaotic delayed neural networks with impulsive perturbation. The proposed results, which do not require the differentiability and monotonicity of the activation functions, can be easily checked via Matlab software. Finally, a numerical example with their simulations is provided to illustrate the effectiveness of the presented synchronization scheme.
Keywords: Stochastically asymptotic synchronization; chaotic neural networks; Markovian jump; impulse; reciprocal convex.
The Multi-Object Tracking Algorithm Research using Kalman Filtering Method
by Shuqing Liu
Abstract: Aiming at the tracking failure caused by occlusion between objects, interleaving or target drift in multi-object tracking process, the new improved algorithm of occlusion prediction tracking based on Kalman filtering and spatiograms was proposed. By combining the color histogram and the distribution of color in space, spatiograms can be used to distinguish between different objects so that we can track the object when interleaving or occlusion between objects occurs. The state of the object can be predicted by the Kalman filtering, and the occlusion mark is used for the object which overlaps with other objects, so that the occluded object which is undetected can be tracked in the next frame video. The 2D MOT 2015 dataset was used in the experimental procedure, and the average accuracy of tracking was 34.1%. The experimental results have shown that the proposed algorithm can improve the performance of multi-object tracking process.
Keywords: Multi-Object Tracking; Kalman Filtering; Spatiograms; Occlusion Prediction.
Special Issue on: Recent Advances in Memetic Computing
Hand Motions Recognition Based on sEMG Nonlinear Feature and Time Domain Feature fusion
by Jiahan Li, Gongfa Li, Ying Sun, Guozhang Jiang, Bo Tao, Shuang Xu
Abstract: In recent years, the development of many rehabilitation robots, bionic prostheses and other sports rehabilitation equipment, which are used to assist the body to restore body movement function, has been paid more and more attention. In the development and design of the current sports rehabilitation equipment or biomimetic prostheses. The classification framework of this paper is a pattern recognition framework. The feature extraction of sEMG is to extract the physical quantity or a set of physical features that fully represent the characteristics of the action class from the electromyogram corresponding to the action of the human hand, in order to distinguish the other types of motion. It's very important step in hand movement recognition. In this paper, the newly developed sEMG nonlinear features AMR are fused with the traditional sEMG time-domain features WL. Feature fusion using SVM-DS fusion algorithm. Hand motions recognition based on feature fusion is improved in accuracy and stability. The accuracy of recognition can be stabilized over 95%.
Keywords: pattern recognition; feature extraction; SVM; D-S evidence theory; Feature fusion; sEMG.
An enhanced cuckoo search using dimension selection
by Lijin Wang
Abstract: This paper proposes an enhanced cuckoo search algorithm using dimension selection. In the proposed strategy, the dimensional distance measure is used to selects a part of dimensions of each solution to search for the new solution in two search components. The dimensions of each solution are selected when those dimensional distances are larger than the average distance of all dimensional distance. A suit of 20 benchmark functions are employed to verify the performance of the proposed algorithm, and the results show the improvement in effectiveness and efficiency of dimension selection.
Keywords: cuckoo search algorithm; dimension selection; dimensional distance; average distance; crossover operator.
A Novel Firefly Algorithm with Self-Adaptive Step Strategy
by Wang Jing
Abstract: In the standard firefly algorithm, the random moving step is very important to the direction of the firefly movement, and the parameter alpha plays an important role in the random moving step. In this paper, we proposed a self-adaptive Step Strategy based on distance control in this paper, and we called it SASFA. Thirteen well-known benchmark functions are used to verify the performance of our proposed method, the computational results show that SASFA is more efficient than many other FA algorithms.
Keywords: firefly algorithm; random moving step; meta-heuristic algorithm; Global optimization;.
A new artificial bee colony based on neighborhood selection
by Xiaoyan Xiong, Jun Tang
Abstract: In this paper, we present a new artificial bee colony (ABC) for solving numerical optimization problems. In the original ABC, a new candidate solution is generated based on the current solution and a randomly selected one. However, the random selection method is unstable. To accelerate the search, a new neighborhood selection is proposed. For each current solution, we firstly randomly select some solutions from the current population. Then, we choose the best one among those solutions as the neighborhood solution to generate new solutions. To verify the performance, we test several classical numerical optimization problems. Simulation results show that our approach outperforms the original ABC and some improved ABC versions.
Keywords: artificial bee colony; neighborhood selection; opposition; global optimization.
One-dimensional deep learning firefly algorithm guided by the best particle
by Zhifeng Xie, Jia Zhao, Hui Sun, Jun Ye, Jiajia Wang, Huasheng Zhu
Abstract: We propose the one-dimensional deep learning firefly algorithm guided by the best particle in order to increase the convergence speed and optimization precision of the firefly algorithm. In each generation of optimization process, the optimal particle is first updated in a fixed number of times according to the newly designed update formula. The update mode is defined as single-dimensional deep learning. After the optimal particle completes single-dimensional deep learning, other fireflies in the population keep the original evolutionary way to update the location and iteratively complete the optimization task. Experiments with 12 benchmark functions show that the proposed algorithm has a higher optimization capacity than the other six modified firefly algorithms.
Keywords: one-dimensional deep learning; firefly algorithm; the optimal particle.
Gaussian bare-bones firefly algorithm
by Hu Peng, Shunxu Peng
Abstract: Firefly algorithm (FA), as a relatively recent emerged swarm intelligence algorithm, is powerful and popular for the complex real parameter global optimization. However, the premature convergence has greatly affected the performance of original FA. To overcome this problem, we proposed a Gaussian bare-bones FA, named GBFA, in which each firefly moves to a Gaussian bare-bones method generated learning object rather than its better neighbors. The experiments are conducted on a set of widely used benchmark functions. Experimental results and comparison with the state-of-the-art FA variants have proved that the proposed algorithm is promising.
Keywords: Firefly algorithm; Swarm intelligence; Gaussian bare-bones; Global optimization.
A hybrid binary harmony search algorithm for solving the winner determination problem
by Geng Lin, Zuoyong Li
Abstract: The winner determination problem (WDP) in combinatorial auctions is to determine an allocation of items to bidders such that each item is allocated to at most one bidder, and the auctioneer's revenue is maximized. This paper proposes a hybrid binary harmony search algorithm for the WDP. Firstly, to enhance the global search ability of the proposed algorithm, a modified harmony improvisation mechanism is developed with a modified memory consideration rule and an adaptive pitch adjustment scheme. Next, a repair operator is employed to guarantee the feasibility of the new candidate harmonies. Finally, a tabu search procedure is presented to improve the local search ability. These strategies make a good balance between intensification and diversification. In the experiments, the performance of the proposed algorithm is validated on five groups of 500 instances. Experimental results and comparisons show that the proposed algorithm is very efficient, and the tabu search procedure significantly improves the performance of the proposed algorithm.
Keywords: harmony search algorithm; local search; winner determination problem; combinatorial optimization.
Special Issue on: Recent Advances in Bio-inspired Computing Paradigms for Security and Privacy of Innovative Computing
An Out-of-Band Mobile Authenticating Mechanism for Controlling Access to data outsourced in the Mobile cloud environment
by Sumit Kumar Yadav, Nisha Saroha, Kavita Sharma
Abstract: Mobile Cloud Computing (MCC), a cloud environment, formed by mobile users at the client-side and cloud servers at the back-end enables users to store and pervasively access a huge amount of data via different mobile devices (smartphones, tablets, PDAs, etc.) in a distributive manner. The "mobile cloud", though meant to resolve the space and processing constraints of mobile devices, increases the risk of data abuse, since data is outsourced on the distrusted cloud servers. Hence, to ensure the security of user's data, of all the security principles (confidentiality, integrity and access control) we argue that controlling access of data with appropriate authentication methods can strive for data protection and integrity. The drawbacks of the current frameworks, such as overloaded computations in key distribution, reduced flexibility, and scalability, are unable to achieve fine-graininess and confidentiality. Moreover, some are not even compatible with MCC environment due to their static nature. Thus, we propose an access control mechanism which is lightweight with minimal computational overhead and provides fine-grained access control for sharing data using out-of-band (OOB) mobile authentication. In this, we perform client-side encryption and decryption using simple hash functions and concatenation operator and achieve dynamic scalability. The encryption algorithms, sharing mechanism, and the use of OOB (out-of-band) mobile authentication are extensively analyzed to prove its efficiency and applicability.
Keywords: Mobile Cloud Computing (MCC); Access Control; Security; Confidentiality; Integrity; Out-Of-Band (OOB) Mobile Authentication; Data Sharing.
Parkinsons Diagnosis Using Ant-Lion Optimization Algorithm
by Prerna Sharma, Rishabh Jain, Moolchand Sharma, Deepak Gupta
Abstract: Parkinsons disease (PD) is a long term progressive disorder of the central nervous system that mainly affects the movement of the body. But there are several limitations in detecting PD at an early stage. In this paper, a binary variant of the recently proposed Ant Lion Optimization (ALO) algorithm has been proposed and implemented for diagnosing patients for Parkinsons disease at early stages. ALO is a recently proposed bio-inspired algorithm, which imitates the hunting patterns of ant-lions or doodlebugs. The proposed algorithm is used to find a minimum number of features that result in higher accuracy using machine learning classifiers. The proposed modified version of ALO extracts the optimal features for the two different Parkinsons Datasets with improved accuracy and computational time. The maximum accuracy achieved by the classifiers after optimal feature selection is 95.91%. The proposed algorithm results have been compared with other related algorithms for the same datasets.
Keywords: Ant Lion Optimization Algorithm; Feature Extraction; Bio-Inspired Algorithm; Parkinson’s disease.
An Improved Chaotic-Based African Buffalo Optimization Algorithm
by Chinwe Peace IGIRI, Yudhveer Singh, Ramesh Chandra Poonia
Abstract: Optimization remains inevitable in any organization as the need to maximize the limited resources persists. It justifies the seemingly endless research in this area. This study explores the effectiveness of chaos to mitigate false or premature convergence problem in African Buffalo Optimization (ABO) algorithm. Chaos employs the ergodic and stochastic properties to handle this limitation. Three resourceful chaotic functions in the literature are evaluated to find the best strategy for ABO improvement. The same strategy is applied across the algorithms under study to provide an unbiased judgment. The study validates the proposed systems performance with a range of nonlinear test functions. The proposed systems result is compared with standard ABO, Particle Swarm Optimization (PSO), and chaotic Particle Swarm Optimization (CPSO) algorithms. Although chaotic ABO (CABO) gave 92% performance in comparison with standard ABO, chaotic PSO, and standard PSO; it requires further investigation. To be more explicit, the reason for no significant difference between chaotic-ABO and standard ABO in some functions calls for further research attention. The present study also highlights the research future scope. In all, the study gives insight to researchers on the appropriate algorithm for a real-world problem.
Keywords: chaotic-optimization; premature convergence; African buffalo optimization; bio-inspired algorithm; nonlinear benchmark optimization problems.
Image Integrity Verification via Reversible Predictive Hiding and Elliptic Curve Diffie-Hellman
by Siddharth Agarwal, J. Jennifer Ranjani
Abstract: This paper presents a medical image integrity verification algorithm which will be able to detect any changes made in the pixel value or the size of a medical image. At the same time, it also provides a secure way of transmitting images over the public domain. This algorithm can not only ensure integrity of the medical image, but also checks if the sender of the image is authentic, making it useful for archiving medical images and remote medical diagnosis. This algorithm essentially has three modules: hashing, data embedding and image encryption. Initially, hash signatures are extracted from the image and are embedded inside the medical image. In the embedding phase, the image is divided into blocks of uneven size. In the raster scanning order, the blocks are embedded with multiple pixels depending on its smoothness. At the receiver end, the signatures are extracted and it can be used to verify the integrity of the medical images. The effectiveness of the embedding algorithm is verified in terms of peak signal to noise ratio, structural similarity index metrics, correlation coefficient. $L^2-$norm is used for verifying the integrity of the images.
Keywords: Reversible data hiding; Elliptic Cryptography; Diffie-Hellman; Image encryption; Integrity verification.
BIO-INSPIRED ALGORITHMS FOR DIAGNOSIS OF BREAST CANCER
by Moolchand Sharma, SHUBBHAM GUPTA, Prerna Sharma, Deepak Gupta
Abstract: Most commonly found cancer among women is Breast cancer. Roughly 12% of women grow breast cancer during their lifetime. It is the second prominent fatal cancer among women. Breast Cancer Diagnosis is necessary during its initial phase for the proper treatment of the patients to lead constructive lives for an extensive period. Many different algorithms are introduced to improve the diagnosis of Breast Cancer, but many have less efficiency. In this work, we have compared different bio-inspired algorithms including Artificial Bee Colony Optimization, Particle Swarm Optimization, Ant Colony Optimization, and Firefly Algorithm. The performances on these algorithms have been measured for UCI Dataset of Wisconsin Diagnostic Breast Cancer, and the results have been calculated using different classifiers on the selected features. After the experiment, it is seen that BPSO has shown maximum accuracy of 96.45% and BFA has shown considerable results of 95.81% with 6 features which is minimum of all algorithms
Keywords: Breast Cancer; Bio-inspired Algorithms; Artificial Bee Colony Optimization; Particle Swarm Optimization; Ant Colony Optimization; Firefly Algorithm; Feature selection; Decision Tree; Linear Support Vector Machines; K-Nearest Neighbor; Random Forest Classifiers.
A Robust Approach to Detect Video Based Attacks to Enhance Security
by Shefali Arora, M.P.S. Bhatia
Abstract: Face authentication has become widespread on smart devices and in various applications these days. Real time detection of human faces in video surveillance systems is challenging due to variations in expression and background conditions. Thus, precise detection of spoofed faces is important to make such security systems robust against potential attacks. Several deep learning based techniques involving the use of Convolutional Neural Networks have proven to be excellent in detection of spoofed faces. In this paper, we have proposed the combined use of spatial and temporal information from facial images using CNN and Long Short Term Memory Networks. We have tested the approach on Idiap Replay-Attack benchmark and compared the results with the application of pre-trained models like InceptionV3, VGGNet and ResNet models to detect replay attacks during video surveillance. Our approach proves to be robust and more efficient for detection of security breaches in real time situations.
Keywords: Video attacks; Replay attacks; Deep Learning; Convolutional Neural Networks; Biometrics; Long Short-Term Memory; Face Spoofing; Real-time detection.