Journal of Intelligent & Fuzzy Systems - Volume 35, issue 2

Authors: Zhang, Lili | Shao, Heshuai | Yao, Kai | Li, Qi | Wang, Huibin

Article Type: Research Article

Abstract: Due to the limited focus range of optical imaging system, and the locations or focus of different objects in the same scene are different, multiple objects cannot be focused at the same time. In order to solve this problem and make the underwater image clearer, we propose a fusion method based on the sparse matrix in this paper. Firstly, we transform the source image into sparse image by sparse transform and get the clearity of the image based on the sparsity. Then, the clearity image will be segmented into focus regions. After that, the focus regions and non-focus regions are fused …respectively based on different fusion algorithms. Finally, the focus regions and non-focus regions are combined to get the enhanced image. The experiments in the end show that the fusion method we proposed in this paper has higher information entropy, correlation entropy, standard deviation, and average gradient, so it can enhance the underwater multi-focus image and can be applied to the underwater object detection. Show more

Keywords: Sparse matrix, underwater multi-focus image, fusion, region segment

DOI: 10.3233/JIFS-169705

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1685-1693, 2018

Authors: Zhou, Chengmin | Li, Fei | Cao, Wen | Wang, Cao | Wu, Yihuai

Article Type: Research Article

Abstract: Contrasted with common obstacle avoidance mode based on single sensor or solo algorithm, this article put forward an intelligent pattern based on Combination from CNN-based Deep Learning Method and liDAR-based Image Processing approach. As for Deep Learning method, a 10-layer Convolutional Neural Network (CNN) is designed which comes to a high recognition accuracy of 97 percent in Tensorflow and success rate of obstacle avoidance is over 90 percent. With regard to liDAR-based Image Processing approach, decision is made by a special method of counting the number of Point Cloud Data (PCD) which is generated by 2D liDAR and a success …rate over 90 percent is achieved as well. When two kinds of methods work together, a robust success rate of 100 percent is realized. Meanwhile, Inertial Measurement Unit (IMU) and Xbox360 are taken into consideration for Pose Estimation and Data Collection. Finally, all functions are integrated in Robot Operation System (ROS) on platform of nVidia Jetson TX1. Show more

Keywords: Obstacle avoidance, deep learning, collaborative system design, 2D liDAR, ROS

DOI: 10.3233/JIFS-169706

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1695-1705, 2018

Authors: Tripathi, Ashish | Mishra, K.K. | Tiwari, Shailesh | Kumar, Naveen

Article Type: Research Article

Abstract: Software cost estimation is the process of predicting the most realistic and valid amount of effort necessary for the development of any software. The cost estimation of any software is a difficult assignment due to the involvement of many factors that anyhow affect the estimation process. In literature, many cost estimation models have been developed for more than a decade to maintain accuracy in estimation of the cost of software projects. But, it is found that these models are inefficient to estimate the exact cost of software development because of uncertainties and lack of accuracy associated with them. In this …paper, Alla F. Sheta models have been taken for optimization, which are the modified versions of the very famous Boehm’s COCOMO model. Parameters of the Sheta models have been tuned enough by the proposed method to estimate and minimize the consequences of different factors that affect the overall software development cost. Experimental work has been carried out in MATLAB environment and analysis of results is performed on the basis of Magnitude of Relative Error (MRE), Prediction (PRED) at 0.25, Value Accounted For (VAF) and Mean Magnitude of Relative Error (MMRE). Estimation accuracy of the proposed work is tested on NASA software project dataset. It is found that the proposed method shows good estimation capabilities over other state-of-the-art cost estimation models. Show more

Keywords: Software cost estimation, EAMD, COCOMO model, NASA dataset, natural phenomena

DOI: 10.3233/JIFS-169707

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1707-1720, 2018

Authors: Bhuvaneshwari, B. | Rajeswari, A.

Article Type: Research Article

Abstract: 3D Reconstruction has been an enduring problem in Image understanding and computer vision. There is an increasing interest on 3D information in the general public due to rapid development of 3D imaging techniques, marketing of 3D movies and games, and low cost of depth cameras. Numerous algorithms that have been proposed for performing 3D reconstruction using different variants of Iterative Closest Point(ICP) algorithm focusses mainly on reducing the computation time. The accuracy of 3D reconstruction is not taken in to consideration. An efficient, accurate, real time and active 3D reconstruction method using Kinect sensor is developed in this paper focusing …on improving the accuracy of 3D reconstruction in less computation time. An Artificial Bee colony based ICP algorithm is proposed by incorporating several efficient variants of ICP algorithm. The proposed algorithm is intended to improve the accuracy and stability of the standard ICP algorithm. The performance of the proposed algorithm is satisfactory when compared with structured light technique and several ICP variants with respect to accuracy, complexity and computation speed. Show more

Keywords: 3D Reconstruction, iterative closest point, kinect sensor, artificial bee colony, structured light technique

DOI: 10.3233/JIFS-169708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1721-1732, 2018

Authors: Zhao, Hong | Hou, Chunning

Article Type: Research Article

Abstract: Smartphone has been used for recognizing the different motion activities. However, current studies focus on either improving algorithm factor or adjusting neural network structure factor rather than on time cost factor and actual application factor. A novel method to consider these four factors comprehensively enhancing recognition of motion state accuracy is proposed. An architecture of the Bi-LSTM neural network and the TensorFlow machine learning system are used to classify the motion state and evaluate its experimental results. In addition, the Bi-LSTM neural network is compared with other neural network structures. Meanwhile, using the data captured by the accelerometer sensor and …gyroscope sensor of the smartphone tests the Bi-LSTM neural network model. Experimental results show that using Bi-LSTM neural network and TensorFlow machine learning system to extract motion state characteristics, this method makes the motion state identification achieve 86.7% accuracy and the Bi-LSTM neural network model is better than other neural network models considering above four factors. The model of Bi-LSTM neural network can be used for other time-series fields such as signal recognition, action analysis, etc. This study provides a new method, which considers the four factors, to enhance the accuracy of the motion state classification. Show more

Keywords: Deep learning, Bi-LSTM neural network, motion state, sensors of smartphone, TensorFlow

DOI: 10.3233/JIFS-169709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1733-1742, 2018

Authors: Gujarathi, Pritam K. | Shah, Varsha A. | Lokhande, Makarand M.

Article Type: Research Article

Abstract: Conversion of the conventional vehicle (CV) into the plug-in hybrid electric vehicle (PHEV) is one of the promising solutions to improve transport sustainability and reduce outdoor air pollution. Energy management is crucial for the performance of PHEV. The paper presents combine rule based-artificial bee colony optimization algorithm for energy management of converted plug-in hybrid electric vehicle (CPHEV). The diesel operated parallel hybrid topology is considered for study with the designed electric powertrain. NOx and PM are considered as optimization parameters along with specific fuel consumption. The performance based on fuel consumption and emissions (NOx and PM) is analyzed by considering …sample Indian urban and highway driving cycle. The complete vehicle is simulated using MATLAB Simulink linked with coding. The results of converted PHEV obtained is compared with conventional one for both driving cycles for analysis of the fuel consumption and emissions considering real-time benchmarking norms. The results indicate that the combine rule based-artificial bee colony strategy keeps pollution under control required as per BSIII norms. Show more

Keywords: Artificial bee colony, emission, optimization, plug-in hybrid electric vehicle

DOI: 10.3233/JIFS-169710

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1743-1753, 2018

Authors: Xia, Min | Zhang, Chong | Weng, Liguo | Liu, Jia | Wang, Ying

Article Type: Research Article

Abstract: Robot path planning is integral to many robotic applications. In this work, three optimization objectives are presented: path length, degree of path smoothness, and degree of security. Due to the lack of local search ability, the optimal solution set is difficult to be obtained with the traditional method especially when the search space is very irregular. And the simple local search algorithm is often trapped into local optimization. A new method with local search is introduced to improve the SPEA2 in this work. The proposed method sets up an external population dedicated to local search, which can increase the local …search ability of the method while retaining good global searching ability. In addition, the new crossover operator and the individual update strategy are used for proposed method. The simulation results shows that the proposed method is better than that of SPEA2, NSGA-2 and PESA. It was found that the model proposed in this work is practical for robot path planning. Show more

Keywords: Multi-objective evolutionary algorithm, SPEA2, robot path planning, adaptive crossover, local search

DOI: 10.3233/JIFS-169711

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1755-1764, 2018

Authors: Sahoo, Samanway | Nandi, Subham Kumar | Barua, Sourav | Pallavi, | Bhowmik, Showmik | Malakar, Samir | Sarkar, Ram

Article Type: Research Article

Abstract: Handwritten word recognition is considered as an active research area since long because of its various real life applications. The key obstacle of this research problem is the huge variation of the writing styles of different individuals. In addition to that the complex shapes of alphabet make the recognition process more difficult. A holistic word recognition approach is proposed here in order to classify 80-class handwritten city name images written in Bangla script. Based on the negative refraction property of the light, a novel shape-based feature vector of size 186 is generated from each of the word images. Effectiveness of …the feature vector is tested on a database containing total 12000 handwritten word images having equal number of samples from each class. The proposed method achieves a reasonably good recognition accuracy of 87.50% which proves better while comparing with some of the recently published feature vectors used for similar job. The reported result is achieved by combining the classifiers namely Sequential Minimal Optimization (SMO), Simple Logistic and CV Parameter Selection embedded with SMO. To verify the robustness of the present method it is also applied on handwritten word images written in Roman and Devanagari scripts separately and it is found that our method obtains satisfactory result on the both the cases. Show more

Keywords: Word recognition, holistic approach, handwritten word, shape descriptor, Bangla script, negative refraction

DOI: 10.3233/JIFS-169712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1765-1777, 2018

Authors: Sharma, Aman | Rani, Rinkle

Article Type: Research Article

Abstract: Human Cancer Cell lines have gained a lot of attention since it helps in studying cancer biology and various treatment options. Recently various large-scale drug screening experiments were performed providing access to genomic and pharmacological data. This data helps in predicting drug responses which eventually contributes to the development of personalized cancer treatment. Heterogeneous nature of cancer raises the serious need for therapeutic agents with an essence of personalized treatment. Thus considering the assumption that similar drugs exhibit similar drug responses, we have developed kernelized similarity based regularization matrix factorization framework for predicting anti-cancer drug responses. Drug-Drug chemical structure similarity …and Tissue-Tissue similarity (gene expression) are taken as key descriptors to formulate the objective function. The kernel function is used to map non-linear relationships between drugs and tissues. Our aim is to provide an efficient anti-cancer drug response prediction approach to establish the protocol for personalized treatment and new drugs designing. The proposed framework is validated using publicly available tumor datasets: GDSC and CCLE. Proposed KSRMF is further compared with three states of art algorithms using GDSC and CCLE drug screens. We have also predicted missing drug response values in the dataset using KSRMF. KSRMF outperforms other counterparts even though gene mutation data is not incorporated while designing the approach. An average mean square error of 3.24 and 0.504 is achieved using GDSC and CCLE drug screens respectively. The obtained results show that the proposed framework has quite potential to improve anti-cancer drug response prediction. Our analysis showed how data integration can help in achieving the goal of personalized cancer treatment. Show more

Keywords: Matrix Factorization, Kernel, Drug Responses Prediction, Personalized Anti-Cancer Treatment

DOI: 10.3233/JIFS-169713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1779-1790, 2018

Authors: Giuffrè, Orazio | Granà, Anna | Tumminello, Maria Luisa | Sferlazza, Antonino

Article Type: Research Article

Abstract: The paper introduces a methodological approach based on genetic algorithms to calibrate microscopic traffic simulation models. The specific objective is to test an automated procedure utilizing genetic algorithms for assigning the most appropriate values to driver and vehicle parameters in AIMSUN. The genetic algorithm tool in MATLAB® and AIMSUN micro-simulation software were used. A subroutine in Python implemented the automatic interaction of AIMSUN with MATLAB® . Focus was made on two roundabouts selected as case studies. Empirical capacity functions based on summary random-effects estimates of critical headway and follow up headway derived from meta-analysis were used as reference for …calibration purposes. Objective functions were defined and the difference between the empirical capacity functions and simulated data were minimized. Some model parameters in AIMSUN, which can significantly affect the simulation outputs, were selected. A better match to the empirical capacity functions was reached with the genetic algorithm-based approach compared with that obtained using the default parameters of AIMSUN. Overall, GA performs well and can be recommended for calibrating microscopic simulation models and solving further traffic management applications that practioners usually face using traffic microsimulation in their professional activities. Show more

Keywords: Genetic algorithm, traffic microsimulation, AIMSUN, passenger car equivalent, roundabout

DOI: 10.3233/JIFS-169714

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 2, pp. 1791-1806, 2018