Journal of Intelligent & Fuzzy Systems - Volume 43, issue 2

Authors: Manzoor, Saher | Tayyaba, Shahzadi | Ashraf, Muhammad Waseem

Article Type: Research Article

Abstract: Microfluidic filtration is an essential process in many biomedical applications. Micro or nanoporous membranes are used for colloidal retention. During the membrane filtration process visualization of various phenomena is challenging. Theoretical models have been proposed to visualize the transport mechanism. In this work, ANSYS Fluent is used for 3D designing of the microfluidic system and Fuzzy simulations are used to study flow rate and velocity, to get the maximum benefit from Anodized Aluminium oxide membrane in practical applications. The proposed method exploits relations between driving force, membrane area, and fluid flow. After optimization of parameters for the filtration, the AAO …membrane with desired pore diameter was fabricated using the two-step anodization method. Scanning electron microscope is used for characterization of fabricated AAO membrane. The simulated and theoretical results using computer-based programs are then compared for manipulation of flow rate during the filtration process. Along with the manipulation of flow rate from nanoporous membrane other challenges faced during the filtration process are also highlighted with possible solutions. Show more

Keywords: AAO, microfluidic analysis, fuzzy analysis, filtration

DOI: 10.3233/JIFS-219309

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2099-2108, 2022

Authors: Imran, Muhammad | Butt, Alvina Rafiq | Qasim, Faheem | Fahad, Hafiz Muhammad | Sher, Falk | Waseem, Muhammad Faisal

Article Type: Research Article

Abstract: ZnO is promising material for the electronic and optoelectronic devices. In present work we have fabricated the ZnO film by DC reactive magnetron sputtering. The variations of reactive and sputtering gases affect the crystallite size and band gap of ZnO film. In present work the ZnO film is prepared at 50 watt power by DC reactive spurting method. The fuzzy simulation has been performed to estimate the best argon oxygen gas ratio which gives the better crystallinity and band-gap. The structural analysis shows that the ZnO film has hexagonal wurtzite structure. The UV-vis spectroscopy has been employed to find the …band gap.the measured band gap value of ZnO is 3.21 eV. The fuzzy rule based system and characterization results are in accordance with each other with a minimal difference of less than 1%. Show more

Keywords: DC Sputtering, Zinc oxide film, band gap, resistivity

DOI: 10.3233/JIFS-219310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2109-2114, 2022

Authors: Awan, Saima Ashraf | Tariq, Muhammad Imran | Zhuang, Peifen

Article Type: Research Article

Abstract: This research analyzed the relationship between openness to agricultural exchange, foreign direct investment, capital, consumer price index, and GDP in Pakistan utilizing time series data for the 1971-2019 period. We used autoregressive distributed lag ARDL-bound testing method to analyze the long-run and short-run correlation between projected variables. The study’s findings also confirmed a positive and important long-term correlation between openness to agricultural trade, foreign direct investment and Pakistan’s economic development. There is no long-run relationship between consumer price and economic growth.

Keywords: Agricultural trade openness, foreign direct investment, ARDL approach

DOI: 10.3233/JIFS-219311

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2115-2119, 2022

Authors: Ma, Yingran | Peng, Yanjun | Wu, Tsu-Yang

Article Type: Research Article

Abstract: Transfer learning technique is popularly employed for a lot of medical image classification tasks. Here based on convolutional neural network (CNN) and sparse coding process, we present a new deep transfer learning architecture for false positive reduction in lymph node detection task. We first convert the linear combination of the deep transferred features to the pre-trained filter banks. Next, a new point-wise filter based CNN branch is introduced to automatically integrate transfer features for the false and positive image classification purpose. To lower the scale of the proposed architecture, we bring sparse coding process to the fixed transferred convolution filter …banks. On this basis, a two-stage training strategy with grouped sparse connection is presented to train the model efficiently. The model validity is tested on lymph node dataset for false positive reduction and our approach indicates encouraging performances compared to prior approaches. Our method reaches sensitivities of 71% /85% at 3 FP/vol. and 82% /91% at 6 FP/vol. in abdomen and mediastinum respectively, which compare competitively to previous approaches. Show more

Keywords: Deep learning, lymph node false positive reduction, transfer learning method, sparse coding algorithm

DOI: 10.3233/JIFS-219312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2121-2133, 2022

Authors: Jia, Han-Dong | Li, Wei | Pan, Jeng-Shyang | Chai, Qing-Wei | Chu, Shu-Chuan

Article Type: Research Article

Abstract: Wireless sensor network (WSN) is a network composed of a group of wireless sensors with limited energy. With the proliferation of sensor nodes, organization and management of sensor nodes become a challenging task. In this paper, a new topology is proposed to solve the routing problem in wireless sensor networks. Firstly, the sensor nodes are layered to avoid the ring path between cluster heads. Then the nodes of each layer are clustered to facilitate the integration of information and reduce energy dissipation. Moreover, we propose efficient multiverse optimization to mitigate the impact of local optimal solution prematurely and the population …diversity declines prematurely. Extensive empirical studies on the CEC 2013 benchmark demonstrate the effectiveness of our new approach. The improved algorithm is further combined with the new topology to handle the routing problem in wireless sensor networks. The energy dissipation generated in routing is significantly lower than that of Multi-Verse Optimizer, Particle Swarm Optimization, and Parallel Particle Swarm Optimization in a wireless sensor network consisting of 5000 nodes. Show more

Keywords: Routing, clustering, layering, WSN, multi-verse optimizer

DOI: 10.3233/JIFS-219313

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2135-2146, 2022

Authors: Feng, Naidan | Wu, Tsu-Yang | Liang, Yongquan

Article Type: Research Article

Abstract: The electrocardiogram (ECG) signal is a kind of time-varying signal, which has the characteristics and difficulties of variability, instability, and noise. Aiming at that, this paper put forward a novel 13-layer deep dynamic neural network model (DDNN) for the ECG signal learning and classification. The proposed DDNN model is a dynamic hybrid deep learning model. It includes a wavelet block, a convolutional block, a recurrent block, and a classification block, which combines the learning property and classification mechanism of convolutional neural network for the large-scale data sets, the learning and memory ability of Long Short-Term Memory (LSTM) for time series, …and the noise reduction and processing ability of wavelet basis for the signals to meet the requirement of the learning and classification of ECG signal characteristics. Sufficient experimental results show that the proposed model is feasible and effective in the electrocardiogram signal pattern classification. Show more

Keywords: Dynamic signal classification, deep dynamic neural network, time-varying signal, ECG signal classification

DOI: 10.3233/JIFS-219314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2147-2154, 2022

Authors: Feng, Qing | Pan, Jeng-Shyang | Du, Zhi-Gang | Peng, Yan-jun | Chu, Shu-Chuan

Article Type: Research Article

Abstract: Antlion Optimization Algorithm (ALO) is a promising bionic swarm intelligence algorithm, which has good robustness and convergence, but there are still many areas to be improved and modified. Aiming at the fact that the ALO algorithm is more likely to fall into the local optimum, proposes three strategies to improve the classic ALO algorithm in this paper. First of all, we adopt a parallel idea in the algorithm, through the communication strategy between groups based on Quantum-Behaved to enhance the diversity of the population. Secondly, we adopted two strategies, Opposition Learning, and Gaussian Mutation, to balance the performance of exploration …and exploitation during the execution of the algorithm, further formed the MSALO algorithm. The CEC2013 Benchmark function is selected as the standard, and MSALO is compared with other intelligent optimization algorithms. The experimental results show that MSALO has stronger optimization performance compared with other intelligent algorithms. Besides, we applied MSALO to the practical scenarios of feature selection, and use SVM classifiers as training evaluators to improve the accuracy of feature extraction from high-dimensional data. Show more

Keywords: ALO, quantum-behaved, opposite learning, gaussian mutation, feature selection

DOI: 10.3233/JIFS-219315

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2155-2166, 2022

Authors: Wu, Jimmy Ming-Tai | Tsai, Meng-Hsiun | Li, Tu-Wei | Pirouz, Matin

Article Type: Research Article

Abstract: Estimating similarity using multiple similarity measures or machine learning prediction models is a popular solution to the link prediction problem. The Relation Pattern Deep Learning Classification (RPDLC) technique is proposed in this study, and it is based on multiple neighbor-based similarity metrics and convolution neural networks. The RPDLC first calculates the characteristics for a pair of nodes using neighbor-based metrics and impact nodes. Second, the RPDLC creates a heat map using node characteristics to assess the similarity of the nodes’ connection patterns. Third, the RPDLC uses convolution neural network architecture to build a prediction model for missing relationship prediction. On …three separate social network datasets, this method is compared to other state-of-the-art algorithms. On all three datasets, the suggested method achieves the greatest AUC, hovering around 99 percent. The use of convolution neural networks and features via relational patterns to create a prediction model are the paper’s primary contributions. Show more

Keywords: Link prediction problem, convolution neural network, relation pattern, social network

DOI: 10.3233/JIFS-219316

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2167-2178, 2022

Authors: Wu, Jimmy Ming-Tai | Tsai, Meng-Hsiun | Cheng, Chao-Chieh | Wu, Mu-En

Article Type: Research Article

Abstract: With the rise in popularity of personal computers and decreasing cost, even a personal computer can execute complex and large calculations. So more researchers can invest in AI and machine learning. Humans can’t handle massive data sets or data that requires a long time to read and evaluate, whereas big data frameworks can read and analyze in a reasonable time. So relevant research has increased recently. In the social sciences, machine learning is used to forecast future trends and the index trend. Keeping up with current events is crucial nowadays to debate countermeasures in time. This study combines economic indicators …from 1988 to 2017 with leading indicators and other types of indicators. The recurrent neural network model predicts economic index trends and tests multiple variables. The proposed methods measure the error in predicting future trends in different models to learn which indicators work well together. Show more

Keywords: Machine learning, leading indicator, recurrent neural network, long short term memory, forecast trend

DOI: 10.3233/JIFS-219317

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2179-2189, 2022

Authors: Moghadam, Arman Balali | Rafsanjani, Marjan Kuchaki | Balas, Valentina Emilia

Article Type: Research Article

Abstract: This study takes a new perspective on the procedural content generation (PCG) evaluation problem, extracts current PCG evaluation methods from previous works, and presents a novel classification of these methods while showing each method’s capabilities. Also, the present study introduces a novel concept called Panda Evaluation. Additionally, the soft and hard launches were presented as two evaluation methods and possible building blocks of PE. A group of papers was analyzed to understand previous works and find new opportunities. In doing so, some missing PCG evaluation areas were found, and some new methods were proposed for future PCG evaluations. To the …best of our knowledge, this is the first time these concepts have been presented in PCG evaluation. Show more

Keywords: Procedural content generation (PCG), platformer, soft launch, panda evaluation (PE), machine learning (ML), evaluation graph

DOI: 10.3233/JIFS-219318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 2191-2210, 2022