Journal of Intelligent & Fuzzy Systems - Volume 39, issue 4

Authors: Khalid, Nadeem

Article Type: Research Article

Abstract: Artificial intelligence learning at higher educational institutions is one of the emerging concepts having vital importance to promote entrepreneurship activities among the university students. However, Malaysian Universities are lacking with the artificial intelligence learning activities. The objective of the study is to examine the role of artificial intelligence learning to promote entrepreneurship performance with the help of entrepreneurial orientation and strategic entrepreneurship. Moreover, the moderating role of government funding and attitude towards entrepreneurship is also examined. To achieve the objective of this study, a survey was carried out among the Malaysian universities. 500 questionnaires were distributed among the universities and …data were collected from the teaching staff. After collection of data, it was analysed with the help of Partial Least Square (PLS)-Structural Equation Modeling (SEM). It is concluded that artificial intelligence learning is most significant to promote entrepreneurial performance among university students. Entrepreneurial orientation and strategic entrepreneurship play a key role to transfer the positive effect of artificial intelligence learning on entrepreneurial performance. Additionally, government funding and attitude towards entrepreneurship also has significant role. Show more

Keywords: Artificial intelligence learning, higher educational institutions, strategic entrepreneurship, government funding, entrepreneurial orientation, performance, entrepreneurial attitude

DOI: 10.3233/JIFS-189026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5417-5435, 2020

Authors: Alhaidar, Abdul Rahman | Sikkandar, Mohamed Yacin | Alkathiry, Abdulaziz A.

Article Type: Research Article

Abstract: Vertical Ground Reaction Force (VGRF) is a force obtained during gait cycle beneath the feet and is used to screen the severity of Parkinson’s disease (PD) patient’s in clinical environment. This article investigates the VGRF signals (left and right) semblance nature among PD patients and control subjects as a function of time and possibility of reconstructing dual tasking VGRF signal from normal walking VGRF signals using radial basis function (RBF) based artificial intelligence (AI). There are many traditional methods for gait analysis and these methods are purely subjective and none made semblance analysis of same subjects gait pattern in different …tasking. In order to overcome the difficulties faced by PD patients, RBF based AI is proposed in this research to reconstruct the dual tasking VGRF signal from normal walking VGRF signal. 93 PD patients with mean age: 66.3 years (63% men), and 73 healthy controls with mean age: 66.3 years (55% men) datasets are used in this work. Proposed RBF network is trained on VGRF signals obtained in normal walking and dual tasking conditions from control. The network was trained with 60% of VGRF data and tested on remaining 40% data. Semblance analysis results are encouraging, and it shows that semblance is high in PD patients than control subjects during dual tasking (P  < 0.05). In order to test the findings of semblance analysis, we explicitly reconstruct VGRF signal of clinically significant dual tasking from VGRF signal of normal walking by the proposed RBF method. Findings proved that the proposed RBF network can reconstruct dual tasking VGRF signal of PD patients from their normal walking VGRF signal with high cross correlation (P  < 0.0001). These findings pave way for a new adjunct tool to diagnose the gait dynamics of PD patients using the proposed reconstruction method. Show more

Keywords: Vertical ground reaction force, signals, semblance, continuous wavelet transform, k-means clustering

DOI: 10.3233/JIFS-189027

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5437-5448, 2020

Authors: Arokiaraj Jovith, A. | Kasmir Raja, S.V. | Razia Sulthana, A.

Article Type: Research Article

Abstract: Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division …Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission. Show more

Keywords: Interference, link state protocol, point-to-point, time division multiple access

DOI: 10.3233/JIFS-189028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5449-5458, 2020

Authors: Somjai, Sudawan | Jermsittiparsert, Kittisak | Chankoson, Thitinan

Article Type: Research Article

Abstract: The adoption of AI is an ongoing phenomenon in today’s economy in all the industries. The purpose of this paper is to examine the economic impact of AI adoption in the region of ASEAN. To achieve this objective, structural questionnaire was developed for the various industry experts in targeted region. A sample of 240 experts was finally obtained over a time span of 6 weeks through online structural questionnaire approach. For measuring AI adoption, twelve items, initial economic impact (seven items), and subsequent economic impact (six items) were finally added in the questionnaire. For analyses purpose, descriptive statistics, structural equation …modelling, and regression analyseswereapplied, examining the both initial and subsequent economic impact of AI adoption. Findings through structural model indicates that overall both initial and subsequent impact are significantly determined by AI adoption in related industries. Additionally, in depth analyses for the individual AI items as their initial and subsequent economic impact indicate that Usage of the data for AI adoption, clear strategy for AI adoption, successful mapping for AI adoption and overall positive attitude towards AI adoption have their significant and positive influence on initial economic indicators. Whereas, as per subsequent economic impact, factors like effective usage of data for AI adoption, assessing the right skills of individuals for AI adoption and positive attitude towards AI adoption are significantly impacting on material investment, capital investment, increasing unemployment, higher economic output, higher return on capital and higher wages for the existing labor. These findings have provided an outstanding evidence in the field of AI and its economic impact in the region of ASEAN and can be considered as initial contribution in related fields. Both industry exports and macroeconomic decision makers can significantly utilize the findings to develop their conceptual framework and understanding for the integration between AI adoption and economy. Additionally, this study can work as reasonable justification for implementing the more adoption of AI in various industries as it has positive economic outcome (both initial and subsequent). However, one of the key limitations of this study is limited sample size and only 240 industry exports were targeted from selected industries in ASEAN. Future study could be reimplemented on similar topic with expanding the sample size for better findings and more generalization. Show more

Keywords: AI adoption, initial-subsequent economic impact, efficiency, economic output, ASEAN

DOI: 10.3233/JIFS-189029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5459-5474, 2020

Authors: Sleaman, Walead Kaled | Yavuz, Sırma

Article Type: Research Article

Abstract: Robot can help human in their everyday life and routine. These are not an indoor robot which was designed to perform desired task, but they can adapt to our environment by themselves and to learn from their own experiences. In this research we focus on high degree of autonomy, which is a must for social robots. For training purpose autonomous exploration and unknown environments is used along with proper algorithm so that robot can adapt to unknown environments. For testing purpose, simulation is carried with sensor fusion method, so that real world noise can be reduced and accuracy can be …increased. This dissertation focuses on the intelligent robot control in autonomous navigation tasks and investigates the robot learning in following aspects. This method is based on human instinct of imitation. In this standard real time data set is provided to the robot for training purpose, it gets train from these data and generalize over all unseen potential situations and environments. Convolutional Neural Network is used to determine the probability and based on that robot can act. After acceptable number of demonstrations, robot can predict output with high accuracy and hence can acquire the independent navigation skills. State-of-the-art reinforcement learning techniques is used to train the robot via interaction with the robots. Convolutional Neural Network is also incorporated for fast generalization. Robot is train based on all past state-action pairs collected during interaction. This training model can predict output which helps robot for autonomous navigation. Show more

Keywords: Deep reinforcement learning, autonomous agent, adaptive agent, autonomous exploration, control mobile robot, deep convolutional neural network

DOI: 10.3233/JIFS-189030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5475-5486, 2020

Authors: Daming, Li | Lianbing, Deng | Zhiming, Cai

Article Type: Research Article

Abstract: The sponge index is the core of the sponge city flood forecast. Whether the model is reasonable or not directly affects the final forecast result. The study of classification problems using neural network models is an important branch of the artificial neural network application field. The classification and pattern recognition functions can be used to achieve flood classification and sponge index monitoring. In this paper, the author analyze the evaluation method of sponge city potential based on neural network and fuzzy mathematical evaluation. After training, the BP neural network model can effectively evaluate the potential of the sponge city, and …based on the input of special information on rain conditions, it can analyze and calculate the flood risk level. It can be seen that this network model has a high mapping capability and can be correctly classified. Therefore, it is feasible to use BP neural network to solve the real-time classification of flood risk. The sponge city potential method and underground drainage system proposed in this paper can provide a reference for promoting sponge city construction. Show more

Keywords: Sponge indicator, monitoring and tracking, neural network algorithm, internet of things

DOI: 10.3233/JIFS-189031

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5487-5498, 2020

Authors: Zhan, Wenjing | Chen, Yue

Article Type: Research Article

Abstract: Artificial intelligence speech recognition mostly judges the accuracy of grammar or sentence in the detection of pronunciation error, but has little research on pronunciation judgment, so it cannot effectively correct the pronunciation. This study analyzes the application of image target recognition in English learning task. Task-based approach emphasizes the process of English learning, not the result, the purposeful communication and meaning expression, encourages learners to open their mouths, and emphasizes that English language learning activities and their tasks are realistic in life. In addition, this paper introduces the DNN adaptive technique based on KL divergence regularization to adapt the acoustic …model. Finally, this paper uses the experimental contrast method to compare and analyze the algorithm of this research with the traditional algorithm. The research shows that the recognition ability of the algorithm for confusing phonemes is improved than that of traditional algorithms, and this conclusion provides a powerful result for the introduction of error correction algorithms into education networks. By using the platform of autonomous learning center, students can improve their English level by completing the tasks chosen by teachers or by themselves and through training. Show more

Keywords: Deep learning, image target recognition, DNN algorithm, English learning

DOI: 10.3233/JIFS-189032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5499-5510, 2020

Authors: Yuan, Qinying

Article Type: Research Article

Abstract: This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218 .

DOI: 10.3233/JIFS-189033

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5511-5520, 2020

Authors: Liu, Ying | Fan, Zhongqi | Qi, Hongliang

Article Type: Research Article

Abstract: This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at https://doi.org/10.3233/JIFS-219218 .

DOI: 10.3233/JIFS-189034

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5521-5534, 2020

Authors: Cui, Jinying

Article Type: Research Article

Abstract: The corpus software has many functions, such as keyword retrieval, context co-occurrence, word list generation and word frequency statistics. It can quickly and accurately provide various corpus and information, such as word-formation collocation, context, word frequency and so on. In this paper, the author analyzes the application of deep learning and target visual detection in English vocabulary online teaching. Deep learning is a kind of machine learning algorithm which includes multi-layer non-linear mapping and tries to obtain high-level abstract representation of data. By extracting features from information, the identifiable components in the image can be extracted. The results show that …the application of corpus in College English vocabulary teaching can promote students’autonomous use of corpus in English vocabulary learning. The simulation experiment improves the performance of the system by choosing parameters, and the classification accuracy is more than 90%. Corpus can enable students to learn real and natural language and master natural collocation. At the same time, corpus can help students understand the semantic and pragmatic norms of words in communication and recognize the characteristics of register variants. Future research can use Map-reduce technology to accelerate the training process, save training time and test more hyperparameters. Show more

Keywords: Corpus, deep learning, target recognition, natural language algorithms, data simulation

DOI: 10.3233/JIFS-189035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5535-5545, 2020