International Journal of Intelligence Science

Journal Information
ISSN / EISSN : 2163-0283 / 2163-0356
Published by: Scientific Research Publishing, Inc. (10.4236)
Total articles ≅ 113
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Ana Lilia Laureano-Cruces, Lourdes Sánchez-Guerrero, Javier Ramírez-Rodríguez, Emiliano Ramírez-Laureano
International Journal of Intelligence Science, Volume 12, pp 57-78; https://doi.org/10.4236/ijis.2022.123005

Abstract:
Little by little, we are entering the new era, intelligent interfaces are absorbing us more and more every day, and artificial intelligence makes its presence in a stealthy way. Virtual humans that represent an evolution of autonomous virtual agents; they are computer programs and in the future capable of carrying out different activities in certain environments. They will give the illusion of being human; they will have a body, and they will be immersed in an environment. They will have a set of senses that will allow them: 1) Sensations and therefore associated expressions; 2) Communication; 3) Learning; 4) Remembering events, among others. By integrating the above, they will have a personality and autonomy, so they will be able to plan with respect to objectives; allowing them to decide and take actions with their body, in other words, they will count on awareness. The applications will be focused on environments that they will inhabit, or as interfaces that will interact with other systems. The application domains will be multiple; one of them being education. This article shows the design of OANNA like an avatar with the role of pedagogical agent. It was modeled as an affective-cognitive structure related to the teaching-learning process linked to a pedagogical agent that represents the interface of an artilect. OANNA, has the necessary animations for intervention within the teaching-learning process.
Yujun Zhou, Xiaowen Ge, Wu Ai
International Journal of Intelligence Science, Volume 12, pp 21-37; https://doi.org/10.4236/ijis.2022.122003

Abstract:
This paper aims to reduce the communication cost of the distributed learning algorithm for stochastic configuration networks (SCNs), in which information exchange between the learning agents is conducted only at a trigger time. For this purpose, we propose the communication-censored distributed learning algorithm for SCN, namely ADMMM-SCN-ET, by introducing the event-triggered communication mechanism to the alternating direction method of multipliers (ADMM). To avoid unnecessary information transmissions, each learning agent is equipped with a trigger function. Only if the event-trigger error exceeds a specified threshold and meets the trigger condition, the agent will transmit the variable information to its neighbors and update its state in time. The simulation results show that the proposed algorithm can effectively reduce the communication cost for training decentralized SCNs and save communication resources.
Lourdes Sánchez-Guerrero, Ana Lilia Laureano-Cruces, Martha Mora-Torres, Javier Ramírez-Rodríguez
International Journal of Intelligence Science, Volume 12, pp 39-56; https://doi.org/10.4236/ijis.2022.123004

Abstract:
Due to the pandemic that is currently being experienced worldwide, educational institutions (HEIs) have had to reinvent and innovate the mode of teaching, in order to continue the process of training students at all educational levels. Higher education has not been immune to this situation, the institutions of this educational level face various challenges in remote teaching: one of them, perhaps the most important, was to answer the question: How to get the student to continue their undergraduate studies remotely? In this sense, it was necessary, on the part of the students and the teachers, a preparation to go from the face-to-face to the virtual mode. This paper addresses the specific case of the analysis of the teaching-learning process of the Teaching-Learning Unit (UEA for its acronym in Spanish) of Structured Programming and Numerical Methods for Engineering at the undergraduate level, at the Universidad Autónoma Metropolitana, Unidad Azcapotzalco (UAM-A) and its relation to the student’s way of thinking. Likewise, the analysis will be carried out if the way of thinking of the students of the sample influences their academic performance when studying the subject of Structured Programming and Numerical Methods for Engineering.
Ahmed Laarfi
International Journal of Intelligence Science, Volume 12, pp 1-8; https://doi.org/10.4236/ijis.2022.121001

Abstract:
This paper reviews the essential biometrics and develops a way to combine them with the Computer and User Information, giving us an Electronic Biometrics ID. This way, distributed databases contain imperative data from much helpful information that supports more security. We reviewed examples of what these databases would look like, which any responsible party could design to be global. As will be mentioned later, we obtain common international databases whose data are modified according to factors such as the owner of the device, the location of the device, and so on. This is very useful for tracking, and it combines biometrics with data set to give us a comprehensive electronic identification.
Mohamed Chakraoui, Abderrafiaa Elkalay, Naoual Mouhni
International Journal of Intelligence Science, Volume 12, pp 9-20; https://doi.org/10.4236/ijis.2022.121002

Abstract:
With the increasing of data on the internet, data analysis has become inescapable to gain time and efficiency, especially in bibliographic information retrieval systems. We can estimate the number of actual scientific journals points to around 40,000 with about four million articles published each year. Machine learning and deep learning applied to recommender systems had become unavoidable whether in industry or in research. In this current, we propose an optimized interface for bibliographic information retrieval as a running example, which allows different kind of researchers to find their needs following some relevant criteria through natural language understanding. Papers indexed in Web of Science and Scopus are in high demand. Natural language including text and linguistic-based techniques, such as tokenization, named entity recognition, syntactic and semantic analysis, are used to express natural language queries. Our Interface uses association rules to find more related papers for recommendation. Spanning trees are challenged to optimize the search process of the system.
Han Jia, Xuecheng Zou
International Journal of Intelligence Science, Volume 11, pp 57-69; https://doi.org/10.4236/ijis.2021.112005

Abstract:
With the development of computer vision researches, due to the state-of-the-art performance on image and video processing tasks, deep neural network (DNN) has been widely applied in various applications (autonomous vehicles, weather forecasting, counter-terrorism, surveillance, traffic management, etc.). However, to achieve such performance, DNN models have become increasingly complicated and deeper, and result in heavy computational stress. Thus, it is not sufficient for the general central processing unit (CPU) processors to meet the real-time application requirements. To deal with this bottleneck, research based on hardware acceleration solution for DNN attracts great attention. Specifically, to meet various real-life applications, DNN acceleration solutions mainly focus on issue of hardware acceleration with intense memory and calculation resource. In this paper, a novel resource-saving architecture based on Field Programmable Gate Array (FPGA) is proposed. Due to the novel designed processing element (PE), the proposed architecture achieves good performance with the extremely limited calculating resource. The on-chip buffer allocation helps enhance resource-saving performance on memory. Moreover, the accelerator improves its performance by exploiting the sparsity property of the input feature map. Compared to other state-of-the-art solutions based on FPGA, our architecture achieves good performance, with quite limited resource consumption, thus fully meet the requirement of real-time applications.
Hamdi Ben Abdessalem, Alexie Byrns, Claude Frasson
International Journal of Intelligence Science, Volume 11, pp 70-96; https://doi.org/10.4236/ijis.2021.112006

Abstract:
Alzheimer’s disease affects millions of persons every year. Negative emotions such as stress and frustration have a negative impact on memory function and Alzheimer's patients experience more negative emotions than healthy adults. Non-pharmacological treatment such as immersion in virtual environments could help Alzheimer patients by reducing their negative emotions, but it has restrictions and requirements. In this work, we present three virtual reality relaxing systems in which the patients are immersed in relaxing environments. We propose to use intelligent agents in order to adapt the relaxing environment to each participant and optimize its relaxation effect. The intelligent agents track the emotions of patients using electroencephalography as input in order to adapt the environments. We designed each system with different levels of intelligence in order to analyze the impact of the adaptation on the patients. Experiments were performed for each system on participants with subjective cognitive decline. Results show that these relaxing systems can reduce negative emotions and improve participants’ memory performance. The positive effects on affective state and memory persisted for a longer period of time and were generally more effective for the systems with more intelligence. We believe that the combination of a relaxing environment, virtual reality, intelligent agents for adapting the environment, and brain assessment is a promising method for helping Alzheimer’s patients.
Xinzheng Xu, Meng Du, Huanxiu Guo, Jianying Chang, Xiaoyang Zhao
International Journal of Intelligence Science, Volume 11, pp 1-16; https://doi.org/10.4236/ijis.2021.111001

Abstract:
Face recognition is a kind of biometric technology that recognizes identities through human faces. At first, the speed of machine recognition of human faces was slow and the accuracy was lower than manual recognition. With the rapid development of deep learning and the application of Convolutional Neural Network (CNN) in the field of face recognition, the accuracy of face recognition has greatly improved. FaceNet is a deep learning framework commonly used in face recognition in recent years. FaceNet uses the deep learning model GoogLeNet, which has a high accuracy in face recognition. However, its network structure is too large, which causes the FaceNet to run at a low speed. Therefore, to improve the running speed without affecting the recognition accuracy of FaceNet, this paper proposes a lightweight FaceNet model based on MobileNet. This article mainly does the following works: Based on the analysis of the low running speed of FaceNet and the principle of MobileNet, a lightweight FaceNet model based on MobileNet is proposed. The model would reduce the overall calculation of the network by using deep separable convolutions. In this paper, the model is trained on the CASIA-WebFace and VGGFace2 datasets, and tested on the LFW dataset. Experimental results show that the model reduces the network parameters to a large extent while ensuring the accuracy and hence an increase in system computing speed. The model can also perform face recognition on a specific person in the video.
Maoguang Wang, Hang Yang
International Journal of Intelligence Science, Volume 11, pp 44-55; https://doi.org/10.4236/ijis.2021.111004

Abstract:
Personal credit risk assessment is an important part of the development of financial enterprises. Big data credit investigation is an inevitable trend of personal credit risk assessment, but some data are missing and the amount of data is small, so it is difficult to train. At the same time, for different financial platforms, we need to use different models to train according to the characteristics of the current samples, which is time-consuming. In view of these two problems, this paper uses the idea of transfer learning to build a transferable personal credit risk model based on Instance-based Transfer Learning (Instance-based TL). The model balances the weight of the samples in the source domain, and migrates the existing large dataset samples to the target domain of small samples, and finds out the commonness between them. At the same time, we have done a lot of experiments on the selection of base learners, including traditional machine learning algorithms and ensemble learning algorithms, such as decision tree, logistic regression, xgboost and so on. The datasets are from P2P platform and bank, the results show that the AUC value of Instance-based TL is 24% higher than that of the traditional machine learning model, which fully proves that the model in this paper has good application value. The model’s evaluation uses AUC, prediction, recall, F1. These criteria prove that this model has good application value from many aspects. At present, we are trying to apply this model to more fields to improve the robustness and applicability of the model; on the other hand, we are trying to do more in-depth research on domain adaptation to enrich the model.
Soumyadeep Samonto, Samarjit Kar, Sagarika Pal, Arif Ahmed Sekh, Bishal Sarkar
International Journal of Intelligence Science, Volume 11, pp 31-43; https://doi.org/10.4236/ijis.2021.111003

Abstract:
In the present scenariom the protection system has become an important issue in the field of the power system. An Intelligent protection system has been introduced in many sectors like low voltage DC breakers, VCB, SF6 and so on. The said protection schemes have been developed to control the moving contacts using intelligent algorithms for tripping overall load against phase to ground faults occurring within the system. The related works have introduced Trapezoidal and Triangular membership functions as input to the fuzzy inference system. It is also found that the Fuzzy Logic Controller has been designed by taking two inputs as current and voltage. The output membership function has been preferred by implementing Trapezoidal and Gaussian membership functions. In this paper, a new concept based over current protection scheme has been introduced. Intelligent relaying technique has been used to trip a particular load against over current fault by introducing multistage cascaded intelligent relaying. Initially, the proposed method is carried out for Stage-I and reported by incorporating fuzzy algorithm by taking current error and current error rates as input using Gaussian membership function to the black box and fed signal to the breaker as output using trapezoidal and triangular membership function respectively to control the loads connected in the system. The best-fit membership function as input to the fuzzy engine is shown here is Gaussian membership function. The analysis reported here by taking both the fault scenario as phase to phase and phase to ground respectively.
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