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Authors:Jianhui Lv, Zhiwei Lin, Hui Cheng, Qingyi Zhang, Lianbo Ma Abstract: International Journal on Artificial Intelligence Tools, Volume 31, Issue 04, June 2022.
Citation: International Journal on Artificial Intelligence Tools PubDate: 2022-06-18T07:00:00Z DOI: 10.1142/S0218213022020055 Issue No:Vol. 31, No. 04 (2022)
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Authors:Xiaomei Zhao, Yubo Jiang Abstract: International Journal on Artificial Intelligence Tools, Volume 31, Issue 04, June 2022. English has become the most widely used language in the world. Everything we do in study, life and work is closely linked with English. With the continuous development of computer technology, machine translation is becoming more and more mature. The convergence of Artificial Intelligence (AI) and language learning is getting increasingly close, which brings great impact and challenge to the language education industry, but also provides an opportunity for the synchronous promotion of the development of the language education industry. With the further development of AI, machine translation can better meet the needs of most general translation, but in the face of professional, diversified, detailed and complex communication translation tasks containing human emotion, machine translation is still difficult to replace human translation. In order to improve the English translation ability of university students, this paper uses AI to propose the innovative factor based Quantum Particle Swarm Optimization-Convolutional Neural Network (QPSO-CNN) algorithm. Through the experiment, at first, the obtained dataset can ensure the accuracy and diversity of the collected results of English translation feature samples to the maximum extent, and the trained QPSO-CNN can be used to analyze the accuracy of the English translation ability of university students. Then, by comparing the convergence curve of QPSO-CNN and back propagation-CNN (BP-CNN), it is concluded that the proposed QPSO-CNN in this paper has been greatly improved in terms of model accuracy and convergence speed. Citation: International Journal on Artificial Intelligence Tools PubDate: 2022-06-18T07:00:00Z DOI: 10.1142/S0218213022400073 Issue No:Vol. 31, No. 04 (2022)
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Authors:Lingling Zhang, Zhenxiong Zhou, Pengyu Ji, Aoxue Mei Abstract: International Journal on Artificial Intelligence Tools, Volume 31, Issue 04, June 2022. When using deep learning methods to model natural language, a recurrent neural network that can map input sequences to output sequences is usually used. Considering that natural language contains more complicated syntactic structures, and the performance of cyclic neural networks in long sentence processing will decrease, scholars have introduced an attention mechanism into the model, which has improved the above problems to a certain extent. The existing attention mechanism still has some shortcomings, such as the inability to explicitly obtain the known syntactic structure information in the sentence, and the poor interpretability of the output probability. In response to the above problems, this article will improve the attention mechanism in the recurrent neural network model. Firstly, the prior information in the natural language sequence is constructed as a graph model through syntactic analysis and other means, and then the graph structure regularization term is introduced into the sparse mapping. A new function netmax is constructed to replace the softmax function in the traditional attention mechanism, thereby improving the performance of the model and making the degree of association. The input values corresponding to larger input samples are closer, making the output of the attention mechanism easier to understand. The innovation of this paper mainly lies in that the weight calculation method which can be widely used in the attention mechanism is proposed by combining the deep learning model with statistical knowledge, which opens a channel to introduce the prior information for the deep learning model in natural language processing tasks. Citation: International Journal on Artificial Intelligence Tools PubDate: 2022-06-18T07:00:00Z DOI: 10.1142/S0218213022400085 Issue No:Vol. 31, No. 04 (2022)
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Authors:Mingsi Sun, Hongwei Zhao Abstract: International Journal on Artificial Intelligence Tools, Volume 31, Issue 04, June 2022. Image recognition technology is an important branch of artificial intelligence research, using the computer to process, analyze and understand the image, in order to identify different patterns of objects. Image recognition technology is currently used in a wide range of applications, such as face recognition, fingerprint recognition, terrain survey, license plate recognition, etc. However, due to the possible existence of multiple categories in images, the blurring of graphic boundaries affects the results of image recognition. In this paper, we propose a multi-category multi-task image recognition method based on deep metric learning (MMDML). Specially, we combine triplet loss function and softmax loss function to construct the loss function, and take ResNet-50 network training to fit the optimal loss function for image recognition. To demonstrate the effectiveness of the proposed method, our method is compared with the other two methods on three common image recognition datasets, namely ImageNet, PASCALVOC, and Caltech. And the experimental results show that our algorithm has the highest Rank-1 and mAP on three datasets. Citation: International Journal on Artificial Intelligence Tools PubDate: 2022-06-18T07:00:00Z DOI: 10.1142/S0218213022400127 Issue No:Vol. 31, No. 04 (2022)
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Authors:Zhifu Zhao Abstract: International Journal on Artificial Intelligence Tools, Volume 31, Issue 04, June 2022. With the rapid development of Internet technology, the network has become an indispensable way of life for undergraduates. The correct guidance of public opinion has also become an important thing in the ideological work of universities. Undergraduates are in an important period of formation and development of thoughts that they are easily to be incited by cyber-rumors. Therefore, it is particularly important to obtain the data of political public opinion in universities and position the hot topics for early detection of political public opinion tendency, which can also avoid the outbreak of major security incidents. With such consideration, this paper obtains multi-source political public opinion data from BBS, Tieba and Weibo of SUN YAT-SEN UNIVERSITY (SYSU) through crawler. We study a text feature extraction method based on Word2Vec & LDA (Latent Dirichlet Allocation), which improves the high-dimensional sparsity in traditional Vector Space Model (VSM) text representation. Meanwhile, based on the classical Single-pass clustering algorithm, this paper studies the Single-pass & HAC clustering algorithm. In addition, a measurement method of hot topic is defined to calculate the heat value of political public opinion. Dictionary and rule based method is used to improve the accuracy of sentiment tendency analysis. The experimental results demonstrate that the effect of topic detection and positioning based on LDA & Word2Vec and Single-pass & HAC algorithm is better than other methods. Citation: International Journal on Artificial Intelligence Tools PubDate: 2022-06-18T07:00:00Z DOI: 10.1142/S0218213022400139 Issue No:Vol. 31, No. 04 (2022)
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Authors:Baiyang Geng Abstract: International Journal on Artificial Intelligence Tools, Volume 31, Issue 04, June 2022. In recent years, legal society has been an important goal of our country, and it becomes a standard approach to solve disputes through law. In general, there are two ways to protect our rights. Firstly, we can find the applicable legal provisions by reading a large number of relevant cases and analogy. The other way is to employ lawyers who will play a key role in the trial process. However, both the approaches have some difficulties. The first difficulty lies in the need of finding similar cases in a large number of legal documents, and the second difficulty is in choosing an appropriate lawyer. In this work, we aim to build a legal consultation system through the excavation of a large number of judicial documents, and provide help in the process of protecting people’s rights. Our work mainly contains three parts that are designing collection framework, proposing text expression method and improving the method of lawyer recommendation. The experimental results show that our method can obviously improve the usability of legal provisions. Citation: International Journal on Artificial Intelligence Tools PubDate: 2022-06-18T07:00:00Z DOI: 10.1142/S0218213022400061 Issue No:Vol. 31, No. 04 (2022)
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Authors:Sonu Jose, Sushil Louis, Sergiu Dascalu, Siming Liu Abstract: International Journal on Artificial Intelligence Tools, Ahead of Print. Bayesian network is a graphical model that is widely used to perform probabilistic reasoning. However, learning the structure of Bayesian network is a complex task. In this paper, we propose a hybrid structure learning algorithm that has two phases: a constraint-based phase to reduce the search space and a score-and-search phase that employs case-injected genetic algorithms for determining the optimal structure from the reduced space of structures. We use a case-injected genetic algorithm-based hybrid approach for the structure learning in order to improve the learning accuracy over similar problems. A case-injected genetic algorithm is the augmentation of a case-based memory with the Genetic Algorithm (GA). Thereby, it finds near-optimal solutions in fewer generations compared to GA. Our method stores relevant or partial solutions in a case-base while solving the problems and utilizes those stored solutions on new similar problems. We use small-to-very large networks for assessing our viability of our approach. In this paper, a series of experiments are conducted on datasets generated from four benchmark Bayesian networks. We compare our method against GA-based hybrid approach and a state-of-the-art algorithm, Max-Min Hill Climbing (MMHC). Presented results indicate an enhanced improvement of our approach over GA and MMHC in learning the Bayesian network structures. Citation: International Journal on Artificial Intelligence Tools PubDate: 2022-06-06T07:00:00Z DOI: 10.1142/S021821302260003X
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Authors:Yasmine Amor, Lilia Rejeb, Rahma Ferjeni, Lamjed Ben Said, Mohamed Ridha Ben Cheikh Abstract: International Journal on Artificial Intelligence Tools, Ahead of Print. Sleep is a fundamental restorative process for human mental and physical health. Considering the risks that sleep disorders can present, sleep analysis is considered as a primordial task to identify the different abnormalities. Sleep scoring is the gold standard for human sleep analysis. The manual sleep scoring task is considered exhausting, subjective, time-consuming and error prone. Moreover, sleep scoring is based on fixed epoch lengths usually of 30 seconds, which leads to an information loss problem. In this paper, we propose an automatic unsupervised sleep scoring model. The aim of our work is to consider different epoch’s durations to classify sleep stages. Therefore, we developed a model based on Hierarchical Multi-Agent Systems (HMASs) that presents different layers where each layer contains a number of adaptive agents working with a specific time epoch. The effectiveness of our approach was investigated using real electroencephalography (EEG) data. Good results were reached according to a comparative study realized with the often used machine learning techniques for sleep stages classification problems. Citation: International Journal on Artificial Intelligence Tools PubDate: 2022-06-01T07:00:00Z DOI: 10.1142/S0218213022500026
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Authors:S. Pradeepa, Jaisaiarun P. Srinivasan, R. Anandalakshmi, P. Subbulakshmi, S. Vimal, A. Tarik Abstract: International Journal on Artificial Intelligence Tools, Ahead of Print. Worldwide, epidemics continue to be a concern on public health. Even with the technological advances, there are still barriers present in predicting the outbreaks. We propose a new methodology known as FREEDOM (Effective Surveillance and Investigation of Water-borne Diseases from data-centric networking using Machine Learning) to perform effective surveillance and investigation of water-borne diseases from social media with next-generation data. In the proposed model, we collected the data from the Twitter media, preprocessed the tweet content, performed hierarchical spectral clustering, and generated the frequent word set from each cluster through the apriori algorithm. At last, the inferences are extracted from the frequent word set through human intervention. From the experimental results, the support and confidence value of the outcome derived from the Apriori algorithm has exhibited the different water-borne diseases that are not listed in the WHO (World Health Organization), and the surveillance of those diseases with percentage ranking and has been achieved using the data-centric networking. They get aligned with precise results portraying real statistics. This type of analysis will empower doctors and health organizations (Government sector) to keep track of the water-borne diseases, their symptoms for early detection, and safe recovery thereby sufficiently reducing the death tolls. Citation: International Journal on Artificial Intelligence Tools PubDate: 2022-04-14T07:00:00Z DOI: 10.1142/S021821302250004X
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Authors:Jing Wang, Zeyuan Yu Abstract: International Journal on Artificial Intelligence Tools, Ahead of Print. Smart Educational Learning (SEL) has recently opened its ways in various changes in scientific discoveries, informatics, globalization, astronautics production, robotics, and artificial intelligence in the Higher Education System. In such an educational system, managing resources to increase education quality with an effective interactive environment has been considered a significant challenging factor for the Students and Teachers. Hence, in this paper, the internet of things assisted Interactive system (IoT-IS) for Smart Learning is used to measure the teachers’ and students’ performance analysis in the SEL platform. The psychometric processes with standards for effective teaching using smart educational learning tools have been discussed based on the higher education system requirements. Furthermore, an active learning strategy with an attention scoring method promotes students’ performance assessment in the higher education system using the interactive system. Facial expression detection and analysis are used and applied in online classroom videos in the SEL. Based on this detection and analysis, the attention of the students is observed. The experimental results show that the method enhances the student performance ratio of 98.5%, an accuracy ratio of 95.3%, an efficiency ratio of 96.7%, a reliability ratio of 93.2%, and a probability ratio of 94.5% compared to other existing methods. Citation: International Journal on Artificial Intelligence Tools PubDate: 2021-11-18T08:00:00Z DOI: 10.1142/S0218213021401011
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