Open Access

ARTICLE

Khadija Khan, Samabia Tehsin^*
Journal on Artificial Intelligence, Vol.4, No.1, pp. 1-13, 2022, DOI:10.32604/jai.2022.028092 - 16 May 2022
Abstract Leukemia is the cancer that starts in the blood cells due to the excess production of immature leucocytes that replace the cells with normal blood cells. Physicians rely on their experience to determine the type and subtype of Leukemia from the blood sample. Most people are misdiagnosed when it comes to its subtypes, the error rates can be up to 40% during the classification process. That too depends on the expertise of the physician. This research represents a Convolutional Neural Network based medical image classifier. The proposed technique can classify Leukemia and its five subtypes. More >

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1106

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ARTICLE

Liu Zhang^1,*, Yi-Fei Chen^1,2, Zi-Quan Pei¹, Jia-Wei Yuan², Nai-Qiao Tang¹
Journal on Artificial Intelligence, Vol.4, No.1, pp. 15-26, 2022, DOI:10.32604/jai.2022.027730 - 16 May 2022
Abstract Analyzing and predicting the learning behavior data of students in blended teaching can provide reference basis for teaching. Aiming at weak generalization ability of existing algorithm models in performance prediction, a BP neural network is introduced to classify and predict the grades of students in the blended teaching. L2 regularization term is added to construct the BP neural network model in order to reduce the risk of overfitting. Combined with Pearson coefficient, effective feature data are selected as the validation dataset of the model by mining the data of Chao-Xing platform. The performance of common More >

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673

Open Access

ARTICLE

Sun Yang-Yang, Yao Jun-Ping^*, Li Xiao-Jun, Fan Shou-Xiang, Wang Zi-Wei
Journal on Artificial Intelligence, Vol.4, No.1, pp. 27-35, 2022, DOI:10.32604/jai.2022.027839 - 16 May 2022
Abstract Deep deterministic policy gradient (DDPG) has been proved to be effective in optimizing particle swarm optimization (PSO), but whether DDPG can optimize multi-objective discrete particle swarm optimization (MODPSO) remains to be determined. The present work aims to probe into this topic. Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO, but also overcome the problem of local optimal solution that MODPSO may suffer. The research findings are of great significance for the theoretical research and application of MODPSO. More >

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896

Open Access

ARTICLE

Qi Wang¹, Zhaoying Liu¹, Ting Zhang^1,*, Shanshan Tu¹, Yujian Li², Muhammad Waqas³
Journal on Artificial Intelligence, Vol.4, No.1, pp. 37-48, 2022, DOI:10.32604/jai.2022.027875 - 16 May 2022
Abstract Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification. However, the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters. To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters, this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines (LC-CKMSVM). The framework consists More >

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902

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ARTICLE

Zeeshan Ahmad¹, Waqas Haider Bangyal¹, Kashif Nisar^2,3,*, Muhammad Reazul Haque⁴, M. Adil Khan⁵
Journal on Artificial Intelligence, Vol.4, No.1, pp. 49-60, 2022, DOI:10.32604/jai.2022.017992 - 16 May 2022
Abstract Huge amount of data is being produced every second for microblogs, different content sharing sites, and social networking. Sentimental classification is a tool that is frequently used to identify underlying opinions and sentiments present in the text and classifying them. It is widely used for social media platforms to find user's sentiments about a particular topic or product. Capturing, assembling, and analyzing sentiments has been challenge for researchers. To handle these challenges, we present a comparative sentiment analysis study in which we used the fine-grained Stanford Sentiment Treebank (SST) dataset, based on 215,154 exclusive texts More >

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