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20 pages, 1421 KiB

Open AccessArticle

Deep Learning-Based Depression Detection from Social Media: Comparative Evaluation of ML and Transformer Techniques

by Biodoumoye George Bokolo and Qingzhong Liu

Electronics 2023, 12(21), 4396; https://doi.org/10.3390/electronics12214396 - 24 Oct 2023

Cited by 4 | Viewed by 4616

Detecting depression from user-generated content on social media platforms has garnered significant attention due to its potential for the early identification and monitoring of mental health issues. This paper presents a comprehensive approach for depression detection from user tweets using machine learning techniques. [...] Read more.

Detecting depression from user-generated content on social media platforms has garnered significant attention due to its potential for the early identification and monitoring of mental health issues. This paper presents a comprehensive approach for depression detection from user tweets using machine learning techniques. The study utilizes a dataset of 632,000 tweets and employs data preprocessing, feature selection, and model training with logistic regression, Bernoulli Naive Bayes, random forests, DistilBERT, SqueezeBERT, DeBERTA, and RoBERTa models. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models’ performance. The results indicate that the RoBERTa model achieves the highest accuracy ratio of 0.981 and the highest mean accuracy of 0.97 (across 10 cross-validation folds) in detecting depression from tweets. This research demonstrates the effectiveness of machine learning and advanced transformer-based models in leveraging social media data for mental health analysis. The findings offer valuable insights into the potential for early detection and monitoring of depression using online platforms, contributing to the growing field of mental health analysis based on user-generated content. Full article

(This article belongs to the Special Issue AI in Knowledge-Based Information and Decision Support Systems)

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18 pages, 13585 KiB

Open AccessArticle

Research on the Application of Prompt Learning Pretrained Language Model in Machine Translation Task with Reinforcement Learning

by Canjun Wang, Zhao Li, Tong Chen, Ruishuang Wang and Zhengyu Ju

Electronics 2023, 12(16), 3391; https://doi.org/10.3390/electronics12163391 - 9 Aug 2023

Viewed by 1896

Abstract

With the continuous advancement of deep learning technology, pretrained language models have emerged as crucial tools for natural language processing tasks. However, optimization of pretrained language models is essential for specific tasks such as machine translation. This paper presents a novel approach that [...] Read more.

With the continuous advancement of deep learning technology, pretrained language models have emerged as crucial tools for natural language processing tasks. However, optimization of pretrained language models is essential for specific tasks such as machine translation. This paper presents a novel approach that integrates reinforcement learning with prompt learning to enhance the performance of pretrained language models in machine translation tasks. In our methodology, a “prompt” string is incorporated into the input of the pretrained language model, to guide the generation of an output that aligns closely with the target translation. Reinforcement learning is employed to train the model in producing optimal translation results. During this training process, the target translation is utilized as a reward signal to incentivize the model to generate an output that aligns more closely with the desired translation. Experimental results validated the effectiveness of the proposed approach. The pretrained language model trained with prompt learning and reinforcement learning exhibited superior performance compared to traditional pretrained language models in machine translation tasks. Furthermore, we observed that different prompt strategies significantly impacted the model’s performance, underscoring the importance of selecting an optimal prompt strategy tailored to the specific task. The results suggest that using techniques such as prompt learning and reinforcement learning can improve the performance of pretrained language models for tasks such as text generation and machine translation. The method proposed in this paper not only offers a fresh perspective on leveraging pretrained language models in machine translation and other related tasks but also serves as a valuable reference for further research in this domain. By combining reinforcement learning with prompt learning, researchers can explore new avenues for optimizing pretrained language models and improving their efficacy in various natural language processing tasks. Full article

(This article belongs to the Special Issue AI in Knowledge-Based Information and Decision Support Systems)

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16 pages, 10565 KiB

Open AccessArticle

Monocular Depth Estimation for 3D Map Construction at Underground Parking Structures

by Jingwen Li, Xuedong Song, Ruipeng Gao and Dan Tao

Electronics 2023, 12(11), 2390; https://doi.org/10.3390/electronics12112390 - 25 May 2023

Viewed by 1776

Abstract

Converting the actual scenes into three-dimensional models has inevitably become one of the fundamental requirements in autonomous driving. At present, the main obstacle to large-scale deployment is the high-cost lidar for environment sensing. Monocular depth estimation aims to predict the scene depth and [...] Read more.

Converting the actual scenes into three-dimensional models has inevitably become one of the fundamental requirements in autonomous driving. At present, the main obstacle to large-scale deployment is the high-cost lidar for environment sensing. Monocular depth estimation aims to predict the scene depth and construct a 3D map via merely a monocular camera. In this paper, we add geometric consistency constraints to address the non-Lambertian surface problems in depth estimation. We also utilize the imaging principles and conversion rules to produce a 3D scene model from multiple images. We built a prototype and conduct extensive experiments in a corridor and an underground parking structure, and the results show the effectiveness for indoor location-based services. Full article

(This article belongs to the Special Issue AI in Knowledge-Based Information and Decision Support Systems)

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22 pages, 2033 KiB

Open AccessArticle

Knowledge-Based Features for Speech Analysis and Classification: Pronunciation Diagnoses

by Lichuan Liu, Wei Li, Sherrill Morris and Mutian Zhuang

Electronics 2023, 12(9), 2055; https://doi.org/10.3390/electronics12092055 - 29 Apr 2023

Viewed by 1048

Abstract

Accurate pronunciation of speech sounds is essential in communication. As children learn their native language, they refine the movements necessary for intelligible speech. While there is variability in the order of acquisition of speech sounds, there are some sounds that are more complex [...] Read more.

Accurate pronunciation of speech sounds is essential in communication. As children learn their native language, they refine the movements necessary for intelligible speech. While there is variability in the order of acquisition of speech sounds, there are some sounds that are more complex and are later developing. The rhotic /r/ is a later-developing sound in English, and some children require intervention to achieve accurate production. Additionally, individuals learning English as a second language may have difficulty learning accurate /r/ production, especially if their native language does not have an /r/, or the /r/ they produce is at a different place of articulation. The goal of this research is to provide a novel approach on how a knowledge-based intelligence program can provide immediate feedback on the accuracy of productions. In the proposed approach, the audio signals will first be detected, after which features of audio signals will be extracted, and finally, knowledge-based intelligent classification will be performed. Based on the obtained knowledge and application scenarios, novel features are proposed and used to classify various speaker scenarios. Full article

(This article belongs to the Special Issue AI in Knowledge-Based Information and Decision Support Systems)

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18 pages, 5343 KiB

Open AccessArticle

Self-Supervised Skill Learning for Semi-Supervised Long-Horizon Instruction Following

by Benhui Zhuang, Chunhong Zhang and Zheng Hu

Electronics 2023, 12(7), 1587; https://doi.org/10.3390/electronics12071587 - 28 Mar 2023

Viewed by 1261

Abstract

Language as an abstraction for hierarchical agents is promising to solve compositional long-time horizon decision-making tasks. The learning of the agent poses significant challenges, as it typically requires plenty of trajectories annotated with languages. This paper addresses the challenge of learning such an [...] Read more.

Language as an abstraction for hierarchical agents is promising to solve compositional long-time horizon decision-making tasks. The learning of the agent poses significant challenges, as it typically requires plenty of trajectories annotated with languages. This paper addresses the challenge of learning such an agent under the scarcity of language annotations. One approach for leveraging unannotated data is to generate pseudo-labels for unannotated trajectories using sparse seed annotations. However, as the scenes of the environment and tasks assigned to the agent are diverse, the inference of language instructions is sometimes incorrect, causing the policy to learn to ground incorrect instructions to actions. In this work, we propose a self-supervised language-conditioned hierarchical skill policy (SLHSP) which utilizes unannotated data to learn reusable and general task-related skills to facilitate learning from sparse annotations. We demonstrate that the SLHSP that learned with less than 10% of annotated trajectories has a comparable performance to one that learned with 100% of annotated data. Our approach to the challenging ALFRED benchmark leads to a notable improvement in the success rate over a strong baseline also optimized for sparsely annotated data. Full article

(This article belongs to the Special Issue AI in Knowledge-Based Information and Decision Support Systems)

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15 pages, 2865 KiB

Open AccessArticle

Knowledge Acquisition and Reasoning Model for Welding Information Integration Based on CNN and Knowledge Graph

by Kainan Guan, Yang Sun, Guang Yang and Xinhua Yang

Electronics 2023, 12(6), 1275; https://doi.org/10.3390/electronics12061275 - 7 Mar 2023

Cited by 1 | Viewed by 1433

Abstract

Knowledge acquisition and reasoning are essential in intelligent welding decisions. However, the challenges of unstructured knowledge acquisition and weak knowledge linkage across phases limit the development of welding intelligence, especially in the integration of domain information engineering. This paper proposes a cognitive model [...] Read more.

Knowledge acquisition and reasoning are essential in intelligent welding decisions. However, the challenges of unstructured knowledge acquisition and weak knowledge linkage across phases limit the development of welding intelligence, especially in the integration of domain information engineering. This paper proposes a cognitive model combining image recognition and a knowledge graph. A CNN is used as the perception layer to obtain direct information. Automated logic rules based on a knowledge graph are described to enable information integration in the knowledge reasoning domain. In addition, a welding knowledge graph of the bogie frame was constructed based on entity and relationship recognition. CNN models with different network structures were compared and trained under supervised conditions. In the results, the InceptionV1 network obtained a high score (0.758 for the thickness relation, 0.642 for the groove form, 0.704 for the joint type, and 0.835 for the base material form). The proposed model showed positive performance in terms of accuracy, interpretation, knowledge coverage, scalability, and portability compared with several other methods. The model can effectively address the abovementioned limitations and is important for welding manufacturing with engineering information integration. Full article

(This article belongs to the Special Issue AI in Knowledge-Based Information and Decision Support Systems)

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18 pages, 6549 KiB

Open AccessArticle

Promoting Adversarial Transferability via Dual-Sampling Variance Aggregation and Feature Heterogeneity Attacks

by Yang Huang, Yuling Chen, Xuewei Wang, Jing Yang and Qi Wang

Electronics 2023, 12(3), 767; https://doi.org/10.3390/electronics12030767 - 3 Feb 2023

Cited by 7 | Viewed by 1545

Abstract

At present, deep neural networks have been widely used in various fields, but their vulnerability requires attention. The adversarial attack aims to mislead the model by generating imperceptible perturbations on the source model, and although white-box attacks have achieved good success rates, existing [...] Read more.

At present, deep neural networks have been widely used in various fields, but their vulnerability requires attention. The adversarial attack aims to mislead the model by generating imperceptible perturbations on the source model, and although white-box attacks have achieved good success rates, existing adversarial samples exhibit weak migration in the black-box case, especially on some adversarially trained defense models. Previous work for gradient-based optimization either optimizes the image before iteration or optimizes the gradient during iteration, so it results in the generated adversarial samples overfitting the source model and exhibiting poor mobility to the adversarially trained model. To solve these problems, we propose the dual-sample variance aggregation with feature heterogeneity attack; our method is optimized before and during iterations to produce adversarial samples with better transferability. In addition, our method can be integrated with various input transformations. A large amount of experimental data demonstrate the effectiveness of the proposed method, which improves the attack success rate by 5.9% for the normally trained model and 11.5% for the adversarially trained model compared with the current state-of-the-art migration-enhancing attack methods. Full article

(This article belongs to the Special Issue AI in Knowledge-Based Information and Decision Support Systems)

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15 pages, 1032 KiB

Open AccessArticle

Cluster-Based JRPCA Algorithm for Wi-Fi Fingerprint Localization

by Li Zhang, Min Zhang, Jingao Xu and Yi Xu

Electronics 2023, 12(1), 153; https://doi.org/10.3390/electronics12010153 - 29 Dec 2022

Viewed by 1303

Abstract

Indoor localization services are emerging as an important application of the Internet of Things, which drives the development of related technologies in indoor scenarios. In recent years, various localization algorithms for different indoor scenarios have been proposed. The indoor localization algorithm based on [...] Read more.

Indoor localization services are emerging as an important application of the Internet of Things, which drives the development of related technologies in indoor scenarios. In recent years, various localization algorithms for different indoor scenarios have been proposed. The indoor localization algorithm based on fingerprints has attracted much attention due to its good performance without extra hardware devices. However, the occurrence of fingerprint mismatching caused by the complexity and variability of indoor scenarios is unneglectable, which degrades localization accuracy. In this article, by combining weighted kernel norm and

L_{2, 1}

-norm, a joint-norm robust principal component analysis (JRPCA in brief) assisted indoor localization algorithm is proposed, which can improve the localization accuracy through aggregating the reference points (RPs) and conducting robust feature extraction based on clustering. More specifically, a one-way hierarchical clustering termination method is proposed to obtain reasonable RP clusters adaptively according to the preset RPs. A two-phase fingerprint matching algorithm of JRPCA based on clustering is proposed to further increase the difference between similar RPs, thus facilitating rapid identification and reinforcing localization accuracy. To validate the proposed algorithm, extensive experiments are conducted in real indoor scenarios. The experimental results confirm that the proposed cluster-based JRPCA algorithm outperforms other existing algorithms in terms of robustness and accuracy. Full article

(This article belongs to the Special Issue AI in Knowledge-Based Information and Decision Support Systems)

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