Journal of Artificial Intelligence and Soft Computing Research最新文献

Bending Path Understanding Based on Angle Projections in Field Environments 基于实地环境中的角度投影理解弯曲路径

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-12-01 DOI: 10.2478/jaiscr-2024-0002

Luping Wang, Hui Wei

{"title":"Bending Path Understanding Based on Angle Projections in Field Environments","authors":"Luping Wang, Hui Wei","doi":"10.2478/jaiscr-2024-0002","DOIUrl":"https://doi.org/10.2478/jaiscr-2024-0002","url":null,"abstract":"Abstract Scene understanding is a core problem for field robots. However, many unsolved problems, like understanding bending paths, severely hinder the implementation due to varying illumination, irregular features and unstructured boundaries in field environments. Traditional three-dimensional(3D) environmental perception from 3D point clouds or fused sensors are costly and account poorly for field unstructured semantic information. In this paper, we propose a new methodology to understand field bending paths and build their 3D reconstruction from a monocular camera without prior training. Bending angle projections are assigned to clusters. Through compositions of their sub-clusters, bending surfaces are estimated by geometric inferences. Bending path scenes are approximated bending structures in the 3D reconstruction. Understanding sloping gradient is helpful for a navigating mobile robot to automatically adjust their speed. Based on geometric constraints from a monocular camera, the approach requires no prior training, and is robust to varying color and illumination. The percentage of incorrectly classified pixels were compared to the ground truth. Experimental results demonstrated that the method can successfully understand bending path scenes, meeting the requirements of robot navigation in an unstructured environment.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"3 1","pages":"25 - 43"},"PeriodicalIF":2.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139188080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpreting Convolutional Layers in DNN Model Based on Time–Frequency Representation of Emotional Speech 基于情感语音的时频表示解读 DNN 模型中的卷积层

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-12-01 DOI: 10.2478/jaiscr-2024-0001

Lukasz Smietanka, Tomasz Maka

{"title":"Interpreting Convolutional Layers in DNN Model Based on Time–Frequency Representation of Emotional Speech","authors":"Lukasz Smietanka, Tomasz Maka","doi":"10.2478/jaiscr-2024-0001","DOIUrl":"https://doi.org/10.2478/jaiscr-2024-0001","url":null,"abstract":"Abstract The paper describes the relations of speech signal representation in the layers of the convolutional neural network. Using activation maps determined by the Grad-CAM algorithm, energy distribution in the time–frequency space and their relationship with prosodic properties of the considered emotional utterances have been analysed. After preliminary experiments with the expressive speech classification task, we have selected the CQT-96 time–frequency representation. Also, we have used a custom CNN architecture with three convolutional layers in the main experimental phase of the study. Based on the performed analysis, we show the relationship between activation levels and changes in the voiced parts of the fundamental frequency trajectories. As a result, the relationships between the individual activation maps, energy distribution, and fundamental frequency trajectories for six emotional states were described. The results show that the convolutional neural network in the learning process uses similar fragments from time–frequency representation, which are also related to the prosodic properties of emotional speech utterances. We also analysed the relations of the obtained activation maps with time-domain envelopes. It allowed observing the importance of the speech signals energy in classifying individual emotional states. Finally, we compared the energy distribution of the CQT representation in relation to the regions’ energy overlapping with masks of individual emotional states. In the result, we obtained information on the variability of energy distributions in the selected signal representation speech for particular emotions.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"9 6","pages":"5 - 23"},"PeriodicalIF":2.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139189498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Organized Operational Neural Networks for The Detection of Atrial Fibrillation 用于检测心房颤动的自组织运行神经网络

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-12-01 DOI: 10.2478/jaiscr-2024-0004

Junming Zhang, Hao Dong, Jinfeng Gao, Ruxian Yao, Gangqiang Li, Haitao Wu

{"title":"Self-Organized Operational Neural Networks for The Detection of Atrial Fibrillation","authors":"Junming Zhang, Hao Dong, Jinfeng Gao, Ruxian Yao, Gangqiang Li, Haitao Wu","doi":"10.2478/jaiscr-2024-0004","DOIUrl":"https://doi.org/10.2478/jaiscr-2024-0004","url":null,"abstract":"Abstract Atrial fibrillation is a common cardiac arrhythmia, and its incidence increases with age. Currently, numerous deep learning methods have been proposed for AF detection. However, these methods either have complex structures or poor robustness. Given the evidence from recent studies, it is not surprising to observe the limitations in the learning performance of these approaches. This can be attributed to their strictly homogenous conguration, which solely relies on the linear neuron model. The limitations mentioned above have been addressed by operational neural networks (ONNs). These networks employ a heterogeneous network configuration, incorporating neurons equipped with diverse nonlinear operators. Therefore, in this study, to enhance the detection performance while maintaining computational efficiency, a novel model named multi-scale Self-ONNs (MSSelf-ONNs) was proposed to identify AF. The proposed model possesses a significant advantage and superiority over conventional ONNs due to their self-organization capability. Unlike conventional ONNs, MSSelf -ONNs eliminate the need for prior operator search within the operator set library to find the optimal set of operators. This unique characteristic sets MSSelf -ONNs apart and enhances their overall performance. To validate and evaluate the system, we have implemented the experiments on the well-known MIT-BIH atrial fibrillation database. The proposed model yields total accuracies and kappa coefficients of 98% and 0.95, respectively. The experiment results demonstrate that the proposed model outperform the state-of-the-art deep CNN in terms of both performance and computational complexity.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"19 5","pages":"63 - 75"},"PeriodicalIF":2.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139189340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metrics for Assessing Generalization of Deep Reinforcement Learning in Parameterized Environments 评估参数化环境中深度强化学习泛化程度的指标

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-12-01 DOI: 10.2478/jaiscr-2024-0003

Maciej Aleksandrowicz, Joanna Jaworek-Korjakowska

引用次数: 0

A Few-Shot Learning Approach for Covid-19 Diagnosis Using Quasi-Configured Topological Spaces 利用准配置拓扑空间进行 Covid-19 诊断的少量学习方法

IF 2.8 3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-12-01 DOI: 10.2478/jaiscr-2024-0005

Hui Liu, Chunjie Wang, Xin Jiang, M. Khishe

{"title":"A Few-Shot Learning Approach for Covid-19 Diagnosis Using Quasi-Configured Topological Spaces","authors":"Hui Liu, Chunjie Wang, Xin Jiang, M. Khishe","doi":"10.2478/jaiscr-2024-0005","DOIUrl":"https://doi.org/10.2478/jaiscr-2024-0005","url":null,"abstract":"Abstract Accurate and efficient COVID-19 diagnosis is crucial in clinical settings. However, the limited availability of labeled data poses a challenge for traditional machine learning algorithms. To address this issue, we propose Turning Point (TP), a few-shot learning (FSL) approach that leverages high-level turning point mappings to build sophisticated representations across previously labeled data. Unlike existing FSL models, TP learns using quasi-configured topological spaces and efficiently combines the outputs of diverse TP learners. We evaluated TPFSL using three COVID-19 datasets and compared it with seven different benchmarks. Results show that TPFSL outperformed the top-performing benchmark models in both one-shot and five-shot tasks, with an average improvement of 4.50% and 4.43%, respectively. Additionally, TPFSL significantly outperformed the ProtoNet benchmark by 12.966% and 11.033% in one-shot and five-shot classification problems across all datasets. Ablation experiments were also conducted to analyze the impact of variables such as TP density, network topology, distance measure, and TP placement. Overall, TPFSL has the potential to improve the accuracy and speed of diagnoses for COVID-19 in clinical settings and can be a valuable tool for medical professionals.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"19 5-6","pages":"77 - 95"},"PeriodicalIF":2.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139195203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Internet of Senses - Potential Applications and Implications 感官互联网-潜在的应用和影响

3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-11-04 DOI: 10.55195/jscai.1316512

Kaan CÖMERT, Mustafa AKKAŞ

引用次数: 0

Machine Learning Based a Comparative Analysis for Detecting Tweets of Earthquake Victims Asking for Help in The 2023 Turkey-Syria Earthquake 基于机器学习的比较分析，检测2023年土耳其-叙利亚地震中寻求帮助的地震受害者的推文

3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-11-04 DOI: 10.55195/jscai.1365639

Anıl UTKU, Ümit CAN

引用次数: 0

An Explainable AI Approach to Agrotechnical Monitoring and Crop Diseases Prediction in Dnipro Region of Ukraine 乌克兰第聂伯罗地区农业技术监测和作物病害预测的可解释人工智能方法

3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-10-01 DOI: 10.2478/jaiscr-2023-0018

Ivan Laktionov, Grygorii Diachenko, Danuta Rutkowska, Marek Kisiel-Dorohinicki

{"title":"An Explainable AI Approach to Agrotechnical Monitoring and Crop Diseases Prediction in Dnipro Region of Ukraine","authors":"Ivan Laktionov, Grygorii Diachenko, Danuta Rutkowska, Marek Kisiel-Dorohinicki","doi":"10.2478/jaiscr-2023-0018","DOIUrl":"https://doi.org/10.2478/jaiscr-2023-0018","url":null,"abstract":"Abstract The proliferation of computer-oriented and information digitalisation technologies has become a hallmark across various sectors in today’s rapidly evolving environment. Among these, agriculture emerges as a pivotal sector in need of seamless incorporation of high-performance information technologies to address the pressing needs of national economies worldwide. The aim of the present article is to substantiate scientific and applied approaches to improving the efficiency of computer-oriented agrotechnical monitoring systems by developing an intelligent software component for predicting the probability of occurrence of corn diseases during the full cycle of its cultivation. The object of research is non-stationary processes of intelligent transformation and predictive analytics of soil and climatic data, which are factors of the occurrence and development of diseases in corn. The subject of the research is methods and explainable AI models of intelligent predictive analysis of measurement data on the soil and climatic condition of agricultural enterprises specialised in growing corn. The main scientific and practical effect of the research results is the development of IoT technologies for agrotechnical monitoring through the development of a computer-oriented model based on the ANFIS technique and the synthesis of structural and algorithmic provision for identifying and predicting the probability of occurrence of corn diseases during the full cycle of its cultivation.","PeriodicalId":48494,"journal":{"name":"Journal of Artificial Intelligence and Soft Computing Research","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136153698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast Attack Detection Method for Imbalanced Data in Industrial Cyber-Physical Systems 工业信息物理系统中不平衡数据的快速攻击检测方法

3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-10-01 DOI: 10.2478/jaiscr-2023-0017

Meng Huang, Tao Li, Beibei Li, Nian Zhang, Hanyuan Huang

引用次数: 0

Towards Ensuring Software Interoperability Between Deep Learning Frameworks 确保深度学习框架之间的软件互操作性

3区计算机科学

Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-10-01 DOI: 10.2478/jaiscr-2023-0016

Youn Kyu Lee, Seong Hee Park, Min Young Lim, Soo-Hyun Lee, Jongwook Jeong

引用次数: 0