【标题】Artificial intelligence to predict climate and weather change
【参考中译】人工智能预测气候和天气变化
【类型】 期刊
【关键词】 Artificial intelligence; Climate change; El nino/Southern oscillation; Sea level rise; Typhoon; Extreme weather; Global warming
【参考中译】 人工智能;气候变化;厄尔尼诺/南方涛动;海平面上升;台风;极端天气;全球变暖
【作者】 Soohwan Jeon; Junkyu Kim
【摘要】 In recent years, the risk of natural disasters has been on the rise due to climate change and extreme weather events driven by global warming, thereby increasing the need for technology that can predict them. Existing weather forecasting technologies that are based on physical and numerical models are not highly accurate and have limitations as certain variables such as global warming are not taken into account. This paper will introduce technologies that utilize artificial intelligence to predict long-term climate change and short- to medium-term extreme weather events. These technologies are not only being actively researched at the basic level, but also are gradually being applied commercially.
【参考中译】 近年来，由于气候变化和全球变暖引发的极端天气事件，自然灾害的风险不断上升，从而增加了对预测自然灾害的技术的需求。现有的基于物理和数字模型的天气预报技术不高准确性，并且存在局限性，因为没有考虑全球变暖等某些变量。本文将介绍利用人工智能预测长期气候变化和中短期极端天气事件的技术。这些技术不仅在基础层面积极研究，而且正在逐步商业化应用。
【来源】 JMST Advances 2024, vol.6, no.1
【入库时间】 2024/6/3
【标题】Early detection of depression through facial expression recognition and electroencephalogram-based artificial intelligence-assisted graphical user interface
【参考中译】通过面部表情识别和基于脑电波的人工智能辅助图形用户界面早期检测抑郁症
【关键词】 Depression; Artificial intelligence; EEG; Emotion recognition; Graphical user interface
【参考中译】 抑郁症;人工智能;脑电;情绪识别;图形用户界面
【作者】 Gajendra Kumar; Tanaya Das; Kuldeep Singh
【摘要】 Psychological disorders have increased globally at an alarming rate. Among these disorders, depression stands out as one of the leading and most prevalent conditions that have affected more than 280 million people. However, it remains widely undiagnosed and untreated due to lack of sensitive and reliable diagnostic tools. This underscores the imperative for the development of a sensitive and accurate diagnostic tool facilitating the early diagnosis of depression symptoms to mitigate the impending mental illness epidemic. To address this need, we developed an artificial intelligence (AI)-assisted tool utilizing facial expression-based emotion recognition and electroencephalogram (EEG) analysis for the detection of depression symptoms along with their severity level assessment. Our approach yielded successful detection of depression symptoms with an accuracy of 93.58%, a sensitivity of 92.70%, a specificity of 93.40%, and an f1-score of 93.68% through facial emotion recognition task. Additionally, severity level detection employing EEG biomarkers achieved an accuracy of 99.75%, a sensitivity of 99.75%, a specificity of 99.92%, and an f1-score of 99.75%. Consequently, a graphical user interface (GUI) tool was developed that seamlessly integrated the AI with facial image and EEG data inputs, enabling efficient recognition of depression from both real-time and pre-recorded data. The resulting AI assistant demonstrates high sensitivity, precision, and accuracy in the early detection of depression, establishing its potential as a reliable diagnostic tool. The application of our tool may be extended to clinicians, therapists, and hospitals for the identification of depression at its early stage.
【参考中译】 心理障碍在全球范围内以惊人的速度增加。在这些疾病中，抑郁症是影响超过2.8亿人的主要和最普遍的疾病之一。然而，由于缺乏敏感和可靠的诊断工具，它仍然普遍没有得到诊断和治疗。这突出表明，必须开发一种敏感和准确的诊断工具，促进抑郁症症状的早期诊断，以缓解即将到来的精神疾病流行。为了满足这一需求，我们开发了一种人工智能(AI)辅助工具，利用基于面部表情的情绪识别和脑电(EEG)分析来检测抑郁症状及其严重程度评估。该方法对抑郁症状的检测准确率为93.58%，敏感度为92.70%，特异度为93.40%，F1-Score为93.68%。此外，使用脑电生物标志物进行严重程度检测的准确率为99.75%，灵敏度为99.75%，特异度为99.92%，F1评分为99.75%。因此，开发了一种图形用户界面(GUI)工具，该工具将人工智能与面部图像和EEG数据输入无缝集成，从而能够从实时和预先记录的数据中高效识别抑郁症。由此产生的人工智能助手在抑郁症的早期检测中表现出高灵敏度、精确度和准确性，确立了其作为可靠诊断工具的潜力。我们的工具的应用可以扩展到临床医生、治疗师和医院，以便在抑郁症的早期阶段进行识别。
【来源】 Neural Computing & Applications 2024, vol.36, no.12
【标题】Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making
【参考中译】用可解释的人工智能增强作物推荐系统：农业决策研究
【关键词】 Crop recommendation systems; Machine learning; EXplainable artificial intelligence; Agriculture; Decision support system
【参考中译】 农作物推荐系统;机器学习;可解释人工智能;农业;决策支持系统
【作者】 Mahmoud Y. Shams; Samah A. Gamel; Fatma M. Talaat
【摘要】 Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide personalized recommendations. In response to the growing demand for transparency and interpretability in agricultural decision-making, this study introduces XAI-CROP an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles. The fundamental objective of XAI-CROP is to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models. The study rigorously compares XAI-CROP with prominent machine learning models, including Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), Gaussian Naive Bayes (GNB), and Multimodal Naive Bayes (MNB). Performance evaluation employs three essential metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2). The empirical results unequivocally establish the superior performance of XAI-CROP. It achieves an impressively low MSE of 0.9412, indicating highly accurate crop yield predictions. Moreover, with an MAE of 0.9874, XAI-CROP consistently maintains errors below the critical threshold of 1, reinforcing its reliability. The robust R~2 value of 0.94152 underscores XAI-CROP's ability to explain 94.15% of the data's variability, highlighting its interpretability and explanatory power.
【参考中译】 作物推荐系统对农民来说是无价的工具，帮助他们做出关于作物选择的明智决定，以优化产量。这些系统利用丰富的数据，包括土壤特性、历史作物表现和流行的天气模式，提供个性化的建议。为了应对农业决策中对透明度和可解释性日益增长的需求，本研究引入了XAI-CROP，这是一种利用可解释人工智能(XAI)原理的创新算法。Xai-Crop的根本目标是让农民能够对推荐过程有可理解的见解，超越传统机器学习模型的不透明性质。该研究严格比较了XAI-CROP与著名的机器学习模型，包括梯度提升(GB)、决策树(DT)、随机森林(RF)、高斯朴素贝叶斯(GNB)和多模式朴素贝叶斯(MNB)。性能评估使用三个基本度量：均方误差(MSE)、平均绝对误差(MAE)和R平方(R2)。实证结果毫不含糊地确立了夏作物的优越业绩。它实现了令人印象深刻的0.9412的低均方误差，表明高度准确的作物产量预测。此外，MAE为0.9874，XAI-CROP始终将误差保持在1的临界阈值以下，从而增强了其可靠性。稳健的R~2值为0.94152，突显了Xai-Crop“S对94.15%的数据的解释能力”，突显了它的可解释性和解释力。
【来源】 Neural Computing & Applications 2024, vol.36, no.11