Kawtar Lehouel, Chaima Saber, Mourad Bouziani, Reda Yaagoubi
Efficiently determining crop water stress is vital for optimising irrigation practices and enhancing agricultural productivity. In this realm, the synergy of deep learning with remote sensing technologies offers a significant opportunity. This study introduces an innovative end-to-end deep learning pipeline for within-field crop water determination. This involves the following: (1) creating an annotated dataset for crop water stress using Landsat 8 imagery, (2) deploying a standalone vision transformer model ViT, and (3) the implementation of a proposed CNN-ViT model. This approach allows for a comparative analysis between the two architectures, ViT and CNN-ViT, in accurately determining crop water stress. The results of our study demonstrate the effectiveness of the CNN-ViT framework compared to the standalone vision transformer model. The CNN-ViT approach exhibits superior performance, highlighting its enhanced accuracy and generalisation capabilities. The findings underscore the significance of an integrated deep learning pipeline combined with remote sensing data in the determination of crop water stress, providing a reliable and scalable tool for real-time monitoring and resource management contributing to sustainable agricultural practices.
{"title":"Remote Sensing Crop Water Stress Determination Using CNN-ViT Architecture","authors":"Kawtar Lehouel, Chaima Saber, Mourad Bouziani, Reda Yaagoubi","doi":"10.3390/ai5020033","DOIUrl":"https://doi.org/10.3390/ai5020033","url":null,"abstract":"Efficiently determining crop water stress is vital for optimising irrigation practices and enhancing agricultural productivity. In this realm, the synergy of deep learning with remote sensing technologies offers a significant opportunity. This study introduces an innovative end-to-end deep learning pipeline for within-field crop water determination. This involves the following: (1) creating an annotated dataset for crop water stress using Landsat 8 imagery, (2) deploying a standalone vision transformer model ViT, and (3) the implementation of a proposed CNN-ViT model. This approach allows for a comparative analysis between the two architectures, ViT and CNN-ViT, in accurately determining crop water stress. The results of our study demonstrate the effectiveness of the CNN-ViT framework compared to the standalone vision transformer model. The CNN-ViT approach exhibits superior performance, highlighting its enhanced accuracy and generalisation capabilities. The findings underscore the significance of an integrated deep learning pipeline combined with remote sensing data in the determination of crop water stress, providing a reliable and scalable tool for real-time monitoring and resource management contributing to sustainable agricultural practices.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Mehedi Hassan, Stephen Karungaru, Kenji Terada
In football or soccer, a referee controls the game based on the set rules. The decisions made by the referee are final and can’t be appealed. Some of the decisions, especially after a handball event, whether to award a penalty kick or a yellow/red card can greatly affect the final results of a game. It is therefore necessary that the referee does not make an error. The objective is therefore to create a system that can accurately recognize such events and make the correct decision. This study chose handball, an event that occurs in a football game (Not to be confused with the game of Handball). We define a handball event using object detection and robotic perception and decide whether it is intentional or not. Intention recognition is a robotic perception of emotion recognition. To define handball, we trained a model to detect the hand and ball which are primary objects. We then determined the intention using gaze recognition and finally combined the results to recognize a handball event. On our dataset, the results of the hand and the ball object detection were 96% and 100% respectively. With the gaze recognition at 100%, if all objects were recognized, then the intention and handball event recognition were at 100%.
{"title":"Robotics Perception: Intention Recognition to Determine the Handball Occurrence during a Football or Soccer Match","authors":"Mohammad Mehedi Hassan, Stephen Karungaru, Kenji Terada","doi":"10.3390/ai5020032","DOIUrl":"https://doi.org/10.3390/ai5020032","url":null,"abstract":"In football or soccer, a referee controls the game based on the set rules. The decisions made by the referee are final and can’t be appealed. Some of the decisions, especially after a handball event, whether to award a penalty kick or a yellow/red card can greatly affect the final results of a game. It is therefore necessary that the referee does not make an error. The objective is therefore to create a system that can accurately recognize such events and make the correct decision. This study chose handball, an event that occurs in a football game (Not to be confused with the game of Handball). We define a handball event using object detection and robotic perception and decide whether it is intentional or not. Intention recognition is a robotic perception of emotion recognition. To define handball, we trained a model to detect the hand and ball which are primary objects. We then determined the intention using gaze recognition and finally combined the results to recognize a handball event. On our dataset, the results of the hand and the ball object detection were 96% and 100% respectively. With the gaze recognition at 100%, if all objects were recognized, then the intention and handball event recognition were at 100%.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141000398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human Immunodeficiency Virus (HIV) is a stigmatizing disease that disproportionately affects African Americans and Latinos among people living with HIV (PLWH). Researchers are increasingly utilizing artificial intelligence (AI) to analyze large amounts of data such as social media data and electronic health records (EHR) for various HIV-related tasks, from prevention and surveillance to treatment and counseling. This paper explores the ethical considerations surrounding the use of AI for HIV with a focus on acceptability, trust, fairness, and transparency. To improve acceptability and trust towards AI systems for HIV, informed consent and a Federated Learning (FL) approach are suggested. In regard to unfairness, stakeholders should be wary of AI systems for HIV further stigmatizing or even being used as grounds to criminalize PLWH. To prevent criminalization, in particular, the application of differential privacy on HIV data generated by data linkage should be studied. Participatory design is crucial in designing the AI systems for HIV to be more transparent and inclusive. To this end, the formation of a data ethics committee and the construction of relevant frameworks and principles may need to be concurrently implemented. Lastly, the question of whether the amount of transparency beyond a certain threshold may overwhelm patients, thereby unexpectedly triggering negative consequences, is posed.
人类免疫缺陷病毒（HIV）是一种令人鄙视的疾病，在 HIV 感染者（PLWH）中，非裔美国人和拉丁裔美国人受到的影响尤为严重。研究人员越来越多地利用人工智能（AI）来分析大量数据，如社交媒体数据和电子健康记录（EHR），以完成从预防和监测到治疗和咨询等各种与 HIV 相关的任务。本文以可接受性、信任度、公平性和透明度为重点，探讨了将人工智能用于艾滋病防治的伦理考虑因素。为提高人工智能系统在艾滋病防治方面的可接受性和信任度，建议采用知情同意和联合学习（FL）方法。关于不公平问题，利益相关者应警惕艾滋病毒人工智能系统进一步污名化，甚至被用作将艾滋病毒感染者定罪的理由。为防止刑事定罪，尤其应研究对数据关联产生的艾滋病毒数据应用不同的隐私保护。参与式设计对于设计更加透明和包容的艾滋病毒人工智能系统至关重要。为此，可能需要同时组建数据伦理委员会，并构建相关框架和原则。最后，我们还提出了一个问题，即超过一定阈值的透明度是否会让患者不堪重负，从而意外引发负面后果。
{"title":"Ethical Considerations for Artificial Intelligence Applications for HIV","authors":"Renee Garett, Seungjun Kim, Sean D. Young","doi":"10.3390/ai5020031","DOIUrl":"https://doi.org/10.3390/ai5020031","url":null,"abstract":"Human Immunodeficiency Virus (HIV) is a stigmatizing disease that disproportionately affects African Americans and Latinos among people living with HIV (PLWH). Researchers are increasingly utilizing artificial intelligence (AI) to analyze large amounts of data such as social media data and electronic health records (EHR) for various HIV-related tasks, from prevention and surveillance to treatment and counseling. This paper explores the ethical considerations surrounding the use of AI for HIV with a focus on acceptability, trust, fairness, and transparency. To improve acceptability and trust towards AI systems for HIV, informed consent and a Federated Learning (FL) approach are suggested. In regard to unfairness, stakeholders should be wary of AI systems for HIV further stigmatizing or even being used as grounds to criminalize PLWH. To prevent criminalization, in particular, the application of differential privacy on HIV data generated by data linkage should be studied. Participatory design is crucial in designing the AI systems for HIV to be more transparent and inclusive. To this end, the formation of a data ethics committee and the construction of relevant frameworks and principles may need to be concurrently implemented. Lastly, the question of whether the amount of transparency beyond a certain threshold may overwhelm patients, thereby unexpectedly triggering negative consequences, is posed.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141004715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohsen Khan Mohammadi, Toni Schneidereit, Ashkan Mansouri Yarahmadi, Michael Breuß
In this study, we developed and explored a methodical image augmentation technique for swimmer localisation in northern German outdoor lake environments. When it comes to enhancing swimmer safety, a main issue we have to deal with is the lack of real-world training data of such outdoor environments. Natural lighting changes, dynamic water textures, and barely visible swimming persons are key issues to address. We account for these difficulties by adopting an effective background removal technique with available training data. This allows us to edit swimmers into natural environment backgrounds for use in subsequent image augmentation. We created 17 training datasets with real images, synthetic images, and a mixture of both to investigate different aspects and characteristics of the proposed approach. The datasets were used to train YOLO architectures for possible future applications in real-time detection. The trained frameworks were then tested and evaluated on outdoor environment imagery acquired using a safety drone to investigate and confirm their usefulness for outdoor swimmer localisation.
{"title":"Investigating Training Datasets of Real and Synthetic Images for Outdoor Swimmer Localisation with YOLO","authors":"Mohsen Khan Mohammadi, Toni Schneidereit, Ashkan Mansouri Yarahmadi, Michael Breuß","doi":"10.3390/ai5020030","DOIUrl":"https://doi.org/10.3390/ai5020030","url":null,"abstract":"In this study, we developed and explored a methodical image augmentation technique for swimmer localisation in northern German outdoor lake environments. When it comes to enhancing swimmer safety, a main issue we have to deal with is the lack of real-world training data of such outdoor environments. Natural lighting changes, dynamic water textures, and barely visible swimming persons are key issues to address. We account for these difficulties by adopting an effective background removal technique with available training data. This allows us to edit swimmers into natural environment backgrounds for use in subsequent image augmentation. We created 17 training datasets with real images, synthetic images, and a mixture of both to investigate different aspects and characteristics of the proposed approach. The datasets were used to train YOLO architectures for possible future applications in real-time detection. The trained frameworks were then tested and evaluated on outdoor environment imagery acquired using a safety drone to investigate and confirm their usefulness for outdoor swimmer localisation.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The allure of team scale and functional diversity has led to the promising adoption of heterogeneous multi-robot systems (HMRS) in complex, large-scale operations such as disaster search and rescue, site surveillance, and social security. These systems, which coordinate multiple robots of varying functions and quantities, face the significant challenge of accurately assembling robot teams that meet the dynamic needs of tasks with respect to size and functionality, all while maintaining minimal resource expenditure. This paper introduces a pioneering adaptive cooperation method named inner attention (innerATT), crafted to dynamically configure teams of heterogeneous robots in response to evolving task types and environmental conditions. The innerATT method is articulated through the integration of an innovative attention mechanism within a multi-agent actor–critic reinforcement learning framework, enabling the strategic analysis of robot capabilities to efficiently form teams that fulfill specific task demands. To demonstrate the efficacy of innerATT in facilitating cooperation, experimental scenarios encompassing variations in task type (“Single Task”, “Double Task”, and “Mixed Task”) and robot availability are constructed under the themes of “task variety” and “robot availability variety.” The findings affirm that innerATT significantly enhances flexible cooperation, diminishes resource usage, and bolsters robustness in task fulfillment.
{"title":"Development of an Attention Mechanism for Task-Adaptive Heterogeneous Robot Teaming","authors":"Yibei Guo, Chao Huang, Rui Liu","doi":"10.3390/ai5020029","DOIUrl":"https://doi.org/10.3390/ai5020029","url":null,"abstract":"The allure of team scale and functional diversity has led to the promising adoption of heterogeneous multi-robot systems (HMRS) in complex, large-scale operations such as disaster search and rescue, site surveillance, and social security. These systems, which coordinate multiple robots of varying functions and quantities, face the significant challenge of accurately assembling robot teams that meet the dynamic needs of tasks with respect to size and functionality, all while maintaining minimal resource expenditure. This paper introduces a pioneering adaptive cooperation method named inner attention (innerATT), crafted to dynamically configure teams of heterogeneous robots in response to evolving task types and environmental conditions. The innerATT method is articulated through the integration of an innovative attention mechanism within a multi-agent actor–critic reinforcement learning framework, enabling the strategic analysis of robot capabilities to efficiently form teams that fulfill specific task demands. To demonstrate the efficacy of innerATT in facilitating cooperation, experimental scenarios encompassing variations in task type (“Single Task”, “Double Task”, and “Mixed Task”) and robot availability are constructed under the themes of “task variety” and “robot availability variety.” The findings affirm that innerATT significantly enhances flexible cooperation, diminishes resource usage, and bolsters robustness in task fulfillment.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140669172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence (AI), the simulation of human intelligence processes by machines, is having a growing impact on healthcare [...]
人工智能（AI）是机器对人类智能过程的模拟，正在对医疗保健产生越来越大的影响 [...]
{"title":"Artificial Intelligence in Healthcare: ChatGPT and Beyond","authors":"Tim Hulsen","doi":"10.3390/ai5020028","DOIUrl":"https://doi.org/10.3390/ai5020028","url":null,"abstract":"Artificial intelligence (AI), the simulation of human intelligence processes by machines, is having a growing impact on healthcare [...]","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.
本文采用人工神经网络（ANN）算法预测工业 4.0/5.0 场景中电压信号的电子电路输出。这种方法适用于预测受未知噪声影响的控制电路可能出现的未校正行为，并重现模拟影响输入正弦电压信号放大的噪声效应的试验台方法，正弦电压信号是受控制造系统的基本和基础信号。所进行的模拟考虑了不同噪声信号改变其时域趋势和频率行为的情况，以证明在控制电路输入端考虑复杂信号（包括添加干扰和噪声）时预测电压输出的可能性。结果表明，只处理注册的电压输出信号，而不考虑噪声曲线（通常是未知的），就有可能构建一个良好的 ANN 训练模型。所提出的模型可作为工业 5.0 制造流程的电子黑盒，实现电路和机器调整程序的自动化。通过分析最先进的 ANN，该研究提供了一种基于 ANN 的创新型多功能解决方案，能够处理各种噪声剖面，而无需事先了解噪声特性。
{"title":"ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems","authors":"A. Massaro","doi":"10.3390/ai5020027","DOIUrl":"https://doi.org/10.3390/ai5020027","url":null,"abstract":"In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nawaf Alharbi, Mustafa Youldash, Duha Alotaibi, Haya Aldossary, Reema Albrahim, Reham Alzahrani, Wahbia Ahmed Saleh, S. O. Olatunji, May Issa Aldossary
Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions.
胎儿缺氧是指子宫内发育中的胎儿缺乏氧气供应。胎儿缺氧会带来潜在风险，导致畸形、先天缺陷甚至死亡。胎儿心动图（CTG）监测是可以检测包括缺氧在内的任何胎儿窘迫迹象的技术之一。由于解释该检测结果至关重要，因此必须在进行这些检测的同时采用不断发展的可用技术，将缺氧情况分为三种情况：正常、可疑或病理。此外，机器学习（ML）是一种不断发展的技术，它有助于医学研究，尤其是胎儿健康预测。尽管医疗机构过去一直在努力检测胎儿缺氧，但通过高效、准确地处理大型数据集中的复杂模式，采用 ML 和深度学习（DL）技术可确保更及时、更准确地检测胎儿缺氧。因此，本综述旨在探讨人工智能模型在心动图检测数据中的应用。本综述的预期成果是为今后的研究提供指导，以提高检测可疑类病例的准确性，这也是以往研究中遇到的挑战，对产科医生有效监测胎儿健康和做出明智决策具有重要意义。
{"title":"Fetal Hypoxia Detection Using Machine Learning: A Narrative Review","authors":"Nawaf Alharbi, Mustafa Youldash, Duha Alotaibi, Haya Aldossary, Reema Albrahim, Reham Alzahrani, Wahbia Ahmed Saleh, S. O. Olatunji, May Issa Aldossary","doi":"10.3390/ai5020026","DOIUrl":"https://doi.org/10.3390/ai5020026","url":null,"abstract":"Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to the critical importance of interpreting the results of this test, it is essential to accompany these tests with the evolving available technology to classify cases of hypoxia into three cases: normal, suspicious, or pathological. Furthermore, Machine Learning (ML) is a blossoming technique constantly developing and aiding in medical studies, particularly fetal health prediction. Notwithstanding the past endeavors of health providers to detect hypoxia in fetuses, implementing ML and Deep Learning (DL) techniques ensures more timely and precise detection of fetal hypoxia by efficiently and accurately processing complex patterns in large datasets. Correspondingly, this review paper aims to explore the application of artificial intelligence models using cardiotocographic test data. The anticipated outcome of this review is to introduce guidance for future studies to enhance accuracy in detecting cases categorized within the suspicious class, an aspect that has encountered challenges in previous studies that holds significant implications for obstetricians in effectively monitoring fetal health and making informed decisions.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140707479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of artificial intelligence (AI) applications in a growing number of domains in recent years has put into focus the ethical, legal, and societal aspects (ELSA) of these technologies and the relevant challenges they pose. In this paper, we propose an ELSA curriculum for data scientists aiming to raise awareness about ELSA challenges in their work, provide them with a common language with the relevant domain experts in order to cooperate to find appropriate solutions, and finally, incorporate ELSA in the data science workflow. ELSA should not be seen as an impediment or a superfluous artefact but rather as an integral part of the Data Science Project Lifecycle. The proposed curriculum uses the CRISP-DM (CRoss-Industry Standard Process for Data Mining) model as a backbone to define a vertical partition expressed in modules corresponding to the CRISP-DM phases. The horizontal partition includes knowledge units belonging to three strands that run through the phases, namely ethical and societal, legal and technical rendering knowledge units (KUs). In addition to the detailed description of the aforementioned KUs, we also discuss their implementation, issues such as duration, form, and evaluation of participants, as well as the variance of the knowledge level and needs of the target audience.
{"title":"Towards an ELSA Curriculum for Data Scientists","authors":"M. Christoforaki, O. Beyan","doi":"10.3390/ai5020025","DOIUrl":"https://doi.org/10.3390/ai5020025","url":null,"abstract":"The use of artificial intelligence (AI) applications in a growing number of domains in recent years has put into focus the ethical, legal, and societal aspects (ELSA) of these technologies and the relevant challenges they pose. In this paper, we propose an ELSA curriculum for data scientists aiming to raise awareness about ELSA challenges in their work, provide them with a common language with the relevant domain experts in order to cooperate to find appropriate solutions, and finally, incorporate ELSA in the data science workflow. ELSA should not be seen as an impediment or a superfluous artefact but rather as an integral part of the Data Science Project Lifecycle. The proposed curriculum uses the CRISP-DM (CRoss-Industry Standard Process for Data Mining) model as a backbone to define a vertical partition expressed in modules corresponding to the CRISP-DM phases. The horizontal partition includes knowledge units belonging to three strands that run through the phases, namely ethical and societal, legal and technical rendering knowledge units (KUs). In addition to the detailed description of the aforementioned KUs, we also discuss their implementation, issues such as duration, form, and evaluation of participants, as well as the variance of the knowledge level and needs of the target audience.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Salman Ibne Eunus, Shahriar Hossain, A. E. M. Ridwan, Ashik Adnan, Md. Saiful Islam, Dewan Ziaul Karim, Golam Rabiul Alam, Jia Uddin
Accidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual detection of such faults periodically can be both time-consuming and costly. In this paper, we have proposed a Deep Learning (DL)-based algorithm for automatic fault detection in railway tracks, which we termed an Ensembled Convolutional Autoencoder ResNet-based Recurrent Neural Network (ECARRNet). We compared its output with existing DL techniques in the form of several pre-trained DL models to investigate railway tracks and determine whether they are defective or not while considering commonly prevalent faults such as—defects in rails and fasteners. Moreover, we manually collected the images from different railway tracks situated in Bangladesh and made our dataset. After comparing our proposed model with the existing models, we found that our proposed architecture has produced the highest accuracy among all the previously existing state-of-the-art (SOTA) architecture, with an accuracy of 93.28% on the full dataset. Additionally, we split our dataset into two parts having two different types of faults, which are fasteners and rails. We ran the models on those two separate datasets, obtaining accuracies of 98.59% and 92.06% on rail and fastener, respectively. Model explainability techniques like Grad-CAM and LIME were used to validate the result of the models, where our proposed model ECARRNet was seen to correctly classify and detect the regions of faulty railways effectively compared to the previously existing transfer learning models.
{"title":"ECARRNet: An Efficient LSTM-Based Ensembled Deep Neural Network Architecture for Railway Fault Detection","authors":"Salman Ibne Eunus, Shahriar Hossain, A. E. M. Ridwan, Ashik Adnan, Md. Saiful Islam, Dewan Ziaul Karim, Golam Rabiul Alam, Jia Uddin","doi":"10.3390/ai5020024","DOIUrl":"https://doi.org/10.3390/ai5020024","url":null,"abstract":"Accidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual detection of such faults periodically can be both time-consuming and costly. In this paper, we have proposed a Deep Learning (DL)-based algorithm for automatic fault detection in railway tracks, which we termed an Ensembled Convolutional Autoencoder ResNet-based Recurrent Neural Network (ECARRNet). We compared its output with existing DL techniques in the form of several pre-trained DL models to investigate railway tracks and determine whether they are defective or not while considering commonly prevalent faults such as—defects in rails and fasteners. Moreover, we manually collected the images from different railway tracks situated in Bangladesh and made our dataset. After comparing our proposed model with the existing models, we found that our proposed architecture has produced the highest accuracy among all the previously existing state-of-the-art (SOTA) architecture, with an accuracy of 93.28% on the full dataset. Additionally, we split our dataset into two parts having two different types of faults, which are fasteners and rails. We ran the models on those two separate datasets, obtaining accuracies of 98.59% and 92.06% on rail and fastener, respectively. Model explainability techniques like Grad-CAM and LIME were used to validate the result of the models, where our proposed model ECARRNet was seen to correctly classify and detect the regions of faulty railways effectively compared to the previously existing transfer learning models.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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