Ramu Shankarappa, Nandini Prasad, R. R. Guddeti, Biju R. Mohan
Nowadays, online examination (exam in short) platforms are becoming more popular, demanding strong security measures for digital learning environments. This includes addressing key challenges such as head pose detection and estimation, which are integral for applications like automatic face recognition, advanced surveillance systems, intuitive human–computer interfaces, and enhancing driving safety measures. The proposed work holds significant potential in enhancing the security and reliability of online exam platforms. It achieves this by accurately classifying students’ attentiveness based on distinct head poses, a novel approach that leverages advanced techniques like federated learning and deep learning models. The proposed work aims to classify students’ attentiveness with the help of different head poses. In this work, we considered five head poses: front face, down face, right face, up face, and left face. A federated learning (FL) framework with a pre-trained deep learning model (ResNet50) was used to accomplish the classification task. To classify students’ activity (behavior) in an online exam environment using the FL framework’s local client device, we considered the ResNet50 model. However, identifying the best hyperparameters in the local client ResNet50 model is challenging. Hence, in this study, we proposed two hybrid bio-inspired optimized methods, namely, Particle Swarm Optimization with Genetic Algorithm (PSOGA) and Particle Swarm Optimization with Elitist Genetic Algorithm (PSOEGA), to fine-tune the hyperparameters of the ResNet50 model. The bio-inspired optimized methods employed in the ResNet50 model will train and classify the students’ behavior in an online exam environment. The FL framework trains the client model locally and sends the updated weights to the server model. The proposed hybrid bio-inspired algorithms outperform the GA and PSO when independently used. The proposed PSOGA not only outperforms the proposed PSOEGA but also outperforms the benchmark algorithms considered for performance evaluation by giving an accuracy of 95.97%.
{"title":"Bio-Inspired Hyperparameter Tuning of Federated Learning for Student Activity Recognition in Online Exam Environment","authors":"Ramu Shankarappa, Nandini Prasad, R. R. Guddeti, Biju R. Mohan","doi":"10.3390/ai5030051","DOIUrl":"https://doi.org/10.3390/ai5030051","url":null,"abstract":"Nowadays, online examination (exam in short) platforms are becoming more popular, demanding strong security measures for digital learning environments. This includes addressing key challenges such as head pose detection and estimation, which are integral for applications like automatic face recognition, advanced surveillance systems, intuitive human–computer interfaces, and enhancing driving safety measures. The proposed work holds significant potential in enhancing the security and reliability of online exam platforms. It achieves this by accurately classifying students’ attentiveness based on distinct head poses, a novel approach that leverages advanced techniques like federated learning and deep learning models. The proposed work aims to classify students’ attentiveness with the help of different head poses. In this work, we considered five head poses: front face, down face, right face, up face, and left face. A federated learning (FL) framework with a pre-trained deep learning model (ResNet50) was used to accomplish the classification task. To classify students’ activity (behavior) in an online exam environment using the FL framework’s local client device, we considered the ResNet50 model. However, identifying the best hyperparameters in the local client ResNet50 model is challenging. Hence, in this study, we proposed two hybrid bio-inspired optimized methods, namely, Particle Swarm Optimization with Genetic Algorithm (PSOGA) and Particle Swarm Optimization with Elitist Genetic Algorithm (PSOEGA), to fine-tune the hyperparameters of the ResNet50 model. The bio-inspired optimized methods employed in the ResNet50 model will train and classify the students’ behavior in an online exam environment. The FL framework trains the client model locally and sends the updated weights to the server model. The proposed hybrid bio-inspired algorithms outperform the GA and PSO when independently used. The proposed PSOGA not only outperforms the proposed PSOEGA but also outperforms the benchmark algorithms considered for performance evaluation by giving an accuracy of 95.97%.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141716012","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}
Modern production relies on data-based analytics for the prediction and optimization of production processes. Specialized data scientists perform tasks at companies and research institutions, dealing with real data from actual production environments. The roles of data preprocessing and data quality are crucial in data science, and an active research field deals with methodologies and technologies for this. While anecdotes and generalized surveys indicate preprocessing is the major operational task for data scientists, a detailed view of the subtasks and the domain of production data is missing. In this paper, we present a multi-stage survey on data science tasks in practice in the field of production. Using expert knowledge and insights, we found data preprocessing to be the major part of the tasks of data scientists. In detail, we found that tackling missing values, finding data point meanings, and synchronization of multiple time-series were often the most time-consuming preprocessing tasks.
{"title":"Inside Production Data Science: Exploring the Main Tasks of Data Scientists in Production Environments","authors":"A. Schmetz, A. Kampker","doi":"10.3390/ai5020043","DOIUrl":"https://doi.org/10.3390/ai5020043","url":null,"abstract":"Modern production relies on data-based analytics for the prediction and optimization of production processes. Specialized data scientists perform tasks at companies and research institutions, dealing with real data from actual production environments. The roles of data preprocessing and data quality are crucial in data science, and an active research field deals with methodologies and technologies for this. While anecdotes and generalized surveys indicate preprocessing is the major operational task for data scientists, a detailed view of the subtasks and the domain of production data is missing. In this paper, we present a multi-stage survey on data science tasks in practice in the field of production. Using expert knowledge and insights, we found data preprocessing to be the major part of the tasks of data scientists. In detail, we found that tackling missing values, finding data point meanings, and synchronization of multiple time-series were often the most time-consuming preprocessing tasks.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141352208","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}
Serhii Postupaiev, R. Damaševičius, R. Maskeliūnas
Using instance segmentation and video inpainting provides a significant leap in real-time football video broadcast enhancements by removing potential visual distractions, such as an occasional person or another object accidentally occupying the frame. Despite its relevance and importance in the media industry, this area remains challenging and relatively understudied, thus offering potential for research. Specifically, the segmentation and inpainting of camera operator instances from video remains an underexplored research area. To address this challenge, this paper proposes a framework designed to accurately detect and remove camera operators while seamlessly hallucinating the background in real-time football broadcasts. The approach aims to enhance the quality of the broadcast by maintaining its consistency and level of engagement to retain and attract users during the game. To implement the inpainting task, firstly, the camera operators instance segmentation method should be developed. We used a YOLOv8 model for accurate real-time operator instance segmentation. The resulting model produces masked frames, which are used for further camera operator inpainting. Moreover, this paper presents an extensive “Cameramen Instances” dataset with more than 7500 samples, which serves as a solid foundation for future investigations in this area. The experimental results show that the YOLOv8 model performs better than other baseline algorithms in different scenarios. The precision of 95.5%, recall of 92.7%, mAP50-95 of 79.6, and a high FPS rate of 87 in low-volume environment prove the solution efficacy for real-time applications.
{"title":"Real-Time Camera Operator Segmentation with YOLOv8 in Football Video Broadcasts","authors":"Serhii Postupaiev, R. Damaševičius, R. Maskeliūnas","doi":"10.3390/ai5020042","DOIUrl":"https://doi.org/10.3390/ai5020042","url":null,"abstract":"Using instance segmentation and video inpainting provides a significant leap in real-time football video broadcast enhancements by removing potential visual distractions, such as an occasional person or another object accidentally occupying the frame. Despite its relevance and importance in the media industry, this area remains challenging and relatively understudied, thus offering potential for research. Specifically, the segmentation and inpainting of camera operator instances from video remains an underexplored research area. To address this challenge, this paper proposes a framework designed to accurately detect and remove camera operators while seamlessly hallucinating the background in real-time football broadcasts. The approach aims to enhance the quality of the broadcast by maintaining its consistency and level of engagement to retain and attract users during the game. To implement the inpainting task, firstly, the camera operators instance segmentation method should be developed. We used a YOLOv8 model for accurate real-time operator instance segmentation. The resulting model produces masked frames, which are used for further camera operator inpainting. Moreover, this paper presents an extensive “Cameramen Instances” dataset with more than 7500 samples, which serves as a solid foundation for future investigations in this area. The experimental results show that the YOLOv8 model performs better than other baseline algorithms in different scenarios. The precision of 95.5%, recall of 92.7%, mAP50-95 of 79.6, and a high FPS rate of 87 in low-volume environment prove the solution efficacy for real-time applications.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376808","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}
Sanjana Banerjee, J. Reynolds, Matt Taggart, Michael Daniele, Alper Bozkurt, Edgar J. Lobaton
Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.
{"title":"Quantifying Visual Differences in Drought-Stressed Maize through Reflectance and Data-Driven Analysis","authors":"Sanjana Banerjee, J. Reynolds, Matt Taggart, Michael Daniele, Alper Bozkurt, Edgar J. Lobaton","doi":"10.3390/ai5020040","DOIUrl":"https://doi.org/10.3390/ai5020040","url":null,"abstract":"Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266082","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}
This study explores the progress of chatbot technology, focusing on the aspect of error correction to enhance these smart conversational tools. Chatbots, powered by artificial intelligence (AI), are increasingly prevalent across industries such as customer service, healthcare, e-commerce, and education. Despite their use and increasing complexity, chatbots are prone to errors like misunderstandings, inappropriate responses, and factual inaccuracies. These issues can have an impact on user satisfaction and trust. This research provides an overview of chatbots, conducts an analysis of errors they encounter, and examines different approaches to rectifying these errors. These approaches include using data-driven feedback loops, involving humans in the learning process, and adjusting through learning methods like reinforcement learning, supervised learning, unsupervised learning, semi-supervised learning, and meta-learning. Through real life examples and case studies in different fields, we explore how these strategies are implemented. Looking ahead, we explore the different challenges faced by AI-powered chatbots, including ethical considerations and biases during implementation. Furthermore, we explore the transformative potential of new technological advancements, such as explainable AI models, autonomous content generation algorithms (e.g., generative adversarial networks), and quantum computing to enhance chatbot training. Our research provides information for developers and researchers looking to improve chatbot capabilities, which can be applied in service and support industries to effectively address user requirements.
{"title":"Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots","authors":"Saadat Izadi, Mohamad Forouzanfar","doi":"10.3390/ai5020041","DOIUrl":"https://doi.org/10.3390/ai5020041","url":null,"abstract":"This study explores the progress of chatbot technology, focusing on the aspect of error correction to enhance these smart conversational tools. Chatbots, powered by artificial intelligence (AI), are increasingly prevalent across industries such as customer service, healthcare, e-commerce, and education. Despite their use and increasing complexity, chatbots are prone to errors like misunderstandings, inappropriate responses, and factual inaccuracies. These issues can have an impact on user satisfaction and trust. This research provides an overview of chatbots, conducts an analysis of errors they encounter, and examines different approaches to rectifying these errors. These approaches include using data-driven feedback loops, involving humans in the learning process, and adjusting through learning methods like reinforcement learning, supervised learning, unsupervised learning, semi-supervised learning, and meta-learning. Through real life examples and case studies in different fields, we explore how these strategies are implemented. Looking ahead, we explore the different challenges faced by AI-powered chatbots, including ethical considerations and biases during implementation. Furthermore, we explore the transformative potential of new technological advancements, such as explainable AI models, autonomous content generation algorithms (e.g., generative adversarial networks), and quantum computing to enhance chatbot training. Our research provides information for developers and researchers looking to improve chatbot capabilities, which can be applied in service and support industries to effectively address user requirements.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266858","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}
Zachery Born, M. Mundt, A. Mian, Jason Weber, J. Alderson
The ability to overcome an opposition in team sports is reliant upon an understanding of the tactical behaviour of the opposing team members. Recent research is limited to a performance analysts’ own playing team members, as the required opposing team athletes’ geolocation (GPS) data are unavailable. However, in professional Australian rules Football (AF), animations of athlete GPS data from all teams are commercially available. The purpose of this technical study was to obtain the on-field location of AF athletes from animations of the 2019 Australian Football League season to enable the examination of the tactical behaviour of any team. The pre-trained object detection model YOLOv4 was fine-tuned to detect players, and a custom convolutional neural network was trained to track numbers in the animations. The object detection and the athlete tracking achieved an accuracy of 0.94 and 0.98, respectively. Subsequent scaling and translation coefficients were determined through solving an optimisation problem to transform the pixel coordinate positions of a tracked player number to field-relative Cartesian coordinates. The derived equations achieved an average Euclidean distance from the athletes’ raw GPS data of 2.63 m. The proposed athlete detection and tracking approach is a novel methodology to obtain the on-field positions of AF athletes in the absence of direct measures, which may be used for the analysis of opposition collective team behaviour and in the development of interactive play sketching AF tools.
在团队运动中，战胜对手的能力取决于对对方队员战术行为的了解。最近的研究仅限于成绩分析师自己的队员，因为无法获得所需的对方球队运动员的地理定位（GPS）数据。然而，在职业澳式足球（AF）中，所有球队运动员的 GPS 数据动画均可通过商业途径获得。本技术研究的目的是从 2019 澳式足球联赛赛季的动画中获取澳式足球运动员的场上位置，以便对任何球队的战术行为进行研究。我们对预先训练好的物体检测模型 YOLOv4 进行了微调，以检测球员，并训练了一个自定义卷积神经网络来追踪动画中的数字。物体检测和运动员追踪的准确率分别达到了 0.94 和 0.98。随后的缩放和平移系数是通过求解优化问题来确定的，以将追踪到的球员号码的像素坐标位置转换为场相关笛卡尔坐标。所得出的方程与运动员的原始 GPS 数据之间的平均欧氏距离为 2.63 米。所提出的运动员检测和跟踪方法是一种在缺乏直接测量手段的情况下获取 AF 运动员场上位置的新方法，可用于分析对手的集体团队行为，以及开发互动式比赛草图 AF 工具。
{"title":"The Eye in the Sky—A Method to Obtain On-Field Locations of Australian Rules Football Athletes","authors":"Zachery Born, M. Mundt, A. Mian, Jason Weber, J. Alderson","doi":"10.3390/ai5020038","DOIUrl":"https://doi.org/10.3390/ai5020038","url":null,"abstract":"The ability to overcome an opposition in team sports is reliant upon an understanding of the tactical behaviour of the opposing team members. Recent research is limited to a performance analysts’ own playing team members, as the required opposing team athletes’ geolocation (GPS) data are unavailable. However, in professional Australian rules Football (AF), animations of athlete GPS data from all teams are commercially available. The purpose of this technical study was to obtain the on-field location of AF athletes from animations of the 2019 Australian Football League season to enable the examination of the tactical behaviour of any team. The pre-trained object detection model YOLOv4 was fine-tuned to detect players, and a custom convolutional neural network was trained to track numbers in the animations. The object detection and the athlete tracking achieved an accuracy of 0.94 and 0.98, respectively. Subsequent scaling and translation coefficients were determined through solving an optimisation problem to transform the pixel coordinate positions of a tracked player number to field-relative Cartesian coordinates. The derived equations achieved an average Euclidean distance from the athletes’ raw GPS data of 2.63 m. The proposed athlete detection and tracking approach is a novel methodology to obtain the on-field positions of AF athletes in the absence of direct measures, which may be used for the analysis of opposition collective team behaviour and in the development of interactive play sketching AF tools.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968199","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}
Samar AboulEla, Nourhan Ibrahim, Sarama Shehmir, Aman Yadav, Rasha F. Kashef
The Internet of Things (IoT) is seeing significant growth, as the quantity of interconnected devices in communication networks is on the rise. The increased connectivity of devices has heightened their susceptibility to hackers, underscoring the need to safeguard IoT devices. This research investigates cybersecurity in the context of the Internet of Medical Things (IoMT), which encompasses the cybersecurity mechanisms used for various healthcare devices connected to the system. This study seeks to provide a concise overview of several artificial intelligence (AI)-based methodologies and techniques, as well as examining the associated solution approaches used in cybersecurity for healthcare systems. The analyzed methodologies are further categorized into four groups: machine learning (ML) techniques, deep learning (DL) techniques, a combination of ML and DL techniques, Transformer-based techniques, and other state-of-the-art techniques, including graph-based methods and blockchain methods. In addition, this article presents a detailed description of the benchmark datasets that are recommended for use in intrusion detection systems (IDS) for both IoT and IoMT networks. Moreover, a detailed description of the primary evaluation metrics used in the analysis of the discussed models is provided. Ultimately, this study thoroughly examines and analyzes the features and practicality of several cybersecurity models, while also emphasizing recent research directions.
{"title":"Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems","authors":"Samar AboulEla, Nourhan Ibrahim, Sarama Shehmir, Aman Yadav, Rasha F. Kashef","doi":"10.3390/ai5020037","DOIUrl":"https://doi.org/10.3390/ai5020037","url":null,"abstract":"The Internet of Things (IoT) is seeing significant growth, as the quantity of interconnected devices in communication networks is on the rise. The increased connectivity of devices has heightened their susceptibility to hackers, underscoring the need to safeguard IoT devices. This research investigates cybersecurity in the context of the Internet of Medical Things (IoMT), which encompasses the cybersecurity mechanisms used for various healthcare devices connected to the system. This study seeks to provide a concise overview of several artificial intelligence (AI)-based methodologies and techniques, as well as examining the associated solution approaches used in cybersecurity for healthcare systems. The analyzed methodologies are further categorized into four groups: machine learning (ML) techniques, deep learning (DL) techniques, a combination of ML and DL techniques, Transformer-based techniques, and other state-of-the-art techniques, including graph-based methods and blockchain methods. In addition, this article presents a detailed description of the benchmark datasets that are recommended for use in intrusion detection systems (IDS) for both IoT and IoMT networks. Moreover, a detailed description of the primary evaluation metrics used in the analysis of the discussed models is provided. Ultimately, this study thoroughly examines and analyzes the features and practicality of several cybersecurity models, while also emphasizing recent research directions.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140973880","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}
Aditya Singh, Kislay Raj, Teerath Meghwar, Arunabha M. Roy
Paddy (Oryza sativa) is one of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much effort and expertise. The grain quality of the product is heavily affected by the weather conditions, irrigation frequency, and many other factors. However, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for rice grain quality assessment, noting that the key characteristics of paddy and rice are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliable ML-based IoT paddy grain quality assessment system utilizing affordable sensors. It involves a specific data collection procedure followed by image processing with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios. To our knowledge, it is the first automated system to precisely provide an overall quality metric. The main feature of our system is its explainability in terms of utilized features and fuzzy rules, which increases the confidence and trustworthiness of the public toward its use. The grain variety used for experiments majorly belonged to the Indian Subcontinent, but it covered a significant variation in the shape and size of the grain.
水稻（Oryza sativa）是世界上消耗量最大的粮食作物之一。从播种到收割、加工、储存和管理等消费过程都需要大量的努力和专业知识。谷物的质量在很大程度上受天气条件、灌溉频率和许多其他因素的影响。然而，质量控制极为重要，因此，对谷物质量进行评估是必要的。鉴于其必要性和艰巨性，我们尝试使用图像处理和机器学习（ML）技术来克服谷物质量评价的局限性和缺陷。由于水稻和大米的主要特征不同，现有的大多数方法都是针对稻谷品质评估而设计的。此外，这些方法的设置复杂且昂贵，并使用黑盒子 ML 模型。为了解决这些问题，我们在本文中提出了一种可靠的基于 ML 的物联网稻谷质量评估系统，该系统利用了经济实惠的传感器。该系统包括一个特定的数据收集程序，然后利用基于 ML 的模型进行图像处理，以预测质量。该系统使用不同的可解释特征对稻谷质量进行分类，如稻谷的形状、大小、水分和成熟度。该系统的精确度在实际场景中进行了测试。据我们所知，这是首个能够精确提供整体质量指标的自动化系统。我们系统的主要特点是利用特征和模糊规则进行解释，这增加了公众对其使用的信心和可信度。用于实验的谷物品种主要属于印度次大陆，但谷物的形状和大小差异很大。
{"title":"Efficient Paddy Grain Quality Assessment Approach Utilizing Affordable Sensors","authors":"Aditya Singh, Kislay Raj, Teerath Meghwar, Arunabha M. Roy","doi":"10.3390/ai5020036","DOIUrl":"https://doi.org/10.3390/ai5020036","url":null,"abstract":"Paddy (Oryza sativa) is one of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much effort and expertise. The grain quality of the product is heavily affected by the weather conditions, irrigation frequency, and many other factors. However, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for rice grain quality assessment, noting that the key characteristics of paddy and rice are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliable ML-based IoT paddy grain quality assessment system utilizing affordable sensors. It involves a specific data collection procedure followed by image processing with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios. To our knowledge, it is the first automated system to precisely provide an overall quality metric. The main feature of our system is its explainability in terms of utilized features and fuzzy rules, which increases the confidence and trustworthiness of the public toward its use. The grain variety used for experiments majorly belonged to the Indian Subcontinent, but it covered a significant variation in the shape and size of the grain.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981227","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}
Faisal Ramzan, Claudio Sartori, Sergio Consoli, Diego Reforgiato Recupero
Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to the inclusion of sensitive information. Additionally, authentic data may be scarce, particularly in specialized domains where acquiring ample, varied, and high-quality data is difficult or costly. This scarcity or limited data availability can limit the training and testing of machine-learning models. In this paper, we address this challenge. In particular, our task is to synthesize a dataset with similar properties to an input dataset about the stock market. The input dataset is anonymized and consists of very few columns and rows, contains many inconsistencies, such as missing rows and duplicates, and its values are not normalized, scaled, or balanced. We explore the utilization of generative adversarial networks, a deep-learning technique, to generate synthetic data and evaluate its quality compared to the input stock dataset. Our innovation involves generating artificial datasets that mimic the statistical properties of the input elements without revealing complete information. For example, synthetic datasets can capture the distribution of stock prices, trading volumes, and market trends observed in the original dataset. The generated datasets cover a wider range of scenarios and variations, enabling researchers and practitioners to explore different market conditions and investment strategies. This diversity can enhance the robustness and generalization of machine-learning models. We evaluate our synthetic data in terms of the mean, similarities, and correlations.
{"title":"Generative Adversarial Networks for Synthetic Data Generation in Finance: Evaluating Statistical Similarities and Quality Assessment","authors":"Faisal Ramzan, Claudio Sartori, Sergio Consoli, Diego Reforgiato Recupero","doi":"10.3390/ai5020035","DOIUrl":"https://doi.org/10.3390/ai5020035","url":null,"abstract":"Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to the inclusion of sensitive information. Additionally, authentic data may be scarce, particularly in specialized domains where acquiring ample, varied, and high-quality data is difficult or costly. This scarcity or limited data availability can limit the training and testing of machine-learning models. In this paper, we address this challenge. In particular, our task is to synthesize a dataset with similar properties to an input dataset about the stock market. The input dataset is anonymized and consists of very few columns and rows, contains many inconsistencies, such as missing rows and duplicates, and its values are not normalized, scaled, or balanced. We explore the utilization of generative adversarial networks, a deep-learning technique, to generate synthetic data and evaluate its quality compared to the input stock dataset. Our innovation involves generating artificial datasets that mimic the statistical properties of the input elements without revealing complete information. For example, synthetic datasets can capture the distribution of stock prices, trading volumes, and market trends observed in the original dataset. The generated datasets cover a wider range of scenarios and variations, enabling researchers and practitioners to explore different market conditions and investment strategies. This diversity can enhance the robustness and generalization of machine-learning models. We evaluate our synthetic data in terms of the mean, similarities, and correlations.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140986057","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}
Stefanos Balaskas, Maria Koutroumani, Maria Rigou, S. Sirmakessis
Blood donation heavily depends on voluntary involvement, but the problem of motivating and retaining potential blood donors remains. Understanding the personality traits of donors can assist in this case, bridging communication gaps and increasing participation and retention. To this end, an eye-tracking experiment was designed to examine the viewing behavior of 75 participants as they viewed various blood donation-related advertisements. The purpose of these stimuli was to elicit various types of emotions (positive/negative) and message framings (altruistic/egoistic) to investigate cognitive reactions that arise from donating blood using eye-tracking parameters such as the fixation duration, fixation count, saccade duration, and saccade amplitude. The results indicated significant differences among the eye-tracking metrics, suggesting that visual engagement varies considerably in response to different types of advertisements. The fixation duration also revealed substantial differences in emotions, logo types, and emotional arousal, suggesting that the nature of stimuli can affect how viewers disperse their attention. The saccade amplitude and saccade duration were also affected by the message framings, thus indicating their relevance to eye movement behavior. Generalised linear models (GLMs) showed significant influences of personality trait effects on eye-tracking metrics, including a negative association between honesty–humility and fixation duration and a positive link between openness and both the saccade duration and fixation count. These results indicate that personality traits can significantly impact visual attention processes. The present study broadens the current research frontier by employing machine learning techniques on the collected eye-tracking data to identify personality traits that can influence donation decisions and experiences. Participants’ eye movements were analysed to categorize their dominant personality traits using hierarchical clustering, while machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbours (KNN), were employed to predict personality traits. Among the models, SVM and KNN exhibited high accuracy (86.67%), while Random Forest scored considerably lower (66.67%). This investigation reveals that computational models can infer personality traits from eye movements, which shows great potential for psychological profiling and human–computer interaction. This study integrates psychology research and machine learning, paving the way for further studies on personality assessment by eye tracking.
{"title":"From Eye Movements to Personality Traits: A Machine Learning Approach in Blood Donation Advertising","authors":"Stefanos Balaskas, Maria Koutroumani, Maria Rigou, S. Sirmakessis","doi":"10.3390/ai5020034","DOIUrl":"https://doi.org/10.3390/ai5020034","url":null,"abstract":"Blood donation heavily depends on voluntary involvement, but the problem of motivating and retaining potential blood donors remains. Understanding the personality traits of donors can assist in this case, bridging communication gaps and increasing participation and retention. To this end, an eye-tracking experiment was designed to examine the viewing behavior of 75 participants as they viewed various blood donation-related advertisements. The purpose of these stimuli was to elicit various types of emotions (positive/negative) and message framings (altruistic/egoistic) to investigate cognitive reactions that arise from donating blood using eye-tracking parameters such as the fixation duration, fixation count, saccade duration, and saccade amplitude. The results indicated significant differences among the eye-tracking metrics, suggesting that visual engagement varies considerably in response to different types of advertisements. The fixation duration also revealed substantial differences in emotions, logo types, and emotional arousal, suggesting that the nature of stimuli can affect how viewers disperse their attention. The saccade amplitude and saccade duration were also affected by the message framings, thus indicating their relevance to eye movement behavior. Generalised linear models (GLMs) showed significant influences of personality trait effects on eye-tracking metrics, including a negative association between honesty–humility and fixation duration and a positive link between openness and both the saccade duration and fixation count. These results indicate that personality traits can significantly impact visual attention processes. The present study broadens the current research frontier by employing machine learning techniques on the collected eye-tracking data to identify personality traits that can influence donation decisions and experiences. Participants’ eye movements were analysed to categorize their dominant personality traits using hierarchical clustering, while machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbours (KNN), were employed to predict personality traits. Among the models, SVM and KNN exhibited high accuracy (86.67%), while Random Forest scored considerably lower (66.67%). This investigation reveals that computational models can infer personality traits from eye movements, which shows great potential for psychological profiling and human–computer interaction. This study integrates psychology research and machine learning, paving the way for further studies on personality assessment by eye tracking.","PeriodicalId":503525,"journal":{"name":"AI","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140991046","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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