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Towards Unsupervised Learning Driven Intelligence for Prediction of Prostate Cancer 利用无监督学习驱动的智能预测前列腺癌
Pub Date : 2024-07-24 DOI: 10.47852/bonviewaia42022210
Ejay Esugbe
Prostate cancer is a widespread and global disease which affects adult males – it is said that key causes of the cancer include age, family history and ethnicity. In this study, the Kaggle prostate cancer dataset, comprising of data of 100 patients with a mixture that both had cancer and did not have cancer, was used alongside machine learning prediction models for the design of unsupervised and automated intelligent systems for the prediction of prostate cancer. Two intelligent systems were designed and underpinned by unsupervised learning algorithms, namely, fuzzy c-means and agglomerative hierarchical clustering, where the various intelligent systems were able to make a prostate cancer prediction with accuracies of over 80% for the various classification metrics, alongside being able to predict an associated stage of the prostate cancer. Both designed intelligent systems offer a complimentary alternative to each other, and their relative merits are discussed in the paper. ry alternative to each other, and their relative merits are discussed in the paper.
前列腺癌是一种普遍存在的全球性疾病,主要影响成年男性--据说癌症的主要诱因包括年龄、家族史和种族。在这项研究中,Kaggle前列腺癌数据集由100名既患癌症又未患癌症的混合患者的数据组成,该数据集与机器学习预测模型一起用于设计用于预测前列腺癌的无监督自动化智能系统。设计了两个智能系统,并以无监督学习算法为基础,即模糊 c-means 和聚类分层聚类,其中各种智能系统能够预测前列腺癌,各种分类指标的准确率超过 80%,同时还能预测前列腺癌的相关分期。本文讨论了这两种设计的智能系统的优点和相对优势。
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引用次数: 0
A Novel Ensemble Deep Learning Based Polyp Detection Using Colonoscopy Dataset 使用结肠镜数据集进行基于深度学习的新型息肉检测
Pub Date : 2024-07-15 DOI: 10.47852/bonviewaia42022549
Sai Rakshana K., Antony Dennis Ananth, Gowri L.
This work addresses the critical task of polyp detection and classification using the SUN colonoscopy video database, which consists of still images annotated with bounding boxes. These images categorize frames into polyp and non-polyp and encompass six distinct classes of polyps: Hyperplastic polyp, Sessile serrated lesion, Low-grade adenoma, Traditional serrated adenoma, High-grade adenoma, and Invasive carcinoma. The approach involves a two-stage classification process. Initially, MobileNetV2 is employed to distinguish between polyp and non-polyp frames. Subsequently, ResNet50 and GoogLeNet are utilized to classify the identified polyps into the six predefined categories. Data augmentation techniques are implemented to address the inherent imbalance in class distribution within the dataset, enhancing model performance and generalizability. The results highlight the effectiveness of GoogLeNet, which achieved an impressive accuracy of 98%, significantly outperforming ResNet50's accuracy of 76.16%. This substantial improvement underscores the potential of GoogLeNet in enhancing the accuracy of polyp classification. The significance of this work lies in its contribution to advancing automated polyp detection and cancer stage classification, crucial for early diagnosis and treatment. These findings provide a foundation for further research and development in this domain, with the potential to improve clinical outcomes through more accurate and timely identification of colorectal polyps.
这项研究利用 SUN 结肠镜检查视频数据库来完成息肉检测和分类的关键任务,该数据库由带边界框注释的静态图像组成。这些图像将帧分为息肉和非息肉,并包含六类不同的息肉:增生性息肉、无柄锯齿状病变、低级别腺瘤、传统锯齿状腺瘤、高级别腺瘤和浸润性癌。该方法包括两个阶段的分类过程。首先,采用 MobileNetV2 对息肉和非息肉帧进行区分。随后,利用 ResNet50 和 GoogLeNet 将识别出的息肉分为六个预定义类别。数据增强技术的应用解决了数据集内固有的类别分布不平衡问题,提高了模型的性能和普适性。结果凸显了 GoogLeNet 的有效性,其准确率达到了令人印象深刻的 98%,大大超过了 ResNet50 的 76.16% 的准确率。这一大幅提升凸显了 GoogLeNet 在提高息肉分类准确性方面的潜力。这项工作的意义在于,它有助于推进息肉自动检测和癌症分期分类,这对早期诊断和治疗至关重要。这些发现为这一领域的进一步研究和开发奠定了基础,有望通过更准确、更及时地识别结直肠息肉来改善临床结果。
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引用次数: 0
Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis 利用新型机器学习分类、数据可视化和分析方法预测红葡萄酒的质量
Pub Date : 2024-05-21 DOI: 10.47852/bonviewaia42021999
Jovial Niyogisubizo, Jean de Dieu Ninteretse, Eric Nziyumva, Marc Nshimiyimana, Evariste Murwanashyaka, Erneste Habiyakare
here is a growing concern among consumers and the wine industry regarding the quality of wine. Traditionally, wine experts determined its quality through tasting, which was time-consuming. Therefore, there is a need to predict wine quality based on specific key features to streamline these tasks. Technological developments like machine learning (ML) approaches have replaced human assessments with computational methods. However, some of these methods have faced criticism due to their low accuracy and lack of interpretability for humans. In this paper, a stacking ensemble method is introduced and demonstrates superior predictive performance when compared to other classification techniques like Logistic Regression (LR), Decision Trees (DT), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), and Random Forest (RF). This evaluation is based on classification metrics such as accuracy, precision, recall, and F1-Score, all under the same conditions. Additionally, outlier detection algorithms were employed to identify exceptional or subpar wines, though their results did not match the accuracy of classification approaches. Lastly, a feature analysis study was conducted to assess the significance of each feature in the model's performance.
消费者和葡萄酒行业对葡萄酒质量的关注与日俱增。传统上,葡萄酒专家通过品尝来确定葡萄酒的质量,这非常耗时。因此,有必要根据特定的关键特征来预测葡萄酒的质量,以简化这些任务。机器学习(ML)方法等技术的发展已经用计算方法取代了人工评估。然而,其中一些方法由于准确度低和缺乏可解释性而受到批评。本文介绍了一种堆叠集合方法,与其他分类技术(如逻辑回归(LR)、决策树(DT)、梯度提升(GB)、自适应提升(AdaBoost)和随机森林(RF))相比,该方法显示出卓越的预测性能。该评估基于相同条件下的分类指标,如准确率、精确度、召回率和 F1 分数。此外,还采用了离群点检测算法来识别特殊或不合格的葡萄酒,尽管其结果与分类方法的准确性不符。最后,还进行了特征分析研究,以评估每个特征在模型性能中的重要性。
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引用次数: 0
Soliton Solutions of Some Ocean Waves Supported by Physics Informed Neural Network Method 物理信息神经网络法支持的某些海浪的孤子解决方案
Pub Date : 2024-05-15 DOI: 10.47852/bonviewaia42022277
Ismail Onder, Abdulkadir Sahiner, A. Seçer, Mustafa Bayram
In this study, we aim to obtain numerical results of the modified Benjamin-Bona-Mahony equation, Ostrovsky-Benjamin-Bona-Mahony equation and Mikhailov-Novikov-Wang equation via the physics-informed neural networks (PINN) method. The equations are modeled for shallow and long water waves, as well as fundamental and phenomenonal models in ocean engineering. According to the implementation, we obtained the PINN solutions of kink, bright, multisoliton (two-soliton) and mixed dark-bright soliton solutions. According to the inference from the obtained results, we achieved good results in some cases compared to other approximate solution methods in the literature. However, it was also observed that the best possible results could not be obtained in cases where the soliton type was intricate and layered. While the results were obtained, the number of hidden layers and the number of neural networks in the layers also varied. These results are shown in tables. Since it is known that the aforementioned models are not solved by the PINN method, we anticipate that the study will lead to other studies in the field of ocean engineering.
在本研究中,我们旨在通过物理信息神经网络(PINN)方法获得修正的本杰明-博纳-马霍尼方程、奥斯特洛夫斯基-本杰明-博纳-马霍尼方程和米哈伊洛夫-诺维科夫-王方程的数值结果。这些方程针对浅水和长水波以及海洋工程中的基本模型和现象模型建模。根据实现方法,我们获得了扭结、明亮、多孤子(双孤子)和暗-明混合孤子的 PINN 解。根据所得结果推断,与文献中的其他近似求解方法相比,我们在某些情况下取得了良好的结果。然而,我们也观察到,在孤子类型复杂且分层的情况下,我们无法获得最佳结果。在获得结果的同时,隐藏层的数量和层中神经网络的数量也各不相同。这些结果见表。由于已知上述模型无法用 PINN 方法求解,我们预计这项研究将引发海洋工程领域的其他研究。
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引用次数: 0
Fuzzy-Based Robot Behavior with the Application of Emotional Pattern Generator 基于模糊的机器人行为与情绪模式发生器的应用
Pub Date : 2024-05-13 DOI: 10.47852/bonviewaia42021212
Laura Trautmann, A. Piros, J. Botzheim
The article deals with the development of human–robot interaction, where the robot measures the user’s emotional state as well as environmental factors by different devices, and it responds to the user with colorful patterns and controls the smart home through the guidance of the Internet of Things system. Before the design process, robots and their functions currently on the market, the role of emotions in communication, and technologies for measuring emotion (such as face recognition, measurement of heart rate, breath, and physical changes) are presented in detail. The designed robot (Em-Patty) uses a previously developed emotion-based automatic pattern generation system based on a fuzzy system, which is a suitable tool for handling emotions. A main and three fuzzy subsystems are fusioned in order to create the most efficient control system for Em-Patty. The nature-inspired design of the robot is also presented, as well as a description of its behavior with the help of a specific case study.
文章论述了人机交互的发展,机器人通过不同的设备测量用户的情绪状态以及环境因素,并通过物联网系统的引导,以丰富多彩的图案回应用户,控制智能家居。在设计过程之前,将详细介绍目前市场上的机器人及其功能、情绪在交流中的作用以及测量情绪的技术(如人脸识别、测量心率、呼吸和身体变化等)。所设计的机器人(Em-Patty)使用了之前开发的基于模糊系统的情绪模式自动生成系统,这是一种适合处理情绪的工具。一个主系统和三个模糊子系统融合在一起,为 Em-Patty 创造了最有效的控制系统。此外,还介绍了该机器人的自然启发设计,以及借助具体案例研究对其行为的描述。
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引用次数: 0
Side Collision Detection Model for Visually Impaired Using Monocular Object-specific Distance Estimation and Multimodal Real-World Location Calculation 利用单目特定物体距离估计和多模态真实世界位置计算的视障人士侧面碰撞检测模型
Pub Date : 2024-04-11 DOI: 10.47852/bonviewaia42022098
Wenqing Song, Yumeng Sun, Qixuan Huang, Junyang Cheok
Targeting the potential risk of side-vehicle collisions when the visually impaired crosses roads, this study proposed a side collision detection model, including monocular distance estimation, multimodal real-world location estimation, future location prediction and collision warning strategies tailored for visually impaired pedestrians. The proposed model employs YOLOv8 and DeepSort for vehicle detection and tracking, utilizing shallow neural networks for distance estimation based on image information and vehicle position data. Predicted vehicle distances are combined with magnetic field sensor and GPS data to compute and store real-world vehicle locations, and these location data will be used for linear regression to forecast future locations. A warning strategy is then implemented to alert users. Experimental validation shows that the monocular distance estimation network has an Absolute Relative Error of 0.043 and an ALE (Average Localization Error) of 1.249m. In real-world location estimation, the view angle ALE is 0.019, and the location ALE is 1.778m. Regarding location prediction, the accuracy in distinguishing stationary and moving vehicles reaches 0.962, and the predicted curve, based on ground truth and predicted locations, exhibits good alignment. The proposed warning strategy, evaluated on Kitti Tracking Dataset and a self-created dataset, accurately detects the majority of potential collision risks.
针对视障人士横穿马路时与侧面车辆发生碰撞的潜在风险,本研究提出了一种侧面碰撞检测模型,包括单目距离估计、多模态真实世界位置估计、未来位置预测以及为视障行人量身定制的碰撞预警策略。建议的模型采用 YOLOv8 和 DeepSort 进行车辆检测和跟踪,利用浅层神经网络根据图像信息和车辆位置数据进行距离估计。预测的车辆距离与磁场传感器和 GPS 数据相结合,计算并存储真实世界中的车辆位置,这些位置数据将用于线性回归,以预测未来的位置。然后实施警告策略,提醒用户注意。实验验证表明,单目距离估计网络的绝对相对误差为 0.043,平均定位误差(ALE)为 1.249 米。在真实世界的位置估计中,视角 ALE 为 0.019,位置 ALE 为 1.778 米。在位置预测方面,区分静止和移动车辆的准确度达到 0.962,基于地面实况和预测位置的预测曲线显示出良好的一致性。在 Kitti 跟踪数据集和一个自建数据集上对所提出的预警策略进行了评估,结果表明该策略能准确检测到大多数潜在的碰撞风险。
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引用次数: 0
Exploring Intervention Techniques for Alzheimer's Disease: Conventional Methods and the Role of AI in Advancing Care 探索阿尔茨海默病的干预技术:传统方法和人工智能在促进护理中的作用
Pub Date : 2024-04-07 DOI: 10.47852/bonview42022497
Karthikeyan Subramanian, Faizal Hajamohideen, Vimbi Viswan, Noushath Shaffi, Mufti Mahmud
Alzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive decline and functional impairment. This study compares conventional intervention techniques with emerging artificial intelligence (AI) approaches to AD. Intervention technique refers to a specific method or approach employed to bring about positive change in a particular situation. In the context of AD, such techniques are crucial as they aim to slow down the progression of symptoms, alleviate behavioral challenges, and support patients and their caretakers in managing the complexities of the condition. Conventional intervention techniques, such as cognitive stimulation and reality orientation, have demonstrated benefits in improving cognitive function and emotional well-being. Conventional intervention approaches are widely preferred as they have a proven track record of effectiveness, personalized response, cost-effectiveness, and patient-centered care. Despite these benefits, they are limited by individual variability in response and long-term effectiveness. On the other hand, AI-based approaches such as Computer Vision and Deep Learning (DL) hold the potential to revolutionize Alzheimer's interventions. These technologies offer early detection, personalized care, and remote monitoring capabilities. They can provide tailored interventions, assist decision-making, and enhance caregiver support. Although AI-based interventions face challenges such as data privacy and implementation complexity, their potential to transform Alzheimer's care is significant. This research paper compares conventional and AI-based approaches. It reveals that while traditional techniques are well-established and have proven benefits, AI-based interventions offer novel opportunities for personalized and advanced care. Combining the strengths of both approaches may lead to more comprehensive and effective interventions for individuals with AD. Continued research and collaboration are crucial to harness the full potential of AI in improving Alzheimer's care and enhancing the quality of life for affected individuals and their caregivers.
阿尔茨海默病(AD)是一种神经退行性疾病,以认知能力下降和功能障碍为特征。本研究将传统的干预技术与新兴的人工智能(AI)方法进行了比较。干预技术是指在特定情况下为带来积极变化而采用的特定方法或途径。就注意力缺失症而言,这类技术至关重要,因为它们旨在减缓症状的发展、缓解行为挑战,并支持患者及其护理人员应对复杂的病情。认知刺激和现实导向等传统干预技术在改善认知功能和情绪福祉方面已取得了明显的效果。传统干预方法在有效性、个性化反应、成本效益和以患者为中心的护理方面都有良好的记录,因此受到广泛青睐。尽管有这些优点,但它们在反应和长期有效性方面受到个体差异的限制。另一方面,计算机视觉和深度学习(DL)等基于人工智能的方法有可能彻底改变阿尔茨海默氏症的干预措施。这些技术可提供早期检测、个性化护理和远程监控功能。它们可以提供量身定制的干预措施,协助决策,并加强对护理人员的支持。虽然基于人工智能的干预措施面临着数据隐私和实施复杂性等挑战,但它们改变阿尔茨海默氏症护理的潜力巨大。本研究论文比较了传统方法和基于人工智能的方法。它揭示出,虽然传统技术已得到广泛认可并具有公认的益处,但基于人工智能的干预措施为个性化和高级护理提供了新的机遇。将这两种方法的优势结合起来,可以为注意力缺失症患者提供更全面、更有效的干预。要充分发挥人工智能在改善阿尔茨海默氏症护理和提高患者及其护理人员生活质量方面的潜力,持续的研究与合作至关重要。
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引用次数: 0
TA’KEED the First Generative Fact-Checking System for Arabic Claims TA'KEED 是首个针对阿拉伯语索赔的生成式事实核查系统
Pub Date : 2024-01-20 DOI: 10.5121/csit.2024.140103
Saud Althabiti, M. Alsalka, Eric Atwell
This paper introduces Ta’keed, an explainable Arabic automatic fact-checking system. While existing research often focuses on classifying claims as "True" or "False," there is a limited exploration of generating explanations for claim credibility, particularly in Arabic. Ta’keed addresses this gap by assessing claim truthfulness based on retrieved snippets, utilizing two main components: information retrieval and LLM-based claim verification. We compiled the ArFactEx, a testing gold-labelled dataset with manually justified references, to evaluate the system. The initial model achieved a promising F1 score of 0.72 in the classification task. Meanwhile, the system's generated explanations are compared with gold-standard explanations syntactically and semantically. The study recommends evaluating using semantic similarities, resulting in an average cosine similarity score of 0.76. Additionally, we explored the impact of varying snippet quantities on claim classification accuracy, revealing a potential correlation, with the model using the top seven hits outperforming others with an F1 score of 0.77
本文介绍 Ta'keed,一个可解释的阿拉伯语自动事实检查系统。现有的研究通常侧重于将索赔分为 "真 "或 "假",而对索赔可信度生成解释的探索却很有限,尤其是在阿拉伯语中。Ta'keed 利用两个主要部分:信息检索和基于 LLM 的索赔验证,根据检索到的片段评估索赔的真实性,从而弥补了这一空白。我们编制了 ArFactEx,这是一个带有人工证明参考文献的黄金标签测试数据集,用于评估该系统。初始模型在分类任务中取得了 0.72 的 F1 分数,成绩喜人。同时,该系统生成的解释与黄金标准解释在句法和语义上进行了比较。研究建议使用语义相似性进行评估,结果是平均余弦相似性得分为 0.76。此外,我们还探索了不同片段数量对索赔分类准确性的影响,发现了潜在的相关性,使用前七次点击的模型优于其他模型,F1 得分为 0.77
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引用次数: 0
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Artificial Intelligence and Applications
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