在过去的十年中，使用深度学习方法从胸部X光片检测到胸部X光片是一个活跃的研究领域。大多数以前的方法试图通过识别负责对模型预测的重要贡献的空间区域来关注图像的患病器官。相比之下，专家放射科医生在确定这些区域是否异常之前首先找到突出的解剖结构。因此，将解剖学知识纳入深度学习模型可能会带来自动疾病分类的大幅改善。在此激励的情况下，我们提出了解剖学XNET，这是一种基于解剖学注意的胸腔疾病分类网络，该网络优先考虑由预识别的解剖区域引导的空间特征。我们通过利用可用的小规模器官级注释来采用半监督的学习方法，将解剖区域定位在没有器官级注释的大规模数据集中。拟议的解剖学XNET使用预先训练的Densenet-121作为骨干网络，具有两个相应的结构化模块，解剖学意识到（$^3 $）和概率加权平均池（PWAP），在凝聚力框架中引起解剖学的关注学习。我们通过实验表明，我们提出的方法通过在三个公开可用的大规模CXR数据集中获得85.78％，92.07％和84.04％的AUC得分来设置新的最先进基准测试。和模拟CXR。这不仅证明了利用解剖学分割知识来改善胸病疾病分类的功效，而且还证明了所提出的框架的普遍性。

The task of locating and classifying different types of vehicles has become a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification and many more. In recent times, Deep Learning models have been dominating the field of vehicle detection. Yet, Bangladeshi vehicle detection has remained a relatively unexplored area. One of the main goals of vehicle detection is its real-time application, where `You Only Look Once' (YOLO) models have proven to be the most effective architecture. In this work, intending to find the best-suited YOLO architecture for fast and accurate vehicle detection from traffic images in Bangladesh, we have conducted a performance analysis of different variants of the YOLO-based architectures such as YOLOV3, YOLOV5s, and YOLOV5x. The models were trained on a dataset containing 7390 images belonging to 21 types of vehicles comprising samples from the DhakaAI dataset, the Poribohon-BD dataset, and our self-collected images. After thorough quantitative and qualitative analysis, we found the YOLOV5x variant to be the best-suited model, performing better than YOLOv3 and YOLOv5s models respectively by 7 & 4 percent in mAP, and 12 & 8.5 percent in terms of Accuracy.

Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of the impact learning over the conventional machine learning algorithm.