For satellite images, the presence of clouds presents a problem as clouds obscure more than half to two-thirds of the ground information. This problem causes many issues for reliability in a noise-free environment to communicate data and other applications that need seamless monitoring. Removing the clouds from the images while keeping the background pixels intact can help address the mentioned issues. Recently, deep learning methods have become popular for researching cloud removal by demonstrating promising results, among which Generative Adversarial Networks (GAN) have shown considerably better performance. In this project, we aim to address cloud removal from satellite images using AttentionGAN and then compare our results by reproducing the results obtained using traditional GANs and auto-encoders. We use RICE dataset. The outcome of this project can be used to develop applications that require cloud-free satellite images. Moreover, our results could be helpful for making further research improvements.

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.

自闭症，也称为自闭症谱系障碍（或ASD），是一种神经系统疾病。它的主要症状包括（口头和/或非语言）交流的难度以及僵化/重复的行为。这些症状通常与正常（对照）个体没有区别，因此这种疾病在幼儿期间仍未诊断，导致治疗延迟。由于学习曲线在最初年龄段是陡峭的，因此对自闭症的早期诊断可以在适当的时间进行足够的干预措施，这可能会对自闭症儿童的成长产生积极影响。此外，传统的自闭症诊断方法需要多次访问专门的精神科医生，但是这一过程可能很耗时。在本文中，我们提出了一种基于学习的方法，可以使用简单和小型动作视频剪辑的主题自闭症诊断。此任务尤其具有挑战性，因为可用的带注释数据的量很小，并且两类（ASD和控制）的样本之间的变化通常是无法区分的。从基线编码器顶部的跨凝结损失学到的二进制分类器的性能不佳也可以明显看出这一点。为了解决这个问题，我们在自我监督和监督的学习框架中采用对比功能学习，并表明这些学习可能会导致二元分类器对此任务的预测准确性显着提高。我们通过对两个公开可用数据集的不同设置进行彻底的实验分析来进一步验证这一点。

复杂系统的一个重要特征是具有许多局部最小值和子结构的问题域。生物系统通过根据环境或发育环境在不同子系统之间切换来管理这些局部最小值。遗传算法（GA）可以模仿此切换性能，并提供一种克服问题域复杂性的手段。但是，标准GA需要其他操作员，该操作员将允许以随机方式进行大规模探索。无梯度的启发式搜索技术适合在离散域中为这种单个客观优化任务提供最佳解决方案，尤其是与明显较慢的基于梯度的方法相比。为此，作者从飞行计划域中转向优化问题。作者比较了这种常见的无梯度启发式搜索算法的性能，并提出了气体的变体。还引入了迭代的链接方法（IC）方法，这是通过触发多个局部搜索而不是突变操作员的单数动作来基于传统链接技术的。作者将表明，使用多个本地搜索可以改善本地随机搜索的性能，从而为许多其他问题域提供了足够的机会。据观察，所提出的GA变体在所有基准测试基准中的平均成本最低，包括提出的问题和IC算法的性能优于其成分。