Based on WHO statistics, many individuals are suffering from visual problems, and their number is increasing yearly. One of the most critical needs they have is the ability to navigate safely, which is why researchers are trying to create and improve various navigation systems. This paper provides a navigation concept based on the visual slam and Yolo concepts using monocular cameras. Using the ORB-SLAM algorithm, our concept creates a map from a predefined route that a blind person most uses. Since visually impaired people are curious about their environment and, of course, to guide them properly, obstacle detection has been added to the system. As mentioned earlier, safe navigation is vital for visually impaired people, so our concept has a path-following part. This part consists of three steps: obstacle distance estimation, path deviation detection, and next-step prediction, done by monocular cameras.

就微电网的运行而言，最佳调度是必须考虑的至关重要问题。在这方面，本文提出了一个有效的框架，用于考虑储能设备，风力涡轮机，微型涡轮机的最佳计划可再生微电网。由于微电网操作问题的非线性和复杂性，使用准确且可靠的优化技术有效解决此问题至关重要。为此，在拟议的框架中，基于教师学习的优化可有效地解决系统中的调度问题。此外，提出了基于双向长期短期记忆的深度学习模型，以解决短期风能预测问题。使用IEEE 33-BUS测试系统检查了建议的框架的可行性和性能以及风力预测对操作效率的影响。此外，澳大利亚羊毛北风现场数据被用作现实世界数据集，以评估预测模型的性能。结果表明，在微电网的最佳计划中，提出的框架的有效性能有效。

如今，推荐系统和搜索引擎在时尚电子商务中发挥积分作用。尽管如此，许多挑战谎言，这项研究试图解决一些问题。本文首先介绍了一种基于内容的时尚推荐系统，它使用并行神经网络作为输入，通过列出商店中可用的类似物品来获取单个时尚项目商店映像。接下来，增强相同的结构以基于用户偏好来个性化结果。然后，这项工作引入了一个背景增强技术，使系统更强大地对域外查询，使其仅使用培训的目录商店图像进行街道到商店建议。此外，本文的最后贡献是推荐任务的新评估度量，称为客观引导的人为评分。该方法是一个完全可定制的框架，可以产生来自人类评分术的主观评估的可解释，可比的分数。

唇读是从唇部运动识别语音的操作。这是一项艰巨的任务，因为在发音时嘴唇的动作是类似的。在对话期间，景观用于描述唇部运动。本文旨在展示如何通过将视频到字符分为两个阶段，即将视频转换为Viseme，然后使用单独的型号将Viseme转换为角色来使用外部文本数据（用于对角色映射）。与正常序列相比，我们所提出的方法通过4 \％改善了4 \％的序列次序列在BBC-oxford唇读数2（LRS2）数据集上序列唇读模型。

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-response assessments. The purpose of this study is to describe and evaluate three active learning methods than can be used to minimize the number of essays that must be scored by human raters while still providing the data needed to train a modern automated essay scoring system. The three active learning methods are the uncertainty-based, the topological-based, and the hybrid method. These three methods were used to select essays included as part of the Automated Student Assessment Prize competition that were then classified using a scoring model that was training with the bidirectional encoder representations from transformer language model. All three active learning methods produced strong results, with the topological-based method producing the most efficient classification. Growth rate accuracy was also evaluated. The active learning methods produced different levels of efficiency under different sample size allocations but, overall, all three methods were highly efficient and produced classifications that were similar to one another.

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

National Association of Securities Dealers Automated Quotations(NASDAQ) is an American stock exchange based. It is one of the most valuable stock economic indices in the world and is located in New York City \cite{pagano2008quality}. The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected and have a volatile and chaotic nature \cite{firouzjaee2022lstm}.In this article, we have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market, and then we have also examined the effect of these indicators on NASDAQ stocks. Then we started to analyze the impact of the feedback on the past prices of NASDAQ stocks and its impact on the current price. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks. The results obtained from the quantitative analysis are consistent with the results of the qualitative analysis of economic studies, and the modeling done with the optimal dynamic experience of machine learning justifies the current price of NASDAQ shares.

Recent advances in distributed artificial intelligence (AI) have led to tremendous breakthroughs in various communication services, from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wirelessly connected devices, random channel fluctuations and the incumbent services running on the same network impact the performance of both distributed learning and the coexisting service. In this paper, we investigate a mixed service scenario where distributed AI workflow and ultra-reliable low latency communication (URLLC) services run concurrently over a network. Consequently, we propose a risk sensitivity-based formulation for device selection to minimize the AI training delays during its convergence period while ensuring that the operational requirements of the URLLC service are met. To address this challenging coexistence problem, we transform it into a deep reinforcement learning problem and address it via a framework based on soft actor-critic algorithm. We evaluate our solution with a realistic and 3GPP-compliant simulator for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delay of the distributed AI service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all network resources.

Deep learning-based object detection is a powerful approach for detecting faulty insulators in power lines. This involves training an object detection model from scratch, or fine tuning a model that is pre-trained on benchmark computer vision datasets. This approach works well with a large number of insulator images, but can result in unreliable models in the low data regime. The current literature mainly focuses on detecting the presence or absence of insulator caps, which is a relatively easy detection task, and does not consider detection of finer faults such as flashed and broken disks. In this article, we formulate three object detection tasks for insulator and asset inspection from aerial images, focusing on incipient faults in disks. We curate a large reference dataset of insulator images that can be used to learn robust features for detecting healthy and faulty insulators. We study the advantage of using this dataset in the low target data regime by pre-training on the reference dataset followed by fine-tuning on the target dataset. The results suggest that object detection models can be used to detect faults in insulators at a much incipient stage, and that transfer learning adds value depending on the type of object detection model. We identify key factors that dictate performance in the low data-regime and outline potential approaches to improve the state-of-the-art.