Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods.

Deep learning-based automated essay scoring (AES) models exhibit a remarkable ability to identify complex patterns within essays and then generate accurate score predictions in an end-to-end training fashion. However, these models face a critical limitation in explaining the specific patterns and features utilized for scoring, which are essential for interpreting the scores and offering constructive feedback to essay authors. Numerous studies have focused on essay scoring, with the aim of modeling prompt-specific, domain-adaptable, or trait-specific AES. While existing surveys on AES cover topics ranging from representation to scoring models, they primarily emphasize scoring models. This study addresses a crucial gap by encompassing research on feedback generation for essay assessment tasks. By delving into essay scoring and feedback generation, we synthesize several existing literature to provide readers with a comprehensive understanding of ongoing research in both deep learning-based essay scoring and automated feedback generation. We categorized the existing essay scoring studies into prompt-specific and cross-prompt AES models, noting that prompt-specific AES is extensively researched category. However, we have only come across a few studies concerning automated feedback generation, likely because of the limited availability of suitable datasets for researching such types of tasks. Moreover, this survey provides insights into approaches for essay representation, prevalent datasets, evaluation metrics, and challenges in automated essay scoring tasks. By shedding light on these aspects, our goal is to delineate the current landscape, identify key research directions, and pave the way for further advancements in automated essay assessment.

When individuals interact with one another to accomplish specific goals, they learn from others’ experiences to achieve the tasks at hand. The same holds for learning in virtual environments, such as video games. Deep multiagent reinforcement learning shows promising results in terms of completing many challenging tasks. To demonstrate its viability, most algorithms use value decomposition for multiple agents. To guide each agent, behavior value decomposition is utilized to decompose the combined Q-value of the agents into individual agent Q-values. A different mixing method can be utilized, using a monotonicity assumption based on value decomposition algorithms such as QMIX and QVMix. However, this method selects individual agent actions through a greedy policy. The agents, which require large numbers of training trials, are not addressed. In this paper, we propose a novel hybrid policy for the action selection of an individual agent known as Q-value Selection using Optimization and DRL (QSOD). A grey wolf optimizer (GWO) is used to determine the choice of individuals’ actions. As in GWO, there is proper attention among the agents facilitated through the agents’ coordination with one another. We used the StarCraft 2 Learning Environment to compare our proposed algorithm with the state-of-the-art algorithms QMIX and QVMix. Experimental results demonstrate that our algorithm outperforms QMIX and QVMix in all scenarios and requires fewer training trials.

In the rapidly advancing Reinforcement Learning (RL) field, Multi-Agent Reinforcement Learning (MARL) has emerged as a key player in solving complex real-world challenges. A pivotal development in this realm is the introduction of the mixing network, representing a significant leap forward in the capabilities of multi-agent systems. Drawing inspiration from COMA and VDN methodologies, the mixing network overcomes limitations in extracting combined Q-values from joint state-action interactions. Previous approaches like COMA and VDN faced constraints in fully utilising the state-provided information during training, limiting their effectiveness. QMIX and QVMinMax addressed this issue by employing neural networks to convert centralized states into weights for a second neural network, akin to hyper-networks. However, these solutions presented challenges such as computational intensity and susceptibility to local minima. To overcome these hurdles, our proposed methodology introduces three key contributions. First, we introduce the state- fusion network, an innovative alternative to traditional mixing, with a self-attention mechanism . Second, to address the local optima problem in MARL algorithms, we leverage the Grey Wolf Optimizer for weight and bias selection, adding a stochastic element for improved optimization. Finally, we comprehensively compare with QMIX, evaluating performance under two optimization methods: Gradient Descent and Stochastic Optimizer. Using the StarCraft II Learning Environment (SC2LE) as our experimental platform, our results demonstrate the superiority of our methodology over QMIX, QVMinMax, and QSOD in absolute performance, particularly when operating under resource constraints. Our proposed methodology contributes to the ongoing evolution of MARL techniques, showcasing advancements in attention mechanisms and optimization strategies for enhanced multi-agent system capabilities.

The current deep learning models detecting relevant web pages show low accuracy because of the poor quality of the training data. In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest. Our experimental results with movies and cellphones data sets show that the average F ₁ -score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to 0.9992 in F ₁ -score.

Video description is one of the most challenging task in the combined domain of computer vision and natural language processing. Captions for various open and constrained domain videos have been generated in the recent past but descriptions for driving dashcam videos have never been explored to the best of our knowledge. With the aim to explore dashcam video description generation for autonomous driving, this study presents DeepRide: a large-scale dashcam driving video description dataset for location-aware dense video description generation. The human-described dataset comprises visual scenes and actions with diverse weather, people, objects, and geographical paradigms. It bridges the autonomous driving domain with video description by textual description generation of the visual information as seen by a dashcam. We describe 16,000 videos (40 seconds each) in English employing 2,700 man-hours by two highly qualified teams with domain knowledge. The descriptions consist of eight to ten sentences covering each dashcam video’s global features and event features in 60 to 90 words. The dataset consists of more than 130K sentences, totaling approximately one million words. We evaluate the dataset by employing location aware vision-language recurrent transformer framework to elaborate on the efficacy and significance of the visio-linguistics research for autonomous vehicles. We provided base line results to evaluate the dataset by employing three existing state-of-the-art recurrent models. The memory augmented transformer performed superior due to its highly summarized memory state for visual information and the sentence history while generating the trip description. Our proposed dataset opens a new dimension of diverse and exciting applications, such as self-driving vehicle reporting, driver and vehicle safety, inter-vehicle road intelligence sharing, and travel occurrence reports.