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In recent years, image semantic segmentation technology has developed rapidly, but image annotation usually requires a significant amount of human and financial resources, especially for remote sensing image annotation, which can be expensive and sometimes even unaffordable. To address this issue, this paper integrates the idea of curriculum learning into the self-training method and screens reliable pseudo-labels through computing image-level confidence, significantly reducing the confirmation error problem. Furthermore, the semi-supervised model in this paper combines implicit semantic enhancement with strong data augmentation, which can reduce the coupling between the teacher model and the student model's prediction distribution and enhance the model's robustness. Finally, the proposed semi-supervised method is experimentally verified using the ISPRS competition dataset and compared with existing state-of-the-art (SOTA) methods. Experimental results show that the proposed semi-supervised segmentation method achieves higher segmentation accuracy compared to self-training methods. Moreover, despite not using iterative training to simplify the training process, the proposed method still yields satisfactory segmentation results. [ABSTRACT FROM AUTHOR]

This paper introduces a novel application of neural style transfer to video sequences, leveraging pre-trained Convolutional Neural Network (CNN) models. By combining content and style losses in an optimization process, the approach transforms video frames into visually captivating compositions, opening new avenues for artistic expression in visual storytelling and filmmaking. The procedure begins with meticulous preprocessing of video frames to align them with the specifications of the powerful VGG16 model. A content model is then constructed to extract activations from the 14th layer of the VGG network, forming the groundwork for nuanced content depiction. Style information is computed by aggregating data from a combination of convolutional layers, resulting in a multifaceted, artistically complex tapestry. The paper introduces a well-balanced loss function that combines style and content losses, serving as the foundation for the ensuing optimization procedure. This careful balancing between style adherence and content authenticity yields visually stunning synthesis, enhancing video sequence quality and artistic resonance. The paper also delves into a detailed analysis of hyper parameters, particularly style weights, and introduces a novel pixel value scaling method for enhanced visual coherence. Experimental findings demonstrate the efficiency and adaptability of the proposed approach, showcasing the seamless integration of content and style for an immersive visual experience. This accomplishment marks a significant stride toward unlocking the creative potential of video content, ushering in a period of enhanced visual storytelling. [ABSTRACT FROM AUTHOR]

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts. [ABSTRACT FROM AUTHOR]

Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector. In quantitative genetics prediction of genetic values is a central issue. Wheat is an allohexaploid (AABBDD) with three distinct genomes. The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat, Therefore, genome sequence approaches based on techniques of Artificial Intelligence (AI) are necessary. This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources andmanaging genetic variation in their breeding program, as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints. In this paper, the hyperparameters of the network are optimized in the CNN to decrease the requirement formanual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks (CNN). [ABSTRACT FROM AUTHOR]

In this paper we aim to analyze the influence of Deep Learning (DL) and Neural Networks (NN), on financial forecasting, as a result we have extensively examined research papers and real life implementations of these sophisticated computer models that forecast patterns and movements in financial markets, in the same way we will explore and compare the development of financial forecasting techniques starting from classical methods to the adoption of deep learning models, like Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM). To be more specific, in this article we delve into the abilities of these models to capture non-linear patterns in financial data such as stock market asset prices. We highlight how they excel in some situations compared to classical methods used for predicting the future of a financial asset. We emphasize how these models outperform conventional techniques for projecting the future of a financial asset in some circumstances, we also address the challenges and barriers associated with using these models to forecasting of financial outcomes, some challenges include overfitting, the need for high quality data, and the requirement for large databases. By examining and combining insights from multiple sources, our goal is to present a global perspective on the current state of neural networks and deep learning used for predicting future changes in the financial sector, we aim to identify areas where these models demonstrate some kind of performance while also showing areas that necessitate more research and development. [ABSTRACT FROM AUTHOR]

The effect of target tracking is not ideal when facing various complex tracking scenarios such as non-rigid deformation of target, frequent occlusion, clutter of target background and interference of similar objects. In this paper, the feature based on deep convolutional neural network is used for target tracking in moving scenes, and a sliding window target segmentation method is proposed to study the impact of data normalization and data set expansion on the final result. In order to select more distinguishing features, principal component analysis is used to process the features of Deep Convolution Neural Network (DCNN), and the features of different network layers of DCNN are compared. The feature coding algorithm is studied, and the extracted DCNN features are encoded by Fisher Vectors algorithm, and compared with the locality-constrained linear encoding technique. Experiments show that the feature based on deep convolutional neural network in this paper can obtain higher accuracy than the traditional feature fusion method. According to the result analysis, the tracking accuracy of deep convolutional neural network algorithm is improved under the condition of illumination variation. In the case of local occlusion, the tracking accuracy is also improved. [ABSTRACT FROM AUTHOR]

The audio detection system enables autonomous cars to recognize their surroundings based on the noise produced by moving vehicles. This paper proposes the utilization of a machine learning model based on convolutional neural networks (CNN) integrated into an embedded system supported by a microphone. The system includes a specialized microphone and a main processor. The microphone enables the transmission of an accurate analog signal to the main processor, which then analyzes the recorded signal and provides a prediction in return. While designing an adequate hardware system is a crucial task that directly impacts the predictive capability of the system, it is equally imperative to train a CNN model with high accuracy. To achieve this goal, a dataset containing over 3000 up-to-5-second WAV files for four classes was obtained from open-source research. The dataset is then divided into training, validation, and testing sets. The training data is converted into images using the spectrogram technique before training the CNN. Finally, the generated model is tested on the testing segment, resulting in a model accuracy of 77.54%. [ABSTRACT FROM AUTHOR]

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer‐learning methodology and a multilabel convolutional neural network for COVID (MLCNN‐COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer‐learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN‐COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three‐dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN‐COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer‐learning model's performance. It shows that three‐dimensional conformers adequately identify negative COVID medicine responses. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning approach for accurate pose estimation in ship unloaders, improving grasping accuracy by reconstructing 3D coordinates. A convolutional neural network optimizes depth map prediction from RGB images, further enhanced by a conditional generative adversarial network to refine quality. Evaluation of simulated ship unloading tasks showed over 90% grasping success rate, outperforming baseline methods. This research offers valuable insights into advanced visual perception and deep learning for next-generation automated cargo handling. [ABSTRACT FROM AUTHOR]

Nowadays, for controlling crime, surveillance cameras are typically installed in all public places to ensure urban safety and security. However, automating Human Activity Recognition (HAR) using computer vision techniques faces several challenges such as lowlighting, complex spatiotemporal features, clutter backgrounds, and inefficient utilization of surveillance system resources. Existing attempts in HAR designed straightforward networks by analyzing either spatial or motion patterns resulting in limited performance while the dual streams methods are entirely based on Convolutional Neural Networks (CNN) that are inadequate to learning the long-range temporal information for HAR. To overcome the above-mentioned challenges, this paper proposes an optimized dual stream framework for HAR which mainly consists of three steps. First, a shots segmentation module is introduced in the proposed framework to efficiently utilize the surveillance system resources by enhancing the lowlight video stream and then it detects salient video frames that consist of human. This module is trained on our own challenging Lowlight Human Surveillance Dataset (LHSD) which consists of both normal and different levels of lowlighting data to recognize humans in complex uncertain environments. Next, to learn HAR from both contextual and motion information, a dual stream approach is used in the feature extraction. In the first stream, it freezes the learned weights of the backbone Vision Transformer (ViT) B-16 model to select the discriminative contextual information. In the second stream, ViT features are then fused with the intermediate encoder layers of FlowNet2 model for optical flow to extract a robust motion feature vector. Finally, a two stream Parallel Bidirectional Long Short-Term Memory (PBiLSTM) is proposed for sequence learning to capture the global semantics of activities, followed by Dual Stream Multi-Head Attention (DSMHA) with a late fusion strategy to optimize the huge features vector for accurate HAR. To assess the strength of the proposed framework, extensive empirical results are conducted on real-world surveillance scenarios and various benchmark HAR datasets that achieve 78.6285%, 96.0151%, and 98.875% accuracies on HMDB51, UCF101, and YouTube Action, respectively. Our results show that the proposed strategy outperforms State-of-the-Art (SOTA) methods. The proposed framework gives superior performance in HAR, providing accurate and reliable recognition of human activities in surveillance systems. [ABSTRACT FROM AUTHOR]