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Personality determines various human daily and working behaviours. Recently, a large number of automatic personality computing approaches have been developed to predict either the apparent or self-reported personality of the subject based on non-verbal audio-visual behaviours. However, most of them suffer from complex and dataset-specific pre-processing steps and model training tricks. In the absence of a standardized benchmark with consistent experimental settings, it is not only impossible to fairly compare the real performances of these personality computing models but also makes them difficult to be reproduced. This paper presents the first reproducible audio-visual benchmark to provide a fair and consistent evaluation of eight existing personality computing models (e.g., audio, visual and audio-visual) and seven standard deep learning models on both self-reported and apparent personality recognition tasks. Building upon a set of benchmarked models, we also investigate the impact of two previously-used long-term modelling strategies for summarising short-term/frame-level predictions on personality computing results. We comprehensively investigate all benchmarked models on two publicly available datasets, ChaLearn First Impression and UDIVA self-reported personality datasets, and conclude: (i) apparent personality traits, inferred from facial behaviours by most benchmarked deep learning models, show more reliability than self-reported ones; (ii) visual models frequently achieved superior performances than audio models on personality recognition; (iii) non-verbal behaviours contribute differently in predicting different personality traits; and (iv) our reproduced personality computing models generally achieved worse performances than their original reported results. [ABSTRACT FROM AUTHOR]

Detecting wafer map anomalies is crucial for preventing yield loss in semiconductor fabrication, although intricate patterns and resource-intensive labeled data prerequisites hinder precise deep-learning segmentation. This paper presents an innovative, unsupervised method for detecting pixel-level anomalies in wafer maps. It utilizes an efficient dual attention module with a knowledge distillation network to learn defect distributions without anomalies. Knowledge transfer is achieved by distilling information from a pre-trained teacher into a student network with similar architecture, except an efficient dual attention module is incorporated atop the teacher network’s feature pyramid hierarchies, which enhances feature representation and segmentation across pyramid hierarchies that selectively emphasize relevant and discard irrelevant features by capturing contextual associations in positional and channel dimensions. Furthermore, it enables student networks to acquire an improved knowledge of hierarchical features to identify anomalies across different scales accurately. The dissimilarity in feature pyramids acts as a discriminatory function, predicting the likelihood of an abnormality, resulting in highly accurate pixel-level anomaly detection. Consequently, our proposed method excelled on the WM-811K and MixedWM38 datasets, achieving AUROC, AUPR, AUPRO, and F1-Scores of (99.65%, 99.35%), (97.31%, 92.13%), (90.76%, 84.66%) respectively, alongside an inference speed of 3.204 FPS, showcasing its high precision and efficiency. [ABSTRACT FROM AUTHOR]

Heart sound auscultation has been applied in clinical usage for early screening of cardiovascular diseases. Due to the high demand for auscultation expertise, automatic auscultation can help with auxiliary diagnosis and reduce the burden of training professional clinicians. Nevertheless, there is a limit to classic machine learning's performance improvement in the era of Big Data. Deep learning has outperformed classic machine learning in many research fields, as it employs more complex model architectures with a stronger capability of extracting effective representations. Moreover, it has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were carried out before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning published in 2017–2022. This work introduces both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis. [ABSTRACT FROM AUTHOR]

Multi-modal classifiers for emotion recognition have become prominent, as the emotional states of subjects can be more comprehensively inferred from Electroencephalogram (EEG) signals and eye movements. However, existing classifiers experience a decrease in performance due to the distribution shift when applied to new users. Unsupervised domain adaptation (UDA) emerges as a solution to address the distribution shift between subjects by learning a shared latent feature space. Nevertheless, most UDA approaches focus on a single modality, while existing multi-modal approaches do not consider that fine-grained structures should also be explicitly aligned and the learned feature space must be discriminative. In this paper, we propose Coarse and Fine-grained Distribution Alignment with Correlated and Separable Features (CFDA-CSF), which performs a coarse alignment over the global feature space, and a fine-grained alignment between modalities from each domain distribution. At the same time, the model learns intra-domain correlated features, while a separable feature space is encouraged on new subjects. We conduct an extensive experimental study across the available sessions on three public datasets for multi-modal emotion recognition: SEED, SEED-IV, and SEED-V. Our proposal effectively improves the recognition performance in every session, achieving an average accuracy of 93.05%, 85.87% and 91.20% for SEED; 85.72%, 89.60%, and 86.88% for SEED-IV; and 88.49%, 91.37% and 91.57% for SEED-V. [ABSTRACT FROM AUTHOR]

Recognizing human emotions from complex, multivariate, and non-stationary electroencephalography (EEG) time series is essential in affective brain-computer interface. However, because continuous labeling of ever-changing emotional states is not feasible in practice, existing methods can only assign a fixed label to all EEG timepoints in a continuous emotion-evoking trial, which overlooks the highly dynamic emotional states and highly non-stationary EEG signals. To solve the problems of high reliance on fixed labels and ignorance of time-changing information, in this paper we propose a time-aware sampling network (TAS-Net) using deep reinforcement learning (DRL) for unsupervised emotion recognition, which is able to detect key emotion fragments and disregard irrelevant and misleading parts. Specifically, we formulate the process of mining key emotion fragments from EEG time series as a Markov decision process and train a time-aware agent through DRL without label information. First, the time-aware agent takes deep features from a feature extractor as input and generates sample-wise importance scores reflecting the emotion-related information each sample contains. Then, based on the obtained sample-wise importance scores, our method preserves top-X continuous EEG fragments with relevant emotion and discards the rest. Finally, we treat these continuous fragments as key emotion fragments and feed them into a hypergraph decoding model for unsupervised clustering. Extensive experiments are conducted on three public datasets (SEED, DEAP, and MAHNOB-HCI) for emotion recognition using leave-one-subject-out cross-validation, and the results demonstrate the superiority of the proposed method against previous unsupervised emotion recognition methods. The proposed TAS-Net has great potential in achieving a more practical and accurate affective brain-computer interface in a dynamic and label-free circumstance. [ABSTRACT FROM AUTHOR]

The document is the table of contents for the May 2024 issue of the IEEE Transactions on Semiconductor Manufacturing. It includes papers on various topics such as yield modeling, analysis, and enhancement, advanced processing, advanced process control, equipment and automation technology, non-silicon materials, emerging areas, and environment, safety, and health. The articles cover subjects such as chip-scale chemical mechanical polishing, threshold voltage deviation monitoring, curvilinear standard cell design, fabrication of silicon nanocone arrays, improving the reliability of through silicon vias, energy consumption and carbon emission reduction in HVAC systems, low-k silicon dioxide synthesis, chemical mechanical polishing of diamond epitaxial layers, conditional variable selection based on deep learning, and eco-friendly dry-cleaning and diagnostics of silicon dioxide deposition chambers. The document also includes an announcement for the call for nominations for the 2024 IEEE EDS Early Career Award. [Extracted from the article]

Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually use the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to that of the NLA with randomly selected regions prompted us to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code and a pre-trained model were uploaded to GitHub (https://github.com/laoyangui/HSPAN) for validation. [ABSTRACT FROM AUTHOR]

Efficient point cloud compression is essential for applications like virtual and mixed reality, autonomous driving, and cultural heritage. This paper proposes a deep learning-based inter-frame encoding scheme for dynamic point cloud geometry compression. We propose a lossy geometry compression scheme that predicts the latent representation of the current frame using the previous frame by employing a novel feature space inter-prediction network. The proposed network utilizes sparse convolutions with hierarchical multiscale 3D feature learning to encode the current frame using the previous frame. The proposed method introduces a novel predictor network for motion compensation in the feature domain to map the latent representation of the previous frame to the coordinates of the current frame to predict the current frame’s feature embedding. The framework transmits the residual of the predicted features and the actual features by compressing them using a learned probabilistic factorized entropy model. At the receiver, the decoder hierarchically reconstructs the current frame by progressively rescaling the feature embedding. The proposed framework is compared to the state-of-the-art Video-based Point Cloud Compression (V-PCC) and Geometry-based Point Cloud Compression (G-PCC) schemes standardized by the Moving Picture Experts Group (MPEG). The proposed method achieves more than 88% BD-Rate (Bjøntegaard Delta Rate) reduction against G-PCCv20 Octree, more than 56% BD-Rate savings against G-PCCv20 Trisoup, more than 62% BD-Rate reduction against V-PCC intra-frame encoding mode, and more than 52% BD-Rate savings against V-PCC P-frame-based inter-frame encoding mode using HEVC. These significant performance gains are cross-checked and verified in the MPEG working group. [ABSTRACT FROM AUTHOR]

Thanks to the many submissions we have received, we can present this second part of our special issue on "AI-eXplained." In here, we continue our mission to demystify the intricate world of artificial intelligence and make it accessible to a broader audience. As AI continues to evolve and integrate into various aspects of our lives, it becomes increasingly important to bridge the gap between experts and those eager to understand the inner workings of AI systems. Our goal remains unchanged—to present AI concepts in a comprehensible, engaging, and interactive manner, empowering our readers to explore and grasp the enchanting world of AI. [ABSTRACT FROM AUTHOR]

The use of helmets is crucial for safeguarding the lives of construction workers. In the construction sector, computer vision technology is extensively employed to detect and monitor the correct usage of helmets by workers. Currently, there are three classical types of helmet detection algorithms: digital image processing, convolutional neural network (CNN), and Transformer. Digital images are based on manual processing of the features, which proves to be inefficient and lacks robustness. CNN exhibits high accuracy but lacks robustness, which limits its effectiveness in complex environments. This paper proposes an algorithm called the Highly Robust Helmet Detection Algorithm (HRHD), designed to attain precise detection of helmet usage at construction sites with varying conditions. The proposed model leverages the YOLO v8s architecture and incorporates the Coordinate Attention module to enhance the model’s focus on important features. It also introduces the Transformer structure to extract global features, and employs the RepConv module to diminish the model’s computational demands, thus achieving a balance between inference speed and detection accuracy. The experiments demonstrate that the proposed model in this paper significantly improves the accuracy compared to YOLO v10 and YOLO v8s. Additionally, the model maintains a rapid inference rate, suggesting substantial potential for application within the construction engineering domain.

Aiming at the incomplete data and difficult prediction in the prediction of firewater system water level, a data filling method is proposed based on the reinforcement learning approach and a deep learning (DL) prediction method is studied based on the long-term short-term memory (LSTM) model in this paper. Firstly, a reinforcement learning-based K-nearest neighbor (KNN) algorithm is designed for the data incompleteness that occurs in the firewater level. The parameters of the KNN algorithm are optimized through the reinforcement learning to enhance the accuracy of data filling. Secondly, to compensate for the shortcomings of the traditional firewater pool level prediction methods, a DL prediction method based on the LSTM network approach is proposed by using the complete data. Through the LSTM three gate functions, the previous information is rounded to avoid causing gradient explosion, rounding the method is more stable and accurate. Finally, the simulation results show that the proposed method in this paper can effectively predict incomplete water level information.

Sign language (SL) is a vital mode of communication, bridging the gap between the hearing impaired and hearing communities. However, SL, despite its paramount importance, has received relatively limited attention from researchers. Its unique structural characteristics, distinct from those of natural languages, present novel challenges that require innovative solutions. Remarkable technological advancements, notably in Artificial Intelligence (AI) and machine learning, offer promising avenues for automated Sign Language translation Systems (SLTS). This review study addresses the crucial need for a comprehensive synthesis of existing research by systematically examining and evaluating the progress made in SLTS. By analyzing 58 research papers, with a particular emphasis on the most frequently cited papers from each year up to 2023, we shed light on the field’s current state, identifying key advancements and challenges. This review followed a systematic approach based on clear guidelines. The methodology involved defining research questions, formulating queries, selecting studies based on clear criteria, and extracting pertinent information to address the research objectives. This review found that deep learning techniques, such as convolutional and recurrent neural networks, have shown high accuracy in sign language recognition, and their performance in recognizing the variety of signs has steadily improved over time. Additionally, integrating non-manual features has proven pivotal in enhancing recognition accuracy. Future research should refine advanced deep learning models and integrate non-manual features to improve system accuracy and applicability. These ongoing advancements hold the potential to revolutionize communication and break down barriers for individuals who rely on sign language as their primary mode of communication.

Hyperspectral imagery, capturing information beyond the visible spectrum, is essential for many remote sensing applications. This paper introduces a novel approach utilizing a 3D Convolutional Neural Network (CNN) enhanced with self-attention mechanism to address the limitations of traditional architectures in hyperspectral image classification. The main problem tackled is the effective extraction of both spatial and spectral features from complex hyperspectral datasets. Our proposed model simultaneously analyzes spatial and spectral dimensions with improved accuracy and adaptability through self-attention. We systematically evaluate the performance of 1D CNN, 2D CNN, and 3D CNN models, alongside Multi-Column Neural Network (MCNN) and Hybrid Spectral Network (Hybrid SN) models, comparing them to our proposed approach. The evaluation, conducted on the Indian Pines and Salinas datasets, highlights significant improvements in classification accuracy and adaptability. Specifically, the proposed 3D CNN with self-attention achieves accuracy values of 98.45% and 91.32% on the Indian Pines and Salinas datasets, respectively. This paper provides critical insights for data scientists and remote sensing experts, contributing to informed decision-making in selecting CNN models tailored to specific application requirements and advancing the field of hyperspectral image classification (HSIC). Furthermore, our approach shows improved handling of the complex spectral variability and spatial heterogeneity inherent in hyperspectral data.

Effective cardiovascular health monitoring relies on precise electrocardiogram (ECG) analysis for early diagnosis and treatment of heart conditions. Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs), have significantly enhanced the automation, accuracy, and personalization of ECG analysis. This review targets both medical professionals and a broader audience interested in deep learning applications. Our work explores the evolution of deep learning techniques in ECG analysis, from early CNN applications to current innovations in real-time processing and privacy-preserving methods. The paper discusses various deep learning models, including hybrid models, Recurrent Neural Networks (RNNs), and attention mechanisms, and their impact on diagnostic accuracy for diseases like myocardial infarction. Additionally, our paper examines ECG-based authentication systems, addressing challenges related to security and privacy, and highlighting recent technological advancements. By providing a detailed overview of these developments, the review offers valuable insights into future directions for deep learning in cardiovascular health monitoring and ECG-based authentication.

Lane detection is a key technical challenge in autonomous driving and advanced driving assistance systems. The current lane detection system has insufficient robustness and low detection accuracy problem in complex and harsh environmental conditions, and lacks research on visual cues without information. In order to accurately and effectively detect multiple lanes in challenging scenarios and meet the real-time requirements of actual driving, a lane detection method based on recurrent cross attention mechanism is proposed in this paper. Firstly, by embedding the recurrent cross attention module in the feature extraction module, the contextual information of surrounding pixels on the intersection path is obtained, and useful information is collected from other regions for accurate lane detection; Secondly, anchor point detection is selected based on the prior position of the designed lane starting position; Finally, combining mixed anchor points to represent lane coordinates, using row and column anchors to learn lane coordinates and improve the real-time performance of lane detection models. The detection model RCCLaneNet designed in this paper was evaluated on the TuSimple and CULane datasets. The experimental results showed that the F1 of this method can reach 97.05% on the TuSimple dataset, 78.5% on the CULane dataset, and the real-time inference speed index FPS can reach +150. The proposed model achieves advanced performance in lane detection tasks in complex scenes while maintaining real-time detection efficiency, and has good universality in actual driving scenarios.

Aiming at the problem that cracks with different shapes and scales on the exterior walls of high buildings are difficult to detect, this paper proposed a wall crack detection method for high-altitude cleaning robots by hybridizing the GAM attention mechanism and RepGFPN. First, the GAM attention mechanism was incorporated into the YOLOV5 backbone network to reduce information and amplify global features to improve the accuracy of feature extraction. Then, the neck network incorporated the RepFPN method to improve the descriptive ability of fused multi-scale features and to increase computational efficiency. Public datasets Concrete Crack Images for Classification, Mixed VOC2007, CrackForest-dataset-master, and UCMerced_LandUse were used for experimental validation. The ablation experiment results show that the average accuracy of mAP is improved by 13.5% after introducing the GAM attention mechanism under the yolov5 s original model, while the method in this paper (GR-YOLO) continues to improve by 4.7%. The experimental results show that the average accuracy mAP of the proposed method (GR-YOLO) is 24.0%, 47.1% and 41.0% higher than that of the model yolov5s + involution, yolov5s + p2 + involution and yolov5s + p2 + involution + CBAM, respectively. The method proposed in this article can more effectively improve the accuracy of crack detection and has important application prospects.

In the context of the current rapid development of the financial market, how to establish an effective stock price index model to avoid investment risks and enhance investment returns for investors has become a subject of great concern. On this basis, a new method of stock price index prediction employing deep neural networks is investigated. This paper employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique to break down the time series of the stock index into its constituent Intrinsic Modal Functions (IMFs). According to the similarity of the values of Fuzzy Entropy (FE), the subsequence is reorganized to become a new sequence, which highlights the fluctuation state of the stock index at different frequencies and improves the forecasting efficiency. In terms of a forecasting method, the combination of Temporal Convolutional Network (TCN), Gated Recurrent Unit (GRU), and Convolutional Block Attention Module (CBAM) is used to forecast the reorganized subsequence, and the final forecast results are reconstructed to obtain the final prediction value. To further evaluate the performance of the proposed CEEMDAN-TCN-GRU-CBAM model, this paper selects four representative stock indices in emerging and developed markets while comparing them with the benchmark model. It uses four evaluation metrics to measure the model performance. The study shows that the proposed model outperforms other benchmark models has better robustness and universality, and has higher forecasting accuracy.

Data compression at the Internet of Things (IoT) edge node aims to minimize data traffic in smart cities. The traditional Huffman Coding Algorithm (HCA) is shown as the most effective compression algorithm for sensor data. However, implementing the algorithm at IoT edge nodes is hindered due to memory limitations; HCA requires a large amount of memory to construct a Huffman tree to compress data. To address this issue, this paper proposes a new lossless Huffman Deep Compression (HDC) algorithm that incorporates the sliding window technique to fit in memory, reduces the complexity of the Huffman tree using deep learning pruning and pooling techniques, and uses pattern matching with pattern weights instead of using symbol matching and symbol frequencies in HCA. This paper introduces a sliding window approach to minimize memory usage, leveraging pattern matching and weights for higher compression and employing deep learning techniques to reduce the Huffman tree size through pruning and pooling. Experiments were performed using the Esp8266 MCU IoT node on eight numerical attributes from sensors of six of Malaysia’s air pollution station datasets. The findings demonstrate that the HDC algorithm has substantially reduced data size (p-value

Non-small cell Lung Cancer (NSCLC) is one of the most common types of lung cancer, accounting for approximately 80% to 85% of lung cancer cases, and survival prediction in this cancer is an essential medical task. Traditional survival prediction methods, which only rely on demographic and clinical variables, fail to fully characterize patients’ pathological characteristics and clinical factors, thus limiting the prediction effect. With the development of medical imaging technology and genomic methods, survival prediction studies have ushered in new analytical perspectives in recent years. However, most of the existing advanced techniques only rely on one class and few classes of medical data, which do not comprehensively characterize patients’ pathological features and clinical conditions, and similarly limit the predictive effect. To this end, in this paper a new method is proposed to solve this problem, that is, using flexible interpretable graph structure to fuse and model the multimodal data (including clinical data and CT images, etc.) of patients, so as to solve the problem of fragmentation and one-sidedness among multi-class data of survival prediction. A new multi-modal fusion graph convolutional network (FGCN) is proposed according to the characteristics of multi-modal graph data. The main body of the network structure is composed of a SAGE graph convolution layer with the self-attention mechanism, which accelerates the convergence of the model compared to the traditional one and ensures that the risk predicted by the model is as close to the actual situation as possible. More importantly, this study is the first to introduce the TopKPooling strategy in this model to address the problems of feature redundancy and excessive noise features arising from multimodal data fusion and to reduce the model complexity. A large number of ablation and comparison experiments and analyses on public non-small cell lung cancer datasets show that the method proposed in this paper achieves better prediction results, with a C-index value of 0.76, which exceeds the currently known advanced techniques, and fully proves the effectiveness and superiority of the method in this paper.

With the rapid development of science and technology, all types of mixed media contain large amounts of data. Traditional single multimedia data can no longer satisfy daily requirements. Therefore, the cross-modal retrieval technology has become an urgent requirement. Consequently, there is a pressing need for cross-modal retrieval technology. Its purpose is to mine the connection between different modal samples, that is, to retrieve another modal sample with approximate semantics through one modal sample. For example, users can retrieve multimedia data such as images or videos with text. However, there are differences in the modal representation of different types of multimedia data, and measuring the correlation between different modes is the main problem of cross-modal retrieval. Currently, the most popular deep learning methods have achieved remarkable results in the field of data processing and graphics. Many researchers have applied deep learning methods to cross-modal retrieval to solve the problem of similarity measurement between different multimedia data. By summarizing the relevant paper methods of cross-modal retrieval, this paper provides a definition of cross-modal retrieval problems, reviews the core ideas of the current mainstream cross-modal retrieval methods in the form of three main methods, lists the commonly used data sets and evaluation methods, and finally analyzes the problems and future research trends of cross-modal retrieval.

A small target object refers to an object whose relative size of the bounding box is very small, usually the ratio of the width of the bounding box to the width and height of the original image is less than 0.1, or the ratio of the area of the bounding box to the area of the original image is less than 0.03, or the absolute size is less than $32^{\ast } 32$ pixels. It has important applications in industrial defect detection, medical image processing, intelligent security, unmanned driving, and many other fields. Although great progress has been made in the field of target detection, which is limited to large target objects, due to the challenges of small size, inconspicuous features and insufficient data samples, the accuracy and speed of small target detection are low. To solve this problem, this paper proposes a novel small target object detection algorithm model: Swin Transformer’s DETR. In this algorithm, Swin Transformer is used as the backbone to extract the global features and local information of small targets, and a three-layer feature pyramid structure is used for feature fusion at the Neck layer to improve the calculation efficiency and model accuracy. Secondly, the detector is optimized, and the detector is replaced by two stages, and the ReLU activation function of FFN layer is replaced by the latest SwiGLU activation function, to avoid the problems of gradient disappearance and explosion and enhance the nonlinearity of the algorithm model. Large resolution size input is adopted on Tiny Person dataset, and its input value is set to [1400,800]. The above analysis is carried out on VOC and Tiny Person datasets, and the detection rates of small target objects are 88.9% and 48.3% respectively. The results show that the Swin Transformer’s DETR algorithm model proposed in this paper performs well on various datasets, and has strong generalization ability, stability and accuracy in different scenarios and datasets, which is higher than other algorithm models.

Ovarian cancer (OC) is one of the most common types of cancer in women. Surgery and chemotherapy are still the most common forms of treatment; however, their success depends on lots of factors describing the type of cancer, the size, shape, and its origin, and thus early and accurate detection could bring lots of benefits to increasing survival rate by applying custom/ personalized and effective treatment. This is why many researchers aim to obtain accurate computer-aided diagnosis (CAD) systems to assist in the early detection and diagnosis of such diseases. The current paper presents a systematic review of new trends in designing different deep learning-based intelligent systems for accurate OC detection and diagnosis. The paper presents the advantages of using deep learning approaches for OC diagnosis, the most used methods, and datasets. It performs a detailed analysis concerning the most preferred, effective, and accurate architecture. 95 articles published in journals and impact conferences were investigated between 2018 and 2024, focusing on 2021 and 2024. All included studies are indexed in PubMed, Scopus, or ISI Web of Science. The authors of the analyzed articles used private databases (the majority) and public databases. Neural networks and combined DL systems recently used for ovarian tumor detection, classification, and segmentation were investigated. The performances obtained by the most relevant systems were also presented. Finally, a comparison with other reviews in the field highlighted the advantages of this article.

Soaring energy demand in power sector demands an intelligent electricity grid which is able to meet the energy requirements of consumers with higher efficiency and reliability. This paper discusses about how Smart Grid (SG) is the solution for such requirements in the energy sector. SG constitutes of three main components: Information Technology (IT), Operational Technology (OT), and Advanced Metering Infrastructure (AMI). It is important to synchronize these three components in order to seamlessly deliver electricity to the consumers connected to the electric network. Cyberattack is increasing sharply in the SG infrastructure and triggers disrupting or malicious manipulation of entire electricity network. It is crucial to maintain the data traffic, power transmission and distribution with high reliability. This paper aims at arriving at the taxonomy of attacks that are subjected to IT, OT and AMI components of SG. This exhaustive taxonomy further opens avenues to evolve mitigation strategies for these attacks which will eventually aid in SG protection. Elaborate discussion about various mitigation strategies for various types of attacks is performed. The systematic approach of methods and techniques along with the type of solution it provides to SG applications is discussed. It is evident from literature that Machine Learning (ML), Deep Learning (DL) and signal processing techniques are very efficient and robust in attack detection and retaliating in SG. ML model pro. Hence the importance of framework and tools in SG environment is analyzed. A case study is also presented that emphasizes on SG Attacks on the specific system and its mitigation strategies.

Early fault warning for large-scale high-speed rotating machinery can effectively reduce unplanned downtime and avoid major safety accidents. Aiming at the problems of difficult screening of multi-source common sensitive features, the challenging training of neural networks with a small number of sensitive features, and the difficulty of directly using generative adversarial networks for early fault warning, this paper constructs an early fault warning model based on multi-source common sensitive features and an improved Wasserstein generative adversarial network, proposing an early fault warning method for rotating machinery. The model was verified by using the open XJTU-SY bearing laboratory data, the P3409A centrifugal pump bearing fault engineering case data of a petrochemical company and the rotor system engineering case data of a circulating hydrogen centrifugal compressor of a petrochemical company. The early fault warning method of rotating machinery proposed in this paper warns the bearing fault of centrifugal pump 160 hours in advance and the rotor system fault of centrifugal compressor 1330 minutes in advance. Compared with the two published methods, the proposed method has better early fault warning effect, better normal and abnormal health index discrimination and less false warning.

With the widespread availability and utilization of 3D meshes across various applications, the need for accurately assessing their visual quality has become increasingly important. Despite the significance of this task, the literature offers few No-Reference (NR) approaches for evaluating the visual quality of 3D meshes. In response to this gap, this paper proposes a novel NR approach tailored specifically to score the quality of 3D meshes. After rendering a 3D mesh into 2D views and patches, a pre-trained convolutional neural network automatically extracts deep features from. These features are then employed in a Multi-Layer Perceptron regressor to predict the quality score of the rendered images. The obtained scores are combined with their corresponding BRISQUE scores, and an additional MLP regressor is used to predict the final score. We present experimental results demonstrating the effectiveness and robustness of our approach across a diverse range of 3D mesh datasets. Comparative analyses with existing NR methods underscore the superior performance and versatility of the proposed approach. Overall, this paper contributes to the advancement of NR techniques for assessing 3D mesh quality, offering a valuable tool for researchers, practitioners, and developers working with 3D models across various domains.

Recently, the combination of convolutional neural network (CNN) and long short-term memory (LSTM) exhibits better performance than single network architecture. Most of these studies connect LSTM networks behind CNNs. When operating on hardware, the current design of CNN-LSTM is similar to a pipeline architecture. However, the classic structure lead to a feature loss when data is sent to LSTM since CNN is not good at extracting temporal features. At the same time, as the depth and scale increases, it will bring a huge amount of computation, which makes hardware implementation difficult. Based on that, a parallel CNN-LSTM architecture is proposed, in which two networks extract features from the input data synchronously, being proven to be more effective than classical CNN-LSTM. This paper designs a parallel CNN-LSTM computing device based on FPGA. The device is divided into control unit and operation unit. Control stream and data stream transport between the two units, ensuring the proper running of the device. A highly parallel multi-channel convolution layer and pooling layer are designed to improve the calculation efficiency. A 4-stage pipeline structure is adopted to implement the LSTM part. This paper makes full use of on-chip BRAM to design a look-up table for activation function approximation, reducing the resource consumption by 95% compared with the traditional polynomial approximation. Finally, we verify our device under cooperative spectrum sensing (CSS) and handwritten classification scenarios. Proposed device reaches higher accuracy in two scenarios compared with classic CNN-LSTM structure as well as faster calculating speed, and the overall project power is limited below 2W. The scalability and limitation of this computing device are also discussed.

Reinforcement learning (RL) has seen an uptick in research interest in recent years, with many papers published in a plethora of different fields, topics and applications. A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. This marriage of deep neural networks and traditional reinforcement learning methods has enabled reinforcement learning algorithms to achieve incredible results in use-cases that were previously deemed to be too complex. One of these highly complex scenarios is the realm of flight. In the last few years the number of papers published in this field has increased dramatically, but there is a fairly big divide between two major categories (quadcopters vs. fixed-wing aircraft). There is published work focusing on reinforcement learning using quadcopters, also known as drones, and there is published work focusing on fixed-wing aircraft, the more traditional aircraft, commonly referred to as airplanes. This paper focuses on the latter and will give an overview over the current state of reinforcement learning in the field of fixed-wing aviation. It will look into available toolkits usable for research as well as give an overview of different fields and tasks within reinforcement learning for fixed-wing aircraft control tasks. It contributes by providing detailed information about the methodologies used in each paper, while pointing out each paper’s contributions as well. A total of 48 papers on fixed-wing RL and airplane control are discussed. With the main focus lying on papers published post 2015, after the publication of Mnih et al.’s impactful deep RL paper.

The convergence of Information Technology (IT), Operational Technology (OT), and Educational Technology (ET) has led to the emergence of the fourth industrial revolution. As a result, a new concept has emerged known as Digital Twins (DT), which is defined as “a virtual representation of various objects or systems that receive data from physical objects/systems to make changes and corrections”. In the aviation industry, numerous attempts have been made to utilize DT in the design, manufacturing, and condition monitoring of aircraft fleets. Among these research efforts, real-time, accurate, fast, and predictive condition monitoring methods play a crucial role in ensuring the safe and efficient performance of aircraft. Using DT for condition and fleet monitoring not only enhances the reliability and safety of aircraft but also reduces operational and maintenance costs. In this paper, the conducted studies on the applications of DT systems for condition monitoring of aircraft units and the aerospace sector are discussed and reviewed. The aim of this review paper is to analyse the current developments of DT systems in the aviation industry as well as explain the remaining challenges of DT systems. Then Finally, future trends of DT systems along with aircraft are presented. Among reviewed papers, most of them have used computational fluid dynamics, finite element methods, and artificial intelligence techniques for developing DT models for aircraft. At the same time, most of these analyses are dedicated to the failure and crack detection body of aircraft as well as engine fault detection. Life prediction is another popular application for using DT in aircraft units that could help the engineers predict the maintenance required for different parts of the aircraft. Finally, the application of DT in marine, power systems, and space programs has been also reviewed and the lessons learned from them have been translated to the aviation sector.

Sentiment analysis extracts valuable insights from textual sources using computation, textual or systematic analysis, and natural language processing. It identifies and measures the attitudes, beliefs, and emotional states individuals express through text data. Recent research on sentiment analysis has largely focused on the English language; therefore, low-resource languages are getting much less attention. Conducting sentiment analysis of low-resource languages is difficult because large datasets and related repositories are unavailable. This paper creates a new dataset for low-resource language (Urdu) to address this issue. The dataset, namely LUCSA-23, consists of more than 65,000 user reviews from various genres, including food, sports, showbiz, apps, and political reviews from developing countries, i.e., Pakistan. Urdu domain experts further annotate the created dataset. This paper proposes an Urdu sentiment analysis approach leveraging the transformer model, i.e., XLM-R and GPT-2. It preprocesses the Urdu text input, generates BERT embeddings, and passes them to the proposed classifier as input for sentiment classification. The proposed classifier is compared with machine/deep/embedded classifiers to evaluate its performance. The findings show that the proposed classifiers outperform existing state-of-the-art approaches with an accuracy of 95%.

This paper introduces a novel method for accurately estimating the 2D coordinates of hand keypoints from single static images, utilizing a sequential convolutional neural network optimized with Maximum Likelihood Estimation Loss. Unlike traditional heatmap-based techniques, our approach eliminates the need to generate label heatmaps and sidesteps the direct optimization of model parameters based on noisy labels. Instead, it concentrates on modeling the distribution of the discrepancies between predicted results and ground truth, rather than the potential presence of noisy labels, thus enabling the direct prediction of hand keypoint coordinates. Furthermore, we propose a sequential training and inference framework that consists of a deep convolutional backbone network and a multi-stage sequential network. Each stage of this network features similar structures, facilitating the progressive and precise prediction of hand keypoint coordinates. Our extensive experimental results demonstrate that our approach is both highly accurate and robust, outperforming mainstream methods under the experimental conditions detailed in this paper.

Automated early detection and classification of paddy diseases help in applying treatment efficiently according to the detected diseases. Early detection also minimises the usage of chemical substances and pesticides and hinders the spread of the disease to healthy crops. On a broader scale, it aids in halting the global spread of diseases. Thus, it ultimately promotes healthier rice crops and increased yield. In this survey paper, we present a thorough exploration of deep learning (DL) models for the classification of paddy diseases. Our paper delves into the motivation behind this research study, reveals different paddy diseases and their associated symptoms, and unravels various deep-learning models employed for disease detection. We have also discussed strategies used by researchers for improving the performance of DL models, along with adaptations tailored for application-specific contexts. Additionally, we illustrate relevant research findings, explore datasets utilised in this domain, and analyse approaches for data augmentation. Through an exhaustive investigation, we emphasise existing research gaps, challenges, and open issues, concluding in a discussion on avenues for future exploration.

The transformer architecture has been focused on many tasks like natural language processes, vision tasks and etc. The most important and general requirement of using the transformer-based architecture is that the model must be trained on a large-scale dataset before it can be fine-tuned for a specific task like classification, object detection and etc. However, in this paper, we find that the transformer architecture has better generalization capability to capture the features from data samples for sleep stage classification than CNN-based architectures, despite using a small-scale dataset without pretraining on large-scale dataset. This outcome contradicts the widely-held belief that a transformer architecture is more effective when trained on large datasets. In this paper, we investigate the attention behavior of a transformer model and demonstrate how global and local attentions influence an attention map in a transformer architecture. Finally, through experiments, we show that restricting global attention using ‘Masked Multi-Head Self-Attention (M-MHSA)’ can lead to improved model generalization in sleep stage classification compared with the previous methodologies and original transformer-based architecture on three different datasets.

Time series forecasting involves justifying assertions scientifically regarding potential states or predicting future trends of an event based on historical data recorded at various time intervals. The field of time series forecasting, supported by diverse deep learning models, has made significant advancements, rendering it a prominent research area. The broad spectra of available time series datasets serve as valuable resources for conducting extensive studies in time series analysis with varied objectives. However, the complexity and scale of time series data present challenges in constructing reliable prediction models. In this paper, our objectives are to introduce and review methodologies for modeling time series data, outline the commonly used time series forecasting datasets and different evaluation metrics. We delve into the essential architectures for trending an input dataset and offer a comprehensive assessment of the recently developed deep learning prediction models. In general, different models likely serve different design goals. We boldly examine the performance of these models under the same time series input dataset with an identical hardware computing system. The measured performance may reflect the design flexibility among all the ranked models. And through our experiments, the SCINet model performs the best in accuracy with the ETT energy input dataset. The results we obtain could give a glimpse in understanding the model design and performance relationship. Upon concluding the paper, we shall provide further discussion on future deep learning research directions in the realm of time series forecasting.

This paper proposes to explore the Radio Frequency Fingerprint (RFF) identification with a virtual database generator. RFF is a unique signature created in the emitter transmission chain by the hardware impairments. These impairments may be used as a secure identifier as they cannot be easily replicated for spoofing purposes. In recent years, the RFF identification relies mainly on Deep Learning (DL), and large databases are consequently needed to improve identification in different environmental conditions. In this paper, we introduce the so-called RiFyFi_VDG, referring to Radio Frequency Fingerprint Virtual Database Generator, and explore individually the impairment impact on the classification accuracy to highlight the most relevant impairment. Different transmission scenarios are then explored, such as the impact of the data type (being a preamble or a payload) and the data size. Design rules of real databases are finally drawn for the different scenarios. We found out that the power amplifier imperfections play the biggest role in RFF accuracy and that average accuracy levels of 94% can be reached when combining the various hardware impairments at the transmitter.

With the rapid development of the Internet, people independently write comments with emotional characteristics on e-commerce platforms, which express consumers’ emotional tendencies towards products or services from multiple perspectives. The sentiment analysis technology of product reviews has attracted more and more attention. In recent years, the Transformer model has performed well in the field of text sentiment analysis. However, in the process of text emotion classification, the Transformer basic model cannot be well obtained when the distance between words is relatively long, and there are problems such as a low accuracy rate and recall rate and too much time spent on the overall training of model construction. Using conditional random field (CRF) as a classifier can effectively solve the problem of too long text segmentation distance, reduce model training time, and increase the accuracy of text sentiment analysis. Therefore, this paper proposes a new method for text sentiment analysis combining the improved Transformer model and a conditional random field. First of all, this paper enhances the Transformer model by adapting the decoder to better suit the task of sentiment classification, and introduces an enhanced version of the Transformer model. It is then classified by combining long-short-term memory (LSTM) and CRF. The experimental results show that in the IMDB data set, the Transformer CRF model has higher accuracy, recall rate, and F1 value, reaching 85.51%, 83.77%, and 85.06%, respectively. Compared with other methods, the results of evaluation indicators further verify that the text method has better recognition performance and generalization ability, and at the same time, it has a better understanding of customers’ emotions towards a specific aspect, provides users with accurate services, effectively improves user satisfaction, and has a high use value for enterprises’ business decisions.

This paper introduces an innovative methodology to disrupt deep-learning (DL) surveillance systems by implementing an adversarial framework strategy, inducing misclassification in live video objects and extending attacks to real-time models. Focusing on the vulnerability of image-categorization models, the study evaluates the effectiveness of face mask surveillance against adversarial threats. A real-time system, employing the ShuffleNet V1 transfer-learning algorithm, was trained on a Kaggle dataset for face mask detection accuracy. Using a white-box Fast Gradient Sign Method (FGSM) attack with epsilon at 0.13, the study successfully generated adversarial frames, deceiving the face mask detection system and prompting unintended video predictions. The findings highlight the risks posed by adversarial attacks on critical video surveillance systems, specifically those designed for face mask detection. The paper emphasizes the need for proactive measures to safeguard these systems before real-world deployment, crucial for ensuring their robustness and reliability in the face of potential adversarial threats.

Situation Awareness (SA) is a process of sensing, understanding and predicting the environment and is an important component in complex systems. The reception of information from the environment tends to be continuous and of a multimodal nature. AI technologies provide a more efficient and robust support by subdividing the different stages of SA objectives into tasks such as data fusion, representation, classification, and prediction. This paper provides an overview of AI and multimodal methods used to build, enhance and evaluate SA in a variety of environments and applications. Emphasis is placed on enhancing perceptual integrity and persistence. Research indicates that the integration of artificial intelligence and multimodal approaches has significantly enhanced perception and comprehension in complex systems. However, there remains a research gap in projecting future situations and effectively fusing multimodal information. This paper summarizes some of the use cases and lessons learned where AI and multimodal techniques have been used to deliver SA. Future perspectives and challenges are proposed, including more comprehensive predictions, greater interpretability, and more advanced visual information.

This paper presents the 6th edition of the “Drone-vs-Bird” detection challenge, jointly organized with the WOSDETC workshop within the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023. The main objective of the challenge is to advance the current state-of-the-art in detecting the presence of one or more Unmanned Aerial Vehicles (UAVs) in real video scenes, while facing challenging conditions such as moving cameras, disturbing environmental factors, and the presence of birds flying in the foreground. For this purpose, a video dataset was provided for training the proposed solutions, and a separate test dataset was released a few days before the challenge deadline to assess their performance. The dataset has continually expanded over consecutive installments of the Drone-vs-Bird challenge and remains openly available to the research community, for non-commercial purposes. The challenge attracted novel signal processing solutions, mainly based on deep learning algorithms. The paper illustrates the results achieved by the teams that successfully participated in the 2023 challenge, offering a concise overview of the state-of-the-art in the field of drone detection using video signal processing. Additionally, the paper provides valuable insights into potential directions for future research, building upon the main pros and limitations of the solutions presented by the participating teams.

Diabetic Retinopathy is one of the leading global causes of vision impairment and blindness in humans. It has seen a rise in prevalence, necessitating the development of advanced automatic detection methods. This paper presents a survey of the evolution in deep learning techniques for diabetic retinopathy detection, emphasizing the transition from traditional machine learning to sophisticated deep learning architectures such as convolutional neural networks. It discusses the role of transfer learning, end-to-end learning, and hybrid models in overcoming medical detection challenges while highlighting the need for artificial intelligence interpretability and real-time screening integration in clinical workflows. Building on this survey, the paper introduces a focused study on cross-dataset deployment of transfer learning for diabetic retinopathy detection and grading. Consequently, this paper evaluates 26 pre-trained models from various convolutional neural network families to provide a comprehensive comparison between the state-of-the-art CNN architectures in the field. Additionally, this study also employs Grad-CAM visualization to interpret the model’s decision-making, bridging advanced artificial intelligence techniques with practical healthcare applications for diabetic retinopathy.

Researchers are working towards automation of ontology construction to manage the ever-growing data on the web. Currently, there is a shift from the use of machine learning techniques towards exploration of deep learning models for ontology construction. Deep learning model are capable of extracting terms, entities, relations, and classifications, and perform axiom learning from the underutilized richness of web-based knowledge. There has been remarkable progress in automatic ontology creation using deep learning models since they can perform word embedding, long-term dependency acquisition, concept extraction from large corpora, and inference of abstracted relationships based on broad corpora. Despite their emerging importance, deep learning models remain underutilized in ontology construction, and there is no comprehensive review of their application in ontology learning. This paper presents a comprehensive review of existing deep-learning models for the construction of ontologies, the strength and the weaknesses presented by the deep learning models for ontology learning as well as promising directions to achieve a more robust deep learning models. The Deep Learning models reviewed include Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long-Short Term Memory (LSTMs), and Gated Recurrent Unit (GRU) as well as their ensembles. While these traditional deep learning models have achieved great success, one of their limitations is that they struggle to understand the meaning and order of data in sequences. CNNs and RNN-based models such as LSTMs and GRUs can be computationally expensive due to their large number of parameters or complex gating mechanisms. Furthermore, RNN models suffer from vanishing gradients, making it difficult to learn long-term relationships in sequences. Additionally, RNN-based models process information sequentially, limiting their ability to take advantage of powerful parallel computing hardware, slowing down training and inference, especially for long sequences. Consequently, there has been a shift towards Generative Pre-Trained (GPT) models and Bidirectional Encoder Representations from Transformers (BERT) models. This paper also reviewed the GPT-3, GPT-4, and the BERT models for extracting terms, entities, relations, and classifications. While GPT models excel in contextual understanding and flexibility, they fall short when handling domain-specific terminology and disambiguating complex relationships. Fine-tuning and domain-specific training data could minimize these shortcomings, and further enhance the performance of GPT in term and relation extraction tasks. On the other hand, the BERT models excel in comprehending context-heavy texts, but struggles with higher-level abstraction and inference tasks due to a lack of explicit semantic knowledge, thus necessitating inference for unspecified relationships. The paper recommends further research on deep learning models for ontology alignment and merging. Also, the ensembling of deep learning models and the use of domain-specific knowledge for ontology learning require further research for ontology construction.

Nowadays, the advancements of wearable consumer devices have become a predominant role in healthcare gadgets. There is always a demand to obtain robust recognition of heterogeneous human activities in complicated IoT environments. The knowledge attained using these recognition models will be then combined with healthcare applications. In this way, the paper proposed a novel deep learning framework to recognize heterogeneous human activities using multimodal sensor data. The proposed framework is composed of four phases: employing dataset and processing, implementation of deep learning model, performance analysis, and application development. The paper utilized the recent KU-HAR database with eighteen different activities of 90 individuals. After preprocessing, the hybrid model integrating Extreme Learning Machine (ELM) and Gated Recurrent Unit (GRU) architecture is used. An attention mechanism is then included for further enhancing the robustness of human activity recognition in the IoT environment. Finally, the performance of the proposed model is evaluated and comparatively analyzed with conventional CNN, LSTM, GRU, ELM, Transformer and Ensemble algorithms. To the end, an application is developed using the Qt framework which can be deployed on any consumer device. In this way, the research sheds light on monitoring the activities of critical patients by healthcare professionals remotely. The proposed ELM-GRUaM model achieved supreme performance in recognizing multimodal human activities with an overall accuracy of 96.71% as compared with existing models.

Several recent studies in dentistry and maxillofacial imaging have concentrated on the Mandibular Canal (MC) segmentation in digital dental radiographs. In this research domain, deep learning approaches have demonstrated promising outcomes. This systematic literature review (SLR) aims to comprehensively analyze and synthesize the recent advancements in applying deep learning techniques for MC segmentation in digital dental radiographs. This review encompasses studies published between 2018 and 2023, sourced from reputable databases, including PubMed, ScienceDirect, IEEE Xplore, Google Scholar, and SpringerLink. This study identified 30 primary research papers focusing on MC segmentation in digital dental radiographs. The review categorizes papers into two groups based on the digital dental radiograph type. The first group uses a 2D digital dental radiograph from a panoramic radiograph and 2D Cone Beam Computed Tomography (CBCT) scans. The second group uses 3D datasets from volumetric data from CBCT scans. The synthesized knowledge from this review is intended to guide researchers, dentists, and oral surgeons in leveraging deep learning advancements in MC segmentation for oral and maxillofacial surgery. Prior studies have faced challenges, including limited public datasets, variations in MC anatomy, time consumption and observer variability in MC annotation, complexities in deep learning models, and lack of practical implementation. To overcome these challenges, it is suggested that more public datasets be collaboratively created and shared within the research community, focusing on handling anatomy variability, improving digital dental radiograph quality, streamlining annotation processes through automated tools, and simplifying deep learning models for practical implementation.

The relentless global population growth and the ever-increasing food demand pose formidable challenges to the agricultural sector. Farmers grapple with the ongoing challenge of wildlife-induced crop damage and human loss, which not only impedes food production but also exacerbates supply and demand imbalances. However, the rise of TinyML enables Edge AI as a promising avenue for implementing resource-efficient deep learning techniques on low-end edge devices. In this paper, we introduce an innovative solution that harnesses the power of Edge AI using tinyML-based deep learning algorithms in conjunction with the Internet of Things (IoT) for animal intrusion detection and deterrence system. The proposed system is developed to create remotely managed defense system tailored to safeguard vast agricultural expanses. It integrates a laser detection system and an AI-CAM with light weight deep learning algorithms for animal intrusion detection and classification. This system also ensures efficient animal deterrence and real-time monitoring for farmers, enabling them to assess the situation with the assistance of an intelligent rover build using IoT. This work emphasizes on proposing a light-weight deep learning model named EvoNet for animal classification. Results reveal that the proposed model achieves the highest accuracy at 96.7%, surpassing other models presented in this paper. However, for edge devices where compact file sizes are crucial, the model also offers comparable accuracy with file sizes as low as 1.63MB with the help of pruning and quantization techniques. This conceptualized solution has the potential to revolutionize agricultural wildlife management, ushering in a new era of crop protection and economic resilience.

Billions of people globally use Android devices (https://backlinko.com/iphone-vs-android-statistics). As such, these devices are highly targeted by security attackers. One of the most threatening attacks is to infect devices with malicious software (malware). Fortunately, there are various ways to counteract these attacks and prevent them. One of these methods is developing a comprehensive malware dataset that researchers can utilize for malware analysis, detection, prediction, and prevention systems. This paper introduces a unique, up-to-date, labeled Android malware dataset (Maloid-DS) comprising a comprehensive set of malware families that reached 345 families with 47,971 malware samples. First, we intensely studied existing datasets utilized by previous research works. These datasets are limited in (a) the number of studied families, (b) the number of samples under each family, (c) the number of new malware samples, (d) the proper categorization of the malware families, (e) the accurate mapping of the sample with its corresponding malware family, (f) providing well structuring of the malware families and subfamilies, and (g) presenting a profound description of each family behavior. All these limitations were seriously tackled by introducing Maloid-DS. The process of creating Maloid is detailed in this paper. Moreover, several case studies are demonstrated in this paper to show the value of Maloid and how different types of analysis systems and AI-based detection and prediction solutions could utilize it. While the full potential of Maloid-DS in real-world scenarios is subject to ongoing research and practical application, it represents a substantial contribution to the cybersecurity community, offering a broad and detailed foundation for protecting Android devices against malware threats.

Ensuring flight safety for unmanned aerial vehicles (UAVs) is a critical concern, necessitating effective mathematical modeling for safety forecasting in both academic and industrial contexts. This study addresses this need by combining the capabilities of deep neural networks and grey analysis to create a comprehensive mathematical modeling approach focused on spatial information service (SIS). The paper introduces a novel spatial information-aware flight security forecasting model for UAVs, emphasizing the transformative impact of the new methodology. Traditionally, factors influencing flight safety are identified and formulated based on SIS chain technology, SIS management rules, SIS business processes, and spatial SIS chain verification. To address the challenges posed by significant data volatility and missing data in the sample dateset, a non-equally spaced GM (1,1) model with an approximate non-simultaneous exponential law series is developed for prediction. Subsequently, multiple influencing factors are encoded and input into a specific BP neural network structure. The paper concludes with simulation experiments to evaluate the proposed model. The results of the simulation analysis demonstrate that the integration of deep learning and grey analysis in the proposed model effectively recognizes flight security risks with high efficiency. This underscores the transformative potential of the new approach in enhancing UAV flight safety forecasting.

Traffic Classification (TC) systems are designed to identify the applications generating network traffic. Recent advancements in TC leverage Deep Learning (DL) techniques, surpassing traditional methods in complex scenarios, including those with encrypted traffic. Notably, state-of-the-art DL-based TC systems have been developed for wireless networks using Physical Layer (L1) packets. This approach overcomes the common limitation in TC research that assumes traffic flows within a wired network under a single network management domain. Despite their benefits, DL-based TC systems often demand significant computational resources, typically available only in cloud environments. Consequently, deploying models at the edge is often infeasible due to their resource-intensive nature, given their original training and optimization for high-resource environments. The inherent challenge lies in adapting these systems for edge computing scenarios, including deployment at access points. In this paper, we propose a novel methodology that exploits expert knowledge in combination with recent advances in Multi-Task Learning (MTL) and Deep Neural Network (DNN) optimization to allow spectrum-based TC systems to run on constrained devices. This paper propose a well-defined and innovative methodology for resource-efficient, spectrum-based TC to address this issue, combining MTL with DNN optimization techniques. Performance evaluations on an NVIDIA Jetson TX2 demonstrate that our most optimized MTL model, handling four TC tasks, can reduce memory requirements by a factor of 2.65x and improve execution time by 3.6x compared to sequential execution of four Single-Task Learning (STL) models in a server-grade configuration, with minimal accuracy impact (less than a 0.5% drop) and energy efficiency of 0.97 millijoules per sample at inference. Compared to other edge platforms such as the Raspberry Pi model 3B+ (RPI3B+) with a low-power Artificial Intelligence (AI)-accelerator such as the Coral Tensor Processing Unit (TPU), the NVIDIA Jetson achieves a 12-fold improvement in energy efficiency with no impact on accuracy.These are the first available results to provide a benchmark for different performance metrics (memory, computing, energy) over heterogeneous constrained devices for this type of TC system.

Early detection of driver behavior is a pivotal aspect in enhancing road safety, focusing on identifying and mitigating risky driving patterns before they lead to accidents. The use of smartphone sensors for data acquisition marks a significant advancement in this field. It allows for continuous, real-time monitoring of driving patterns without the need for specialized equipment. In this study, we leverage a publicly available smartphone motion sensor dataset, utilizing accelerometer and gyroscope data from a Samsung Galaxy S21 to analyze driving behaviors classified as slow, normal, and aggressive. This research introduces a novel feature engineering technique named the RKnD (Random forest, K-nearest classifier, Decision tree) probabilistic feature engineering technique, which integrates three prominent machine learning (ML) models. This blend offers a robust analysis of driver behavior, leveraging the strengths of each algorithm. This paper emphasizes the importance of data balancing in machine learning, employing the Synthetic Minority Oversampling Technique (SMOTE) to enhance the reliability of the predictions. Furthermore, k-fold cross-validation is used to ensure the model’s consistency and accuracy across original features and the proposed RKnD probabilistic features of the data sets. By achieving such high accuracy, the study demonstrates the potential of smartphone-based systems to significantly improve road safety. This paper introduces a novel approach utilizing smartphone motion sensor data to detect driver behaviors with a remarkable accuracy rate of 99.63%. This research stands out for its application of machine learning techniques in a practical, accessible manner. This pioneering approach named RKnD feature engineering sets a new standard in the realm of smart transportation systems, opening avenues for further innovations in the field, and filling a gap in road safety analysis to avoid road accidents. Future research on RKnD should streamline its algorithm for real-time use, diversify datasets, integrate advanced Deep Learning for complex pattern detection, and undertake real-world testing to validate practicality and uncover challenges.

In view of the low segmentation accuracy for small-scale object and insufficient segmentation of local boundary for semantic segmentation methods based on Deep Learning, this paper proposes an image semantic segmentation approach based on attention mechanism and feature fusion. On the basis of ensuring the overall accuracy, the segmentation accuracy of small-scale object and local boundary is improved, and it meets the requirement of accurately segmenting the object in the complex background. Firstly, an image semantic segmentation model based on hybrid cascade and feature fusion is proposed, and the hybrid concatenation and multi-cores pooling methods are used to extract deeper semantic information. Then, a cross-stages fusion approach is designed to divide the backbone network of the encoder stage in the network and the improved Atrous Spatial Pyramid Pooling module into three stages to fully utilize the different semantic information of the shallow and deep layers. Thirdly, the attention mechanism is introduced into the hybrid cascade and feature fusion image semantic segmentation network model, and image semantic segmentation model based on cross-stages and attention mechanisms is explained. Self attention is added to channel attention enhances the connection between feature maps, and one-dimensional convolution in the spatial attention mechanism is used to increase the spatial receptive field. The final results on the public dataset PASCAL VOC2012 and SUIM show that MIoUs have reached 86.68% and 61.55% respectively, and it proved that the overall accuracy of the approach proposed in this paper is higher than other ones.

This work reviews the critical challenge of data scarcity in developing Transformer-based models for Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), specifically focusing on Motor Imagery (MI) decoding. While EEG-BCIs hold immense promise for applications in communication, rehabilitation, and human-computer interaction, limited data availability hinders the use of advanced deep-learning models such as Transformers. In particular, this paper comprehensively analyzes three key strategies to address data scarcity: data augmentation, transfer learning, and the inherent attention mechanisms of Transformers. Data augmentation techniques artificially expand datasets, enhancing model generalizability by exposing them to a wider range of signal patterns. Transfer learning utilizes pre-trained models from related domains, leveraging their learned knowledge to overcome the limitations of small EEG datasets. By thoroughly reviewing current research and methodologies, this work underscores the importance of these strategies in overcoming data scarcity. It critically examines the limitations imposed by limited datasets and showcases potential solutions being developed to address these challenges. This comprehensive survey, focusing on the intersection of data scarcity and technological advancements, aims to provide a critical analysis of the current state-of-the-art in EEG-BCI development. By identifying research gaps and suggesting future directions, the paper encourages further exploration and innovation in this field. Ultimately, this work aims to contribute to the advancement of more accessible, efficient, and accurate EEG-BCI systems by addressing the fundamental challenge of data scarcity.

Deep learning has revolutionized object detection, with YOLO (You Only Look Once) leading in real-time accuracy. However, detecting moving objects in visual streams presents distinct challenges. This paper proposes a refined YOLOv8 object detection model, emphasizing motion-specific detections in varied visual contexts. Through tailored preprocessing and architectural adjustments, we heighten the model’s sensitivity to object movements. Rigorous testing against KITTI, LASIESTA, PESMOD, and MOCS benchmark datasets revealed that the modified YOLOv8 outperforms the state-of-the-art detection models, especially in environments with significant movement. Specifically, our model achieved an accuracy of 90%, a mean Average Precision (mAP) of 90%, and maintained a processing speed of 30 frames per second (FPS), with an Intersection over Union (IoU) score of 80%. This paper offers a detailed insight into object trajectories, proving invaluable in areas like security, traffic management, and film analysis where motion understanding is critical. As the importance of dynamic scene interpretation grows in artificial intelligence and computer vision, the proposed enhanced YOLOv8 detection model highlights the potential of specialized object detection and underscores the significance of our findings in the evolving field of object detection.