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Accurate net load forecasting contributes to increasing the integration of renewable energy sources and reducing the operating cost of the power grid. In recent years, deep learning models have been applied in net load forecasting. However, there is still a large scope for them to improve the extraction of complex multi-scale temporal features in the net load. In addition, most researches do not establish explicit mechanisms to consider the impact of relevant factors on net load forecasting, such as renewable energy outputs and load demand. Therefore, in this paper, we propose a multivariate Transformer-based model, named TCAMS-Trans, to achieve direct net load forecasting. First, TCAMS-Trans exploits multi-scale patch-based variable-independent embedding to model potential multi-scale patterns in the load data. Subsequently, TCAMS-Trans designs a new hierarchical encoder–decoder pair configured with temporal-channel attention block (TCA block) to further extract multi-scale features and capture the connections between the net load and the relevant factors. The temporal attention in TCA block is used to mine complex temporal dependency along the time dimension, and the channel attention is used to capture the inter-variable dependency along the variable dimension. Experiments conducted on three publicly available datasets show that TCAMS-Trans has higher prediction accuracy than the current state-of-the-art models at different prediction lengths. The model proposed in this paper provides a new powerful solution for net load forecasting. [ABSTRACT FROM AUTHOR]

Today, wind and solar energy sources have opened their place in the power system due to their environmental appeal. With the presence of these renewable energy sources (RESs), forecasting net load (NL) and its ramp are of huge importance owing their fundamental role in evaluating and improving the flexibility of the power grid. However, it is challenging to analysis flexibility and NL ramps using NL forecasts due to the uncertainty of RESs. Furthermore, the surge in the number of electric vehicles (EVs) as notable electricity consumers, and how their charging strategy can affect the NL curve are matters of concern. Most NL forecasting and flexibility analyses overlook the concurrent integration of wind and solar sources. Moreover, the impact of controlled EVs charging on grid flexibility remains largely unexplored. Existing studies also fail to assess the proper combination of wind and solar energy sources for future flexibility needs. This paper presents a framework for forecasting NL and detecting ramps with the simultaneous presence of EVs and RESs to investigate the impact of controlled charging of EVs on flexibility. Investigating charging of EVs as a solution is delved deeply in this paper to reduce NL ramps and improve flexibility. The numerical results show that controlled EVs charging may lead to 3% ramp reduction and 52% increment in the system flexibility. This paper also proposes to forecast and find the proper combination of RESs for the next years, leading to a 75% increment in the system flexibility compared to the business as usual RESs penetration. [Display omitted] [ABSTRACT FROM AUTHOR]

With the rapid development of blockchain and digital currencies, cybercrimes targeting blockchain transactions are proliferating. Various abnormal transaction behaviors caused by malicious nodes and malicious transactions have emerged in blockchain systems. Therefore, blockchain transaction security research is a hot issue in blockchain research. Deep learning has made remarkable achievements in image and audio. However, deep learning is less effective when processing text-like data than tree-based models. Therefore, for the highly unbalanced textual financial dataset, this paper designs a deep learning anomaly detection method called 1D SA-Inception by combining CNN with Transformer structure on the basis of traditional one-dimensional CNN (1D-CNN), introduces the Inception structure to learn the information of different scales of tabular data, and introduces the Transformer (self-attention) self-attention mechanism for the research of blockchain anomaly transaction detection technology, effectively improving the prediction effect. Finally, by conducting experiments on the extreme class imbalance dataset, the results show that the 1D SA-Inception designed in this paper outperforms the traditional CNN with AUC, G-mean, and F1 indexes of 91.05 %, 84.95 %, and 83.52 %, respectively, on the textual dataset. This method can more efficiently achieve the detection of abnormal transaction behavior, safeguard consumer rights and interests, and ensure the safe development of the financial sector. [ABSTRACT FROM AUTHOR]

• Enhances network anomaly detection and security using ML. • ML methods for network threat detection are evaluated. • Optimizes ML model training hyperparameter modification, validation, and transparency. • Wireless Sensor networks provide real-time anomaly detection. • Scalability, model interpretability, and attack resistance are addressed. Robust solutions are essential for protecting complex network systems in the constantly changing cybersecurity scenario. This investigation examines the role of machine learning (ML) in improving the safety of digital infrastructure by examining network anomaly detection and security defense. We evaluate the effectiveness of key ML techniques, including deep learning (DL), ensemble methods, and supervised and unsupervised learning, in recognizing abnormalities and reducing dangers. Real-time monitoring and adaptive reactions are emphasized in the analysis of ML-based systems integration into comprehensive security frameworks. This section also addresses challenges like interpretability, harmful assaults, and model resilience. Real-world case examples emphasize the importance of ML in the advancement of network security. The paper continues with a discussion of ML's future role in the optimization of security measures and the mitigation of emergent threats. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents an integrated Unmanned Aerial Vehicle (UAV) imagery system and artificial intelligence-based smart farming for saffron cultivation. This work is about monitoring the growth of saffron flowers through UAV imagery every week or month during the pre-harvesting time while the percentage of the covered area by leaf and saffron flowers is calculated. This is crucial for predicting saffron growing areas over cultivated regions. From the vertical viewing of UAV imagery from above, it is problematic from the bird's eye perspective to identify the particular saffron-growing areas of things, so the multi-class classification of flowers (subjects) recognition system is proposed. So, the implementation of this work is divided into four phases. The first two phases (Phase 1 and Phase 2) are about the algorithms for Saffron growing region detections and predictions using the modified You Only Look Once (YOLO) higher version model followed by statistical analysis based region corner detections. The outcomes of the first two phases are used to form a database of saffron flowers with different scales, orientations, and deformations. This is further used to build a two-class classification model in the third phase (Phase 3) that discriminates saffron vs. non-saffron flowers. In the fourth phase, the two-class classification model is extended to a multi-class flower recognition model for recognizing 900 flower images database available in the largest social media website dedicated exclusively to gardening in the Phase 4. Extensive experiments are conducted, and the performances are compared with some existing state-of-the-art methods that show the superiority of the proposed system. [ABSTRACT FROM AUTHOR]

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, greatly avoiding power waste. As a result, time series forecasting in the field of power can bring great benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous researches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Season-aware Block to enhance the model's ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement, which can help power generation companies reduce costs and further reduce prices to benefit consumers; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Season-aware Block through visualization experiments through visualization techniques. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common cause of deaths among women due to cancer. A computer-assisted prognosis and diagnosis of breast cancer, which effectively reduces the mortality rate, is rapidly advancing field through medical imaging. Therefore, in an attempt, this paper introduces a novel deep learning and machine learning based hybrid framework for the classification of mammographic masses. Deep learning models are used to extract the deep features from the suspicious mass candidates obtained from a mass detection system while an optimized ensemble classifier is employed for the categorization of the detected mass lesions. The optimization is done via a nature-inspired tunicate swarm optimization where the cost function incorporates sensitivity and false positives to improved efficiency. Several experiments are performed with the proposed system and evaluated on 68 and 500 mammograms datasets from two standard databases—mini-MIAS and DDSM, respectively. In the experiment, false positive reduction, the model achieved sensitivities of 93.8% and 92.3% with false positives per image of 0.18 and 0.32, respectively for the mini-MIAS and DDSM datasets. For the characterization of malignant tumor against benign lesions and normal tissues, the introduced approach attained an A z value of 0.99 with an accuracy of 97.76% for the mini-MIAS dataset while the same for the DDSM dataset are 0.95 and 95.74%, respectively. The accuracy of a three-class categorization (normal, benign, and malignant) is 93.61% in the mini-MIAS dataset and 88.15% in the DDSM dataset, with malignancy F 1 scores of 0.86 and 0.80, respectively. [ABSTRACT FROM AUTHOR]

Deaf-mute individuals encounter substantial difficulties in their daily lives due to communication impediments. These individuals may encounter difficulties in social contact, communication, and capacity to acquire knowledge and engage in employment. Recent studies have contributed to decreasing the gap in communication between deaf-mute people and normal people by studying sign language interpretation. In this paper, a real-time Arabic avatar system is created to help deaf-mute people communicate with other people. The system translates text or spoken input into Arabic Sign Language (ArSL) movements that the avatar makes using deep-learning-based translation. The dynamic generation of the avatar movements allows smooth and organic real-time communication. In order to improve the precision and effectiveness of ArSL translation, this study depends on a state-of-the-art deep learning model, which makes use of YOLOv8, to recognize and interpret sign language gestures in realtime. The avatar is trained on three diverse datasets of Arabic sign language images, namely Sign-language-detection Image (SLDI), Arabic Sign Language (ArSL), and RGB Arabic Alphabet Sign Language (AASL), enabling it to accurately capture the nuances and variations of hand movements. The best recognition accuracy of the suggested approach was 99.4% on the AASL dataset. The experimental results of the suggested approach demonstrate that deaf-mute people will be able to communicate with others in Arabic-speaking communities more effectively and easily. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) holds significant promise for advancing natural disaster management through the use of predictive models that analyze extensive datasets, identify patterns, and forecast potential disasters. These models facilitate proactive measures such as early warning systems (EWSs), evacuation planning, and resource allocation, addressing the substantial challenges associated with natural disasters. This study offers a comprehensive exploration of trustworthy AI applications in natural disasters, encompassing disaster management, risk assessment, and disaster prediction. This research is underpinned by an extensive review of reputable sources, including Science Direct (SD), Scopus, IEEE Xplore (IEEE), and Web of Science (WoS). Three queries were formulated to retrieve 981 papers from the earliest documented scientific production until February 2024. After meticulous screening, deduplication, and application of the inclusion and exclusion criteria, 108 studies were included in the quantitative synthesis. This study provides a specific taxonomy of AI applications in natural disasters and explores the motivations, challenges, recommendations, and limitations of recent advancements. It also offers an overview of recent techniques and developments in disaster management using explainable artificial intelligence (XAI), data fusion, data mining, machine learning (ML), deep learning (DL), fuzzy logic, and multicriteria decision-making (MCDM). This systematic contribution addresses seven open issues and provides critical solutions through essential insights, laying the groundwork for various future works in trustworthiness AI-based natural disaster management. Despite the potential benefits, challenges persist in the application of AI to natural disaster management. In these contexts, this study identifies several unused and used areas in natural disaster-based AI theory, collects the disaster datasets, ML, and DL techniques, and offers a valuable XAI approach to unravel the complex relationships and dynamics involved and the utilization of data fusion techniques in decision-making processes related to natural disasters. Finally, the study extensively analyzed ethical considerations, bias, and consequences in natural disaster-based AI. [ABSTRACT FROM AUTHOR]

In order to process speech, most state-of-the-art experimental methods employ convolutional neural networks (CNNs), which operate on a continuous, 1-dimensional (1-D) time stream. In an audio signal, the mel-spectrogram facilitates the representation of attributes of the utterances' in the frequency domain (which corresponds to the speech spectrum). Moreover, for a time-series speaker signal, CNNs are superior to machine or transfer learning models in capturing characteristics from long-form talks. This paper introduces a jump-connected 1-D CNN that employs a combined loss function for speaker recognition. The suggested model uses a 1-D convolutional layer combined with jump connections to extract speaker-specific characteristics; this reduces time-based and frequency-based variability for faster computing. A combined softmax loss, stable L2-norm, and smooth L1-norm loss function guide the proposed compact convolutional neural networks (CCNN) to identify the correct spokesman with improved efficacy. We evaluated the proposed framework using various standard and real-time audio datasets. The experimental findings demonstrate that the proposed CCNN outperforms existing approaches by reducing the equal error rate by 9.02 %. Also, our recommended voiceprint identification model achieves an impressive average speaker recognition rate of 98.76 %. Simultaneously, the reliability of the 1-D CCNN is tested under various conditions. Other fields of study, like language modelling, could employ this approach after some fine-tuning. Relevance of the work: Speaker recognition is an area of interest in which machine learning (ML) and deep learning (DL) schemes, when combined, have the potential to make history in the areas of forensic sciences, automation, and authentication. Using a modest CNN can enhance the identification and verification process by ignoring many issues such as false positives, background noise, and so on. Expanding this process would facilitate raga identification and disease treatment therapies. [ABSTRACT FROM AUTHOR]

Phishing has emerged as a significant cyber threat, resulting in huge financial frauds for internet users annually. This malicious activity uses social engineering and upgraded methodologies (like file archiver in the browser, content injection, calendar phishing, more convincing fake websites or emails, voice manipulation, or other tools designed to deceive and exploit the target's confidence) to extract sensitive information from unsuspected victims. In order to mitigate these attacks, several methods and tools have been devised; various detection techniques and block phishing websites, and browser extensions that notify users about suspicious websites. Our work elaborates on meticulous analysis of the detection of phishing attacks by classifying them into four broader categories based on the adopted methodologies like List-Based Detection, Heuristic-Based Detection, machine learning (ML)-based, and deep learning (DL)-based. Additionally, it summarizes the popular devised schemes, highlighting their advantages and limitations, and how these are suitable for the different types of deployments. [Display omitted] • This research work elaborates on VPNs, proxy links, and advanced detection techniques, including heuristics-based and deep learning models, to comprehensively combat phishing attacks. • This paper provides comprehensive reports with detected URLs, timestamps, attack types, probability scores, and mitigation suggestions. • This study is to create a comprehensive understanding on detection of different types of Phishing techniques. [ABSTRACT FROM AUTHOR]

Correct sign language recognition helps deaf people communicate normally with hearing people. However, existing sign language recognition techniques have low recognition accuracy due to insufficient feature extraction. In this paper, we propose an approach to improve the accuracy of sign language recognition. Firstly, we design a one-dimensional convolutional neural network (CNN) using the skip connection approach Secondly, we propose an improved multi-head attention mechanism that incorporates bi-directional long short-term memory (BiLSTM) networks within a multi-head attention mechanism. Thirdly, we position this improved multi-head attention mechanism behind the final convolutional layer of the proposed CNN architecture and denote the resultant architecture as BMCNN. Finally, we verify the performance of our approach with BMCNN architecture through ten-fold cross-validation on the sensing dataset. Our approach achieves 99.13% accuracy in sign language recognition. The results show that our proposed approach outperforms the traditional machine learning approaches and other state-of-the-art techniques in this field. [Display omitted] • Proposing an approach based on sensing data and deep learning. • The convolutional neural network using skip connection enhances feature extraction. • The improved multi-head attention extracts temporal properties and key features. • Realizing real-time accurate sign language recognition. [ABSTRACT FROM AUTHOR]

• A LSTM model with attention was proposed to predict power of hybrid vehicle. • Training data was collected from dyno with perturbation for data diversity. • The predictors were handled using feature extraction and noise reduction. • The effects were compared and analyzed with different contamination degree data. • The robustness of proposed model was assessed on real data from different vehicles. Hybrid vehicles are pivotal in transitioning to sustainable transportation, but effective energy management is challenged by data contamination. This paper proposes a novel approach, utilizing deep learning trained with diverse data from existing energy management systems, to assess its adaptability to varying contamination levels. The training dataset covers emission test cycles, loadings, and driving styles, with dimensionality and noise reduction applied to control parameters. A Long Short-Term Memory (LSTM) prediction model with attention mechanism forecasts power demand, outperforming five other deep learning models. The study demonstrates exceptional performance, achieving an average Root Mean Square Error (RMSE) of 4.9761, surpassing RMSE values from 7.9497 to 9.1933. Real driving conditions yield a Mean Absolute Error (MAE) of 3.965 and RMSE of 5.908, highlighting the model's adaptability and predictive accuracy despite data contamination. Such robustness makes it valuable for hybrid electric vehicle energy management systems research and development. [Display omitted] [ABSTRACT FROM AUTHOR]

• A method of exchange rate market trend prediction based on sentiment analysis. • Forecast the exchange rate market trend by combining CNN with LSTM. • The "emotional" factors in the text data of Weibo are excavated. • It can still perform stably on small data sets. Exchange rate market is an important part of the global economy to reflect national economic development.With the rise of the Internet, more and more people choose to express their opinions on the Internet. Therefore, we believe that the comments in the online text will also reflect the trend of the exchange rate market. In this paper, a hybrid model of exchange rate market trend prediction based on sentiment analysis is proposed. The sentiment analysis module is constructed by combining Word2vec with Long Short-Term Memoryto extract "emotion" factors in text data and improve the performance of the model.After data cleaning, the model generates word vector and conducts sentiment analysis to get three emotional classifications of "positive", "ordinary" and "negative" by crawling the contents of "exchange rate" entries on "Weibo", and obtains the emotional weight of each day.Then, the emotional weights of about 3 months were randomly inserted into the historical exchange rate data of 3-4 years to conduct the mixed training combining Convolutional Neural Network and LSTM.In this paper, the historical exchange rate data of RMB against US dollar, Japanese yen, Euro and Australian dollar in 3-4 years are used as experimental samples. Experimental results show that compared with some traditional models, the method proposed in this paper is used to conduct comparative analysis by Mean absolute error, Mean squared error, Mean absolute percentage error and R2_Score after inserting small emotional data. The results show that our proposed method outperforms all comparison models in all indicators. Taking the trend prediction of the Australian dollar/RMB exchange rate as an example, our method outperforms the comparison models by an average of about 25.32% in the MAE results, 38.51% in the MSE results, and 2.76% in the MAPE results, R2_Score The average result of Score is 6.16% better than the comparison models, indicating a certain degree of robustness. In order to verify the effectiveness of the emotional weights we extracted in predicting exchange rate trends, we extracted the proposed method from the ''emotional analysis'' module and conducted a self comparison. The results showed that the results of the module with the addition of ''emotional analysis'' were significantly better than those without the addition of ''emotional analysis" module. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurate diagnosis of Lithium-ion batteries (Li-ion batteries) degradation plays a critical role in improving the maintenance of energy storage technology. This paper presents a method based on a novel deep network model combined with a data transformation technique to diagnose Li-ion battery degradation modes. Different from conventional studies based on specific experimental and numerical methods to estimate and predict the degradation, the proposed method is based on data-driven approach, by leveraging datasets consisting of voltage/capacity curves, these were converted into incremental capacity (IC) curves and then transformed into images using the gramian angular summation field (GASF) technique. The study adopted two models: Inception-v3 and the proposed model, both underwent fine-tuning and a subsequent transfer learning process. Degradation modes, namely loss of lithium inventory (LLI) and the loss of active materials in both the positive (LAMPE) and negative electrodes (LAMNE), were diagnosed in relation to IC curves. Finally, the model was tested using two different datasets, and the results showed that the proposed method achieved high performance, especially across three Li-ion batteries, three degradation modes, three cells, and various cycles (totaling 378 cases) the proposed method outperformed in 233 cases, thereby outperforming other methods in comparison. Our method provides a flexible data-driven approach that accurately predicts various degradation modes across different cell chemistries throughout their lifespan. [ABSTRACT FROM AUTHOR]

Distributed generation sources (e.g. photovoltaic, wind power) are raised to fit the energy demands recently in proficient way. Further, adoption of renewable energy sources to the smart grid causes severe issues in quality of power signals. These problems can be overcome with correct identification and classification of power quality disturbances (PQDs) efficiently by the both producer and consumer. In this work, a new method for classification of PQDs based on signal processing technique and deep convolutional neural network (DCNN) is proposed. The proposed method compress the raw data of PQDs by using discrete wavelet transform (DWT) and multi-resolution analysis (MRA). Moreover, CNN has the ability to automatic feature selection and classification in a single stage instead of conventional methods i.e. artificial neural networks (ANNs). In this paper, DWT 1D-CNN is designed to extract and optimize multi-scale characteristics of disturbances. Further, 1-D convolutional, pooling and batch-normalization layers are added to facilitate normalize data, better generalization ability and, effective classification process against noisy data. A typical 11Kv distribution network is simulated in MATLAB to prove the validity of the proposed method in an essential part of smart grid. [ABSTRACT FROM AUTHOR]

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure. [ABSTRACT FROM AUTHOR]

In today's digital age, the proliferation of network-connected devices has triggered a surge in cyberattacks. Distributed Denial-of-Service (DDoS) attacks pose a particularly formidable challenge to network security by disrupting access to vital services. While numerous researchers have proposed DDoS detection methods utilizing machine learning and deep learning techniques, developing a robust and reliable DDoS intrusion detection system remains challenging. This challenge is exacerbated by issues such as highly imbalanced data, multi-classification, and computational complexity. This paper proposes an innovative feature selection approach to create a robust intrusion detection system capable of detecting and classifying recent common DDoS attack types. We evaluate the performance of our model on the CICDDoS2019 benchmark dataset. Our experimental results demonstrate that our proposed model outperforms existing methods, achieving a detection accuracy of 96.82%, a recall of 96.82%, a precision of 96.76%, and an F1 score of 96.50%. Additionally, our model exhibits faster prediction times, with the ability to predict an attack in just 0.189 ms. Notably, our approach, combined with preprocessing and feature selection techniques, outperforms previous works and baseline models in DDoS attack classification. [Display omitted] • An innovative hybrid feature extraction method for training of DDoS attack detection model. • We propose a robust CNN model integrated with an Inception mechanism designed to classify various types of DDoS attacks. • Evaluated the network performance of the proposed system in terms of various parameters and compared the results with existing studies and baseline models. • The proposed model outperforms more than 96% in all performance metrics (Precision, Recall, Accuracy, and F1 Score). • The proposed model achieves the fastest prediction time of 0.189 ms among existing models in the literature. [ABSTRACT FROM AUTHOR]

• Manual liver segmentation from CT scans is error-prone and time-intensive, necessitating the need for automated methods. • A novel automated framework, GWO-SwinUNet combines Gray Wolf optimized Swin Transformer with UNet architecture for liver segmentation. • GWO-SwinUNet achieves superior segmentation accuracy with remarkable Dice and Jaccard coefficients. • The hybrid approach enhances fine-grained detail extraction while ensuring robustness and convergence speed. Liver segmentation in medical images, particularly CT scans, is crucial for diagnosing and treating liver diseases. However, manual segmentation by radiologists is time-consuming and prone to errors, necessitating automated solutions. Although deep learning methods have shown promise, issues such as model overfitting, convergence, and sensitivity to noise persist. In response, this paper introduces GWO-SwinUNet, a novel liver segmentation framework that combines the Gray Wolf optimized Swin Transformer with the UNet architecture. By leveraging this hybrid approach, the method enhances fine-grained details while maintaining robustness and convergence speed. Extensive experimentation on diverse datasets and comparisons with state-of-the-art methods demonstrate the superior performance of GWO-SwinUNet, achieving a Dice coefficient of 0.988 and a Jaccard coefficient of 0.979. A thorough ablation study further validates the efficacy of the proposed strategy. In summary, GWO-SwinUNet represents a significant advancement in liver segmentation, harnessing the strengths of transformer-based architectures and optimization techniques to enhance accuracy and efficiency in medical image analysis. [Display omitted] [ABSTRACT FROM AUTHOR]

Mobile devices have become an essential element in our day-to-day lives. The chances of mobile attacks are rapidly increasing with the growing use of mobile devices. Exploiting vulnerabilities from devices as well as stealing personal information, are the principal targets of the attackers. Researchers are also developing various techniques for detecting and analyzing mobile malware to overcome these issues. As new malware gets introduced frequently by malware developers, it is very challenging to come up with comprehensive algorithms to detect this malware. There are many machine-learning and deep-learning algorithms have been developed by researchers. The accuracy of these models largely depends on the size and quality of the training dataset. Training the model with a diversified dataset is necessary to predict new malware accurately. However, this training process may raise the issue of privacy loss due to the disclosure of sensitive information of the users. Researchers have proposed various techniques to mitigate this issue, such as differential privacy, homomorphic encryption, and federated learning. This survey paper explores the significance of applying federated learning to the mobile operating systems, contrasting traditional machine learning and deep learning approaches for mobile malware detection. We delve into the unique challenges and opportunities of the architecture of in-built mobile operating systems and their implications for user privacy and security. Moreover, we assess the risks associated with federated learning in real-life applications and recommend strategies for developing a secure federated learning framework in the domain of mobile malware detection. [ABSTRACT FROM AUTHOR]

• A comprehensive and up-to-date review of deep learning-based encryption for digital images is presented. • A detail overview of image encryption using deep learning models along with recent applications are discussed. • Comparative analysis of the reviewed methods and their limitations are discussed to guide future directions. With the innovation of high-throughput big data storage technology and the construction of digital information systems in media applications, images are the common and most popular media carriers and are generated and transmitted in huge quantities. Due to the significant advantages and high value of digital image content, there is an urgent demand to protect their privacy and prevent leakage. In the past few years, deep learning models have brought more options for image encryption algorithm design. This paper offers a comprehensive survey of recent digital image encryption using deep learning models. First, a strong motivation for image encryption using deep learning models along with recent applications is introduced. Next, various state-of-the-art deep learning-based encryption techniques are discussed. The technical summary of different popular techniques is then given in tabular form. Lastly, by deeply investigating existing deep learning-based encryption, we establish some important research challenges and possible solutions, including standard security metrics. [Display omitted] [ABSTRACT FROM AUTHOR]

Localization models, particularly for indoor applications like autonomous vehicles and smart manufacturing in Industry 4.0, have emerged as a crucial research area because of the growing need for precise positioning. In this context, distributed cell-free MIMO have demonstrated capabilities for a higher fine-grained resolution and accuracy. This paper explores different approaches to address challenges related to positioning and corresponding data transmission and model compression problems in cell-free massive MIMO systems. We present a novel deep-learning powered CSI compression model for reducing the signalization data associated with the localization task, minimizing the fronthaul capacity requirement by at least a factor of 2, without a significant loss of accuracy. In our research, we also focus on the challenges of deploying deep end-to-end neural networks on resource-constrained embedded platforms. Two approaches are proposed: model modification and magnitude-based pruning. Our results show that the pruned end-to-end localization model has a smaller size by 36% at the cost of an increase on the localization error by a factor of 1.5 (precision is reduced from approximately 5 mm to 7.8 mm). [ABSTRACT FROM AUTHOR]

Accurate short-term wind power prediction is crucial for the efficient and safe operation of wind power systems. To enhance the accuracy of short-term wind power prediction, this paper proposes a hybrid short-term wind power prediction model, IDBO-VMD-TCN-GRU-Attention, based on the Improved Dung Beetle Optimizer algorithm (IDBO), Variational Modal Decomposition (VMD), Temporal Convolutional Network (TCN), Gated Recurrent Unit (GRU), and Attention Mechanism. The IDBO is used to optimize the parameters of the VMD. Then the optimized IDBO-VMD is used to decompose the original data into modal components with lower volatility to fully extract the data features. These modal components are inputted into the TCN-GRU-Attention to predict the short-term wind power and obtain the final prediction value. The model is tested and validated using real data from four different months and compared against 11 other models. Results demonstrate that our proposed model significantly reduces the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by an average of at least 22.86 %, 18.82 %, and 19.99 %, respectively, across the four different months. This indicates that the proposed model provides a superior fit to the data, offers higher prediction accuracy, and holds significant practical value. [ABSTRACT FROM AUTHOR]

Many IoT devices are presently in use without sufficient security measures. The vulnerability of these devices to malware highlights the necessity for effective methods to identify malicious activity while protecting data privacy. Federated Learning is a collaborative learning technique that can create a fair model while keeping the local data private. To this end, this paper presents the SIM-FED, a novel model for detecting malware in IoT devices. This model utilizes both deep learning and federated learning algorithms. The SIM-FED offers a privacy-preserving solution for IoT malware detection, without sharing data, enhancing security in distributed environments. The model utilizes a lightweight one-dimensional CNN with optimized hyperparameters, reducing preprocessing, and computational overhead. Various federated aggregation strategies are evaluated, and the FedAvg strategy is selected to integrate the results of local models in the proposed model. Moreover, the model's resilience against white-box and black box cyber-attacks is assessed, revealing minimal performance degradation and highlighting its robustness, which is often overlooked in previous studies. The performance of the SIM-FED is assessed by conducting a series of experiments using the IoT-23 dataset. The results show that the SIM-FED outperforms other deep learning and federated learning models in terms of all evaluation metrics, achieving a remarkable 99.52 % accuracy. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attacks are common and increasing in frequency. It renders the service inaccessible to legitimate users and degrades network performance. A complex structural environment called a Cyber–Physical System (CPS) is created by combining computation, connectivity, and physical parameters. Software-Defined Networking (SDN) is an emerging architecture that separates the data plane from the network plane. A central logic control resides in the control plane, making SDN vulnerable to DDoS attacks. SDN design ideas are broadened and applied to create software-defined Cyber–Physical Systems. Deep learning powers many artificial intelligence apps and services, enhancing automation by performing cognitive tasks without human intervention. It can perform feature extraction and classification on both small and large datasets. This paper presents a variety of Deep Learning models for efficiently detecting DDoS attacks in the SD-CPS framework through a scalable and adaptable SDN-based architecture. We could determine which Deep Learning techniques work best under various attack scenarios by examining multiple Deep Learning techniques. The Deep Learning models performed above 99% accuracy in classifying binary and multiclass data over unknown traffic when tested on two recent security datasets, the SDN-specific dataset and the CICDDoS2019 dataset. [Display omitted] • The paper utilizes two datasets, one tailored to SDNs and the CICDDoS2019 dataset. • Three distinct deep learning approaches identify binary and multiclass attacks. • The binary accuracy is over 99% for the datasets, which is better than prior studies. • Multiclass detects attack type with accuracy greater than 99±% for both datasets. • The individual accuracy for type of attack ranges between 98%–99% in most cases. [ABSTRACT FROM AUTHOR]

• We propose a personalized course recommendation method based on BERT model, which integrates deep learning and big data technology based on MOOC system. • We design a domain feature difference learning strategy, which is able to learn the difference features between course contents to better extract information in the text and improve the recommendation performance of the model. • We experimentally demonstrate the effectiveness of the method proposed in this paper on the open MoocCube datasets. Finding the courses that users are interested in quickly in the massive data can make a very important contribution to the accurate dissemination of knowledge. In this paper, we integrate the deep learning and big data technology to investigate a personalized recommendation method based on Massive Open Online Course (MOOC) system. Based on the Bidirectional Encoder Representations from Transformers (BERT) model, we propose some corresponding strategies to improve the accuracy of the recommendation system. First, we introduce the acquisition and preprocessing of the open dataset. Second, we design a recommendation model framework by taking advantage of the BERT model and incorporating a self-attention mechanism. Finally, to obtain deep feature information between course texts, we design a domain feature difference learning strategy to improve the model's recommendation performance. The results of our experiments prove that the proposed model in this paper performs good recommendation results compared with other methods. [Display omitted] [ABSTRACT FROM AUTHOR]

• A comprehensive overview of recent advances in visual odometry (VO), focusing on deep learning models. • An in-depth model comparison, highlighting explainability and interpretability aspects. • A summarization of research gaps and challenges in the context of explainable AI (XAI) in visual odometry. • A discussion on advances in XAI techniques for visual odometry, allowing transparent models. • An overview of practical implications of visual odometry advancements in real-time applications. Visual Odometry (VO) is a crucial process for estimating camera motion in real-time based on visual information captured. The emergence of deep learning has significantly transformed VO and Explainable Artificial Intelligence (XAI) in Deep Vision-Based Odometry. This survey paper explores the latest advancements in VO facilitated by deep learning methods, focusing on explainability and interpretability. It provides an overview of state-of-the-art deep learning techniques and dissects each model into its elemental building blocks to understand their explainable and interpretable aspects. The survey also highlights research gaps in optical flow robustness, occlusion and dynamic objects, real-time processing, drift correction, semantic awareness, and sensor integration. The aim is to catalyze future innovations in deep learning-based VO and stimulate dialogue about potential directions for the next wave of research, emphasizing explainability and interpretability as integral components of advanced systems. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents an effective and accurate method to analyze the network traffic risk of enterprise public cloud financial system by using deep learning algorithm. To build the analytical model, the collected data is preprocessed to extract relevant features that contribute to risk analysis. The pre-processed data is used to train the deep learning model. The model is designed to learn patterns and relationships in network traffic data. The network traffic risk analysis module is constructed by combining the deep learning model with the system architecture of the enterprise public cloud financial system. Through a trained deep learning model, the module is able to accurately identify abnormal behavior in network traffic and provide corresponding risk assessments. The results show that the proposed system module can accurately identify abnormal behavior in network traffic and provide risk assessment. [ABSTRACT FROM AUTHOR]

A modern smart grid tends to have increasingly various uncertain renewable generations. Due to different geographical areas and network topology constraints, operations of a smart grid become complicated and challenging. Moreover, using existing methods, power flow estimation in real-time could be time-consuming and computationally expensive. This paper proposes an efficient deep learning approach to estimate real-time power flow solutions of the smart grid with high penetration of uncertain PV generations using synchrophasor data considering measurement noise. The performance and effectiveness of the proposed convolutional long short-term memory (ConvLSTM) with time-series cross-validation technique are examined using synchrophasor data with Gaussian noise in the IEEE 39 bus test system. The proposed ConvLSTM approach shows better robust performance than weighted least square (WLS) state estimation and long short-term memory (LSTM) approaches. In addition, state measurements and confidence intervals are employed to confirm the accuracy of estimated real-time power flow results. The accurate real-time power flow estimation is crucial to determining dynamic available transfer capability (ATC) results and efficient operations of smart grids. [Display omitted] • The proposed deep learning ConvLSTM approach provides accurate real-time power flow results. • The real-time power flow estimation employs synchrophasor data with measurement noise. • The proposed approach is efficient and effective for a power system with uncertain PV. • The accurate results are confirmed by means of state measurements and confidence intervals. [ABSTRACT FROM AUTHOR]

Advancements in deep learning for speaker identification are constrained by the limited availability of data, especially in law enforcement applications. This has led to the emergence of few-shot speaker identification, a technique that classifies unseen test samples with the help of a few support samples. Despite several attempts to advance few-shot speaker identification, significant challenges persist, including the extraction of robust speaker embeddings, the problem of overfitting, and the issue of prototype shift error. This paper proposes a few-shot speaker identification system employing a novel architecture called the Channel Attention-based Convolutional Recurrent Neural Network (CACRN-Net) with three-dimensional (3D) log Mel spectrogram inputs to mitigate overfitting and enhance the accuracy of speaker embeddings. Furthermore, a self-attention mechanism alleviates prototype shift errors caused by noisy data. The proposed framework is compared to existing methods using VCTK and Voxceleb1 speech corpora through 5-way, 5-shot learning experiments. To assess the performance of the framework in speech variability conditions, we utilized the IIT Guwahati (IITG) multi-variability (MV) speech database. The proposed approach outperforms state-of-the-art techniques, achieving a substantial enhancement in speaker identification with a 2.73 % accuracy improvement on the VCTK database and a 2.3 % improvement on Voxceleb1. [ABSTRACT FROM AUTHOR]

The rapid growth of online music education has led to increased security risks from cyber intrusions. This paper proposes public cloud networks oriented deep neural networks for effective intrusion detection in online music education environments. Specifically, a novel intrusion detection framework is developed, comprising fuzzy logic based feature selection, chronological salp swarm algorithm optimized deep belief networks, and gated recurrent unit integrated convolutional neural networks. Detailed methodologies are presented for the fuzzy logic system, chronological salp optimization, deep belief network architecture, and convolutional neural networks. Comprehensive experiments are conducted on the NSL-KDD and CICIDS2017 datasets. Various deep neural networks are evaluated and compared, including multi-layer perceptrons, convolutional neural networks, deep belief networks, recurrent neural networks, and the proposed models. Experimental results demonstrate that the proposed fuzzy feature selection and chronological salp swarm algorithm optimized deep belief network achieves a test accuracy of 97.33 %, outperforming other peer models. The gated recurrent unit integrated convolutional neural network obtains a test accuracy of 98.46 %, superior to state-of-the-art methods. While the experiments on the newly created dataset for intrusion detection in cloud-based online music education demonstrate that the proposed models outperform the benchmarks. The experiments verify the effectiveness of the proposed deep learning frameworks for intrusion detection in online music education cloud networks. [ABSTRACT FROM AUTHOR]

• The Intelligent Microbat Routing (IMR) and deep learning (DL) based Popularity Content Caching (PCC) IMR-PCC hybrid intelligent architecture for the caching and routing achieved the better QoS performances in vehicular edge networks. • This paper introduced the popularity based content features for achieving the better learning rate in the intelligent caching. • The framework utilized OMNET++ interfaced with Python3.8 platform for the implementation which results in better implementation of the model. • The paper focusses on the joint routing and caching technique for the vehicular edge and IoT networks. • IMR-PCC achieved the better Quality of Service (QoS) as well as executes cache performance which is highly suitable for the vehicular IoT networks. Vehicular networks are considered as one of the paramount components of intelligent transportation systems. With the help of new advanced technologies like Internet of things (IoT), and many wireless applications such as 5 G/6 G, these vehicular networks has changed its face of applications in terms of computations, communications and privacy. The arising vehicular applications such as crash awareness and emergency brake requires better communication, storage resources, and computation with severe execution prerequisites on response time and network bandwidth. But still, efficient data computation with an intelligent routing mechanism still remains to be darker side of the research. To achieve the improvisation in above parameters, this paper proposes the hybrid combination of the Intelligent Microbat Routing (IMR) and Deep learning based Popularity Content Caching (PCC) techniques which has been targeted for IoT based vehicular networks. This paper introduces the Reinforced Gated Recurrent Units (R-GRU) with High Speed Extreme Learning Machines for better prediction of contents' popularity that aids for better caching. The proposed IMR-PCC architecture consists of four different tasks such as popularity prediction, cache placement, data retrieval and effective routing. The extensive experimentation is carried out to evaluate the IMR-PCC by implementing the dynamic network topology with multi factors, which directly impacts on the performance of the complete system. Nearly 300,000 contents were collected randomly and the complete algorithm was implemented in the OMNET++ interfaced with Python 3.8. Finally from the experimental results, the proposed IMR-PCC architecture proved its excellence in terms of accuracy (98.6%), precision (98.6%), recall (98.6%), F1-Score (98.8%), cache hit ratios (0.25), latency (0.3), Packet Delivery Ratio (PDR) (99%) than other existing methodologies such as Q-learning, BLSTME, MPCF, ICN. So the proposed IMR-PCC achieved the better Quality of Service (QoS) as well as executes cache performance which is highly suitable for the vehicular IoT networks. [Display omitted] [ABSTRACT FROM AUTHOR]

With the boom in Internet and information technology, cyber-attacks are becoming more frequent and sophisticated, especially Advanced Persistent Threat (APT) attacks. Unlike traditional attacks, APT attacks are more targeted, stealthy, and adversarial, rendering it challenging to manually analyze threat behaviors for APT detection, attribution, and response. Therefore, the research community has focused on intelligent defense methods. Intelligent threat profiling is dedicated to analyzing APT attacks and improving defense capability with Knowledge Graph and Deep Learning methods. With this insight, this paper provides the first systematic review of intelligent threat profiling techniques for APT attacks, covering three aspects: data, methods, and applications. The contents include data processing techniques, threat modeling, representation, reasoning methods, etc. Furthermore, this paper summarizes the latest research in applications, proposes the research framework and technical architecture, and provides insights into future research trends. This paper contributes to recognizing the advantages and challenges of intelligent threat profiling. It paves the way for integrating knowledge graphs and deep learning to achieve intelligent security. [Display omitted] • The first review paper on intelligent threat profiling of Advanced Persistent Threat. • Summarizes the research findings on three aspects: data, methods and applications. • Proposes the research framework and technical architecture of intelligent threat profiling. • Analyzes the challenges and provides insights into future research trends. [ABSTRACT FROM AUTHOR]

• Dual fusion feature pyramids offer better performance. • The candidate box obtained by using CIOU is more friendly. • Transfer learning and data augmentation can improve the problem of data scarcity. Rail surface defects are serious to the quality and safety of railroad system operation. Due to the diversity and randomness of rail defects form, the detection of rail surface defects is a challenging task. Therefore, this paper proposes a new surface defect detection network based on Mask R-CNN to detect rail defects. The detection network is designed with a new feature pyramid for multi-scale fusion; a new evaluation metric complete intersection over union (CIOU) is used in the region proposal network to overcome the limitations of intersection over union (IOU) in some special cases; in the training phase, both transfer learning and data augmentation are used to solve the problem of small defective datasets. The experimental evaluation shows that the model proposed in this paper achieves 98.70% mean average precision (MAP) on the proposed dataset and can locate the defect location more accurately. [Display omitted] [ABSTRACT FROM AUTHOR]

Modern businesses benefit significantly from advances in computer vision technology, one of the important sectors of artificially intelligent and computer science research. Advanced computer vision issues like image processing, object recognition, and biometric authentication can benefit from using deep learning methods. As smart devices and facilities advance rapidly, current networks such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing demand. Classification of images, object classification, and facial recognition software are some of the most difficult computer vision problems that can be solved using deep learning methods. As a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to solve the above challenges by introducing a new methodology named Optimizing Computer Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency algorithms (DL-EA) for computer vision to address the issues mentioned and improve the system's outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper to recognize pattern recognition and intelligent management systems and provide driven methodology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can summarize the significant areas for future research and potential solutions, including image enhancement, machine vision, and access control. [ABSTRACT FROM AUTHOR]

Non-contact facial video-based physiological signal measurement using remote photoplethysmography (rPPG) has shown great potential in many applications (e.g. telemedicine, driver fatigue detection, sleep detection) and has achieved reliable results in the controlled environment. With the development of deep learning, more and more methods have been proposed. However, these methods were usually implemented based on model variations that have achieved better results in computer vision tasks, and finally chose average pooling layer or one-dimensional convolution layer directly reduce dimension. Such these operations ignored the fact that the physiological signals of one individual are consistent in different facial regions. In this paper, we propose a novel remote photoplethysmography framework that utilizes graph neural network to extract physiological signal, named GraphPhys. And we design the Average Relative GraphConv based on the fact that the physiological signals of one individual are always consistent in different facial regions for the remote physiological signal measurement task. We apply GraphPhys to two different architectures for rPPG methods, and the methods based on GraphPhys significantly outperform the original methods on the VIPL-HR (Niu et al., 2019) dataset. To the best of our knowledge, this is the first work to utilize graph neural network to aggregate physiological signals. The code is available at https://github.com/Xiong-JiaHao/GraphPhys. [ABSTRACT FROM AUTHOR]

Traditional network traffic analysis methods fall short in addressing the requirements of complex network environments. Therefore, the introduction of advanced technologies, such as deep learning, is necessary to enhance the accuracy and efficiency of network traffic analysis. This paper designs a risk analysis method of enterprise cloud financial system network traffic based on deep learning to improve the level and effect of network security assurance. A deep learning based traffic analysis framework has been constructed, which includes several main steps such as data preprocessing, feature extraction, model training, and result evaluation. The test results on the experimental data set show that this method can identify network attacks and abnormal traffic more quickly and accurately than the traditional network traffic analysis methods, and has better practicability and application prospects. [ABSTRACT FROM AUTHOR]

Students' actions in the classroom play a key role in their studying performance in class. This study focuses on the automatic recognition of students' spontaneous actions in a real classroom environment. A database of students' spontaneous actions is first established in this study. Our database consists of 4917 images of students' spontaneous actions, including the actions of students in 10 learning states. Based on the characteristics of students' actions in the classroom, we proposed a new 11-layer convolutional neural network algorithm based on an enhanced deep super resolution network. In this algorithm, a data augmentation method is introduced for data processing. The experimental results show that the proposed algorithm effectively improved the accuracy of action recognition on the database established in this paper. Such a spontaneous database proposed in this paper provides good data support for the study of students' actions in the classroom. [ABSTRACT FROM AUTHOR]

'Fake news' refers to the misinformation presented about issues or events, such as COVID-19. Meanwhile, social media giants claimed to take COVID-19 related misinformation seriously, however, they have been ineffectual. This research uses Information Fusion to obtain real news data from News Broadcasting, Health, and Government websites, while fake news data are collected from social media sites. 39 features were created from multimedia texts and used to detect fake news regarding COVID-19 using state-of-the-art deep learning models. Our model's fake news feature extraction improved accuracy from 59.20% to 86.12%. Overall high precision is 85% using the Recurrent Neural Network (RNN) model; our best recall and F1-Measure for fake news were 83% using the Gated Recurrent Units (GRU) model. Similarly, precision, recall, and F1-Measure for real news are 88%, 90%, and 88% using the GRU, RNN, and Long short-term memory (LSTM) model, respectively. Our model outperformed standard machine learning algorithms. [Display omitted] • The research uses the process of Information Fusion to obtain real news data. • We have used Deep Learning classifiers to propose 39 novel features for text. • The paper proposes sentiment features, linguistic features and name entity base features. After extracting features from the text, our new features detect COVID-19 related fake news with an accuracy of 86.12%. Thus, accuracy is increased by 20% with the novel features. • This paper compares Deep Learning algorithm results with the standard Machine Learning (ML) algorithms using the same fusion of dataset and shows a higher accuracy. Therefore, it confirms the superiority of our Deep Learning model. [ABSTRACT FROM AUTHOR]

• A 3D detection network based on key points. • Adjusting the grasping point. • A method to generate best grasping posture. In robotic grasping tasks, different kinds of objects with different poses and positions are often distributed in the scene, which makes the design a feasible visual solution for robotic grasping very challenging. In this paper, we propose a robotic grasping method based on a 3D detection network, which reduces the influence of different camera positions in image recognition. First, a convolutional neural network is designed to recognize objects in RGB images and calculate the 3D bounding boxes and center points of objects. Second, to further improve the stability of the whole grasping system, we propose a strategy for calculating the best object grasping posture. Finally, robotic control is implemented to grasp objects in real scenes. Experimental results show that the 3D detection accuracy reached 88%, and the successful grasp rate reached 94% in real scenes. In summary, the grasping system designed in this paper can generate appropriate grasping postures effectively and achieve high successful grasp rates, meeting the higher requirements of grasping tasks. [Display omitted] [ABSTRACT FROM AUTHOR]

The automated detection of spilled loads on the highway can help quick finding of offending vehicles and prevent traffic congestion. A real-time spilled loads detection algorithm based on the improved You Only Look Once v4 (YOLOv4) is proposed in this paper. Image synthesis method is used to manually generate dataset to solve the problem of insufficient spilled loads images. Six categories of spilled loads are divided into 35,468 samples as a dataset (80% for training and 20% for testing). K-means clustering and Convolutional Block Attention Module (CBAM) were introduced to YOLOv4 Channel pruning was carried out to reduce the computational complexity and resource consumption of the model. The proposed model, of which average precision=98.3%, recall= 0.962, precision=0.98, performs better than other 5 types classifiers and is applied to estimate spilled loads, where the model size and GFLOPs is 134.535 MB and 70 respectively. Compared with the original model, the reduction is 47.6% and 34%, saving computing resources and speeding up the detection speed on the premise of ensuring the detection accuracy. This paper paves a new way for automated spilled load detection with deep learning method, improving the efficiency and accuracy of spilled load detection. [ABSTRACT FROM AUTHOR]

• Data security is becoming a more sensitive and important challenge with the increase of AI. • FL provides secure models with no data sharing that leads to a highly efficient privacy-preserving solution. • The main focus of FL is on the image processing applications which ensure that data trained on the model are secure and protected. • FL is concerned about the communication between edge devices and the server and tries to minimize. • Frameworks are discussed along with their usage in federated learning. Nowadays, data privacy is an important consideration in machine learning. This paper provides an overview of how Federated Learning can be used to improve data security and privacy. Federated Learning is made up of three distinct architectures that ensure that privacy is never jeopardised. Federated learning is a type of collective learning in which individual edge devices are trained and then aggregated on the server without sharing edge device data. On the other hand, federated learning provides secure models with no data sharing, resulting in a highly efficient privacy-preserving solution that also provides security and data access. We discuss the various frameworks used in federated learning, as well as how federated learning is used with machine learning, deep learning, and datamining. This paper focuses on image processing applications that ensure that data trained on the model is secure and protected. We provide a comprehensive overview of the key issues raised in recent literature, as well as an accurate description of the related research work. [Display omitted] [ABSTRACT FROM AUTHOR]

Document forgery is a significant problem for ages due to paper-based documents' pervasive use. Classical destructive approaches for this problem, such as chromatography and electrophoresis, cannot be implemented as they flaw the document under analysis. Hyperspectral imaging - non-destructive approach that assists in finding the unique features of an image under investigation through their unique spectral signatures. It captures multiple narrow-band images at the electromagnetic spectrum, which is difficult through conventional imaging. Deep learning approaches for hyperspectral images have attained state-of-the-art results for solving many complex and challenging problems. Supervised classification of hyperspectral images is a tedious task since obtaining image labels and labeling the training data is a time-consuming and expensive process. In this paper, an unsupervised approach for classification of hyperspectral document images is proposed. To propose an unsupervised deep learning approach for ink mismatch detection in hyperspectral document images using spectral features. CAE-LR approach is proposed that uses Convolutional Autoencoder (CAE) for feature extraction and utilizing them for ink mismatch detection through Logistic Regression (LR). We evaluated the performance of CAE-LR on UWA writing ink hyperspectral images dataset for blue and black inks. Artificially similar color inks of different types (2∼5) were mixed in varying proportions to detect ink mismatch. Additionally, results are compared with three machine learning algorithms with variants of each, CNN, and five state-of-art methods used by the researchers. Experimental results illustrated that the CAE-LR outperforms all the above – mentioned approaches by achieving the state of art results, which depicts the efficacy of unsupervised deep learning approach for ink mismatch detection in hyperspectral document images. [ABSTRACT FROM AUTHOR]

As the content of the food Review text becomes more and more complex and the types of the food Review text representation become more and more diverse, emotion analysis becomes more and more important in dealing with problems. At present are faced with the problem, this paper puts forward a new type of complementary fusion model, the text pretreatment by Bert training text model, make the content of the food review text more coherent, keep on convolutional neural network logic timing relationships, before and after the statement of text vector of the local feature extraction, using the method of bidirectional long short-term memory network (BLSTM) capture and text context information related to global long-range dependence, using the weighted formula of attention mechanism to improve the comment text statement attention emotional words and then with the weight of characteristic value after conditions with the airport of sorting the output. Through a comparative experiment on the data set of JD food review data set reviews, the results show that BCBLAC, the feature fusion model proposed in this paper, is much more accurate than the existing model in the food Review text language classification. [ABSTRACT FROM AUTHOR]

• Interpretable DL Model: Our unique deep learning model interprets modulation mechanisms, efficiently extracting time and phase features with minimal resources. • Efficient Performance: Our approach achieves top performance on RML2016 and RML2018 datasets with fewer model parameters. • Fastest Embedded Speed: Outperforming 8 DL-based methods, our approach demonstrates the fastest speed on Jetson Nano and Raspberry Pi systems. Automatic Modulation Classification (AMC) constitutes a fundamental technology for enabling automatic demodulation in Cognitive Communication Systems (CCS). Due to the size, weight, and power (SWaP) constraints of embedded computers employed in CCS, there are limited computational and memory resources. While deep neural networks possess strong feature representation and high accuracy recognition capabilities, they usually come with a high number of network parameters and high computational complexity, thereby reducing the real-time processing ability of CCS. Therefore, neural network structures intended for CCS must be lightweight and computationally efficient. In this paper, we propose a high-performance and resource-friendly network model based on an analysis of the modulation mechanism of communication signals. The network extracts phase features and short-time features sequentially using directional convolutional filters. Long short-term memory (LSTM) units are then used to extract long-term features, and only one fully connected layer is used for classification. Experiments with a standard dataset consisting of 11 communication modulation types demonstrate that our proposed model achieves an accuracy greater than 84.5%, even when the signal-to-noise ratio (SNR) is 0 dB, and the model has only 29187 parameters. On a Jetson Nano embedded platform, the model achieves a processing speed of up to 375366 in-phase and quadrature samples/s. Overall, the results suggest that our proposed approach is both lightweight and highly efficient, making it more suitable for CCS applications. [Display omitted] [ABSTRACT FROM AUTHOR]

White blood cells (WBCs) are crucial constituents of the blood that protect the human body against infections and viruses. The classification of WBCs in a blood smear image is used to diagnose a range of haematological disorders. The manual identification of WBCs can result in potential errors, necessitating the need for automated systems that can assist in classification. Recently, various deep learning models, such as DenseNet121, Xception, MobileNetV2, ResNet50, and VGG16, have been used to classify WBCs. However, the available classification models are black boxes because their decisions are difficult for humans to understand without further exploration. The interpretability and explainability of these models are essential, as their decisions can have severe consequences for patients. In this paper, we integrate an explainable AI (XAI) technique called local interpretable model-agnostic explanations (LIME) with the DenseNet121 classification model for WBC classification. Interpretable results would allow the users to understand and verify the model's predictions, enhancing their confidence in the automated diagnosis. • Comparison of deep learning models for leukocyte classification. • Inclusion of explainability to visually explain the final decision. • The results show high explainability of the prediction mechanism. [Display omitted] [ABSTRACT FROM AUTHOR]

The use of Neural Architecture Search (NAS) techniques is becoming more widespread. NAS methods have been essential for automating and accelerating the laborious and erroneous process of designing and tuning new Deep Learning (DL) approaches. The past few years have seen increasing number of research works on NAS and associated hyper-parameter optimization (HPO). They have arguably had the biggest influence on classification and object detection tasks, where they have produced state-of-the-art results. Despite the notable success to date, it is still difficult and not always practicable to apply NAS to real-world situations. In general, the structure of Convolution Neural Networks (CNNs) is too sophisticated to be used in platforms with limited resources, such as IoT, mobile, and embedded systems. In this paper, we revisit several state-of-the-art NAS and HPO methods from a multi-perspective view both in non-federated and distributed federated learning environments. Earlier attempts to critically analyze the components of NAS or HPO are only limited to non-federated environment, where NAS and HPO related computations need to be performed on a single CPU/ multiple GPUs and where all the data was readily accessible for performing the task of learning. In this article, we extend the discussion and critically analyze the important components of NAS and HPO methods in federated learning environment, where computations need to be performed on multiple computational units in federation and where only local data can be used by a single computational unit. Due to unavailability of entire data in federated learning environment, the earlier NAS methods (i.e., non-federated NAS methods) and the earlier HPO methods (i.e., non-federated HPO methods) cannot be used directly without modifications. We discuss these modifications made by researchers in non-federated NAS and HPO methods to be used in federated learning environment and provide discussion for future avenues. [ABSTRACT FROM AUTHOR]

• Artificial intelligence and deep learning have good prospects in computer network security. • Improving computer network security detection rate. • Reducing false positives in computer network security. With the continuous progress of the information age and science and technology, how to combine complex and changeable data with self-protection ability has become an urgent problem that needs to be solved. In the computer system, the traditional technology for protection is somewhat passive. To this end, this paper analyzes computer network security monitoring under artificial intelligence (AI) deep learning technology and discusses its development trend, which monitors network security to find and address problems in time. The results show that the deep learning technology of computer network security detection can improve its security performance with high safety performance, high detection rate, and low false alarm rate. It can monitor the vulnerabilities in the computer network in time and realize effective security attack detection of the computer network. Deep learning technology for computer network security detection can improve its security performance, timely monitor vulnerabilities in computer networks, and achieve effective security attack detection on computer networks [Display omitted] [ABSTRACT FROM AUTHOR]

With the rapid evolution of the Internet of Things (IoT), network advancement has significantly influenced the increasing number of devices and advanced enhancements linked with it. Indeed the increasing number prevalence and sophistication of emerging cyber-attacks have highlighted the necessity for designing robust security application. In this paper, the Remora-based Deep Maxout Network model is Proposed. Here, the input data is acquired and forwarded to the pre-processing phase, wherein the missing value imputation approach is employed for creating a complete dataset. Later, the pre-processed data is then subjected to dimension transformation from the transformed data; the Convolutional Neural Network features are extracted, followed by feature selection based on Canberra distance. Here, detection is carried out using a Deep Maxout Network whose weights and training parameters are modified using the Remora Optimization Algorithm. However, the proposed model has delivered superior results with a high testing accuracy of 0.945 [ABSTRACT FROM AUTHOR]

Medical imaging is crucial for monitoring the progression of a diagnosis. However, poor-quality images, particularly radiographs which are commonly used for their affordability, can lack sufficient visual information and lead to lengthy and inaccurate diagnoses, reducing clinicians' ability to examine and decreasing the accuracy of their assumptions. To address this issue, this paper proposes an Xray-Net approach that utilizes self-supervised pixel-contrast stretching to improve low-contrast images. The approach consists of three main phases: (1) Pre-processing, which involves obtaining X-ray contrast-based classification through the measurement of average brightness, (2) Development of a conventional neural network model, and (3) Contrast-adaptive stretching, where the stretching control coefficients are predicted adaptively based on input image information. The proposed approach was evaluated using quantitative and qualitative experiments on three X-ray databases, and six different quality measures were considered. The results obtained by Xray-Net demonstrate superior low-contrast enhancement performance compared to several state-of-the-art methods. • Low-contrast radiography enhancement by adaptive intensity transformation. • Adaptive intensity transformation using pixel-stretching-based learning. • Deep model-based learning approach for self-supervised pixel stretch. • Evaluation of the proposed approach with SOTA, qualitatively and quantitatively. • Proposed approach achieves enhanced radiographs compared to related methods. [ABSTRACT FROM AUTHOR]