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With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 % , 50 % , 34 % and 45 % respectively. [ABSTRACT FROM AUTHOR]

Facial features extracted from deep convolutional networks are susceptible to background, individual identity and other factors. It interferes with facial expression recognition when mixed with useless features. Considering that different scale features have rich semantic and texture information respectively, this paper takes VGG-16 as the basic network structure and combines multiscale features to obtain richer feature information. In addition, the input feature map elements are enhanced or suppressed by the attention module in order to extract salient features more accurately. The proposed method was validated on two commonly used expression data sets CK+ and RAF-DB, and the recognition rates were 98.77 and 82.83%, respectively. Experimental results show the superiority of this method. [ABSTRACT FROM AUTHOR]

Comic inpainting is essential for language localization and adapting comics into animations, involving the removal of speech boxes, textboxes, and sound effects while maintaining artistic integrity. This paper introduces an automated approach leveraging the Manga inpainting method by Xie et al., adapted for western comics like Tex Willer. Tex Willer comics, known for their fusion of Italian art and American Western imagery, pose unique challenges due to their distinct styles. Customized Manga Restoration and Structural Line Extraction codes are employed, along with automated mask generation using deep CNN-based speech balloon detection and segmentation. Our contributions include the development of a precise model tailored to Tex Willer Comics, resulting in visually coherent inpainting results. The paper follows a structured format, covering State-of-the-Art, methodology, results, and conclusions, aiming to streamline the process of western comic inpainting for professionals in the comics and animation industries. [ABSTRACT FROM AUTHOR]

With the increasing complexity and cost of industrial control systems and the rapid development of information technology, the tolerance of industrial control equipment to performance degradation, productivity decline, and hidden safety hazards is getting lower and lower. Immediate detection of failures of industrial control equipment is of great significance for the safety of industrial control systems and reducing maintenance costs. Faced with these challenges, the traditional fault diagnosis technology based on expert knowledge has been insufficient to meet the requirements of accuracy and real-time fault diagnosis of industrial control systems due to its high cost and low efficiency. Aiming at the problem that the fault diagnosis method in the current industrial control environment is not systematic, this paper proposes a Safety Fault diagnosis system for industrial intelligent control equipment based on DevOps concept and deep CNN. Based on the in-depth analysis of the principles of CNN, this paper improves the model's robustness from the perspective of data set enhancement, fault diagnosis performance, and noise immunity analysis. Finally, it is proved through experiments that the fault diagnosis model can effectively deal with the lack of fault samples by adopting the fault data set enhancement method based on periodic overlapping sampling. Our fault diagnosis system based on DevOps and CNN proposed in this paper has high scalability and can accurately predict fault input. [ABSTRACT FROM AUTHOR]

The recognition of telugu hand written text is been one of the problems in many applications. To overcome the problem a deep learning technique is proposed in this work i.e. a Dense convolutional neural network (DCNN) model. A telugu dataset which is taken form IIIT-HW-Telugu is utilized to perform the proposed model. In this paper a four stage telugu word retrieval is performed, initially thinning of image is performed using morphological operation, secondly Densenet-CNN is applied for thinning image, thirdly perform OCR based image segmentation, finally two models like HARRIS and BRISK features to extract the features and evaluate information from the given input HWT images. The parameters evaluated are hamming distance, PSNR, MSE, Noise Sensitivity and rate of thinning. The proposed model outperformed well compared to other methods. The PSNR obtained using proposed model is 54.74, hamming distance is 1.2. [ABSTRACT FROM AUTHOR]

Purpose: This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches. Design/methodology/approach: Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data. Findings: Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work. Research limitations/implications: Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners. Practical implications: Improving the pre-trained model's performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully. Social implications: Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with "crossing the chasm" from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios. Originality/value: Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications. [ABSTRACT FROM AUTHOR]

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system. [ABSTRACT FROM AUTHOR]

Crack detection methods of high-rise building walls based on traditional computer vision (CV) heavily rely on manual selection and extraction of design features. Convolutional neural network (CNN)-based CV can actively learn the features of cracks and adapt to complex backgrounds, solving the limitations of traditional crack detection methods. This paper explores faster region-CNN, single shot multibox detector (SSD), You Only Look Once for crack detection, and Mask R-CNN for crack segmentation and proposes a novel automatic crack geometric quantification method by combining CNN-based object detection and segmentation. The contents include (1) crack detection and bounding box extraction, exploring a variety of models, selecting the best model to detect the image taken by an unmanned aerial vehicle (UAV), and extracting the crack region; (2) crack segmentation, using the detection results of the first part as input for more accurate detection and segmentation of cracks; and (3) a novel pixel-level geometric quantization method of crack based on Hough straight-line detection, mainly including crack length and width. Then, the pixel level is transformed into the actual geometric quantization to simply determine the crack severity. The three models generated in these three parts can be used for managing exterior wall cracks in high-rise buildings for different inspection purposes. [ABSTRACT FROM AUTHOR]

In the realm of prognosticating the remaining useful life (RUL) of pivotal components, such as aircraft engines, a prevalent challenge persists where the available historical life data often proves insufficient. This insufficiency engenders obstacles such as impediments in performance degradation feature extraction, inadequacies in capturing temporal relationships comprehensively, and diminished predictive accuracy. To address this issue, a 1D CNN-GRU prediction model for few-shot conditions is proposed in this paper. In pursuit of more comprehensive data feature extraction and enhanced RUL prognostication precision, the Convolutional Neural Network (CNN) is selected for its capacity to discern high-dimensional features amid the intricate dynamics of the data. Concurrently, the Gated Recurrent Unit (GRU) network is leveraged for its robust capability in extracting temporal features inherent within the data. We combine the two to construct a CNN-GRU hybrid network. Moreover, the integration of data distribution alongside correlation and monotonicity indices is employed to winnow the input of multi-sensor monitoring parameters into the CNN-GRU network. Finally, the engine RULs are predicted by the trained model. In this paper, experiments are conducted on a sub-dataset of the National Aeronautics and Space Administration (NASA) C-MAPSS multi-constraint dataset to validate the effectiveness of the method. Experimental results have demonstrated that this method has high accuracy in RUL prediction tasks, which can powerfully demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Agriculture plays a crucial role in fostering sustainable growth through the integration of various technological advancements such as image processing, artificial intelligence, deep learning, and the Internet of Things (IoT). The global population is increasing on a daily basis. The increasing demand within the agriculture industry has necessitated the collective enhancement of plant cultivation and field productivity. This paper emphasizes the significance of effectively managing the crop during its initial growth phase as well as during the harvesting era. Image processing and artificial neural networks are employed as distinct methodologies for detecting illnesses on leaves. When capturing images using drones, the resulting images undergo a process of segmentation and transformation, resulting in the identification of three distinct vectors that represent diseases. These vectors include colour, texture, and morphology. This paper reviews on various disease classification strategies that can be utilized for the detection of plant diseases. [ABSTRACT FROM AUTHOR]

Detecting arrhythmia through electrocardiogram (ECG) signals is a challenging task, and some methods based on deep neural networks have been proposed to assist in the diagnosis of arrhythmia. However, the performance of existing models in obtaining imbalanced key points of ECG data and integrating long-distance information needs to be improved. In this study, we propose an adaptive arrhythmia classification network (ADLNet) based on deformable convolution and bidirectional LSTM. This network uses the Bi-LSTM gating mechanism to obtain and control the cumulative information of the context, enhance the ability of the model to understand the context, and mine the long-term sequential information in the lead. Considering the imbalance of the key points of the focus of ECG and the gradient disappearance of the deep network, the residual deformable convolution module with deformable receptive field is introduced to better obtain the local features of ECG and enhance the ability of feature representation. In addition, the adaptive fusion module adaptively combines the features mined from the first two with different weight coefficients to improve the performance of the model. The proposed network was evaluated on the 12-lead ECG data set of the 2018 China Physiological Signal Challenge 2018, and the F1 of ADLNet reached 0.861, which is 3% higher than the baseline method. The experimental results show that the performance of the ADLNet proposed in this paper is better than that of the mainstream arrhythmia classification network. This study has demonstrated that the adaptive combination of deformable convolutional models and Bi-LSTM models can achieve excellent ECG classification performance without the assistance of other medical features. [ABSTRACT FROM AUTHOR]

The paper classification method aims to correctly divide the paper data according to the similarity of its content. However, how to accurately classify according to the content expressed in the paper has always been a problem that various classification algorithms need to face. At present, there is a kind of paper classification method based on deep learning and implemented by the encoder‐decoder structure. This method inputs the words from a large number of papers into encoder, after calculating by NN (neural network) algorithm, the similarity degree of different papers is compared to achieve the purpose of classification. However, this type of method only considers the similarity between words, a NN algorithm can only calculate a large number of word information once, and it cannot find the regularity of classification through word information. But it has a difference with the similarity of the content. This paper starts from the perspective of considering the content, its label information is extracted, and the input vector of encoder‐decoder structure is formed with labels and words. This improves the original paper classification method based on encoder‐decoder structure. Firstly, the label information is based on the content, which can reflect the content of the paper. Secondly, the classification method which combines label information and word information can reflect the content of the paper comprehensively. Thirdly, the label information is independent of word information and NN algorithm is used separately to make this part of the content more consistent in the encoder‐decoder structure. Finally, the label information and the word information are combined, respectively, with the output values obtained by different NN algorithms to realize the classification of the content. This paper proves the effectiveness of the proposed method by evaluating the paper data in web of science and obtaining relevant experimental results. [ABSTRACT FROM AUTHOR]

This paper aims to use deep learning algorithms to identify and study the stability of breakwater armor blocks. It introduces a posture identification model for fender blocks using a Mask Region-based Convolutional Neural Network (R-CNN), which has been enhanced by considering factors affecting breakwater fender blocks. Furthermore, a wave prediction model for breakwaters is developed by integrating Bidirectional Encoder Representations from Transformers (BERTs) with Bidirectional Long Short-Term Memory (BiLSTM). The performance of these models is evaluated. The results show that the accuracy of the Mask R-CNN and its comparison algorithms initially increases and then decreases with higher Intersection Over Union (IOU) thresholds, peaking at 95.16% accuracy at an IOU threshold of 0.5. The BERT-BiLSTM wave prediction model maintains a loss value around 0.01 and an accuracy of approximately 90.00%. These results suggest that the proposed models offer more accurate stability assessments of breakwater armor blocks. By combining the random forest prediction model with BiLSTM, the wave characteristics and fender posture can be predicted better, offering reliable decision support for breakwater engineering. [ABSTRACT FROM AUTHOR]

About five million people lose their lives every year to lung cancer, making it one of the leading causes of mortality worldwide. In the last few years, a lot of methods of detection of lung cancer were improved however these could not efficiently diagnose cancer. In this paper, a convolutional neural network (CNN) of robust deep learning is developed. CNN precision raises the deeper that is, however, it causes overfitting or vanishing gradient problems simultaneously. To solve the issue, the CNN used resort to parallel CNN. It used Pictures the LIDC-IDRI consortium image collection has thoracic CT images that have been annotated for lung cancer diagnosis and screening purposes. The presented model includes result models of CNN which are integrated via ensemble methods and it compare with every model. Results of the simulation illustrate that the presented method's accuracy has developed by 2.18 percent in comparison with the method of the main paper [ABSTRACT FROM AUTHOR]

Separation of speech and music plays a vital role in multiple fields related to audio and speech processing. The spectrograms of speech and music show distinct patterns. This serves as the motivation for the differentiation of speech and music signals in an audio segment. The patterns have been further emphasized using Sobel edge kernels, Mel-spectrograms. For the inception of this paper, we have made a dataset from "All India Radio" news archives which is having separate and overlapped speech and music data in different languages. The different input features are extracted from these audio segments and further emphasized before feeding them to the different classifiers for distinguishing speech and music frames. We also compared the different classification algorithms for their varied performance in terms of accuracy. We have found that the convolutional neural network based approach on Mel-spectrograms and MFCC-delta-RNN methods have given a significantly better result compared to other approaches. Further, we wanted to see how these approaches work in the audio data of different languages, hence, we have applied the proposed method in three different languages such as Bengali, Punjabi, and Tamil. We have seen that the performance of the proposed method in all languages is consistent. The paper has also attempted to solve the problem of classifying audio segments with overlapped speech and music regions and achieved a good level of accuracy. [ABSTRACT FROM AUTHOR]

Since the dawn of civilization, handwriting has been one of the most important forms of communication. However, as handwriting differs from person to person, writer identification has become a promising application of pattern recognition to identify the actual writer of a handwritten document. Handwriting can be either online or offline, depending on how it was obtained. Users can write directly on tablets, smartphones, touch screens, PDAs, and other devices using input devices with online handwriting, whereas offline handwriting is done with a pen and paper. With the advent of artificial intelligence, and most importantly deep learning techniques, the development of writer identification systems based on offline handwritten documents has gained a lot of attention. Deep learning models have the capability of automatic feature extraction, which results in increased performance. From the literature survey, it was revealed that least attention has been paid towards the development of deep learning-based writer identification systems for offline Urdu handwritten documents, unlike the English and Arabic scripts. Therefore, in this paper, we proposed an offline Urdu handwritten writer identification system using a deep learning model inspired by the VGG-16 model of CNN. The model was trained and tested on a novel Urdu handwritten dataset contributed by 318 distinct Urdu writers, resulting in an overall training accuracy of 98.71% and a testing accuracy of 99.11%. The results achieved showed that the proposed model outperformed the already existing writer identification techniques. [ABSTRACT FROM AUTHOR]

The rapid advancement of autonomous vehicle technology has brought into focus the critical need for enhanced road safety systems, particularly in the areas of road damage detection and surface classification. This paper explores these two essential components, highlighting their importance in autonomous driving. In the domain of road damage detection, this study explores a range of deep learning methods, particularly focusing on one-stage and two-stage detectors. These methodologies, including notable ones like YOLO and SSD for one-stage detection and Faster R-CNN for two-stage detection, are critically analyzed for their efficacy in identifying various road damages under diverse conditions. The review provides insights into their comparative advantages, balancing between real-time processing and accuracy in damage localization. For road surface classification, the paper investigates the classification techniques based on both environmental conditions and material road composition. It highlights the role of different convolutional neural network architectures and innovations at the neural level in enhancing classification accuracy under varying road and weather conditions. The main finding of this work is that it offers a comprehensive overview of the current state of the art, showcasing significant strides in utilizing deep learning for road analysis in autonomous vehicle systems. The study concludes by underscoring the importance of continued research in these areas to further refine and improve the safety and efficiency of autonomous driving. [ABSTRACT FROM AUTHOR]

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]

AI plays a pivotal role in predicting plant growth in agricultural contexts and in creating optimized environments for cultivation. However, unlike agriculture, the application of AI in aquaculture is predominantly focused on diagnosing animal conditions and monitoring them for users. This paper introduces an Automated Fish-feeding System (AFS) based on Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), aiming to establish an automated system akin to smart farming in the aquaculture sector. The AFS operates by precisely calculating feed rations through two main modules. The Fish Growth Measurement Module (FGMM) utilizes fish data to assess the current growth status of the fish and transmits this information to the Feed Ration Prediction Module (FRPM). The FRPM integrates sensor data from the fish farm, fish growth data, and current feed ration status as time-series data, calculating the increase or decrease rate of ration based on the present fish conditions. This paper automates feed distribution within fish farms through these two modules and verifies the efficiency of automated feed distribution. Simulation results indicate that the FGMM neural network model effectively identifies fish body length with a minor deviation of less than 0.1%, while the FRPM neural network model demonstrates proficiency in predicting ration using a GRU cell with a structured layout of 64 × 48. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of deep learning technology, a large number of excellent convolutional neural networks (CNNs) have been proposed, many of which are based on improvements to classical methods. Based on the Inception family of methods, depthwise separable convolution was applied to Xception to achieve lightweighting, and Inception-ResNet introduces residual connections to accelerate model convergence. However, existing improvements for the Inception module often neglect further enhancement of its receptive field, while increasing the receptive field of CNNs has been widely studied and proven to be effective in improving classification performance. Motivated by this fact, three effective expansion modules are proposed in this paper. The first expansion module, Inception expand (Inception-e) module, is proposed to improve the classification accuracy by concatenating more and deeper convolutional branches. To reduce the number of parameters for Inception e, this paper proposes a second expansion module—Equivalent Inception-e (Eception) module, which is equivalent to Inception-e in terms of feature extraction capability, but which suppresses the growth of the parameter quantity brought by the expansion by effectively reducing the redundant convolutional layers; on the basis of Eception, this paper proposes a third expansion module—Lightweight Eception (Lception) module, which crosses depthwise convolution with ordinary convolution to further effectively reduce the number of parameters. The three proposed modules have been validated on the Cifar10 dataset. The experimental results show that all these extensions are effective in improving the classification accuracy of the models, and the most significant effect is the Lception module, where Lception (rank = 4) on the Cifar10 dataset improves the accuracy by 1.5% compared to the baseline model (Inception module A) by using only 0.15 M more parameters. [ABSTRACT FROM AUTHOR]

Numerous vitamin deficiencies might manifest as one or more easily recognizable signs and symptoms that emerge in various parts of the body. Through examination of photos of their eyes, lips, tongue, and nails, the program gives users the opportunity to identify any vitamin deficiencies they may have without giving blood samples. The CNN algorithm, which is based on deep learning, is used to carry out this operation. Here, the dataset of the eyes, lips, tongue, and lips has been considered. Following the analysis of the dataset, pre-processing is carried out, and the data is subsequently trained using the CNN method. Following training, the model is stored, and OpenCV is used for testing. [ABSTRACT FROM AUTHOR]

The world population is growing very fast and the lifestyle of human beings is changing with time and place. So, there is a need for disease management which includes disease diagnosis, its detection and classification, cure and lastly for future disease prevention. The outermost protective layer of a human body is the skin. Skin not only impacts a person's health but also psychologically impacts one's life. Computer-aided systems are very helpful in skin disease detection and classification and their application is growing rapidly in healthcare. This literature review paper aims to help the researchers to get a synthesized and appropriate information for the same. We have included papers from 2021 to 2023 for the review from the Scopus database. 45 studies are selected for the review of which 32 studies use deep learning techniques, 11 use machine learning techniques and 2 studies use a hybrid approach. The studies are compared on various parameters like models, datasets, and performance metrics. The work also identified some of the challenges like dealing with noise and also explained disease symptoms. [ABSTRACT FROM AUTHOR]

Writing is a commonplace activity that individuals partake in regularly. However, the implications behind it are often overlooked. When we write, various psychological factors come into play as the pen creates letters on the paper. Handwriting analysis has long been a subject of study, attracting researchers from diverse disciplines such as graphology, psychology, paleography, neuroscience, criminology, and computer science. Among the promising applications of handwriting analysis is gender classification, where a system can predict the gender of a writer based on their handwriting style. Since each individual's handwriting is unique, and variations exist between the handwriting of different genders, an automatic gender classification system can exploit these differences to make predictions. This paper presents a deep-learning-based gender classification system specifically designed for Urdu handwriting. The proposed approach utilizes a CNN network trained and tested on a self-created dataset contributed by 200 distinct male and 200 female Urdu writers. Through this method, the gender classification system achieved an impressive overall accuracy of 99.63%. The results obtained demonstrate that our technique for Urdu handwriting-based writer identification surpasses existing approaches. In the future, we intend to explore transfer learning techniques to further advance this field. [ABSTRACT FROM AUTHOR]

Multiclass classification of brain tumors from magnetic resonance (MR) images is challenging due to high inter-class similarities. To this end, convolution neural networks (CNN) have been widely adopted in recent studies. However, conventional CNN architectures fail to capture the small lesion patterns of brain tumors. To tackle this issue, in this paper, we propose a global transformer network dubbed GT-Net for multiclass brain tumor classification. The GT-Net mainly comprises a global transformer module (GTM), which is introduced on the top of a backbone network. A generalized self-attention block (GSB) is proposed to capture the feature inter-dependencies not only across spatial dimension but also channel dimension, thereby facilitating the extraction of the detailed tumor lesion information while ignoring less important information. Further, multiple GSB heads are used in GTM to leverage global feature dependencies. We evaluate our GT-Net on a benchmark dataset by adopting several backbone networks, and the results demonstrate the effectiveness of GTM. Further, comparison with state-of-the-art methods validates the superiority of our model. [ABSTRACT FROM AUTHOR]

Deep learning and the Internet of Things (IoT) are rapidly advancing technologies with wide-ranging applications, notably in areas such as face detection. There is a growing need for sophisticated touchless authentication systems within this domain. While numerous security methods are available, many of them suffer from various shortcomings, including issues like forgotten passwords and the potential transmission of diseases through touch-based authentication techniques. This paper proposes IoT-based intelligent control face detection systems that leverage deep learning (DL) models. These systems are designed to significantly enhance security without relying on manually crafted rules. Face detection plays a pivotal role in safeguarding society, identifying wrongdoers, and bolstering community safety. However, challenges persist, particularly in achieving high accuracy with face recognition control systems in uncontrolled environments and real-time scenarios, such as intersections. In response to these challenges, our paper introduces a novel model that harnesses a combination of deep convolutional neural networks (CNNs) with self-attention mechanisms. Our experimental results demonstrate that this model can rapidly detect whole-scale images in a single forward pass. Notably, our proposed method achieved an outstanding accuracy rate of 99.7%. In comparison to existing state-of-the-art methods, our approach exhibits superior efficiency. This work showcases how the integration of IoT and DL, particularly with the use of a CNN with self-attention, outperforms other CNN-based approaches in terms of both speed and accuracy. [ABSTRACT FROM AUTHOR]

Breast cancer remains a substantial public health challenge, marked by a rising prevalence. Accurate early detection is paramount for effective treatment and improved patient outcomes in breast cancer. The diversity of breast tumors and the complexity of their microenvironment present significant challenges. Establishing a reliable breast calcification and micro-calcification detection approach is an ongoing issue that researchers must continue to investigate. The goal is to develop an effective methodology that contributes to increased patient survival. Therefore, this paper introduces a novel approach for classifying breast calcifications in mammography, aiming to distinguish between benign and malignant cases. Aiming to address these challenges, we proposed our hybrid approach for breast calcification classification in mammogram images. The proposed approach starts with an image pre-processing phase that includes noise reduction and enhancement filters. Afterward, we proposed our hybrid classification architecture. It includes two branches: First, the vision transformer (ViT++) branch for contextual features. Secondly, a CNN branch based on transfer learning techniques for visual features. Using the CBIS-DDSM dataset, the application of our proposed ViT++ architecture reached the maximum accuracy of 96.12%. Further, the application of the VGG16 as a single feature extractor had a much lower accuracy of 61.96%. Meanwhile, the combination of these techniques in the same architecture improved the accuracy to 99.22%. Three different pre-trained feature extractors were applied in the CNN branch: Xception, VGG16, and RegNetX002. However, the best-obtained outcomes were from the combination of the ViT++ and the VGG16. The experimental findings indicate that the proposed strategy for breast calcification detection has the potential to surpass the performance of currently top-ranked methods, particularly in terms of classification accuracy. [ABSTRACT FROM AUTHOR]

Recently, image dehazing methods based on deep learning have achieved good results, but most of them are aimed at synthetic hazy images that have corresponding haze-free images. However, in some practical applications, the acquired images always contain haze in hazy weather, but it is difficult to get the corresponding haze-free images. So the image dehazing for real hazy images is a very important and challenging problem due to the absence of the corresponding haze-free images. In this paper, we propose an unpaired image dehazing method to solve the problem of unpaired real hazy images, which includes two parts: one is the unpaired training module and the other one is the supervised training module. For the unpaired training module, we first use cycle generative adversarial network to generate the dehazed images; then we use perceptual loss to make the features of the dehazed images and the unpaired haze-free images more consistency to improve the image dehazing performance; Finally, we propose to introduce instinct properties of clear images to further improve the image dehazing performance. For the supervised training module, we design a pseudo labeling scheme to train the model by using the dehazed images generated from the unpaired training module as pseudo haze-free images, so that we can conduct supervised training to further improve the performance of haze removal. Besides, we dedicate to collect a real hazy images dataset from real high-speed railway scenarios, named HRHI (High-speed Railway Hazy Image) dataset, which contains real hazy images and unpaired haze-free images. In order to verify the generality of our method, we also collect a real sandstorm images dataset from real high-speed railway scenarios, named HRSI (High-speed Railway Sandstorm Image) dataset. Expensive experimental results on HRHI dataset and the generic hazy image dataset show the effectiveness of our method compared with other state-of-the-art image dehazing methods, and the experimental results on HRSI dataset prove the generality of our method. [ABSTRACT FROM AUTHOR]

In smart transportation, assisted driving relies on data integration from various sensors, notably LiDAR and cameras. However, their optical performance can degrade under adverse weather conditions, potentially compromising vehicle safety. Millimeter-wave radar, which can overcome these issues more economically, has been re-evaluated. Despite this, developing an accurate detection model is challenging due to significant noise interference and limited semantic information. To address these practical challenges, this paper presents the TC–Radar model, a novel approach that synergistically integrates the strengths of transformer and the convolutional neural network (CNN) to optimize the sensing potential of millimeter-wave radar in smart transportation systems. The rationale for this integration lies in the complementary nature of CNNs, which are adept at capturing local spatial features, and transformers, which excel at modeling long-range dependencies and global context within data. This hybrid approach allows for a more robust and accurate representation of radar signals, leading to enhanced detection performance. A key innovation of our approach is the introduction of the Cross-Attention (CA) module, which facilitates efficient and dynamic information exchange between the encoder and decoder stages of the network. This CA mechanism ensures that critical features are accurately captured and transferred, thereby significantly improving the overall network performance. In addition, the model contains the dense information fusion block (DIFB) to further enrich the feature representation by integrating different high-frequency local features. This integration process ensures thorough incorporation of key data points. Extensive tests conducted on the CRUW and CARRADA datasets validate the strengths of this method, with the model achieving an average precision (AP) of 83.99% and a mean intersection over union (mIoU) of 45.2%, demonstrating robust radar sensing capabilities. [ABSTRACT FROM AUTHOR]

Edge detection is an essential image processing act that is crucial for many computer vision applications such as object detection, image segmentation, face recognition, text recognition, medical imaging, and autonomous vehicles. Deep learning is the most advanced and widely used tool of them all. In this paper, we present a novel deep learning model and use image datasets to test it. Our model uses a fractional calculus tool, which could enhance gradient approaches' performances. Specifically, we approximate the fractional-order derivative-order neural network (GLFNet) using a Grünwald–Letnikov fractional definition. First, the original dataset is subjected to a Grünwald–Letnikov fractional order. After that, the CNN model is updated with the new dataset, concluding the standard CNN procedure. The training rate, the improvement in the F-measure for identifying the effective edge while maintaining the CNN model's memory consumption, and the values of the loss errors between the prediction and training processes were all tested using the MNIST dataset. Our experiments show that GLFNet considerably enhances edge detection. GLFNet outperformed CNN with an average loss error ratio of 15.40, suggesting fewer loss mistakes. The F1-measure ratio of 0.81 indicates that GLFNet can compete with CNN in terms of precision and recall. The training time for GLFNet was lowered by an average ratio of 1.14 when compared to CNN, while inference time was faster with a ratio of 1.14, indicating increased efficiency. These findings demonstrate the efficacy of introducing Grünwald–Letnikov fractional convolution into deep learning models, resulting in more precise and reliable edge detection while preserving comparable memory utilization. [ABSTRACT FROM AUTHOR]

The communication barriers experienced by deaf and hard-of-hearing individuals often lead to social isolation and limited access to essential services, underlining a critical need for effective and accessible solutions. Recognizing the unique challenges this community faces—such as the scarcity of sign language interpreters, particularly in remote areas, and the lack of real-time translation tools. This paper proposes the development of a smartphone-runnable sign language recognition model to address the communication problems faced by deaf and hard-of-hearing persons. This proposed model combines Mediapipe hand tracking with particle filtering (PF) to accurately detect and track hand movements, and a convolutional neural network (CNN) and bidirectional long short-term memory based gesture recognition model to model the temporal dynamics of Sign Language gestures. These models use a small number of layers and filters, depthwise separable convolutions, and dropout layers to minimize the computational costs and prevent overfitting, making them suitable for smartphone implementation. This article discusses the existing challenges handled by the deaf and hard-of-hearing community and explains how the proposed model could help overcome these challenges. A MediaPipe + PF model performs feature extraction from the image and data preprocessing. During training, with fewer activation functions and parameters, this proposed model performed better to other CNN with RNN variant models (CNN + LSTM, CNN + GRU) used in the experiments of convergence speed and learning efficiency. [ABSTRACT FROM AUTHOR]

Hazy weather reduces contrast, narrows the dynamic range, and blurs the details of the remote sensing image. Additionally, color fidelity deteriorates, causing color shifts and image distortion, thereby impairing the utility of remote sensing data. In this paper, we propose a lightweight remote sensing-image-dehazing network, named LRSDN. The network comprises two tailored, lightweight modules arranged in cascade. The first module, the axial depthwise convolution and residual learning block (ADRB), is for feature extraction, efficiently expanding the convolutional receptive field with little computational overhead. The second is a feature-calibration module based on the hybrid attention block (HAB), which integrates a simplified, yet effective channel attention module and a pixel attention module embedded with an observational prior. This joint attention mechanism effectively enhances the representation of haze features. Furthermore, we introduce a novel method for remote sensing hazy image synthesis using Perlin noise, facilitating the creation of a large-scale, fine-grained remote sensing haze image dataset (RSHD). Finally, we conduct both quantitative and qualitative comparison experiments on multiple publicly available datasets. The results demonstrate that the LRSDN algorithm achieves superior dehazing performance with fewer than 0.1M parameters. We also validate the positive effects of the LRSDN in road extraction and land cover classification applications. [ABSTRACT FROM AUTHOR]

This article explores the application of a new optimal convolutional neural network (CNN) to segment remote sensing. The paper designs a modified version of the firefly algorithm to provide an optimal structure of the CNN. The method is then applied to segment the remote-sensing images. The authors compare the proposed method with several other architectures on two multispectral remote sensing datasets, evaluating model performance and accuracy. The findings demonstrate that the projected CNN method provides the most efficient outcomes and can be a viable option for remote sensing analysis and classification due to their relative ease of training and high accuracy rates. Furthermore, the authors discuss the potential for further requests of CNNs in remote sensing statistics analysis. [ABSTRACT FROM AUTHOR]

Traditional leaf chlorophyll estimation using Soil Plant Analysis Development (SPAD) devices and spectrophotometers is a high-cost mechanism in agriculture. Recently, research on chlorophyll estimation using leaf camera images and machine learning has been seen. However, these techniques use self-defined image color combinations where the system performance varies, and the potential utility has not been well explored. This paper proposes a new method that combines an improved contact imaging technique, the images' original color parameters, and a 1-D Convolutional Neural Network (CNN) specifically for tea leaves' chlorophyll estimation. This method utilizes a smartphone and flashlight to capture tea leaf contact images at multiple locations on the front and backside of the leaves. It extracts 12 different original color features, such as the mean of RGB, the standard deviation of RGB and HSV, kurtosis, skewness, and variance from images for 1-D CNN input. We captured 15,000 contact images of tea leaves, collected from different tea gardens across Assam, India to create a dataset. SPAD chlorophyll measurements of the leaves are included as true values. Other models based on Linear Regression (LR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), and K-Nearest Neighbor (KNN) were also trained, evaluated, and tested. The 1-D CNN outperformed them with a Mean Absolute Error (MAE) of 2.96, Mean Square Error (MSE) of 15.4, Root Mean Square Error (RMSE) of 3.92, and Coefficient of Regression ( R 2 ) of 0.82. These results show that the method is a digital replication of the traditional method, while also being non-destructive, affordable, less prone to performance variations, and simple to utilize for sustainable agriculture. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have been mainly powerful and important artificial intelligence techniques, which are exploited in various computer vision applications, such as facial point detection (FPD), owing their versatility and high performance. The role of DCNNs in this area is pivotal due to their ability to learn hierarchical features, which are essential for recognizing complex patterns. On the other hand, DCNNs have considerable computational complexity due to their topology nature. An FPGA-based SoC design is needed for CNN acceleration due to the rapid development, energy efficiency, low latency and high reconfigurability of FPGAs. Despite their performance benefits, FPGA-based SoCs also come with several limitations like complexity of design. This paper proposes a dynamic partial reconfiguration (DPR) and hybrid architecture for DCNN accelerators. We propose (i) a GPU-based software implementation for DCNN-based FPD, (ii) a CNN-based acceleration and optimization method using the high-level synthesis technique, (iii) a DPR-based hybrid architecture to improve the performance of the suggested approach. To validate our design, four scenarios are put forward. The experimental results prove that the proposed work achieves a better performance in terms of reconfigurability and execution time, hardware cost, and power consumption. [ABSTRACT FROM AUTHOR]

In situations where traditional feedback methods face high complexity and rely on channel sparsity, many deep learning-based CSI compression feedback methods have shown the potential to fully capture the diversity and multiplexing gains of Massive MIMO technology in Frequency Division Duplex (FDD) mode. In order to further enhance the accuracy of obtaining downlink channel state information at the Base Station (BS) side, this paper proposes a novel neural network, SfNet. Based on visual analysis, the convolutional layers in the encoder are designed to extract long-distance time-delay correlations of the same antenna across different subcarriers. The SimAM module is introduced to mitigate clustering effects. In the decoder, a spatial-temporal joint modeling approach is presented, utilizing the Convolutional Block Attention Module (CBAM) module to preserve the original spatial structure features lost during the dimension reduction of the CSI matrix to sequence data. Subsequently, two layers of Transformer multi-head attention are employed to achieve more global time-series modeling.The experimental results indicate that, compared to CLNet, the average complexity of SfNet's encoder is reduced by 3.4%. It meets the demand for lightweight devices on the user side. In terms of accuracy, SfNet exhibits an average improvement of 30.17% compared to TransNet in outdoor environments. [ABSTRACT FROM AUTHOR]

The Internet of Things is a disruptive technology that has changed the face of many industries. On the contrary, the unpresidential growth of IoT has also introduced many cybersecurity challenges. An adversary can exploit a zero-day vulnerability in an IoT to create a botnet of things. An IoT botnet is a group of compromised Internet of Things weaponized to launch cyber attacks. Machine learning and other artificial intelligence techniques are being used to combat the wide range of cyberattacks on the Internet of Things. However, in order to overcome challenges such as early diagnosis, real-time monitoring, and adaptability to different threats, these Machine Learning approaches still require significant feature engineering. In order to identify IoT botnet assaults early on, this paper suggests using a customized convolutional neural network (CCNN) model. The four phases of the model are feature extraction, attack detection, mitigation, and pre-processing. The class imbalance has been improved and the input data pre-processed using the Enhanced Synthetic minority oversampling approach. Furthermore, flow-based features, raw attributes, mean, median, standard deviation, improved entropy, mutual information, and other statistical features are retrieved and regarded as part of the feature set. The CCNN model provides the detection or classification output during the attack detection phase, which operates depending on the features derived from the input data. Additionally, a mitigation process based on entropy has been suggested to locate the attacker node, aiding in the removal of the susceptible attacker IoT node from the network. The compromised IoT node is removed through the entropy-based mitigation method, which establishes the entropy formulation based on the node's activity. The suggested model's specificity is 97.09%, compared to the minimal specificity reached by conventional techniques, including CNN (83.58%), RNN (86.17%), RF (60.46%), SVM (78.50%), and DNN (84.12%) and SMIE (88.42%), respectively. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN) models have become very powerful machine learning due to their high classification accuracy especially for applications dealing with images. This paper presents an automatic system for Maghrebian (Algerian, Tunisian and Moroccan) accent recognition using CNN 2D model. The CNN model is fed by images constructed from the Mel frequency cepstral coefficients (MFCC) over time. Accents are recognized independently to utterance and its start time position within the speech signal. The silence part that is presented in speech signals, causes confusion in accent recognition. An SVM classifier is trained to remove the silence basing on 12 MFCC. To show the silence effect, accent recognition is performed with and without silence elimination using 5 folds cross validation technique. Many CNN architectures were studied and evaluated using 150 audio files of 30 speakers from the three Maghrebian accents for 1, 1.5 and 2 s test hearing durations (HD). The classification accuracy (CA) of the accent recognition is improved by about 2% after silence elimination and achieves 99.33% with a hearing duration of 1.5 s using the smallest best CNN model. [ABSTRACT FROM AUTHOR]

There has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unfurl at a fast pace on digital platforms leading to various issues such as prejudice, violence and even genocide. Considering the acceptance of Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques in varied application domains, it would be intriguing to consider these techniques for automated hate speech detection. In literature, there have been efforts to recognize and categorize hate speech using varied Machine Learning (ML) and Deep Learning (DL) techniques. Hence, considering the need and provocations for hate speech detection we aim to present a comprehensive review that discusses fundamental taxonomy as well as recent advances in the field of online hate speech identification. There is a significant amount of literature related to the initial phases of hate speech detection. The background section provides a detailed explanation of the previous research. The subsequent section that follows is dedicated to examining the recent literature published from the year 2020 onwards. The paper presents some of the hate speech datasets considered for hate speech detection. Furthermore, the paper discusses different data modalities, namely, textual hate speech detection, multi‐modal hate speech detection and multilingual hate speech detection. Apart from systematic review on hate speech detection, the paper also implement several multi‐label models to compare the performance of hate speech detection by employing classic ML technique namely, Logistic Regression and DL technique namely, Long Short‐Term Memory (LSTM) and a multiclass multi‐label architecture. In the implemented architecture, we have derived two new elements to quantify the hatefulness and intensity of hatred to improve the results for hate speech detection using Indonesian tweet dataset. Empirical Analysis of the model reveals that the implemented approach outperforms and is able to achieve improved results for the underlying dataset. [ABSTRACT FROM AUTHOR]

Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels. [ABSTRACT FROM AUTHOR]

Simulating animal movement has long been a central focus of study in the area of wildlife behaviour studies. Conventional modelling methods have difficulties in accurately representing changes over time and space in the data, and they generally do not effectively use telemetry data. Thus, this paper introduces a new and innovative deep reinforcement learning technique known as AnimalEnvNet. This approach combines historical trajectory data and remote sensing images to create an animal agent using deep reinforcement learning techniques. It overcomes the constraints of conventional modelling approaches. We selected pandas as the subject of our research and carried out research using GPS trajectory data, Google Earth images, and Sentinel-2A remote sensing images. The experimental findings indicate that AnimalEnvNet reaches convergence during supervised learning training, attaining a minimal mean absolute error (MAE) of 28.4 m in single-step prediction when compared to actual trajectories. During reinforcement learning training, the agent has the capability to replicate animal locomotion for a maximum of 12 iterations, while maintaining an error margin of 1000 m. This offers a novel approach and viewpoint for mimicking animal behaviour. [ABSTRACT FROM AUTHOR]

Image denoising and super-resolution play vital roles in imaging systems, greatly reducing the preprocessing cost of many AI techniques for object detection, segmentation, and tracking. Various advancements have been accomplished in this field, but progress is still needed. In this paper, we have proposed a novel technique named the Enhanced Learning Enriched Features (ELEF) mechanism using a deep convolutional neural network, which makes significant improvements to existing techniques. ELEF consists of two major processes: (1) Denoising, which removes the noise from images; and (2) Super-resolution, which improves the clarity and details of images. Features are learned through deep CNN and not through traditional algorithms so that we can better refine and enhance images. To effectively capture features, the network architecture adopted Dual Attention Units (DUs), which align with the Multi-Scale Residual Block (MSRB) for robust feature extraction, working sidewise with the feature-matching Selective Kernel Extraction (SKF). In addition, resolution mismatching cases are processed in detail to produce high-quality images. The effectiveness of the ELEF model is highlighted by the performance metrics, achieving a Peak Signal-to-Noise Ratio (PSNR) of 42.99 and a Structural Similarity Index (SSIM) of 0.9889, which indicates the ability to carry out the desired high-quality image restoration and enhancement. [ABSTRACT FROM AUTHOR]

Timely and accurate rainfall prediction is crucial to social life and economic activities. Because of the influence of numerous factors on rainfall, making precise predictions is challenging. In this study, the northern Xinjiang region of China is selected as the research area. Based on the pattern of rainfall in the local area and the needs of real life, rainfall is divided into four levels, namely 'no rain', 'light rain', 'moderate rain', and 'heavy rain and above', for rainfall levels nowcasting. To solve the problem that the existing model can only extract a single time dependence and cause the loss of some valuable information in rainfall data, a prediction model named DFFNet, which is based on dual-branch feature fusion, is proposed in this paper. The two branches of the model are composed of Transformer and CNN, which are used to extract time dependence and feature interaction in meteorological data, respectively. The features extracted from the two branches are fused for prediction. To verify the performance of DFFNet, the India public rainfall dataset and some sub-datasets in the UEA dataset are chosen for comparison. Compared with the baseline models, DFFNet achieves the best prediction performance on all the selected datasets; compared with the single-branch model, the training time consumption of DFFNet on the two rainfall datasets is reduced by 21% and 9.6%, respectively, and it has a faster convergence speed. The experimental results show that it has certain theoretical value and application value for the study of rainfall nowcasting. [ABSTRACT FROM AUTHOR]

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In response to the challenge of timely fault identification in the spindle bearings of machine tools operating in complex environments, this study proposes a method based on a combination of infrared imaging with an Informer and a CNN + Swin Transformer. The aim is to achieve real-time monitoring of bearing faults, precise fault localization, and classification of fault severity. To accomplish this, an angular contact ball bearing was chosen as the research subject. Initially, an infrared image dataset was constructed, encompassing various fault positions and degrees, by simulating different forms of bearing faults. Subsequently, an Informer-based bearing temperature prediction model was established to select faulty bearing data. Lastly, the faulty data were input into the CNN + Swin Transformer model for bearing fault recognition and classification. The results demonstrate that the Informer model accurately identifies abnormal temperature rises during bearing operation, effectively screening out faulty bearings. Under steady-state conditions, the model achieves a classification accuracy of 97.8%. Furthermore, after employing the Informer screening process, the proposed model exhibits a recognition precision of 98.9%, surpassing other models such as CNN, SVM, and Swin Transformer, which are mentioned in this paper. [ABSTRACT FROM AUTHOR]

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models will be presented. The study will be conducted over a limited COVID-19 x-ray. The study relies on neutrosophic set and theory to convert the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images, and they are the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. The dataset used in this research has been collected from different sources. The dataset is classified into four classes {COVID-19, normal, pneumonia bacterial, and pneumonia virus}. This study aims to review the effect of neutrosophic sets on deep transfer learning models. The selected deep learning models in this study are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures. To test the performance of the conversion to the neutrosophic domain, more than 36 trials have been conducted and recorded. A combination of training and testing strategies by splitting the dataset into (90–10%, 80–20%, 70–30) is included in the experiments. Four domains of images are tested, and they are, the original domain, the True (T) domain, the Indeterminacy (I) domain, and the Falsity (F) domain. The four domains with the different training and testing strategies were tested using the selected deep transfer models. According to the experimental results, the Indeterminacy (I) neutrosophic domain achieves the highest accuracy possible with 87.1% in the testing accuracy and performance metrics such as Precision, Recall, and F1 Score. The study concludes that using the neutrosophic set with deep learning models may be an encouraging transition to achieve better testing accuracy, especially with limited COVID-19 datasets. [ABSTRACT FROM AUTHOR]

Prediction of a driver's braking intention enables the advanced driver assistance system (ADAS) to intervene in the braking system as early as possible, which may shorten braking distance and improve driving safety. This paper proposes a novel deep learning model called LSTM-CNN-Attention that combines a long short-term memory (LSTM) neural network, convolutional neural network (CNN), and Attention mechanism for extracting spatiotemporal features of multi-sensor data to improve prediction accuracy. The proposed model inherits both temporal and spatial feature extraction abilities from LSTM and CNN. The LSTM-CNN-Attention model has a parallel architecture, which enhances the feature extraction ability of the model for multi-sensor time series data and improves the prediction accuracy of the driver's braking intention before the braking action. Furthermore, a driving simulator is set up to sample driving data for training and evaluating the proposed method. According to the results of the experiment, the model obtains up to 3.16% higher accuracy than the baseline models such as LSTM, CNN, and bidirectional LTSM (Bi-LSTM). Additionally, the influence of sliding window size and prediction horizon on the performance of the method is investigated. A method of tuning hyperparameters using the genetic algorithm is presented. The results demonstrate that the prediction accuracy increases by about 2% after being optimized by GA. [ABSTRACT FROM AUTHOR]

Although CNN shows high performance in the image recognition field, it has great disadvantages. They are to take a long time to perform machine learning due to a lack of system resources, and to consume a lot of power due to the great volume of computation. A convolution layer is a key element of convolutional neural network processing. This paper presents a convolution layer hardware using processing elements with a low-power multiplier. The low-power multiplier reduces the switching activity by increasing the exchange rate between the multiplier and multiplicand. The proposed convolution layer hardware was implemented on the Intel DE1-SoC FPGA Board using Verilog-HDL. The performance was verified by comparing the exchange rate with the existing multipliers when performing multiplications to process convolutional operations for the MNIST image database. [ABSTRACT FROM AUTHOR]

Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality's temporal and spatial distribution characteristics. In this study, Xi'an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi'an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model's hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model's accuracy and its promising application prospects. [ABSTRACT FROM AUTHOR]

This research paper presents a review of the use of convolutional neural networks (CNNs) for human crowd analysis. The paper discusses the challenges and limitations of methods and highlights the potential of CNNs in addressing these limitations. This study reveals and provides an in-depth analysis of the different techniques, architectures, and algorithms used in CNNs for human crowd analysis and their respective advantages and limitations. Additionally, the paper discusses the potential applications of CNNs in crowd analysis, including pedestrian detection, crowd counting, and crowd behavior recognition. The review also provides insights into the performance evaluation metrics commonly used in this area and the datasets used for training and testing CNNs. Overall, this review provides a comprehensive overview of the latest developments in the use of CNNs for human crowd analysis, as well as insights into future research directions in this field. [ABSTRACT FROM AUTHOR]