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Emergency communication is a key link to ensure the stable operation of communication networks. And large-scale multiple-input multiple-output (MIMO) technology can significantly improve coverage of communication systems. This work aims to optimize the backhaul process through deep neural network to improve the response speed and data transmission efficiency of base stations. Specifically, the proposed method uses deep neural network to represent the backhaul process of base stations, and explores data transmission under high efficiency and low delay. Simulation experiments are designed to verify performance advantages of the proposed method in large-scale MIMO wireless ad hoc networking scenarios, by using three comparison metrics. The experimental results show that the proposed method has a significant performance improvement in data transmission rate, system stability and resource utilization compared with traditional methods. Besides, the feasibility and effectiveness of the algorithm in practical application are verified by simulation and actual environment test. To sum up, the proposed algorithm can provide a new direction for the technical research in the field of emergency communication. [ABSTRACT FROM AUTHOR]

Sedimentary basins, integral to Earth's geological history and energy resource exploration, undergo complex changes driven by sedimentation, subsidence and geological processes. Gravity anomaly inversion is a crucial technique offering insights into subsurface structures and density variations. Our study addresses the challenge of complex subsurface structure assessment by leveraging deep neural networks to invert observed gravity anomalies. Optimization approaches traditionally incorporate known density distributions obtained from borehole data or geological logging for inverting basement depth in sedimentary basins using observed gravity anomalies. Our study explores the application of deep neural networks in accurate architectural assessment of sedimentary basins and demonstrates their significance in mineral and hydrocarbon exploration. Recent years have witnessed a surge in the use of machine learning in geophysics, with deep learning models playing a pivotal role. Integrating deep neural networks, such as the feedforward neural networks, has revolutionized subsurface density distribution and basement depth estimation. This study introduces a deep neural network specifically tailored for inverting observed gravity anomalies to estimate two‐dimensional basement relief topographies in sedimentary basins. To enhance computational efficiency, a one‐dimensional discrete cosine transform based discretization approach is employed. Synthetic data, generated using non‐Gaussian fractals, compensates for the scarcity of true datasets for training the deep neural network model. The algorithm's robustness is validated through noise introduction with comparisons against an efficient and traditional global optimization‐based approach. Gravity anomalies of real sedimentary basins further validate the algorithm's efficacy, establishing it as a promising methodology for accurate and efficient subsurface imaging in geological exploration. [ABSTRACT FROM AUTHOR]

Most of the existing research works on stock price prediction ignore the synergistic effect of multi-source factors. Therefore, an enterprise stock price prediction method based on multi-source information fusion based on deep neural network (DNN) is proposed. Based on the condition of multi-source information fusion, the original data related to enterprise stock price are collected from multiple sources, and the structure level and model framework of the method are constructed. The feature extraction technology of DNN is used to extract the features useful for stock price prediction from the pre-processed data, and the DNN structure considering multi-source information fusion is established. Finally, feature extraction and evaluation settings are completed based on data variables. LOSS, ACCURACY and other indicators were used for analysis. The results show that compared with typical prediction methods, this method can make use of multiple information sources more comprehensively and improve the prediction accuracy. In addition, the proposed method has certain flexibility and can adapt to the characteristics and changes of different stock markets [ABSTRACT FROM AUTHOR]

Coronary Artery Bypass Graft (CABG) is a common cardiac surgery, but it continues to have many associated risks, including the need for blood transfusions. Previous research has shown that blood transfusion during CABG surgery is associated with an increased risk for infection and mortality. The current study aims to use modern techniques, such as deep neural networks and data synthesis, to develop models that can best predict the need for blood transfusion among CABG patients. Results show that neural networks with synthetic data generated by DataSynthesizer have the best performance. Implications of results and future directions are discussed. [ABSTRACT FROM AUTHOR]

Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. However, the population-level association does not fully account for individual variability in intelligence. To address this, studies have emerged recently to predict individual subject's intelligence or neurocognitive scores. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual subject's verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, and FSIQ), which reflect fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input image channels, six deep learning models, and two outcome settings, in 850 healthy and autistic subjects 6–64 years of age. Our findings indicate a statistically significant potential of T1-weighted sMRI in predicting intelligence, with a Pearson correlation exceeding 0.21 (p < 0.001). Interestingly, we observed that an increase in the complexity of deep learning models does not necessarily translate to higher accuracy in intelligence prediction. The interpretations of our 2D and 3D CNNs, based on GradCAM, align well with the Parieto-Frontal Integration Theory (P-FIT), reinforcing the theory's suggestion that human intelligence is a result of interactions among various brain regions, including the occipital, temporal, parietal, and frontal lobes. These promising results invite further studies and open new questions in the field. [ABSTRACT FROM AUTHOR]

The geo-position spoofing detection in the images enables the tamper-proof protection of the images to ensure the authenticity of the images. Currently, available techniques for geo-position spoofing detection primarily depend on geo-position data stored in the database. In addition, these techniques are limited in performance due to the shortage of prior information, require more training samples and achieve poor generalization. To address the aforementioned limitations in this research, Eques Chimp Optimization-Deep Convolutional Neural Network (ECO-DCNN) is proposed to enhance the ability of the model to adapt to the evolving spoofing methods, handle noisy images and maintain computational efficiency. The significance of the research relies upon effectively tracing the source cameras by extracting the Photo Response NonUniformity noise (PRNU) from the images, which is then subjected to spoofing detection using Eques Chimp Optimized Deep Neural Network (ECO-DCNN). The ECO is designed by hybridizing the decision-making characters and social aspects of Eques and Chimp in the adaptive parameter tuning of the detection model to guarantee image integrity. Furthermore, for the Training Percentage (TP) analysis, the model exhibits a remarkable performance of 97.00% accuracy, 97.10% sensitivity, 97.19% specificity and 0.97 MCC. Similarly, the K-fold analysis achieves the values with accuracy, sensitivity, specificity and MCC for the model are 96.00%, 96.89%, 95.96% and 0.96%, respectively. [ABSTRACT FROM AUTHOR]

This concept will incorporate unmanned aerial vehicles (UAV) into the upcoming sixth-generation (6G) cellular network. UAVs have gained increasing interest for their ability to serve as a relay for efficiently boosting the range and communication rate of wireless networks. In the contexts of 6G and the Internet of Things (IoT), mm-Wave spectrum and non-orthogonal multiple access (NOMA) are expected to offer ubiquitous connectivity for many users. However, there is a chance of blocking severe conditions on the wireless terrestrial link between end users and the base station. Instead, because UAVs fly at a high altitude, it is much more likely that they will establish a line of sight (LoS) link. The multi-UAV network that enables an uplink (UL) NOMA cellular system is the main topic of this article. In addition, hybrid beamforming is used and operates in the mm-wave band. During hybrid beam forming, the Deep neural network (DNN) approach is used to classify each UAV's various directions of arrival. We develop a new design for the sum-rate maximization problem for the uplink mmWave transmission of the UAV-aided NOMA 6G network. The performance in terms of achievable sum rate compared to UL NOMA interconnections without the aid of a UAV network is better. [ABSTRACT FROM AUTHOR]

Social relationship prediction has garnered significant attention in the development of artificial intelligence technology due to its potential to promote socioeconomic development. Meantime, the increasing popularity and adoption of intelligent devices located in the Internet of Things (IoT) environment have generated abundant data, which can serve as a basis for social relationship prediction. Nevertheless, the highly fragmented distribution of user data and the implementation of data protection policies can lead to the dilemma of "data scarcity" during the process of social relationship prediction. The application of federated learning can effectively address data segregation and fragmentation. Additionally, existing studies on social relationship prediction fail to consider users' temporal sequence information. To this end, we present a novel Federated Deep Relationship Prediction framework, named FDRP, which adopts the principle of vertical federated learning. Specifically, under the IoT environment, the offline operation initially assigns virtual items and ratings to users. Subsequently, the online operation executes social relationship prediction, where the client converts the sparse data into dense vectors and extracts the overall temporal sequence features, as well as the server performs the model parameter aggregation. Through extensive experiments conducted on the Epinions dataset, FDRP demonstrates excellent prediction accuracy and convergence speed, effectively mitigating the risk of inference attacks. [ABSTRACT FROM AUTHOR]

The advent of voice assistance technology and its integration into smart devices has facilitated many useful services, such as texting and application execution. However, most assistive technologies lack the capability to enable the system to act as a human who can localize the speaker and selectively spot meaningful keywords. Because keyword spotting (KWS) and sound source localization (SSL) are essential and must operate in real time, the efficiency of a neural network model is crucial for memory and computation. In this paper, a single neural network model for KWS and SSL is proposed to overcome the limitations of sequential KWS and SSL, which require more memory and inference time. The proposed model uses multi-task learning to utilize the limited resources of the device efficiently. A shared encoder is used as the initial layer to extract common features from the multichannel audio data. Subsequently, the task-specific parallel layers utilize these features for KWS and SSL. The proposed model was evaluated on a synthetic dataset with multiple speakers, and a 7-module shared encoder structure was identified as optimal in terms of accuracy, direction of arrival (DOA) accuracy, DOA error, and latency. It achieved a KWS accuracy of 94.51%, DOA error of 12.397°, and DOA accuracy of 89.86%. Consequently, the proposed model requires significantly less memory owing to the shared network architecture, which enhances the inference time without compromising KWS accuracy, DOA error, and DOA accuracy. [ABSTRACT FROM AUTHOR]

Frequency-hopping (FH) communication adversarial research is a key area in modern electronic countermeasures. To address the challenge posed by interfering parties that use deep neural networks (DNNs) to classify and identify multiple intercepted FH signals—enabling targeted interference and degrading communication performance—this paper presents a batch feature point targetless adversarial sample generation method based on the Jacobi saliency map (BPNT-JSMA). This method builds on the traditional JSMA to generate feature saliency maps, selects the top 8% of salient feature points in batches for perturbation, and increases the perturbation limit to restrict the extreme values of single-point perturbations. Experimental results in a white-box environment show that, compared with the traditional JSMA method, BPNT-JSMA not only maintains a high attack success rate but also enhances attack efficiency and improves the stealthiness of the adversarial samples. [ABSTRACT FROM AUTHOR]

The Norwegian Environment Agency is responsible for updating a map of undisturbed nature, which is performed every five years based on aerial photos. Some of the aerial photos are already up to five years old when a new version of the map of undisturbed nature is published. Thus, several new nature interventions may have been missed. To address this issue, the timeliness and mapping accuracy were improved by integrating Sentinel-2 satellite imagery for the detection of new roads across Norway. The focus on new roads was due to the fact that most new nature interventions include the construction of new roads. The proposed methodology is based on applying U-Net on all the available summer images with less than 10% cloud cover over a five-year period, with an aggregation step to summarize the predictions. The observed detection rate was 98%. Post-processing steps reduced the false positive rate to 46%. However, as the false positive rate was still substantial, the manual verification of the predicted new roads was needed. The false negative rate was low, except in areas without vegetation. [ABSTRACT FROM AUTHOR]

Under the present Indian government initiative, allweather roads are being taken up for four pilgrimage locations in the Uttarakhand state of India. The Rishikesh to Gangotri road axis is a major road used by local citizens and tourists. Rainfall and numerous anthropogenic activities become the primary reasons for landslide hazards in the area. An accurate Landslide Susceptibility Map (LSM) for any area is of paramount importance for the decision makers of land-use planning. The present study gives a comparative analysis of recent advanced algorithms, i.e. CatBoost, LightGBM and deep neural network topology for generating the LSM by following pixel-based. Fourteen causative factors along with landslide inventory of 154 locations are used for the study. LSM are generated based on JENKS natural break criteria using all the algorithms and their performance comparison is evaluated. Overall accuracy for train and test data, prediction accuracy, area under receiver operating characteristics (AUROC) score for test data, and computational time for model fit on train data; are the criteria used for performance evaluation of each algorithm. In this study, it is observed that LSM can be generated at considerably fast pace if CatBoost or LightGBM is used while deep neural network-based topology gives marginally better results on all other performance measure. [ABSTRACT FROM AUTHOR]

Flotation is a physical and chemical process that uses the difference between mineral surface properties to separate valuable minerals from worthless minerals through the attachment of bubbles. The visual information of the flotation foam surface can reflect the flotation effect and will be closely related to the flotation conditions, directly indicating the degree of mineralization of the foam layer. Considering the problem that coal slurry foam images are difficult to segment due to the mutual adhesion of bubbles and fuzzy boundaries, in this work, we proposed a new and effective segmentation algorithm for extracting coal slurry flotation foam edge information. Based on the encoder and decoder structure, a selective multi-branch input segmentation deep neural network model was designed. We evaluated the proposed method and model using a self-built coal slurry foam image segmentation dataset. The intersection over union, mean intersection over union, and PA evaluation indicators of this method increased by 12.8376%, 7.2716%, and 1.457%, respectively, compared with those of the U-Net model. The experimental results showed that the selective branch segmentation network model had high accuracy and robustness in extracting coal slurry bubble edge information, and it could effectively solve the problem of difficult segmentation of coal slurry foam images. This study provided a foundation for optimizing flotation process parameters and realizing intelligent flotation site parameters. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a diabetic issue that influences the eyes. It harms the veins of the light-delicate tissue behind the eye (retina). DR is an issue of diabetes and the main source of visual deficiency. Since there are trained doctors to diagnose the disease, but to make their work easier, ensemble architectures were proposed for the prediction of grading of DR using modern technology known as deep learning. Deep neural network results in a better diagnostic system in comparison to machine learning networks. ResNET 101 and Ensemble CNN architectures were proposed using Adam Optimization for the detection of DR. From the results, it has been observed that for any constant size Network architecture, the loss cannot be further reduced after a certain number known as Bayes error. With the help of a learning curve, it can easily understand that the proposed architecture costs are decreasing after every iteration. The Ensemble and ResNET 101 model attain 87.67% and 81.28% accuracy, respectively, and 0.5046 and 0.5328 cross-entropy loss, respectively. The proposed Ensemble model results in an 18.4% improvement over the Inception V3 model and 7.8% over the proposed ResNet 101 model. [ABSTRACT FROM AUTHOR]

Clinical sleep diagnosis traditionally relies on polysomnography (PSG) and expert manual classification of sleep stages. Recent advancements in deep learning have shown promise in automating sleep stage classification using a single PSG channel. However, variations in PSG acquisition devices and environments mean that the number of PSG channels can differ across sleep centers. To integrate a sleep staging method into clinical practice effectively, it must accommodate a flexible number of PSG channels. In this paper, we proposed FlexSleepTransformer, a transformer-based model designed to handle varying number of input channels, making it adaptable to diverse sleep staging datasets. We evaluated FlexSleepTransformer using two distinct datasets: the public SleepEDF-78 dataset and the local SleepUHS dataset. Notably, FlexSleepTransformer is the first model capable of simultaneously training on datasets with differing number of PSG channels. Our experiments showed that FlexSleepTransformer trained on both datasets together achieved 98% of the accuracy compared to models trained on each dataset individually. Furthermore, it outperformed models trained exclusively on one dataset when tested on the other dataset. Additionally, FlexSleepTransformer surpassed state-of-the-art CNN and RNN-based models on both datasets. Due to its adaptability with varying channels numbers, FlexSleepTransformer holds significant potential for clinical adoption, especially when trained with data from a wide range of sleep centers. [ABSTRACT FROM AUTHOR]

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients. • The MFF-DTA model is proposed to accurately predict the affinity relationship between drugs and target proteins. • The model includes multiple feature extractors to capture global and local key features. • We create a comprehensive and information-rich representation. • The model has demonstrated strong performance on public datasets. [ABSTRACT FROM AUTHOR]

Similar waveforms and overlapping frequency ranges make distinguishing blasting vibrations and microseismic signals challenging, causing interference with coal mine microseismic monitoring systems. To address this problem, we propose a spectrum dataset (MSData) reflecting the spectrum features of both signal types and present a signal classification network (SCNet) combining CNNs and Transformers for signal classification. The network can learn multi-dimensional features of both signals from MSData and automatically and efficiently identify the two signal types. Experimental results yield F1-scores of 0.991 for microseismic signals and 0.993 for blasting vibration signals, meeting engineering application requirements. [ABSTRACT FROM AUTHOR]

The intelligibility of speech is a primary component to assess the severity level of Dysarthria, a speech disorder, which is caused not only due to weakness in vocal motor muscles but also difficulty in controlling its movements. Prior information about the severity of Dysarthria, aids the therapist during the rehabilitation process. This paper introduces a novel hybrid architecture comprising Gaussian Mixture Model and Neural Network (GMM-NN) for categorizing Dysarthric severity into four classes based on speech intelligibility. Mel Frequency Cepstral Coefficients (MFCC) extracted from the segmented speech signals are used to train the classifier. The proposed model produced a 1.9% improvement in accuracy when compared to the baseline Gaussian Mixture Model (GMM). The Gaussian Mixture Model Deep Neural Network (GMM-DNN) and Gaussian Mixture Model Feed Forward Neural Network (GMM-FFNN) architectures showed an accuracy of 96.7% and 96.42% with F1 scores of 0.9649, 0.9604 respectively. [ABSTRACT FROM AUTHOR]

A deep neural network (DNN) was trained to generate a multiparametric ultrasound (mpUS) volume from four input ultrasound-based modalities (acoustic radiation force impulse [ARFI] imaging, shear wave elasticity imaging [SWEI], quantitative ultrasound-midband fit [QUS-MF], and B-mode) for the detection of prostate cancer. A DNN was trained using co-registered ARFI, SWEI, MF, and B-mode data obtained in men with biopsy-confirmed prostate cancer prior to radical prostatectomy (15 subjects, comprising 980,620 voxels). Data were obtained using a commercial scanner that was modified to allow user control of the acoustic beam sequences and provide access to the raw image data. For each subject, the index lesion and a non-cancerous region were manually segmented using visual confirmation based on whole-mount histopathology data. In a prostate phantom, the DNN increased lesion contrast-to-noise ratio (CNR) compared to a previous approach that used a linear support vector machine (SVM). In the in vivo test datasets (n = 15), the DNN-based mpUS volumes clearly portrayed histopathology-confirmed prostate cancer and significantly improved CNR compared to the linear SVM (2.79 ± 0.88 vs. 1.98 ± 0.73, paired-sample t -test p < 0.001). In a sub-analysis in which the input modalities to the DNN were selectively omitted, the CNR decreased with fewer inputs; both stiffness- and echogenicity-based modalities were important contributors to the multiparametric model. The findings from this study indicate that a DNN can be optimized to generate mpUS prostate volumes with high CNR from ARFI, SWEI, MF, and B-mode and that this approach outperforms a linear SVM approach. [ABSTRACT FROM AUTHOR]

\nPlain Language SummaryBrain tumors present a formidable diagnostic challenge due to their aberrant cell growth. Accurate determination of tumor location and size is paramount for effective diagnosis. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are pivotal tools in clinical diagnosis, yet tumor segmentation within their images remains challenging, particularly at boundary pixels, owing to limited sensitivity. Recent endeavors have introduced fusion-based strategies to refine segmentation accuracy, yet these methods often prove inadequate. In response, we introduce the Parallel-Way framework to surmount these obstacles. Our approach integrates MRI and PET data for a holistic analysis. Initially, we enhance image quality by employing noise reduction, bias field correction, and adaptive thresholding, leveraging Improved Kalman Filter (IKF), Expectation Maximization (EM), and Improved Vibe Algorithm (IVib), respectively. Subsequently, we conduct multi-modality image fusion through the Dual-Tree Complex Wavelet Transform (DTWCT) to amalgamate data from both modalities. Following fusion, we extract pertinent features using the Advanced Capsule Network (ACN) and reduce feature dimensionality via Multi-objective Diverse Evolution-based selection. Tumor segmentation is then executed utilizing the Twin Vision Transformer with dual attention mechanism. Implemented our Parallel-Way framework which exhibits heightened model performance. Evaluation across multiple metrics, including accuracy, sensitivity, specificity, F1-Score, and AUC, underscores its superiority over existing methodologies.Diagnosing brain tumors is challenging due to their abnormal growth. Accurately determining the tumor’s location and size is crucial for effective treatment. Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are important tools in clinical diagnosis, but accurately segmenting tumors in their images, especially at the edges, is difficult due to limited sensitivity. To address this, we propose the Parallel-Way framework, which integrates MRI and PET data for a comprehensive analysis. First, we enhance image quality using noise reduction, bias field correction, and adaptive thresholding techniques like the Improved Kalman Filter (IKF), Expectation Maximization (EM), and Improved Vibe Algorithm (IVib). Next, we merge the MRI and PET data using the Dual-Tree Complex Wavelet Transform (DTWCT) to combine information from both modalities. After fusion, we extract relevant features using the Advanced Capsule Network (ACN) and reduce their complexity with Multi-objective Diverse Evolution-based selection. Tumor segmentation is then performed using the Twin Vision Transformer with a dual attention mechanism. Our Parallel-Way framework aims to improve the accuracy of brain tumor segmentation, overcoming limitations of existing methods. By integrating data from MRI and PET and applying advanced image processing and machine learning techniques, we believe our approach will provide a more reliable and accurate method for diagnosing and treating brain tumors. Evaluations using metrics such as accuracy, sensitivity, specificity, F1-Score, and AUC demonstrate that our method is superior to existing approaches, providing a more reliable and accurate way to diagnose brain tumors. [ABSTRACT FROM AUTHOR]

Structure and properties of the electrode/electrolyte interface significantly influence the electrochemical processes of energy storage and conversion, yet the challenge lies in accurate description of both molecular characteristics and external field effects. Here, we develop a mesoscopic thermodynamic model that calculates the thermodynamic properties of electrolytes based on chemical potential, and its efficiency is enhanced by a deep neural network. The deep neural network enhanced mesoscopic thermodynamic (DeepMT) model effectively bridges the gap between micro‐level characteristics of ions and macro‐level effects of external field, enabling precise presentation of ion density distributions over complex conditions. Our result indicates that the DeepMT model not only demonstrates a computational efficiency improvement of approximately four orders of magnitude over direct theoretical calculations, but also accurately predicts interface properties including ion adsorption, surface charge, and differential capacitance through the statistical analysis of density distributions. [ABSTRACT FROM AUTHOR]

This work focuses on an intelligent management framework for vocal music media resources, with use of deep neural networks and web crawlers. The web driver module is used to simulate the Google browser access mode to collect and preprocess the data of the China vocal music network webpage project, and the delay code is added to solve the problem of Internet Protocol (IP) restrictions on crawling webpages. After parsing, the content of the project description is obtained and temporarily stored in the local file through the panda’s module to realize the automatic collection of project data. Membership functions of four basic types of emotions are designed. By utilizing the analytic hierarchy process, the experience and wisdom of many music experts are gathered. The weights of activity and the evoking force in determining the music emotion membership value are discussed, trying to make the membership function closer to reality. Through the application of the music emotion analysis model, the music emotion analysis technology is combined with the recommendation algorithm, and the music recommendation algorithm combined with the emotion analysis is realized. The experimental test proves that the algorithm has higher accuracy than the traditional music recommendation algorithm. [ABSTRACT FROM AUTHOR]

Simple Summary: In the duck breeding process, the rapid and accurate identification of gender is currently a key challenge. This study proposes a method based on sound signal discrimination to address this issue. The vocalizations of one-day-old male and female ducklings were extracted and the signal features were obtained through professional processing. Classification models were established using a backpropagation neural network (BPNN), a deep neural network (DNN), and a convolutional neural network (CNN). In total, 70% of the dataset was used for training, and 30% was used for testing. The test results showed that the BPNN model had the highest accuracy rate of 93.33% in identifying male ducks, while the CNN model had the highest accuracy rate of 95% in identifying female ducks. From the perspective of overall model performance, the CNN model performed the best, with the highest accuracy rate and F1-score of 84.15% and 84.32%, respectively. This study can provide a technical reference and a foundation for future voice recognition technologies for different poultry breeds and applications. Gender recognition is an important part of the duck industry. Currently, the gender identification of ducks mainly relies on manual labor, which is highly labor-intensive. This study aims to propose a novel method for distinguishing between males and females based on the characteristic sound parameters for day-old ducks. The effective data from the sounds of day-old ducks were recorded and extracted using the endpoint detection method. The 12-dimensional Mel-frequency cepstral coefficients (MFCCs) with first-order and second-order difference coefficients in the effective sound signals of the ducks were calculated, and a total of 36-dimensional feature vectors were obtained. These data were used as input information to train three classification models, include a backpropagation neural network (BPNN), a deep neural network (DNN), and a convolutional neural network (CNN). The training results show that the accuracies of the BPNN, DNN, and CNN were 83.87%, 83.94%, and 84.15%, respectively, and that the three classification models could identify the sounds of male and female ducks. The prediction results showed that the prediction accuracies of the BPNN, DNN, and CNN were 93.33%, 91.67%, and 95.0%, respectively, which shows that the scheme for distinguishing between male and female ducks via sound had high accuracy. Moreover, the CNN demonstrated the best recognition effect. The method proposed in this study can provide some support for developing an efficient technique for gender identification in duck production. [ABSTRACT FROM AUTHOR]

In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms. [ABSTRACT FROM AUTHOR]

UAVs are commonly utilized for the detection of insulator faults in transmission lines. The successful execution of such missions depends on two pivotal factors; the detection of insulator faults, which reduces losses in transmission lines, and the monitoring of the status of the UAV during the mission. Due to the vulnerability of UAV wings to defects, particularly lagging defects, it is crucial for the UAV to promptly land if it has loose wings to prevent severe damage. This article employs vibration data acquired from a wing-mounted sensor on the UAV to monitor its status. To determine the state of the UAV, a novel classifier is introduced, which aggregates votes from support vector machines (SVM), probabilistic neural networks (PNN), and deep neural networks (DNN) using Type-1 and Interval Type-2 Takagi–Sugeno–Kang Fuzzy System. Furthermore, the UAV-captured images of insulator in transmission lines are utilized for insulator fault detection. A similar approach is adopted for insulator fault detection, except that Multilayer Perceptron (MLP) is used instead of PNN, and ResNet-50 (Residual Network) is employed for feature extraction from insulator images. The software's cell phone interface, designed specifically for mobile devices, presents the graphical representation of UAV status and insulator fault detection. Furthermore, the generalizability of the proposed classifier for other applications is evaluated using two test datasets sourced from the UCI Machine Learning repository. The findings indicate that the proposed method performs better than renowned classifiers such as PNN, MLP, ANFIS, Fuzzy classifier, SVM, and ResNet-50. [ABSTRACT FROM AUTHOR]

This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure. [ABSTRACT FROM AUTHOR]

The objective of this work is to predict the methane hydrate phase boundary equilibrium temperature in the presence of ionic liquids (ILs) using machine learning techniques to overcome the limitations of the existing empirically proposed models. To achieve the objectives of this work, five deep neural networks (DNN) algorithms; Adadelta, Ftrl, Adagrad, Adam, and RMSProp coupled with six activation functions (elu, leaky relu, sigmoid, relu, tanh, and selu) were used on 610 experimental datasets from literature. The independent variables used to predict the ILs methane hydrate boundary temperature were pressure (2.39–100.43 MPa), concentration (0.10–50 wt.%), and ILs molecular weight (91.11–339.50 gmol−1). The study revealed that Adadelta DNN optimization algorithm and elu activation functions gave the best predictions with an average RMSE of 0.6727 and 0.6989, respectively. The findings suggest that the use of Adadelta coupled with elu accurately predicts the methane hydrate phase boundary condition in the presence of ionic liquids. The excellent performance of Adadelta and elu resides in their ability to predict exponential data trends which is the fundamental behavior of hydrate phase behavior condition. This work pioneered the use of machine learning techniques to predict hydrate behavior conditions in IL systems. Thus, the findings in this work will enhance the development of simple hydrate phase behavior properties predictive software for IL systems. [ABSTRACT FROM AUTHOR]

In this work, small-sized CaO–PdNps nanocomposites were synthesized using Lawsonia inermis leaf extract as a reducing and capping agent. X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR) were used to analyze the produced nanocomposites. Based on these investigations, the average particle size was found to be 8±12nm, with a predominantly spherical morphology. The resulting nanocomposite was then tested for catalytic activity in methylene blue reduction. The mass of the catalyst and dye, the concentration of sodium borohydride ([NaBH4]), and the reaction time were all optimized using a central composite design (CCD). A significant correlation between predicted and experimental degradation percentages was shown by the CCD model (R2=0.9901), which also proved statistically significant (p-value=0.0016). Furthermore, a deep learning neural network (DLNN) was used to improve the prediction accuracy over and beyond CCD. There was a good connection between the two models as a result of using the CCD output as DLNN validation data. Mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and R2 were used to assess the performance of the DLNN. The results (MAE=0.001, MSE=0.01, RMSE=0.001, MAPE=0.001, R2=0.999) validated that all four metrics were successful in forecasting the percentage of degradation. [ABSTRACT FROM AUTHOR]

In this study, our goal is to integrate classical mathematical models with deep neural networks by introducing two novel deep neural network models for image segmentation known as Double-well Nets. Drawing inspirations from the Potts model, our models leverage neural networks to represent a region force functional. We extend the well-known MBO (Merriman--Bence--Osher) scheme to solve the Potts model. The widely recognized Potts model is approximated using a doublewell potential and then solved by an operator-splitting method, which turns out to be an extension of the well-known MBO scheme. Subsequently, we replace the region force functional in the Potts model with a UNet-type network, which is data-driven and is designed to capture multiscale features of images, and also introduce control variables to enhance effectiveness. The resulting algorithm is a neural network activated by a function that minimizes the double-well potential. What sets our proposed Double-well Nets apart from many existing deep learning methods for image segmentation is their strong mathematical foundation. They are derived from the network approximation theory and employ the MBO scheme to approximately solve the Potts model. By incorporating mathematical principles, Double-well Nets bridge the MBO scheme and neural networks, and offer an alternative perspective for designing networks with mathematical backgrounds. Through comprehensive experiments, we demonstrate the performance of Double-well Nets, showcasing their superior accuracy and robustness compared to state-of-the-art neural networks. Overall, our work represents a valuable contribution to the field of image segmentation by combining the strengths of classical variational models and deep neural networks. The Double-well Nets introduce an innovative approach that leverages mathematical foundations to enhance segmentation performance. [ABSTRACT FROM AUTHOR]

A few decades ago, quality assessment was a significant challenge in agriculture, even with tiny amounts of agricultural products. They are subject to diseases, pesticides, and environmental variables that have led to challenges to quality and safety. The quality evaluation of these items serves a crucial function in quality control and increases productivity before consumption. This paper proposes a deep neural network for solving Indian tomato diversity problems based on the assessment of their features. This study was carried out using four different categories of south Indian cherry tomatoes, i.e., spot, BER, calyx, and non-calyx. Thirty (30) relevant features were taken from an RGB-HIS color space: textural color moments, gray level co-occurrence matrix (GLCM), and defective proportion. They were then normalized in MS-EXCEL with a Min-Max function and fed into a multilayer perception deep neural network (MLP-DNN) and a convolutional neural network (CNN), respectively. The MLP-DNN was made with ten (10) hidden layers and was used with a fine-tuned convolutional neural network (CNN) of Alex net architecture with eight (8) learned layers to do grading and detection separately using MATLAB 2018a software. The results of training, testing, and validating both networks showed that they did an excellent job of solving the two-class problem (good and bad), with an overall prediction accuracy of more than 97% and a bit of loss function in both cases. However, the algorithms can be improved and employed to detect defects and sort fruits or vegetables of all kinds in the agro-industry with a larger dataset to reduce overfitting and increase validation accuracy in the future. [ABSTRACT FROM AUTHOR]

To better apply deep convolutional neural networks for expression recognition in UAV-enabled B5G/6G networks, we propose a deep network expression recognition method based on transfer learning and fine-tuning on facial expression datasets. Initially, we train our model on a large-scale facial attribute dataset and subsequently fine-tune it on a facial expression dataset. This strategy effectively lowers the high costs and dependence associated with annotating facial expression datasets, while enhancing the accuracy and training speed of our model. This method is particularly suited for UAVs equipped with on-board cameras and image processing capabilities, enabling real-time expression recognition for various applications such as crowd monitoring, search and rescue, and human-UAV interaction. Compared to traditional methods that train exclusively on facial expression datasets, our method significantly reduces the number of training iterations on facial expression datasets and significantly improves the generalization ability of the model, especially for UAV applications. We use a large-scale facial attribute dataset, which is more closely related to the facial expression recognition task, as our source dataset, forming a contrast with methods that typically use a facial recognition dataset as the transfer learning source dataset. The experimental results on the CK + dataset, integrated with UAV-enabled B5G/6G networks, show that our method achieves a facial expression recognition accuracy of 97.6%, a significant improvement over the 97.3% accuracy rate of methods that only train on facial expression datasets, with less training time as well. [ABSTRACT FROM AUTHOR]

Precoding is a critical signal processing technique used in wireless communication systems to enhance transmission performance. This paper initially provides a brief overview of various conventional precoding algorithms. The exploration includes various nonlinear precoding algorithms which outperform linear techniques in high signal-to-noise ratio (SNR) scenarios. Moreover, the paper delves into practical considerations and provides insights into selecting the most suitable technique for specific communication scenarios. The deep learning-based hybrid precoder is designed using the MDL-AltMin algorithm and simulated results show a spectral efficiency of (SE) 40.96 dB compared with different precoding algorithms. The precoder model is modified with a Hybrid Precoding Net algorithm with a spectral efficiency of 21.07 dB for high values of SNR such as −30 and again compared with deep learning precoders. The HPNet model is proposed with a spectral efficiency of 23.04 dB with and without CSI (Channel State Information) at the transmitter. Furthermore, the HPNet model is compared with optimal digital precoders. [ABSTRACT FROM AUTHOR]

Robotic fruit harvesting has great potential to revolutionize agriculture, but detecting cherry tomatoes in farming environments still faces challenges in accuracy and efficiency. To overcome the shortcomings of existing cherry tomato detection methods for harvesting, this study introduces a deep-learning-based cherry tomato detection scheme for robotic harvesting in greenhouses using multimodal RGB-D perception and an improved YOLOv7-tiny Cherry Tomato Detection (YOLOv7-tiny-CTD) network, which has been modified from the original YOLOv7-tiny by eliminating the "Objectness" output layer, introducing a new "Classness" method for the prediction box, and incorporating a new hybrid non-maximum suppression. Acquired RGB-D images undergo preprocessing such as color space transformation, point cloud normal vector angle computation, and multimodal regions of interest segmentation before being fed into the YOLOv7-tiny-CTD. The proposed method was tested using an AGV-based robot in a greenhouse cherry tomato farming facility. The results indicate that the multimodal perception and deep learning method improves detection precision and accuracy over existing methods while running in real time, and the robot achieved over 80% successful picking rates in two-trial mode in the greenhouse farm, showing promising potential for practical harvesting applications. [ABSTRACT FROM AUTHOR]

As a crucial spatial decision support tool, Geographic Information Systems (GISystems) are widely used in fields such as digital watersheds, resource management, environmental assessment, and regional governance, with their core strength lying in the integration of geographic simulation models from various disciplines, enabling the analysis of complex geographical phenomena and the resolution of comprehensive spatial problems. With the rapid advancement of artificial intelligence, deep neural network-based geographic simulation models (DNN-GSMs) have increasingly replaced traditional models, offering significant advantages in simulation accuracy and inference speed, and have become indispensable components in GISystems. However, existing integration methods do not adequately account for the specific characteristics of DNN-GSMs, such as their formats and input/output data types. To address this gap, we propose a novel tight integration framework for DNN-GSMs, comprising four key interfaces: the data representation interface, the model representation interface, the data conversion interface, and the model application interface. These interfaces are designed to describe spatial data, the simulation model, the adaptation between spatial data and the model, and the model's application process within the GISystem, respectively. To validate the proposed method, we construct a spatial morphology simulation model based on CNN-LSTM, integrate it into a GISystem using the proposed interfaces, and conduct a series of predictive experiments on island morphology evolution. The results demonstrate the effectiveness of the proposed integration framework for DNN-GSMs. [ABSTRACT FROM AUTHOR]

An effective approach to addressing the speech separation problem is utilizing a time–frequency (T-F) mask. The ideal binary mask (IBM) and ideal ratio mask (IRM) have long been widely used to separate speech signals. However, the IBM is better at improving speech intelligibility, while the IRM is better at improving speech quality. To leverage their respective strengths and overcome weaknesses, we propose an ideal threshold-based mask (ITM) to combine these two masks. By adjusting two thresholds, these two masks are combined to jointly act on speech separation. We list the impact of using different threshold combinations on speech separation performance under ideal conditions and discuss a reasonable range for fine tuning the thresholds. By using masks as a training target, to evaluate the effectiveness of the proposed method, we conducted supervised speech separation experiments applying a deep neural network (DNN) and long short-term memory (LSTM), the results of which were measured by three objective indicators: the signal-to-distortion ratio (SDR), signal-to-interference ratio (SIR), and signal-to-artifact ratio improvement (SAR). Experimental results show that the proposed mask combines the strengths of the IBM and IRM and implies that the accuracy of speech separation can potentially be further improved by effectively leveraging the advantages of different masks. [ABSTRACT FROM AUTHOR]

Financial fraud detection has been an urgent technical demand in cyberspace. It highly relies on clear extraction and deep representation toward complex relationships inside financial social networks. As consequence, this study combines both knowledge graph and deep learning to deal with such issue. Thus, an intelligent financial fraud detection model based on knowledge graph guidance and deep neural network is proposed in this paper. First, a new knowledge graph based on financially related systems is constructed, which includes multiple entities and relationships. Then, an adversarial learning-based neural network structure is formulated to extract financial attributes. Finally, the detection results can be output according to the extracted factors. Empirically, the proposal is implemented on a real-world dataset for performance evaluation. The experimental results show that it has higher accuracy and effectiveness compared to traditional fraud detection methods. The proposed detection model can not only identify known fraudulent behaviors, but also predict potential fraud patterns based on consumer habits, thereby improving the security and reliability of financial transactions. It can also update the knowledge graph in real-time, enabling it to cope with emerging fraud methods and variants. [ABSTRACT FROM AUTHOR]

The effective prediction of heart disorders is crucial for timely intervention and treatment before a cardiac event occurs. While various machine learning models have been developed for this purpose, many struggle to handle high-dimensional data effectively, limiting their performance. In this work, efforts have been made to enhance the performance and computational efficiency of deep learning classifiers using hyperparameters. The study utilized heart sound data from normal and diseased patients obtained from standard online repositories. The hyperparameter tuned modified CNN-based Inception Network model achieved an accuracy of 99.65% ± 0.23% on the test dataset, along with a sensitivity of 98.8% ± 0.12% and specificity of 98.2% ± 0.15%. Thus the hyperparameter-tuned CNN-based Inception Network model outperformed its counterparts, making it the most effective model for diagnosing heart disorders. [ABSTRACT FROM AUTHOR]

Accurately predicting the payload movement and ensuring efficient control during dynamic tower crane operations are crucial for crane safety, including the ability to predict payload mass within a safe or normal range. This research utilizes deep learning to accurately predict the normal and abnormal payload movement of tower cranes. A scaled-down tower crane prototype with a systematic data acquisition system is built to perform experiments and data collection. The data related to 12 test case scenarios are gathered, and each test case represents a specific combination of hoisting and slewing motion and payload mass to counterweight ratio, defining tower crane operational variations. This comprehensive data is investigated using a novel attention-based deep neural network with Tree-Structured Parzen Estimator optimization (TPE-AttDNN). The proposed TPE-AttDNN achieved a prediction accuracy of 0.95 with a false positive rate of 0.08. These results clearly demonstrate the effectiveness of the proposed model in accurately predicting the tower crane payload moving condition. To ensure a more reliable performance assessment of the proposed AttDNN, we carried out ablation experiments that highlighted the significance of the model's individual components. [ABSTRACT FROM AUTHOR]

Predicting robust earthquake spectra is challenging, especially for data sparse regions such as India. Often, alternatives to the traditional data-driven regression analysis are used to develop empirical models for such regions. Advancing these efforts, the present study aims at exploring an alternative machine learning technique called Transfer learning, wherein a non-parametric deep neural network is trained for response (Sa) and Fourier spectra (FAS) of Himalayas, which uses network parameters that were derived for a large comprehensive database (NGA-West2). While the FAS is derived using magnitude, distance, focal depth, and site class, the Sa is scaled using FAS and significant duration. [ABSTRACT FROM AUTHOR]

Rationale: Clinical abbreviations pose a challenge for clinical decision support systems due to their ambiguity. Additionally, clinical datasets often suffer from class imbalance, hindering the classification of such data. This imbalance leads to classifiers with low accuracy and high error rates. Traditional feature‐engineered models struggle with this task, and class imbalance is a known factor that reduces the performance of neural network techniques. Aims and Objectives: This study proposes an attention‐based bidirectional long short‐term memory (Bi‐LSTM) model to improve clinical abbreviation disambiguation in clinical documents. We aim to address the challenges of limited training data and class imbalance by employing data generation techniques like reverse substitution and data augmentation with synonym substitution. Method: We utilise a Bi‐LSTM classification model with an attention mechanism to disambiguate each abbreviation. The model's performance is evaluated based on accuracy for each abbreviation. To address the limitations of imbalanced data, we employ data generation techniques to create a more balanced dataset. Results: The evaluation results demonstrate that our data balancing technique significantly improves the model's accuracy by 2.08%. Furthermore, the proposed attention‐based Bi‐LSTM model achieves an accuracy of 96.09% on the UMN dataset, outperforming state‐of‐the‐art results. Conclusion: Deep neural network methods, particularly Bi‐LSTM, offer promising alternatives to traditional feature‐engineered models for clinical abbreviation disambiguation. By employing data generation techniques, we can address the challenges posed by limited‐resource and imbalanced clinical datasets. This approach leads to a significant improvement in model accuracy for clinical abbreviation disambiguation tasks. [ABSTRACT FROM AUTHOR]

Bone illnesses arise at a young age itself, so bone age assessment (BAA) is primarily utilized in paediatrics to identify their growth. Several BAA-related methods are employed to determine bone maturity; however, they do not give accuracy and the rate of error increases. To overcome this issue, in this manuscript, TW3-based Region of Interest (ROI) identification and classification with Chaotic ANN (CANN), as well as Deep Neural Network optimized with Evolved Gradient Direction optimizer (EVGO) architecture, is proposed for an Automated BAA classification. In this, Hand X-Ray images are taken from the Digital Hand Atlas (DHA) dataset and the Radiological Society of North America (RSNA) database. Here, Tanner-Whitehouse (TW3) and Shallow convolution neural network (SCNN) is utilized for extracting the ROI regions such as radius, ulna, and short bones (RUS) from the wrist region of the left hand. After that, the extracted ROI feature data are given to CANN for predicting BAA. Then, the predicted data are classified using the DNN-EVGO network. The bone age evaluation is then classified depending on maturity phases. The proposed approach is activated in MATLAB. Simulation outcomes of TW3- SCNN- CANN- DNN-EVGO- BAA methods for DHA database attain higher accuracy of 45.75%, 37.64%, 24.64% when analysed to the existing models, such as Faster Region-Convolutional Neural network oriented feature learning with optimal trained Recurrent Neural Network for BAA for paediatrics (TW3-F-R-CNN-AF-SFO), Multi-objective segmentation approach for BAA under parameter tuning- U-net architecture, and TW3-based fully automated bone age assessment system using deep neural networks (TW3-F-R-CNN-VGGNet-CNN-BAA). [ABSTRACT FROM AUTHOR]

The evolving behavior of the attacks may affect the decision boundaries of the trained machine learning models. The issue has not been well investigated, especially with hypervisor-based security solutions where virtual machine (VM)'s network artifacts are introspected and analyzed. In this paper, we proposed a sustainable and explainable flow-filtering-based concept drift-driven network intrusion detection approach, called 'SFC-NIDS' which introspects network activities by analyzing VM traffic profile. The VM traffic is captured and pre-processed at the hypervisor to extract important network artifacts. The redundant and trivial network flows have been filtered using the proposed gradient descent-based flow filtering mechanism and validated using explainability. SFC-NIDS employs auto-encoders to reconstruct the traffic features to capture additional patterns. Afterward, the 1D-convolution neural network has been employed to learn and detect malicious attack flows. The model's sustainability is ensured by integrating the drift detection mechanism with the decision model to retrain it with evolving attack patterns. The approach has been validated with virtual network traffic artifacts collected at the hypervisor and provides 98.9% accuracy, 99.03%, and F1-Score. In addition, the approach has also been validated using the KDD99 dataset, showcasing an accuracy of 99.97% and an F1-Score of 99.98%. [ABSTRACT FROM AUTHOR]

Flooding disaster causes huge impacts on the socio-economic world. In the inundated area, some geo-referenced images are shared through some media posts, which assist in providing alertness to the critical volunteers and managing the financial loss crisis. In this work, the Adaptive Billiards-Inspired Optimization (A-BIO) and Optimized Ensemble-learning-based detection (OED) with map reduce framework is proposed for flood disaster detection. Initially, the big data is gathered and processed for detection. During the map phase, data preprocessing is performed to enhance the performance of the data, which helps in removing the noise or unwanted attributes. Furthermore, the reduction phase can be done through weighted feature selection, where the features to be selected and the weight is optimized through A-BIO, which assists in getting the most significant features for improving the performance and reducing the complexity of the designed model. Finally, OED is performed by a set of classifiers like Convolutional Neural Networks, Adaboost, XGBoost, Long Short-Term Memory, and Deep Neural Networks, where the parameters of ensemble learning classifiers are optimized by A-BIO algorithm. Finally, through the performance analysis, this detection model can provide high accuracy and better detection performance to avoid the huge impacts of flood disasters. [ABSTRACT FROM AUTHOR]

Underwater object detection is a crucial aspect of monitoring the aquaculture resources to preserve the marine ecosystem. In most cases, Low-light and scattered lighting conditions create challenges for computer vision-based underwater object detection. To address these issues, low-colorfulness and low-light image enhancement techniques are explored. This work proposes an underwater image enhancement technique called Underwater Image Colorfulness Enhancement MIRNet (UICE-MIRNet) to increase the visibility of small, multiple, dense objects followed by underwater object detection using YOLOv4. UICE-MIRNet is a specialized version of classical MIRNet, which handles random increments of brightness features to address the visibility problem. The proposed UICE-MIRNET restrict brightness and also works on the improvement of the colourfulness of underwater images. UICE-MIRNet consists of an Underwater Image-Colorfulness Enhancement Block (UI-CEB). This block enables the extraction of low-colourful areas from underwater images and performs colour correction without affecting contextual information. The primary characteristics of UICE-MIRNet are the extraction of multiple features using a convolutional stream, feature fusion to facilitate the flow of information, preservation of contextual information by discarding irrelevant features and increasing colourfulness through proper feature selection. Enhanced images are then trained using the YOLOv4 object detection model. The performance of the proposed UICE-MIRNet method is quantitatively evaluated using standard metrics such as UIQM, UCIQE, entropy, and PSNR. The proposed work is compared with many existing image enhancement and restoration techniques. Also, the performance of object detection is assessed using precision, recall, and mAP. Extensive experiments are conducted on two standard datasets, Brackish and Trash-ICRA19, to demonstrate the performance of the proposed work compared to existing methods. The results show that the proposed model outperforms many state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The structure of a novel computational hyperbolic tangent sigmoid deep neural network (HTS-DNN) is presented for the numerical solutions of the hepatitis B virus model, which is based on the antibody immune response. The mathematical model is categorized as healthy and hepatocytes, capsids, antibodies and free viruses. A novel process based on the HTS-DNN is exploited by using two hidden layers with 20 and 30 numbers of neurons. The optimization is performed through Bayesian regularization, which is one of the reliable procedures used in the optimization of various problems. A dataset is obtained through the Runge–Kutta solver, which is used to reduce the mean square error by dividing the training, testing and verification data as 70%, 16% and 14%. Moreover, the statistical representations in the sense of error histogram, regression, and state transitions also approve the accuracy of the scheme. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the application of machine learning (ML) techniques for predicting the spatiotemporal evolution of thermal fields during additive manufacturing (AM) processes. AM, also known as three-dimensional printing, has gained significant attention in various industries due to its potential for rapid prototyping and customized production. However, accurately predicting and controlling the thermal behavior during the AM process is crucial for ensuring the quality and reliability of the printed components. Traditional physics-based models (PBM) often face challenges in capturing AM's complex dynamics and inherent uncertainties. In recent years, ML algorithms, particularly neural networks (NNs), have shown promising results in predicting the thermal field evolution. This paper reviews the existing literature and highlights the critical methodologies and recent advancements in NN-based predictions. It explores novel perspectives by discussing the hybrid modeling approaches, including the combination of PBMs with NNs. This overview highlights the evolving landscape of predictive techniques in the context of AM and underscores the potential for enhancing accuracy and efficiency in thermal field prediction. The paper also discusses the challenges and outlines future directions for enhancing the accuracy and efficiency of thermal field prediction in AM. By synthesizing current research, this overview will guide researchers and practitioners toward leveraging NNs effectively for optimizing thermal management in AM processes. The insights presented underscore the transformative potential of NN predictions in advancing AM capabilities. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for modeling unsaturated flow using deep neural networks (DNNs) integrated with spacetime radial basis functions (RBFs). Traditional methods for simulating unsaturated flow often face challenges in computational efficiency and accuracy, particularly when dealing with nonlinear soil properties and complex boundary conditions. Our proposed model emphasizes the capabilities of DNNs in identifying complex patterns and the accuracy of spacetime RBFs in modeling spatiotemporal data. The training data comprise the initial data, boundary data, and radial distances used to construct the spacetime RBFs. The innovation of this approach is that it introduces spacetime RBFs, eliminating the need to discretize the governing equation of unsaturated flow and directly providing the solution of unsaturated flow across the entire time and space domain. Various error evaluation metrics are thoroughly assessed to validate the proposed method. This study examines a case where, despite incomplete initial and boundary data and noise contamination in the available boundary data, the solution of unsaturated flow can still be accurately determined. The model achieves RMSE, MAE, and MRE values of 10−4, 10−3, and 10−4, respectively, demonstrating that the proposed method is robust for solving unsaturated flow in soils, providing insights beyond those obtainable with traditional methods. [ABSTRACT FROM AUTHOR]

In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits strong non-linearity between pilot subcarriers. Since the channel delay profile is generally unknown, this non-linearity cannot be modeled precisely. A neural network (NN)-based receiver effectively tackles this challenge by learning and compensating for the non-linearity through NN training. The performance of the DNN-based UA communication receiver was tested recently in river trials in Western Australia. The results obtained from the trials prove that the DNN-based receiver performs better than the conventional least-squares (LS) estimator-based receiver. This paper suggests that UA communication using DNN receivers holds great potential for revolutionizing underwater communication systems, enabling higher data rates, improved reliability, and enhanced adaptability to changing underwater conditions. [ABSTRACT FROM AUTHOR]

In order to ensure the flight safety of small unmanned aerial vehicles (UAVs), a deep neural network-fused mathematical modeling approach is put up for reliable flight control of small UAVs. First, engine torque, thrust eccentricity and initial stop angle are taken into full consideration. A six-degree-of-freedom nonlinear model is formulated for small UAVs, concerning both ground taxiing and air flight status. Then, the model was linearized using the principle of small disturbances. The linearized model expressions for both ground taxiing and air flight were provided. In addition, radial basis function neural networks are used for online approximation to address the nonlinearity and uncertainty caused by changes in aircraft aerodynamic parameters. At the same time, to compensate for the external disturbance and the approximation error of the neural network, the system robustness is improved by selecting reasonable design parameters. This helps the whole flight control system obtain better tracking control performance. At last, some simulation experiments are carried out to evaluate the performance of the proposed mathematical modeling framework. The simulation results show that the proposal has stronger convergence ability, smaller prediction error, and better performance. Thus, proper proactivity can be acknowledged. [ABSTRACT FROM AUTHOR]

In today's information age, intelligent manufacturing has become a significant trend of future industrial development. The neural network plays a vital role as the basis of smart manufacturing. This is a kind of intelligent algorithm that can simulate human brain organization, with strong learning ability and computing power, and has achieved remarkable results in the application field; the most typical representative is deep neural network, especially in recent years, an artificial intelligence developed based on deep neural network has entered the application stage, providing a new idea and model for industrial production. Based on this, taking the clothing production process as an example, this paper combined transfer learning and network fine-tuning technology to build a Faster RCNN model based on the ResNet-101 network to realize the positioning and recognition of clothing style map features and create an intelligent generation platform for clothing process flow on this basis. [ABSTRACT FROM AUTHOR]