Deep Learning Research Articles

Optimization of Interference Identification for Wireless Communication Based on Global Convolutional Neural Network

With the widespread adoption of smartphones and the rise of 5G technology, wireless networks fully show their bright future. In order to study the interference factors and optimization methods in the process of wireless communication, this paper studies the wireless communication interference recognition algorithm and power allocation algorithm based on global convolutional neural network, proposes a wireless communication interference recognition system model based on global convolutional neural network, and trains the system. The specific conclusions are as follows: (1) The basic modulation recognition neural network algorithm is constructed based on deep learning theory; (2) As the number of D2D users increases, the time used by the WMMSE algorithm and the equal power allocation algorithm in the traditional method increases. (3) The QL algorithm-based on greedy- is compared with the system-based QL algorithm proposed in this paper. On 200-time nodes, the SINK value of the QL algorithm based on -greedy is increased from 2.6 to 3.3, and the convergence speed is also greatly improved. The system proposed in this paper has a faster learning speed, can accelerate the learning of strategies, and can save a lot of computing resources, and effectively reduce the complexity of operations.

A deep learning approach for marine snow synthesis and removal

A deep learning approach for marine snow synthesis and removal

From Sequence to System: Enhancing IVT mRNA Vaccine Effectiveness through Cutting-Edge Technologies.

The COVID-19 pandemic has spotlighted the potential of in vitro transcribed (IVT) mRNA vaccines with their demonstrated efficacy, safety, cost-effectiveness, and rapid manufacturing. Numerous IVT mRNA vaccines are now under clinical trials for a range of targets, including infectious diseases, cancers, and genetic disorders. Despite their promise, IVT mRNA vaccines face hurdles such as limited expression levels, nonspecific targeting beyond the liver, rapid degradation, and unintended immune activation. Overcoming these challenges is crucial to harnessing the full therapeutic potential of IVT mRNA vaccines for global health advancement. This review provides a comprehensive overview of the latest research progress and optimization strategies for IVT mRNA molecules and delivery systems, including the application of artificial intelligence (AI) models and deep learning techniques for IVT mRNA structure optimization and mRNA delivery formulation design. We also discuss recent development of the delivery platforms, such as lipid nanoparticles (LNPs), polymers, and exosomes, which aim to address challenges related to IVT mRNA protection, cellular uptake, and targeted delivery. Lastly, we offer insights into future directions for improving IVT mRNA vaccines, with the hope to spur further progress in IVT mRNA vaccine research and development.

Predicting schedule adherence of engineering changes – a case study on effectivity date adherence prediction using machine learning

Engineering changes (EC), redesigns of components, are common with complex products. Their realisation into production systems is a lengthy process and thorough control is imperative. As with most complex processes, however, this is a resource-intensive task and support is required to direct scarce resources. In other domains, predictive business process monitoring (PBPM) research has demonstrated that complex processes can be monitored using machine learning approaches. However, it is indicated that predictive performance is domain sensitive. Therefore, we investigate and show how PBPM can be applied to the EC process by predicting adherence to planned implementation dates. As the outcome of a case study comparing 30 predictive models, our research indicates that EC effectivity date adherence prediction, and thus pre-emptive EC schedule monitoring, is possible. However, performance comparison hints that shallow learning algorithms outperform deep learning algorithms. Furthermore, as the optimal algorithm depends on the deployment scenario, it is demonstrated how cost curves are better decision criteria for choosing models compared to threshold dependent metrics. Based on these findings, this article offers a blueprint for developing machine learning models for predicting the EC schedule adherence and lays the base for further research towards automatic EC scheduling and control.

External validation and performance analysis of a deep learning-based model for the detection of intracranial hemorrhage.

We aimed to investigate the external validation and performance of an FDA-approved deep learning model in labeling intracranial hemorrhage (ICH) cases on a real-world heterogeneous clinical dataset. Furthermore, we delved deeper into evaluating how patients' risk factors influenced the model's performance and gathered feedback on satisfaction from radiologists of varying ranks. This prospective IRB approved study included 5600 non-contrast CT scans of the head in various clinical settings, that is, emergency, inpatient, and outpatient units. The patients' risk factors were collected and tested for impacting the performance of DL model utilizing univariate and multivariate regression analyses. The performance of DL model was contrasted to the radiologists' interpretation to determine the presence or absence of ICH with subsequent classification into subcategories of ICH. Key metrics, including accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, were calculated. Receiver operating characteristics curve, along with the area under the curve, were determined. Additionally, a questionnaire was conducted with radiologists of varying ranks to assess their experience with the model. The model exhibited outstanding performance, achieving a high sensitivity of 89% and specificity of 96%. Additional performance metrics, including positive predictive value (82%), negative predictive value (97%), and overall accuracy (94%), underscore its robust capabilities. The area under the ROC curve further demonstrated the model's efficacy, reaching 0.954. Multivariate logistic regression revealed statistical significance for age, sex, history of trauma, operative intervention, HTN, and smoking. Our study highlights the satisfactory performance of the DL model on a diverse real-world dataset, garnering positive feedback from radiology trainees.

MBI-TCMR: A TRADITIONAL CHINESE FORMULA RECOMMENDATION DEEP LEARNING MODEL BASED ON MULTI-TASK META LEARNING AND BIOLOGICAL NETWORK STRUCTURE

The performance of existing traditional Chinese medicine (TCM) recommendation models is generally poor because of their weak generalization ability, overfitting, and inability to use known biological networks. Therefore, building a TCM recommendation model based on artificial intelligence has currently become an important bioinformatics task. This study aimed to design a multitask meta-learning model with good biological interpretation for TCM formula recommendation (MBI-TCMR) for deep learning regularization. This method was based on the known biological network structure to sparse the deep learning network, solve the overfitting problem of the model, and enhance the biological interpretability of the model. Furthermore, a multi-learning framework based on meta-learning was also proposed. The framework allowed the MBI-TCMR model to mine knowledge of TCM formulas and quickly adapt to different types of TCM formula recommendation tasks. Finally, we used a gradient-based deep learning feature backtracking method to calculate the feature weight for each neuron. This weight could provide valuable explanatory information for researchers to study how the model made its medicine recommendations. We designed three independent experiments. The experimental results showed that the hit ratio (HR), AUC, and recall and precision value of the MBI-TCMR model outperformed the existing TCM formula recommendation models. The MBI-TCMR model’s HR of top 1–10 reached 0.15–0.9 (Gynecologic Disease Dataset). HR was 10 for the MBI-TCMR model, which was an improvement of 11.1% compared with the best baseline model. The bio-enrichment analysis showed that the model exhibited good bio-interpretation. In summary, this study proposed a novel TCM formula recommendation model, which expanded the application of the artificial intelligence model and achieved good results.

Deep Learning Based Music Recommendation Systems: A Review of Algorithms and Techniques

Abstract. This paper provides a thorough examination of the utilisation of deep learning in music recommendation systems, which have transformed consumer discovery and engagement with music on streaming platforms. Scalability challenges and the cold-start problem are among the constraints that conventional recommendation methods, such as content-based filtering and collaborative filtering, encounter, which hinder their ability to deliver personalised recommendations that are effective. The processing of multi-modal and sequential data is significantly improved by deep learning methodologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders. This results in more precise and contextually pertinent music recommendations. Moreover, hybrid models that amalgamate deep learning with conventional techniques augment recommendation precision by synthesising user interaction data with acoustic characteristics. This paper examines essential performance metrics employed in the assessment of music recommendation systems, including precision, recall, F1-score, and Mean Reciprocal Rank (MRR). It also tackles issues including computational complexity, bias, and ethical dilemmas pertaining to data privacy.

Analysis of athletes’ technical action based on deep learning

In order to raise athletes’ technical proficiency and overall performance, this paper uses deep learning technology to precisely assess table tennis technical activities. The paper builds a hybrid neural network model for technical action analysis of table tennis players, based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in deep learning. First, CNN extracts the spatial characteristics from the video frames. After that, an LSTM processes these data in a time series to accurately recognize and classify the movements of athletes. Lastly, through trials, the model is trained and evaluated using the Table Tennis Tracking Network (TTNET) dataset. The experimental findings demonstrate that: (1) In table tennis technical action analysis, the suggested deep learning model performs better than other comparative models. The efficacy of combining convolution with sequence learning is fully demonstrated by the CNN-LSTM hybrid model, which performs best in all indicators when compared to the CNN, LSTM, Multi-Objective Function (MOF), and Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) combined models. Its accuracy rate, precision rate, recall rate, and F1 score are 0.923, 0.918, 0.925, and 0.921, respectively. In contrast, the performances of LSTM and CNN are also excellent, but the performance of MOF model is relatively low; (2) In the classification of technical actions, the model has the highest classification accuracy of service, reaching 0.930, and the classification accuracy of other technical actions such as forehand stroke, backhand stroke and spike is also above 0.9, and the time sequence consistency index also maintains a high level, indicating that the model can effectively identify and analyze table tennis technical actions. In addition, the performance evaluation of real-time feedback shows that the model can achieve low processing time and feedback delay in video data processing with different lengths, which ensures the real-time and reliability in practical applications. These results show that the proposed model can not only provide accurate technical action recognition, but also provide timely and effective feedback in practical application, which has high practical value. The results of this paper prove the potential of deep learning technology in the analysis of athletes’ technical actions, and provide scientific basis and effective tools for the technical training and optimization of table tennis players.

Multi-Sensor Fusion and Deep Learning: A New Frontier in Stabilization Algorithm Optimization

The rapid development of automation technology has highlighted the importance of stabilization algorithms in ensuring the stability and functionality of smart devices. These algorithms are critical for maintaining balance and precision, especially in applications such as unmanned aerial vehicles (UAVs), handheld devices, and autonomous systems. This research focuses on the application of stabilization algorithms across various domains, analyzing their principles, advantages, limitations, and potential optimization strategies. The study employs a literature review to investigate the integration of multi-sensor fusion and deep learning techniques to improve system stability. It shows that multi-sensor fusion significantly enhances system robustness by mitigating external disturbances, while deep learning improves autonomous decision-making in dynamic environments, particularly in UAV path planning and task execution. The findings confirm the crucial role of stabilization algorithms in modern systems and provide insights into future optimization directions. By combining deep learning with sensor fusion, future systems are expected to achieve greater stability and autonomy, contributing to the advancement of UAVs and other intelligent systems.

A Review of 3D Human Pose Estimation

Abstract. Visual perception and human body recognition are fundamental capabilities required for effective and safe interactions between artificial intelligence (AI), computer vision, and humans in real-world scenarios. Recent groundbreaking developments in AI and computer vision have resulted in major advancements in human body recognition technology. However, research in human body recognition is still in the early stages of the product lifecycle. Identifying the three-dimensional locations of the joints in the human body from pictures or videos is known as 3D posture estimation. Although it is widely used in areas like human motion analysis and robotics, it continues to be a difficult task due to challenges such as depth ambiguity and the scarcity of robust datasets. Over the past decade, numerous methods have been developed, many of which are based on deep learning, significantly improving the performance of existing benchmarks. A comprehensive literature review of this field is crucial for future development. However, in nowadays,more and more such research has mainly concentrated on traditional techniques, requirement for a comprehensive examination of tools based on deep learning. This paper delivers a thorough overview of current deep learning-based 3D pose estimation algorithms, outlining their advantages and limitations while providing a detailed understanding of the field. It also explores commonly used benchmark datasets and methods for analyzing human poses in unlabeled field images, providing a thorough comparative analysis. Finally, insights are provided to aid in the design of future models and algorithms.

Short-Term Photovoltaic Power Prediction Based on Deep Learning

Abstract. In order to cope with the surge in global demand for clean energy and the challenges under the goals of "carbon peaking" and "carbon neutrality", this paper discusses the short-term prediction of photovoltaic (PV) power based on deep learning. This paper first summarizes the theoretical basis of deep learning and its application in PV power prediction, then expounds the key steps such as data preprocessing, data augmentation, and feature construction, and introduces the specific applications of deep learning models such as convolutional neural network, recurrent neural network and its variants long short-term memory network and gated recurrent unit in PV power prediction. Finally, this paper summarizes the progress of the current technology and looks forward to the future development direction of photovoltaic power prediction including multi-factor fusion prediction and optimization of deep learning models.

Development and Evaluation of a Deep Learning Model to Reduce Exomass-Related Metal Artefacts in Cone-Beam Computed Tomography of the Jaws.

To develop and evaluate a deep learning (DL) model to reduce metal artefacts originating from the exomass in cone-beam computed tomography (CBCT) of the jaws. Five porcine mandibles, each featuring six tubes filled with a radiopaque solution, were scanned using four CBCT units before and after the incremental insertion of up to three titanium, titanium-zirconium, and zirconia dental implants in the exomass of a small field of view. A conditional denoising diffusion probabilistic model, using DL techniques, was employed to correct images from exomass-related metal artefacts across the CBCT units and implant scenarios. Three examiners independently scored the image quality of all datasets, including those without an implant (ground truth), with implants in the exomass (original), and DL-generated ones. Quantitative analysis compared contrast-to-noise ratio (CNR) to validate artefact reduction using repeated measures analysis of variance in a factorial design followed by Tukey test (α = 0.05). The visualisation of the hard tissues and overall image quality was reduced in the original and increased in the DL-generated images. The score variation observed in the original images was not observed in the DL-generated images, which generally scored higher than the original images. DL-generated images revealed significantly greater CNR than both the ground truth and their corresponding original images, regardless of the material and quantity of dental implants and the CBCT unit (p < 0.05). Original images revealed significantly lower CNR than the ground truth (p < 0.05). The developed DL model demonstrated promising performance in correcting exomass-related metal artefacts in CBCT of the jaws.

Prioritization of Software Bugs Using Entropy‐Based Measures

ABSTRACTOpen‐source software is evolved through the active participation of users. In general, a user request for bug fixing, the addition of new features, and feature enhancements. Due to this, the software repositories are increasing day by day at an enormous rate. Additionally, user distinct requests add uncertainty and irregularity to the reported bug data. The performance of machine learning algorithms drastically gets influenced by the inappropriate handling of uncertainty and irregularity in the bug data. Researchers have used machine learning techniques for assigning priority to the bug without considering the uncertainty and irregularity in reported bug data. In order to capture the uncertainty and irregularity in the reported bug data, the summary entropy–based measure in combination with the severity and summary weight is considered in this study to predict the priority of bugs in the open‐source projects. Accordingly, the classifiers are build using these measures for different machine learning techniques, namely, k‐nearest neighbor (KNN), naïve Bayes (NB), J48, random forest (RF), condensed nearest neighbor (CNN), multinomial logistic regression (MLR), decision tree (DT), deep learning (DL), and neural network (NNet) for bug priority prediction This research aims to systematically analyze the summary entropy–based machine learning classifiers from three aspects: type of machine learning technique considered, estimation of various performance measures: Accuracy, Precision, Recall, and F‐measure and through existing model comparison. The experimental analysis is carried out using three open‐source projects, namely, Eclipse, Mozilla, and OpenOffice. Out of 145 cases (29 products X 5 priority levels), the J48, RF, DT, CNN, NNet, DL, MLR, and KNN techniques give the maximum F‐measure for 46, 35, 28, 11, 15, 4, 3, and 1 cases, respectively. The result shows that the proposed summary entropy–based approach using different machine learning techniques performs better than without entropy‐based approach and also entropy‐based approach improves the Accuracy and F‐measure as compared with the existing approaches. It can be concluded that the classifier build using summary entropy measure significantly improves the machine learning algorithms' performance with appropriate handling of uncertainty and irregularity. Moreover, the proposed summary entropy–based classifiers outperform the existing models available in the literature for predicting bug priority.

Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer.

Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images. We developed a tumor-aware recurrent registration (TRACER) deep learning (DL) method and evaluated its suitability for VBA. TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF). Multiple CLSTM steps are used to compute a progressive sequence of deformations. Input conditioning was applied by including tumor segmentations with 3D image pairs as input channels. Bidirectional tumor rigidity, image similarity, and deformation smoothness losses were used to optimize the network in an unsupervised manner. TRACER and multiple DL methods were trained with 204 3D computed tomography (CT) image pairs from patients with lung cancers (LC) and evaluated using (a) Dataset I (N = 308 pairs) with DL segmented LCs, (b) Dataset II (N = 765 pairs) with manually delineated LCs, and (c) Dataset III with 42 LC patients treated with RT. TRACER accurately aligned normal tissues. It best preserved tumors, indicated by the smallest tumor volume difference of 0.24%, 0.40%, and 0.13 % and mean square error in CT intensities of 0.005, 0.005, 0.004, computed between original and resampled moving image tumors, for Datasets I, II, and III, respectively. It resulted in the smallest planned RT tumor dose difference computed between original and resampled moving images of 0.01 and 0.013 Gy when using a female and a male reference. TRACER is a suitable method for inter-patient registration involving LC occurring in both fixed and moving images and applicable to voxel-based analysis methods.

An Adaptive Cruise Control Algorithm Based on DDPG Algorithm Based on Deep Reinforcement Learning Under Variable Acceleration Conditions

Adaptive cruise control (ACC) dynamically regulates a vehicle's speed to preserve a secure gap from the preceding vehicle, enhancing road safety. In this study, ACC is examined through the lens of deep reinforcement learning, with a focus on the Deep Deterministic Policy Gradient (DDPG) technique. The reward function takes into account the speed error, and two modesspeed control and distance controlare implemented. The proposed ACC strategy is trained and validated through simulations on the MATLAB/Simulink platform. The experimental results indicate that the reward function converges rapidly, confirming the suitability of the DDPG algorithm for automotive ACC research.

PDCF-DRL: a contention window backoff scheme based on deep reinforcement learning for differentiating access categories

AbstractIn wireless networks, the Contention Window (CW) is a crucial parameter for wireless channel sharing among numerous stations, directly influencing overall network performance. With the rapid growth and increasing diversity of traffic in wireless networks, differentiating and serving various types of traffic to enhance network performance has become a pressing issue that needs to be addressed. Deep Reinforcement Learning (DRL) optimizes decision-making through the interaction between an agent and its environment, enabling it to handle complex state spaces and high-dimensional data, making it particularly suitable for dynamically adjusting the contention window and adapting to environmental changes in real time. Access Category (AC) is used to differentiate various types of traffic to support different quality of service (QoS) requirements. Therefore, this paper proposes the integration of DRL techniques into the EDCA mechanism, utilizing AC to differentiate network traffic, while the DRL technology adjusts the contention window to adequately ensure QoS for different types of traffic and enhance overall network performance. This paper proposes a CW back off scheme based on DRL for differentiating ACs. The scheme uses DRL technology to observe the channel collision rate and sense the current network conditions, thereby adaptively adjusting the CW size for ACs. In addition, it dynamically adjusts the back off strategy according to the perceived current network conditions to optimize the data transmission process. In the range of 20–120 stations, the scheme was tested in both single AC and multiple AC traffic scenarios, demonstrating excellent performance. The collision rate consistently remained below 18%, while the normalized throughput was maintained above 76%. Additionally, there is a significant improvement compared to existing deep reinforcement learning-based optimization schemes. Experimental results show that this scheme effectively discriminates between different ACs, resulting in lower latency and higher throughput for high-priority traffic. Furthermore, adaptively adjusting the CW size and improving the back off strategy maintains a low collision rate and stable throughput even under heavy load conditions, significantly improving the overall network performance.

Deep Reinforcement Learning for Multi-Objective Real-Time Pump Operation in Rainwater Pumping Stations

Rainwater pumping stations located near urban centers or agricultural areas help prevent flooding by activating an appropriate number of pumps with varying capacities based on real-time rainwater inflow. However, relying solely on rule-based pump operations that monitor only basin water levels is often insufficient for effective control. In addition to maintaining a low maximum water level to prevent flooding, pump operation at rainwater stations also requires minimizing the number of pump on/off switches. Reducing pump switch frequency lowers the likelihood of mechanical failure and thus decreases maintenance costs. This paper proposes a real-time pump operation method for rainwater pumping stations using Deep Reinforcement Learning (DRL) to meet these operational requirements simultaneously, based only on currently observable information such as rainfall, inflow, storage volume, basin water level, and outflow. Simulated rainfall data with various return periods and durations were generated using the Huff method to train the model. The Storm Water Management Model (SWMM), configured to simulate the Gasan rainwater pumping station located in Geumcheon-gu, Seoul, South Korea, was used to conduct experiments. The performance of the proposed DRL model was then compared with that of the rule-based pump operation currently used at the station.

A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort study.

To develop and validate deep learning (DL) models using preoperative contrast-enhanced CT images for tumor auto-segmentation and microsatellite instability(MSI) prediction incolorectal cancer (CRC). Patients with CRC who underwent surgery or biopsy between January 2018 and April 2023 were retrospectively enrolled. Mismatch repair protein expression was determined via immunohistochemistry or fluorescence multiplex polymerase chain reaction-capillary electrophoresis. Manually delineated tumor contours using arterial and venous phase CT images by three abdominal radiologists are served as ground truth. Tumor auto-segmentation used nnU-Net. MSI prediction employed ViT or convolutional neural networks models, trained and validated with arterial and venous phase images (image model) or combined clinical-pathological factors (combined model). The segmentation model was evaluated using patch coverage ratio, Dice coefficient, recall, precision, and F1-score. The predictive models' efficacy was assessed using areas under the curves and decision curve analysis. Overall, 2180 patients (median age: 61years ± 17 [SD]; 1285 males) were divided into training (n = 1159), validation (n = 289), and independent external test (n = 732) groups. High-level MSI status was present in 435 patients (20%). In the external test set, the segmentation model performed well in the arterial phase, with patch coverage ratio, Dice coefficient, recall, precision, and F1-score values of 0.87, 0.71, 0.72, 0.74, and 0.71, respectively. For MSI prediction, the combined models outperformed the clinical model (AUC = 0.83 and 0.82 vs 0.67, p < 0.001) and two image models (AUC = 0.75 and 0.77, p < 0.001). Decision curve analysis confirmed the higher net benefit of the combined model compared to the other models across probability thresholds ranging from 0.1 to 0.45. DL enhances tumor segmentation efficiency and, when integrated with contrast-enhanced CT and clinicopathological factors, exhibits good diagnostic performance in predicting MSI in CRC.

A Review of The Application of Natural Language Processing in Human-Computer Interaction

Over the past few decades, advancements in computing technologies have propelled research in Human-Computer Interaction (HCI). Recently, the rise of artificial intelligence, particularly deep learning and pre-trained models like BERT and GPT, has revolutionized Natural Language Processing (NLP), making HCI applications more intelligent and personalized. NLP has emerged as a critical technology in enhancing HCI, enabling more intuitive and efficient communication between users and machines. This review explores the various applications of NLP in HCI, highlighting its significant role in user interface design, chatbots, and virtual assistants. Specifically, the paper examines how NLP techniques such as intent recognition, sentiment analysis, and language generation contribute to the creation of more responsive and user-friendly interfaces through voice input, personalized experiences, and optimized feedback mechanisms. Furthermore, the challenges and limitations of implementing NLP in HCI are discussed, particularly concerning data privacy, model effectiveness, and the ethical implications of deploying NLP systems, with a focus on privacy and trustworthiness. Finally, the paper considers future research directions in this field, emphasizing the importance of interdisciplinary collaboration in overcoming current barriers and enhancing the applicability of NLP in real-world contexts, while urging developers to prioritize responsible AI practices in their designs.

Research Advances in Facial Expression Recognition Technology

As a key area in artificial intelligence and computer vision, facial expression analysis technology has been taking hold rapidly, yielding dazzling advances. This technology extracts even the subtlest facial movements to identify when a human is sad, happy, or angry. It has extensive application value in markets like mental diagnosis, secure monitoring, and intelligent interaction. The paper will provide a brief overview of the kernel technology of facial expressions recognition: geometric features, appearance features, and deep learning. And compared the experimental results of different experiments, as a goal, the experiments compare functions of several methods in a multi-dataset context, concentrating the primary problems in the tasks like dataset bias, real-time requirements, and different light levels and partial occlusions in various environments. Eventually, the paper forecasts the imminent growth trend in data fusion and deep learning improvement. This also indicates that emerging technologies, especially in terms of applications, will have the upper hand.