Effectiveness Of Deep Learning Research Articles

Fast Aerodynamic Prediction of Airfoil with Trailing Edge Flap Based on Multi-Task Deep Learning

Conventional methods for solving Navier–Stokes (NS) equations to analyze flow fields and aerodynamic forces of airfoils with trailing edge flaps (TEFs) are known for their significant time cost. This study presents a Multi-Task Swin Transformer (MT-Swin-T) deep learning framework tailored for swift prediction of velocity fields and aerodynamic coefficients of TEF-equipped airfoils. The proposed model combines a Swin Transformer (Swin-T) for flow field prediction with a multi-layer perceptron (MLP) dedicated to lift coefficient prediction. Both networks undergo gradient updates through the shared encoder component of the Swin Transformer. Such a trained network model for computational fluid dynamics simulations is both effective and robust, significantly improving the efficiency of complex aerodynamic shape design optimization and flow control. The study further investigates the impact of integrating multi-task learning loss functions, skip connections, and the network’s structural design on prediction accuracy. Additionally, the effectiveness of deep learning in improving the aerodynamic simulation efficiency of airfoils with TEF is examined. Results demonstrate that the multi-task deep learning approach provides accurate predictions for TEF airfoil flow fields and lift coefficients. The strategic combination of these tasks during network training, along with the optimal selection of loss functions, significantly enhances prediction accuracy compared with the single-task network. In a specific case study, the MT-Swin-T model demonstrated a prediction time that was 1/7214 of the time necessitated by CFD simulation.

SDR implementation of a light deep learning model based CNN for joint spectrum sensing and AMC

Automatic modulation classification (AMC) aims to blindly recognize the modulation type of a received signal in wireless systems. It is also a critical component of non-cooperative communication systems after the detection of the presence of a signal. In this paper, we introduce a robust approach, termed DET-AMC (joint Detection and Automatic Modulation Classification), employing Convolutional Neural Networks (CNNs) trained via transfer learning methodology. The main advantage of our approach is its ability to handle a wide range of modulation types, including 10 different schemes generated in Gnuradio and their detection using the same model. Through extensive experimentation, we evaluate the performance of our light CNN-based DET-AMC method across varying signal-to-noise ratio (SNR) levels, as well as in the presence of phase noise and frequency offset. We find that the CNN’s learned features, obtained through transfer learning, exhibit robustness, particularly in low SNR and various challenging conditions, leading to accurate modulation classification. In general, our approach outperforms existing methods by using the effectiveness of deep learning in capturing relevant discriminative features. Additionally, our model offers a robust solution for join detection and AMC by achieving an accurate probability of detection and modulation classification without the need for manual feature engineering or the consideration of frequency offset, phase noise or noise estimation. Our model achieves 100% accuracy for synthetic and real data at an SNR equal to -10 dB for detection, and 100% and 98% for classification of synthetic and real signals at −4 dB, respectively.

Ensemble Deep Learning Methods for Detecting Skin Cancer

Skin cancer is a common and possibly fatal condition. Effective treatment results are greatly influenced by early identification. Deep learning (DP) algorithms have demonstrated encouraging outcomes in skin cancer detection computer-aided diagnostic systems. This article investigates the many forms of skin cancer, such as melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), and offers a system for detecting skin cancer utilizing convolutional neural network (CNN) approaches, particularly the multi-model ResNet (M-ResNet) architecture. We present a ResNet architecture that is capable of handling deep networks and has increased skin cancer detection performance. The proposed approach uses a thorough pipeline to find skin cancer. The dataset first goes through pre-processing (PP) procedures, such as picture resizing, normalization, and augmentation approaches, to improve the model's capacity for generalization. The multi-model assembles, leading to improved accuracy, sensitivity, and specificity in skin cancer LEARNING Classification SYSTEM (SC-LCS) tasks. In this study FINAL highlights, the effectiveness of deep learning (DL)techniques, specifically the multi-model ResNet architecture, AND skin cancer LEARNING classification SYSTEM (SC-LCS) for skin cancer detection. The suggested framework seems to have promising results in accurately identifying different types of skin cancer, assisting in diagnosis and therapy at an early stage. Further research and development in this field can potentially contribute to improving healthcare systems and reducing the global burden of skin cancer-related EFFECTED and DEATH RATE.

Component Fourier Neural Operator for Singularly Perturbed Differential Equations

Solving Singularly Perturbed Differential Equations (SPDEs) poses computational challenges arising from the rapid transitions in their solutions within thin regions. The effectiveness of deep learning in addressing differential equations motivates us to employ these methods for solving SPDEs. In this paper, we introduce Component Fourier Neural Operator (ComFNO), an innovative operator learning method that builds upon Fourier Neural Operator (FNO), while simultaneously incorporating valuable prior knowledge obtained from asymptotic analysis. Our approach is not limited to FNO and can be applied to other neural network frameworks, such as Deep Operator Network (DeepONet), leading to potential similar SPDEs solvers. Experimental results across diverse classes of SPDEs demonstrate that ComFNO significantly improves accuracy compared to vanilla FNO. Furthermore, ComFNO exhibits natural adaptability to diverse data distributions and performs well in few-shot scenarios, showcasing its excellent generalization ability in practical situations.

Identifying Diabetic Retinopathy in the Human Eye: A Hybrid Approach Based on a Computer-Aided Diagnosis System Combined with Deep Learning.

Diagnosing and screening for diabetic retinopathy is a well-known issue in the biomedical field. A component of computer-aided diagnosis that has advanced significantly over the past few years as a result of the development and effectiveness of deep learning is the use of medical imagery from a patient's eye to identify the damage caused to blood vessels. Issues with unbalanced datasets, incorrect annotations, a lack of sample images, and improper performance evaluation measures have negatively impacted the performance of deep learning models. Using three benchmark datasets of diabetic retinopathy, we conducted a detailed comparison study comparing various state-of-the-art approaches to address the effect caused by class imbalance, with precision scores of 93%, 89%, 81%, 76%, and 96%, respectively, for normal, mild, moderate, severe, and DR phases. The analyses of the hybrid modeling, including CNN analysis and SHAP model derivation results, are compared at the end of the paper, and ideal hybrid modeling strategies for deep learning classification models for automated DR detection are identified.

The Effect of SPOC Hybrid Model on Deep Learning Effectiveness: A Systematic Literature Review

The hybrid model of SPOC (Small Private Online Course) may have an impact on the effectiveness of deep learning. Nevertheless, few studies have validated the relationship between SPOC and deep learning effectiveness. This systematic review focuses on exploring the impact of the blended model of SPOC on deep learning effectiveness. The article delves into the three major SPOC categories in deep learning and the effects of deep learning in SPOC mode. In addition, the article explores various factors that influence the effectiveness of SPOC on deep learning. To accomplish this, an exhaustive review of the relevant literature was conducted to reveal potential connections and interactions between the SPOC blended model and deep learning effectiveness. This study provides educators and researchers with insights on how to more effectively combine SPOC and deep learning to optimize teaching and learning experiences.

THE APPLICATION OF LINEAR REGRESSION AND ARTIFICIAL NEAURAL NETWORKS TO FORECAST THE AMOUNT OF ELECTRONIC WASTE IN THAILAND

This research aims to investigate the input factors influencing e-waste generation and analyze their predictive capabilities using linear regression techniques and neural networks. The study utilizes data collected from the population, Gross Domestic Product (GDP), inflation, and the amount of e-waste obtained from the Pollution Control Department. By comparing the performance of linear regression and neural networks, this research seeks to identify the most effective approach for modeling and forecasting e-waste generation based on the selected input factors. The findings will contribute to improved understanding and prediction of e-waste patterns, aiding policymakers and waste management authorities in developing sustainable strategies for e-waste management. The data were forecast the possible volume of e-waste in the future. A model using neural network modeling techniques be dividing data into different layers:3 input layers,3 hidden layers,1 bias, and 1 output layer.The effect of deep learning is to get a Learning Rate: LR = 0.01 that doesnt increase the loss value. This allows the model to be trained as fast as possible, with a loss of0.0056, Os 92 ms/step. Neural Network learned with data, and reworked it to fit this data 500 times (Epochs = 500), where Root Mean Squared Error: RMSE= 0.0751, RMSE approaches 0, showing that the model is highly accurate.

Effectiveness of deep learning in early‐stage oral cancer detections and classification using histogram of oriented gradients

AbstractEarly detection of oral cancer (OC) improves survival prospects. Artificial intelligence (AI) is gaining popularity in diagnostic medicine. Oral cancer is a primary global health concern, accounting for 177,384 deaths in 2018; most cases occur in low‐ and middle‐income countries. Automated disease identification in the oral cavity may be facilitated by the ability to identify both possibly and definite malignant lesions. This study aimed to examine the evidence currently available on the effectiveness of AI in diagnosing OC. They highlighted the ability of AI to analyse and identify the early stages of OC. Furthermore, radial basis function networks (RBFN) were employed to develop automated systems to generate intricate patterns for this challenging operation. The stochastic gradient descent algorithm (SGDA) selected the model parameters that best matched the predicted and observed results. It can be used. The initial data was collected for this study to evaluate. Two deep learning‐based computer vision algorithms have been developed to recognize and categorize oral lesions, which is necessary for the early detection of oral cancer. Several examples of HoG include the Canny edge detector, SIFT (scale invariant and feature transform), and SIFT (scale invariant and feature transform). In computer vision and image processing, it is used to find objects. We investigated the potential uses of deep learning‐based computer vision techniques in oral cancer and the viability of an automated system for OC recognition based on photographic images. That made calculations to determine the accuracy, sensitivity, specificity, and receiver operating characteristic curve areas across all validation datasets, including internal, external, and clinical validation (AUC). The RBFN‐SDC model outperformed all others. For 1000 data points, the accuracy of the RBFN‐SDC model is 99.99%, while the accuracy of the R‐CNN, CNN, DCNN, and SVM models is 91.54%, 90.14%, 93.89%, and 94.87%, respectively.

Generative Deep Learning for Visual Animation in Landscapes Design

The biggest challenge for architecture designers is the time required for the design process. Especially landscape architects who have different work limits from architects in general. In contrast to architects in general, who are assisted in producing design plans by building standards, building requirements, and space programs that adapt to the type of project being undertaken. At the same time, some design jobs demand high-productivity landscape animation presentation in a short time. The long process involved in designing animation often makes it difficult for designers to produce optimal work. This study proposes generative zooming animation with artificial intelligence support to shorten the designer’s work process and energy optimization. Deep learning with Vector Quantized Generative Adversarial Network and Contrastive Language-Image Pre-Training was used to generate alternative landscape designs from text prompt-based and compile them in animation. Our experiment shows that one frame can be generated roughly in 3.636 ± 0.089 s, which is significantly faster than the conventional method to create animation. Moreover, our method is able to achieve a good-quality image, which scored 3.2904 using inception score evaluation. The effectiveness of deep learning in visual landscape and animation creation can help designers speed up the design process. Furthermore, working time efficiency without compromising design quality will increase designer productivity and economic growth.

Deep learning-based switchable network for in-loop filtering in high efficiency video coding

<span lang="EN-US">The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering.</span>

Mapping Knowledge Domain Analysis in Deep Learning Research of Global Education

With the rapid development of the global digital knowledge economy, educational activities are facing more challenges. Sustainable development education aims to cultivate students’ thinking ability to better integrate with the contemporary world view, so classroom practice should involve innovative teaching and learning. The goal of sustainable development education is to cultivate talents with high-level thinking and sustainable development abilities. The concept of deep learning emphasizes mobilizing students’ internal motivation, focusing on problem-solving ability, improving students’ critical thinking level, and developing students’ lifelong learning ability. The concept of deep learning has evolved with the times. The introduction of the concept of deep learning in teaching can enhance students’ understanding of the nature of knowledge, cultivate students’ high-level thinking, and enable students to achieve better learning results. Integrating the concept of deep learning into teaching has extremely important significance and value for sustainable development education. It has become a hot topic in the world to comprehensively analyze the research status of deep learning and explore how deep learning can help education achieve sustainable development. In this study, CiteSpace (6.1.R2) visualization analysis software was used to visualize and quantitatively analyze the literature on deep learning in the Social Science Citation Index (SSCI). The visualized analysis is conducted on the annual publication amount, authors, institutions, countries, keywords, and high-frequency cited words of deep learning, to obtain the basic information, development status, hot spots, and evolution trends of deep learning research. The results show that the annual publication volume of deep learning is on the rise; deep learning research has entered a rapid growth stage since 2007; the United States has published the most papers and is the center of the global deep learning research collaboration network; the countries involved in the study were often interconnected, but the institutions and authors were relatively dispersed; research in the field of deep learning mainly focuses on concept exploration, influencing factors, implementation strategies and effectiveness of deep learning; learning method, learning strategy, curriculum design, interactive learning environment are the high-frequency keywords of deep learning research. It can be seen that deep learning research has the characteristics of transnationality, multidisciplinary nature and multi-perspective. In addition, this paper systematically analyzes the latest progress in global deep learning research and objectively predicts that using intelligent technology to design appropriate teaching and learning scenarios and evaluation methods may become the future development trend of deep learning. The research results of this paper will help readers to have a comprehensive understanding of deep learning research, provide deeper and more targeted resources for integrating deep learning concepts into teaching, and promote better sustainable development of education.

Research on the Effectiveness of Deep Learning−Based Agency Cost Suppression Strategy: A Case Study of State−Owned Enterprises in Mainland China

The mixed ownership reform aims to improve the property rights structure of the state−owned enterprises (SOEs) and reduce agency costs, and the current mixed reform strategies mainly include equity blending by introducing external non−state capital, executive assignments, and employee stock ownership. In this paper, 953 valid data of A−shares listed in Shanghai and Shenzhen from 2008 to 2020 are used as samples to construct the indicators of mixed reform strategy by the literature statistics method. After obtaining multiple impact indicators, the regression impact model of corporate agency cost suppression strategy is constructed by MATLAB software using a machine learning algorithm. On this basis, the performance of multiple machine learning algorithms is compared, and it is found that the integrated optimization−based bag−boosting model is used to study the effect of hybrid reform strategy to reduce the agency costs of SOEs, and the proportional setting of indicators when the effect is optimal is also explored. Finally, the laws of different influencing factors on the agency costs of enterprises are explored separately by the eigenvalue method. The results of the study show that the proportion of shareholding of the first largest non−state shareholder is sin−functional with the agency costs of SOEs when non−state majority shareholders are introduced into SOEs’ equity mix, and the agency costs tend to decrease after SOEs become privately held enterprises. The greater the number and proportion of supervisors appointed by non−state shareholders, the greater the supervisory restraint effect on SOE managers and the better the effect of suppressing agency costs. The participation of non−state−owned shareholders in the company’s business decisions by appointed executives and the special resource advantages of SOEs intensify the occurrence of the self−interest of appointed executives and the increase of agency costs of SOEs. The implementation of an employee stock ownership plan plays the role of employee supervision and restraint on SOE managers, which reduces the agency costs of SOEs. Based on this, it can provide support for the government to improve the hybrid reform policy and promote the process layer by layer, and also provide theoretical reference for SOEs to deepen the equity mix, incentivize employee shareholding, and empower non−state shareholders to govern and thus reduce agency costs.

Deep learning course development and evaluation of artificial intelligence in vocational senior high schools.

This study aimed to develop cross-domain deep learning courses of artificial intelligence in vocational senior high schools and explore its impact on students’ learning effects. It initially adopted a literature review to develop a cross-domain SPOC-AIoT Course with SPOC (small private online courses) and the Double Diamond 4D model in vocational senior high schools. Afterward, it adopted participatory action research (PAR) and a questionnaire survey and conducted analyses on the various aspects of the technology acceptance model by SmartPLS. Further, this study explored the impact on the effects of deep learning and knowledge-ability learning of artificial intelligence after 16 weeks of course teaching among 36 Grade I students from the electrical and electronic group of a vocational senior high school. This study revealed that (1) the four stages of the SPOC-AIoT Teaching Mode of the Double Diamond 4D model may effectively guide students to learn AIoT knowledge and skills. (2) Based on the technology acceptance model, the analysis of learning and participation in SmartPLS indicated that this model conformed to the academic fitness requirements of the overall model. (3) After learning with the SPOC-AIoT Teaching Mode, the learning effects of students in AIoT have been significantly improved to a positive aspect. Finally, some suggestions were put forward to promote the development of the SPOC-AIoT Teaching Mode Course in the future.

Incorporating deep learning into capacitive images for smartphone user authentication

We incorporate deep learning techniques into capacitive images of body parts (ear, four fingers, and thumb) to improve the performance of user authentication in smartphones. Use of a capacitive touchscreen as an image sensor has several advantages, such as it is less sensitive to poor illumination conditions, occlusions, and pose variations. Also, it does not need an additional hardware like iris or fingerprint scanner. Use of capacitive images for user authentication is not new. However, the performance, specially, false reject rates (FRRs) of the state-of-the-art capacitive image-based systems are poor. In this paper, we focus on improving the performance and leverage deep learning. Deep learning techniques demonstrated spectacular performance in previous physical biometrics-based research. However, to our knowledge, effectiveness of deep learning is still unexplored in capacitive touchscreen-based user authentication. In order to bridge this research gap, we devise a multi-modal deep learning model, namely UASNet, and compare its performance with a large set of uni- and multi-modal baselines. Using the UASNet, we achieve an accuracy of 99.77%, an EER of 0.48%, and an FRR of 1.19% at FAR of 0.06%.

Deep learning for behaviour classification in a preclinical brain injury model.

The early detection of traumatic brain injuries can directly impact the prognosis and survival of patients. Preceding attempts to automate the detection and the assessment of the severity of traumatic brain injury continue to be based on clinical diagnostic methods, with limited tools for disease outcomes in large populations. Despite advances in machine and deep learning tools, current approaches still use simple trends of statistical analysis which lack generality. The effectiveness of deep learning to extract information from large subsets of data can be further emphasised through the use of more elaborate architectures. We therefore explore the use of a multiple input, convolutional neural network and long short-term memory (LSTM) integrated architecture in the context of traumatic injury detection through predicting the presence of brain injury in a murine preclinical model dataset. We investigated the effectiveness and validity of traumatic brain injury detection in the proposed model against various other machine learning algorithms such as the support vector machine, the random forest classifier and the feedforward neural network. Our dataset was acquired using a home cage automated (HCA) system to assess the individual behaviour of mice with traumatic brain injury or non-central nervous system (non-CNS) injured controls, whilst housed in their cages. Their distance travelled, body temperature, separation from other mice and movement were recorded every 15 minutes, for 72 hours weekly, for 5 weeks following intervention. The HCA behavioural data was used to train a deep learning model, which then predicts if the animals were subjected to a brain injury or just a sham intervention without brain damage. We also explored and evaluated different ways to handle the class imbalance present in the uninjured class of our training data. We then evaluated our models with leave-one-out cross validation. Our proposed deep learning model achieved the best performance and showed promise in its capability to detect the presence of brain trauma in mice.

When is deep learning better and when is shallow learning better: qualitative analysis

In many practical situations, deep neural networks work better than the traditional ‘shallow’ ones; however, in some cases, the shallow neural networks lead to better results. At present, deciding which type of neural networks will work better is mostly done by trial and error. It is therefore desirable to come up with some criterion of when deep learning is better and when shallow is better. In this paper, we argue that this depends on whether the corresponding situation has natural symmetries: if it does, we expect deep learning to work better, otherwise we expect shallow learning to be more effective. Our general qualitative arguments are strengthened by the fact that in the simplest case, the connection between symmetries and effectiveness of deep learning can be theoretically proven.

Effect of Deep Learning on College Students’ Career Planning

It is difficult for college students to find jobs after graduation, which is the most important problem to be solved now. This paper chooses the statistical analysis method to analyze the career planning of college students under different circumstances. Four aspects are analyzed, which are decision-making action, current situation evaluation, career exploration, and self-understanding level. The main conclusions of this paper are as follows. In this study, the gender differences of college students have a certain impact on their career. Generally speaking, the career planning level of boys is higher than that of girls. The job-hunting needs of college graduates are students who enter social work. Family factors affect the level of college students’ career planning. It is found that students’ school experience is the most important factor affecting the level of career planning, and school experience is also reflected in whether students have class committee experience.

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification

3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.

Computer Vision: The Effectiveness of Deep Learning for Emotion Detection in Marketing Campaigns

Computer Vision: The Effectiveness of Deep Learning for Emotion Detection in Marketing Campaigns

CİLT SEGMENTASYONUNDA DERİN ÖĞRENME İLE MAKİNE ÖĞRENİMİNİN KARŞILAŞTIRILMASI

In this study, a skin segmentation study is investigated with deep learning methods. The skin segmentation problem is chosen as a case study. The main reason for this is that there are numerous studies on this subject and the abundance of available data sets. In addition, images containing skin pixels contain multiple attributes. That's why human images are very suitable for comparative studies on machine learning and deep learning. In the first stage of this study, skin segmentation will be done by using RGB space, which contains deep information as an attribute in machine learning. At the same time, to show the success of the deep learning algorithm, the effect of deep learning will be tested by converting images to grayscale, and success differences will be given.