Convolutional Neural Network Method Research Articles

U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

Abstract. Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard to the growing satellite dataset available for methane point sources. Here, we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels × 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point-source observability, Ops=Q/(UWΔB), as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h−1 in GHGSat-C1 images over surfaces with low background noise and successfully handles larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates, while the CNN method is biased towards the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2–4 m s−1 is optimal for detection and quantification of point sources from satellite data.

Lung Cancer Detection Using a Modified Convolutional Neural Network (CNN)

<p><span>Image processing is used to classify lung images with malignant or normal nodules. The Convolutional Neural Network (CNN) method is often used to classify images. This study uses a modified CNN architecture with various layers, filters, batch size, dropout, and epoch values. Variations were made to determine the best accuracy value and reduce the overfitting value of the proposed CNN architecture. This study implements the method using the Keras library with the Python programming language. The data is in the form of CT-Scan images of lung cancer and normal lungs. The results of several experiments from the proposed model produce an accuracy value of 95% using three layers, 128 filters on the first layer, 256 on the second layer, and 512 filters on the third layer, then with 32 batch sizes, 0.5 dropout.</span></p>

Assessment of Tree Damage with the Forest Health Monitoring (FHM) Method and the Convolutional Neural Network (CNN) Method

So far, the assessment or measurement of tree damage has only been done using the Forest Health Monitoring (FHM) method. This study aims to determine the types of tree damage using Forest Health Monitoring (FHM) and Convolutional Neural Network (CNN) methods. The research was conducted at the TAHURA WAR Utilization Block and the Computer Science Laboratory at FMIPA Lampung University. Measuring the type of tree damage using the FHM method is carried out on trees that are in the FHM cluster. Identification of tree damage types with the CNN algorithm using the MobileNet architecture. The results showed that there were 13 types of tree damage found, with five types of tree damage that were commonly found (> 60 cases): open wounds (218 cases), cancer (94 cases), Broken / Cracks and stems (87 cases), broken or dead branches (73 cases), and loss of dominant shoots (69 cases). As for the identification results with the CNN method, there were nine out of 13 types of damage that obtained precision, recall, and F1 scores of 100%. Thus, five types of dominant tree damage were found, one of which was open wounds (218 cases), and nine types of tree damage obtained high accuracy values.

Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas.

Prompt histopathological diagnosis with accuracy is required for soft tissue sarcomas (STSs) which are still challenging. In addition, the advances in artificial intelligence (AI) along with the development of pathology slides digitization may empower the demand for the prediction of behavior of STSs. In this article, we explored the application of deep learning for prediction of prognosis from histopathological images in patients with STS. Our retrospective study included a total of 35 histopathological slides from patients with STS. We trained Inception v3 which is proposed method of convolutional neural network based survivability estimation. F1 score which identify the accuracy and area under the receiver operating characteristic curve (AUC) served as main outcome measures from a 4-fold validation. The cohort included 35 patients with a mean age of 64 years, and the mean follow-up period was 34 months (2-66 months). Our deep learning method achieved AUC of 0.974 and an accuracy of 91.9% in predicting overall survival. Concerning with the prediction of metastasis-free survival, the accuracy was 84.2% with the AUC of 0.852. AI might be used to help pathologists with accurate prognosis prediction. This study could substantially improve the clinical management of patients with STS.

IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model

This research aims to develop a monitoring system and temperature prediction model in neonatal premature infant incubators by applying the internet of things (IoT) concept and the 1-dimensional convolutional neural network (1D-CNN) method. The system is designed by integrating sensors, actuators, and microcontrollers connected through Wi-Fi network with message queue telemetry transport (MQTT) protocol. Sensor data in the incubator is stored in a database and displayed in real-time on a web application. The data in the database is also used for creating a temperature prediction model in the incubator. Test results indicate that the best model configuration consists of 5 neurons in the first layer, 20 neurons in the second layer, and a dense layer with 100. The evaluation of this model yields a high level of accuracy with an root mean square error (RMSE) of 0.200 °C, MSE of 0.004 °C, mean absolute error (MAE) of 0.152 °C, and mean absolute percentage error (MAPE) of 0.4%. Based on the error values obtained between the predicted and actual values from each evaluation technique in the model, it can be concluded that the range between the real and predicted values is approximately 0.2 °C. Overall, this research contributes to improving the quality of care for premature infants.

Implementation of Convolutional Neural Network (CNN) Method for Fish Processed Cuisine Image Identification Application with Google Maps Features

The rapid advancement of science and technology encourages dynamic transformation in various sectors, especially in the field of information and communication technology (ICT), especially with the existence of Android-based smartphones. This advancement revolutionizes the way we access information, especially about various processed fish dishes in Indonesia. However, despite the plethora of culinary offerings, travelers often find it difficult to discover traditional dishes through social media platforms. To bridge this gap, a new app that utilizes artificial neural networks has been developed. The app allows users to photograph and upload images of processed fish dishes to recognize and provide detailed descriptions and recipes. In addition, integrating Google Maps makes it easy for users to find nearby places that serve these dishes. Testing the app with a dataset consisting of 1577 images of six types of processed fish dishes yielded promising results, with accuracy reaching 97.57% over 120 epochs. This innovation not only preserves cultural heritage but also enhances the culinary experience for locals and tourists.

Recent Advances and Applications of Graph Convolution Neural Network Methods in Materials Science

With the development of artificial intelligence (AI), AI plus science is increasingly valued, presenting new perspectives to scientific research. The research on using machine learning (including deep learning) to discover patterns from data and predict targeted material properties has received widespread attention, which will have a profound impact in material science studies. In recent years, there has been an increased interest in the use of deep learning in materials science, which has led to significant progress in both fundamental and applied research. One of the most notable advancements is the development of graph convolutional neural network models, which combine graph neural networks and convolutional neural networks to achieve outstanding results in materials science and bridge effectively the deep learning models and material properties predictions. The availability of large materials databases due to the rise of big data has further enhanced the relevance of these models in the field. We present, in this article, a comprehensive overview of graph convolutional neural network models, explaining their fundamental principles and highlighting a few examples of their applications in materials science, as well as current trends. The limitations and challenges that these models face, as well as the potential for future research in this dynamic area are also discussed.

A classification framework with multi-level fusion of object-based analysis and convolutional neural network: a case study for land use classification in mining areas

ABSTRACT To enhance the accuracy of land use classification in mining areas, the Object-based Convolutional Neural Network (OCNN) method has been widely used. However, existing researches tend to neglect the importance of decision-level fusion, focusing only on feature-level fusion. This study proposes a new classification framework with Multi-level Fusion of Object-based analysis and CNN (MFOCNN) to achieves high-accuracy land use classification in mining areas. First, Simple Linear Iterative Cluster (SLIC) is employed to generate image objects, which serve as the basic unit for classification. Second, an improved DenseNet is proposed to extract deep features from image patches, which represent image objects, and provide the classification result. Third, handcrafted features including spectral, textural and geometric of the image objects are extracted and fused with the deep features to obtain the classification result with random forest classifier. Finally, the Dempster-Shafer (DS) evidence theory is applied to fuse the two previously described classification results at the decision-level to obtain the final result. Experiments conducted in the mining areas of Erdos using Gaofen-6 images demonstrate that the proposed MFOCNN achieves the best visual performance and accuracy among all tested methods. The MFOCNN, with its feature-level fusion and decision-level fusion, significantly improves the accuracy of land use classification in mining areas. The results suggest that the proposed MFOCNN is a promising method for achieving high-accuracy land use classification in mining areas.

Locating and Grading of Lidar-Observed Aircraft Wake Vortex Based on Convolutional Neural Networks

Aircraft wake vortices are serious threats to aviation safety. The Pulsed Coherent Doppler Lidar (PCDL) has been widely used in the observation of aircraft wake vortices due to its advantages of high spatial-temporal resolution and high precision. However, the post-processing algorithms require significant computing resources, which cannot achieve the real-time detection of a wake vortex (WV). This paper presents an improved Convolutional Neural Network (CNN) method for WV locating and grading based on PCDL data to avoid the influence of unstable ambient wind fields on the localization and classification results of WV. Typical WV cases are selected for analysis, and the WV locating and grading models are validated on different test sets. The consistency of the analytical algorithm and the CNN algorithm is verified. The results indicate that the improved CNN method achieves satisfactory recognition accuracy with higher efficiency and better robustness, especially in the case of strong turbulence, where the CNN method recognizes the wake vortex while the analytical method cannot. The improved CNN method is expected to be applied to optimize the current aircraft spacing criteria, which is promising in terms of aviation safety and economic benefit improvement.

Utilizing CNN to predict homogeneous thermo-mechanical properties of conductive layers for reliability numerical analysis in electronics

This study explores the application of Convolutional Neural Networks (CNN) in predicting the partitioned homogeneous properties (PHPs) of electronic product wiring structures, aiming to enhance the efficiency of reliability analysis through Finite Element Analysis (FEA). A systematic and novel method was developed to generate the input partitioned wiring diagram image sets that foster model generalization and universality. The performance of the CNN-based approach was assessed through regression analysis by employing a leave-one-out cross-validation (LOOCV) approach across three PCBs. Additionally, the predicted PHPs of one of the PCBs were applied in product-level FEA to validate their reliability, further demonstrating the practical applicability of the CNN-based method. The results demonstrate that a well-trained CNN model can accurately predict the properties of previously unencountered wiring structures, thereby facilitating direct application in product-level reliability FEA and improving the efficiency of reliability assessment. Furthermore, efficiency evaluation revealed that the CNN-based method offers significant advantages in terms of time and cost economy compared to the mesoscopic FEA method in determining, highlighting its potential for broader application in electronic product reliability analysis. The findings provide preliminary insights and propose strategies to enhance the applicability of CNN methods in this domain, ultimately aiming to improve the efficiency and reduce costs in reliability assessments, thereby streamlining the overall product development process.

Obstructive Sleep Apnea Syndrome Identification Using CNN-LSTM Hybrid Model

Obstructive Sleep Apnea (OSA) is a common sleep problem. It causes breathing issues during sleep. This disturbs oxygen intake and sleep patterns. Detecting OSA early and accurately is important for treatment. In our study, we look into using advanced deep learning for OSA detection. We focus on Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and a new combined method. We also check the impact of different functions and methods on OSA detection accuracy. Our tests reveal some interesting facts. The Nadam optimizer gives the best OSA detection at 95.22% accuracy. The combined CNN and LSTM method achieves 95.38% accuracy. Using LSTM with the sigmoid function, we get a 93.80% accuracy. These results stress the importance of choosing suitable models and methods for OSA detection. Our study helps improve diagnostic tools for OSA. These findings can aid in early and improved treatment of OSA, giving people a better quality of life and health.

Fault-tolerant Integration of Charge and Discharge Information Security for Multi-Scale Time Series Models

In this paper, the power mode of winding and converter topology are integrated. Considering the filter characteristics and electromagnetic torque control of the system during charging/discharging, the integrated structures of one-phase full bridge, three-phase full bridge and three-phase four-arm bridge are studied. The single-phase all-bridge integrated structure can completely suppress the electromagnetic torque generated by the motor during charging and discharging, and make the filtering effect of the system better. In this project, a multi-scale full convolutional deep neural network method based on time series is studied. The multi-scale full convolution time series model is used for training, and the future continuous signals can be monitored according to the trained model based on the power signal data collected from high-voltage power lines. Experiments show that the proposed method can effectively extract the required data.

A convolutional neural network based on an evolutionary algorithm and its application

PM2.5 concentration predictions can provide air pollution control, management, and early warning. However, the PM2.5 data with high-dimensionality, complexity, and dynamics pose a great challenge to achieve optimal prediction results. Convolutional Neural Networks (CNNs) has unique advantages in processing complex data and contributes to state-of-the-art performances. However, designing the architecture and selecting the learning rate for CNN are time-consuming and requires prior knowledge. Evolutionary algorithms, with the advantages of global convergence, ergodicity, robustness and adaptability, are the most commonly used methods to design the optimal framework for CNNs. Therefore, to improve the predictive performance of CNNs, this paper proposes an improved CNN method (EBRO-ICNN) which employs the enhanced battle royale optimization (EBRO) algorithm and proportional-derivative (PD) control. Firstly, the EBRO algorithm with multistrategy collaborative optimization is introduced, and validated by CEC2017 benchmark functions, which demonstrates EBRO strong global exploration capability, fast convergence speed, and low time complexity. Next, PD control is applied to adjust the learning rate of CNN (ICNN) dynamically, which improves the efficiency and stability of the network training process. At the same time, the ICNN model is optimized using the EBRO algorithm, which can reduce the human interference, generate the optimal framework automatically and enhance the prediction accuracy effectively. Finally, a private air quality dataset and two public datasets are utilized to evaluate the performance of the EBRO-ICNN model, considering 3 error evaluation metrics and 7 prediction comparison models. The experimental results demonstrate that the EBRO-ICNN model exhibits great accuracy and stability.

Improvement Model for Speaker Recognition using MFCC-CNN and Online Triplet Mining

Various biometric security systems, such as face recognition, fingerprint, voice, hand geometry, and iris, have been developed. Apart from being a communication medium, the human voice is also a form of biometrics that can be used for identification. Voice has unique characteristics that can be used as a differentiator between one person and another. A sound speaker recognition system must be able to pick up the features that characterize a person's voice. This study aims to develop a human speaker recognition system using the Convolutional Neural Network (CNN) method. This research proposes improvements in the fine-tuning layer in CNN architecture to improve the Accuracy. The recognition system combines the CNN method with Mel Frequency Cepstral Coefficients (MFCC) to perform feature extraction on raw audio and K Nearest Neighbor (KNN) to classify the embedding output. In general, this system extracts voice data features using MFCC. The process is continued with feature extraction using CNN with triplet loss to obtain the 128-dimensional embedding output. The classification of the CNN embedding output uses the KNN method. This research was conducted on 50 speakers from the TIMIT dataset, which contained eight utterances for each speaker and 60 speakers from live recording using a smartphone. The accuracy of this speaker recognition system achieves high-performance accuracy. Further research can be developed by combining different biometrics objects, commonly known as multimodal, to improve recognition accuracy further.

AI Driven Applications for Precision Weed Management in Agricultural Crops

Agriculture plays a most important role in our Indian economy and therefore lowering the cost of production and improving the quality of agricultural products is highly demanded. A weed is a plant which grows in wrong place at the wrong time and doing mostly harm than crops. Weed competes with the crops for water, light, nutrients and space, and therefore it prevents crop yields. This paper proposes a new method in a contrary way, which combines deep learning and image processing technology to prevent these weeds. Machine learning technologies, are becoming crucial in agriculture to increase productivity, where advanced automation and control have been required. Based on large training datasets and pre-trained models, (Deep Learning) DL-based Convolutional Neural Networks (CNN) methods have proven to be more accurate than previous traditional techniques. Recently, Deep Learning (DL) has gained much attention due to its advantages in object detection, classification, and feature extraction. The system implementation of image processing technique for weed detection, a trained image is taken as a sample in order to demonstrate the difference between weed and the crop. Yolo frame work is used for annotate boundary boxes to the image with datasets. The effectiveness of the (You Only Live Once) YOLO-WEED system for real-time Unmanned Aerial Vehicle (UAV) weed detection, given its high speed and high accuracy in detection. After certain steps, we get desired output, where the weeds are separated from the crop that has been taken in the sample image. Key Words: DeepLearning,CNN, Unmanned Aerial Vehicle (UAV), Precision Weed Detection.

Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network

COVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a Differential Evolution Chaotic Whale Optimization Algorithm (DECWOA) based Convolutional Neural Network (CNN) method for identifying and classifying COVID-19 chest X-ray images. The DECWOA based CNN model improves the accuracy and convergence speed of the algorithm. This method is evaluated {by} Chest X-Ray (CXR) dataset and attains better results in terms of accuracy, precision, sensitivity, specificity, and F1-score values of about 99.89}%, 99.83%, 99.81%, 98.92%, and 99.26% correspondingly. The result shows that the proposed DECWOA based CNN model provides accurate and quick identification and classification of COVID-19 compared to existing techniques like ResNet50, VGG-19, and Multi-Model Fusion of Deep Transfer Learning (MMF-DTL) models.

Recommendation System Building based on CNN and TF-IDF Approaches

Online shopping has becoming increasingly popular. Customers remain engaged with shopping sites due to effective product recommendations. By analyzing users’ past actions and preferences, shopping platforms effectively identify customer interests and desires, enhancing their overall shopping experience. This paper presents a recommendation system that displays 5 items relevant to the product currently being viewed by the customer. The system processes images with an adapted ResNet-50, a deep learning image classification model in convolutional neural network (CNN) and use the acquired embedded vector to determine similarities with cosine functions. Additionally, the system employs term frequency-inverse document frequency (TF-IDF) method for text analysis in product descriptions, generating word embedding that assists recommendations. This blend of visual and textual analysis ensures that the suggestions closely match the item’s category. The system achieves 94.72% accuracy in subcategories’ classification, a 4% increase compared to using the CNN method alone, confirming its effectiveness in recommending relevant items.

Wind Power Forecasting Based on CNN and LSTM Models

Nowadays more and more sustainable and environmentally friendly energy sources are demanded and wind power is playing an increasingly significant role in the energy market. For example, according to the Chinese Academy of Sciences, China plans to generate more than ten times the current scale of wind power in the next decade. Accurate prediction of wind generation is essential for the scheduling, operation, and energy trading of power systems. In this study, a method combining long and short-term memory network (LSTM) and convolutional neural network (CNN) is proposed for wind power generation prediction. In this study, a wind power generation data set from Longyuan Power Group is used to adapt to the input requirements of the model. The data are entered into the LSTM model to capture long-term trends in the data. Next, this paper uses the CNN to process the local spatial features to improve the prediction accuracy. Combining the outputs of these two models, this paper obtains an efficient wind power generation prediction method. In order to evaluate the performance of the proposed method, root mean square error is used to analyze the prediction data. The results show that the combined method of LSTM and CNN is better than individual methods in prediction accuracy and stability. In conclusion, this study proposes a wind power forecasting method combining LSTM and CNN. This method helps to improve the dispatching and operation efficiency of the power system and provides strong support for the promotion of wind power in the energy market.

CRIECNN: Ensemble convolutional neural network and advanced feature extraction methods for the precise forecasting of circRNA-RBP binding sites

Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availability and accuracy, necessitating advanced approaches. In this study, we propose CRIECNN (Circular RNA-RBP Interaction predictor using an Ensemble Convolutional Neural Network), a novel ensemble deep learning model that enhances circRNA-RBP binding site prediction accuracy. CRIECNN employs advanced feature extraction methods and evaluates four distinct sequence datasets and encoding techniques (BERT, Doc2Vec, KNF, EIIP). The model consists of an ensemble convolutional neural network, a BiLSTM, and a self-attention mechanism for feature refinement. Our results demonstrate that CRIECNN outperforms state-of-the-art methods in accuracy and performance, effectively predicting circRNA-RBP interactions from both full-length sequences and fragments. This novel strategy makes an enormous advancement in the prediction of circRNA-RBP interactions, improving our understanding of circRNAs and their regulatory roles.

Free-surface multiple attenuation and seismic deghosting for blended data using convolutional neural networks

Simultaneous source acquisition has become common over the past few decades for marine seismic surveys because of the increased efficiency of seismic acquisition by limiting the time, reducing the cost, and having less environmental impact than conventional single-source (or unblended) acquisition surveys. For simultaneous source acquisition, seismic sources at different locations are fired with time delays, and the recorded data are referred to as the blended data. The air-water interface (or free surface) creates strong multiples and ghost reflections for blended seismic data. The multiples and/or ghost reflections caused by a source in the blended data overlap with the primary reflections of another source, thus creating a strong interference between the primary and multiple events of different sources. We develop a convolutional neural network (CNN) method to attenuate free-surface multiples and remove ghost reflections simultaneously from the blended seismic data. The CNN-based solution that we develop operates on single traces and is not sensitive to the missing near-offset traces, missing traces, and irregular/sparse acquisition parameters (e.g., for ocean-bottom node acquisition and time-lapse monitoring studies). We illustrate the efficacy of our free-surface multiple attenuation and seismic deghosting method by presenting synthetic and field data applications. The numerical experiments demonstrate that our CNN-based approach for simultaneously attenuating free-surface multiples and removing ghost reflections can be applied to the blended data without the deblending step. Although the interference of primaries and multiples from different shots in the blended data makes free-surface multiple attenuation harder than the unblended data, we determine that our CNN-based method effectively attenuates free-surface multiples in the blended synthetic and field data even when the delay time for the blending is different in the training data than in the data to which the CNN is applied.