Deep Neural Network Approach Research Articles

Assessing built microclimate with building group frontal projection maps: A sun-path-dependent deep transfer learning neural network approach

Climate conditions play a pivotal role in estimating buildings' energy consumption. For urban-scale building energy dynamic simulation, the typical meteorological year weather represents a universal climate condition for the entire city. However, the varying terrain roughness, environment conditions, human-built architectures jointly formulate a unique microclimate condition for each building. This study intends to develop flexible and reliable approach to assess a building's microclimate condition based on its surrounding environment morphological features. The proposed approach leverages deep transfer learning neural networks that combines seasonal sun path trajectories and front projection maps of building groups. To validate the proposed method, a case study was conducted on a campus environment with wireless environmental sensing systems. The findings of this study demonstrate that the projection matrices and sun path can improve the assessment methods solely based on the dynamic impacts of urban morphological features and can forecast microclimate dynamics for urban-scale building energy simulation.

Classification of Brain Tumors: A Comparative Approach of Shallow and Deep Neural Networks

Brain tumors can be generated anywhere in the brain, with an extensive size range and morphology that makes it challenging to identify and classify. Classifying brain tumors is essential for developing personalized treatment plans. Different types of brain tumors have different responses to treatment, and an accurate classification can help medical professionals develop treatment plans tailored to each patient’s needs. Therefore, this case study aimed to classify T1-weighted contrast-enhanced images of three types of tumors through various approaches, from shallow neural networks to fine-tuning deep neural networks trained. Comparing shallow and deep neural network approaches could help to understand the trade-offs between their performance, interoperability, interpretability, benefits, limitations, scopes, and overall, choosing the best method for a given problem.

Deep neural networks approach with transfer learning to detect fake accounts social media on Twitter

<span>The massive use of social media makes people take actions that have a negative impact on cyberspace, such as creating fake accounts that aim to commit crimes such as spam and fraud to spread false information. Fake accounts are difficult to detect in the traditional way because fake accounts always use photos, names, and unreal information, there are several criteria that can identify a fake account such as no information, few followers, and minimal activity. In the traditional model, it is difficult to detect fake accounts on many Twitters social media accounts, so the application of the deep learning model with the convolutional neural network (CNN) algorithm and the application of deep learning can help detect fake accounts. This study will use data on Twitter social media so that this research produces good accuracy for the scenarios described at the methodology stage. This research produces an accuracy of 86% for the deep learning model with the CNN algorithm, and with the traditional model, it produces an accuracy of 51% while the use of transfer learning produces an accuracy of 93.9%.</span>

Tool Condition Monitoring for milling process using Convolutional Neural Networks

Tool condition monitoring has emerged as an essential approach in the machining industry to drive cost reductions and enhance productivity. In the era of Industry 4.0, tool wear monitoring can be done using various Internet of Things sensors, such as those recording acoustic emission, vibration, temperature, and pressure. Signals acquired can be processed by extracting numeric features or converting them into images.In this study, experimental milling campaigns with different input machine parameters were conducted, leading to the creation of a dataset based on tool wear measurements and acoustic emission registrations. Then we investigated a deep convolutional neural networks approach to classify image representations of the registered acoustic signals, aiming to predict the status of tool wear. Since the dataset was imbalanced, a class weights approach was used to improve model performance. An accuracy exceeding 90% was reached, demonstrating a great potential of the model in predicting tool wear state.

Illegal Microdumps Detection in Multi-Mission Satellite Images With Deep Neural Network and Transfer Learning Approach

Illegal Microdumps Detection in Multi-Mission Satellite Images With Deep Neural Network and Transfer Learning Approach

Advancing soil erosion prediction in Wadi Sahel-Soummam watershed Algeria: A comparative analysis of deep neural networks (DNN) and convolutional neural networks (CNN) models integrated with GIS

This study employs adaptive deep learning (utilizing DNN and CNN approaches) to accurately predict soil erosion, a crucial aspect of sustainable soil resource management. The goal is to develop fuzzy logic models for erosion forecasting in a large watershed with limited in-puts, comparing them to predictions from the Revised Universal Soil Loss Equation (RUSLE). Integration of GIS enables analysis of satellite data, providing crucial details like land use, slope, rainfall distribution, and flow direction. This synergistic approach enhances erosion prediction capabilities and yields spatial erosion distributions. Producing precise erosion risk maps within GIS is crucial for prioritizing high-risk areas and implementing effective conservation methods in the Wadi Sahel watershed, Algeria. The assessment in the Oued Sahel-Soummam watershed involved overlaying five RUSLE factor maps using Arc GIS spatial analysis, resulting in an aver-age annual soil loss of 4.22 tons per hectare. The DNN and CNN models were integrated with GIS for detailed calculation of annual average soil loss (tons per hectare per year) and mapping erosion risk areas in Wadi Sahel-Soummam watershed. Using the CNN model, estimated annual soil loss in Sahel-Soummam wadi was about 4.00 tons per hectare per year, while the DNN model estimated around 4.13 tons per hectare per year. This study employed two deep learning models for erosion prediction, with the DNN model featuring six hidden layers performing no-tably better than the compared CNN model.

Cross-Domain Collaborative Filtering: A Deep Neural Network Approach for Accurate and Diverse Recommendations

The rapid proliferation of data and the intricate nature of user behavior in the online realm have presented new hurdles for recommendation systems, which aim to suggest pertinent items to users. Among the notable challenges, ensuring the provision of accurate recommendations across diverse domains stands out. This research paper proposes an innovative solution to tackle this challenge by developing a cross-domain recommendation system that leverages the collaborative filtering technique to generate precise and varied recommendations spanning multiple domains. The system gathers user behavior and item attribute data from various domains and employs a collaborative filtering algorithm integrated with a deep neural network. The model makes use of a Neural Network with CReLU activation along with the embeddings of the user and the item followed by concealed layers. The output layer employs tanh activation to guarantee that recommendations fall within [-1, 1]. Adam optimizer, MSE loss, and an accuracy metric are utilised in training. This architecture captures user-item interactions effectively, resulting in precise personalised recommendations resulting an accuracy of 96.5% - 97.5%. To validate the effectiveness of the model, extensive evaluations are conducted on real-world datasets encompassing movies and books. The results demonstrate that the proposed system outperforms state-of-the-art recommendation systems in terms of accuracy, as measured by precision(89%), recall(90%), and AUC(0.74) scores. Furthermore, the system exhibits robust performance even in scenarios characterized by cold-start problems and data sparsity.

Battery State of Charge and State of Health Estimation Using a New Hybrid Deep Neural Network Approach

Battery State of Charge and State of Health Estimation Using a New Hybrid Deep Neural Network Approach

A Hybrid Deep Neural Network Approach to Recognize Driving Fatigue Based on EEG Signals

A Hybrid Deep Neural Network Approach to Recognize Driving Fatigue Based on EEG Signals

A Convolutional Deep Neural Network Approach for miRNA Clustering

A Convolutional Deep Neural Network Approach for miRNA Clustering

A Deep Neural Network Approach for Classification of GNSS Interference and Jammer

A Deep Neural Network Approach for Classification of GNSS Interference and Jammer

Data-driven prediction of indoor airflow distribution in naturally ventilated residential buildings using combined CFD simulation and machine learning (ML) approach

Predicting indoor airflow distribution in multi-storey residential buildings is essential for designing energy-efficient natural ventilation systems. The indoor environment significantly impacts human health and well-being, considering the substantial time spent indoors and the potential health and safety risks faced daily. To ensure occupants’ thermal comfort and indoor air quality, airflow simulations in the built environment must be efficient and precise. This study proposes a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we investigate the viability of employing a Deep Neural Network (DNN) model for accurately forecasting indoor airflow dispersion. The quantitative results reveal the DNN’s ability to faithfully reproduce indoor airflow patterns and temperature distributions. Furthermore, DNN approaches to investigate indoor airflow in the residential building achieved an 80% reduction in the time required to anticipate testing scenarios compared with CFD simulation, underscoring the potential for efficient indoor airflow prediction. This research underscores the feasibility and effectiveness of a data-driven approach, enabling swift and accurate indoor airflow predictions in naturally ventilated residential buildings. Such predictive models hold significant promise for optimizing indoor air quality, thermal comfort, and energy efficiency, thereby contributing to sustainable building design and operation.

Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications.

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample

Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.

Physics-informed neural network reconciles Australian displacements and tectonic stresses

Stress orientation information is invaluable to evaluate active tectonic forces within the Earth’s crust. The global dataset provided by the World Stress Map offers a rich resource of stress indicators, facilitating the calibration of mechanical models to extract complete stress and displacement fields. However, traditional inversion processes are hampered by the manual tuning of geomechanical properties and boundary conditions to reconcile simulations with observations. In this study, we introduce ML-SEISMIC (machine learning for stress estimation integrating satellite image and computational modelling), a physics-informed deep neural network approach to autonomously align stress orientation data with an elastic model. It nearly completely bypasses the need for explicit boundary condition inputs and yields comprehensive distributions of material properties, displacements, and stress tensors. Application of this methodology to Australia, coupled with precise global navigation satellite systems observations, unveils a robust and scale-independent interpolation framework. Additionally, it pinpoints regions where stress orientation reinterpretation is warranted. Our results present a streamlined yet powerful process, offering a substantial leap forward in geodynamic investigations. This approach promises to unify velocity and stress orientation observations with physical models, ushering in a new era of insights into Earth’s dynamic processes.

Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living: A 3D Vision Approach.

Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable for everyday environments. We explore various configurations, including multi-camera setups placed at different distances and angles, as well as performing daily activities in different directions. An integral component of our study involves combining gait analysis with the monitoring of activities of daily living (ADLs), given the paramount relevance of this integration in the context of Ambient Assisted Living. To achieve this, we investigate cutting-edge Deep Neural Network approaches, such as the Temporal Convolutional Network, Gated Recurrent Unit, and Long Short-Term Memory Autoencoder. Additionally, we scrutinize different data representation formats, including Euclidean-based representations, angular adjacency matrices, and rotation matrices. Our system's performance evaluation leverages both publicly available datasets and data we collected ourselves while accounting for individual variations and environmental factors. The results underscore the effectiveness of our proposed configurations in accurately classifying abnormal gait, thus shedding light on the optimal setup for non-invasive and efficient data collection.

Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning

In the rapidly advancing field of biomedical engineering, effective real-time control of artificial limbs is a pressing research concern. Addressing this, the current study introduces a pioneering method for augmenting task recognition in prosthetic control systems, combining a ReliefF-based Deep Neural Networks (DNNs) approach. This paper has leveraged the MILimbEEG dataset, a comprehensive rich source collection of EEG signals, to calculate statistical features of Arithmetic Mean (AM), Standard Deviation (SD), and Skewness (S) across various motor activities. Supreme Feature Selection (SFS), of the adopted time-domain features, was performed using the ReliefF algorithm. The highest scored DNN-ReliefF developed model demonstrated remarkable performance, achieving accuracy, precision, and recall rates of 97.4 %, 97.3 %, and 97.4 %, respectively. In contrast, a traditional DNN model yielded accuracy, precision, and recall rates of 50.8 %, 51.1 %, and 50.8 %, highlighting the significant improvements made possible by incorporating SFS. This stark contrast underscores the transformative potential of incorporating ReliefF, situating the DNN-ReliefF model as a robust platform for forthcoming advancements in real-time prosthetic control systems.

Novel parameter-free and parametric same degree distribution-based dimensionality reduction algorithms for trustworthy data structure preserving

As an effective dimensionality reduction method, Same Degree Distribution (SDD) has been demonstrated to be able to maintain better data structure than other dimensionality reduction methods, including Principal Component Analysis (PCA), Multidimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Laplacian Eigen-maps (LE), Uniform Manifold Approximation and Projection (UMAP) and t-Stochastic Neighbour Embedding (t-SNE). In addition, SDD does not require tuning the number of neighbours or perplexity to scale the structure capturing performance. Instead, it requires tuning the degree of degree-distribution ranging in e certain interval. Hence, tuning the degree of degree-distribution makes SDD a less costly method than other methods that require tuning the number of neighbours or perplexity. Although these advantages, SDD is still an expensive method compared with parameter-free methods such as PCA and MDS. A parameter-free SDD is proposed based on standard SDD, with two main differences: 1) it does not require tuning the degree of degree-distribution in the entire range from 1 to 15, but only uses degree 1; and 2) it re-scales the pairwise distances in the range [0, 2] instead of range [0, 1]. A theoretical analysis is presented to prove the better performance of parameter-free SDD. In addition, the performances of the proposed parameter-free SDD and the standard SDD have been experimentally compared in terms of structure capturing and computational time. This paper also proposes a parametric version of SDD using a deep neural network approach to learn the mapping based on the samples of the original data and their corresponding embedded representations in a low dimensional space. Comparative experiments have been undertaken with SDD and other methods such as Isomap, t-SNE and UMAP to demonstrate the effectiveness of the proposed parametric SDD with several popular synthetic and real datasets such as Churn, SEER Breast Cancer, AVletters (LIPS Reading) and MNIST.

Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data

It is a difficult undertaking to reliably estimate global terrestrial evapotranspiration (ET) using the Visible Infrared Imaging Radiometer Suite (VIIRS) at high spatial and temporal scales. We employ deep neural networks (DNN) to enhance the estimation of terrestrial ET on a global scale using satellite data. We accomplish this by merging five algorithms that are process-based and that make use of VIIRS data. These include the Shuttleworth–Wallace dual-source ET method (SW), the Priestley–Taylor-based ET algorithm (PT-JPL), the MOD16 ET product algorithm (MOD16), the modified satellite-based Priestley–Taylor ET algorithm (MS-PT), and the simple hybrid ET algorithm (SIM). We used 278 eddy covariance (EC) tower sites from 2012 to 2022 to validate the DNN approach, comparing it to Bayesian model averaging (BMA), gradient boosting regression tree (GBRT) and random forest (RF). The validation results demonstrate that the DNN significantly improves the accuracy of daily ET estimates when compared to three other merging methods, resulting in the highest average determination coefficients (R2, 0.71), RMSE (21.9 W/m2) and Kling–Gupta efficiency (KGE, 0.83). Utilizing the DNN, we generated a VIIRS ET product with a 500 m spatial resolution for the years 2012–2020. The DNN method serves as a foundational approach in the development of a sustained and comprehensive global terrestrial ET dataset. The basis for characterizing and analyzing global hydrological dynamics and carbon cycling is provided by this dataset.

Machine learning identification of Pseudomonas aeruginosa strains from colony image data.

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual 'fingerprints' that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data.