Hybrid Deep Learning Algorithm Research Articles

Toward Better Pedestrian Trajectory Predictions: The Role of Density and Time-to-Collision in Hybrid Deep-Learning Algorithms.

Predicting human trajectories poses a significant challenge due to the complex interplay of pedestrian behavior, which is influenced by environmental layout and interpersonal dynamics. This complexity is further compounded by variations in scene density. To address this, we introduce a novel dataset from the Festival of Lights in Lyon 2022, characterized by a wide range of densities (0.2-2.2 ped/m2). Our analysis demonstrates that density-based classification of data can significantly enhance the accuracy of predictive algorithms. We propose an innovative two-stage processing approach, surpassing current state-of-the-art methods in performance. Additionally, we utilize a collision-based error metric to better account for collisions in trajectory predictions. Our findings indicate that the effectiveness of this error metric is density-dependent, offering prediction insights. This study not only advances our understanding of human trajectory prediction in dense environments, but also presents a methodological framework for integrating density considerations into predictive modeling, thereby improving algorithmic performance and collision avoidance.

Automatic Classification of Stegano files using the Features of Natural Language Processing and Hybrid Deep Learning Algorithms

The corporate sector has adopted communication as its principal medium due to the improvements of the digital era. In the past, in-person meetings, purchases, settlements, business talks, and profile-taking were all done. However, everything has gone digital in the present period. Over the last several years, there has been a noticeable exponential rise in the amount of complicated texts and documents that require a deeper comprehension of machine learning techniques in order to recognize languages in a variety of applications. Outstanding results in the processing of natural languages have been obtained by a number of Artificial Intelligent techniques. A key component of machine learning and deep learning techniques' efficacy is their capacity to understand intricate models and non-linear connections in data. Choosing the right architecture, frameworks, and algorithms for classifying input, like text files, managing audio and video files, however, is a difficult undertaking. The proposed work's goal is to identify text, audio messages, chatbots, and smart records using natural language processing. Using a hybrid deep learning approach, text, voice, and video recordings are used as inputs to be classified. Problem Synopsis: With contact becoming more and more important to business, companies have created advanced NLP programs. Through chatbots, digital records, phone conversations, and messages, these NLP swiftly fulfil human desires. Customer preferences, desires, and demands have been found to be more strongly impacted by the ease of communication and connection. Today's service providers use chatbots, digital records, messaging apps, email, and phone conversations as their main methods of communication for almost all of their trade channels of preference, client queries, and transactions. Proposed Method: The study shows how input stegano file is processed automatically based on user reactions, text message replies, and audio record identification during communications using text content, voice messages, and audio using the features of Natural Language Processing and hybrid Deep Learning Algorithms

Application of the improved dung beetle optimizer, muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area, China

Due to the lack of accurate data and complex parameterization, the prediction of groundwater depth is a challenge for numerical models. Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas. In this study, two new models are applied to the prediction of groundwater depth in the Ningxia area, China. The two models combine the improved dung beetle optimizer (DBO) algorithm with two deep learning models: The Multi-head Attention–Convolution Neural Network–Long Short Term Memory networks (MH-CNN-LSTM) and the Multi-head Attention–Convolution Neural Network–Gated Recurrent Unit (MH-CNN-GRU). The models with DBO show better prediction performance, with larger R (correlation coefficient), RPD (residual prediction deviation), and lower RMSE (root-mean-square error). Compared with the models with the original DBO, the R and RPD of models with the improved DBO increase by over 1.5%, and the RMSE decreases by over 1.8%, indicating better prediction results. In addition, compared with the multiple linear regression model, a traditional statistical model, deep learning models have better prediction performance.摘要本研究将两个新模型应用于位于中国西北干旱半干旱区的宁夏地区地下水深度预测. 这两个模型将改进的蜣螂优化 (DBO) 算法与两个深度学习模型相结合, 即多头注意力-卷积神经网络-长短期记忆网络和多头注意力-回旋神经网络-门控递归单元. 带有DBO的模型预测结果表现出更大的相关系数 (R) , 残差预测偏差 (RPD) 和较低的均方根误差 (RMSE) , 预测结果更好. 此外, 与DBO模型相比, 改进后的DBO模型的R和RPD增加了1.5%以上, RMSE降低了1.8%以上, 表明预测结果更好. 与传统的统计模型多元线性回归模型相比, 深度学习模型具有更好的预测性能.

Automatic Classification of Stegano files using the features of Natural Language Processing and hybrid Deep Learning Algorithms

The corporate sector has adopted communication as its principal medium due to the improvements of the digital era. In the past, in-person meetings, purchases, settlements, business talks, and profile-taking were all done. However, everything has gone digital in the present period. Over the last several years, there has been a noticeable exponential rise in the amount of complicated texts and documents that require a deeper comprehension of machine learning techniques in order to recognize languages in a variety of applications. Outstanding results in the processing of natural languages have been obtained by a number of Artificial Intelligent techniques. A key component of machine learning and deep learning techniques' efficacy is their capacity to understand intricate models and non-linear connections in data. Choosing the right architecture, frameworks, and algorithms for classifying input, like text files, managing audio and video files, however, is a difficult undertaking. The proposed work's goal is to identify text, audio messages, chatbots, and smart records using natural language processing. Using a hybrid deep learning approach, text, voice, and video recordings are used as inputs to be classified. Problem Synopsis: With contact becoming more and more important to business, companies have created advanced NLP programs. Through chatbots, digital records, phone conversations, and messages, these NLP swiftly fulfil human desires. Customer preferences, desires, and demands have been found to be more strongly impacted by the ease of communication and connection. Today's service providers use chatbots, digital records, messaging apps, email, and phone conversations as their main methods of communication for almost all of their trade channels of preference, client queries, and transactions. Proposed Method: The study shows how input stegano file is processed automatically based on user reactions, text message replies, and audio record identification during communications using text content, voice messages, and audio using the features of Natural Language Processing and hybrid Deep Learning Algorithms

An intelligent credit card fraudulent activity detection using hybrid deep learning algorithm

An intelligent credit card fraudulent activity detection using hybrid deep learning algorithm

Novel cloud-AIoT fault diagnosis for industrial diesel generators based hybrid deep learning CNN-BGRU algorithm

The industrial diesel generator is essential to provide electric power during emergencies and requires accurate failure diagnosis in preventative maintenance services. This research presents a novel AIoT system-based hybrid deep learning algorithm for online monitoring of the anomaly conditions of 125 kW/250 kW industrial generators. Firstly, the IoT modules with different precise sensors are developed to be installed on 125 kW and 250 kW industrial generators to simulate other faults conditions in the laboratory. Then, the supervised learning operations are deployed to train deep learning algorithms with collected labeled data. The proposed algorithm, a hybrid convolution neural network bidirectional gate recurrent unit (CNN-BGRU), is deployed to extract deep features from long sequential historical signals and classify the anomaly conditions. The experiments in different 10 s, 20 s, and 30 s sampling frequencies are performed to evaluate and analyze with various benchmarks, which demonstrate efficiencies of the proposed CNN-BGRU method with other traditional deep learning algorithms, including RNN, CNN, GRU, LSTM, BRNN, and BGRU methods. The proposed hybrid method accomplishes the supreme augmentation of 29.43 % loss, 36.55 % validating loss, 25.86 % MAE, 27.00 % validating MAE, 16.78 % MSE, 20.64 % validating MSE, 6.36 % Pre, 25.34 % validating Pre, 17.30 % Rec, and 26.91 % validating Rec compared with state-of-the-art approaches. The AIoT system-based hybrid CNN-BGRU algorithm provides better improvement and higher accuracy in fault diagnosis of firefighting pumps for maintenance service in Industry 4.0.

IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm

The most significant threat that networks established in IoT may encounter is cyber attacks. The most commonly encountered attacks among these threats are DDoS attacks. After attacks, the communication traffic of the network can be disrupted, and the energy of sensor nodes can quickly deplete. Therefore, the detection of occurring attacks is of great importance. Considering numerous sensor nodes in the established network, analyzing the network traffic data through traditional methods can become impossible. Analyzing this network traffic in a big data environment is necessary. This study aims to analyze the obtained network traffic dataset in a big data environment and detect attacks in the network using a deep learning algorithm. This study is conducted using PySpark with Apache Spark in the Google Colaboratory (Colab) environment. Keras and Scikit-Learn libraries are utilized in the study. ‘CICIoT2023’ and ‘TON_IoT’ datasets are used for training and testing the model. The features in the datasets are reduced using the correlation method, ensuring the inclusion of significant features in the tests. A hybrid deep learning algorithm is designed using one-dimensional CNN and LSTM. The developed method was compared with ten machine learning and deep learning algorithms. The model’s performance was evaluated using accuracy, precision, recall, and F1 parameters. Following the study, an accuracy rate of 99.995% for binary classification and 99.96% for multiclassification is achieved in the ‘CICIoT2023’ dataset. In the ‘TON_IoT’ dataset, a binary classification success rate of 98.75% is reached.

Health monitoring of an assembly-type structure using raw time-domain EMI signals and a novel hybrid deep learning algorithm

Deep learning models are widely used to extract features from data through supervised or unsupervised methods. However, when these two methods are used separately, their generalization ability is insufficient. In this study, a structural health monitoring technology based on a novel hybrid deep learning (HDL) model and time-domain electromechanical impedance (TEMI) has been proposed. The HDL is a highly efficient combination of deep auto-encoder (DAE) and two-dimensional convolutional neural network (2D CNN). The DAE is used for unsupervised reconstruction of the high-level features of 1D TEMI signals, and then, the features extracted by DAE are arranged and fed into a supervised 2D CNN that is designed to learn the optimal feature representations of the multi-label signals. This increases effectively the model’s learnability and generalizability. The robustness of the developed approach has been tested using TEMI signals that are collected from a scaled model of a rectangular pipe gallery subjected to transversal joint damage. Results indicate that the maximal identification errors of joint damage severity are 2.4% and 9.33% when the corresponding structural conditions are used and unused by the training process of HDL, respectively.

A Hybrid Deep Learning Algorithm for Tropospheric Zenith Wet Delay Modeling with the Spatiotemporal Variation Considered

The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotemporal characteristics and influencing factors of ZWD. The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined in the proposed algorithm to make a novel architecture, namely, the hybrid CNN-LSTM (CL) algorithm, combining CNN for local spatial feature extracting and LSTM for complex sequence dependency training. Data from 46 radiosonde sites in South America spanning from 2015 to 2021 are used to develop models of ZWD under three strategies, i.e., model CL-A without surface parameters, model CL-B with surface temperature, and model CL-C introducing surface temperature and water vapor pressure. The modeling accuracy of the proposed models is validated using the data from 46 radiosonde sites in 2022. The results indicate that CL-A demonstrates slightly better accuracy compared to the Global Pressure and Temperature 3 (GPT3) model; CL-B shows a precision increase of 14% compared to the Saastamoinen model, and CL-C exhibits accuracy improvements of 30% and 12% compared to the Saastamoinen and Askne and Nordius (AN) model, respectively. Evaluating the models’ generalization capabilities at non-modeled sites in South America, data from six sites in 2022 were used. CL-A shows overall better performance compared to the GPT3 model; CL-B’s accuracy is 19% better than the Saastamoinen model, and CL-C’s accuracy is enhanced by 33% and 10% compared to the Saastamoinen and AN model, respectively. Additionally, the proposed hybrid algorithm demonstrates a certain degree of improvement in both modeling accuracy and generalization accuracy for the South American region compared to individual CNN and LSTM algorithm.

An ensemble neural network model for predicting the energy utility in individual houses

With the rapid increase in the world's human population and the advancement of technology, energy utilization has substantially increased. To sustain a stable stream of energy, it is vital to anticipate power utilization beforehand. This issue makes predicting short-term electrical time series use difficult. In this research, the user proposed a neural network with a deep learning prototype that performs energy prediction by combining. Long Short-Term Memory (LSTM) and Feed Forward Neural Networks (FFNN). In order to enhance the performance, the ensemble FFNN-LSTM network and the Stationary Wavelet Transform (SWT) method are combined to create a hybrid deep learning algorithm. The SWT's reduction of instability and expansion of data dimensionality may improve the FFNN-LSTM forecasting accuracy. The suggested technique's predicting effectiveness is even further improved by the ensemble Long short-term neural network. On the basis of a real-world household energy usage database gathered by the "UK-DALE" project, validation studies were carried out. Researchers determined that the LSTM-FFNN based SWT produced a better result in terms of accuracy and energy utilization than other existing results after contrasting the experimental findings to the baseline, which is the conventional LSTM and Moving Average (MA) based upon the Root Mean Squared Error (RMSE) score.

BiLSTM-ANN: early diagnosis of Alzheimer’s disease using hybrid deep learning algorithms

BiLSTM-ANN: early diagnosis of Alzheimer’s disease using hybrid deep learning algorithms

Early Detection of Stress and Anxiety Based Seizures in Position Data Augmented EEG Signal Using Hybrid Deep Learning Algorithms

Early Detection of Stress and Anxiety Based Seizures in Position Data Augmented EEG Signal Using Hybrid Deep Learning Algorithms

A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm

A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed level

A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm

A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed level

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection–diffusion–reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms.

Enhancing the accuracy of wind speed estimation model using an efficient hybrid deep learning algorithm

Estimation of wind speed holds great significance for efficient operation of wind farms and grid stability. Randomness of wind speed poses a great challenge for accurate estimation of wind speed. Hence for addressing this problem, a convolutional neural network (CNN) and long short-term memory (LSTM) based novel hybrid deep learning (DL) short-term is proposed. The prediction model is built using pre-processed wind speed dataset from Jaisalmer district, Rajasthan. For designing the proposed model, the samples of the dataset are bifurcated into four seasons such as autumn, summer, winter and spring and the prediction performance are evaluated for each season separately. The proposed hybrid algorithm (CNN-LSTM) is implemented and a comparison is made with different DL algorithms. The predictive performance are calculated by observing the statistical metrics such as root mean squared error (RMSE), mean absolute error (MAE), relative RMSE, normalized RMSE and coefficient of determination (R2). It is observed that the value of statistical metrics such as MAE, RMSE, relative RMSE and normalized RMSE of the proposed algorithm are lower than the other DL models for all the four seasons. The higher value of R2 obtained by the CNN-LSTM indicates the model is best fitted with the dataset. Conclusively, the proposed model demonstrates improved adaptability and higher prediction accuracy in comparison to the predictive skills of models utilizing the same dataset.

Machine learning algorithm for soybean leaf disease detection

Agriculture is now the primary source of income for nearly 45% of India's population. This makes it important to aid farmers with crop cultivation, protection, and harvesting. Soybean is one of the most widely grown plants in India, especially in the states of Madhya Pradesh and Maharashtra. But soybean crops are prone to diseases like yellow mosaicand anthracnose, amongst others. We propose hybrid deep learning algorithm to detect diseases in soybean plant leavesusing computer vision techniques. In the experimentation, various classification algorithms were compared using accuracy.

Ethnicity Classification: Discriminating Between Iranian and Asian Populations Using Hybrid Deep Learning Algorithm

The rapid advancement of deep learning techniques has opened new avenues for solving complex classification problems in various domains. This research explores the application of Convolutional Neural Networks (CNN), specifically the VGG16 architecture, in conjunction with Support Vector Machines (SVM) for the classification of ethnicity using Iranian and Asian facial images. The classification of ethnicity based on facial features is a challenging task due to the subtle and complex variations within and between different ethnic groups. The proposed methodology involves a two-step process. First, the VGG16 CNN model is utilized to extract high-level features from the facial images. The pre-trained VGG16 model, known for its depth and representational power, is fine-tuned on the dataset to capture relevant ethnic features. The extracted features are then fed into an SVM classifier, which is trained to distinguish between Iranian and Asian facial characteristics. A comprehensive dataset consisting of labeled Iranian and Asian facial images is compiled and preprocessed for training and evaluation. The model's performance is assessed using metrics such as accuracy. Various experiments are conducted to optimize hyperparameters and validate the generalization capability of the proposed model. Additionally, visualization techniques are employed to provide insights into the features learned by the CNN and the decision boundaries established by the SVM. The results indicate that the combined approach of CNN-VGG16 and SVM yields promising accuracy in the classification of ethnicity based on facial images.

Stress Level Based Emotion Classification Using Hybrid Deep Learning Algorithm

Stress Level Based Emotion Classification Using Hybrid Deep Learning Algorithm

Sustainable Environmental Design Using Green IOT with Hybrid Deep Learning and Building Algorithm for Smart City

Sustainable Environmental Design Using Green IOT with Hybrid Deep Learning and Building Algorithm for Smart City