Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data

Maxout neurons for deep convolutional and LSTM neural networks in speech recognition

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Prediction concept needs human-like thinking and ideas. ANN helps make a program for describing the solutions to the prediction problems. To gain accurate prediction, there is a need for deep thinking in the knowledge system discovered. Deep learning helps form such next-level type of ANN thinking. Deep learning models do not require humans for programming the problems. It can determine the prediction by itself by learning the dataset. This paper describes the prediction by considering sequential model for continuous accessing of data with the help of multi-layers. Deep learning is very helpful for predicting larger dataset. This paper proposes an algorithmic model known as a Comprehensive Deep Recurrent Artificial Neural Networks (CDRANN) by hybridizing LSTM (long short-term memory) with RNN (recurrent neural network) backtrack solver for better future stock prediction. This paper also explains the need for hybridizing evolutionary model CDRANN to create a novel change in future prediction.KeywordsCDRANN (Comprehensive Deep Recurrent Artificial Neural Networks)LSTM (long short-term memory)RNN (recurrent neural network)Stock marketPrediction

Over the past few decades, research has focused heavily on automatic speech recognition (ASR). Although ASR for a few languages is close to reality; ASR for the low resource languages like Malayalam is still in its infancy. Here, in this work, the authors discuss the experiment conducted on accented data of Malayalam speech using two approaches. One approach uses the spectral features for modeling using deep convolutional neural network and the other uses the influential features of speech signals for modeling using LSTM-RNN approach. The proposed methodology comprises three distinct stages; dataset preparation, feature extraction, classification, and hence the construction of deep learning models that recognize the accent-based spoken sentences in the Malayalam language. Mel-frequency cepstral coefficient (MFCC) algorithm, short-term Fourier transform (STFT), and mel spectrogram methodologies are used for feature engineering and hence the features that represent the speech signals are used for constructing the accented ASR system for the Malayalam language using long short-term memory (LSTM) a recurrent neural network (RNN). The spectrogram dataset has been constructed for the speech dataset and used for constructing the ASR model with deep convolutional neural network (DCNN). The result of the experiment shows that LSTM-based RNN outperforms DCNN for the proposed dataset that has been constructed for the experiment in the natural recording environment.

This study investigates the application of advanced deep learning models to forecast fossil energy prices, a critical factor influencing global economic stability. Unlike previous research, this study conducts a comparative analysis of Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN) models. The evaluation metrics employed include Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results reveal that recurrent architectures, particularly GRU, LSTM, and Bi-LSTM, consistently outperform feedforward and convolutional models, demonstrating superior ability to capture temporal dependencies and nonlinear dynamics in energy markets. In contrast, the RNN and DNN show relatively weaker generalization capabilities. Additionally, visualizations of actual versus predicted prices for each model further emphasize superior forecasting accuracy of recurrent models. The results highlight the potential of deep learning in enhancing investment and policy decisions. Additionally, the results provide significant implications for policymakers and investors by emphasizing the value of accurate energy price forecasting in mitigating market volatility, improving portfolio management, and supporting evidence-based energy policies.

Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as “smart farming”. Utilizing a variety of information and agricultural technologies, crops are observed for their general health and productivity. This requires monitoring the condition of field crops and looking at many other indicators. The goal of smart agriculture is to reduce the amount of money spent on agricultural inputs while keeping the quality of the final product constant. The Internet of Things (IoT) has made smart agriculture possible through data collection and storage techniques. For example, modern irrigation systems use effective sensor networks to collect field data for the best plant irrigation. Smart agriculture will become more susceptible to cyber-attacks as its reliance on the IoT ecosystem grows, because IoT networks have a large number of nodes but limited resources, which makes security a difficult issue. Hence, it is crucial to have an intrusion detection system (IDS) that can address such challenges. In this manuscript, an IoT-based privacy-preserving anomaly detection model for smart agriculture has been proposed. The motivation behind this work is twofold. Firstly, ensuring data privacy in IoT-based agriculture is of the utmost importance due to the large volumes of sensitive information collected by IoT devices, including on environmental conditions, crop health, and resource utilization data. Secondly, the timely detection of anomalies in smart agriculture systems is critical to enable proactive interventions, such as preventing crop damage, optimizing resource allocation, and ensuring sustainable farming practices. In this paper, we propose a privacy-encoding-based enhanced deep learning framework for the difficulty of data encryption and intrusion detection. In terms of data encoding, a novel method of a sparse capsule-auto encoder (SCAE) is proposed along with feature selection, feature mapping, and feature normalization. An SCAE is used to convert information into a new encrypted format in order to prevent deduction attacks. An attention-based gated recurrent unit neural network model is proposed to detect the intrusion. An AGRU is an advanced version of a GRU which is enhanced by an attention mechanism. In the results section, the proposed model is compared with existing deep learning models using two public datasets. Parameters such as recall, precision, accuracy, and F1-score are considered. The proposed model has accuracy, recall, precision, and F1-score of 99.9%, 99.7%, 99.9%, and 99.8%, respectively. The proposed method is compared using a variety of machine learning techniques such as the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

In this study, we investigate the effectiveness of various deep learning algorithms for customer segmentation in the banking sector, aiming to enhance targeted service delivery and customer experience. We employ a comprehensive pipeline encompassing data collection, preprocessing, feature selection, feature extraction, model development, and rigorous evaluation. Our dataset, derived from real-world banking customer profiles, was processed using normalization, encoding, and dimensionality reduction techniques. We implemented and compared eight models: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), CNN-LSTM Hybrid, Autoencoder-Based Network, and Deep Neural Network (DNN). Among them, the Autoencoder-Based model achieved the highest accuracy of 91.56%, outperforming others in terms of segmentation clarity and computational efficiency. These findings suggest that deep learning methods, particularly Autoencoder-Based architectures, offer robust solutions for real-time banking customer segmentation, enabling institutions to tailor products and services more effectively.

Lip reading, the process of interpreting speech by visually observing the movements of the lips, has emerged as a critical area of research with applications spanning communication aids for the hearing impaired, silent speech interfaces, and enhanced human-computer interaction. This paper reviews recent advancements in lip reading technologies, focusing on the integration of machine learning and computer vision techniques. We explore state-of-the-art methods including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models that have significantly improved the accuracy and robustness of lip reading systems. The study highlights the importance of large annotated datasets, such as LipNet and LRW, which have facilitated the training of deep learning models. Additionally, we examine multimodal approaches that combine visual information with audio signals to enhance performance, especially in noisy environments. Despite substantial progress, challenges remain in addressing speaker variability, low resolution, and real-time processing. Future research directions are discussed, emphasizing the need for more diverse datasets, improved model generalization, and real-world application testing. This comprehensive review underscores the potential of advanced lip reading technologies to revolutionize communication accessibility and human-computer interaction. This paper presents the method for Vision based Lip Reading system that uses convolutional neural network (CNN) with attention-based Long Short-Term Memory (LSTM). The dataset includes video clips pronouncing words sentence. The pretrained CNN is used for extracting features from pre- processed video frames which then are processed for learning temporal characteristics by LSTM. The SoftMax layer of architecture provides the result of lip reading. In the present work experiments are performed with two pre-trained models. The system provides 80% accuracy using Tensorflow and ensemble learning. Keywords— CNN; RNN; LSTM; Tensorflow; lip reading; deep learning

The escalating global freshwater shortage is driven by socio-economic development, changing consumption patterns, and systemic inefficiencies, with semi-arid regions like Dodoma, Tanzania, being especially vulnerable. Traditional statistical and regression-based models for predicting water deficits have proven insufficient in capturing the complex, nonlinear interactions among climatic, hydrological, and anthropogenic factors. To address this gap, this study proposes a deep learning-based predictive framework utilising advanced algorithms, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) to improve the forecasting of water deficits. Using a thirteen-year (13) dataset collected from the semi-arid climate region of Dodoma, encompassing meteorological, hydrological, and socioeconomic variables. The models were trained and evaluated using performance metrics such as Root Mean Square Error (RMSE) and R-squared (R²). The DNN model demonstrated superior performance with an RMSE of 0.049 and an R² of 1.000, significantly outperforming other models. LSTM, CNN, and RNN models showed moderate to weak predictive accuracy, particularly in handling long-term dependencies and extreme deficit events. The key finding of this study is that the DNN model provides highly reliable and accurate water deficit predictions, making it the most effective among the tested deep learning approaches. This result highlights the value of incorporating deep learning into water resource planning, especially in data-scarce, semi-arid regions. The study concludes that DNN-based models should be prioritised for operational deployment in early warning systems and decision-making platforms. Future work should explore hybrid architectures, hyperparameter tuning, and integration with real-time data sources to enhance robustness and applicability

The superiority of various Deep Neural Networks (DNN) models, such as Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Recurrent Neural Networks (RNN), has been proven in various real-world applications and has received much attention. However, different DNN models include various types of operations. For example, CNN models are usually composed of convolutional layers (Conv) and fully-connected layers (FC). Furthermore, some light CNN models such as MobileNet adopt depthwise separable convolution with depthwise convolution (DWC) and pointwise convolution (PWC) to compress the models. In addition to regular convolution, de-convolution (De-Conv) is also widely used in many GAN models. Moreover, many RNN models also employ long-short-term memory (LSTM) to control update of internal states and data. Such a high diversity of various DNN operations poses great design challenges in implementing reconfigurable Deep Learning (DL) accelerators, which can support various types of DNN operations. Most recent DL accelerators focus only on some DNN operations, which lacks computing flexibility. In this paper, by exploiting the sparsity in current DNN models, we design sparsity-aware DL hardware accelerators that can support efficient computation of various DNN operations, including Conv, DeConv, DWC, PWC, FC, and LSTM. Through reconfiguring dataflow and parallelizing different operations, the proposed designs not only improve system performance but also increase hardware utilization with a significant reduction of power consumption in memory accesses and arithmetic computations.

Historical information in the Canadian Maritime Judiciary increases with time because of the need to archive data to be utilized in case references and for later application when determining verdicts for similar cases. However, such data are typically stored in multiple systems, making its reachability technical. Utilizing technologies like deep learning and sentiment analysis provides chances to facilitate faster access to court records. Such practice enhances impartial verdicts, minimizing workloads for court employees, and decreases the time used in legal proceedings for claims during maritime contracts such as shipping disputes between parties. This paper seeks to develop a sentiment analysis framework that uses deep learning, distributed learning, and machine learning to improve access to statutes, laws, and cases used by maritime judges in making judgments to back their claims. The suggested approach uses deep learning models, including convolutional neural networks (CNNs), deep neural networks, long short-term memory (LSTM), and recurrent neural networks. It extracts court records having crucial sentiments or statements for maritime court verdicts. The suggested approach has been used successfully during sentiment analysis by emphasizing feature selection from a legal repository. The LSTM + CNN model has shown promising results in obtaining sentiments and records from multiple devices and sufficiently proposing practical guidance to judicial personnel regarding the regulations applicable to various situations.

With the increase in E-Commerce businesses in the last decade,the sentiment analysis of product reviews has gained a lot of attention in linguistic research. In literature, the survey depicts the majority of the research done emphasizes on mere polarity identification of the reviews. The proposed system emphasized on classifying the sentiment polarity and the product aspect identification from the reviews. Proposed work experimented with traditional machine learning techniques as well as deep neural networks such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory(LSTM) Networks. The proposed system gives a better understanding of these algorithms by comparing the outcomes. The Deep Learning approach in the proposed work successfully provides a mechanism which identifies the review polarity and intensity of the reviews and also analyses the short form words used by people in the reviews. The experimental results in this work, applied on amazon product dataset, shows that the LSTM model works the best for sentiment analysis and intensity of reviews with 93% accuracy. This research work also predicts polarity for short-form word reviews which is the common trend these days while writing the reviews.

This study assesses the effectiveness of deep learning models, specifically Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), in assessing synthetic grip strength data for stress ball and pencil grips. The aim is to determine the optimal model for grip strength classification and tackle issues such as temporal data and class imbalance. Synthetic data was created, preprocessed, and utilized to train the models on Google Colab employing TensorFlow and Keras. Hyperparameters, such as CNN kernel dimensions and RNN/LSTM sequence lengths, were refined. Performance was evaluated by accuracy, precision, F1 scores, and loss metrics. The RNN model surpassed both CNN and LSTM, with an accuracy of 94.99% and a precision of 0.56, particularly excelling in the classification of “Normal” grip strength instances. CNN and LSTM had difficulties with time dependencies and class imbalance, leading to reduced accuracy and increased loss. Although RNN has proven to be the most effective approach, subsequent research should concentrate on mitigating class imbalance and investigating hybrid models to improve performance.

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

While the growing accessibility of technology improves usability, it also creates more opportunities for cybercriminals to exploit vulnerabilities, significantly accelerating the proliferation of cybersecurity attacks. Deep learning (DL) approaches present significant advancements over conventional machine learning (ML) techniques by automating feature selection and extraction while minimizing external dependencies. This study proposes a deep learning-based model to enhance cyberattack detection and ensure that data security goals are achieved. This study employs a quantitative research design, utilizing simulation and modeling as the primary analytical tools. The dataset used is the Canadian Institute for Cybersecurity Intrusion Detection System (CIC-IDS-2017) dataset. Three distinct DL algorithms are used to design the detection models, namely, Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). The performance comparison metrics are F1-score, accuracy, sensitivity, false positive rate (FPR), specificity, positive predictive value (PPV), false negative rate (FNR), and negative predictive value (NPV). Optimization concepts are integrated to enhance the detection efficiency in web-based systems, including loss functions, gradient-based optimization, and efficient model generalization techniques. The results of k-fold cross-validation show LSTM’s higher scores for F1-score (94.6%), recall (94.7%), accuracy (94.8%), and precision (94.6%). LSTM outperformed RNN and DNN, achieving the highest accuracy, precision, specificity, and sensitivity at 94.7%, 94.3%, 98.9%, and 94.7% respectively, validating LSTM's superior generalizability for cyberattack detection tasks.