Convolutional Recurrent Neural Network Research Articles

Applications of deep learning in mathematics education: A review

Traditional classroom settings often fail to account for individual differences, leading to disparities in student performance. Deep learning models offer a solution by analyzing performance data to provide targeted interventions and immediate feedback, thereby personalizing educational experiences. This review examines the transformative potential of deep learning in mathematics education through various applications and methodologies. Studies show that deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can analyze complex student data, identify learning patterns, and offer real-time, personalized support. Notable applications include automated classroom feedback systems, intelligent tutoring systems, and predictive models for student performance. Despite the promising results, challenges such as data quality, computational resources, model interpretability, and potential biases remain. Future research should focus on creating inclusive datasets, improving computational efficiency, and enhancing model transparency to fully harness the benefits of deep learning in educational settings.

Using acoustic emission technique for structural health monitoring of laminate composite: A novel CNN-LSTM framework

This research has developed a real-time end-to-end deep learning model for structural health monitoring (SHM) method for composite impact damage diagnosis based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The acoustic emission (AE) signals collected under low-velocity impacts by means of piezoelectric sensors on composite materials are used for training deep learning networks. Based on the impact load curves, specimens are categorized into minor failure, intermediate failure, and severe failure. The convolved signals are segmented and reconstructed at a given length for the following LSTM module. The average accuracies for basic CNN, CNN– Recurrent Neural Network (RNN), CNN-LSTM, and CNN– Gated Recurrent Unit (GRU) are respectively 88.7 %, 92.6 %, 98 %, and 95.4 %. A sensitivity analysis on sub-signal length was conducted on the CNN-LSTM model, revealing that the model achieved its best performance when the sub-signal length was set at 16. The model attained prediction accuracies of 97.4 %, 100 %, and 100 %, respectively, for minor failure, intermediate failure, and severe failure cases.

An improved convolutional recurrent neural network for stock price forecasting

<p>Stock price forecasting is a challenging area of research, particularly due to the complexity and unpredictability of financial markets. The accuracy of prediction models is influenced by various factors, including nonlinearity, seasonality, and economic shocks. Deep learning has demonstrated better forecasts of stock prices than traditional approaches. This study, therefore, proposed a new approach to improve forecasting system based on an end-to-end convolutional recurrent neural network (CRNN) with attention mechanism. Our approach first investigates local stock price features using 1D convolutional neural network, and then employs a bidirectional long short-term memory (Bi-LSTM) network for forecasting. This model stands out by effectively utilizing contextual data and representing the temporal character of data. The Bi-LSTM is helpful for understanding the history and future contextual information since it uncovers both past and future contexts of stock data. Furthermore, integrating attention mechanism within the CRNN represents a significant improvement. This allows our model to handle long input sequences more effectively and capture the inherent stochasticity in stock prices, which is often missed by traditional models. The effectiveness of our approach is investigated using data on 10 stock indexes from Yahoo Finance. The results show that our method outperforms ARIMA, LSTM, and conventional methods.</p><p> </p>

Radar-Based Fall Detection: A Survey.

Fall detection, particularly critical for high-risk demographics like the elderly, is a key public health concern where timely detection can greatly minimize harm. With the advancements in radio frequency technology, radar has emerged as a powerful tool for human detection and tracking. Traditional machine learning algorithms, such as Support Vector Machines (SVM) and k-Nearest Neighbors (kNN), have shown promising outcomes. However, deep learning approaches, notably Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have outperformed in learning intricate features and managing large, unstructured datasets. This survey offers an in-depth analysis of radar-based fall detection, with emphasis on Micro-Doppler, Range-Doppler, and Range-Doppler-Angles techniques. We discuss the intricacies and challenges in fall detection and emphasize the necessity for a clear definition of falls and appropriate detection criteria, informed by diverse influencing factors. We present an overview of radar signal processing principles and the underlying technology of radar-based fall detection, providing an accessible insight into machine learning and deep learning algorithms. After examining 74 research articles on radar-based fall detection published since 2000, we aim to bridge current research gaps and underscore the potential future research strategies, emphasizing the real-world applications possibility and the unexplored potential of deep learning in improving radar-based fall detection.

Efficient and novel multidomain feature analysis model with incremental optimizations for enhancing pediatric myelodysplastic syndrome detection

Pediatric myelodysplastic syndromes (MDS) are complicated, thus early and accurate diagnosis is essential for treatment planning and patient care. Diagnostic processes often use discrete data domain analysis, which reduces accuracy and delays diagnosis. This work addresses these limitations by introducing an advanced Multi domain Feature Analysis Model (MFAM) enhanced with incremental optimizations to improve pediatric MDS detection. Traditional pediatric MDS diagnosis relies on subjective evaluations and limited data fusion, not modern computational methods. These constraints may reduce diagnosis accuracy and postpone action. The proposed MFAM integrates data from Clinical History, Physical Examination, Blood Cell Counts, Peripheral Blood Smear, Bone Marrow Aspiration and Biopsy, Cytogenetic Analysis, Flow Cytometry, Genetic Testing, Iron Studies, and Bone Marrow Cytology to overcome these challenges. The MFAM increases feature variance by fusing Bidirectional Long Short-Term Memory (BiLSTM) with Bidirectional Gated Recurrent Units (BiGRU). Deep Q Learning with Graph Recurrent Convolutional Neural Networks (DQGRCNN) boosts efficiency. Additionally, the model integrates the Vector Autoregressive Moving Average with Exogenous Inputs (VARMAX) to facilitate early prediction of paediatric MDS. These enhancements have resulted in significant improvements in the precision of paediatric MDS detection by 4.5%, accuracy by 3.5%, recall by 2.3%, Area Under the Curve (AUC) by 1.5%, and specificity by 2.4% while reducing diagnostic delays by 8.5%. Furthermore, the model enhances the precision of predictive analysis by 2.9%, accuracy by 3.5%, recall by 2.5%, AUC by 2.9%, specificity by 5.5%, and reduces delays in predictive analysis by 8.5%. The MFAM presented in this paper revolutionizes the diagnosis and treatment of paediatric MDS by efficiently combining diverse diagnostic data, employing advanced transformation and fusion techniques, and optimizing responses through DQGRCNN. The integration of VARMAX further enables early prediction of the disease. MFAM will enhance diagnostic precision, therapy start, and clinical outcomes for young MDS patients.

Portfolio Prediction using Deep Learning

This research investigates the use of Long Short-Term Memory (LSTM) networks for predicting stock prices and optimizing investment portfolios. Utilizing a dataset comprising the top 30 U.S. companies by market capitalization from 2009-12-31 to 2021-12-31, excluding AbbVie, Meta, and Tesla due to their later market listings, we demonstrate that LSTM models outperform conventional Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) in generating accuracy and portfolio returns. By efficiently capturing long-term dependencies within stock price data, LSTMs offer more reliable predictions, which are crucial for optimizing investment portfolios. Our methodology involves data collection and preprocessing, model building using LSTM architecture, and evaluating performance using metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean percentage error (MPE). The results indicate that LSTM models not only enhance prediction accuracy but also improve portfolio returns compared to equally weighted and market capitalization weighted portfolios. This research provides significant insights into the benefits of using advanced deep learning techniques like LSTMs for financial market predictions and portfolio management.

Intelligent void identification of particle packing system of caved ore and rock

Analyzing the void structure of the packing system of caved ore and rock and establishing the causal relationship between void structure and macroscopic properties are critical research areas in the field of gravity flow. However, the current method of analyzing void structure primarily involves manual threshold segmentation, leading to low analysis efficiency and the need for improved accuracy. This study introduces a novel approach using a dataset of two-dimensional Computed Tomography (CT) slices of an irregular limestone particle packing system. The primary goal is to enhance existing algorithms by focusing on data and network aspects to address the challenges in identifying this type of image dataset. As a result, a model named Data Attention gate with Recurrent Residual convolutional neural network based on U-net (DA2RU-net) is developed and evaluated for its convergence, accuracy, and robustness. The findings indicate the following: (1) To identify voids in the packing system of caved ore and rock using CT slices, it is advisable to employ 1000 images with dimensions between 300 and 400 pixels. It is essential to ensure that both large and small voids are represented in the image dataset. (2) The DA2RU-net model demonstrates superior performance with an average Dice value of 0.9859 for identifying large voids and 0.9654 for identifying small voids, surpassing other iterations of the U-net model and traditional algorithms. (3) The DA2RU-net model shows robustness to variations in brightness levels.

Degradation-aware deep unfolding network with transformer prior for video compressive imaging

In video snapshot compressive imaging (SCI) systems, video reconstruction methods are used to recover spatial–temporal-correlated video frame signals from a compressed measurement. While unfolding methods have demonstrated promising performance, they encounter two challenges: (1) They lack the ability to estimate degradation patterns and the degree of ill-posedness from video SCI, which hampers guiding and supervising the iterative learning process. (2) The prevailing reliance on 3D-CNNs in these methods limits their capacity to capture long-range dependencies. To address these concerns, this paper introduces the Degradation-Aware Deep Unfolding Network (DADUN). DADUN leverages estimated priors from compressed frames and the physical mask to guide and control each iteration. We also develop a novel Bidirectional Propagation Convolutional Recurrent Neural Network (BiP-CRNN) that simultaneously captures both intra-frame contents and inter-frame dependencies. By plugging BiP-CRNN into DADUN, we establish a novel end-to-end (E2E) and data-dependent deep unfolding method, DADUN with transformer prior (TP), for video sequence reconstruction. Experimental results on various video sequences show the effectiveness of our proposed approach, which is also robust to random masks and has wide generalization bounds.

Advanced Earthquake Magnitude Prediction Using Regression and Convolutional Recurrent Neural Networks

Earthquake magnitude prediction is critical in seismology, with significant implications for disaster risk management and mitigation. This study presents a novel earthquake magnitude prediction model by integrating regression analysis with Convolutional Recurrent Neural Networks (CRNNs). It utilises Convolutional Neural Networks (CNNs) for spatial feature extraction from 2-dimensional seismic signal images and Long Short-Term Memory (LSTM) networks to capture temporal dependencies. The innovative model architecture incorporates residual connections and specialised regression techniques for sequential data. Validated against a comprehensive seismic dataset, the model achieves a Mean Squared Error (MSE) of 0.1909 and a Root Mean Squared Error (RMSE) of 0.4369, with a coefficient of determination of 0.79772. These metrics, alongside a correlation coefficient of 0.8980, demonstrate the model's accuracy and consistency in predicting earthquake magnitudes, establishing its potential for enhancing seismic risk assessment and informing early warning systems.

Damage detection and classification of carbon fiber-reinforced polymer composite materials based on acoustic emission and convolutional recurrent neural network

Carbon fiber-reinforced polymer (CFRP) composite laminates generate acoustic emission signals during the tensile process, which can be used to identify the type of acoustic emission (AE) signal at a given moment and determine the current damage condition. However, due to the large number and complexity of AE signals generated during the tensile process of carbon fiber composite laminates, it is challenging to manually identify and annotate them. To address this issue, we propose a low-complexity classification and detection network model based on the convolutional recurrent neural network (CRNN) architecture. First, we construct a dataset of composite damage signals for a single damage category and use log-mel spectrogram features to train the model for that specific category of damage signals. Then, we utilize the trained model to recognize the AE signals of CFRP composite laminates. The proposed method achieves an accuracy of 99.02% in classifying single damage modes in the test set and effectively distinguishes the types of AE signals generated during the damage process of CFRP composite laminates. Compared with the classic classification model using AE signals, the number of parameters in our proposed low-complexity CRNN model is reduced by 94.42%. It also demonstrates that 19.4% of the AE signals during the damage process are in a superimposed state, indicating the simultaneous occurrence of multiple damage modes in CFRP composite laminates.

Methodology for assessing the impact of emergencies on the spread of infectious diseases

The spread of infectious diseases is significantly influenced by emergencies, particularly military conflicts, which disrupt healthcare systems and increase the risks of epidemics. The full-scale Russian invasion of Ukraine has exacerbated these challenges, causing environmental damage, mass displacement, and the breakdown of healthcare services, all of which contribute to the spread of infectious diseases. This study aims to develop a comprehensive methodology for assessing the impact of emergencies on the spread of infectious diseases, focusing on the full-scale invasion of Ukraine. The object of this study is to address epidemic threats posed by emergencies, particularly the increased spread of infectious diseases due to war-related disruptions. The subject of this study is methods and models of infectious disease transmission under conditions of emergencies, emphasizing the Russian full-scale invasion of Ukraine. The tasks of this study are to provide an analysis of the current state of research and develop a methodology for assessing the impact of emergencies on the spread of infectious diseases. The proposed methodology includes several key components. Comprehensive data from public health organizations includes infectious disease statistics, demographic shifts, healthcare disruptions, and environmental factors exacerbated by emergencies. Data preprocessing removes inconsistencies, standardization of formats, and normalization for population size differences. Machine learning models, including convolutional neural networks and recurrent neural networks, have been developed to simulate the spread of diseases based on demographic, environmental, and healthcare-related variables. Deep learning models analyze spatial and temporal patterns, whereas compartmental models such as SIR estimate changes in reproductive numbers (R₀ and Re). Additionally, models of excess mortality incorporate mixed effects to account for regional and time-based variations. The methodology incorporates real-time monitoring of epidemic threats using real-time data from multiple sources, enabling dynamic assessments of disease spread and facilitating predictive modeling. The models were trained on historical data and validated using cross-validation techniques to ensure robustness and reliability, with a specific focus on the pre- and post-invasion phases in Ukraine.Results: The study provides a comprehensive framework for collecting and processing data on infectious diseases and epidemic threats in emergencies. The proposed model introduces advanced machine learning and epidemiological models trained on pre- and post-invasion data to analyze disease transmission patterns and forecast future epidemic dynamics. Conclusion: The proposed methodology addresses current gaps in infectious disease during emergencies by integrating real-time data and machine learning techniques. This research improves decision-making in public health management and biosafety during crises, particularly in war-affected regions like Ukraine.

CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network

Deep learning (DL) has brought new perspectives and methods to automatic modulation recognition (AMR), enabling AMR systems to operate more efficiently and reliably in modern wireless communication environments through its powerful feature learning and complex pattern recognition capabilities. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are used for sequence recognition tasks, face two main challenges, respectively: the ineffective utilization of global information and slow processing speeds due to sequential operations. To address these issues, this paper introduces CTRNet, a novel automatic modulation recognition network that combines a CNN with Transformer. This combination leverages Transformer’s ability to adequately capture the long-distance dependencies between global sequences and its advantages in sequence modeling, along with the CNN’s capability to extract features from local feature regions of signals. During the data preprocessing stage, the original IQ-modulated signals undergo sliding-window processing. By selecting the appropriate window sizes and strides, multiple subsequences are formed, enabling the network to effectively handle complex modulation patterns. In the embedding module, token vectors are designed to integrate information from multiple samples within each window, enhancing the model’s understanding and modeling ability of global information. In the feedforward neural network, a more effective Bilinear layer is employed for processing to capture the higher-order relationship between input features, thereby enhancing the ability of the model to capture complex patterns. Experiments conducted on the RML2016.10A public dataset demonstrate that compared with the existing algorithms, the proposed algorithm not only exhibits significant advantages in terms of parameter efficiency but also achieves higher recognition accuracy under various signal-to-noise ratio (SNR) conditions. In particular, it performs relatively well in terms of accuracy, precision, recall, and F1-score, with clearer classification of higher-order modulations and notable overall accuracy improvement.

CNN Based Automatic Speech Recognition: A Comparative Study

Recently, one of the most common approaches used in speech recognition is deep learning. The most advanced results have been obtained with speech recognition systems created using convolutional neural network (CNN) and recurrent neural networks (RNN). Since CNNs can capture local features effectively, they are applied to tasks with relatively short-term dependencies, such as keyword detection or phoneme- level sequence recognition. This paper presents the development of a deep learning and speech command recognition system. The Google Speech Commands Dataset has been used for training. The dataset contained 65.000 one-second-long words of 30 short English words. That is, %80 of the dataset has been used in the training and %20 of the dataset has been used in the testing. The data set consists of one-second voice commands that have been converted into a spectrogram and used to train different artificial neural network (ANN) models. Various variants of CNN are used in deep learning applications. The performance of the proposed model has reached %94.60.

Review Paper on Deep RL for Video Summarization

In recent years, the explosive growth of video content has heightened the need for efficient summarization techniques to distill lengthy videos into concise, informative summaries. Traditional approaches to video summarization often rely on heuristic-based methods or supervised learning techniques, which can be limited by their reliance on predefined features or extensive labeled datasets. To address these limitations, this paper explores the application of Deep Reinforcement Learning (DRL) for video summarization. DRL offers a dynamic framework where an agent learns to optimize summarization strategies through interaction with the video content, enabling adaptive and context-aware summarization. We propose a novel DRL-based framework that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract and represent temporal features from video frames. Actor- critic architecture is utilized, where the actor generates candidate summaries and the critic evaluates their quality based on a reward function designed to balance in formativeness and brevity. We introduce a new reward function that incorporates both content relevance and diversity to encourage the generation of summaries that effectively capture key moments and maintain narrative coherence. Experimental results on benchmark video datasets demonstrate that our DRL-based approach significantly outperforms traditional methods in terms of both summary quality and user satisfaction. The proposed method not only achieves state-of-the-art performance but also offers greater flexibility and adaptability to diverse video content. This work highlights the potential of DRL in advancing video summarization and opens avenues for future research in optimizing video content extraction and representation

CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy

ObjectivesTo develop a deep learning model combining CT scans and clinical information to predict overall survival in advanced hepatocellular carcinoma (HCC).MethodsThis retrospective study included immunotherapy-treated advanced HCC patients from 52 multi-national in-house centers between 2018 and 2022. A multi-modal prognostic model using baseline and the first follow-up CT images and 7 clinical variables was proposed. A convolutional-recurrent neural network (CRNN) was developed to extract spatial-temporal information from automatically selected representative 2D CT slices to provide a radiological score, then fused with a Cox-based clinical score to provide the survival risk. The model’s effectiveness was assessed using a time-dependent area under the receiver operating curve (AUC), and risk group stratification using the log-rank test. Prognostic performances of multi-modal inputs were compared to models of missing modality, and the size-based RECIST criteria.ResultsTwo-hundred seven patients (mean age, 61 years ± 12 [SD], 180 men) were included. The multi-modal CRNN model reached the AUC of 0.777 and 0.704 of 1-year overall survival predictions in the validation and test sets. The model achieved significant risk stratification in validation (hazard ratio [HR] = 3.330, p = 0.008), and test sets (HR = 2.024, p = 0.047) based on the median risk score of the training set. Models with missing modalities (the single-modal imaging-based model and the model incorporating only baseline scans) can still achieve favorable risk stratification performance (all p < 0.05, except for one, p = 0.053). Moreover, results proved the superiority of the deep learning-based model to the RECIST criteria.ConclusionDeep learning analysis of CT scans and clinical data can offer significant prognostic insights for patients with advanced HCC.Critical relevance statementThe established model can help monitor patients’ disease statuses and identify those with poor prognosis at the time of first follow-up, helping clinicians make informed treatment decisions, as well as early and timely interventions.Key PointsAn AI-based prognostic model was developed for advanced HCC using multi-national patients.The model extracts spatial-temporal information from CT scans and integrates it with clinical variables to prognosticate.The model demonstrated superior prognostic ability compared to the conventional size-based RECIST method.Graphical

基于透射光谱技术的马铃薯种薯黑心病检测研究

Black heart disease is one of the screening indicators of seed potatoes, which has a serious impact on the quality and yield of potato, and at present there are fewer non-destructive testing methods for internal defects of seed potatoes. This paper aims to utilize non-destructive testing technology to quickly identify qualified and black hearted seed potatoes, and then to protect yield and food security. In this paper, transmission spectroscopy system was utilized to collect the spectral data of 104 qualified seed potatoes and 104 black hearted seed potatoes in 450~940 nm band. Subsequently, four algorithms, namely Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and First-order Derivative (FD), were utilized to pre-process the seed potatoes spectral data to improve the data noise ratio. Feature wavelength extraction was made using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) to enhance the sample data characteristics and improve the model interpretability. The construction of classification models for qualified and black hearted seed potatoes relied on two deep learning techniques, Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), which were trained and tested for the feature bands respectively. The experimental results showed that SG-CARS-CNN was the optimal combination of classification algorithms, and the classification accuracies of both the training set and the test set reached 100%, which improved the accuracy of the test set by 3.85% compared with that of the traditional machine learning algorithms, and provided an accurate method for the rapid screening of qualified seed potatoes.

State of Health Prediction in Electric Vehicle Batteries Using a Deep Learning Model

Accurately estimating the state of health (SOH) of lithium-ion batteries plays a significant role in the safe operation of electric vehicles. Deep learning (DL)-based approaches for estimating state of health (SOH) have consistently been the focus of study in recent years. In the current era of electric mobility, the utilization of lithium-ion batteries (LIBs) has evolved into a necessity for energy storage. Ensuring the safe operation of EVs requires a precise assessment of the state-of-health (SOH) of LIBs. To estimate battery SOH accurately, this paper employs a deep learning (DL) algorithm to enhance the estimation accuracy of SOH to obtain accurate SOH measurements. This research introduces the Diffusion Convolutional Recurrent Neural Network (DCRNN) with a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm (DCRNN + SVM-RFE) for enhancing classification and feature selection performance. The data gathered from the dataset were pre-processed using the min–max normalization method. The Center for Advanced Life Cycle Engineering (CALCE) dataset from the University of Maryland was employed to train and evaluate the model. The SVM-RFE algorithm was used for feature selection of pre-processed data. DCRNN algorithm was used for the classification process to enhance prediction precision. The DCRNN + SVM-RFE model’s performance was calculated using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), Mean Squared Error (MSE), and Root MSE (RMSE) metric values. The proposed model generates accurate results for SOH prediction; all RMSEs are within 0.02%, MAEs are within 0.015%, MSEs were within 0.032%, and MAPEs are within 0.41%. The mean values of RMSE, MSE, MAE, and MAPE were 0.014, 0.026, 0.011, and 0.32, respectively. Experiments confirmed that the DCRNN + SVM-RFE model has the highest accuracy among those that predict SOH.

A visible light positioning system for coal mine personnel based on convolutional recurrent neural network

Visible Light Positioning (VLP) technology exhibits promising potential for underground coal mine positioning. While numerous studies have explored VLP in indoor environments, attention to VLP systems for underground mines still needs to be improved. In this paper, based on the traditional indoor visible light communication channel model, the effects of unique features on VLP, such as irregular walls, inclined and rotating receiving ends, and coal dust particles in underground mines, are considered and modeled in detail. In order to improve the accuracy and reliability of personnel VLP in complex mine environments, this paper proposes a deep-learning-based VLP system for underground personnel in coal mines, which consists of two LEDs and four photodetectors (PDs), where the four PDs are mounted on miners' helmets, in conjunction with the actual mine environment. Due to environmental factors and noise, there are apparent spatio-temporal characteristics in the received signal strength of the PDs. Therefore, the system adopts a convolutional recurrent neural network (CRNN) for feature extraction. In the experimental phase, the method's average localization error was 11.24 cm, with 90% of the localization errors within 20.4 cm. The experimental results show that the system has high positioning accuracy and meets the requirements of personnel positioning in underground mines.

Evolutionary Optimization of Deep Learning Models: Enhance Performance through Hyperparameter and Structural Tuning with Evolutionary Algorithms

Evolutionary optimization is a strong way to improve the performance of deep learning models by using evolutionary algorithms (EAs) to fine-tune both structure elements and hyperparameters. This method is based on biological evolution and uses processes like crossing, mutation, and selection to make model setups better over time. When the model and hyperparameter space get more complicated, it can be hard to use traditional methods for adjusting hyperparameters, like grid search or random search, because they need a lot of computing power and don't work as well. Evolutionary algorithms, on the other hand, can quickly move through these high-dimensional spaces and find the best or almost best designs that make the model work better. This research looks into how evolutionary algorithms can be used to improve deep learning models. It focuses on two main areas: setting hyperparameters and making changes to the models' structures. Hyperparameters, like learning rate, batch size, and the number of layers, have a big effect on how the model learns and how accurate it is in the end. The evolutionary method encodes these hyperparameters into chromosomes and then iteratively evolves a population of models, picking the best ones based on a fitness function that usually shows how accurate or lost the models are. Random changes are made by mutations, and traits from high-performing models are combined in crosses. This creates variety and stops things from convergent too soon. Changing the model's design, such as the amount of neurons in each layer, the type of activation functions, and the patterns of connections, is called structural tweaking. These building blocks can be changed on the fly by evolutionary algorithms, which can find new, efficient designs that human creators might miss. This feature is especially helpful when making complicated networks like convolutional neural networks (CNNs) or recurrent neural networks (RNNs), where the best structure isn't always clear. Results from real life show that evolutionary optimization not only makes models more accurate, but it also makes them more stable and able to generalize. This method gives a scalable and adaptable strategy for optimizing deep learning models. This makes it a very useful tool for students and professionals who want to get the best performance in a wide range of situations.

Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks

Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper proposes two models for enhancing QoS through efficient and sustainable resource allocation and optimization of base stations. The first model, a Hybrid Quantum Deep Learning approach, incorporates Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). CNNs handle resource allocation, network reconfiguration, and slice aggregation tasks, while RNNs are employed for functions like load balancing and error detection. The second model introduces a novel neural network named the Base Station Optimizer net. This network includes various parameters as input and output information about the condition of the base station within the network. Node coverage, number of users, node count and user locations, operating frequency, etc., are different parametric inputs considered for evaluation, providing a binary decision (ON or SLEEP) for each base station. A dynamic allocation strategy aims for network lifetime maximization, ensuring sustainable operations and power consumption are minimized across the network by 2 dB. The QoS performance of the Hybrid Quantum Deep Learning model is evaluated for many devices based on slice characteristics and congestion scenarios to attain an impressive overall accuracy of 98%.