Convolutional Neural Network Hyperparameters Research Articles

Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network

This study presents the application of deep learning technology in torsional capacity evaluation of reinforced concrete (RC) beams. A data-driven model based on 2D convolutional neural network (CNN) is established, where model inputs contain the beam width, beam height, stirrup width, stirrup height, concrete compressive strength, steel ratio of longitudinal reinforcement, yield strength of longitudinal reinforcement, steel ratio of transverse reinforcement, yield strength of transverse reinforcement and stirrup spacing. To enhance the prediction accuracy of the proposed model, an improved bird swarm algorithm (IBSA) is leveraged to optimise the hyperparameters of CNN in the training phase. A comprehensive dataset, comprising 268 groups of laboratory tests of RC beams collected from published articles, is used for model development and validation. The results show that the proposed 2D CNN with hyperparameter optimisation exhibits high performance in predicting torsional strength of RC beams, which outperforms other machine learning models, building codes and empirical formula in terms of a series of evaluation metrics.

An improved method for fault diagnosis of rolling bearings of power generation equipment in a smart microgrid

In the construction of smart microgrids for petrochemical enterprises, the generating unit is an important part, and the rolling bearings are one of the key components of the generator. The condition of the rolling bearing directly affects the safe operation of the entire generating unit and an accurate fault diagnosis of the bearing not only can improve the stability of the smart microgrid, but also can reduce the risk of loss of the factory. This study proposes an improved fault diagnosis method based on variational modal decomposition (VMD) and a convolutional neural network (CNN). The VMD algorithm was used to remove random noise in the original signal and a CNN was used to extract useful data from the vibration signal processed by VMD. Since the modal number and penalty parameter of the VMD are difficult to choose and they have a profound impact on the decomposition results, differential evolution (DE) was used as the optimization method and envelope entropy was used as the fitness function to optimize the VMD parameters. Since it is difficult to ensure the best fit of the hyper-parameters of CNN, this study proposes a method for using the DE algorithm to obtain suitable hyper-parameters for the CNN, and then used the CNN to diagnose a fault. The test results using the vibration data of Case Western Reserve University show that the combination of VMD and CNN can improve the convergence speed more than 10% and the accuracy to over 99.6%.

Noninvasive acoustic time-of-flight measurements in heated, hermetically-sealed high explosives using a convolutional neural network

We present a data-driven technique for measuring the time-of-flight through material sealed within a container. Time-of-flight measurement provides a noninvasive means of quantifying the sound speed profile within a material by transmitting an acoustic burst and then measuring the time required for the burst to arrive at an opposing receiver. In a hermetically-sealed cylindrical container, a portion of the acoustic energy propagates through the material as a bulk wave, while the remainder of the acoustic energy propagates around the container walls as guided waves. As a result, interference from the guided waves obscures the bulk arrival, inhibiting measurement of the sound speed. The technique uses a Convolutional Neural Network (CNN) to identify critical features in the measured waveforms and identify bulk wave arrivals. We demonstrate this time-of-flight measurement technique on high explosive-filled containers as they are heated from room temperature to detonation. This is a particularly challenging application for acoustic time-of-flight measurements as the high explosives have significant sound speed gradients as they undergo heating, and they lead to significant attenuation of the bulk wave, as opposed to the guided waves, which do not suffer significant attenuation. We characterize the performance of the CNN as a function of the high explosive temperature and as a function of the CNN hyperparameters. We then provide physical insight into the error trends.

Application of edge computing combined with deep learning model in the dynamic evolution of network public opinion in emergencies.

The aim is to clarify the evolution mechanism of Network Public Opinion (NPO) in public emergencies. This work makes up for the insufficient semantic understanding in NPO-oriented emotion analysis and tries to maintain social harmony and stability. The combination of the Edge Computing (EC) and Deep Learning (DL) model is applied to the NPO-oriented Emotion Recognition Model (ERM). Firstly, the NPO on public emergencies is introduced. Secondly, three types of NPO emergencies are selected as research cases. An emotional rule system is established based on the One-Class Classification (OCC) model as emotional standards. The word embedding representation method represents the preprocessed Weibo text data. Convolutional Neural Network (CNN) is used as the classifier. The NPO-oriented ERM is implemented on CNN and verified through comparative experiments after the CNN's hyperparameters are adjusted. The research results show that the text annotation of the NPO based on OCC emotion rules can obtain better recognition performance. Additionally, the recognition effect of the improved CNN is significantly higher than the Support Vector Machine (SVM) in traditional Machine Learning (ML). This work realizes the technological innovation of automatic emotion recognition of NPO groups and provides a basis for the relevant government agencies to handle the NPO in public emergencies scientifically.

Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

This research utilizes metaheuristic optimization inspired by the Egyptian Vulture Optimization (EVO) technique. Biomedical image segregation is developed to reduce the complex association of hyperparameters of Convolutional Neural networks (CNN). The complex attributes of CNN include the type of kernel, size of the kernel, size of the batch, epoch counts, momentum, learning rate, activation function, convolution layer, and dropout. However, the life cycle of an Egyptian vulture influences the optimization technique to resolve complexity and increase the accuracy of CNN. The proposed CNN-based EVO model was evaluated in comparison to ANN-based and deep learning-based classifiers utilizing brain MRI image datasets. The results achieved have confirmed the efficiency and performance of the proposed CNN-based EVO model, in which the average detection accuracy and precision were 93% and 95%, respectively.

Decoding sensorimotor information from superior parietal lobule of macaque via Convolutional Neural Networks

Despite the well-recognized role of the posterior parietal cortex (PPC) in processing sensory information to guide action, the differential encoding properties of this dynamic processing, as operated by different PPC brain areas, are scarcely known. Within the monkey’s PPC, the superior parietal lobule hosts areas V6A, PEc, and PE included in the dorso-medial visual stream that is specialized in planning and guiding reaching movements. Here, a Convolutional Neural Network (CNN) approach is used to investigate how the information is processed in these areas. We trained two macaque monkeys to perform a delayed reaching task towards 9 positions (distributed on 3 different depth and direction levels) in the 3D peripersonal space. The activity of single cells was recorded from V6A, PEc, PE and fed to convolutional neural networks that were designed and trained to exploit the temporal structure of neuronal activation patterns, to decode the target positions reached by the monkey. Bayesian Optimization was used to define the main CNN hyper-parameters. In addition to discrete positions in space, we used the same network architecture to decode plausible reaching trajectories. We found that data from the most caudal V6A and PEc areas outperformed PE area in the spatial position decoding. In all areas, decoding accuracies started to increase at the time the target to reach was instructed to the monkey, and reached a plateau at movement onset. The results support a dynamic encoding of the different phases and properties of the reaching movement differentially distributed over a network of interconnected areas. This study highlights the usefulness of neurons’ firing rate decoding via CNNs to improve our understanding of how sensorimotor information is encoded in PPC to perform reaching movements. The obtained results may have implications in the perspective of novel neuroprosthetic devices based on the decoding of these rich signals for faithfully carrying out patient’s intentions.

A Novel MRI Diagnosis Method for Brain Tumor Classification Based on CNN and Bayesian Optimization.

Brain tumor is one of the most aggressive diseases nowadays, resulting in a very short life span if it is diagnosed at an advanced stage. The treatment planning phase is thus essential for enhancing the quality of life for patients. The use of Magnetic Resonance Imaging (MRI) in the diagnosis of brain tumors is extremely widespread, but the manual interpretation of large amounts of images requires considerable effort and is prone to human errors. Hence, an automated method is necessary to identify the most common brain tumors. Convolutional Neural Network (CNN) architectures are successful in image classification due to their high layer count, which enables them to conceive the features effectively on their own. The tuning of CNN hyperparameters is critical in every dataset since it has a significant impact on the efficiency of the training model. Given the high dimensionality and complexity of the data, manual hyperparameter tuning would take an inordinate amount of time, with the possibility of failing to identify the optimal hyperparameters. In this paper, we proposed a Bayesian Optimization-based efficient hyperparameter optimization technique for CNN. This method was evaluated by classifying 3064 T-1-weighted CE-MRI images into three types of brain tumors (Glioma, Meningioma, and Pituitary). Based on Transfer Learning, the performance of five well-recognized deep pre-trained models is compared with that of the optimized CNN. After using Bayesian Optimization, our CNN was able to attain 98.70% validation accuracy at best without data augmentation or cropping lesion techniques, while VGG16, VGG19, ResNet50, InceptionV3, and DenseNet201 achieved 97.08%, 96.43%, 89.29%, 92.86%, and 94.81% validation accuracy, respectively. Moreover, the proposed model outperforms state-of-the-art methods on the CE-MRI dataset, demonstrating the feasibility of automating hyperparameter optimization.

The analysis and optimization of CNN Hyperparameters with fuzzy tree modelfor image classification

The meaningful performance of convolutional neural network (CNN) has enabled the solution of various state-of-the-art problems. Although CNNs achieve satisfactory results in computer-vision problems, they still have some difficulties. As the designed CNN models are deepened to achieve much better accuracy, computational cost and complexity increase. It is significant to train CNNs with suitable topology and training hyperparameters that include initial learning rate, minibatch size, epoch number, filter size, number of filters, etc. because the initialization of hyperparameters affects classification results. On the other hand, it is not possible to make a definite inference for the hyperparameter initialization and there is uncertainty. This study is carried out to model uncertainty using fuzzy inference system (FIS). The designed fuzzy model provides estimation of classification result depending on CNN topology and training hyperparameters. GoogleNet and Inceptionv3 that contain inception-modules, ShuffleNet that contains shuffleblocks, DenseNet201 that contains dense-blocks, EfficientNet, ResNet18, ResNet50, ResNet101, and MobileNetv2 that contain residual-blocks, and InceptionResNetv2 that includes both inception-modules and residual-blocks were evaluated as CNN models. Test sample dataset was obtained by training CNN models with various training hyperparameter combinations. CNN models were trained on Animal Diagnostics Lab (ADL) which is a histopathological dataset includes healthy and inflamed kidney, lung, and spleen images. A new FIS tree model that is more computationally efficient and easier to understand than a single FIS was designed and classification accuracy prediction of CNN models depending on hyperparameter combinations was performed. The best, the worst, and the average classification accuracies obtained with CNN models that use best training hyperparameter set are 97.70%, 93.60%, and 96.30%, respectively. Moreover, Cifar10 and Cifar100 benchmark datasets were experimented to reveal true capability and limitations of the proposed approach. Experimental results indicate that the designed FIS tree model provides a successful hyperparameter evaluation mechanism with an average RMSE value of 1.2652.

A hybrid deep learning model based low‐rate DoS attack detection method for software defined network

AbstractThe low‐rate DoS (LDoS) attack is a new kind of network attack which has the characteristics such as low speed and good concealment. The software defined network, as a new type of network architecture, also faces the threat from LDoS attacks. In this article, we propose a detection method of LDoS attacks based on a hybrid deep learning model CNN‐GRU: the convolutional neural network (CNN) and the gated recurrent unit (GRU). First, we extract field values such as n_packets and n_bytes, from the flow rule, and construct the average numbers of packets and bytes as the input data of the hybrid model. Then, to enhance the detection performance of the hybrid model, we improve the sailfish algorithm to optimize the hyperparameters of CNN and GRU automatically in the training process. Finally, we adopt hyperparameter optimized CNN and GRU to extract deeper spatial and temporal features of input data, respectively, which achieves accurate detection of the LDoS attack. The experimental results demonstrate that the proposed hybrid deep learning model‐based method outperforms other traditional machine learning algorithms in terms of detection efficiency and accuracy.

Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data

Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of COVID-19 patients is critical. Deep Learning has shown promising performance in a variety of domains and emerged as a key technology in Artificial Intelligence. Recent advances in visual recognition are based on image classification and artefacts detection within these images. The purpose of this study is to classify chest X-ray images of COVID-19 artefacts in changed real-world situations. A novel Bayesian optimization-based convolutional neural network (CNN) model is proposed for the recognition of chest X-ray images. The proposed model has two main components. The first one utilizes CNN to extract and learn deep features. The second component is a Bayesian-based optimizer that is used to tune the CNN hyperparameters according to an objective function. The used large-scale and balanced dataset comprises 10,848 images (i.e., 3616 COVID-19, 3616 normal cases, and 3616 Pneumonia). In the first ablation investigation, we compared Bayesian optimization to three distinct ablation scenarios. We used convergence charts and accuracy to compare the three scenarios. We noticed that the Bayesian search-derived optimal architecture achieved 96% accuracy. To assist qualitative researchers, address their research questions in a methodologically sound manner, a comparison of research method and theme analysis methods was provided. The suggested model is shown to be more trustworthy and accurate in real world.

Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Deep learning techniques, particularly convolutional neural networks (CNNs), have exhibited remarkable performance in solving vision-related problems, especially in unpredictable, dynamic, and challenging environments. In autonomous vehicles, imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs. In this regard, globally, researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results. Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs. However, to the best of our knowledge, these techniques are yet to be applied to address the problem of imitation-learning-based steering angle prediction. Thus, in this study, we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters, which are employed to solve the steering angle prediction problem. To validate the performance of each hyperparameters’ set and architectural parameters’ set, we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set: optimizer, Adagrad; learning rate, 0.0052; and nonlinear activation function, exponential linear unit. As per our findings, we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones. Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach. Infield testing was also performed using the model trained with the optimal architecture, which we developed using our approach.

An Experimental Approach to Diagnose Covid-19 Using Optimized CNN

The outburst of novel corona viruses aggregated worldwide and has undergone severe trials to manage medical sector all over the world. A radiologist uses x-rays and Computed Tomography (CT) scans to analyze images through which the existence of corona virus is found. Therefore, imaging and visualization systems contribute a dominant part in diagnosing process and thereby assist the medical experts to take necessary precautions and to overcome these rigorous conditions. In this research, a Multi-Objective Black Widow Optimization based Convolutional Neural Network (MBWO-CNN) method is proposed to diagnose and classify covid-19 data. The proposed method comprises of four stages, preprocess the covid-19 data, attribute selection, tune parameters, and classify covid-19 data. Initially, images are fed to preprocess and features are selected using Convolutional Neural Network (CNN). Next, Multi-objective Black Widow Optimization (MBWO) method is imparted to finely tune the hyper parameters of CNN. Lastly, Extreme Learning Machine Auto Encoder (ELM-AE) is used to check the existence of corona virus and further classification is done to classify the covid-19 data into respective classes. The suggested MBWO-CNN model was evaluated for effectiveness by undergoing experiments and the outcomes attained were matched with the outcome stationed by prevailing methods. The outcomes confirmed the astonishing results of the ELM-AE model to classify covid-19 data by achieving maximum accuracy of 97.53%. The efficacy of the proposed method is validated and observed that it has yielded outstanding outcomes and is best suitable to diagnose and classify covid-19 data.

Neural architecture search via standard machine learning methodologies

<abstract><p>In the context of deep learning, the more expensive computational phase is the full training of the learning methodology. Indeed, its effectiveness depends on the choice of proper values for the so-called hyperparameters, namely the parameters that are not trained during the learning process, and such a selection typically requires an extensive numerical investigation with the execution of a significant number of experimental trials. The aim of the paper is to investigate how to choose the hyperparameters related to both the architecture of a Convolutional Neural Network (CNN), such as the number of filters and the kernel size at each convolutional layer, and the optimisation algorithm employed to train the CNN itself, such as the steplength, the mini-batch size and the potential adoption of variance reduction techniques. The main contribution of the paper consists in introducing an automatic Machine Learning technique to set these hyperparameters in such a way that a measure of the CNN performance can be optimised. In particular, given a set of values for the hyperparameters, we propose a low-cost strategy to predict the performance of the corresponding CNN, based on its behavior after only few steps of the training process. To achieve this goal, we generate a dataset whose input samples are provided by a limited number of hyperparameter configurations together with the corresponding CNN measures of performance obtained with only few steps of the CNN training process, while the label of each input sample is the performance corresponding to a complete training of the CNN. Such dataset is used as training set for a Support Vector Machines for Regression and/or Random Forest techniques to predict the performance of the considered learning methodology, given its performance at the initial iterations of its learning process. Furthermore, by a probabilistic exploration of the hyperparameter space, we are able to find, at a quite low cost, the setting of a CNN hyperparameters which provides the optimal performance. The results of an extensive numerical experimentation, carried out on CNNs, together with the use of our performance predictor with NAS-Bench-101, highlight how the proposed methodology for the hyperparameter setting appears very promising.</p></abstract>

Research on Landslide Risk Assessment Based on Convolutional Neural Network

Landslide risk assessment (LRA) is of great significance to reduce the loss of landslide disaster and improve the effect of disaster prevention and reduction. At present, there are few studies on LRA, and the theories and methods need to be innovated. In view of this, convolutional neural network (CNN) is applied to LRA. Based on 9 landslide hazard conditioning factors and 5 vulnerability conditioning factors, a CNN framework suitable for LRA (LR-CNN framework) was constructed by using Bayesian method to optimize the CNN hyperparameters. In order to obtain more stable model and assessment results, 5-fold cross-validation method was adopted. The LR-CNN framework and the risk expression of the United Nations Department of human affairs (risk expression of UNDHA) method were applied to Tumen City, China, and the two methods were analyzed and compared. From the rationality test results, both methods meet the rationality test standard. From the landslide risk mapping, both methods can obtain better LRA results. From the area within each risk class, LR-CNN framework is slightly inferior to risk expression of UNDHA method, but from the perspective of landslide distribution and landslide density, LR-CNN framework is superior to risk expression of UNDHA method. It can be seen that the application of CNN to LRA is feasible and superior to the risk expression of UNDHA method to a certain extent.

OPFaceNet: OPtimized Face Recognition Network for noise and occlusion affected face images using Hyperparameters tuned Convolutional Neural Network

Face recognition is considered as important research in computer vision applications, and it is regarded as the basic biometric security system. Research related to face recognition has been done in the past several years. Still, many more challenges associated with this field need to be addressed. Some literary works have designed the face recognition model on the relatively controlled environments; yet, their performance in general settings has been substandard This paper develops an Optimal Face Recognition Network (OPFaceNet) to recognize the face images affected by high noise and occlusion. The feature patterns subjected to noise like LBP, FLBP, and NRLBP are extracted. The average of all three patterns is given to the proposed Convolutional Neural Network (CNN) classifier. As the main contribution, the CNN model is enhanced by optimizing the Fitness Sorted Rider Optimization Algorithm (FS-ROA). This algorithm optimizes the hyperparameters of CNN like Convolutional layer, Pooling Layer, Fully connected layer, number of Hidden layers, and Type of Pooling. Finally, the simulation results show that the system achieves a good recognition rate of 97.2% and is robust against variations in terms of occlusion and noise when benchmarked over diverse datasets.

Detection of atrial fibrillation using variable length genetic algorithm and convolutional neural network

AbstractAtrial fibrillation (AF) is the most prevalent cardiac arrhythmia and it is considered as one of the most important risk factor for death, stroke, hospitalization, and heart failure. It is possible to detect AF by analyzing electrocardiogram (ECG) of patients. To work on clean signals and reduce errors resulted from noise, we have used Butterworth filter. The short‐term Fourier transform was used to analyze ECG segments to obtain ECG spectrogram images. Convolutional neural network (CNN) models have been proposed for improving automatic detection of AF. The number of convolutional layers varies in different CNN models, and as the model become deeper, more hyper parameters are added. So in this article, variable length genetic algorithm was used in order to optimize hyper parameters of CNN. The results of experiments that performed on the MIT‐BIH AF database showed that the proposed method achieved 100%, 98.90%, and 99.95% for the sensitivity, specificity, and accuracy, respectively, so the proposed method outperforms the deep CNNs. Hence, the proposed method is an accurate and efficient method for detection of AF.

Image classification of different clove (Syzygium aromaticum) quality using deep learning method with convolutional neural network algorithm

The objective of this study is to classify the quality of dried clove flowers using deep learning method with Convolutional Neural Network (CNN) algorithm, and also to perform the sensitivity analysis of CNN hyperparameters to obtain best model for clove quality classification process. The quality of clove as raw material in this study was determined according to SNI 3392-1994 by PT. Perkebunan Nusantara XII Pancusari Plantation, Malang, East Java, Indonesia. In total 1,600 images of dried clove flower were divided into 4 qualities. Each clove quality has 225 training data, 75 validation data, and 100 test data. The first step of this study is to build CNN model architecture as first model. The result of that model gives 65.25% reading accuracy. The second step is to analyze CNN sensitivity or CNN hyperparameter on the first model. The best value of CNN hyperparameter in each step then to be used in the next stage. Finally, after CNN hyperparameter carried out the reading accuracy of the test data is improved to 87.75%.

Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images

The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application.

MODE-CNN: A fast converging multi-objective optimization algorithm for CNN-based models

Convolutional neural networks (CNNs) have been used to solve many problems in computer science with a high level of success, and have been applied in many fields in recent years. However, most of the designs of these models are still tuned manually; obtaining the highest performing CNN model is therefore very time-consuming, and is sometimes not achievable. Recently, researchers have started using optimization algorithms for the automatic adjustment of the hyper-parameters of CNNs. In particular, single-objective optimization algorithms have been used to achieve the highest network accuracy for the design of a CNN. When these studies are examined, it can be seen that the most significant problem in the optimization of the parameters of a CNN is that a great deal of time is required for tuning. Hence, optimization algorithms with high convergence rates are needed for the parameter optimization of deep networks. In this study, we first develop an algorithm called MODE-CNN, based on the multi-objective differential evolution (MODE) algorithm for parameter optimization of CNN or CNN-based methods. MODE-CNN is then compared with four different multi-objective optimization algorithms. This comparison is carried out using 16 benchmark functions and four different metrics, with 100 independent runs. It is observed that the algorithm is robust and competitive compared to alternative approaches, in terms of its accuracy and convergence. Secondly, the MODE-CNN algorithm is used in the parameter optimization of a CNN-based method, developed previously by the authors, for the segmentation and classification of medical images. In this method, there are three parameters that influence the test time and accuracy: the general stride (GS), neighbour distance (ND), and patch accuracy (PA). These parameters need to be optimized to give the highest possible accuracy and lowest possible test time. With the MODE-CNN algorithm, the most appropriate GS, ND, and PA values are obtained for the test time and accuracy. As a result, it is observed that the MODE-CNN algorithm is successful, both in comparison with multi-objective algorithms and in the parameter optimization of a CNN-based method.

Unified Deep Learning approach for Efficient Intrusion Detection System using Integrated Spatial–Temporal Features

Intrusion detection systems (IDS) differentiate the malicious entries from the legitimate entries in network traffic data and helps in securing the networks. Deep learning algorithms have been greatly employed in the network security field for large scale data in modern cyberspace networks because of their ability to learn the deeply integrated features. However, learning both space and time aspects of system information are very challenging for any individual deep knowledge model. While Convolutional Neural Networks (CNN) effectively acquires the spatial aspects, the Long Short-Term Memory (LSTM) neural networks perform better for temporal features. Integrating the benefits of these models has the potential for improving the large scale IDS. In this paper, a high accurate IDS model is proposed by using a unified model of Optimized CNN (OCNN) and Hierarchical Multi-scale LSTM (HMLSTM) for effective extraction and learning of spatial–temporal features. The proposed IDS model performs the pre-processing, feature extraction through network training and network testing and final classification. In the OCNN–HMLSTM model, the Lion Swarm Optimization (LSO) is used to tune the hyper-parameters of CNN for the optimal configuration of learning spatial features. The HMLSTM learns the hierarchical relationships between the different features and extracts the time features. Lastly, the unified IDS approach utilizes the extracted spatial–temporal features for categorizing the network data. Tests are performed over public IDS datasets namely NSL-KDD, ISCX-IDS and UNSWNB15. Assessing the performance of OCNN–HMLSTM​ against the contemporary IDS methods, the proposed model performs better intrusion detection with high accuracy of above 90% with less false values and better classification coefficients.