Convolutional Neural Network Technique Research Articles

Cloudy Weather Prediction Using CNN Models and Satellite Images

Abstract: Cloudy weather classification is a vital task in meteorology and remote sensing, facilitating various applications such as weather forecasting, climate monitoring, and environmental analysis. In this study, we explore the application of convolutional neural network (CNN) techniques for classifying cloudy weather conditions using the Cloudy Weather Dataset sourced from Kaggle. The primary CNN architectures investigated include AlexNet, LeNet, and ResNet. The dataset undergoes preprocessing steps, including resizing and normalization to floating-point representation. Additionally, for calculating cloud cover percentage, the images are processed through grayscaling followed by thresholding. The performance of each CNN model is evaluated based on metrics of accuracy, that is providing insights into their effectiveness for cloudy weather classification

Enabling temporal–spectral decoding in multi-class single-side upper limb classification

This manuscript presents a novel approach for decoding pre-movement patterns from brain signals using a two-stage-training temporal–spectral neural network (TTSNet). The TTSNet employs a combination of filter bank task-related component analysis (FBTRCA) and convolutional neural network (CNN) techniques to enhance the classification of single-upper limb movements in non-invasive brain–computer interfaces (BCIs).In our previous work, we introduced the FBTRCA method which utilized filter banks and spatial filters to handle spectral and spatial information, respectively. However, we observed limitations in the temporal decoding phase, where correlation features failed to effectively utilize temporal information because of misaligned onset and noisy spikes. To address this issue, our proposed method focuses on analyzing multi-channel signals in the temporal–spectral domain. The TTSNet first divides the signals into various filter banks, employing task-related component analysis to reduce dimensionality and eliminate noise, respectively. Subsequently, a CNN is employed to optimize the temporal characteristics of the signals and extract class-related features. Finally, the class-related features from all filter banks are concatenated and classified using the fully connected layer.To evaluate the effectiveness of our proposed method, we conducted experiments on two publicly available datasets. In binary classification tasks, the TTSNet achieved an improved accuracy of 0.7707 ± 0.1168, surpassing the performance of EEGNet (accuracy: 0.7340 ± 0.1246) and FBTRCA (accuracy: 0.7487 ± 0.1250). In multi-class tasks, TTSNet achieved an accuracy of 0.4588 ± 0.0724, exhibiting a 4.27% and 3.95% accuracy increase over EEGNet and FBTRCA, respectively.The findings of this study suggest that the proposed TTSNet method holds promise for detecting limb movements and assisting in the rehabilitation of stroke patients. The classification of single-side limb movements is expected to facilitate the interaction between patients and external environment by increasing the number of control commands in BCIs.

Proactive Analysis and Detection of Cyber-attacks using Deep Learning Techniques

Objectives: This study objective is to create a proactive forensic framework with a classification model to identify the malicious content to avoid cyber-attacks. Methods: In this proposed work, a novel framework is introduced to analyze and detect network attacks before it happens. It monitors the network packet flow, captures the packets, analyzes the packet flow proactively, and detects cyber-attacks using different machine learning algorithms and Deep Convolution Neural network (CNN) technique. The KDD dataset is used in this experiment with 30% for testing and 80% for training. Findings: The simulation results show that the detection percentage of the proposed framework reaches a maximum of 95.92% in different scenarios. It is approximately 10% higher than the existing proactive frameworks for example Gawand’s model, Ahmetoglu’s model and many more. Novelty and applications: The proposed framework is a proactive model which detects the cyber-attack in prior to avoid cyber-attacks. The deep CNN model highly efficient for detecting cyber-attack. Keywords: Proactive Forensic Framework, Deep CNN, Classification Algorithms, Cyber attack detection, Intrusion Detection System

A hybrid deep learning approach to integrate predictive maintenance and production planning for multi-state systems

This paper develops a data-driven approach to dynamically integrate tactical production and predictive maintenance planning for a multi-state system composed of several series-parallel machines. The objective is to determine an integrated lot-sizing and preventive maintenance strategy that will minimize the sum of maintenance and production costs, while satisfying the demand for all products over the entire horizon. A rolling horizon planning strategy is adopted to continuously update the production and maintenance plans based on new data obtained through sensors. Unlike the existing integrated models, we develop a hybrid deep learning (DL) approach to coordinate maintenance and production decisions for a multi-state system composed of multiple machines. To accurately predict the health condition of each machine, the developed hybrid DL method combines the powers of convolutional neural network (CNN), long-short-term memory (LSTM), and attention technique. We use multi-state reliability theory to estimate the production capacity. Furthermore, a genetic algorithm is developed to solve large-scale problems. Benchmarking data are used to compare the results of our data-driven approach with a model-based approach, a pure LSTM, and a CNN-LSTM approach. This comparison is based on prediction accuracy, solution quality, and computational time. The obtained results show the superiority of the suggested CNN-LSTM-attention data-driven framework integrating maintenance and production.

Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for Smart Machine

In smart meter technology, the reliability of Backplane to Bezel (BTB) Liquid Crystal Displays (LCDs) is crucial for efficient functioning within intelligent grid systems. Defects in these LCD screens can significantly impact overall smart meter performance, necessitating effective detection methods for proper management and utilization. Current detection approaches, which combine manual and automatic methods based on machine vision, have shown unsatisfactory performance. This research addresses this challenge by proposing a novel method for advanced fault detection in BTB LCDs of smart meters, Automatic Testing Technology of BTB Liquid Crystal Display Advanced Fault Detection in Smart Meter for smart machine (BTB-LCD-FD- MCDSGAN) is proposed. The study begins by collecting datasets specifically tailored for LCD screen localization and defect detection. To enhance data quality, a Window Adaptive Extended Kalman Filter (WAEKF) is applied during preprocessing for noise removal. Feature extraction follows, utilizing Parameterized Multi Synchrosqueezing Transforms (PMST) with a primary focus on Gray Level Co-occurrence Matrix features. These extracted features serve as input for classification by a Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDSGAN), categorizing defects into five types likes normal display, no display, abnormal display, liquid crystal rupture, and incomplete display. Furthermore, the proposed method optimizes the MCDSGAN weight parameter using the Red Fox Optimization Algorithm (RFOA) to achieve accurate LCD fault defect prediction. The entire approach is implemented in Python, performance metrics likes accuracy, precision, recall, F-score, computational time are thoroughly analyzed. Performance of proposed BTB-LCD-FD-MCDSGAN approach attains 20.89%, 33.67% and 25.98% high accuracy, and 17.98%, 23.78% and 33.45% higher recall compared with existing methods such as automatic detection of display defects for smart meters depend on deep learning (AD-AM-DL), deep learning-enabled image content-adaptive field sequential color LCDs by mini-LED backlight (DL-AFSC-LCD) and new multi category defect detection method depend on convolutional neural network technique for TFT-LCD panels (MDF-CNN-LCD), methods respectively.

An optimal model for detection of lung cancer using convolutional neural network

In terms of frequency and mortality, lung cancer ranks second among all cancers worldwide for both men and women. It is suggested that pattern classification and machine learning be applied to the identification and categorization of lung cancer. Convolution neural network (CNN) techniques divide the input data into groups according to the distinctive characteristics of the input. Using a standard approach to analyze a large number of computed tomography images, early detection of lung cancer can save lives. The suggested effort is centered on identifying the precise type of cancer and making predictions about whether it is benign or aggressive. The deployment of proposed model is an attempt to improve the accuracy of the system. The proposed work showed an overall accuracy of 98.4% during the detection of lung cancer and 98.8% accuracy towards the prediction of specific type in the lung cancer. Mean average precision score of 97.17% and 98.75% test and validation respectively. 0.96, 0.93, and 0.95 for malignant test data.

DEF: Deep Ensemble Neural Network Classifier for Android Malware Detection

Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected from the Internet sources and Microsoft are used for the empirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the ELAML algorithm for obtaining intermediate features required by CNN and LSTM. Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.

Constraining primordial non-Gaussianity using neural networks

ABSTRACT We present a novel approach to estimate the value of primordial non-Gaussianity (fNL) parameter directly from the cosmic microwave background (CMB) maps using a convolutional neural network (CNN). While traditional methods rely on complex statistical techniques, this study proposes a simpler approach that employs a neural network to estimate fNL. The neural network model is trained on simulated CMB maps with known fNL in range of [−50, 50], and its performance is evaluated using various metrics. The results indicate that the proposed approach can accurately estimate fNL values from CMB maps with a significant reduction in complexity compared to traditional methods. With 500 validation data, the $f^{\rm output}_{\rm NL}$ against $f^{\rm input}_{\rm NL}$ graph can be fitted as y = ax + b, where $a=0.980^{+0.098}_{-0.102}$ and $b=0.277^{+0.098}_{-0.101}$, indicating the unbiasedness of the primordial non-Gaussianity estimation. The results suggest that the CNN technique can be widely applied to other cosmological parameter estimation directly from CMB images.

Road Accident Severity Detection In Smart Cities

Ensuring safety, in cities is a focus in the development of urban areas requiring new and creative methods for categorizing and managing accidents. Traditional approaches often face challenges in evaluating accident seriousness within changing city environments. This research utilizes Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to create a system that categorizes accidents into three severity levels; minor, moderate and severe. By leveraging learning capabilities, our method boosts the precision and efficiency of safety protocols in cities. The outcomes exhibit promising results in categorizing accident severity offering a tool for enhancing urban safety infrastructure. Through empowering cities to handle accidents, our model establishes a foundation for safety initiatives. In essence, this study contributes to enhancing safety standards in cities promoting resilience and sustainability, within settings.

A novel Bagged-CNN architecture for short-term wind power forecasting

ABSTRACT Wind power forecasting plays a pivotal role in ensuring the dependable integration of wind energy into power grids. This research introduces a novel Bagged-CNN architecture tailored for short-term wind power forecasting. This architecture harnesses the combined strengths of the Bagging ensemble technique and Convolutional Neural Networks (CNN) to enhance prediction accuracy and robustness. Our approach demonstrates superior forecasting capabilities by exploiting the spatial and temporal features captured by CNNs. To validate our proposed architecture, we have conducted a comprehensive evaluation across three distinct wind farms, each characterized by unique geographical and climatic conditions. We systematically compared the performance of our Bagged-CNN model against several standalone machine-learning algorithms commonly employed in wind power forecasting. The hyperparameters of our Bagged-CNN model are meticulously optimized using the GRID search algorithm, which systematically explores a predefined parameter space. Notably, our predictions shed light on the substantial impact of training data period length on wind power prediction accuracy. Our research underscores the potential of coupling Bagging and CNN techniques for short-term wind power forecasting. The Bagged-CNN architecture, as proposed, serves as a robust framework for capturing intricate spatial and temporal patterns in wind power data, thereby facilitating more precise and dependable predictions.

Intelligent Diagnosis of Lung Cancer Using Deep Learning

Introduction: Lung cancer, a highly prevalent disease worldwide, poses a significant risk to individuals. Nodules, which manifest as minuscule masses in the lungs, serve as crucial indicators of the early stages of the disease, with the possibility of being either benign or malignant in nature. Prompt diagnosis of this ailment plays a pivotal role in saving patients' lives, thus rendering the utilization of computed aided diagnosis methods exceedingly valuable within this domain.Material and Methods: The methodology employed for presentation purposes is deeply rooted in the principles of deep learning, a field that epitomizes the amalgamation of artificial intelligence and neuroscience. Delving into the specifics, the initial phase of this process entails the preprocessing of data, wherein the lung area is meticulously isolated from computed tomography (CT) scan images. Subsequently, in the second stage, the identification of nodules is facilitated through the employment of the mask region convolutional neural network (RCNN) technique, which effectively entails the delineation of masks and bounding boxes. The third and final step involves the classification of the identified nodules, achieved through the utilization of a singular convolutional neural network, ultimately segregating the nodules into three distinct categories: benign, malignant, and ambiguous. In order to evaluate the efficacy of the proposed method, the LIDC-IDRI dataset was employed as a means of testing, thereby furnishing tangible evidence to substantiate the claim that the presented method is on par with its counterparts within the realm of detecting and classifying lung nodules.Results: It is worth noting that the proposed method has yielded a remarkable accuracy rate of 95% in the phase of nodule detection, further bolstering its credibility and reliability. Furthermore, the accuracy rate achieved during the step of nodule classification stands at an impressive 97.3%, thereby cementing the efficacy of the proposed method in a comprehensive manner.Conclusion: The purpose of this work to provide an intelligent system for reducing the amount of the workload of the physicians in this field. After examining and studying some data set, the LIDC-IDRI data set is presented, for this work because of being suitable for two works of detecting nodule place and nodule classification, lots of data, and because of being reliable and used in previous reliable works that can provide the ability to compare the results.

Cancer Guard: Early Detection of Breast Cancer

Breast cancer stands as the most prevalent form of cancer among women, globally contributing to the highest number of cancer-related deaths. Timely detection of abnormalities significantly enhances the prospects of successful treatment and reduces mortality rates. Hence an automatic detection will be very useful for medical practitioner. This research introduces a novel framework for enhancing breast cancer detection and stage classification by integrating image processing techniques such as Gray Level Co-occurrence Matrix (GLCM) and Convolutional Neural Network (CNN) techniques. Initially, mammographic images undergo preprocessing to improve quality, followed by GLCM feature extraction for capturing textural information. With the help of GLCM technique, accuracy of the network can be increased by extracting various features. A CNN model is then employed for automatic feature learning and classification. This framework enhances the accuracy of distinguishing between malignant and benign tissues and extends to stage detection, enabling classification into various stages. Experimental results demonstrate the effectiveness of the proposed approach in achieving high precision and recall rates, suggesting potential for clinical integration to improve patient outcomes and streamline healthcare workflows.

An end-to-end machine learning approach with explanation for time series with varying lengths

AbstractAn accurate prediction of complex product quality parameters from process time series by an end-to-end learning approach remains a significant challenge in machine learning. A special difficulty is the application of industrial batch process data because many batch processes generate variable length time series. In the industrial application of such methods, explainability is often desired. In this study, a 1D convolutional neural network (CNN) algorithm with a masking layer is proposed to solve the problem for time series of variable length. In addition, a novel combination of 1D CNN and class activation mapping (CAM) technique is part of this study to better understand the model results and highlight some regions of interest in the time series. As a comparative state-of-the-art unsupervised machine learning method, the One-Nearest Neighbours (1NN) algorithm combined with dynamic time warping (DTW) was used. Both methods are investigated as end-to-end learning methods with balanced and unbalanced class distributions and with scaled and unscaled input data, respectively. The FastDTW and DTAIDistance algorithms were investigated for the DTW calculation. The data set is made up of sensor signals that was collected during the production of plastic parts. The objective was to predict a quality parameter of plastic parts during production. For this research, the quality parameter will be a difficult or only destructively measurable parameter and both methods will be investigated for their applicability to this prediction task. The application of the proposed approach to an industrial facility for producing plastic products shows a prediction accuracy of 83.7%. It can improve the reverence method by approximately 1.4%. In addition to the slight increase in accuracy, the CNN training time was significantly reduced compared to the DTW calculation.

HaCk: Hand Gesture Classification Using a Convolutional Neural Network and Generative Adversarial Network-Based Data Generation Model

According to global data on visual impairment from the World Health Organization in 2010, an estimated 285 million individuals, including 39 million who are blind, face visual impairments. These individuals use non-contact methods such as voice commands and hand gestures to interact with user interfaces. Recognizing the significance of hand gesture recognition for this vulnerable population and aiming to improve user usability, this study employs a Generative Adversarial Network (GAN) coupled with Convolutional Neural Network (CNN) techniques to generate a diverse set of hand gestures. Recognizing hand gestures using HaCk typically involves a two-step approach. First, the GAN is trained to generate synthetic hand gesture images, and then a separate CNN is employed to classify gestures in real-world data. The evaluation of HaCk is demonstrated through a comparative analysis using Leave-One-Out Cross-Validation (LOO CV) and Holdout Cross-Validation (Holdout CV) tests. These tests are crucial for assessing the model’s generalization, robustness, and suitability for practical applications. The experimental results reveal that the performance of HaCk surpasses that of other compared ML/DL models, including CNN, FTCNN, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models. Specifically, the improvement percentages for the LOO CV Test are 17.03%, 20.27%, 15.76%, 13.76%, 10.16%, 5.90%, and 15.90%, respectively. Similarly, for the Holdout CV Test, HaCk outperforms HU, ZM, GB, GB-ZM, GB-HU, CDCGAN, GestureGAN, GGAN, MHG-CAN, and ASL models, with improvement percentages of 56.87%, 15.91%, 13.97%, 24.81%, 23.52%, 17.72%, 15.72%, 12.12%, 7.94%, and 17.94%, respectively.

Efficient Quantum Image Classification Using Single Qubit Encoding.

The domain of image classification has been seen to be dominated by high-performing deep-learning (DL) architectures. However, the success of this field, as seen over the past decade, has resulted in the complexity of modern methodologies scaling exponentially, commonly requiring millions of parameters. Quantum computing (QC) is an active area of research aimed toward greatly reducing problems of complexity faced in classical computing. With growing interest toward quantum machine learning (QML) for applications of image classification, many proposed algorithms require usage of numerous qubits. In the noisy intermediate-scale quantum (NISQ) era, these circuits may not always be feasible to execute effectively; therefore, we should aim to use each qubit as effectively and efficiently as possible, before adding additional qubits. This article proposes a new single-qubit-based deep quantum neural network for image classification that mimics traditional convolutional neural network (CNN) techniques, resulting in a reduced number of parameters compared with previous works. Our aim is to prove the concept of the initial proposal by demonstrating classification performance of the single-qubit-based architecture, as well as to provide a tested foundation for further development. To demonstrate this, our experiments were conducted using various datasets including MNIST, Fashion-MNIST, and ORL face datasets. To further our proposal in the context of the NISQ era, our experiments were intentionally conducted in noisy simulation environments. Initial test results appear promising, with classification accuracies of 94.6%, 89.5%, and 82.5% achieved on the subsets of MNIST, FMNIST, and ORL face datasets, respectively. In addition, proposals for further investigation and development were considered, where it is hoped that these initial results can be improved.

Wide-band spectrum sensing with convolution neural network using spectral correlation function

Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.

Diagnosis of skull-base invasion by nasopharyngeal tumors on CT with a deep-learning approach

PurposeTo develop a convolutional neural network (CNN) model to diagnose skull-base invasion by nasopharyngeal malignancies in CT images and evaluate the model’s diagnostic performance.Materials and methodsWe divided 100 malignant nasopharyngeal tumor lesions into a training (n = 70) and a test (n = 30) dataset. Two head/neck radiologists reviewed CT and MRI images and determined the positive/negative skull-base invasion status of each case (training dataset: 29 invasion-positive and 41 invasion-negative; test dataset: 13 invasion-positive and 17 invasion-negative). Preprocessing involved extracting continuous slices of the nasopharynx and clivus. The preprocessed training dataset was used for transfer learning with Residual Neural Networks 50 to create a diagnostic CNN model, which was then tested on the preprocessed test dataset to determine the invasion status and model performance. Original CT images from the test dataset were reviewed by a radiologist with extensive head/neck imaging experience (senior reader: SR) and another less-experienced radiologist (junior reader: JR). Gradient-weighted class activation maps (Grad-CAMs) were created to visualize the explainability of the invasion status classification.ResultsThe CNN model’s diagnostic accuracy was 0.973, significantly higher than those of the two radiologists (SR: 0.838; JR: 0.595). Receiver operating characteristic curve analysis gave an area under the curve of 0.953 for the CNN model (versus 0.832 and 0.617 for SR and JR; both p < 0.05). The Grad-CAMs suggested that the invasion-negative cases were present predominantly in bone marrow, while the invasion-positive cases exhibited osteosclerosis and nasopharyngeal masses.ConclusionsThis CNN technique would be useful for CT-based diagnosis of skull-base invasion by nasopharyngeal malignancies.

Numerical approximation based on deep convolutional neural network for high‐dimensional fully nonlinear merged PDEs and 2BSDEs

This paper proposes two efficient approximation methods to solve high‐dimensional fully nonlinear partial differential equations (NPDEs) and second‐order backward stochastic differential equations (2BSDEs), where such high‐dimensional fully NPDEs are extremely difficult to solve because the computational cost of standard approximation methods grows exponentially with the number of dimensions. Therefore, we consider the following methods to overcome this difficulty. For the merged fully NPDEs and 2BSDEs system, combined with the time forward discretization and ReLU function, we use multiscale deep learning fusion and convolutional neural network (CNN) techniques to obtain two numerical approximation schemes, respectively. Finally, three practical high‐dimensional test problems involving Allen–Cahn, Black–Scholes–Barenblatt, and Hamilton–Jacobi–Bellman equations are given so that the first proposed method exhibits higher efficiency and accuracy than the existing method, while the second proposed method can extend the dimensionality of the completely NPDEs–2BSDEs system over 400 dimensions, from which the numerical results highlight the effectiveness of proposed methods.

Predicting Cyclone Michaung's Wrath: Leveraging Deep Learning Technique for Intensity and Track Forecasting

Tropical cyclones pose significant threats to coastal regions, necessitating accurate and timely monitoring systems for effective disaster management. In this study, Convolutional Neural Networks (CNN) is used to identify and track the tropical cyclone Michaung using a comprehensive dataset comprising INSAT3D captured INFRARED and RAW Cyclone Imagery over the Indian Ocean from 2012 to 2021. The raw data, sourced from the MOSDAC server, has been meticulously labeled by timestamp and corresponding coordinates on the intensity-time graph of each cyclone directory. Our methodology involves training the CNN model on this extensive dataset and subsequently validating its efficacy by predicting satellite images of cyclone Michaung obtained from the MODIS satellite. The integration of both INSAT3D and MODIS imagery enhances the robustness of our model, providing a more comprehensive understanding of cyclone dynamics. Results indicate a high level of accuracy in cyclone identification, showcasing the potential of CNN techniques in satellite image analysis for cyclone monitoring. The approach not only contributes to the field of meteorology but also establishes a framework for the utilization of diverse satellite datasets in training neural networks for real-time cyclone detection and tracking. This research represents a significant step toward leveraging advanced machine learning techniques to enhance the efficiency and accuracy of tropical cyclone monitoring systems, ultimately aiding in early warning and disaster preparedness efforts.

Deep convolutional neural networks with Bee Collecting Pollen Algorithm (BCPA)-based landslide data balancing and spatial prediction

Predicting the landslide-prone area is critical for various applications, including emergency response, land planning, and disaster mitigation. There needs to be a thorough landslide inventory in current studies and appropriate sampling uncertainty issues. Landslide risk mapping has expanded significantly as machine learning techniques have developed. However, one of the primary issues in Landslide Prediction is data imbalance (DI). This is problematic since it is challenging or expensive to generate an accurate inventory map of landslides based on previous data. This study proposes a novel landslide prediction method using Generative Adversarial Networks (GAN) for generating the synthetic data, Synthetic Minority Oversampling Technique (SMOTE) for overcoming the data imbalance problem, and Bee Collecting Pollen Algorithm (BCPA) for feature extraction. Combining 184 landslides and ten criteria, including topographic wetness index (TWI), aspect, distance from the road, total curvature, sediment transport index (STI), height, slope, stream, lithology, and slope length, a geographical database was produced. The data was generated using GAN, a Deep Convolutional Neural Network (DCNN) technique to populate the dataset. The proposed DCNN-BCPA approach findings were merged with current machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM), logistic regression (LR). The model’s accuracy, precision, recall, f-score, and RMSE were measured using the following metrics: 92.675%, 96.298%, 90.536%, 96.637%, and 45.623%. This study suggests that harmonizing landslide data may have a substantial impact on the predictive capabilities of machine learning models.