Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract Population imaging studies rely on good-quality medical imagery before quantifying downstream images. This study provides an automated approach for image quality assessment (IQA) from cardiovascular magnetic resonance (CMR) imaging at scale. We identify four common CMR imaging artefacts: respiratory motion, cardiac motion, Gibbs ringing, and aliasing. Four datasets, including UK Biobank, York University (YU), the Universidad Carlos III (UCIII) and CMR-Tehran, were used to perform the experiments. This study proposes two deep-learning models for CMR IQA in spatial and frequency domains. The presented spatial-domain model also has domain adaptation. The accuracies of supervised 4-fold cross-validation experiments for UK Biobank, YU, UCIII and CMR-Tehran datasets are 99.41%, 75.78%, 89.46% and 67.87% for the spatial-domain and 87.46%, 63.76%, 80.25% and 58.48% for the frequency-domain. Domain adaptation results, considering UK Biobank as the source set and YU, UCIII and CMR-Tehran as the target sets, show the domain shift gap coverage between the datasets to the extent of +11.91%, +3.93% and +16.57%, respectively. Besides, by training and testing the spatial-domain model on 30,125 images from the UK Biobank, an accuracy of 89.56% was obtained in a training time of 394.80 seconds. Meanwhile, the frequency-domain model with training and testing on 180,750 images achieves an accuracy of 87.99% in a training time of 255.04 seconds. Thus, the frequency-domain model can achieve almost the same accuracy yet 1.548 times faster than the spatial model. The proposed models can detect four common CMR imaging artefacts by receiving images or the corresponding k-spaces.

Abstract Abstract— Sea Surface Temperature (SST) data reveal the temporal and spatial distribution of warm anticyclonic eddies and cold cyclonic eddies, impacting ocean behavior. The SST combined products attained adequate decisions to permit the recognition of mesoscale eddies with the introduction of altimeter operations and the availability of two or more altimeters at the same time. Climate change impacts ocean circulation and atmospheric anomalies linked to SST variations. Ocean eddies, vital for material and energy transport, require precise identification to advance oceanography. This study uses SST data from CMEMS in the Atlantic to introduce EddyNet, a deep-learning model for automatic eddy detection and classification. EddyNet's encoder-decoder architecture includes a pixel-wise classification layer, labeling each pixel as "0" (non-eddy), "1" (anticyclonic), or "2" (cyclonic). The high-resolution feature representation outperforms existing models, marking a significant leap in eddy detection accuracy and reliability. This study introduces EddyNet, a deep-learning model based on the U-Net architecture for automatic eddy detection and classification using Sea Surface Temperature (SST) data. The model was trained and evaluated on satellite imagery from the Copernicus Marine Environment Monitoring Service (CMEMS), achieving a training accuracy of 78.55%, a Dice score of 31.99%, and a precision of 0.9259. The recall values for different classes indicate that the model correctly identifies 99.51% of non-eddy pixels, 51.57% of anticyclonic eddy pixels, and 57.19% of cyclonic eddy pixels. These results demonstrate the effectiveness of deep learning in mesoscale eddy detection and highlight the potential for further optimization in classifying eddy structures with higher precision.

Abstract Abstract— Activation functions are essential for extracting meaningful relationships from real-world data in deep learning models. The design of activation functions is critical, as they directly influence the performance of these models. Nonlinear activation functions are commonly preferred since linear functions can limit a model’s learning capacity. Nonlinear activation functions can either have fixed parameters, which are predefined before training, or adjustable ones that modify during training. Fixed-parameter activation functions require the user to set the parameter values prior to model training. However, finding suitable parameters can be time-consuming and may slow down the convergence of the model. In this study, a novel fixed-parameter activation function is proposed and its performance is evaluated using benchmark MNIST datasets, demonstrating improvements in both accuracy and convergence speed.

Abstract— Activation functions are essential for extracting meaningful relationships from real-world data in deep learning models. The design of activation functions is critical, as they directly influence the performance of these models. Nonlinear activation functions are commonly preferred since linear functions can limit a model’s learning capacity. Nonlinear activation functions can either have fixed parameters, which are predefined before training, or adjustable ones that modify during training. Fixed-parameter activation functions require the user to set the parameter values prior to model training. However, finding suitable parameters can be time-consuming and may slow down the convergence of the model. In this study, a novel fixed-parameter activation function is proposed and its performance is evaluated using benchmark MNIST datasets, demonstrating improvements in both accuracy and convergence speed.

Abstract Abstract— The issue of intrusion in security presents a fundamental challenge that can lead to serious damage in IT systems. Intrusion Detection Systems (IDS) serve as effective tools for identifying intrusion activities and generating alerts. However, traditional IDS methods often face issues such as low accuracy and long training times. Therefore, enhancing the performance and efficiency of these systems is crucial. The proposed approach in this study leverages evolutionary optimization algorithms combined with machine learning approaches to improve accuracy and training speed in IDS and better manage large volumes of data. This combination leads to the development of an Evolutionary Neural Network (ENN) that enhances and optimizes IDS performance. In this approach, BUZOA and Ant Colony Optimization (ACO) algorithms are used for feature selection, and decision tree, k-nearest neighbor, support vector machine, and deep neural network algorithms are used for classification and intrusion detection. The dataset used in this research is from the CICDDOS2019 database, containing 54,000 samples and 22 initial features. The experimental results indicate that among the metaheuristic algorithms BUZOA and ACO, and their combinations with decision tree, k-nearest neighbor, and support vector machine, the BUZOA-CNN hybrid algorithm with an average RMSE of 0.0117 and an accuracy of 96.32% performs better than other algorithms.

Abstract Abstract— Detecting white blood cells (WBC) in microscopic images is essential in medical diagnosis. Manual analysis of these images is time-consuming and has a high error rate. Using object detection for WBCs detection with deep convolutional neural networks (CNN) can be considered a practical and effective solution. In this study, a CNN model is proposed to classify these images. In order to achieve optimal training performance, CNNs have many hyperparameters, such as dropout rate, number of hidden units in each hidden layer, activation function, loss function and optimizer, which need to be optimized. Therefore, a hyperparameter optimization approach based on a genetic algorithm is suggested, which can then be used to select the best combination parameters to improve accuracy and efficiency in detecting white blood cells in microscopic images. This new approach is significant and flexible for medical technicians to use in clinical practice for examining blood cell microscopy. In this research, the images were classified into five classes and the mean accuracy of the model for the five classes was 87%, which is considered a good accuracy for classification into five classes.

Abstract Understanding dynamic systems like disease outbreaks, social influence, and information diffusion requires effective modeling of complex networks. Traditional evaluation methods for static networks often fall short when applied to temporal networks. This paper introduces DGSP-GCN (Dynamic Graph Similarity Prediction based on Graph Convolutional Network), a deep learning-based framework that integrates graph convolutional networks with dynamic graph signal processing techniques to provide a unified solution for evaluating generative models and detecting anomalies in dynamic networks. DGSP-GCN assesses how well a generated network snapshot matches the expected temporal evolution, incorporating an attention mechanism to improve embedding quality and capture dynamic structural changes. The approach was tested on five real-world datasets: WikiMath, Chickenpox, PedalMe, MontevideoBus, and MetraLa. Results show that DGSP-GCN outperforms baseline methods, such as time series regression and random similarity assignment, achieving the lowest error rates (MSE of 0.0645, MAE of 0.1781, RMSE of 0.2507). These findings highlight DGSP-GCN's effectiveness in evaluating and detecting anomalies in dynamic networks, offering valuable insights for network evolution and anomaly detection research.

Abstract With the rapid expansion of the Internet and social networks, the volume of images available online has increased dramatically, making it challenging for users to find relevant content. To address this problem, we propose incorporating emotion analysis as a key factor in understanding user preferences, thereby creating a more personalized and effective image recommendation system. In this article, we examine two approaches to utilizing emotional features in image recommendation. The first approach integrates emotional features directly into the feature vector used for training the recommendation model. The second approach refines recommendations through emotion-based postprocessing, where emotional proximity between users and images is used to re-rank recommendations. This study emphasizes the value of emotion analysis in advancing the personalization and efficacy of social image recommendation systems. Experimental results indicate that both approaches significantly improve recommendation performance, achieving higher metrics such as Recall@k and Precision@k. These findings demonstrate that emotional analysis enhances personalization and effectiveness in social image recommendation systems.