Recent Advances in Computer Science and Communications - Online First

Cybersecurity requirements for ensuring data security during research manuscript transit on the journal website require continuous improvement and adherence to best practices. Research data loss can have significant negative consequences across multiple dimensions including time and financial loss. The present research investigates security vulnerabilities during the real-time transit of manuscripts on a journal website.

Procedure: Website Access: The journal website was accessed, and manuscript components (main manuscript, figures, tables, graphical abstract, funding sources, suggested reviewer, and cover letter) were uploaded.

Operating system: Kali Linux, designed for penetration testing and security auditing was used.

Tools and software: Nmap (Version 7.95-2) for network discovery and security auditing. Nikto (2.5.0) for web server vulnerability scanning, and Tor (13.0.13) to anonymize web activities. Firefox (127.0.2) as the web browser, and VMware Workstation with Kali Rolling (2023.2 in a virtual environment.

Testing phase: Initial upload of the manuscript and supplementary materials. Upload of figures, tables, and graphical abstract. Inclusion of funding sources, suggested reviewers, and cover letter.

Data Collection and Analysis: Network traffic and potential vulnerabilities were monitored on Nmap, Nikto, and Tor.

Activities were conducted in the virtual environment of VMware Workstation for controlled and replicable setup.

Output measures: Identified and documented potential security gap or vulnerabilities leading to data theft during manuscript transit.

An Nmap scan of XXXXXXXX.com (IP: yyyyyyyyyyy) revealed six open ports: 80 (HTTP Apache), 443 (SSL/SMTP Exim), 587 (SMTP Exim), 993 (IMAPS), and 995 (POP3S). each server showed potential vulnerabilities. The scan took 86.15 seconds.

The results demonstrate a high risk of exposing sensitive information due to open ports, the presence of potentially outdated services, and the possibility of incomplete detection due to filtered ports pose a high risk of sensitive data during manuscript transit on the website of the journal.

Cardio Vascular Disease (CVD), a primary cause of death worldwide, includes a variety of heart-related disorders like heart failure, arrhythmias, and coronary artery disease (CAD), where plaque buildup narrows the heart muscle's blood vessels and causes angina or heart attacks. Genetics, congenital anomalies, bad diet, lack of exercise, smoking, and chronic diseases including hypertension and diabetes can cause cardiac disease.

The symptoms can range from chest pain and shortness of breath to exhaustion and palpitations and diagnosis usually involves a medical history, physical examination, and electrocardiograms (ECGs), and stress testing. Lifestyle adjustments, medicines, angioplasty, and bypass grafts or heart transplants are possible treatments. Preventive measures include healthy living, risk factor management, and frequent checkups, which are few measures, whereas advanced algorithms can analyze massive volumes of ECG and MRI data to find patterns and anomalies that humans may overlook.

The deep learning models increase arrhythmia, coronary artery disease, and heart failure diagnosis accuracy and speed. They enable predictive analytics, early intervention, and personalized treatment programs, increasing cardiac care results. The proposed DNN model consists of a 3-layer architecture having input, hidden, and output layers. In the hidden layer, 2 layers, namely, the dense layer and batch normalization layer are added to enhance its accuracy.

Three different optimizers namely Adam, AdaGrad, and AdaDelta are tested on 50 epochs and 32 batch sizes for predicting cardiovascular disease. Adam optimizer has the highest accuracy of 85% using the proposed deep neural network.

Considering the singularity of collaborative filtering algorithms in recommending learning resources and the problem that existing knowledge graph convolutional networks cannot deeply mine the neighbourhood information of learning resource nodes in application scenarios with less neighbourhood information, a multi-domain fusion convolutional network learning resource recommendation model based on knowledge graphs is proposed here.

This study aimed to improve the accuracy and personalization of recommendations of filtering algorithms.

First, the model mapped learner nodes, learning resources, and their neighbour nodes into low-dimensional dense vectors. Second, the multi-domain fusion layer and the multi-domain aggregator were used to obtain the fused multi-domain learning resource vector. Finally, the learner vector and the multi-domain learning resource vector were fed into the prediction layer to calculate the interaction probability.

To verify the effectiveness of the algorithm, we conducted a comparative experiment using the publicly available datasets MOOPer and MOOCCubeX. The experimental results showed that the proposed model outperformed baseline models, such as CKE, MKR, KGCN, DEKGCN, and KGIN, in terms of evaluation metrics, such as AUC, ACC, and F1. At the same time, when the neighborhood information was limited, the AUC, ACC, and F1 values of the proposed model still maintained the optimal value.

Compared to the optimal baseline model, the effectiveness of the proposed model has been proven.

With the rapid development of society, motor vehicles have become one of the main means of transportation. However, as the number of motor vehicles continues to increase, traffic safety accidents also continue to appear, bringing serious threats to people's lives, and property safety. Fatigue driving is one of the important causes of traffic safety accidents.

To address this problem, a target detection algorithm called VA-YOLO is designed to improve the speed and accuracy of facial recognition for fatigue checking. The algorithm employs a lightweight backbone network, VanillaNet, instead of the traditional backbone network, which reduces the computational and parametric quantities of the model. The SE attention mechanism is also introduced to enhance the model's attention to the target features, which further improves the accuracy of target detection. Finally, in terms of the bounding box regression loss function, the SIoU loss function is used to reduce the error.

The experimental results show that, compared toYolov8n, the VA-YOLO algorithm improves the accuracy by 1.3% while the number of parameters decreases by 30%.

This shows that the VA-YOLO algorithm has a significant advantage in realizing the balance between the number of parameters and accuracy, which is important for improving the speed and accuracy of fatigue driving detection.

”Artificial Intelligence will revolutionize our lives” is a phrase frequently echoed. The influence of Artificial Intelligence (AI) and Machine Learning (ML) extends across various aspects of our daily lives, encompassing health, education, economics, the environment, and more.

A particularly formidable challenge lies in decision support, especially in critical scenarios such as natural disaster management, where artificial intelligence significantly amplifies its ongoing capacity to assist in making optimal decisions. In the realm of disaster management, the primary focus often centers on preventing or mitigating the impact of disasters. Consequently, it becomes imperative to anticipate their occurrence in terms of both time and location, enabling the effective implementation of necessary strategies and measures. In our research, we propose a disaster forecasting framework based on a Multi-Layer Perceptron (MLP) empowered by the Particle Swarm Optimization (PSO) algorithm. The PSO-MLP is further fortified by the incorporation of the Artificial Bee Colony (ABC) algorithm for feature selection, pinpointing the most critical elements. Subsequently, we employ the LIME (Local Interpretable Model-agnostic Explanations) model, a component of eXplainable Artificial Intelligence (XAI). This comprehensive approach aims to assist managers and decision-makers in comprehending the factors influencing the determination of the occurrence of such disasters and increases the performance of the PSO-MLP model. The approach, specifically applied to predict snow avalanches, has yielded impressive results.

The obtained accuracy of 0.92 and an AUC of 0.94 demonstrate the effectiveness of the proposed framework. In comparison, the prediction precision achieved through an SVM is 0.75, while the RF classifier yields 0.86, and XGBoost reaches 0.77. Notably, the precision is further enhanced to 0.81 when utilizing XGBoost optimized by the grid-search.

These results highlight the superior performance of the proposed methodology, showcasing its potential for accurate and reliable snow avalanche predictions compared to other established models.

Unbalanced datasets present a significant challenge in machine learning, often leading to biased models that favor the majority class. Recent oversampling techniques like SMOTE, Borderline SMOTE, and ADASYN attempt to mitigate these issues. This study investigates these techniques in conjunction with machine learning models like SVM, Decision Tree, and Logistic Regression. The results reveal critical challenges such as noise amplification and overfitting, which we address by refining the oversampling approaches to improve model performance and generalization.

In order to address this challenge of unbalanced datasets, the minority class is oversampled to accommodate the majority class. Oversampling techniques such SMOTE (Synthetic Minority Oversampling Technique), Borderline SMOTE and ADASYN (Adaptive Synthetic Sampling) are used in this work.

To perform the comprehensive analysis of various oversampling methods for taking acre of class imbalance issue using ML methods.

The proposed methodology uses BERT technique which removes the pre-processing step. Various proposed oversampling techniques in the literature are used for balancing the data, followed by feature extraction followed by text classification using ML algorithms. Experiments are performed using ML classification algorithms like Decision tree (DT), Logistic regression (LR), Support vector machine (SVM) and Random forest (RF) for categorizing the data.

The results show improvement corresponding SVM using Borderline SMOTE, resulting in an accuracy of 71.9% and MCC value of 0.53.

The suggested method assists in the evolution of fairer and more effective ML models by addressing this basic issue of class imbalance.

Deformable image registration is an essential task in medical image analysis. The UNet model, or the model with the U-shaped structure, has been popularly proposed in deep learning-based registration methods. However, they easily lose the important similarity information in the up-sampling stage, and these methods usually ignore the inherent inverse consistency of the transformation between a pair of images. Furthermore, the traditional smoothing constraints used in the existing methods can only partially ensure the folding of the deformation field.

An inverse consistent deformable medical image registration network(ICSANet) based on the inverse consistency constraint and the similarity-based local attention is developed. A new UNet network is constructed by introducing similarity-based local attention to focus on the spatial correspondence in the high-similarity space. A novel inverse consistency constraint is proposed, and the objective function of the new form is presented with the combination of the traditional constraint conditions. Experiment: The performance of the proposed method is compared with the typical registration models, such as the VoxelMorph, PVT, nnFormer, and TransMorph-diff model, on the brain IXI and OASIS datasets.

Experimental results on the brain MRI datasets show that the images can be deformed symmetrically until two distorted images are well matched. The quantitative comparison and visual analysis indicate that the proposed method performs better, and the Dice index can be improved by at least 12% with only 10% parameters.

This paper presents a new medical image registration network, ICSANet. By introducing a similarity attention gate, it accurately captures high-similarity spatial correspondences between source and target images, resulting in better registration performance.

To address the challenges of low visibility, object recognition difficulties, and low detection accuracy in foggy weather, this paper introduces the WViT-YOLO real-time fog detection model, built on the YOLOv5 framework. The NVIT-Net backbone network, incorporating NTB and NCB modules, enhances the model's ability to extract both global and local features from images.

An efficient convolutional C3_DSConv module is designed and integrated with channel attention mechanisms and ShuffleAttention at each upsampling stage, improving the model's computational speed and its ability to detect small and blurry objects. The Wise-IOU loss function is utilized during the prediction stage to enhance the model's convergence efficiency.

Experimental results on the publicly available RTTS dataset for vehicle detection in foggy conditions demonstrate that the WViT-YOLO model achieves a 3.2% increase in precision, a 9.5% rise in recall, and an 8.6% improvement in mAP50 compared to the baseline model. Furthermore, WViT-YOLO shows a 9.5% and 8.6% mAP50 improvement over YOLOv7 and YOLOv8, respectively. For detecting small and blurry objects in foggy conditions, the model demonstrates approximately a 5% improvement over the benchmark, significantly enhancing the detection network's generalization ability under foggy conditions.

This advancement is crucial for improving vehicle safety in such weather. The code is available at https://github.com/QinghuaZhang1/mode.

The use of 20% blended biofuels to fossil fuels is one of the important targets of the Government of India to address the impacts of Climate Change, energy-related environmental pollution, and illnesses due to air pollution.

The National Policy on Biofuels 2018 (NPB 2018) is in place to boost the emerging production of biofuels and, therefore, respond to different international agreements, including the Sustainable Development Goals (SDGs) and the Paris Agreement. Hence, this article examined the production of biofuels in India in line with Agenda 2030 to project the share to be taken by biofuels as its contribution to the country’s energy needs.

The results were compromising; it was observed that the data from 2000 up to 2017 were not on the side of realizing the targets of production and consumption of biofuels in India, whereas the data from 2018 up to now showed a hope of achieving 2030 set goal of E20 petrol in 2025-26, and E5 diesel in 2030. It was clear that the production of bioethanol was boosting compared to its sibling biodiesel, and renewable energy will continue to have a hard take a good share in the total annual energy used in India.

It is recommended to share data between different stakeholders to promote more research, as the low performance in achieving the targets was due to poor communication and missing technology, rather than the lack of feedstock or unavailability of production facilities.