International trade requires transparent visibility of the goods transportation. High-quality data related to containers is essential for container movement across the border speed. However, customs and port authorities face information incorrectness and inconsistency, which are significant determinants that decrease the performance of container clearance in supply chain activities. The Seamless Integrated Data Pipeline principle has been proposed to overcome the mentioned data quality shortcomings and enhance supply chain visibility. Based on the Data Pipeline idea, we proposed the Distributed Trust Backbone (DTB) as a model of secure information exchange between parties within the supply chain activity. However, the supply chain data is highly dynamic. Access control on dynamic resources is the key to enabling secure data exchange and clear visibility. We take this challenge up in this paper. We propose an access control mechanism based on the supply chain Data Pipeline concept and apply it to the DTB model. The elaboration on the concrete detail of the system is presented in this paper. The prototype has been developed and performed in the simulation tests. It reduces 58% of requesting data for supply chain activities. The results of the experiments show that our proposed method performs 100% access control to data with BigO(1) accessing the Access Control List. It can ensure that the information for decision-making in the supply chain is of high quality. The supply chain visibility is clearer and speeds up a modern information exchange system of supply chains.

Nurses play a crucial role in healthcare, directly influencing patient care quality. With a global nursing shortage, enhancing nursing efficiency and care quality is urgently needed. This foundational study explores the advantages of text and data processing techniques to determine NANDA-I nursing diagnoses using both subjective and objective patient data recorded by nurses. By employing text data similarity analysis and a prototype of the predictive model, our research aims to rene the nursing assessment process and facilitate the automation of nursing diagnoses. This work highlights the accuracy of BERT-based assessment pattern matching to support nursing practices and sets a platform for future research to address the nursing shortage effectively.

Image denoising using supervised learning effectively removes image noise by learning from available data. However, it may lack efficiency when faced with insufficient data, such as in the case of single images or blind noise. This challenge has led to the adoption of unsupervised learning methods, which utilize the inherent properties of noise to extract and enhance image features. This research aims to leverage the benefits of the downsampling effect for noise removal, even though downsampling may impact image features. Therefore, deep learning must be used to restore image details lost during downsampling. This research proposes the Noisy Low-Resolution to Noisy Super-Resolution (NLR2NSR) framework, which leverages image downsampling to simultaneously reduce image and noise features. A super-resolution network is then used to restore the image features. Experimental results show that under conditions where noise features are less prominent than image features, the NLR2NSR can effectively remove noise and preserve image features using only noisy data for training. However, the NLR2NSR has limitations in handling high-level noise.

This research investigates a multilingual cross-modal pedestrian information search (PIS) technique based on graph-text modal fusion. Initially, we used a combination of replacement neural networks to improve the English Language-Based Pedestrian Information Search model with Graph-Text Modal Fusion (GTMFLPIS) performance. In addition, existing research lacks GTMFLPIS models for other languages. Therefore, we propose to train GTMFLPIS models for Chinese. The Chinese GTMFLPIS model was trained using our previously constructed Chinese CUHK-PEDES dataset. The Rank1 of the Chinese RN50_PMML12V2 model reached 0.5989. In addition, we found that a single model could not adapt to the limitations of multiple languages. Therefore, we propose a novel architecture to implement a single-model multilingual cross-modal GTMFLPIS model in this research. We propose RN50_DBMCV2 and ENB7_DBM-CV2, both of which have improved performance over the existing ones. We constructed a bilingual dataset using our Chinese CUHK-PEDES dataset and existing English CUHK-PEDES dataset to test our novel multilingual cross-modal GTMFLPIS model. In addition, we found that the loss function significantly impacts the model during our experiments. Therefore, we optimized the performance of the existing loss functions for cross-modal GTMFLPIS models. Our proposed CCMPM loss function improves the performance of the model by 2%. The experimental results of this research show that our proposed model has advantages in improving the accuracy of PIS.

The deep learning (DL) techniques used for image classification might not deliver the desired level of classification accuracy as some features belonging to some class of a dataset are missed during feature extraction. The ensemble learning (EL) based model improves classification accuracy by combining the strengths of individual classifiers. As a result, those features that were missed during feature extraction by a specific DL technique will be taken care of by another DL technique in an ensemble DL approach. In this paper, averaging EL (AENet), weighted averaging EL (WAENet), and stacking EL (StackedNet) approaches are proposed, considering the DenseNet201, EcientNetB0, and ResNetRS101 as base models. The predictions of the base models are averaged to generate the AENet. The WAENet is constructed by assigning weights to each base model based on their prediction and then taking their average. Similarly, the Stacked-Net is developed by considering the DenseNet201, EcientNetB0, and ResNetRS101 as base-learners and ResNetRS101 as meta-learner. Analysed performance of the considered pre-trained base models and the developed EL models on the standard and application-specific datasets such as MiniImageNet, CIFAR10, CIFAR100, Plant Village (PV), Tomato, Covid-19 and 9IndianFood. 80% of the datasets were used to train and 20% to test the base and proposed models. The models are trained for an epoch of 30, considering a learning rate of 0.001 and adam optimizer. The stackedNet delivered better results than others.

Nowadays, leading manufacturers of enterprise-grade networking devices offer the dynamic ARP inspection (DAI) feature in their Ethernet Switches to detect and prevent ARP cache poisoning attacks from malicious hosts. However, MikroTik Ethernet switches do not yet support this feature. Within MikroTik-based networks, three potential approaches exist to prevent ARP cache poisoning attacks, each with drawbacks. This paper proposes an innovative approach called Gateway-controlled ARP (GCA) to prevent ARP cache poisoning attacks on a router-on-a-stick (RoaS) network using MikroTik networking devices, where a single router performs inter-VLAN routing through one physical interface. With this approach, all Ethernet switches are configured to forward ARP messages from hosts directly to the router for inspection and handling. A RouterOS script based on the GCA approach was implemented and executed on the router to handle all incoming ARP requests from any host in all VLANs, ensuring all hosts receive legitimate ARP responses from the router. This approach can effectively prevent spoofed ARP packets sent by malicious attackers. This approach was tested and evaluated on an actual RoaS network, focusing on processing time, CPU Load, and response time. The evaluation results show that the approach effectively prevents ARP cache poisoning attacks.

Advances in technology and data availability have helped in improving the quality of care and in predicting health issues in infants. Currently, Information and Communication technology aids in reaching the essentiality and the applications of infant health to a greater extent. Over a few decades, researchers have dived into sensing and the prediction of Artificial Intelligence (AI) for infant health. Since these healthcare systems deal with large amounts of data, significant development is seen in several computing platforms. AI, including both machine learning (ML) and deep learning (DL), plays a crucial role in the medical industry in the prediction and classification of various infant diseases. The prediction of diseases in infants using extubation readiness and their utility ranges is still lacking. Thus, the present study aims to present a complete review of the adaption of ML and DL approaches to infant health prediction. The current review paper provides a complete overview of the research predicting infant health issues. Effectual comparisons are made among the AI approaches performing infant disease prediction. Furthermore, the paper identifies the research gaps and the future direction of the research in the present domain. A comprehensive form of analysis of the current landscapes involved in predicting infant health issues using AI is presented.

We present Codepickle, a groundbreaking solution designed to overcome Python version constraints and enhance portability, especially within distributed Python grids and volunteer computing environments. Unlike existing serialization libraries such as Cloudpickle and Dill, which rely on Python bytecode and are prone to version conflicts, Codepickle offers a robust alternative. Our methodology includes innovative adjustments for function serialization and shared variable management. Experimental results reveal challenges like code sourcing and nonlocal variable handling. Performance benchmarks highlight Codepickle's significant advantages over Cloudpickle, including better portability and reduced message sizes. Notably, Codepickle achieves message sizes that are 84% of those produced by Cloudpickle especially for small code segments, with comparable execution performance. Proposed enhancements target critical issues such as lambda functions and cross-version compatibility. This comprehensive study not only demonstrates Codepickle's transformative potential but also underscores the ongoing quest for advanced serialization techniques in Python's distributed computing landscape.

This Data fusion has become a significant issue in diagnostic imaging, particularly in medical applications like radiation and guided image surgery. Medical image fusion aims to enhance the precision of tumor diagnosis, by preserving the salient information and characteristics of the original images in the fused image. It has been shown that guided filters are capable of maintaining edges well. In this paper, we propose a novel cross-guided filter-based fusion approach for multimodal medical images utilizing convolutional neural networks. The cross-guided filter is used in the proposed algorithm to extract the detailed features from the source images. Convolutional neural networks are used to generate the feature weights of source images derived from the detail layers. The weighted average rule is used to merge the source images based on these weights. We used thirty distinct types of medical images from diverse sources to compare the effectiveness of the proposed strategy to that of existing methods, both numerically and visually. The experimental findings demonstrated that, in terms of both objective evaluation and qualitative image quality, the suggested system performs better than other standard methods already in use. The quantitative results show that compared to existing methods under consideration for comparison, the proposed algorithm improves mutual information by 25%, image entropy by 9.5%, spatial frequency by 21%, standard deviation by 18.1%, structural similarity index by 30%, and edge strength of the fused image by 39%.