This study presents an advanced modification of the You Only Look Once version 7 (YOLOv7) model named Gallbladder YOLOv7 (GB-YOLOv7). GB-YOLOv7 integrates a Normalization-based Attention Module (NAM) and a Global Attention Mechanism (GAM) into the backbone and head architecture. Several image preprocessing methods are also employed, including median filtering and Contrast-Limited Adaptive Histogram Equalization (CLAHE). The framework includes three attention mechanism-based models, including Coordinate and Global Attention Mechanism (CordGAM-YOLOv7), Dual Global Attention Mechanism (DualGAM-YOLOv7), and Normalization-based Attention Module YOLOv7 (NAM-YOLOv7), enabling a meticulous comparative analysis with GB-YOLOv7. Results demonstrate its superior performance across all metrics compared to both traditional and newer YOLO versions: achieving a Recall of 91.3% (vs YOLOv8's 78.1% and YOLOv9's 84.8%), a Mean Average Precision of 94.0%, and a Specificity of 96.2% (vs YOLOv11's 90.6%). GB-YOLOv7 also shows significant improvements in Matthews Correlation Coefficient (MCC) (72.7% vs YOLOv7's 67.3%) and F1-score (90.0% vs You Only Look Once version 9 ( YOLOv9)'s 81.4%), while maintaining greater parameter efficiency (24.34M vs YOLOv7's 36.58M), showcasing its potential as a cutting-edge tool for more effective gallbladder cancer detection.

Stress among university students can be defined as a state of mental tension caused by difficult academic endeavours. Nonetheless, stress at the tertiary (i.e., University) level has been the subject of much research as accurately predicting mental stress is crucial for promoting student well-being and academic success. In recent research, Machine Learning (ML) and Deep Learning (DL) models have been utilized for determining the academic stress. However, majority of these works utilizes isolated ML or DL models without having an in-depth assessment of the feature interpretation (i.e., the mental stressors) with respect to model performance. In this study, a novel hybrid CNN-SVM model is proposed to predict the mental stress among the university students. The rationale here is that the feature extraction capability of Convolutional Neural Networks (CNN) blended with the classification proficiency of Support Vector Machines (SVM) would better extract the relevant mental stressors towards accurate stress prediction. The model is trained and tested with the student mental stress data as measured using the PSS-10 model from 15 top-ranked universities in Bangladesh. Alongside, Explainable Artificial Intelligence (XAI) is used to validate the model performance. Reported results reveal superior performance of the hybrid CNN-SVM model in relation to conventional ML and DL models, achieving an accuracy of 97.34% with precision 97.38%, recall 97.34%, and f1-score 97.36%. XAI assessment shows that the improvement is due to the models' efficacy in utilizing the essential mental stressors than the conventional models. We argue that the overall contribution can be used for early detection of student mental stress and assist in planning for student counseling, health assessment, and academic process improvement.

ABSTRACT Colorectal cancer (CRC) is characterized by the uncontrollable growth of cancerous cells within the rectal mucosa. In contrast, colon polyps, precancerous growths, can develop into colon cancer, causing symptoms like rectal bleeding, abdominal pain, diarrhea, weight loss, and constipation. It is the leading cause of death worldwide, and this potentially fatal cancer severely afflicts the elderly. Furthermore, early diagnosis is crucial for effective treatment, as it is often more time‐consuming and laborious for experts. This study improved the accuracy of CRC multi‐class classification compared to previous research utilizing diverse datasets, such as NCT‐CRC‐HE‐100 K (100,000 images) and CRC‐VAL‐HE‐7 K (7,180 images). Initially, we utilized various image processing techniques on the NCT‐CRC‐HE‐100 K dataset to improve image quality and noise‐freeness, followed by multiple feature extraction and selection methods to identify prominent features from a large data hub and experimenting with different approaches to select the best classifiers for these critical features. The third ensemble model (XGB‐LightGBM‐RF) achieved an optimum accuracy of 99.63% with 40 prominent features using univariate feature selection methods. Moreover, the third ensemble model also achieved 99.73% accuracy from the CRC‐VAL‐HE‐7 K dataset. After combining two datasets, the third ensemble model achieved 99.27% accuracy. In addition, we trained and tested our model with two different datasets. We used 80% data from NCT‐CRC‐HE‐100 K and 20% data from CRC‐VAL‐HE‐7 K, respectively, for training and testing purposes, while the third ensemble model obtained 98.43% accuracy in multi‐class classification. The results show that this new framework, which was created using the third ensemble model, can help experts figure out what kinds of CRC diseases people are dealing with at the very beginning of an investigation.

The General Data Protection Regulation (GDPR) is the new European data protection law whose compliance affects organisations in several aspects related to the use of consent and personal data. With emerging research and innovation in data management solutions claiming assistance with various provisions of the GDPR, the task of comparing the degree and scope of such solutions is a challenge without a way to consolidate them. With GDPR as a linked data resource, it is possible to link together information and approaches addressing specific articles and thereby compare them. Organisations can take advantage of this by linking queries and results directly to the relevant text, thereby making it possible to record and measure their solutions for compliance towards specific obligations. GDPR text extensions (GDPRtEXT) uses the European Legislation Identifier (ELI) ontology published by the European Publications Office for exposing the GDPR as linked data. The dataset is published using DCAT and includes an online webpage with HTML id attributes for each article and its subpoints. A SKOS vocabulary is provided that links concepts with the relevant text in GDPR. To demonstrate how related legislations can be linked to highlight changes between them for reusing existing approaches, we provide a mapping from Data Protection Directive (DPD), which was the previous data protection law, to GDPR showing the nature of changes between the two legislations. We also discuss in brief the existing corpora of research that can benefit from the adoption of this resource.

Cloud computing is rapidly changing the digital service landscape. A proliferation of Cloud providers has emerged, increasing the difficulty of consumer decisions. Trust issues have been identified as a factor holding back Cloud adoption. The risks and challenges inherent in the adoption of Cloud services are well recognised in the computing literature. In conjunction with these risks, the relative novelty of the online environment as a context for the provision of business services can increase consumer perceptions of uncertainty. This uncertainty is worsened in a Cloud context due to the lack of transparency, from the consumer perspective, into the service types, operational conditions and the quality of service offered by the diverse providers. Previous approaches failed to provide an appropriate medium for communicating trust and trustworthiness in Clouds. A new strategy is required to improve consumer confidence and trust in Cloud providers. This paper presents the operationalisation of a trust label system designed to communicate trust and trustworthiness in Cloud services. We describe the technical details and implementation of the trust label components. Based on a use case scenario, an initial evaluation was carried out to test its operations and its usefulness for increasing consumer trust in Cloud services.

A data location control model for Cloud services is presented that uses an authorization system as its core control element. The model is intended for use by enterprises that collect personal data from end users that can potentially be stored and processed at multiple geographic locations. By adhering to the model's authorization decisions, the enterprise can address end users' concerns about the location of their data by incorporating the their preferences about the location of their personal data into authorization policy. The model also ensures that the end users have visibility into the location of their data and are informed when the location of their data changes. A prototype of the model has been implemented that provides the data owner with an interface that allows their location preferences to be expressed. These preferences are stored internally as XACML policy documents. Thereafter, movements or remote duplications of the data must be authorized by submitting requests to an ISO/IEC 10181-3:1996 compliant Policy Enforcement Point. End users can, at any time, view up-to-date information on the locations where their data is stored via a web interface. Furthermore, XACML obligations are used to ensure that end users are informed whenever the location of their data changes.