Modeling Suspicious Email Detection using Enhanced Feature Selection

The paper presents a suspicious email detection model which incorporates enhanced feature selection. In the paper we proposed the use of feature selection strategies along with classification technique for terrorists email detection. The presented model focuses on the evaluation of machine learning algorithms such as decision tree (ID3), logistic regression, Na\"ive Bayes (NB), and Support Vector Machine (SVM) for detecting emails containing suspicious content. In the literature, various algorithms achieved good accuracy for the desired task. However, the results achieved by those algorithms can be further improved by using appropriate feature selection mechanisms. We have identified the use of a specific feature selection scheme that improves the performance of the existing algorithms

Cooperation Services in a Structural Computing Environment

Colloque avec actes et comité de lecture. internationale.International audienceEnvironments for structural computing have seen significant recent development. Generally, they provide several hypermedia facilities involving several applications. Users within these environments may perform activities involving several applications and handle several types of hypermedia data. In this context, they need within the structural computing environment some cooperation facilities that help them to coordinate their activities and manage their roles in cooperation. This paper proposes to integrate some cooperation services into the Construct structural computing environment

"If only had I known":a qualitative study investigating a treatment of patients with a hip fracture with short time stay in hospital

Hip fractures are amongst the leading causes of admission to an orthopaedic ward. Systematized pathways with reduced admission time have become increasingly common as an essential tool for quality development and to improve efficiency in the hospital setting.  The aim of this study was to clarify if the patients feel empowered and able to perform self-care after short time stay in hospital (STSH) due to a hip fracture. The study used descriptive phenomenology to describe experiences of the pathway. Field studies were conducted in hospitals and in the patients' homes.  Interviews were performed with 10 patients recruited from two wards at a Danish University hospital, 4 family members and 15 health professionals from three hospitals.  The open attitude of reflective lifeworld research guided the analysis. The findings revealed that patients felt unprepared and insecure about their future, but also had a strong desire to be in charge of their own lives.  Of all the patients interviewed, none had any recollection of the information given to them by health professionals during their hospital admission. This study demonstrates that empowerment of patients with hip fractures is not adequately achieved in the pathway with STSH

Monkeypox detection using deep neural networks

BACKGROUND: In May 2022, the World Health Organization (WHO) European Region announced an atypical Monkeypox epidemic in response to reports of numerous cases in some member countries unrelated to those where the illness is endemic. This issue has raised concerns about the widespread nature of this disease around the world. The experience with Coronavirus Disease 2019 (COVID-19) has increased awareness about pandemics among researchers and health authorities.METHODS: Deep Neural Networks (DNNs) have shown promising performance in detecting COVID-19 and predicting its outcomes. As a result, researchers have begun applying similar methods to detect Monkeypox disease. In this study, we utilize a dataset comprising skin images of three diseases: Monkeypox, Chickenpox, Measles, and Normal cases. We develop seven DNN models to identify Monkeypox from these images. Two scenarios of including two classes and four classes are implemented.RESULTS: The results show that our proposed DenseNet201-based architecture has the best performance, with Accuracy = 97.63%, F1-Score = 90.51%, and Area Under Curve (AUC) = 94.27% in two-class scenario; and Accuracy = 95.18%, F1-Score = 89.61%, AUC = 92.06% for four-class scenario. Comparing our study with previous studies with similar scenarios, shows that our proposed model demonstrates superior performance, particularly in terms of the F1-Score metric. For the sake of transparency and explainability, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-Cam) were developed to interpret the results. These techniques aim to provide insights into the decision-making process, thereby increasing the trust of clinicians.CONCLUSION: The DenseNet201 model outperforms the other models in terms of the confusion metrics, regardless of the scenario. One significant accomplishment of this study is the utilization of LIME and Grad-Cam to identify the affected areas and assess their significance in diagnosing diseases based on skin images. By incorporating these techniques, we enhance our understanding of the infected regions and their relevance in distinguishing Monkeypox from other similar diseases. Our proposed model can serve as a valuable auxiliary tool for diagnosing Monkeypox and distinguishing it from other related conditions.</p

Efficacy of a web-based healthcare innovation to advance the quality of life and care of patients with an implantable cardioverter defibrillator (ACQUIRE-ICD): a randomized controlled trial

AIMS: Modern clinical management of patients with an implantable cardioverter defibrillator (ICD) largely consists of remote device monitoring, although a subset is at risk of mental health issues post-implantation. We compared a 12-month web-based intervention consisting of goal setting, monitoring of patients' mental health-with a psychological intervention if needed-psychoeducational support from a nurse, and an online patient forum, with usual care on participants' device acceptance 12 months after implantation.METHODS AND RESULTS: This national, multi-site, two-arm, non-blinded, randomized, controlled, superiority trial enrolled 478 first-time ICD recipients from all 6 implantation centres in Denmark. The primary endpoint was patient device acceptance measured by the Florida Patient Acceptance Survey (FPAS; general score range = 0-100, with higher scores indicating higher device acceptance) 12 months after implantation. Secondary endpoints included symptoms of depression and anxiety. The primary endpoint of device acceptance was not different between groups at 12 months [B = -2.67, 95% confidence interval (CI) (-5.62, 0.29), P = 0.08]. Furthermore, the secondary endpoint analyses showed no significant treatment effect on either depressive [B = -0.49, 95% CI (-1.19; 0.21), P = 0.17] or anxiety symptoms [B = -0.39, 95% CI (-0.96; 0.18), P = 0.18].CONCLUSION: The web-based intervention as supplement to usual care did not improve patient device acceptance nor symptoms of anxiety and depression compared with usual care. This specific web-based intervention thus cannot be recommended as a standardized intervention in ICD patients.</p

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Hyperform: A Hypermedia System Development Environment

Development of hypermedia systems is a complex matter. The current trend toward open, extensible and distributed multiuser hypermedia systems adds additional complexity to the development process. As a means of reducing this complexity, we have seen an increasing interest in hyperbase management systems that allow hypermedia system developers to abstract from the intricacies and complexity of the hyperbase layer and fully attend to application and user interface issues. Design, development and deployment experiences of a dynamic, open and distributed multiuser hypermedia system development environment called Hyperform is presented. Hyperform is based on the concepts of extensibility, tailorability and rapid prototyping of hypermedia system services. Open, extensible hyperbase management systems permit hypermedia system developers to tailor hypermedia functionality for specific applications and serve as a platform for research. The Hyperform development environment is comprised of multiple instances of four component types: (1) a hyperbase management system server, (2) a tool integrator, (3) editors and (4) participating tools. Hyperform has been deployed in Unix environments and experiments have shown tha

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Proceedings of th