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

"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

Patient@home Ecosystem

Abstract Patient@home was the largest Danish project within health and welfare technology. The aim was to support the health and care sector with innovative technologies to meet the increasing demands of an aging and more chronically ill population. The underlying hypothesis was that the increased use of technology could help minimize hospital admissions in both number and duration. New technologies enable carefully selected groups of patients to stay longer in their own homes to the benefit of both patients (and their relatives) and the health and care sectors. A unique ecosystem for the development, assessment, and testing of health and welfare technology was developed, refined and matured from 2012 to 2018. The ecosystem covered the entire value chain from research to implementation; it had a broad national coverage and an international outreach with more than 100 partners (including 66 private companies). The ecosystem provided a set of services that supported knowledge and evidence based development and assessment. Numerous contributing partners were aligned to each focus on what they do best. A “best practice” method for successful product and service development was established based on examination of successful projects in the portfolio. The ecosystem deployed specialized methods for technology assessment and smart innovation management

Issues in the Design of EHTS: A Multiuser Hypertext System for Collaboration

This paper describes the architecture, issues from the design, and experiences from the use of EHTS (Emacs HyperText System), a multiuser hypertext system for collaboration. EHTS consists of a text editor, a graphical browser, and an active hypertext database named HyperBase. HyperBase is built on the client-server model and has been designed especially to support collaboration among its users, by providing an event mechanism and a fine-grained lock mechanism. Events from HyperBase are used to notify the editor and browser about important actions on the shared data, enabling them to monitor changes. Four categories of issues from the design and experiences from the use of EHTS are reported: architecture, collaboration, user interface, and data model. Based on our experiences with the client-server model, we suggest a new and improved architecture for the event driven multiuser hypertext system. One major lesson learned is that events and fine-grained locks can provide powerful support for data sharing among multiple users simultaneously working in the same environment. The need for a flexible data model is another lesson learned. It is difficult to predict what data model objects actually will be needed, when the data model is designed before the user interface

Workspaces: The HyperDisco Approach to Internet Distribution

Hypermedia concepts are currently being deployed in a variety of information systems such as the World Wide Web, software development environments, large engineering enterprises, collaborative authoring systems, and digital library systems. The complex requirements of these application areas have resulted in extensive research into hypermedia infrastructures. The HyperDisco project is about design, development, deployment and assessment of hypermedia infrastructures. Previous HyperDisco experiments have dealt with integration of a small set of tools supporting authoring and extension of the integrated tools to support multiple collaborating users and multiple versions of shared files. These experiments were conducted on a local area network using a single centralized workspace. The latest version of HyperDisco supports collaboration and versioning over multiple workspaces distributed across the Internet. This paper gives a brief overview of HyperDisco, describes the workspace concept and reports on the latest experiments: (1) an experiment that allows the use of multiple workspaces on a local area network, (2) an experiment that allows workspaces to be distributed across the Internet, and (3) an experiment focusing on hypermedia modeling and presentation issues of distributed workspaces

Using the Flag Taxonomy to Study Hypermedia System Interoperability

Interoperability between existing systems, program packages, tools and applications with various degrees of hypermedia awareness is a complex and important challenge facing the hypermedia community. This paper presents a general framework (called the Flag Interoperability Matrix) to discuss and examine hypermedia system interoperability based on the concepts and principles of the Flag taxonomy of open hypermedia systems. The purposes of the Flag Interoperability Matrix are to provide a framework to classify, describe concisely and compare different approaches to hypermedia system interoperability, and provide an overview of the design space of hypermedia system interoperability. The Flag Interoperability Matrix is used to examine existing interoperability approaches. Based on a systematic analysis of possible approaches to hypermedia system interoperability, the paper explores one solution to hypermedia system interoperability that seems particularly promising with respect to handling the growing number of applications with increasing but incomplete awareness of hypermedia structure concepts