Fault Tolerant Spectral/Spatial Optical Code Division Multiple Access Passive Optical Network.

High-capacity communication networks are built to provide high throughput and low latency to accommodate the growing demand for bandwidth. However, the provision of these features is subject to a robust underlying network, which can provide high capacity with maximum reliability in terms of the system's connection availability. This work optimizes an existing 2D spectral-spatial optical code division multiple access (OCDMA) passive optical network (PON) to maximize connection availability while maintaining desirable communication capacity and capital expenditure.

Transformer-based active learning for multi-class text annotation and classification.

Objective: Data-driven methodologies in healthcare necessitate labeled data for effective decision-making. However, medical data, particularly in unstructured formats, such as clinical notes, often lack explicit labels, making manual annotation challenging and tedious.

Methods: This paper introduces a novel deep active learning framework designed to facilitate the annotation process for multiclass text classification, specifically using the SOAP (subjective, objective, assessment, plan) framework, a widely recognized medical protocol.

Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations.

Background: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking.

Objective: This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress.

Methods: Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants.

Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers.

The human brain is an extremely intricate and fascinating organ that is made up of the cerebrum, cerebellum, and brainstem and is protected by the skull. Brain stroke is recognized as a potentially fatal condition brought on by an unfavorable obstruction in the arteries supplying the brain. The severity of brain stroke may be reduced or controlled with its early prognosis to lessen the mortality rate and lead to good health.

Visual pollution real images benchmark dataset on the public roads.

The term quality of life (QoL) refers to a wide range of multifaceted concepts that often involve subjective assessments of both positive and negative aspects of life. It is difficult to quantify QoL as the word has varied meanings in different academic areas and may have different connotations in different circumstances. The five sectors most commonly associated with QoL, however, are Health, Education, Environmental Quality, Personal Security, Civic Engagement, and Work-Life Balance.

Multilayer perceptron-based self-care early prediction of children with disabilities.

Early identification of children with self-care impairments is one of the key challenges professional therapists face due to the complex and time-consuming detection process using relevant self-care activities. Due to the complex nature of the problem, machine-learning methods have been widely applied in this area. In this study, a feed-forward artificial neural network (ANN)-based self-care prediction methodology, called multilayer perceptron (MLP)-progressive, has been proposed.

Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images.

Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test.

A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions.

Multimodal emotion recognition has gained much traction in the field of affective computing, human-computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals.

Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images.

Brain MR images are the most suitable method for detecting chronic nerve diseases such as brain tumors, strokes, dementia, and multiple sclerosis. They are also used as the most sensitive method in evaluating diseases of the pituitary gland, brain vessels, eye, and inner ear organs. Many medical image analysis methods based on deep learning techniques have been proposed for health monitoring and diagnosis from brain MRI images.

Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment.

Clinical decision support systems (CDSSs) represent the latest technological transformation in healthcare for assisting clinicians in complex decision-making. Several CDSSs are proposed to deal with a range of clinical tasks such as disease diagnosis, prescription management, and medication ordering. Although a small number of CDSSs have focused on treatment selection, areas such as medication selection and dosing selection remained under-researched.

Balancing Complex Signals for Robust Predictive Modeling.

Robust predictive modeling is the process of creating, validating, and testing models to obtain better prediction outcomes. Datasets usually contain outliers whose trend deviates from the most data points. Conventionally, outliers are removed from the training dataset during preprocessing before building predictive models.

A practical approach towards causality mining in clinical text using active transfer learning.

Objective: Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defined and schema driven information systems. The objective of this research work is to create a framework, which can convert clinical text into causal knowledge.

COVID-19 Knowledge Resource Categorization and Tracking: Conceptual Framework Study.

Background: Since the declaration of COVID-19 as a global pandemic by the World Health Organization, the disease has gained momentum with every passing day. Various private and government sectors of different countries allocated funding for research in multiple capacities. A significant portion of efforts has been devoted to information technology and service infrastructure development, including research on developing intelligent models and techniques for alerts, monitoring, early diagnosis, prevention, and other relevant services.

Incidence of Cardiac Arrhythmias in Acute Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention and Associated Outcomes During the First 24 Hours.

Background Acute myocardial infarction (AMI) is the most life-threatening manifestation of coronary artery diseases. The majority of deaths in AMI are due to arrhythmias. Therefore, the aim of this study was to evaluate the incidence and risk factors and outcomes of cardiac arrhythmias in AMI patients undergoing primary percutaneous coronary intervention (PCI) during the first 24 hours of the index hospitalization.

An Empirical Method of Automatic Pattern Extraction for Clinical Text Classification.

Clinical text classification is an indispensable and extensively studied problem in medical text processing. Existing research primarily employs machine learning and pattern based approaches to address the stated problem. In general, pattern based approaches perform better than other methods.

On computing critical factors based healthy behavior index for behavior assessment.

Objective: Ubiquitous computing has supported personalized health through a vast variety of wellness and healthcare self-quantification applications over the last decade. These applications provide insights for daily life activities but unable to portray the comprehensive impact of personal habits on human health. Therefore, in order to facilitate the individuals, we have correlated the lifestyle habits in an appropriate proportion to determine the overall impact of influenced behavior on the well-being of humans.

Semantic preservation of standardized healthcare documents in big data.

Background: Standardized healthcare documents have a high adoption rate in today's hospital setup. This brings several challenges as processing the documents on a large scale takes a toll on the infrastructure. The complexity of these documents compounds the issue of handling them which is why applying big data techniques is necessary.

A Multimodal Deep Log-Based User Experience (UX) Platform for UX Evaluation.

The user experience (UX) is an emerging field in user research and design, and the development of UX evaluation methods presents a challenge for both researchers and practitioners. Different UX evaluation methods have been developed to extract accurate UX data. Among UX evaluation methods, the mixed-method approach of triangulation has gained importance.

Multi-model-based interactive authoring environment for creating shareable medical knowledge.

Objective: Technologically integrated healthcare environments can be realized if physicians are encouraged to use smart systems for the creation and sharing of knowledge used in clinical decision support systems (CDSS). While CDSSs are heading toward smart environments, they lack support for abstraction of technology-oriented knowledge from physicians. Therefore, abstraction in the form of a user-friendly and flexible authoring environment is required in order for physicians to create shareable and interoperable knowledge for CDSS workflows.

Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval.

Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construct knowledge-based complex queries. To overcome the difficulty in constructing knowledge-based complex queries, we utilized the knowledge base (KB) of the clinical decision support system (CDSS), which has the potential to provide sufficient contextual information.

H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus.

Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body's resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient's data.

Prospective, multi-center study of the effectiveness of orthodontic/orthognathic surgery care in the United Kingdom.

Introduction: The aim of this study was to evaluate the effectiveness of orthodontic/orthognathic surgical care provided in the North West region of England. It was an observational, prospective cohort study at 13 maxillofacial clinics in the United Kingdom.

Methods: The 131 patients comprised 47 males (35.