Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction.

The early detection and accurate diagnosis of cardiovascular diseases is vital to reduce global morbidity and death rates. In this work, the quantum-inspired seagull optimization algorithm (QISOA) combined with a deep belief network (DBN) is proposed to improve the identification of cardiovascular disorders. As part of preprocessing, cleaning, transformation, and standardization are performed to eliminate noise, inconsistencies, and scaling issues in the data.

Multi scale multi attention network for blood vessel segmentation in fundus images.

Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB).

Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival.

Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence.

A multi-modal geospatial-temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns.

Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integrates temporal dependencies with geographic information through a multi-modal approach that combines attention mechanisms and Recurrent Neural Networks (RNNs).

A comprehensive review of sensor node deployment strategies for maximized coverage and energy efficiency in wireless sensor networks.

Wireless Sensor Networks (WSNs) have paved the way for a wide array of applications, forming the backbone of systems like smart cities. These systems support various functions, including healthcare, environmental monitoring, traffic management, and infrastructure monitoring. WSNs consist of multiple interconnected sensor nodes and a base station, creating a network whose performance is heavily influenced by the placement of sensor nodes.

A Review of Datasets, Optimization Strategies, and Learning Algorithms for Analyzing Alzheimer's Dementia Detection.

Alzheimer's Dementia (AD) is a progressive neurological disorder that affects memory and cognitive function, necessitating early detection for its effective management. This poses a significant challenge to global public health. The early and accurate detection of dementia is crucial for several reasons.

Gauss Markov and Flow Balanced Vector Radial Learning network traffic classification on IoT with SDN.

Recent evolution in connected devices modelled a massive stipulation for network traffic resources and classification. Software-defined networking (SDN) enables ML techniques with the Internet of Things (IoT) to automate network traffic. This helps to reduce accuracy and improves latency.

Integrating cat boost algorithm with triangulating feature importance to predict survival outcome in recurrent cervical cancer.

Cervical cancer is one of the most dangerous malignancies in women. Prolonged survival times are made possible by breakthroughs in early recognition and efficient treatment of a disease.The existing methods are lagging on finding the important attributes to predict the survival outcome.

Analysis of issues related to nursing law: Examination of news articles using topic modeling.

Purpose: The objective of this study was to analyze proposed Korean nursing legislation as depicted in newspaper articles, to highlight issues related to the legislative process for this potential law, and to better understand social awareness regarding this matter.

Methods: The study focused on articles from 11 leading newspapers in Korea, published between February 2020 and August 2023, that pertained to nursing legislation. The articles were retrieved from the BigKinds database.

Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection.

Alzheimer's Disease (AD) is a brain disorder that causes the brain to shrink and eventually causes brain cells to die. This neurological condition progressively hampers cognitive and memory functions, along with the ability to carry out fundamental tasks over time. From the symptoms it is very difficult to detect during its early stage.

Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning.

Pipelines are observed one of the economic modes of transport for transporting oil, gas, and water between various locations. Most of the countries in the world transport petroleum and other flammable products through underground pipelines. The underground and aboveground pipelines are facing various damages due to corrosion, dents, and ruptures due to the environment and operational fluid conditions.

Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images.

COVID-19, a global pandemic, has killed thousands in the last three years. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight against it. Computer Tomography (CT) scans help diagnose and monitor COVID-19, especially in severe cases.

Review of Internet of Things-Based Artificial Intelligence Analysis Method through Real-Time Indoor Air Quality and Health Effect Monitoring: Focusing on Indoor Air Pollution That Are Harmful to the Respiratory Organ.

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient.

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics.

With rapid urbanization, awareness of environmental pollution is growing rapidly and, accordingly, interest in environmental sensors that measure atmospheric and indoor air quality is increasing. Since these IoT-based environmental sensors are sensitive and value reliability, it is essential to deal with missing values, which are one of the causes of reliability problems. Characteristics that can be used to impute missing values in environmental sensors are the time dependency of single variables and the correlation between multivariate variables.

Ratio of Mediastinal Lymph Node SUV to Primary Tumor SUV in F-FDG PET/CT for Nodal Staging in Non-Small-Cell Lung Cancer.

Purpose: Following determination of the maximum standardized uptake values (SUVmax) of the mediastinal lymph nodes (SUV-LN) and of the primary tumor (SUV-T) on F-FDG PET/CT in patients with non-small-cell lung cancer (NSCLC), the aim of the study was to determine the value of the SUV-LN/SUV-T ratio in lymph node staging in comparison with that of SUV-LN.

Methods: We retrospectively reviewed a total of 289 mediastinal lymph node stations from 98 patients with NSCLC who were examined preoperatively for staging and subsequently underwent pathologic studies of the mediastinal lymph nodes. We determined SUV-LN and SUV-R for each lymph node station on F-FDG PET/CT and then classified each station into one of three groups based on SUV-T (low, medium and high SUV-T groups).

Semi-Quantitative Analysis of Post-Transarterial Radioembolization (90)Y Microsphere Positron Emission Tomography Combined with Computed Tomography (PET/CT) Images in Advanced Liver Malignancy: Comparison With (99m)Tc Macroaggregated Albumin (MAA) Single Photon Emission Computed Tomography (SPECT).

Objectives: The purpose of this study is to evaluate the correlation between pretreatment planning technetium-99m ((99m)Tc) macroaggregated albumin (MAA) SPECT images and posttreatment transarterial radioembolization (TARE) yttirum-90 ((90)Y) PET/CT images by comparing the ratios of tumor-to-normal liver counts.

Methods: Fifty-two patients with advanced hepatic malignancy who underwent (90)Y microsphere radioembolization from January 2010 to December 2012 were retrospectively reviewed. Patients had undergone (99m)Tc MAA intraarterial injection SPECT for a pretreatment evaluation of microsphere distribution and therapy planning.

Correlation between Semi-Quantitative (18)F-FDG PET/CT Parameters and Ki-67 Expression in Small Cell Lung Cancer.

Purpose: The aim of this study was to evaluate the relationship between semiquantitative parameters on (18)F-FDG PET/CT including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) and the expression level of Ki-67 in small-cell lung cancer (SCLC).

Methods: Ninety-four consecutive patients with SCLC were enrolled in this study. They underwent (18)F-FDG PET/CT for initial evaluation of SCLC, and we measured SUVmax, avgSUVmean, MTVsum, and TLGtotal on (18)F-FDG PET/CT images.

18F-FDG PET/CT of Bronchial Mucous Gland Adenoma.

Bronchial mucous gland adenoma is a very rare benign tumor that arises from the bronchial mucous-secreting glands. Its detection and appearance using F-FDG PET/CT has not been well characterized. We present a case of a 59-year-old man with FDG-avid mucous gland adenoma that mimicked lung cancer on F-FDG PET/CT.

Value of (18)F-FDG PET/CT in the Detection of Ovarian Malignancy.

Purpose: Ovarian cancer is a leading cause of gynecologic malignancy. As symptoms of ovarian cancer are nonspecific, only 20 % of ovarian cancers are diagnosed while they are still limited to the ovaries. Thus, early and accurate detection of disease is important for an improved prognosis.

The role of interim 18F-FDG PET/CT in predicting early response to neoadjuvant chemotherapy in breast cancer.

Aim: The aim of this study was to prove the diagnostic value of interim 18F-Fluorodeoxyglucose-positron-emission tomography combined with computed tomography (PET/CT) scan for predicting pathological complete response (pCR) compared to other factors in neoadjuvant chemotherapy.

Patients And Methods: Twenty-seven patients with breast cancer were included in this retrospective study. They all underwent scheduled neoadjuvant chemotherapy.

The Clinical Role of Dual-Time-Point (18)F-FDG PET/CT in Differential Diagnosis of the Thyroid Incidentaloma.

Thyroid incidentalomas are common findings during imaging studies including (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography/computed tomography (PET/CT) for cancer evaluation. Although the overall incidence of incidental thyroid uptake detected on PET imaging is low, clinical attention should be warranted owing to the high incidence of harboring primary thyroid malignancy. We retrospectively reviewed 2,368 dual-time-point (18)F-FDG PET/CT cases that were undertaken for cancer evaluation from November 2007 to February 2009, to determine the clinical impact of dual-time-point imaging in the differential diagnosis of thyroid incidentalomas.