SS-DRPL: self-supervised deep representation pattern learning for voice-based Parkinson's disease detection.

Parkinson's disease (PD) is a globally significant health challenge, necessitating accurate and timely diagnostic methods to facilitate effective treatment and intervention. In recent years, self-supervised deep representation pattern learning (SS-DRPL) has emerged as a promising approach for extracting valuable representations from data, offering the potential to enhance the efficiency of voice-based PD detection. This research study focuses on investigating the utilization of SS-DRPL in conjunction with deep learning algorithms for voice-based PD classification.

TSSG-CNN: A Tuberculosis Semantic Segmentation-Guided Model for Detecting and Diagnosis Using the Adaptive Convolutional Neural Network.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium. It primarily impacts the lungs but can also endanger other organs, such as the renal system, spine, and brain. When an infected individual sneezes, coughs, or speaks, the virus can spread through the air, which contributes to its high contagiousness.

Percutaneous image-guided management of a misplaced pericardiocentesis catheter into the inferior vena cava.

Misplacement of pericardiocentesis catheter in central veins is a rare complication that can be managed with several methods. In this case, we report a percutaneous image-guided plug-assisted management of a misplaced pericardiocentesis catheter into the inferior vena cava through a transhepatic tract successfully occluded. This minimally invasive technique was not previously described in this setting and had a favorable long-term outcome.

Survey on Blockchain-Based Data Storage Security for Android Mobile Applications.

This research paper investigates the integration of blockchain technology to enhance the security of Android mobile app data storage. Blockchain holds the potential to significantly improve data security and reliability, yet faces notable challenges such as scalability, performance, cost, and complexity. In this study, we begin by providing a thorough review of prior research and identifying critical research gaps in the field.

Automated Categorization of Multiclass Welding Defects Using the X-ray Image Augmentation and Convolutional Neural Network.

The detection of weld defects by using X-rays is an important task in the industry. It requires trained specialists with the expertise to conduct a timely inspection, which is costly and cumbersome. Moreover, the process can be erroneous due to fatigue and lack of concentration.

Identification, 3D-Reconstruction, and Classification of Dangerous Road Cracks.

Advances in semiconductor technology and wireless sensor networks have permitted the development of automated inspection at diverse scales (machine, human, infrastructure, environment, etc.). However, automated identification of road cracks is still in its early stages.

Accuracy and reproducibility of a cone beam CT-based virtual parenchymal perfusion algorithm in the prediction of SPECT/CT anatomical and volumetric results during the planification of radioembolization for HCC.

Objectives: To evaluate anatomical and volumetric predictability of a cone beam computed tomography (CBCT)-based virtual parenchymal perfusion (VPP) software for the single-photon-emission computed tomography (SPECT)/CT imaging results during the work-up for transarterial radioembolization (TARE) procedure in patients with hepatocellular carcinoma (HCC).

Methods: VPP was evaluated retrospectively on CBCT data of patients treated by TARE for HCC. Tc macroaggregated albumin particles (Tc-MAA) uptake territories on work-up SPECT/CT was used as ground truth for the evaluation.

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention.

River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields.

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans.

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19.

Olive Disease Classification Based on Vision Transformer and CNN Models.

It has been noted that disease detection approaches based on deep learning are becoming increasingly important in artificial intelligence-based research in the field of agriculture. Studies conducted in this area are not at the level that is desirable due to the diversity of plant species and the regional characteristics of many of these species. Although numerous researchers have studied diseases on plant leaves, it is undeniable that timely diagnosis of diseases on olive leaves remains a difficult task.

Blockchain for Modern Applications: A Survey.

Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital cryptocurrencies, such as Bitcoin and Ethereum.

Tuberculosis Disease Diagnosis Based on an Optimized Machine Learning Model.

Computer science plays an important role in modern dynamic health systems. Given the collaborative nature of the diagnostic process, computer technology provides important services to healthcare professionals and organizations, as well as to patients and their families, researchers, and decision-makers. Thus, any innovations that improve the diagnostic process while maintaining quality and safety are crucial to the development of the healthcare field.

An Adaptive Topology Management Scheme to Maintain Network Connectivity in Wireless Sensor Networks.

The roots of Wireless Sensor Networks (WSNs) are tracked back to US military developments, and, currently, WSNs have paved their way into a vast domain of civil applications, especially environmental, critical infrastructure, habitat monitoring, etc. In the majority of these applications, WSNs have been deployed to monitor critical and inaccessible terrains; however, due to their unique and resource-constrained nature, WSNs face many design and deployment challenges in these difficult-to-access working environments, including connectivity maintenance, topology management, reliability, etc. However, for WSNs, topology management and connectivity still remain a major concern in WSNs that hampers their operations, with a direct impact on the overall application performance of WSNs.

An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors.

Experts have predicted that COVID-19 may prevail for many months or even years before it can be completely eliminated. A major problem in its cure is its early screening and detection, which will decide on its treatment. Due to the fast contactless spreading of the virus, its screening is unusually difficult.

Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification.

The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals.

DHPV: a distributed algorithm for large-scale graph partitioning.

Big graphs are part of the movement of "Not Only SQL" databases (also called NoSQL) focusing on the relationships between data, rather than the values themselves. The data is stored in vertices while the edges model the interactions or relationships between these data. They offer flexibility in handling data that is strongly connected to each other.

Blockchain for the Internet of Vehicles: A Decentralized IoT Solution for Vehicles Communication Using Ethereum.

The concept of smart cities has become prominent in modern metropolises due to the emergence of embedded and connected smart devices, systems, and technologies. They have enabled the connection of every "thing" to the Internet. Therefore, in the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in newly developed smart cities.

[The value of radiotherapy in the treatment of aggressive and localised gastric lymphomas].

Purpose: To evaluate the treatment results of localised aggressive gastric lymphomas with favourable prognosis using chemotherapy and radiotherapy.

Patients And Methods: Between February 1993 and December 2004, nine patients with primary gastric high-grade lymphoma have been treated by the Lymphoma Committee of Sfax (Tunisia). The sex ratio was 1.

Ethnic and socio-cultural specificities in Tunisia have no impact on the prevalence of anti-Saccharomyces cerevisiae antibodies in Crohn's disease patients, their relatives or associated clinical factors.

Background: In Western Europe and the USA, the presence of anti-Saccharomyces cerevisiae antibodies (ASCAs) in Crohn's disease (CD) patients and their healthy relatives suggests that ASCAs may be influenced by genetic and/or environmental factors.

Objectives: To assess the prevalence of ASCAs in Tunisian patients with CD or ulcerative colitis (UC), and unaffected family members, in relation to clinical phenotype. Patients and methods.

[Tumors of the vaterian region indications, findings and prognosis : about 49 cases].

This retrospective study is about 49 cases of tumors of the vaterian region collected between 1976 and 2001. Tumors of the vaterian region represented 7% of the bilio-pancreatic tumors. The study was about 18 women and 31 men, with sex-ratio of 1.

[Cutaneous metastasis of Crohn's disease].

Introduction: Involvement of the skin in Crohn's disease is rare. We report the case of a young woman presenting cutaneous nodules revealing a Crohn's disease.

Case Report: R.