Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI.

The influence of the exposome on major health conditions like cardiovascular disease (CVD) is widely recognized. However, integrating diverse exposome factors into predictive models for personalized health assessments remains a challenge due to the complexity and variability of environmental exposures and lifestyle factors. A machine learning (ML) model designed for predicting CVD risk is introduced in this study, relying on easily accessible exposome factors.

Early Life Stress Detection Using Physiological Signals and Machine Learning Pipelines.

Pregnancy and early childhood are two vulnerable times when immunological plasticity is at its peak and exposure to stress may substantially raise health risks. However, to separate the effects of adversity during vulnerable times of the lifetime from those across the entire lifespan, we require deeper phenotyping. Stress is one of the challenges which everyone can face with this issue.

Analysis of the Security and Reliability of Cryptocurrency Systems Using Knowledge Discovery and Machine Learning Methods.

Cryptocurrency, often known as virtual or digital currency, is a safe platform and a key component of the blockchain that has recently attracted much interest. Utilizing blockchain technology, bitcoin transactions are recorded in blocks that provide detailed information on all financial transactions. Artificial intelligence (AI) has significant applicability in several industries because of the abundance and processing capacity of large data.

NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security.

Social media evidence is the new topic in digital forensics. If social media information is correctly explored, there will be significant support for investigating various offenses. Exploring social media information to give the government potential proof of a crime is not an easy task.

Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines.

The popularity of cryptocurrency in recent years has gained a lot of attention among researchers and in academic working areas. The uncontrollable and untraceable nature of cryptocurrency offers a lot of attractions to the people in this domain. The nature of the financial market is non-linear and disordered, which makes the prediction of exchange rates a challenging and difficult task.

A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach.

The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers.

Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing.

Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management.

Towards a Secure Thermal-Energy Aware Routing Protocol in Wireless Body Area Network Based on Blockchain Technology.

The emergence of biomedical sensor devices, wireless communication, and innovation in other technologies for healthcare applications result in the evolution of a new area of research that is termed as Wireless Body Area Networks (WBANs). WBAN originates from Wireless Sensor Networks (WSNs), which are used for implementing many healthcare systems integrated with networks and wireless devices to ensure remote healthcare monitoring. WBAN is a network of wearable devices implanted in or on the human body.