Academic Articles & Research Papers

Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass express...

Machine Learning in Agriculture: A Review

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive revi...

Investment portfolio optimization with supervised learning and attention mechanism

Portfolio optimization is a process that involves distribution of capital with the purpose of maximizing returns and at the same time minimizing risks. The current paper discusses the use of Transformer networks in supervised learning for p...

Leveraging Cloud Technology for Personalized Multiple Sclerosis Care: A Comprehensive Data Management and Visualization Approach

This project develops a cloud-based solution for securely managing clinical data and patient-reported outcomes (PROMs) for multiple sclerosis (MS) patients. Utilizing REDCap for data collection, we incorporated clinical outcomes and PROMs f...

Exploring the application and challenges of fNIRS technology in early detection of Parkinson’s disease

BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a cri...

Application of MRI-Based Radiomics in Preoperative Prediction of NF2 Alteration in Intracranial Meningiomas

PurposeThis study aimed to investigate the feasibility of predicting NF2 mutation status based on the MR radiomic analysis in patients with intracranial meningioma.MethodsThis retrospective study included 105 patients with meningiomas, incl...

Mechanisms of the interaction between aging of the male reproductive system and obesity, and clinical management strategies (Review).

Integrating InVEST and machine learning to model mangrove habitat degradation trend in in Northern Persian Gulf, Iran

Abstract The expansion of human activities and environmental changes in coastal desert and semi-desert ecosystems significantly impact habitat quality. This study used remote sensing data on the Google Earth Engine (GEE) platform to generat...

Trinity: In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics

The ability to perform machine learning (ML) tasks in a database management system (DBMS) is a new paradigm for conventional database systems as it enables advanced data analytics on top of well-established capabilities of DBMSs. However, t...

Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This st...

Synergistic multi-altimeter for estimating water level in the coastal zone of Beibu Gulf using SEL, ALES + and BFAST algorithms

Accurately monitoring and predicting the large-scale dynamic changes of water levels in coastal zones is essential for its protection, restoration and sustainable development. However, there has been a challenge for achieving this goal usin...

AN IMPROVED GRU BASED ON RECURRENT ATTENTION UNIT AND SELF- ATTENTION TECHNIQUE FOR TEXT SENTIMENT ANALYSIS

In text sentiment analysis, a crucial challenge is that conventional word vectors fail to capture lexical ambiguity. The Gated Recurrent Unit (GRU), an advanced variant of RNN, is extensively utilized in natural language processing tasks su...

Analysis of EMC Factors on Electronic Devices Using PLS-SEM Method: A Case Study in Vietnam

Electronic equipment is indispensable in the industrial 4.0 era. Electromagnetic Compatibility issues with electronic devices are increasingly concerning. The phenomenon of electromagnetic field compatibility is getting higher and higher. T...

Information disclosure and funding success of green crowdfunding campaigns: a study on GoFundMe

Abstract Crowdfunding has become important in increasing financial support for the development of green technologies. Self-disclosed information significantly affects supporters’ decisions and is important for the success of green project f...

A Deep Dive into a Groundbreaking Approach to Machine Learning-Powered E-Learning

Information retrieval aims to find the most important data for specific queries. The challenge is retrieving relevant data efficiently due to the large search area. Existing solutions lead to unnecessary processing costs. Additionally, iden...

Operational Digital Shadow for Onshore Wind Energy Systems

Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative as...

Spatial analysis of smart tourism contrasts A comparison between tourist attractions in Cairo and Dubai

Technology has undergone rapid advancements, significantly impacting various sectors, with the tourism industry emerging as one of the most benefited. This research examines several key aspects, including: An overview of Cairo and Dubai as ...

Ride the green: How does the national carbon market affect corporate breakthrough green innovation?

Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage...

Calibrated Machine Learning with Temporal Leakage Controls for Rare-Event Forecasting of Port Activity Anomalies: Evidence from Selected African Countries and Indonesia

Port activity anomalies are rare but may signal operational constraints relevant to supply chains. The aim of the study was to develop and empirically evaluate a calibrated machine learning framework for forecasting rare, algorithmically de...