International Journal of Advanced Science and Computer Applications

Prediction of shear wall residential beam height based on machine learning

2024-05-19T04:07:24+00:00

The beam height is an important design parameter that influences structural properties such as load-bearing capacity and stability of beams. In the early stages of structural design, the existing methods for determining beam height mainly include empirical formulae. However, empirical methods are highly subjective, lack accuracy, and are poorly adapted to complex conditions. This paper establishes a beam height prediction model for shear wall residential structures. Using structural design data from projects built by a real estate company across various regions in China, a large dataset of beam heights was collected. The Permutation Feature Importance (PFI) method and six unique machine learning (ML) models were used to rank the importance of input variables. The Gradient Boosting (GB) model, consistent with the feature ranking obtained from PFI, was selected. The model evaluation method was then used to select the number of input features for the GB model, and grid search and K-fold cross-validation were employed to optimize the GB prediction model. This model was compared with a prediction model obtained from a Back Propagation Neural Network (BPNN). Finally, the SHAP method was used to interpret the "black box" machine learning model. The results show that the GB model has higher accuracy compared to the BPNN model, and the input features of the proposed GB model contribute to the beam height in accordance with mechanical laws, demonstrating the model's rationality. The research findings can provide a reference for initial beam height design.

A Secure Storage For Medical Information Scheme Using Blockchain

2024-05-01T18:56:54+00:00

Nowadays, many companies, organizations, hospitals and individuals have adopted centralized data storage systems to store and share data. However, these systems create a single point of failure and involve a centralized entity or third party, which can cause concern for users. Decentralized storage systems are therefore needed to overcome the drawbacks of the traditional approach. However, in the face of centralization issues, this paper proposes a combination of Hyperledger Fabric, InterPlanetary File System (IPFS), Attribute-Based Access Control (ABAC), and proxy re-encryption to enhance the security and transparency features of decentralized storage systems. Thus, the proposed scheme provides a secure decentralized system storage of medical information using a consortium blockchain

UAV Formation Control Using Enhanced Behavior Mechanism And Artificial Potential Field

2024-04-03T02:13:51+00:00

Inspired by formation flight of pigeon flock, this paper proposes a enhanced method of autonomous formation control of multiple Unmanned Aerial Vehicles (UAVs) that can maintain high symmetry based on pigeon flock behavior mechanism. Addressing the instability of formation in the original method, the follow improvements have been made. Firstly, improve leadership of top three UAVs, Secondly, modify artificial potential field strategies for top two followers. Finally, through a series of simulation experiments, it is verified that the UAVs can form the expected formation under the autonomous formation control, and can maintain the formation under the complex motion of leader UAV.

Knowledge Graph-based JingFang Drug Efficacy Analysis With a Supportive Randomized Controlled Influenza-like Illness Clinical Trial

2024-05-24T04:35:16+00:00

This paper presents a novel methodology for drug efficacy analysis using a knowledge graph, validated by a randomized controlled clinical trial. To provide a comprehensive understanding of drug treatment effects, a learning-based workflow is developed to mine drug-disease entities and relations from literature. These relations build a knowledge graph used for clustering-based drug efficacy analysis. Our tool reports the learned relatedness between drugs and diseases, indicating efficacy levels. JingFang is identified as effective for flu and colds. To validate this, a clinical trial was conducted on Influenza-like illness. Between August 25 and October 12, 2020, 106 patients were randomly assigned in a 1:1 ratio to either the combined group (53) or the control group (53). Patients in the combined group received Xinkangtai Ke and JingFang, while the control group received Xinkangtai Ke only for 7 days. The combined group's cure rate was 92.5% (49) compared to 81.1% (43) in the control group (p=0.0852). The very effective rate was 98.1% (52) in the combined group versus 92.5% (49) in the control group (p=0.3692). For middle-aged and elderly participants, the combined group's recovery rate was significantly higher than the control group's (100% vs 78.4%, p=0.0059, 95% CI: 21.6 (8.3, 38.2)). No adverse effects were observed in either group. The results indicate that JingFang is effective for patients with Influenza-like illnesses, especially those over 34 years old. This study highlights the potential of knowledge graph-based analysis in drug efficacy research.

Real-Time Monitoring For Detecting Lake Pollution And Biotic Conservation

2024-05-15T04:24:36+00:00

This research unveils a comprehensive system designed to tackle plastic pollution in lakes autonomously, eliminating the necessity for human intervention. By harnessing sensor data and camera imagery processed through the YOLO algorithm, the system identifies plastic debris. It then calculates the debris density and compares it against a preset threshold. Once the threshold is exceeded, an automated email alert containing the density data is sent to relevant authorities. Additionally, water quality sensors are integrated to continuously monitor environmental conditions. Regular updates are provided to enable proactive measures in pollution prevention. This endeavor showcases the utilization of advanced technology to address environmental challenges and safeguard aquatic ecosystems' health. By employing automated detection and monitoring mechanisms, the system offers a sustainable approach to combat plastic pollution in lakes, fostering environmental conservation endeavors.

Artificial Intelligence for Human Learning & Behaviour Change

2024-04-26T10:56:53+00:00

This paper explores the potential of artificial intelligence (AI) in facilitating human learning and promoting behaviour change. By employing machine learning algorithms, natural language processing, and data analysis, AI systems can provide personalized learning experiences, identify learning gaps, and adapt to individual learning styles. Furthermore, AI can be utilized to create nudges and interventions that encourage positive behaviour change, offering promising applications in fields such as health, finance, and environmental conservation. The paper also discusses ethical considerations and challenges, emphasizing the importance of transparency, fairness, and privacy in AI-driven learning and behaviour change systems