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Earthquakes are a big threat. They kill people, wreck buildings, and mess up economies. We need to predict them well to lower risks and handle disasters better. This project uses deep learning methods, such as neural networks and Long Short-Term Memory (LSTM) models, to guess earthquake sizes and depths....

Understanding human gait patterns is essential for medical diagnostics, rehabilitation, and prosthetic development. This study presents an advanced deep learning approach that combines Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) to analyze lower...

Artificial Intelligence is a mechanical discipline that has now become inseparable in the modern era. AI has made automation possible in different applications ranging from remote controls to autonomous vehicles, so that complicated sequences of tasks can now become feasible. This project discusses quick...

Generic term was designed by Professor Mae made in 1990 to a design concept, its meaning is: to all products or commodities and the environment are designed to be, as far as possible to eliminate age differences, environmental differences and the case of ability among a by most people use a common design....

A credit system is the need of higher education development. The grade management is the basis and core of the credit system. Therefore, it is necessary to estimate score as soon as possible. Estimation by analogy is a common method to estimate effort; it is used to estimate scores in the paper. As the...

Motion tracking is an important application in video processing. By tracking motion, we can detect the presence of moving object, track and analyze the motion of object and even make prediction of the next step of a selected object. Motion tracking can be applied to facial recognition, intelligent transportation...

Network freight platforms face the complex task of truck-goods matching, which involves considering user conditions and demands. The matching process evaluates ten indicators, such as vehicle type and reputation, using intuitionistic fuzzy rough sets to represent scores and degrees of satisfaction, dissatisfaction,...

The issue of exchange rate forecasting has always been a hot topic, and with the increasingly close relationship between the UK and Chinese import and export trade, the GBP/CNY exchange rate has received increasing attention. Forecasting the GBP/CNY can help trading companies on both sides to effectively...

This paper proposes a novel hybrid forecasting model combining autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) with incorporating moving average and the annual seasonal index for Thailand's cassava export (i.e., native starch, modified starch, and sago). The...

Suicide is a serious public health problem in today's society. It is of great social significance to conduct in-depth research on suicide prevention. In this study, suicide risk assessment methods based on random forest algorithm were investigated. The random matching and traversal matching algorithms...

In order to reduce the performance of the federated learning model caused by data heterogeneity in traffic flow prediction, this paper proposes an improved G-FedProx framework, and combines the real-time traffic status, weather and holiday data obtained by Amap Application Programming Interface (API)...

To determine optimum extraction process of volatile oil from flower of Scindepus aureus and analyze its chemical composition. The essential oils were extracted from S. aureus by microwave-assisted extraction (MAE), distillation time, solid-liquid ratio. Microwave Power were investigated with extraction...

The learning achievement is the important index of students appraising and award grants, therefore, the scores of courses become the core content of the student management. A system is established to estimate scores to improve the learning effect based on a large number of students’ grade data bases...

The Herfindahl-Hirschman Index (HHI) is a key metric for evaluating market concentration and competitive dynamics, frequently employed in antitrust evaluations, merger analysis, and industry structure research. This study examines the predictive capabilities of univariate and multivariate time series...

In order to improve the accuracy of railway freight volume prediction, we adopted the BP neural network method combined with grey relational analysis (GRA). Given that BP neural network algorithm are prone to local minimum, slow learning convergence, and diversity of structural selection problems, we...

Stock prediction has long had a high profile among investors under the incentives of profit maximization. However, as a result of the instability and chaos of the financial stock market, predicting stock prices is challenging. To address this problem, the discrete wavelet transformation (DWT) is applied...

Accurate crop yield prediction is vital for enhancing food security and supporting data-driven agricultural planning. Existing models often fail to capture the intricate relationships between environmental conditions, farming practices, and crop responses. This study presents a novel deep learning-based...

Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this...

Through improved select tactics and genetic operators, the accelerating genetic algorithm (AGA) and simulated annealing algorithm (SA) were combined to form a new algorithm called accelerating genetic and simulated annealing algorithm (AGSA). A modified method to develop the flow rate prediction model...

Gross domestic product (GDP) refers to the final result of production activities of all resident units in a country or region within a certain period of time. There are a variety of GDP forecasting methods, which can be classified into three types: Time Series Analysis, Regression Analysis and VAR Model....

With the increasing influence of Bitcoin in the market, the prediction of Bitcoin price has also received a lot of attention. Accurately predicting Bitcoin prices is of great importance to both traders and investors. This research selects the Bitcoin price from 2020 to 2023 during the epidemic period...

Polyphenols, flavonoids, triterpenoid and VitaminC from Clinacanthus nutans were extracted by microwave-assisted extraction (MAE) technology. The optimal extracting conditions were established as distilled water as solvent, solid-liquid ratio 1:45 (g/ml), irradiation power 160W, and every extraction...

This study examines the use of the support vector regression (SVR) approach in modeling and predicting the membrane water content of Proton Exchange Membrane Fuel Cell (PEMFC) under two influence factors, including the impedance of single-PEM-chip and operating temperature. The leave-one-out cross validation...

Better methods of forecasting the concentrations of air pollution are important in developmental of sound policies and decisions at a time when environmental concerns and the need for mitigating climate change has become important. This paper describes how climate pollution can be predicted from climate...

Under the background of the "Double First-Class" university plan, the sharing mechanism of large-scale instruments plays an indispensable role in science and education integration in universities. This paper first collects usage data (from September 2020 to September 2022) of large campus-shared...

Due to the rapid price fluctuations in the stock market, traditional stock price prediction methods struggle to achieve satisfactory prediction performance. We use the CNN-LSTM model to predict stock prices at the industry level in this paper. The data used in this paper come from the first-level industries...

Financial statements are pivotal for forecasting the future performance of stocks. Harnessing the random forest machine learning model, this study aims to enhance the prediction of quarterly stock returns by focusing on twelve critical financial indicators. This paper utilized Principal Component Analysis...

This study was conducted to identify key parameters such as temperature, humidity, carbon dioxide (CO2), and relative temperature-humidity index (RTHI) that affect the inside and outside environment of the Swine barn. Moreover, the climate of the Swine barn is always related to the Swine’s body temperature...

This research focuses on stock price prediction using Random Forest and XGBoost, with the dataset of Microsoft (2020 - 2025) from Yahoo Finance. Given the complexity of accurately predicting stock price fluctuations, this study first aims to compare the performance of the two models in both regression...

Stock price prediction is an important topic in market analysis, and accurate prediction can support investor’s investment decisions. Because of its benefits in processing time series data, deep learning techniques—particularly the Long Short-Term Memory (LSTM) technique—have been increasingly popular...

The exact price of financial derivatives, especially options, is still a core of quantitative finance and risk management. Traditional continuous-time models, such as the Black-Scholes-Merton (BSM) framework, provide a theoretical foundation but they make simplifying assumptions, mainly constant volatility,...

This empirical study investigated the impact of face occupancy on the generalization performance of Convolutional Neural Networks (CNNs), specifically focusing on three widely-used architectures: ResNet50, VGG16, and MobileNetV2. The face occupancy ratio, defined as the proportion of the image occupied...

The credit system is the need of higher education development. It is the basis and core of the credit system. Therefore, it is necessary to establish a reasonable course recommendation system. Collaborative filtering is a method of group intelligence, which has been successfully applied in many fields...

This study evaluated the operational efficiency and client awareness of HSAC services in Butuan City, as mandated by Section 17 of the Republic Act (RA) 11201. Using a descriptive comparative design, the study analyzed data from 71 HSAC clients in Butuan City, focusing on service efficiency, the quality-of-service...

This work proposed a novel Convolutional Neural Network (CNN) that can classify fruits and predict when they will be ripe is presented with higher accuracy. The architecture consists of task-specific nodes for classification and regression and a centralized node for shared feature extraction. Preprocessing...

Data envelopment analysis (DEA) model is widely used to calculate the technical efficiency of agricultural production, but it is facing the defects of poor flexibility and slower speed. For this reason, we propose an evaluation method of agricultural production technical efficiency that integrates the...

Weather forecasting plays a crucial role in people’s lives and provides important information for social and economic activities. Yet still poses major challenges due to the uncertain nature of climatic conditions and lack of regional data procuring. The Internet of Things (IoT) has significantly transformed...

Objective The goal of this study is to develop and verify an AI-based predictive analytics system for hospital waste management in response to the challenges of conventional biomedical waste management practices, which are reactive and inefficient in dealing with waste spikes [11]. Novelty:...

With the deepening urbanization process, metro systems have become one of the important components of urban passenger transportation. However, the growing passenger flow poses severe operational challenges, giving rise to the demand for short-term passenger flow prediction (STPFP). This study employs...

The relationship between stock prices and economic development is well-established, and the study of stock price prediction methods is crucial for gaining insights into the economy. This research aims to enhance the accuracy of stock price prediction by leveraging a combination of convolutional neural...

This study assessed the engagement and satisfaction in Technology for Teaching and Learning (TTL) of the pre-service teachers through the flexible learning modality. The descriptive correlation research design was employed. The research instrument used in the study was adapted from Gray & Diloreto...

To improve the deformation prediction accuracy of foundation pit engineering, a centralized and distributed fusion model for multi-source monitoring data is proposed based on the correlation analysis of multi-source monitoring data. Furthermore, genetic algorithm (GA) is used to optimize Long Short-Term...

Pectin is usually isolated using the conventional extraction methods are time consuming. Pectin from sentul peel has been successfully isolated by using Microwave Assisted Extraction (MAE). This method has a short processing time and little solvent needed. This study is aimed to determine the effect...

Urban traffic congestion has become a global challenge, causing economic losses, environmental pollution, and reduced quality of life. Accurate short-term traffic flow prediction is essential for optimizing traffic management, improving travel efficiency, and supporting the development of intelligent...

Efficient inventory management is critical in e-commerce, where inaccurate demand forecasts can lead to costly stock outs or excess inventory. This study presents a scalable, machine learning-based framework to optimize warehouse operations. Historical sales data—including product/store identifiers,...

The research evaluates the relationship between Bitcoin returns and US Gold prices using both traditional and deep learning predictive models. It analyzes Bitcoin’s volatility through time-series data from 2017 to 2025 to assess its impact on US Gold prices, which have traditionally exhibited greater...

With the application of artificial intelligence algorithm in the financial field, it soon becomes an interesting issue and a research hotspot to predict stock price. In this paper, LSTM and ARIMA models are adopted to explore the attracting stock price prediction. Besides, forecasting accuracy is comprehensively...

Gold prices play a pivotal role in the global financial system, but they often experience short-term volatility, long-term trends, and frequent regime switching. To address this, this paper proposes a new architecture, building on the CNN architecture and combining ordinary convolution with dilated convolution...

Solve the problem that the accuracy of scoring prediction is not high due to insufficient learning of the features of two graph data (user social graph and user item graph) in the social recommendation system (GraphRec), and improve the accuracy of the social recommendation system. In this paper, the...

The building industry’s high energy consumption and greenhouse gas emissions make accurate heating and cooling load prediction crucial. Traditional white box and black box methods have limitations. This paper explores Deep Neural Networks (DNNs) and the Deep - FM model, which combines Factorization Machines...