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Predicting Market Trends : A Stock Prices Forecasting with Artificial Neural Network for Apple Inc. and Microsoft Corp.
Get PDFMachine learning plays a crucial role in predicting stock prices, as it aids investors in making well-informed decisions amidst the vast array of stocks traded on the stock exchange. The unpredictability of stock price behaviour, influenced by numerous factors, adds complexity to this process. Consequently, numerous studies have explored the use of machine learning for stock price forecasting. Hence, this study employs an Artificial Neural Network model as a machine learning algorithm for forecasting stock prices. The model is based on daily stock prices for Apple Inc. and Microsoft Corp. obtained from Yahoo Finance. Data preprocessing entailed normalizing stock prices to ensure that the input features were on a similar scale. The model was trained using a backpropagation approach, with weights optimized based on the mean square error loss function. The proposed model's performance is evaluated using the Root Mean Square Error (RMSE) and Absolute Error (AE) to assess its effectiveness in analyzing the data. The results show that the ANN model can accurately and reliably forecast stock prices. The RMSE and AE metrics demonstrated that the ANN model could effectively capture the underlying trends in stock price movements, giving investors valuable insights for decision-making
Determinants of Organizational Citizenship Behavior among University Employees in Shandong, China
Get PDFThis study is centered on a comprehensive examination of the factors that influence employee organizational citizenship behavior (OCB). In particular, it delves into the intricate relationships between employee job performance, personal development, and job satisfaction, and how these variables impact the manifestation of organizational citizenship behavior within the workplace. The study employs a quantitative analysis method, allowing for a detailed and data-driven exploration of these relationships. The target population for this study consisted of lecturers from Shandong University. Using purposive sampling, 500 questionnaires were distributed, resulting in 412 valid responses, achieving a response rate of 82.4%. Among the respondents, 183 were male, representing 44.42% of the sample, while 229 were female, accounting for 55.58%. Through rigorous data collection and analysis, this study substantiates the positive influence of job performance, personal development, and job satisfaction on employee OCB. These findings underscore the interconnectedness between various facets of employee engagement and their propensity towards altruistic behaviors within the organizational context. The validation of these relationships provides empirical support for the hypotheses posited in the study, consolidating existing theoretical frameworks and contributing to a deeper understanding of the mechanisms driving employee behavior and organizational effectivenes
Exploring the relationship between strategic planning and educational performance : a systematic literature review
No full textIn the evolving landscape of education, strategic planning has emerged as a critical tool for enhancing institutional performance and achieving educational excellence. This systematic literature review aims to explore the
intricate link between strategic planning and educational performance by synthesizing findings from a diverse array of studies. The review addresses the pressing need for a structured approach to improving educational outcomes. To achieve this, we conducted an extensive search of scholarly
articles from reputable databases such as Scopus and Web of Science, focusing on studies published between 2020 and 2024. The flow of the study based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework. The database found (n=28) final primary data was analyzed. The finding was divided into three themes which
are: i) competency development and assessment in education; ii) strategic planning and management in higher education; and iii) technological integration and innovation in education. The results highlight the significance of implementing a comprehensive and flexible framework for
strategic planning that aligns with the specific requirements and environments of educational institutions. In conclusion, this research offers insightful information to educators, administrators and policymakers who
want to use strategic planning to support long-term improvements in student performance and eventually advance the larger objective of educational excellence
Impact of the Probiotic on the Modulation of Vaginal Bacterial and Fungal Microbiota in HPV‐Positive Women
Get PDFThis study examined the effects of administering the probiotic Lactiplantibacillus plantarum Probio87 on the vaginal microbiota of HPV-positive women, highlighting alterations in bacterial and fungal communities and their potential influence on vaginal and overall health. In a double-blind, randomized, placebo-controlled study, participants consumed daily doses of Probio87 (1 × 109 CFU/sachet) or a placebo for 12 weeks. Vaginal swabs were collected, and DNA was extracted for 16S rRNA and the internal transcribed spacer (ITS) region amplification via PCR, followed by sequencing of bacterial and fungal microbiota. The probiotic group showed improved alpha and beta diversity, with an increase in beneficial Lactobacillus (p = 0.014) and Phascolarctobacterium faecium (p = 0.041), while decreasing the abundance of pathogens like Streptococcus and Candida (p = 0.011). Additionally, vaginal health questionnaires linked probiotic use to improved vaginal and mental health in HPV-positive women. L. plantarum Probio87 demonstrated clinical and psychological benefits in HPV-positive women by balancing the vaginal microbiome, reducing pathogenic species, and promoting the growth of beneficial microorganisms
The Effects of Flipped Classroom on Learning Performance in Management Courses at Vocational and Technical Colleges in China
No full textThis study examined the effects of the flipped classroom approach on learning performance among first-year management students in five vocational and technical colleges in Anhui Province, China. A mixed-methods quasi-experimental design was employed, integrating both quantitative and qualitative approaches. During a six-week teaching experiment, 50 classes comprising 1,000 students were randomly assigned to
experimental and control groups. The experimental group adopted the flipped classroom model, while the control group followed traditional teaching approaches. The primary
objective was to assess the flipped classroom's effects in enhancing learning performance, including academic achievement, successful intelligence, and learning attitudes. The research framework identified the independent variables as the flipped classroom and traditional classroom approaches, as well as demographic profiles (such as college and gender). The dependent variable was learning performance, with cognitive style serving as a moderating variable. Control variables included grade level, teaching focus, and teaching materials. The experimental results showed that the flipped classroom significantly outperformed the traditional classroom. Flipped students had a 41.642-point increase in academic achievement test scores compared to 30.998 points for traditional students. Successful
intelligence scores rose by 9.146 points in the flipped group and decreased by 0.07 points in the traditional group. Learning attitudes improved by 33.092 points in the flipped group, compared to a 0.394-point decrease in the traditional group. Findings indicated that the flipped classroom model significantly improved academic achievements, successful
intelligence, and fostered a more positive learning attitude compared to traditional teaching methods. However, cognitive style did not moderate the effects of the flipped classroom approach. This study provided empirical evidence on the effects of the flipped classroom in China's Vocational and Technical Colleges (VTCs), while explored the moderating role of cognitive styles to deepen the understanding of how different teaching methods impact
learning performance. It contributed to the broader discussion on personalized and student-centered learning approaches in China's Vocational and Technical Education (VTE). Future research could expand by diversifying samples, conducting longitudinal studies, incorporating additional variables (e.g., self-efficacy, learning motivation), and
exploring cross-disciplinary contexts. Keywords: Flipped Classroom, Vocational and Technical Colleges, Learning Performance, Cognitive Style
Sijil Penyertaan Perbadanan Perpustakaan Awam Kedah atas penyertaan sebagai Peserta Persidangan di dalam Seminar Antarabangsa Kedah Historiografi Nusantara pada 27-28 Mei 2025.
No full textImpact of global value chain on China’s automobile industry upgrading: The mediating effect of technology investment and moderating effect of foreign direct investment
No full textThe automobile industry is a cornerstone of China’s economy, with its upgrading significantly influencing overall economic growth. Global value chain (GVC), as a
framework that connects global markets and production resources, substantially impacts an industry’s international competitiveness. This study employs the Malmquist Productivity Index based on the Data Envelopment Analysis (DEA) method to assess China’s automobile industry (CAI). Additionally, a system Generalized Method of Moments (GMM) approach is utilized to investigate the influence of GVC on the upgrading process while further examining the mediating
and moderating roles of technological innovation and foreign direct investment (FDI). The findings show that GVC has a significant negative effect on upgrading CAI, with technology investment acting as a mediating factor; specifically, GVC hinders industry upgrading by inhibiting technology investment. Furthermore, FDI mitigates the negative impact of GVC on the upgrading of CAI. Based on these findings, this study offers policy implications to promote the upgrading of CAI
Active and Passive Islanding Detection for Photovoltaic System Integration
No full textIn the context of photovoltaic (PV) system integration with the power grid, ensuring prompt and reliable islanding detection holds paramount importance for safety and operational stability. This study employs both active and passive islanding detection methods, assessing their efficacy through simulation and analysis. The active approach involves injecting a controlled test signal to manipulate PV systems, while the passive method monitors grid frequency deviations during islanding. Results indicate that both methods successfully and swiftly detect islanding events, with active detection identifying them within 0.5 seconds and passive detection within approximately 1 second. The study underscores the significance of these techniques for maintaining safe and stable PV-grid integration and conforms to IEEE 1547 standards through droop control strategies, all validated within a MATLAB/Simulink-based PV system simulation
Investigation on Blade Angles Effect on Micro-Gravitational Water Vortex System via Simulation and Experimental Analysis
Get PDFMicro gravitational water vortex power plants offer a promising solution for generating electricity from renewable energy sources with minimal hydraulic pressure requirements. Increased energy generation results from an increased vortex formation. Thus, the design of a micro gravitational water vortex system aims to achieve a powerful artificial air-core vortex. This study focused on investigating the effect of different blade angles in a Kaplan turbine within a micro gravitational water vortex system. Computational fluid dynamics (CFD) simulations conducted using Ansys software provided detailed insights into fluid flow dynamics, while experimental validation of the numerical results involved computational design and modelling, fabrication of the blades prototype on a 3D printer, and experimental data collection. Three blade angles at 140°, 150°, and 160°, were examined to determine its effects on power efficiency, approximate power input, and output. Simulation results indicate that a blade angle of 140° yields the highest power efficiency, reaching an impressive value of 95.40%. This efficiency outperforms the other blade profiles investigated. Experimental results showed reasonable consistency with the numerical predictions, with errors between numerical and experimental value ranging from 4.5% to 27.6%. Findings presented in this paper emphasised the importance of blade angle selection in optimizing the performance of micro gravitational water vortex power plants. Valuable insights for the design and operation of this eco-friendly technology aid the advancement of green energy generation systems
A Multi-Criteria Recommendation Technique for Personalized Tourism Experiences
Get PDFThis research aims to design a multi-Criteria recommendation technique for the tourism domain. In the past few decades, the growth of the World Wide Web has led to an unprecedented amount of information available to us. This phenomenon has resulted in what we now call information overload, where the sheer volume of data surpasses our capacity to manage it effectively. In order to address this issue, it is crucial to ensure that accurate information is communicated to the appropriate audience, as proper information dissemination is key. Like other industries, the tourism industry faces the challenge of information overload. The abundance of information can be overwhelming for both tourists and industry stakeholders. Some tourists like guided tours, while others prefer exploring independently, and that is where e-tour guides can be helpful. E-tour guides are digital tools like apps or websites that inform tourists about their destination. In this context, recommendation systems and information dissemination are closely related. Recommendation systems use algorithms to analyze user data and provide personalized recommendations based on their past behaviour and preferences. Both recommendation systems and information dissemination aim to provide users with relevant and useful information. This study aims to develop a multi-criteria recommendation system that effectively addresses the issue of information overload in the tourism industry by delivering pertinent information to the right users. The proposed method combines several techniques, including deep learning and traditional techniques, such as matrix factorization, to address common challenges like scalability and data sparseness. The approach is designed to provide personalized recommendations based on user location, site, and other relevant criteria. By emphasizing the filtering and provision of the most relevant information based on user location and site, the user experience and engagement can be significantly improved. This study utilizes the Convolutional Matrix Factorization (CMF) algorithm due to its compatibility with tourism. The proposed algorithm, CMF with ResNet, combines the power of CMF with the superior performance of ResNet to overcome the limitations of CMF and achieve even better results for recommendation tasks. Using ResNet, the algorithm can learn more complex and nuanced patterns in the data, leading to more accurate recommendations. In the end, compared to all the tested algorithms, the proposed method outperformed and achieved higher scores on error measurement metrics. Additionally, it can handle complex problems like sparse data or very small amounts of training samples. The proposed method also provided more relevant recommendations to users compared to all other tested algorithms. Overall, the proposed method offers a novel and effective solution to the information overload challenge in the tourism industry