Text describes the development of health promoting schools movement (from principles to practice, from practice to policy), main principles and concept, differences betweem school health promotion and health promoting schools. Special emphases is given to the criteria and set of quidelines towards which schools aspiring to the status of health promoting school were required to work.

Health promoting schools

This paper introduces recently developed Aquila Optimization Algorithm specifically configured for Multi-Robot space exploration. The proposed hybrid framework "Coordinated Multi-Robot Exploration Aquila Optimizer" (CME-AO) is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based methodology, known as Aquila Optimizer (AO). A novel parallel communication protocol is also embedded to improve multi-robot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Numerous simulations under different environmental conditions were then performed to validate the effectiveness of the proposed algorithm. Algorithm consistency aspects in achieving the expected results (area explored rate and time) is demonstrated through statistical means. A perspective analysis is then performed by comparing the performance of the CME-AO algorithm with latest state of art contemporary algorithms namely conventional CME and CME-WO (CME Whale Optimizer). The comparison duly accommodates all pertinent aspects such as % area explored, number of failed runs, and time taken for map exploration for different environments. Results indicate that the proposed algorithm presents two distinct advantages over the other conventional state of the art CME based techniques a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time, with almost no fail runs. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other techniques either fails (stuck locally) or takes longer exploration time. 

A Centralized Strategy for Multi-Agent Exploration

The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

https://www.mdpi.com/2079-9292/11/19/3149/pdf?version=1665556490

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section.

/pdf/a-novel-deep-learning-technique-for-detecting-emotional-smy4ysfn.pdf

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

This paper introduces recently developed Aquila Optimization Algorithm specifically configured for Multi-Robot space exploration. The proposed hybrid framework “Coordinated Multi-Robot Exploration Aquila Optimizer” (CME-AO) is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based methodology, known as Aquila Optimizer (AO). A novel parallel communication protocol is also embedded to improve multi-robot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Numerous simulations under different environmental conditions were then performed to validate the effectiveness of the proposed algorithm. Algorithm consistency aspects in achieving the expected results (area explored rate and time) is demonstrated through statistical means. A perspective analysis is then performed by comparing the performance of the CME-AO algorithm with latest state of art contemporary algorithms namely conventional CME and CME-WO (CME Whale Optimizer). The comparison duly accommodates all pertinent aspects such as % area explored, number of failed runs, and time taken for map exploration for different environments. Results indicate that the proposed algorithm presents two distinct advantages over the other conventional state of the art CME based techniques a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time, with almost no fail runs. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other techniques either fails (stuck locally) or takes longer exploration time.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09933766.pdf

Fusion of modern meta-heuristic optimization methods using arithmetic optimization algorithm for global optimization tasks

Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance.

/pdf/a-new-stock-price-forecasting-method-using-active-deep-2tldj4hz.pdf

A New Stock Price Forecasting Method Using Active Deep Learning Approach

Robust flight control system design of a fixed wing UAV using optimal dynamic programming

Combination of political optimizer, particle swarm optimizer, and convolutional neural network for brain tumor detection

M-commerce has very rapidly developed into a very powerful way of reaching out to the consumer. M-commerce has been a massive success in terms of users’ adoption in some markets like Japan, while, astonishingly, not as thriving in others. However, its acceptance and level of adoption are low in Palestine compared to other countries. The research main objective is to classify the key variables that influence the acceptance of M-commerce among higher education students in Palestine by developing an M-commerce adoption Model based on an extension of the Technology Acceptance Model (TAM).  A total of 430 questionnaires were collected and analyzed using Structural Equation Modelling (SEM) technique. The findings revealed that perceived usefulness, perceived ease of use, personnel innovation, security and privacy, subjective norms, and perceived trust are found to have an important effect on consumer behavioral intention to adopt M-commerce. These results will benefit stakeholders involved in M-commerce activities such as service providers, retailers, consumers, academicians, and students.

Hamzah Alkhazaleh

Papers

Fusion of modern meta-heuristic optimization methods using arithmetic optimization algorithm for global optimization tasks

A New Stock Price Forecasting Method Using Active Deep Learning Approach

Robust flight control system design of a fixed wing UAV using optimal dynamic programming

Combination of political optimizer, particle swarm optimizer, and convolutional neural network for brain tumor detection

Determinants of M-Commerce Adoption: An Empirical Study