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Research Article
Automated Forex Trading Bot Using MQL4: A Reinforcement Learning-based Approach
Jamari Markus,
Baku Agyo Raphael,
Ismaila Jesse Mazadu*
Issue:
Volume 13, Issue 3, September 2025
Pages:
49-62
Received:
29 April 2025
Accepted:
14 May 2025
Published:
27 August 2025
Abstract: Foreign exchange (Forex) trading is a high-liquidity, high-volatility global market that requires rapid decision-making. Manual trading often suffers from human limitations, including emotional biases and inconsistent decision-making. This paper presents the design and implementation of an automated Forex trading bot developed using MetaQuotes Language 4 (MQL4) and reinforcement learning (RL) strategies. The system integrates real-time market data analysis, technical indicators (MACD, RSI, Bollinger Bands), and dynamic risk management. Leveraging a custom RL environment, the bot adapts to changing market conditions, learns optimal strategies, and executes trades with reduced human intervention. Simulation results show improved trading performance, higher Sharpe ratios, and reduced drawdown compared to manual strategies. The bot architecture consisted of distinct layers, including the market data input layer, decision engine, order execution module, and risk management system. The bot was tested in a demo trading environment over a one-week period. Results demonstrated a win rate of 62%, a profit factor of 1.45, and a maximum drawdown of 4.2%. These outcomes validate the bot's ability to achieve stable performance under simulated market conditions. This study underscores the potential of AI-driven automation for enhancing algorithmic trading efficacy.
Abstract: Foreign exchange (Forex) trading is a high-liquidity, high-volatility global market that requires rapid decision-making. Manual trading often suffers from human limitations, including emotional biases and inconsistent decision-making. This paper presents the design and implementation of an automated Forex trading bot developed using MetaQuotes Lang...
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Research Article
Emotionally Intelligent User Interfaces of Social Media: The Role of Emotional Intelligence in User Interface Design and UX Pattern on Social Media on Human Behaviour
Munim Ahsan Chowdhury Primon*
Issue:
Volume 13, Issue 3, September 2025
Pages:
63-68
Received:
26 March 2025
Accepted:
9 April 2025
Published:
2 September 2025
Abstract: Social Media platforms are increasing user engagements with their attractive User Interfaces (UI) design playing a crucial role in maintaining user involvement. Use of Emotional Intelligence (EI) into the UI transforming user interaction and engagement pattern significantly. With an emphasis on how design decisions affect user behaviour, this study explores the nuanced function of EIUIs (Emotionally Inteligence User Interfaces) in the context of social media. It specifically draws attention to the dangers of manipulative techniques like "dark patterns"-purposeful interface strategies intended to influence users to take actions they might not have otherwise taken-as well as the possibility of constructive involvement. By investigating these practices, the paper hopes to clarify the wider ethical consequences of interface design as well as the duty of platforms and designers to promote transparency, autonomy, and confidence in digital interactions. This paper aims to contribute to the discourse on user interface design by providing a comprehensive understanding of EIUIs' potential and pitfalls in the context of social media interactions. Additionally, this study focusses on finding information of the pattern of Emotionally Intelligent User Interfaces (EIUI) design to enhance user engagements emotionally and tend to take decisions and the impact of the design pattern in user experience.
Abstract: Social Media platforms are increasing user engagements with their attractive User Interfaces (UI) design playing a crucial role in maintaining user involvement. Use of Emotional Intelligence (EI) into the UI transforming user interaction and engagement pattern significantly. With an emphasis on how design decisions affect user behaviour, this study...
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Research Article
Evaluation of Bangladesh Shipbuilding State and Major Challenges
Hossain Khandakar Akhter*
Issue:
Volume 13, Issue 3, September 2025
Pages:
69-85
Received:
28 July 2025
Accepted:
13 August 2025
Published:
19 September 2025
DOI:
10.11648/j.acis.20251303.13
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Abstract: The shipbuilding industry is considered as one of the most profitable and sustainable industries around the globe. Shipbuilding has long been an appealing industry for many develop and developing countries like Japan, South Korea, and China. Historically, this sector has lacked strong global regulation and often faced problems of over-investment, as it involves diverse technologies, stimulates numerous backward and forward linkage industries, creates significant employment opportunities, generates income, and remains global in nature. Such patterns have been evident among all major shipbuilding nations throughout history. Today, China is the global shipbuilding market leader. Bangladesh, endowed with its traditional shipbuilding expertise and glorious history, progressed to become a small but competitive player in the global market, though it could not sustain its momentum. The global shipbuilding industry is projected to reach approximately USD 200 billion soon, and Bangladesh holds the potential to secure at least USD 4 billion of this market share. By addressing challenges such as low productivity, the need for advanced technology integration, and safety standards, Bangladesh’s shipbuilding sector can significantly enhance its international competitiveness and contribute meaningfully to national development and economic growth. This study evaluates the status of local shipbuilding by depicting the challenges within the context of global trends, technological progress, and future market share.
Abstract: The shipbuilding industry is considered as one of the most profitable and sustainable industries around the globe. Shipbuilding has long been an appealing industry for many develop and developing countries like Japan, South Korea, and China. Historically, this sector has lacked strong global regulation and often faced problems of over-investment, a...
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Research Article
Neural Network-Based Online Model Identification and Nonlinear Adaptive Predictive Control of Self-Balancing Two-Wheel LEGO Mindstorms NXTway-GS Robot
Issue:
Volume 13, Issue 3, September 2025
Pages:
86-115
Received:
21 June 2025
Accepted:
5 July 2025
Published:
25 September 2025
DOI:
10.11648/j.acis.20251303.14
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Abstract: The main challenge of the well-celebrated Levenberg-Marquardt algorithm (LMA) is the selection of the searching direction and adaptation parameters. Secondly, the implementation of the LMA for online model identification has faced challenges as it is a batch optimization. As a third challenge, the solution of the Levenberg-Marquardt based on the full-Newton nonlinear optimization (FNNO) for online applications have been limited due to its unguaranteed positive definiteness. This paper presents two versions of the modified Levenberg-Marquardt algorithm (MLMA) for neural network model identification and adaptive predictive control for the online dynamic model identification and adaptive control of a self-balancing two-wheel LEGO Mindstorms NXTway-GS robot. The first version is the online-window-approach of the modified Levenberg-Marquardt algorithm (OWA-MLMA) based on approximate Guass-Newton algorithm (AGNA) for training neural network model predictor. The second version is a neural network-based adaptive predictive control (APC) algorithm based on the full-Newton nonlinear optimization of the modified Levenberg-Marquardt algorithm (FNNO-MLMA) for online adaptive control. A NNARMAX model predictor for the NXT robot is first trained and validated using the OWA-MLMA based on AGNA. The validated NNRAMAX model is then used for the design of the NN-APC based on FNNO-MLMA. Finally, the model identification based on OWA-MLMA and APC based on FNNO-MLMA schemes are simulated in closed-loop for online NNARMAX model identification and adaptive predictive control of the NXT robot. The comparison of the proposed OWA-MLMA based on AGNA shows superior performance over the recursive incremental back-propagation algorithm (INCBPA) while the proposed NN-APC based on FNNO-MLMA shows excellent control and good tracking performances over a discrete-time fixed-parameter proportional-integral-derivative (PID) controller for the NXT robot control. The simulation results show that the developed OWA-MLMA based on AGNA and the APC based on FNNO-MLMA can be adapted for the online dynamic modeling, automation, control and robotics applications.
Abstract: The main challenge of the well-celebrated Levenberg-Marquardt algorithm (LMA) is the selection of the searching direction and adaptation parameters. Secondly, the implementation of the LMA for online model identification has faced challenges as it is a batch optimization. As a third challenge, the solution of the Levenberg-Marquardt based on the fu...
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Research Article
Analytical Study of History and Development of Smart Port
Hossain Khandakar Akhter*
Issue:
Volume 13, Issue 3, September 2025
Pages:
116-130
Received:
13 July 2025
Accepted:
11 August 2025
Published:
25 September 2025
DOI:
10.11648/j.acis.20251303.15
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Abstract: Ports are essential for local and international trade, connecting countries and facilitating the movement of goods. It plays a vital role of global economy as 75% of trade and commerce of planet by value passes through the port. Actually, ports are always considered as capital infrastructures and those cover wide range of financial associates. Smart ports are modern and highly facilitated port which uses digital, automated and other smart technologies to enhance efficiency, accountability, sustainability, security, and competitiveness in monitoring, operations, management and effective 24/7 service. Smart shipping and smart ports frequently utilize digital tools such as sensors, data analytics, augmented reality, big data, digital twins, and automation to improve cargo movement, minimize waste and emissions, and provide superior services to shippers, shipping companies, customs authorities, freight-forwarders, local communities, stakeholders and others. They may also feature renewable energy sources, electric charging stations, onshore power supply, and smart infrastructure for logistics and transportation. The economic benefits are often less directly tied to port activities and more linked to the dynamics of the supporting supply-chains. This operational support becomes beneficial and effective, and playing a crucial role to enhancing national and international competitiveness. This analytical paper will assess the history and development of ports in the context of advanced technology. Today’s smart ports and shipping are always used smart technology - like AI, ML, DL, data science, etc and facilities for effective and efficient service, safety, operation and better management.
Abstract: Ports are essential for local and international trade, connecting countries and facilitating the movement of goods. It plays a vital role of global economy as 75% of trade and commerce of planet by value passes through the port. Actually, ports are always considered as capital infrastructures and those cover wide range of financial associates. Smar...
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Research Article
Research on Short-Term Electricity Consumption Forecasting Based on VMD-xLSTM
Issue:
Volume 13, Issue 3, September 2025
Pages:
131-139
Received:
27 August 2025
Accepted:
13 September 2025
Published:
25 September 2025
DOI:
10.11648/j.acis.20251303.16
Downloads:
Views:
Abstract: Enhancing the accuracy of electricity consumption forecasting is essential for the safe and stable operation of power systems and the strategic planning of grid companies. To achieve high-precision short-term electricity consumption forecasting, this paper proposes a VMD-xLSTM forecasting model that combines Variational Modal Decomposition (VMD) with an extended Long Short-Term Memory network (xLSTM). This novel hybird model firstly uses the VMD decomposition algorithm to decompose the original electricity consumption time series into multiple modal components and a residual component. Subsequently, leveraging the powerful long-term dependency capturing capabilities of the xLSTM neural network, each decomposed component is independently established the model and forecasted. Finally, the prediction results of each component are superimposed to reconstruct the overall predicted value of electricity consumption. Experimental results demonstrate that the proposed VMD-xLSTM model performs outstandingly, with a Symmetric Mean Absolute Percentage Error (SMAPE) of only 0.86%. This is notably lower than that of the traditional Long Short-Term Memory network (LSTM) at 2.33%, the standalone xLSTM model at 2.15%, and the VMD-LSTM model at 0.90%. Additionally, all other prediction error evaluation metrics of this model are comprehensively superior to the compared models, clearly verifying the VMD-xLSTM model's effectiveness in short-term electricity consumption forecasting tasks through comparative experiments.
Abstract: Enhancing the accuracy of electricity consumption forecasting is essential for the safe and stable operation of power systems and the strategic planning of grid companies. To achieve high-precision short-term electricity consumption forecasting, this paper proposes a VMD-xLSTM forecasting model that combines Variational Modal Decomposition (VMD) wi...
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