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An Algorithmic Trading Approach Merging machine learning With Multi-Indicator Strategies for Optimal Performance

IEEE ACCESS 2024年 12卷 188154-188173页

作者： Sukma, Narongsak Namahoot, Chakkrit Snae Naresuan Univ Fac Sci Phitsanulok 65000 Thailand Naresuan Univ Fac Sci Ctr Excellence Nonlinear Anal & Optimizat Phitsanulok 65000 Thailand

This study investigates the integration of machine learning techniques with multi-indicator strategies in algorithmic trading to overcome the limitations of traditional trading methods. As financial markets become increasingly complex and volatile, innovative approaches are essential to improve predictive accuracy and adaptability. This research aims to develop an algorithmic trading framework that dynamically selects relevant indicators to optimize trading performance. Researchers propose a flexible and computationally efficient model that takes advantage of advanced machine learning algorithms alongside multiple technical indicators, designed to adapt to changing market conditions. An empirical analysis evaluates the effectiveness of this approach against traditional trading indicators. The results demonstrate that the proposed algorithm achieved an impressive total return of 901%, significantly outperforming traditional indicators such as Moving Average (MA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, On-Balance Volume (OBV), and Ichimoku. Furthermore, the model maintained a high win rate during the backtesting, showcasing its robustness under various market conditions. These findings highlight the potential of hybrid strategies that combine machine learning with technical analysis. This research emphasizes the need for adaptive trading models that can respond to the dynamic nature of financial markets. The integration of machine learning with multi-indicator strategies is shown to enhance trading performance. Future work should focus on improving the interpretability of the model and exploring diverse datasets to further enhance the effectiveness of algorithmic trading methodologies.

关键词： machine learning algorithms machine learning Prediction algorithms Adaptation models Heuristic algorithms Predictive models Accuracy Computational modeling Analytical models Decision making Undervalued stocks algorithmic approaches machine learning predictive modeling financial markets

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Advanced machine learning Based Malware Detection Systems

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IEEE ACCESS 2024年 12卷 115296-115305页

作者： Kim, Song-Kyoo Feng, Xiaomei Al Hamadi, Hussam Damiani, Ernesto Yeun, Chan Yeob Nandyala, Sivaprasad Macao Polytech Univ Fac Appl Sci Macau Peoples R China Univ Dubai Engn & Informat Technol Dubai U Arab Emirates Khalifa Univ Sci & Technol Ctr Cyber Phys Syst EECS Dept Abu Dhabi U Arab Emirates Technol Innovat Inst Secure Syst Res Ctr Abu Dhabi U Arab Emirates

In the area of machine learning (ML) training data optimization through the construction of compact data, the focus of this paper is presented. The concept of compact data design, aimed at creating an optimized dataset that maximizes benefits without the need to manage a vast amount of complex data, is introduced. Improvements in the methods for optimizing ML training have been incorporated into the development of artificial intelligence (AI) systems. The introduction of understanding ML training datasets as a facet of Explainable AI (XAI), comprehensible to humans, has been made. Among the methods of XAI, the evaluation of input feature importance stands out as a way to enhance the accuracy of complex ML models. The innovative method of compact data design for optimizing ML training through dataset reduction is proposed. The performance of an ML-based malware detection system, along with its variant utilizing compact data, has been assessed, demonstrating the maintenance of 99% accuracy. By applying a 76% reduced input dataset, the speed of ML training with the novel compact data design could be maximized, suggesting that an ML system trained in this manner could achieve statistically equivalent accuracy with only 57% of the original data sample size.

关键词： machine learning Malware Feature extraction machine learning algorithms Correlation Accuracy Supervised learning Classification algorithms Data analysis Artificial intelligence Compact data data reduction machine learning malware security data complexity artificial intelligence supervised learning robust classification

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Improving Accuracy and Speed of Deep learning algorithms for Real-Time Data Analysis in machine learning

Improving Accuracy and Speed of Deep Learning Algorithms for...

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Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Fatima M. Inamdar Mahendra Kumar Singar Preeti Naval Ashutosh Pattanaik M.P. Santhoshkumar Maheswaran S Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Department of Civil Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Electronics and Communication Engineering Kongu Engineering College Erode Tamil Nadu INDIA

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This have a look at examines using deep learning algorithms for stepped forward accuracy and velocity in real-time facts evaluation in gadget studying. The goal is to offer a comprehensive assessment of recently developed optimization strategies for deep gaining knowledge of algorithms, as well as techniques to enhance their accuracy and overall performance. Additionally, the paper evaluates the potential of deep learning algorithms for actual-time information analysis, in phrases of their accuracy, pace, reliability, and scalability. In the end, the paper offers a survey of to be had datasets for deep learning, and gives hints for in addition studies. The results of this examine suggest that deep mastering algorithms can be used to enhance accuracy and speed of system getting to know for actual-time data evaluation, and that further studies must attention on exploring the ability of optimization techniques for accomplishing higher accuracy and pace.

关键词： Deep learning Knowledge engineering Surveys machine learning algorithms Scalability Software algorithms Real-time systems

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CoopGBFS: A Federated learning and Game-Theoretic-Based Approach for Personalized Security, Recommendation in 5G Beyond IoT Environments for Consumer Electronics

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 2648-2656页

作者： Shafiq, Muhammad Yadav, Rahul Javed, Abdul Rehman Mohsan, Syed Agha Hussnain Guangzhou Univ Dept Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150009 Peoples R China Lebanese Amer Univ Dept Elect & Comp Engn Byblos 1102 2801 Lebanon Zhejiang Univ Ocean Coll Opt Commun Lab Zhoushan 316021 Peoples R China

In 5G IoT for consumer electronics security recommendation systems, machine learning (ML) based Federated learning (FL) methods plays an important for the detection of cyberattacks. For this objective, several different models for effective feature selection and recommendation systems are presented in the literature. However, presented models prone to misclassify 5G Internet of Things (IoT) malicious traffic due to inappropriate feature recommendation system. To address the issue, In this paper, firstly, an Automatic Data set Generator (ADG) method is proposed and then designed ADG algorithm for the effective features set dataset. Then based on proposed ADG algorithm we proposed a new recommendation model CoopGBFS, based on cooperative game theory, federated learning and correlations with algorithm accuracy to address the problem. Finally, based on proposed CoopGBFS method an algorithm named CoopGBFS is developed and designed to select and recommend effective features set for 5G IoT network security. For the proposed approach evaluation, Bot-IoT dataset and four well-known ML algorithms are utilized. However, from the result analysis, it is cleared that the presented method is efficient for 5G IoT malicious traffic detection features recommendation for Consumer Electronics.

关键词： Feature extraction 5G mobile communication Consumer electronics Cyberattack Internet of Things machine learning algorithms Game theory CoopGBFS recommendation system 5G IoT network cyberattacks detection federated learning for consumer electronics

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Utilizing Hybrid machine learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin

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WATER 2024年第3期16卷

作者： Mao, Yimin Li, Yican Teng, Fei Sabonchi, Arkan K. S. Azarafza, Mohammad Zhang, Maosheng Shaoguan Univ Sch Informat & Engn Shaoguan 512005 Peoples R China China Geol Survey Xian Ctr Mineral Resources Survey Xian 710054 Peoples R China Imam Jafar Al Sadiq Univ Dept Comp Tech Engn Baghdad 10011 Iraq Univ Tabriz Fac Civil Engn Geotech Unit Tabriz *** Iran Xi An Jiao Tong Univ Sch Human Settlement Environm & Civil Engn Xian 712000 Peoples R China

The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a network comprising 13 perennial rivers, along withnumerous small springs and direct precipitation onto the lake's surface. Among these contributors, approximately half of the inflow is attributed to the Zarrineh River and the Simineh River. Remarkably, Lake Urmia lacks a natural outlet, with its water loss occurring solely through evaporation processes. This study employed a comprehensive methodology integrating ground surveys, remote sensing analyses, and meticulous documentation of historical landslides within the basin as primary information sources. Through this investigative approach, we preciselyidentified and geolocated a total of 512 historical landslide occurrences across the Urmia Lake drainage basin, leveraging GPS technology for precision. Thisarticle introduces a suite of hybrid machine learning predictive models, such as support-vector machine (SVM), random forest (RF), decision trees (DT), logistic regression (LR), fuzzy logic (FL), and the technique for order of preference by similarity to the ideal solution (TOPSIS). These models were strategically deployed to assess landslide susceptibility within the region. The outcomes of the landslide susceptibility assessment reveal that the main high susceptible zones for landslide occurrence are concentrated in the northwestern, northern, northeastern, and some southern and southeastern areas of the region. Moreover, when considering the implementation of predictions using different algorithms, it became evident that SVM exhibited superior performance regardingboth accuracy (0.89) and precision (0.89), followed by RF, with and accuracy of 0.83 and a precision of 0.83. However, it is noteworthy that TOPSIS yielded the lowest accuracy value among the algorithms assessed.

关键词： landslide susceptibility hybrid artificial intelligence models soft computing Urmia Lake drainage basin geographic information systems machine learning algorithms

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Quantum Multi-Agent Reinforcement learning for Autonomous Mobility Cooperation

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IEEE COMMUNICATIONS MAGAZINE 2024年第6期62卷 106-112页

作者： Park, Soohyun Kim, Jae Pyoung Park, Chanyoung Jung, Soyi Kim, Joongheon Korea Univ Seoul South Korea Ajou Univ Suwon South Korea

For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms suffer from huge parameter utilization and convergence difficulties with many agents. To tackle these problems, a quantum MARL (QMARL) algorithm based on the concept of actor-critic network is proposed, which is beneficial in terms of scalability, to deal with the limitations in the noisy intermediate-scale quantum (NISQ) era. Additionally, our QMARL is also beneficial in terms of efficient parameter utilization and fast convergence due to quantum supremacy. Note that the reward in our QMARL is defined as task precision over computation time in multiple agents, thus, multi-agent cooperation can be realized. For further improvement, an additional technique for scalability is proposed, which is called projection value measure (PVM). Based on PVM, our proposed QMARL can achieve the highest reward by reducing the action dimension into a logarithmic-scale. Finally, we can conclude that our proposed QMARL with PVM outperforms the other algorithms in terms of efficient parameter utilization, fast convergence, and scalability.

关键词： Artificial neural networks Quantum computing Qubit Training Reinforcement learning machine learning algorithms Convergence

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Eight quick tips for biologically and medically informed machine learning

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PLOS COMPUTATIONAL BIOLOGY 2025年第1期21卷 e1012711页

作者： Oneto, Luca Chicco, Davide Univ Genoa Dipartimento Informat Bioingn Robot & Ingn Sistemi Genoa Italy Univ Milano Bicocca Dipartimento Informat Sistemist & Comunicaz Milan Italy Univ Toronto Inst Hlth Policy Management & Evaluat Toronto ON Canada

machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine learning, in contrast to studies that lack domain knowledge and treat all variables equally (uninformed machine learning). While the application of informed machine learning to bioinformatics and health informatics datasets has become more seamless, the likelihood of errors has also increased. To address this drawback, we present eight guidelines outlining best practices for employing informed machine learning methods in biomedical sciences. These quick tips offer recommendations on various aspects of informed machine learning analysis, aiming to assist researchers in generating more robust, explainable, and dependable results. Even if we originally crafted these eight simple suggestions for novices, we believe they are deemed relevant for expert computational researchers as well.

关键词： machine learning machine learning algorithms Artificial intelligence Medical law Bioinformatics Dose prediction methods Open source software Statistical models

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Maximizing supply chain performance leveraging machine learning to anticipate customer backorders

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 194卷

作者： Ali, Abdulrahim Jayaraman, Raja Azar, Elie Maalouf, Maher Khalifa Univ Dept Management Sci & Engn POB 127788 Abu Dhabi U Arab Emirates Carleton Univ Dept Civil & Environm Engn Ottawa ON K1S 5B6 Canada State Univ Zanzibar Dept Comp Sci & Informat Technol Zanzibar Tanzania

The complexity of global supply chains, with their multi-tiered and lengthy structures, presents significant challenges for effectively planning inventory replenishment, accurately forecasting demand, and managing customer backorders. To maintain customer loyalty and avoid extended waiting periods, companies need to have an efficient system for predicting product backlog for customers without overstocking. Traditional statistical techniques like regression analysis can forecast demand and the probability of customer backorders. Nevertheless, they are restricted to modeling linear relationships and may not be well-suited for capturing complex relationships. On the other hand, analytical methods like machine learning (ML) show great promise. However, ML algorithms can be computationally intensive, especially when dealing with many predictors, which can make models complex and increase computational costs. Thus, simpler models with fewer attributes can make data collection and model complexity more manageable. In this study we assess the efficacy and accuracy of simplified ML algorithms, the impact of utilizing limited, high-impact predictors in supply chain backorder prediction. Using publicly available data sets from Kaggle, we developed two sets of models: one with 22 predictors and another with only the top five predictors. The results demonstrate a significant decrease in computational costs, ranging from 30 % to 98 %, with only a marginal reduction in accuracy and F1-score, ranging from 0.6% to 4.2 %. These findings underscore the potential for simpler backorder prediction models, which helps streamline data collection with lower computational cost. This study enhances the current literature by providing insights into optimizing customer backorder prediction with potential generalization for various types of supply chains. It strikes a balance between accuracy and computational efficiency, making the findings valuable for practical implementation across various i

关键词： Backorder prediction Supply chain machine learning algorithms Logistic regression Feature selection

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Log-Scale Quantization in Distributed First-Order Methods: Gradient-Based learning From Distributed Data

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10948-10959页

作者： Doostmohammadian, Mohammadreza Qureshi, Muhammad I. Khalesi, Mohammad Hossein Rabiee, Hamid R. Khan, Usman A. Semnan Univ Fac Mech Engn Semnan *** Iran Tufts Univ Dept Elect & Comp Engn Medford MA 02155 USA Sharif Univ Technol Comp Engn Dept Tehran *** Iran

Decentralized strategies are of interest for learning from large-scale data over networks. This paper studies learning over a network of geographically distributed nodes/agents subject to quantization. Each node possesses a private local cost function, collectively contributing to a global cost function, which the considered methodology aims to minimize. In contrast to many existing papers, the information exchange among nodes is log-quantized to address limited network-bandwidth in practical situations. We consider a first-order computationally efficient distributed optimization algorithm (with no extra inner consensus loop) that leverages node-level gradient correction based on local data and network-level gradient aggregation only over nearby nodes. This method only requires balanced networks with no need for stochastic weight design. It can handle log-scale quantized data exchange over possibly time-varying and switching network setups. We study convergence over both structured networks (for example, training over data-centers) and ad-hoc multi-agent networks (for example, training over dynamic robotic networks). Through experimental validation, we show that (i) structured networks generally result in a smaller optimality gap, and (ii) log-scale quantization leads to a smaller optimality gap compared to uniform quantization. Note to Practitioners-Motivated by recent developments in cloud computing, parallel processing, and the availability of low-cost CPUs and communication networks, this paper considers distributed and decentralized algorithms for machine learning and optimization. These algorithms are particularly relevant for decentralized data mining, where data sets are distributed across a network of computing nodes. A practical example of this is the classification of images over a networked data centre. In real-world scenarios, practical model nonlinearities such as data quantization must be addressed for information exchange among the computing nodes. T

关键词： Quantization (signal) Convergence Optimization Cost function Costs Heuristic algorithms Distributed databases machine learning algorithms Ad hoc networks Training Distributed algorithm data classification quantization graph theory optimization

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Leader Selection and Follower Association for UE-Centric Distributed learning in Future Wireless Networks

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IEEE ACCESS 2024年 12卷 154160-154172页

作者： Parsaeefard, Saeedeh Roessel, Sabine Ghavamabad, Anousheh Gholami Zaus, Robert Raaf, Bernhard Apple Inc Cupertino CA 95014 USA Apple Technol Engn BV & Co KG D-80335 Munich Germany

This paper focuses on UE-centric DL algorithms where UEs initiate requests to adapt AI/ML models for better performance in dynamic environment, e.g., locally refined AI/ML models among a set of headsets or smartphones. This new setup requires selecting a set of UEs as aggregators (here called leaders) and another set as followers, where all UEs update their models based on their local data, and followers share theirs with leaders for aggregation. From a networking perspective, the first question is how to select leaders and associate followers efficiently. This results in a high dimensional mixed integer programming problem and involves internal UE state information and state information among UEs, called external state information in this paper. To address this challenge, we introduce two new indices: a Leader Internal Index (LII), which is a function of the internal states of each device, demonstrating the willingness to be a leader such as battery life and AI/hardware accelerators, and a Leader eXternal Index (LXI), which is a function of external state information among UEs, such as trust, channel condition, and any aspect relevant for associating a follower with a leader. These two indices transform the highly complex leader selection and follower association problem into a better tractable formulation. More importantly, LIIs and LXIs allow to keep the internal and external state information of this problem inside of each device without compromising users' privacy. We present the necessary constraints and objective function, propose a distributed algorithm, and explore the feasibility, challenges, and implementation within 5G and 6G networks. Our simulation results show that the performance of the distributed algorithm is only 8.9% lower compared to the optimal solution if the related threshold is adjusted efficiently. Also, the message-passing overhead of our approach is linear with the number of users while it effectively translates an NP-hard problem into a

关键词： Servers 6G mobile communication Wireless networks Adaptation models Real-time systems Data models Computational modeling machine learning algorithms Indexes Stochastic processes Distributed management Distributed machine learning (DL) algorithms fine-tuning federated learning subnetworks in 6G private networks in 6G

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