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An Optimization Method-Assisted Ensemble Deep Reinforcement learning Algorithm to Solve Unit Commitment Problems

IEEE ACCESS 2023年 11卷 100125-100136页

作者： Qin, Jingtao Gao, Yuanqi Bragin, Mikhail Yu, Nanpeng Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

Unit commitment (UC) is a fundamental problem in the day-ahead electricity market, and it is critical to solve UC problems efficiently. Mathematical optimization techniques like dynamic programming, Lagrangian relaxation, and mixed-integer quadratic programming (MIQP) are commonly adopted for UC problems. However, the calculation time of these methods increases at an exponential rate with the number of generators and energy resources, which is still the main bottleneck in the industry. Recent advances in artificial intelligence have demonstrated the capability of reinforcement learning (RL) to solve UC problems. Unfortunately, the existing research on solving UC problems with RL suffers from the curse of dimensionality when the size of UC problems grows. To deal with these problems, we propose an optimization method-assisted ensemble deep reinforcement learning algorithm, where UC problems are formulated as a Markov Decision Process (MDP) and solved by multi-step deep Q-learning in an ensemble framework. The proposed algorithm establishes a candidate action set by solving tailored optimization problems to ensure relatively high performance and the satisfaction of operational constraints. Numerical studies on three test systems show that our algorithm outperforms the baseline RL algorithm in terms of computation efficiency and operation cost. By employing the output of our proposed algorithm as a warm start, the MIQP technique can achieve further reductions in operational costs. Furthermore, the proposed algorithm shows strong generalization capacity under unforeseen operational conditions.

关键词： Optimization Costs machine learning algorithms Heuristic algorithms Ions Deep learning Uncertainty Reinforcement learning Deep reinforcement learning multi-step return optimization methods unit commitment

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A Novel Deep learning-Based Hybrid Method for the Determination of Productivity of Agricultural Products: Apple Case Study

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IEEE ACCESS 2023年 11卷 7808-7821页

作者： Bal, Fatih Kayaalp, Fatih Kirklareli Univ Dept Software Engn TR-39010 Kirklareli Turkiye Duzce Univ Dept Comp Engn TR-81620 Duzce Turkiye

The production of agricultural products and the high yield in these products are of critical importance for the continuation of human life. In recent years, machine learning and deep learning technologies have been widely used in determining agricultural productivity. The purpose of this study was to estimate the yield of apple fruit by using a novel deep learning-based hybrid method. First, by using images belonging to the golden and royal gala apple varieties, a classification was made with the help of a convolutional neural network (CNN) that was designed for the study. Then, using classical machine learning algorithms and bagging and boosting algorithms, a hybrid application was performed by classifying the images whose feature extractions were done with the designed CNN. The results of the study, presented on 4 separate datasets (Datasets A, B, C, and D), were evaluated based on accuracy, precision, recall, F-measure, and Cohen kappa scores. Considering the accuracy results for Datasets B, C, and D, it was determined that the hybrid model that gave the best result was the CNN-SVM model. For Dataset A, the CNN-SVM and CNN-Gradient Boosting hybrid models gave the best and same accuracy. Dataset C was determined as the most appropriate dataset in terms of the more balanced distribution of train, test, and validation size in the datasets, the results of the proposed hybrid CNN model, and the evaluation of the results of the model. For Dataset C, it was found that the accuracy of the hybrid model was 99.70%. Precision, recall, f-measure, and Cohen kappa scores were 99%. The results of the study revealed that the hybrid models showed effective results in determining the productivity of apple fruit through images belonging to the golden and royal gala varieties.

关键词： Generative adversarial networks Feature extraction Data models Classification algorithms Convolutional neural networks machine learning algorithms machine learning Apple yield prediction deep learning ensemble methods machine learning smart farm

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A Parallel Framework for Constraint-Based Bayesian Network learning via Markov Blanket Discovery

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第6期34卷 1699-1715页

作者： Srivastava, Ankit Chockalingam, Sriram P. Aluru, Srinivas Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA Georgia Inst Technol Inst Data Engn & Sci Atlanta GA 30332 USA

Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale networks. In this article, we present a parallel framework to scale BN structure learning algorithms to tens of thousands of variables. Our framework is applicable to learning algorithms that rely on the discovery of Markov blankets (MBs) as an intermediate step. We demonstrate the applicability of our framework by parallelizing three different algorithms: Grow-Shrink (GS), Incremental Association MB (IAMB), and Interleaved IAMB (Inter-IAMB). Our implementations are available as part of an open-source software called ramBLe, and are able to construct BNs from real data sets with tens of thousands of variables and thousands of observations in less than a minute on 1024 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, we demonstrate using simulated data sets that our proposed parallel framework can scale to BNs of even higher dimensionality. Our implementations were selected for the reproducibility challenge component of the 2021 student cluster competition (SCC'21), which tasked undergraduate teams from around the world with reproducing the results that we obtained using the implementations. We discuss details of the challenge and the results of the experiments conducted by the top teams in the competition. The results of these experiments indicate that our key results are reproducible, despite the use of completely different data sets and experiment infrastructure, and validate the scalability of our implementations.

关键词： Random variables Scalability Probability distribution Markov processes machine learning algorithms Bayes methods Software algorithms Bayesian networks constraint-based learning parallel machine learning gene networks reproducibility

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The Role of machine learning in Identifying Students At-Risk and Minimizing Failure

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IEEE ACCESS 2023年 11卷 1224-1243页

作者： Pek, Reyhan Zeynep Ozyer, Sibel Tariyan Elhage, Tarek Ozyer, Tansel Alhajj, Reda Istanbul Medipol Univ Dept Comp Engn TR-34810 Istanbul Turkiye Ankara Medipol Univ Dept Comp Engn TR-06050 Ankara Turkiye ABC Private Sch Abu Dhabi U Arab Emirates Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada Univ Southern Denmark Dept Heath Informat DK-5230 Odense Denmark

Education is very important for students' future success. The performance of students can be supported by the extra assignments and projects given by the instructors for students with low performance. However, a major problem is that students at-risk cannot be identified early. This situation is being investigated by various researchers using machine learning techniques. machine learning is used in a variety of areas and has also begun to be used to identify students at-risk early and to provide support by instructors. This research paper discusses the performance results found using machine learning algorithms to identify at-risk students and minimize student failure. The main purpose of this project is to create a hybrid model using the ensemble stacking method and to predict at-risk students using this model. We used machine learning algorithms such as Naive Bayes, Random Forest, Decision Tree, K-Nearest Neighbors, Support Vector machine, AdaBoost Classifier and Logistic Regression in this project. The performance of each machine learning algorithm presented in the project was measured with various metrics. Thus, the hybrid model by combining algorithms that give the best prediction results is presented in this study. The data set containing the demographic and academic information of the students was used to train and test the model. In addition, a web application developed for the effective use of the hybrid model and for obtaining prediction results is presented in the report. In the proposed method, it has been realized that stratified k-fold cross validation and hyperparameter optimization techniques increased the performance of the models. The hybrid ensemble model was tested with a combination of two different datasets to understand the importance of the data features. In first combination, the accuracy of the hybrid model was obtained as 94.8% by using both demographic and academic data. In the second combination, when only academic data was used, the acc

关键词： Predictive models Data models machine learning Stacking machine learning algorithms Prediction algorithms Data mining At-risk students classification dropout prediction hybrid model machine learning techniques stacking ensemble model student performance prediction

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Cerebras Architecture Deep Dive: First Look Inside the Hardware/Software Co-Design for Deep learning

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IEEE MICRO 2023年第3期43卷 18-30页

作者： Lie, Sean Cerebras Syst Sunnyvale CA 94085 USA

The compute and memory demands for deep learning and machine learning (ML) have increased by several orders of magnitude in just the last couple of years, and there is no end in sight. Traditional improvements in processor performance alone struggle to keep up with the exponential demand. A new chip architecture co-designed with the ML algorithms can be better equipped to satisfy this unprecedented demand and enable the ML workloads of the future. This article describes the Cerebras architecture and how it is designed specifically with this purpose, from the ground up, as a wafer-sized chip to enable emerging extreme-scale ML models. It uses fine-grained data flow compute cores to accelerate unstructured sparsity, distributed static random-access memory for full memory bandwidth to the data paths, and a specially designed on-chip and off-chip interconnect for ML training. With these techniques, the Cerebras architecture provides unique capabilities beyond traditional designs.

关键词： Semiconductor device modeling Deep learning Hardware machine learning algorithms Computational modeling Memory management Distributed databases Software Product design

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machine learning for Detecting the WestRock Ransomware Attack Using BGP Routing Records

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IEEE COMMUNICATIONS MAGAZINE 2023年第3期61卷 20-26页

作者： Li, Zhida Rios, Ana Laura Gonzalez Trajkovic, Ljiljana Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

Border Gateway Protocol (BGP) enables Internet data routing. Hence, its anomalies affect Internet connectivity and cause routing discon-nections, route flaps, and oscillations. Detection of anomalous BGP routing dynamics is a topic of great interest in cybersecurity. In this article, we survey machine learning algorithms for detecting BGP anomalies and intrusions. Gradient boosting decision tree and deep learning algorithms are evaluated by creating models using collected routing records during the WestRock ransomware event. BCPGuard, a BGP anomaly detection tool, has been developed to integrate various stages of the anomaly detection process.

关键词： Routing Ransomware machine learning algorithms Border Gateway Protocol Logic gates Routing protocols Internet

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Enhancing the Efficiency of Gaussian Naïve Bayes machine learning Classifier in the Detection of DDOS in Cloud Computing

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IEEE ACCESS 2023年 11卷 124597-124608页

作者： Naiem, Sarah Khedr, Ayman E. Idrees, Amira M. Marie, Mohamed I. Helwan Univ Fac Comp & Artificial Intelligence Cairo 11795 Egypt Future Univ Egypt Fac Comp & Informat Technol Cairo 11835 Egypt

Distributed Denial of services is one of the most dangerously planned attacks in cloud computing, resulting in huge losses of data and money for both the cloud services providers and the users of these services. Many efforts have been performed to help protect the cloud from these attacks using machine learning techniques. This study focuses on enhancing the efficiency of the Gaussian Naive Bayes classifier, considered one of the cheapest and fastest classifiers. Still, it has some problems resulting from its equation's statistical nature. The nature of this classifier is based on multiplication, resulting in inaccurate classification due to the zero-frequency issue and the fact that it assumes that features are independent. This research proposed a framework handling the selection of a set of highly independent features following an iterative feature selection approach using the Pearson Correlation Coefficient, Mutual Information, and Chi-squared and then selecting other subsets of features from these sets to reach a set of highly independent features. After that, we used a specific algorithm handling the data pre-processing to handle the zero-frequency problem where we used the Mode to replace the missing values, and if the mode was zero, it used the mean instead. Still, if the record's label is zero, we get the value of the previous record with zero labels. After that, we handled the data imbalances using SMOTE. These enhancements increased both accuracy for the mutual information model by 2% and the average overall accuracy and precision by 1.5%.

关键词： Classification algorithms machine learning algorithms Naive Bayes classifiers cloud computing algorithmic efficiency DDoS attacks

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machine learning FOR 6G ENHANCED ULTRA-RELIABLE AND LOW-LATENCY SERVICES

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IEEE WIRELESS COMMUNICATIONS 2023年第2期30卷 48-54页

作者： Liu, Yan Deng, Yansha Nallanathan, Arumugam Yuan, Jinhong Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Kings Coll London Dept Engn London England Queen Mary Univ London Sch Elect Engn & Comp Sci London England Univ New South Wales Telecommun Grp Sydney NSW Australia

Ultra-reliable and low-latency communications (URLLC), as one of the major communication services of the fifth-generation (5G) and the sixth-generation (6G) cellular networks, is critical to supporting a variety of emerging mission-critical applications. However, the modern mobile networks could not satisfy the latency and reliability requirements, as well as other Quality of Service (QoS) requirements, including spectrum efficiency, energy efficiency, capacity, jitter, round-trip delay, network coverage, etc. To fulfill diverse QoS requirements for various URLLC applications, machine learning (ML) solutions are promising for future 6G networks. In this article, we first categorize the 6G URLLC vision into three connectivity characteristics, including ubiquitous connectivity, deep connectivity, and holographic connectivity, with their corresponding unique QoS requirements. We then identify potential challenges in meeting these connectivity requirements, and investigate promising ML solutions to achieve the intelligent connectivity for the 6G URLLC service. We further discuss how to implement the ML algorithms to guarantee the QoS requirements for different URLLC scenarios, including mobility URLLC, massive URLLC, and broadband URLLC. Finally, we present a case study of downlink URLLC channel access problems, solved by centralized deep reinforcement learning (CDRL) and federated DRL (FDRL), respectively, which validates the effectiveness of machine learning for URLLC services.

关键词： 6G mobile communication machine learning algorithms Mission critical systems Quality of service Reinforcement learning Ultra reliable low latency communication Downlink

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Leak detection in real water distribution networks based on acoustic emission and machine learning

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ENVIRONMENTAL TECHNOLOGY 2023年第25期44卷 3850-3866页

作者： Fares, Ali Tijani, I. A. Rui, Zhang Zayed, Tarek Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Kowloon Hong Kong Peoples R China

Water scarcity as well as social and economic damages caused by the increasing amounts of non-revenue water in the water distribution networks (WDNs) have been prompting innovative solutions. A great deal of potable water is wasted due to leakage in the WDNs all over the world. Hence, various leak detection approaches have been explored, including the promising application of acoustic devices. Exploiting the benefits of technological advances in acoustic devices, signal processing, and machine learning (ML), this study aimed to develop a sophisticated system for leak detection in WDNs. Different from laboratory-based studies, this study was conducted on real WDNs in Hong Kong and lasted for about two years. Utilizing acoustic emissions acquired using wireless noise loggers, various ML algorithms were explored to develop inspection models for in-service and buried WDNs. ML classification algorithms can identify patterns in the acquired signals for leak and no-leak statuses. Thus, a combination of features describing acoustic signals in time and frequency domains was utilized to facilitate the development of ML models. Separately for metal and non-metal WDNs, ten well-known ML algorithms were used to develop leak detection models. The validation results demonstrate the promising application of noise loggers and ML for leak detection in real WDNs. Support Vector machine (SVM), Artificial Neural Network (ANN), and Deep learning (DL) leak detection models demonstrated a largely stable performance and a very good accuracy, particularly for new unlabelled cases.

关键词： Acoustic signals noise loggers machine learning algorithms water distribution networks water leak detection

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A Topical Review of Quantum and Classical machine learning Approaches to Disaster Escape Routing Problems

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IEEE ACCESS 2025年 13卷 81494-81506页

作者： Vinil, A. Iyer, Parameswaran Chetan, Jetain Bembale, Aniket Singh, Nagendra Birla Inst Technol & Sci Pilani BITS Pilani Hyderabad Campus Hyderabad 500078 India C DAC CINE NIT Silchar Campus Silchar 788010 Assam India

The pathfinding problem in a graph has been solved using several classical algorithms, notably Dijkstra's and A* algorithms. However, most classical algorithms are most effective on static graphs. They either cannot be adapted to dynamic graphs or become computationally expensive. The challenge of processing dynamic graphs, which demands significant computational resources, can be addressed using Classical and Quantum machine learning methods. For this review, we consider a dynamically changing graph representing a disaster-stricken city. Our problem, termed the Disaster Escape Routing problem, aims to find the optimal path within this dynamic graph. We review and analyze an existing hybrid quantum-classical machine learning model alongside classical machine learning models specifically for this problem. We also explore Variational Quantum Circuits and Encoding Methods. Our study suggests that hybrid quantum-classical machine learning, Graph Neural Networks, and Temporal Graph Networks offer high performance in terms of path prediction and accuracy in finding the optimal path. This review also identifies Kolmogorov Arnold Networks as a promising approach to solving escape routing problems. Additionally, an integrated approach combining strengths of all the models has been hypothesized to enhance emergency escape route planning.

关键词： Quantum computing Routing machine learning Heuristic algorithms Reviews Graph neural networks machine learning algorithms Integrated circuit modeling Disasters Prediction algorithms quantum computing vehicle routing

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