| 1. |
- Gholami, Omid, 1979-, et al.
(author)
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A Heuristic Approach to Solving the Train Traffic Re-Scheduling Problem in Real Time
- 2018
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In: Algorithms. - : MDPI. - 1999-4893. ; 11:4, s. 1-18
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Journal article (peer-reviewed)abstract
- Effectiveness in managing disturbances and disruptions in railway traffic networks, when they inevitably do occur, is a significant challenge, both from a practical and theoretical perspective. In this paper, we propose a heuristic approach for solving the real-time train traffic re-scheduling problem. This problem is here interpreted as a blocking job-shop scheduling problem, and a hybrid of the mixed graph and alternative graph is used for modelling the infrastructure and traffic dynamics on a mesoscopic level. A heuristic algorithm is developed and applied to resolve the conflicts by re-timing, re-ordering, and locally re-routing the trains. A part of the Southern Swedish railway network from Karlskrona centre to Malmö city is considered for an experimental performance assessment of the approach. The network consists of 290 block sections, and for a one-hour time horizon with around 80 active trains, the algorithm generates a solution in less than ten seconds. A benchmark with the corresponding mixed-integer program formulation, solved by commercial state-of-the-art solver Gurobi, is also conducted to assess the optimality of the generated solutions.
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| 2. |
- Åleskog, Christoffer, et al.
(author)
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Recent Developments in Low-Power AI Accelerators : A Survey
- 2022
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In: Algorithms. - : MDPI. - 1999-4893. ; 15:11
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Journal article (peer-reviewed)abstract
- As machine learning and AI continue to rapidly develop, and with the ever-closer end of Moore’s law, new avenues and novel ideas in architecture design are being created and utilized. One avenue is accelerating AI as close to the user as possible, i.e., at the edge, to reduce latency and increase performance. Therefore, researchers have developed low-power AI accelerators, designed specifically to accelerate machine learning and AI at edge devices. In this paper, we present an overview of low-power AI accelerators between 2019–2022. Low-power AI accelerators are defined in this paper based on their acceleration target and power consumption. In this survey, 79 low-power AI accelerators are presented and discussed. The reviewed accelerators are discussed based on five criteria: (i) power, performance, and power efficiency, (ii) acceleration targets, (iii) arithmetic precision, (iv) neuromorphic accelerators, and (v) industry vs. academic accelerators. CNNs and DNNs are the most popular accelerator targets, while Transformers and SNNs are on the rise.
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| 3. |
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| 4. |
- Al-Douri, Yamur K., et al.
(author)
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Time Series Forecasting using a Two-level Multi-objective Genetic Algorithm : A case study of cost data for tunnel fans
- 2018
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In: Algorithms. - : MDPI. - 1999-4893. ; 11:8
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Journal article (peer-reviewed)abstract
- The aim of this study is to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level is for the process of forecasting time series cost data, while the second level evaluates the forecasting. The first level implements either a multi-objective GA based on the ARIMA model or based on the dynamic regression model. The second level utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with the ARIMA model only. The results show the drawbacks of time series forecasting using the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In the second level, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.
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| 5. |
- Björklund, Henrik, et al.
(author)
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Uniform vs. Nonuniform Membership for Mildly Context-Sensitive Languages : A Brief Survey
- 2016
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In: Algorithms. - : MDPI. - 1999-4893. ; 9:2
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Journal article (peer-reviewed)abstract
- Parsing for mildly context-sensitive language formalisms is an important area within natural language processing. While the complexity of the parsing problem for some such formalisms is known to be polynomial, this is not the case for all of them. This article presents a series of results regarding the complexity of parsing for linear context-free rewriting systems and deterministic tree-walking transducers. We discuss the difference between uniform and nonuniform complexity measures and how parameterized complexity theory can be used to investigate how different aspects of the formalisms influence how hard the parsing problem is. The main results we survey are all hardness results and indicate that parsing is hard even for relatively small values of parameters such as rank and fan-out in a rewriting system.
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| 6. |
- Carlsson, Leo S., et al.
(author)
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Fibers of Failure : Classifying Errors in Predictive Processes
- 2020
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In: Algorithms. - : MDPI. - 1999-4893. ; 13:6
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Journal article (peer-reviewed)abstract
- Predictive models are used in many different fields of science and engineering and are always prone to make faulty predictions. These faulty predictions can be more or less malignant depending on the model application. We describe fibers of failure (FIFA), a method to classify failure modes of predictive processes. Our method uses MAPPER, an algorithm from topological data analysis (TDA), to build a graphical model of input data stratified by prediction errors. We demonstrate two ways to use the failure mode groupings: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode. We demonstrate FIFA on two scenarios: a convolutional neural network (CNN) predicting MNIST images with added noise, and an artificial neural network (ANN) predicting the electrical energy consumption of an electric arc furnace (EAF). The correction layer on the CNN model improved its prediction accuracy significantly while the inspection of failure modes for the EAF model provided guiding insights into the domain-specific reasons behind several high-error regions.
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| 7. |
- Dey, Polash, et al.
(author)
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Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains
- 2021
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In: Algorithms. - Basel, Switzerland : MDPI. - 1999-4893. ; 14:8, s. 1-20
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Journal article (peer-reviewed)abstract
- Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks’ prices follow the same trend, a single model cannot be used to predict the movement of all types of stock’s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs—simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)—and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies’ datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability.
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| 8. |
- Edler, Daniel, et al.
(author)
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Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap
- 2017
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In: Algorithms. - : MDPI AG. - 1999-4893. ; 10:4
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Journal article (peer-reviewed)abstract
- Comprehending complex systems by simplifying and highlighting important dynamical patterns requires modeling and mapping higher-order network flows. However, complex systems come in many forms and demand a range of representations, including memory and multilayer networks, which in turn call for versatile community-detection algorithms to reveal important modular regularities in the flows. Here we show that various forms of higher-order network flows can be represented in a unified way with networks that distinguish physical nodes for representing a complex system's objects from state nodes for describing flows between the objects. Moreover, these so-called sparse memory networks allow the information-theoretic community detection method known as the map equation to identify overlapping and nested flow modules in data from a range of different higher-order interactions such as multistep, multi-source, and temporal data. We derive the map equation applied to sparse memory networks and describe its search algorithm Infomap, which can exploit the flexibility of sparse memory networks. Together they provide a general solution to reveal overlapping modular patterns in higher-order flows through complex systems.
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| 9. |
- Georgiadis, Georgios, 1981, et al.
(author)
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Overlays with Preferences: Distributed, Adaptive Approximation Algorithms for Matching with Preference Lists
- 2013
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In: Algorithms. - : MDPI AG. - 1999-4893. ; 6:4, s. 824-856
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Journal article (peer-reviewed)abstract
- A key property of overlay networks is the overlay nodes’ ability to establish connections (or be matched) to other nodes by preference, based on some suitability metric related to, e.g., the node’s distance, interests, recommendations, transaction history or available resources. When there are no preference cycles among the nodes, a stable matching exists in which nodes have maximized individual satisfaction, due to their choices, however no such guarantees are currently being given in the generic case. In this work, we employ the notion of node satisfaction to suggest a novel modeling for matching problems, suitable for overlay networks. We start by presenting a simple, yet powerful, distributed algorithm that solves the many-to-many matching problem with preferences. It achieves that by using local information and aggregate satisfaction as an optimization metric, while providing a guaranteed convergence and approximation ratio. Subsequently, we show how to extend the algorithm in order to support and adapt to changes in the nodes’ connectivity and preferences. In addition, we provide a detailed experimental study that focuses on the levels of achieved satisfaction, as well as convergence and reconvergence speed.
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| 10. |
- Islam, Md. Saiful, et al.
(author)
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A Review on Recent Advancements in FOREX Currency Prediction
- 2020
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In: Algorithms. - : MDPI. - 1999-4893. ; 13:8
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Research review (peer-reviewed)abstract
- In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.
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