Article
Computer Science, Information Systems
Pritpal Singh
Summary: The study proposed an improved quantum optimization algorithm to address the issues of universe of discourse selection and fuzzy degree determination in fuzzy time series models. By integrating this algorithm with the FTS modeling approach, a hybrid model called FQTSFM was developed, which shows fast convergence and accurate forecasting results.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Shivani Pant, Sanjay Kumar
Summary: This study proposes a novel computational fuzzy time series forecasting method based on intuitionistic fuzzy sets and self-organized direction aware clustering. The method shows good performance in forecasting accuracy with optimized weights using grey wolf optimization, applied to predict enrolments of the University of Alabama and market price of SBI share at Bombay stock exchange.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Thermodynamics
Xiaoping Xiong, Guohua Qing
Summary: This paper proposes a new hybrid forecasting framework to improve the forecasting accuracy of day-ahead electricity prices. The proposed model consists of three valuable strategies: an adaptive copula-based feature selection algorithm, a new method of signal decomposition technique based on decomposition denoising strategy, and a Bayesian optimization and hyperband optimized long short-term memory model. Cross-validation using five datasets from the PJM electricity market shows that the proposed hybrid algorithm is more effective and practical for day-ahead electricity price forecasting.
Article
Computer Science, Artificial Intelligence
Zhenyu Song, Cheng Tang, Shuangbao Song, Yajiao Tang, Jinhai Li, Junkai Ji
Summary: This paper proposes a complex network-based firefly algorithm (CnFA) to address the limitations of the traditional algorithm and achieve better performance. Experimental results demonstrate that CnFA achieves satisfactory results in various optimization tasks and can enhance the performance of other population-based evolutionary algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Junkai Ji, Minhui Dong, Qiuzhen Lin, Kay Chen Tan
Summary: Wind energy is one of the fastest growing renewable energy resources, and accurate wind power forecasting is crucial for power system planning and wind farm operation. By using artificial neural networks and the dendritic neuron model (DNM) with a dynamic structure for wind speed time series prediction, prediction accuracy can be significantly improved. By utilizing the states of matter search (SMS) optimization algorithm, the dendritic neural regression (DNR) can efficiently capture nonlinear correlations and achieve competitive results in wind speed forecasting compared to other advanced techniques.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2021)
Article
Multidisciplinary Sciences
Radha Mohan Pattanayak, H. S. Behera, Sibarama Panigrahi
Summary: A new fuzzy time-series forecasting model is proposed in the research, which handles uncertainty and non-determinism by considering neutrosophic entropy and triangular membership value. The research focuses mainly on three concepts: adaptive method, fuzzy logical relationships, and forecasted values calculation.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Mathematics
Wen-Jie Liu, Yu-Ting Bai, Xue-Bo Jin, Ting-Li Su, Jian-Lei Kong
Summary: In this paper, an adaptive Broad Echo State Network (ABESN) is proposed for time series forecasting. By optimizing the structure and hyperparameters, the ABESN demonstrates better learning ability for nonstationary time series data and achieves higher forecasting accuracy.
Article
Computer Science, Artificial Intelligence
Yaoguo Dang, Yifan Zhang, Junjie Wang
Summary: To address the problem of the grey multivariate prediction model's inability to accurately simulate systems with periodic oscillations, a novel multivariate grey model named the GM(1,N|sin) power model is proposed. This model incorporates a power exponential term and dynamic sinusoidal function to represent the nonlinear relationship and periodic oscillations of the independent and dependent variables, respectively. Through case studies on electricity consumption and PM2.5 concentrations, the GM(1,N|sin) power model outperforms alternative models in accurately predicting time series with periodic oscillations.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Peng Liu, Qilong Han, Ting Wu, Wenjian Tao
Summary: The Industrial Internet of Things (IIoT) is crucial for digital management in industrial enterprises, enhancing productivity and reducing management costs. Anomaly detection is becoming increasingly important for IIoT security. However, the complex topology and randomness of IIoT present challenges in effectively extracting data neighborhood information. This article proposes a new multivariate time-series (MTSs) anomaly detection framework, utilizing an intuition-based neutrosophic representation method and an automatic learning graph structure. Experimental results on public anomaly detection datasets demonstrate the robustness and interpretability of our method in IIoT anomaly detection tasks.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Thermodynamics
Ping Jiang, Hufang Yang, Hongmin Li, Ying Wang
Summary: The study developed a novel forecasting system based on fuzzy time series for energy consumption forecasting, which shows excellent performance in small-sample forecasting. By utilizing information granularity and fuzzy c-means clustering for fuzzification, the system improves accuracy and stability in forecasting the energy consumption structure.
Article
Computer Science, Information Systems
Ruijin Wang, Xikai Pei, Juyi Zhu, Zhiyang Zhang, Xin Huang, Jiayi Zhai, Fengli Zhang
Summary: This paper proposes a model fusion-based time series forecasting method to improve the accuracy and efficiency of predictions using multivariate grey model and artificial fish swarm algorithm. Two fusion models based on data decomposition and weighted summation achieve good prediction results in different scenarios.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Milad Shahvaroughi Farahani, Seyed Hossein Razavi Hajiagha
Summary: The article focuses on predicting stock price indices using artificial neural network and metaheuristic algorithms, along with feature selection and error evaluation, and compares the results with time series models.
Article
Environmental Sciences
Harshal Dhake, Yashwant Kashyap, Panagiotis Kosmopoulos
Summary: The rapid growth of solar energy requires accurate solar irradiance forecasts. Traditional techniques are time-consuming and only accurate for short-term forecasts. Deep learning techniques like LSTM networks can learn and predict complex time series data, but poor performance may occur due to improper hyperparameter configuration. This work introduces two new algorithms for LSTM hyperparameter tuning and a FFT-based data decomposition technique. Results show significant improvement in fitness and reduction in RMSE for 90 min ahead forecast using the optimized workflow, compared to other techniques for solar energy forecasting.
Article
Computer Science, Artificial Intelligence
Jianping Li, Jun Hao, QianQian Feng, Xiaolei Sun, Mingxi Liu
Summary: This paper proposes a heterogeneous ensemble forecasting model with multi-objective programming for nonlinear time series, and validates it using the Baltic Dry Index's time series data. Experimental results demonstrate the model's superior robustness in conducting out-of-sample predictions under different lead times.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Salihu A. Abdulkarim, Andries P. Engelbrecht
Summary: Several studies have applied particle swarm optimization algorithms to train neural networks for time series forecasting, with good performance results. This study introduces a dynamic PSO algorithm for training NNs in forecasting non-stationary time series, outperforming standard PSO and Rprop algorithms. These findings suggest the potential of dynamic PSO in real-world forecasting applications.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Francesco Pistolesi, Michele Baldassini, Beatrice Lazzerini
Summary: More than one in four workers worldwide suffer from back pain, resulting in the loss of 264 million work days annually. In the U.S., it costs $50 billion in healthcare expenses each year, rising up to $100 billion when accounting for decreased productivity and lost wages. The impending Industry 5.0 revolution emphasizes worker well-being and their rights, such as privacy, autonomy, and human dignity. This paper proposes a privacy-preserving artificial intelligence system that monitors the posture of assembly line workers. The system accurately assesses upper-body and lower-body postures while respecting privacy, enabling the detection of harmful posture habits and reducing the likelihood of musculoskeletal disorders.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Xavier Boucher, Camilo Murillo Coba, Damien Lamy
Summary: This paper explores the new business strategies of digital servitization and smart PSS delivery, and develops conceptual prototypes of smart PSS value offers for early stages of the design process. It presents the development and experimentation of a modelling language and toolkit, and applies it to the design of a smart PSS in the field of heating appliances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Dieudonne Tchuente, Jerry Lonlac, Bernard Kamsu-Foguem
Summary: Artificial Intelligence (AI) is becoming increasingly important in various sectors of society. However, the black box nature of most AI techniques such as Machine Learning (ML) hinders their practical application. This has led to the emergence of Explainable artificial intelligence (XAI), which aims to provide AI-based decision-making processes and outcomes that are easily understood, interpreted, and justified by humans. While there has been a significant amount of research on XAI, there is currently a lack of studies on its practical applications. To address this research gap, this article proposes a comprehensive review of the business applications of XAI and a six-step framework to improve its implementation and adoption by practitioners.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Francois-Alexandre Tremblay, Audrey Durand, Michael Morin, Philippe Marier, Jonathan Gaudreault
Summary: Continuous high-frequency wood drying, integrated with a traditional wood finishing line, improves the value of lumber by correcting moisture content piece by piece. Using reinforcement learning for continuous drying operation policies outperforms current industry methods and remains robust to sudden disturbances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Luyao Xia, Jianfeng Lu, Yuqian Lu, Wentao Gao, Yuhang Fan, Yuhao Xu, Hao Zhang
Summary: Efficient assembly sequence planning is crucial for enhancing production efficiency, ensuring product quality, and meeting market demands. This study proposes a dynamic graph learning algorithm called assembly-oriented graph attention sequence (A-GASeq), which optimizes the assembly graph structure to guide the search for optimal assembly sequences. The algorithm demonstrates superiority and broad utility in real-world scenarios.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Mutahar Safdar, Padma Polash Paul, Guy Lamouche, Gentry Wood, Max Zimmermann, Florian Hannesen, Christophe Bescond, Priti Wanjara, Yaoyao Fiona Zhao
Summary: Metal-based additive manufacturing can achieve fully dense metallic components, and the application of machine learning in this field has been growing rapidly. However, there is a lack of framework to manage these machine learning models and guidance on the fundamental requirements for a cross-disciplinary platform to support process-based machine learning models in industrial metal AM.
COMPUTERS IN INDUSTRY
(2024)