Article
Green & Sustainable Science & Technology
Chengli Zheng, Wen-Ze Wu, Wanli Xie, Qi Li, Tao Zhang
Summary: The consumption of hydroelectricity in China is on the rise, and a new method utilizing grey modeling technique has been developed to estimate this consumption. The novel model constructed in the study eliminates inherent errors and adjusts nonlinear parameters, improving prediction accuracy. Forecasts suggest a continuous increase in hydroelectricity consumption in the future, albeit with a slightly slower growth rate.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Chemistry, Multidisciplinary
Shebiao Hu, Kun Li
Summary: This paper proposes a product-demand forecasting model based on multilayer Bayesian network to accurately predict demand by introducing hidden layer variables and volatility factors. The experimental results show that the method has a good prediction effect and provides a new idea for demand forecasting in the supply chain.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Stephen Oyewumi Oladipo, Yanxia Sun, Abraham Olatide Amole
Summary: This study focuses on the accurate prediction of electricity consumption using hybrid modeling techniques, with Lagos districts in Nigeria as a case study. The research investigates the performance of three evolutionary algorithms for optimizing the parameters of adaptive network-based fuzzy inference systems. It also examines the impact of clustering techniques on other key hyperparameters of the ANFIS.
Article
Green & Sustainable Science & Technology
Mei-Li Shen, Cheng-Feng Lee, Hsiou-Hsiang Liu, Po-Yin Chang, Cheng-Hong Yang
Summary: This study introduces a new forecasting approach, FSPSOSVR, which combines particle swarm optimization, random forest feature selection, and support vector regression for accurately predicting exchange rates. Empirical results show that the FSPSOSVR algorithm consistently outperforms competing models and has practical relevance for foreign exchange carry trades.
Article
Thermodynamics
Yanmei Huang, Najmul Hasan, Changrui Deng, Yukun Bao
Summary: Accurate day-ahead peak load forecasting is crucial for power dispatching and is of great interest to investors, energy policy makers, and government. This study proposes a novel MEMD-PSO-SVR hybrid model for precise electricity peak load prediction, which has been validated using real-world load data sets from Australia.
Article
Computer Science, Artificial Intelligence
Naresh Kumar, Seba Susan
Summary: The study optimizes the hyperparameters of fuzzy time series forecasting for the COVID-19 pandemic using Particle Swarm Optimization, proposing nested FTS-PSO and exhaustive search FTS-PSO techniques. The exhaustive search FTS-PSO outperformed all methods in forecasting coronavirus confirmed cases, demonstrating its effectiveness in achieving optimal hyperparameter values.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Claudiu Pozna, Radu-Emil Precup, Erno Horvath, Emil M. Petriu
Summary: This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The algorithm is applied to the optimal tuning of proportional-integral-fuzzy controllers for position control of integral-type servo systems, resulting in reduced energy consumption. A comparison with other metaheuristic algorithms is provided at the end of the article.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Engineering, Civil
Saeed Mozaffari, Saman Javadi, Hamid Kardan Moghaddam, Timothy O. Randhir
Summary: A simulation-optimization hybrid model using the PSO algorithm was developed to forecast groundwater levels in aquifers. The model outperformed other models in terms of RMSE and R 2 , providing a reliable tool for decision support and management of similar aquifers.
WATER RESOURCES MANAGEMENT
(2022)
Article
Chemistry, Multidisciplinary
Chen Bao, Yongwei Miao, Jiazhou Chen, Xudong Zhang
Summary: With the increasing demand for intelligent custom clothing, the development of highly accurate human body dimension prediction tools using artificial neural network technology has become essential. The proposed GRFN model combines multiple techniques to enhance prediction accuracy and correction values, outperforming traditional SVR models.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Jagriti Saini, Maitreyee Dutta, Goncalo Marques
Summary: Indoor air pollution has a significant impact on human health, with PM10 being closely associated with severe health issues. Monitoring and predicting PM10 concentration in indoor environments using the Fuzzy Inference System Tree model can help building occupants to take preventive measures, thus improving public health and safety. Research findings indicate that the model optimized with genetic algorithm demonstrates superior performance in enhancing air quality.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Multidisciplinary Sciences
El-Sayed M. El-kenawy, Seyedali Mirjalili, Nima Khodadadi, Abdelaziz A. Abdelhamid, Marwa M. Eid, M. El-Said, Abdelhameed Ibrahim
Summary: This study proposes a high-accuracy wind speed prediction method based on a weighted ensemble model. The proposed algorithm outperforms existing algorithms and demonstrates stability and robustness through statistical analysis.
Article
Chemistry, Analytical
Ruijie Wang, Xun Wen, Fangmin Xu, Zhijian Ye, Haiyan Cao, Zhirui Hu, Xiaoping Yuan
Summary: Device-to-device (D2D) communication is a promising wireless communication technology that reduces the traffic load of the base station and improves spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further enhance throughput, but interference suppression becomes more complex due to new links. This paper proposes a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization (PSO) to address this challenge.
Article
Thermodynamics
D. J. Krishna Kishore, M. R. Mohamed, K. Sudhakar, K. Peddakapu
Summary: The photovoltaic system is attractive for its ability to generate more power without pollution and be environmentally friendly. However, under partial shading, the system faces difficulties. To mitigate these issues, this work proposes a hybrid method called TLABC, which combines teaching-learning and artificial bee colony. Simulation results show that the proposed method outperforms other methods in terms of Standard Deviation, Mean Absolute Error, Successful rate, and efficiency.
Article
Construction & Building Technology
Guo-Feng Fan, Ya Zheng, Wen-Jing Gao, Li-Ling Peng, Yi-Hsuan Yeh, Wei-Chiang Hong
Summary: This paper proposes a novel hybrid method, the EWT-MOLSTM-SVR model, for power load forecasting, which combines Long Short-term Memory (LSTM) with the dual optimization of particle swarm algorithm and butterfly algorithm. Experimental results show that the model significantly outperforms other models such as Recurrent Neural Network (RNN), General Regression Neural Network (GRNN), and PSO-SVM in improving forecast accuracy.
ENERGY AND BUILDINGS
(2023)
Article
Computer Science, Information Systems
Manzoor Ellahi, Muhammad Rehan Usman, Waqas Arif, Hafiz Fuad Usman, Waheed A. Khan, Gandeva Bayu Satrya, Kamran Daniel, Noman Shabbir
Summary: Renewable Energy Sources are effective alternatives to traditional fuels, but they have limitations. This paper presents a method for wind speed and power forecasting using neural networks and optimization algorithms. The results show that neural networks provide more accurate predictions when trained and tuned using the given optimization algorithms.
Article
Computer Science, Artificial Intelligence
Mehdi Rajabi Asadabadi, Elizabeth Chang, Keiran Sharpe
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Theory & Methods
A. S. M. Kayes, Wenny Rahayu, Paul Watters, Mamoun Alazab, Tharam Dillon, Elizabeth Chang
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Computer Science, Interdisciplinary Applications
Yu Zhang, Min Wang, Morteza Saberi, Elizabeth Chang
Article
Computer Science, Hardware & Architecture
Hamed Aboutorab, Omar K. Hussain, Morteza Saberi, Farookh Khadeer Hussain, Elizabeth Chang
Summary: Risk management is crucial in global supply chains, with most studies focusing on managerial rather than technical perspectives. This paper specifically evaluates techniques for risk identification in the current networked supply chain environment to inform improvements needed for effective risk management.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Energy & Fuels
Petros Koutroumpinas, Yu Zhang, Steve Wallis, Elizabeth Chang
Summary: Reducing energy waste is crucial for Remote Industrial Plants (RIP), especially during the COVID-19 pandemic where balancing energy efficiency and OHS becomes challenging. The AI Empowered Cyber Physical Ecosystem (AECPE) has shown promising results in reducing energy costs, optimizing environmental conditions, and enhancing OHS for industries.
Article
Computer Science, Interdisciplinary Applications
Yu Zhang, Min Wang, Morteza Saberi, Elizabeth Chang
Summary: Research on academic paper ranking has gained attention, but lacks reliable evaluation standards. Researchers analyze their algorithms using expert decisions and predefined benchmarks, but different methods lead to different results. This study provides a guideline on conducting comprehensive analysis using multiple benchmarks.
Article
Computer Science, Interdisciplinary Applications
Mehdi Rajabi Asadabadi, Morteza Saberi, Nima Salehi Sadghiani, Ofer Zwikael, Elizabeth Chang
Summary: This paper proposes an automated approach to quality management and product improvement using online product reviews. By performing text mining, it effectively captures the voice of the customer and utilizes the extracted information to guide the product improvement process. This approach enhances quality management processes in organizations and advances customer-oriented product improvement.
JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT
(2023)
Article
Computer Science, Theory & Methods
Michael Zipperle, Florian Gottwalt, Elizabeth Chang, Tharam Dillon
Summary: Traditional IDS cannot handle the increasing and sophisticated cyberattacks effectively, while recent research has explored the potential of utilizing data provenance for Host-based IDS to improve detection performance and reduce false alarms. This survey aims to provide a detailed evaluation, propose a novel taxonomy, discuss current issues, and encourage researchers to tackle challenges in data collection, graph summarization, intrusion detection, and real-world benchmark datasets for PIDS.
ACM COMPUTING SURVEYS
(2023)
Article
Engineering, Electrical & Electronic
C. P. Koushik, P. Vetrivelan, Elizabeth Chang
Summary: This paper proposes a Markov chain-based opportunistic routing protocol, which predicts node mobility using historical movement information and proposes a neighbor table-based relay node selection procedure. The performance of the proposed protocol is evaluated through simulation experiments and compared with existing routing protocols, demonstrating its superiority.
IETE JOURNAL OF RESEARCH
(2023)
Article
Chemistry, Analytical
K. Kalaivanan, G. Idayachandran, P. Vetrivelan, A. Henridass, V. Bhanumathi, Elizabeth Chang, P. Sam Methuselah
Summary: Real-time smart applications are now possible due to advancements in communication and sensor technology. Wireless sensor networks (WSNs) are used to collect data from disaster sites and accurately locate detected information for safe rescue operations. The T-based Routing Topology (TRT) is suggested for data collection in landmine-affected areas, utilizing sensors, GPS, and cameras to locate explosive materials with high accuracy. The efficiency of this method is evaluated using Network Simulator-2 (NS-2).
Proceedings Paper
Automation & Control Systems
Michael Zipperle, Florian Gottwalt, Yu Zhang, Omar Hussain, Elizabeth Chang, Tharam Dillon
Summary: Traditional Intrusion Detection Systems are unable to keep up with sophisticated cyber-attacks, but Provenance-based Intrusion Detection Systems using fine-grained event correlation show increased detection accuracy and reduced false-alarm rates. However, manually creating rules by security experts is time-consuming and lacks high-quality standards. To address this, an automated rule generation framework is proposed to promptly identify similar attacks and affected systems.
2022 IEEE INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, INTL CONF ON CLOUD AND BIG DATA COMPUTING, INTL CONF ON CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Yu Zhang, Min Wang, Morteza Saberi, Elizabeth Chang
TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING
(2020)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Morteza Saberi, Zahra Saberi, Mehdi Rajabi Aasadabadi, Omar Khadeer Hussain, Elizabeth Chang
ADVANCES IN E-BUSINESS ENGINEERING FOR UBIQUITOUS COMPUTING
(2020)
Article
Computer Science, Hardware & Architecture
Tharam S. Dillon, Yi-Ping Phoebe Chen, Elizabeth Chang, Mukesh Mohania, Vish Ramakonar
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2020)
Editorial Material
Automation & Control Systems
Elizabeth Chang, Kit Yan Chan, Ponnie Clark, Vidy Potdar
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)