Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Detecting and predicting the topic change of Knowledge-based Systems: A topic-based bibliometric analysis from 1991 to 2016

© 2017 The journal Knowledge-based Systems (KnoSys) has been published for over 25 years, during which time its main foci have been extended to a broad range of studies in computer science and artificial intelligence. Answering the questions: “What is the KnoSys community interested in?” and “How does such interest change over time?” are important to both the editorial board and audience of KnoSys. This paper conducts a topic-based bibliometric study to detect and predict the topic changes of KnoSys from 1991 to 2016. A Latent Dirichlet Allocation model is used to profile the hotspots of KnoSys and predict possible future trends from a probabilistic perspective. A model of scientific evolutionary pathways applies a learning-based process to detect the topic changes of KnoSys in sequential time slices. Six main research areas of KnoSys are identified, i.e., expert systems, machine learning, data mining, decision making, optimization, and fuzzy, and the results also indicate that the interest of KnoSys communities in the area of computational intelligence is raised, and the ability to construct practical systems through knowledge use and accurate prediction models is highly emphasized. Such empirical insights can be used as a guide for KnoSys submissions

Aplikasi Jaringan Saraf Tiruan Dan Particle Swarm Optimization Untuk Peramalan Indeks Harga Saham Bursa Efek Indonesia

Jakarta Composite Index (JCI) is the main stock index in Indonesia Stock Exchange, which indicates the movement of the performance of all stocks listed. The data of stock price index often experience rapid fluctuations in a short time, so it is needed to carry out an analysis to help investor making the right investment decisions. Forecasting JCI is one of the activities that can be done because it helps to predict the value of the stock price in accordance with the past patterns, so it can be a consideration to make a decision. In this research, there are two forecasting models created to predict JCI, which are Artificial Neural Network (ANN) model with (1) Backpropagation algorithm (BP) and (2) Backpropagation algorithm model combined with Particle Swarm Optimization algorithm (PSO). The development of both models is done from the stage of the training process to obtain optimal weights on each network layer, followed by a stage of the testing process to determine whether the models are valid or not based on the tracking signals that are generated. ANN model is used because it is known to have the ability to process data that is nonlinear such as stock price indices and PSO is used to help ANN to gain weight with a fast computing time and tend to provide optimal results. Forecast results generated from both models are compared based on the error of computation time and forecast error. ANN model with BP algorithm generates computation time of training process for 4,9927 seconds with MSE of training and testing process is respectively 0,0031 and 0,0131, and MAPE of forecast results is 2,55%. ANN model with BP algorithm combined with PSO generates computation time of training process for 4,3867 seconds with MSE of training and testing process is respectively 0,0030 and 0,0062, and MAPE of forecast result is 1,88%. Based on these results, it can be concluded that ANN model with BP algorithm combined with PSO provides a more optimal result than ANN model with BP algorithm

The cross-association relation based on intervals ratio in fuzzy time series

The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modifed steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating  very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Triangular Fuzzy Time Series for Two Factors High-order based on Interval Variations

Fuzzy time series (FTS) firstly introduced by Song and Chissom has been developed to forecast such as enrollment data, stock index, air pollution, etc. In forecasting FTS data several authors define universe of discourse using coefficient values with any integer or real number as a substitute. This study focuses on interval variation in order to get better evaluation. Coefficient values analyzed and compared in unequal partition intervals and equal partition intervals with base and triangular fuzzy membership functions applied in two factors high-order. The study implemented in the Shen-hu stock index data. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 0.28% for Shen-hu stock index daily data. Based on the result, this research can be used as a reference to determine the better interval and degree membership value in the fuzzy time series.

Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve‐fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine‐tuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine‐tuning, fuzzy rule‐based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods

APLIKASI JARINGAN SARAF TIRUAN DAN PARTICLE SWARM OPTIMIZATION UNTUK PERAMALAN INDEKS HARGA SAHAM BURSA EFEK INDONESIA

Jakarta Composite Index (JCI) is the main stock index in Indonesia Stock Exchange, which indicates the movement of the performance of all stocks listed. The data of stock price index often experience rapid fluctuations in a short time, so it is needed to carry out an analysis to help investor making the right investment decisions. Forecasting JCI is one of the activities that can be done because it helps to predict the value of the stock price in accordance with the past patterns, so it can be a consideration to make a decision. In this research, there are two forecasting models created to predict JCI, which are Artificial Neural Network (ANN) model with (1) Backpropagation algorithm (BP) and (2) Backpropagation algorithm model combined with Particle Swarm Optimization algorithm (PSO). The development of both models is done from the stage of the training process to obtain optimal weights on each network layer, followed by a stage of the testing process to determine whether the models are valid or not based on the tracking signals that are generated. ANN model is used because it is known to have the ability to process data that is nonlinear such as stock price indices and PSO is used to help ANN to gain weight with a fast computing time and tend to provide optimal results. Forecast results generated from both models are compared based on the error of computation time and forecast error. ANN model with BP algorithm generates computation time of training process for 4,9927 seconds with MSE of training and testing process is respectively 0,0031 and 0,0131, and MAPE of forecast results is 2,55%. ANN model with BP algorithm combined with PSO generates computation time of training process for 4,3867 seconds with MSE of training and testing process is respectively 0,0030 and 0,0062, and MAPE of forecast result is 1,88%. Based on these results, it can be concluded that ANN model with BP algorithm combined with PSO provides a more optimal result than ANN model with BP algorithm

Evolving integrated multi-model framework for on line multiple time series prediction

Time series prediction has been extensively researched in both the statistical and computational intelligence literature with robust methods being developed that can be applied across any given application domain. A much less researched problem is multiple time series prediction where the objective is to simultaneously forecast the values of multiple variables which interact with each other in time varying amounts continuously over time. In this paper we describe the use of a novel Integrated Multi-Model Framework (IMMF) that combined models developed at three di erent levels of data granularity, namely the Global, Local and Transductive models to perform multiple time series prediction. The IMMF is implemented by training a neural network to assign relative weights to predictions from the models at the three di erent levels of data granularity. Our experimental results indicate that IMMF signi cantly outperforms well established methods of time series prediction when applied to the multiple time series prediction problem