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Journal articles on the topic 'Prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Burbey, Ingrid, and Thomas L. Martin. "A survey on predicting personal mobility." International Journal of Pervasive Computing and Communications 8, no. 1 (2012): 5–22. http://dx.doi.org/10.1108/17427371211221063.

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PurposeLocation‐prediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal location‐prediction. Location‐prediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, location‐prediction turned personal, predicting individuals' next locations given their current locations.Design/methodology/approachThis paper includes an overview of prediction techniques and reviews several location‐predi

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Hanappi, Hardy. "Predictions and Hopes: Global Political Economy Dynamics of the Next Ten Years." Advances in Social Sciences Research Journal 11, no. 8 (2024): 66–87. http://dx.doi.org/10.14738/assrj.118.17381.

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Predictions and hopes are different things. Predictions are based on past empirical observations. They single out what seem to be essential variables and the relationships between them and assume that their importance will prevail in the future. Hopes add a component to a prediction, namely an evaluation, which refers back to the entity that produces the prediction. More favourable predictions are hoped to become a reality while others, which would see the entity in a worse position, are not hoped for. A closer look reveals that with a consideration of what predictions are used for by an entit

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Verhun, Volodymyr, and Mykhailo Granchak. "M&A PREDICTIONS: RECONSIDERING THEIR VALUE, END-USERS, AND METHODOLOGIES." Actual Problems of International Relations, no. 160 (2024): 138–51. http://dx.doi.org/10.17721/apmv.2024.160.1.138-151.

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The article explores market participants who may benefit from M&A predictions and how their goals may impact the requirements for M&A predictions. These participants (also called end-users of M&A predictions) are company shareholders considering selling their business, shareholders and company management considering acquiring one or a few other companies, shareholders and company management competing with potential M&A targets or buyers, and advisory firms providing investment banking services in the industries where M&A deals occur. Analyzing their goals while applying M&a

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Sun, Zhaoyue, Jiazheng Li, Gabriele Pergola, and Yulan He. "ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25228–36. https://doi.org/10.1609/aaai.v39i24.34709.

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Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and develop

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Tang, Li, Ping He Pan, and Yong Yi Yao. "EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices." International Journal of Computers Communications & Control 13, no. 2 (2018): 268–79. http://dx.doi.org/10.15837/ijccc.2018.2.3187.

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This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clus

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Yan, Xiaohui, Tianqi Zhang, Wenying Du, Qingjia Meng, Xinghan Xu, and Xiang Zhao. "A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years." Journal of Marine Science and Engineering 12, no. 1 (2024): 159. http://dx.doi.org/10.3390/jmse12010159.

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Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator

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Zhang, Chenglong, and Hyunchul Ahn. "E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors." Education Sciences 13, no. 11 (2023): 1130. http://dx.doi.org/10.3390/educsci13111130.

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This study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s time series was considered, and data from previous semesters were used to predict the current semester’s results. Each course was predicted separately so that the research reflected reality as closely as possible. Three different methods for selecting training data were listed. Since the at-risk pre

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Zhuang, Wei, Zhiheng Li, Ying Wang, Qingyu Xi, and Min Xia. "GCN–Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting." Applied Sciences 14, no. 5 (2024): 2181. http://dx.doi.org/10.3390/app14052181.

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Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to me

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Rather, Akhter Mohiuddin. "A Hybrid Intelligent Method of Predicting Stock Returns." Advances in Artificial Neural Systems 2014 (September 7, 2014): 1–7. http://dx.doi.org/10.1155/2014/246487.

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This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of

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Harahap, Rahma Sari, Iskandar Muda, and Rina Br Bukit. "Analisis penggunaan metode Altman Z-Score dan Springate untuk mengetahui potensi terjadinya Financial Distress pada perusahaan manufaktur sektor industri dasar dan kimia Sub Sektor semen yang terdaftar di Bursa Efek Indonesia 2000-2020." Owner 6, no. 4 (2022): 4315–25. http://dx.doi.org/10.33395/owner.v6i4.1576.

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The objective of the research is to find out the result of predicting bankruptcy, using Altman Z-Score and Springate methods in the manufacturing companies of basic industrial and chemistry sectors, cement sub-sector listed on BEI (Indonesia Stock Exchange) in the period of 2000-2020 and to determine the most accurate predicting method of bankruptcy to be applied in the manufacturing companies in basic industrial and chemistry sectors, cement sub-sector. The research employs descriptive quantitative method. The samples are taken by using purposive sampling method with three manufacture compani

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PALOPOLI, LUIGI, and GIORGIO TERRACINA. "CooPPS: A SYSTEM FOR THE COOPERATIVE PREDICTION OF PROTEIN STRUCTURES." Journal of Bioinformatics and Computational Biology 02, no. 03 (2004): 471–95. http://dx.doi.org/10.1142/s0219720004000697.

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Predicting the three-dimensional structure of proteins is a difficult task. In the last few years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them specialized to work on either some aspects of the predictions or on some categories of proteins; however, they are still not sufficiently accurate and reliable for predicting all kinds of proteins. In this context, it is useful to jointly apply different predicti

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Lelis, Levi, Sandra Zilles, and Robert Holte. "Improved Prediction of IDA*'s Performance via Epsilon-Truncation." Proceedings of the International Symposium on Combinatorial Search 2, no. 1 (2021): 108–16. http://dx.doi.org/10.1609/socs.v2i1.18198.

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Korf, Reid, and Edelkamp launched a line of research aimed at predicting how many nodes IDA* will expand with a given cost bound. This paper advances this line of research in three ways. First, we identify a source of prediction error that has hitherto been overlooked. We call it the ``discretization effect''. Second, we disprove the intuitively appealing idea that a ``more informed'' prediction system cannot make worse predictions than a ``less informed'' one. More informed systems are more susceptible to the discretization effect, and in several of our experiments the more informed system ma

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Lelis, Levi, Sandra Zilles, and Robert Holte. "Time Complexity of Iterative-Deepening A*: The Informativeness Pathology (Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1800–1801. http://dx.doi.org/10.1609/aaai.v25i1.8053.

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Korf, Reid, and Edelkamp launched a line of research aimed at predicting how many nodes IDA* will expand with a given depth bound. This paper advances this line of research in three ways. First, we identify a source of prediction error that has hitherto been overlooked. We call it the "discretization effect." Second, we disprove the intuitively appealing idea that a "more informed" prediction system cannot make worse predictions than a ``less informed'' one. More informed systems are more susceptible to the discretization effect, and in our experiments the more informed system makes poorer pre

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Je-Gal, Hong, Young-Seo Park, Seong-Ho Park, et al. "Time-Series Explanatory Fault Prediction Framework for Marine Main Engine Using Explainable Artificial Intelligence." Journal of Marine Science and Engineering 12, no. 8 (2024): 1296. http://dx.doi.org/10.3390/jmse12081296.

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As engine monitoring data has become more complex with an increasing number of sensors, fault prediction based on artificial intelligence (AI) has emerged. Existing fault prediction models using AI significantly improve the accuracy of predictions by effectively handling such complex data, but at the same time, the problem arises that the AI-based models cannot explain the rationale of their predictions to users. To address this issue, we propose a time-series explanatory fault prediction framework to provide an explainability even when using AI-based fault prediction models. It consists of a

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Wu, Xinhua, Nan Chen, Qianyun Du, Shuangshuang Mao, and Xiaoming Ju. "Short-term wind power prediction model based on ARMA-GRU-QPSO and error correction." Journal of Physics: Conference Series 2427, no. 1 (2023): 012028. http://dx.doi.org/10.1088/1742-6596/2427/1/012028.

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Abstract Power system dispatch benefits from accurate wind power predictions. To increase the prediction precision for wind power, this paper proposes a combined model for predicting short-term wind power based on the autoregressive moving average-gated recurrent unit (ARMA-GRU). Firstly, we build the ARMA model and GRU model respectively to predict wind power. Then we optimize the combined model’s weights by quantum particle swarm algorithm (QPSO). Finally, we build an error correction model for the prediction errors to acquire the final results for the wind power predictions. Our experimenta

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Chen, Zixuan, Guojie Wang, Xikun Wei, et al. "Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China." Atmosphere 15, no. 2 (2024): 155. http://dx.doi.org/10.3390/atmos15020155.

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Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI

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Jin, Long, Cai Yao, and Xiao-Yan Huang. "A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity." Monthly Weather Review 136, no. 12 (2008): 4541–54. http://dx.doi.org/10.1175/2008mwr2269.1.

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Abstract A new nonlinear artificial intelligence ensemble prediction (NAIEP) model has been developed for predicting typhoon intensity based on multiple neural networks with the same expected output and using an evolutionary genetic algorithm (GA). The model is validated with short-range forecasts of typhoon intensity in the South China Sea (SCS); results show that the NAIEP model is clearly better than the climatology and persistence (CLIPER) model for 24-h forecasts of typhoon intensity. Using identical predictors and sample cases, predictions of the genetic neural network (GNN) ensemble pre

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Ahmadi, Farrokh, Abbas Toloie Eshlaghi, and Reza Radfar. "Examining and Comparing the Efficiency of MLP and SimpleRNN Algorithms in Cryptocurrency Price Prediction." Management Strategies and Engineering Sciences 6, no. 3 (2024): 121–37. https://doi.org/10.61838/msesj.6.3.12.

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Cryptocurrencies have been widely identified and established as a new form of electronic currency exchange, carrying significant implications for emerging economies and the global economy. This research focused on the "examination and comparison of the efficiency of MLP and SimpleRNN algorithms in predicting cryptocurrency prices" using the Python programming language. Price predictions for Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple were made using two deep learning algorithms (including the MLP algorithm and the SimpleRNN algorithm) over the period from 2017 to 2023. The results of

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Sudriyanto, Sudriyanto, Fatimatus Syahro, and Novi Fitriani. "PERBANDINGAN PERFORMA MODEL MACHINE LEARNING SUPPORT VECTOR MACHINE, NEURAL NETWORK, DAN K-NEAREST NEIGHBORS DALAM PREDIKSI HARGA SAHAM." Jurnal Advanced Research Informatika 2, no. 1 (2023): 13–21. http://dx.doi.org/10.24929/jars.v2i1.2983.

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This study aims to analyze the performance of three prediction models, namely K-Nearest Neighbors (K-NN), Neural Network (NN), and Support Vector Machine (SVM), in predicting the stock price of PT Astra International Tbk (ASII.JK). The research encompasses the initial stages through evaluation using optimal parameters for these three algorithms. The research findings reveal that the K-NN prediction model has the lowest Root Mean Square Error (RMSE) value, with a value of 0.037, indicating the most accurate prediction compared to the other models. Despite the NN model having an RMSE of 0.048, w

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Badjate, Sanjay L., and Sanjay V. Dudul. "Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series." Applied Computational Intelligence and Soft Computing 2009 (2009): 1–21. http://dx.doi.org/10.1155/2009/364532.

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Multistep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in the recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present so

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Yang, Jiapeng. "Goldman Sachs’s Price Forecast Based on ARIMA and LSTM." Highlights in Business, Economics and Management 24 (January 22, 2024): 2194–201. http://dx.doi.org/10.54097/zk7c4c90.

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The prediction of stock prices is a common and crucial problem in trading. Correctly predicting future stock prices enables traders to determine the optimal time to buy and sell stocks, increasing the probability of making profits. This study focuses on predicting the closing price of Goldman Sachs. Initially, an ARIMA (4,1,6) benchmark model was established based on the AIC information criteria for time series prediction. The model was then applied to make forward predictions. Subsequently, a two-layer LSTM model was constructed. The prediction results of both models were visualized, and the

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alan, Gun, Kavin Kumar, Su rya, and Kalai Chelvi. "Stock Market Prediction." International Academic Journal of Science and Engineering 9, no. 1 (2022): 18–22. http://dx.doi.org/10.9756/iajse/v9i2/iajse0909.

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Researchers have been investigating various approaches to accurately forecast stock market prices. Trading professionals can gain better insights regarding data, such as potential trends, by using useful prediction tools. Additionally, since the study predicts future market conditions, investors stand to gain significantly. Using machine learning algorithms for predicting is one such approach. The goal of this study is to increase the accuracy of stock market predictions made using stock valuation. Many academics have developed various approaches to address this issue, primarily using conventi

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Fang, Yiheng. "Prediction of the Ammonia Nitrogen Content with Improved Grey Model by Markov Chain." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 156–61. http://dx.doi.org/10.54097/zee1cd17.

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Water pollution prediction plays a crucial role in environmental protection and sustainable development. This study proposes an innovative approach to enhance the accuracy of water pollution prediction by combining the grey prediction model (GM) with Markov chain analysis. This research focuses on predicting the concentration of ammonia nitrogen (NH3-N) in Dongting Lake, a significant water body. Grey prediction models (GM) are utilized to forecast NH3-N content, addressing the challenge posed by incomplete or insufficient data. However, due to the dynamic nature of water quality indicators, G

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Wadi, Faska Aris Y. K., Putu Sugiartawan, Ni Nengah Dita Adriani, and Ni Nengah Dita Adriani. "Analisa Prediksi Time Series Jumlah Kasus Covid-19 Dengan Metode BPNN Di Bali." Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) 4, no. 1 (2022): 24–33. http://dx.doi.org/10.33173/jsikti.124.

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The COVID-19 pandemic has not yet subsided. This epidemic has spread to almost all countries in the world. In Indonesia, especially in the province of Bali, which experienced a large number of additional positive cases, recoveries and deaths from COVID-19, an analysis was carried out. The purpose of this analysis is to be able to obtain accuracy in predicting the addition of COVID-19 cases, recoveries and deaths in the province of Bali, predictions are made using the covid-19 time series data used in making predictions. what was done obtained the best and not good prediction accuracy, predicti

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Siemens, Angela, Spencer J. Anderson, S. Rod Rassekh, Colin J. D. Ross, and Bruce C. Carleton. "A Systematic Review of Polygenic Models for Predicting Drug Outcomes." Journal of Personalized Medicine 12, no. 9 (2022): 1394. http://dx.doi.org/10.3390/jpm12091394.

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Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The searc

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Masdiantini, Putu Riesty, and Ni Made Sindy Warasniasih. "Laporan Keuangan dan Prediksi Kebangkrutan Perusahaan." Jurnal Ilmiah Akuntansi 5, no. 1 (2020): 196. http://dx.doi.org/10.23887/jia.v5i1.25119.

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This study aims to determine differences in bankruptcy predictions at company’s sub-sector of cosmetics and household listed on the Indonesia Stock Exchange (IDX) using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model, and to determine the bankruptcy prediction model that is the most accurate of the five bankruptcy prediction models. This study uses secondary data in the form of company financial statements for the period 2014-2018. Data analysis techniques in this study used the Kruskal-Wallis test. The results showed there were differences in bankruptcy pre

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Jeong, Jiseok, and Changwan Kim. "Comparison of Machine Learning Approaches for Medium-to-Long-Term Financial Distress Predictions in the Construction Industry." Buildings 12, no. 10 (2022): 1759. http://dx.doi.org/10.3390/buildings12101759.

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A method for predicting the financial status of construction companies after a medium-to-long-term period can help stakeholders in large construction projects make decisions to select an appropriate company for the project. This study compares the performances of various prediction models. It proposes an appropriate model for predicting the financial distress of construction companies considering three, five, and seven years ahead of the prediction point. To establish the prediction model, a financial ratio was selected, which was adopted in existing studies on medium-to-long-term predictions

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Shaji, Hima Elsa, Arun K. Tangirala, and Lelitha Vanajakshi. "Joint clustering and prediction approach for travel time prediction." PLOS ONE 17, no. 9 (2022): e0275030. http://dx.doi.org/10.1371/journal.pone.0275030.

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Modeling and prediction of traffic systems is a challenging task due to the complex interactions within the system. Identification of significant regressors and using them to improve travel time predictions is a concept of interest. In previous studies, such regressors were identified offline and were static in nature. In this study, an iterative joint clustering and prediction approach is proposed to accurately predict spatiotemporal patterns in travel time. The clustering module is tied to the prediction module, and a prediction model is trained on each cluster. The combined clustering and p

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Liu, Gaoxiang, Xin Yu, and Danyang Liu. "Predictive Model for Long-Term Lane Occupancy Rate Based on CT-Transformer and Variational Mode Decomposition." Applied Sciences 14, no. 12 (2024): 5346. http://dx.doi.org/10.3390/app14125346.

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Lane occupancy is a crucial indicator of traffic flow and is significant for traffic management and planning. However, predicting lane occupancy is challenging due to numerous influencing factors, such as weather, holidays, and events, which render the data nonsmooth. To enhance lane occupancy prediction accuracy, this study introduces a fusion model that combines the CT-Transformer (CSPNet-Attention and Two-stage Transformer framework) with the Temporal Convolutional Neural Network-Long Short-Term Memory (TCN-LSTM) models alongside the Variational Mode. This includes a long-term lane occupanc

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Li, Ze. "Prediction of MBTI Personality Leveraging Machine Learning Algorithms." Applied and Computational Engineering 8, no. 1 (2023): 580–87. http://dx.doi.org/10.54254/2755-2721/8/20230275.

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In this study, the author attempted to implement a machine learning approach to determine users' corresponding MBTI personality types by relying only on the content of their online forum postings. Models based on different algorithms are built and trained, and the natural language of the collected data set is converted into machine language for machine learning and used in subsequent tests to determine the correctness of the predicting results. The data set is collected from the forum and divided into two parts, the training set is leveraged to train the model and the test data set is leverage

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Cheng, Dehe, Jinlong Li, Shuwei Guo, et al. "Genomic Prediction for Germplasm Improvement Through Inter-Heterotic-Group Line Crossing in Maize." International Journal of Molecular Sciences 26, no. 6 (2025): 2662. https://doi.org/10.3390/ijms26062662.

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Germplasm improvement is essential for maize breeding. Currently, intra-heterotic-group crossing is the major method for germplasm improvement, while inter-heterotic-group crossing is also used in breeding but not in a systematic way. In this study, five inbred lines from four heterotic groups were used to develop a connected segregating population through inter-heterotic-group line crossing (CSPIC), which comprised 5 subpopulations with 535 doubled haploid (DH) lines and 15 related test-cross populations including 1568 hybrids. Significant genetic variation was observed in most subpopulations

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Merrill, Zachary, Subashan Perera, and Rakié Cham. "Torso Segment Parameter Prediction in Working Adults." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 1257–61. http://dx.doi.org/10.1177/1541931218621289.

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Body segment parameters (BSPs) such as segment mass, center of mass, and radius of gyration are used as inputs in static and dynamic ergonomic and biomechanical models used to predict joint and muscle forces, and related risks of musculoskeletal injury. Because these models are sensitive to BSP values, accurate and representative parameters are necessary for injury risk prediction. While previous studies have determined segment parameters in the general population, as well as the impact of age and obesity levels on these parameters, estimated errors in the prediction of BSPs can be as large as

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Busari, Ibrahim, Debabrata Sahoo, R. Daren Harmel, and Brian E. Haggard. "A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems." Journal of Natural Resources and Agricultural Ecosystems 1, no. 2 (2023): 63–76. http://dx.doi.org/10.13031/jnrae.15647.

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Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital for efficient ML model development. ML models for toxin production and monitoring are limited. Abstract. Harmful algal blooms (HABs) are detrimental to livestock, humans, pets, the environment, and the global economy, which calls for a robust approach to their management. While process-based models can inform practitioners about HAB enabling conditions, they have inherent limitations in accurately predicting harmful algal blooms. To address these limitations, Machine

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de Zarzà, I., J. de Curtò, Enrique Hernández-Orallo, and Carlos T. Calafate. "Cascading and Ensemble Techniques in Deep Learning." Electronics 12, no. 15 (2023): 3354. http://dx.doi.org/10.3390/electronics12153354.

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In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions into another set of NN. Our exploration starts from an initial preliminary study and extends to various ensemble techniques including bagging, stacking, and finally cascading. The cascading ensemble involves training a second layer of models on the predictions of the first. This cascading str

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Hu, Guang, and Yue Tang. "GERPM: A Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model." Mathematics 11, no. 14 (2023): 3160. http://dx.doi.org/10.3390/math11143160.

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Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERP

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Wu, Menglong, Yicheng Ye, Nanyan Hu, Qihu Wang, Huimin Jiang, and Wen Li. "EMD-GM-ARMA Model for Mining Safety Production Situation Prediction." Complexity 2020 (June 8, 2020): 1–14. http://dx.doi.org/10.1155/2020/1341047.

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In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used

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Roulston, Mark S., and Kim Kaivanto. "Joint-outcome prediction markets for climate risks." PLOS ONE 19, no. 8 (2024): e0309164. http://dx.doi.org/10.1371/journal.pone.0309164.

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Predicting future climate requires the integration of knowledge and expertise from a wide range of disciplines. Predictions must account for climate-change mitigation policies which may depend on climate predictions. This interdependency, or “circularity”, means that climate predictions must be conditioned on emissions of greenhouse gases (GHGs). Long-range forecasts also suffer from information asymmetry because users cannot use track records to judge the skill of providers. The problems of aggregation, circularity, and information asymmetry can be addressed using prediction markets with join

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Spichak, V. V., and O. K. Zakharova. "Neural Network Modeling of Electromagnetic Prediction of Geothermal Reservoir Properties." Физика земли 2023, no. 1 (2023): 67–80. http://dx.doi.org/10.31857/s0002333723010064.

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This work conducts neural network modeling of temperature, thermal conductivity, and permeability predictions for depths greater than those drilled, as well as for the immediate vicinity of the exploratory borehole. For this purpose, we use data from three boreholes drilled earlier in the Soultz-sous-Forêts geothermal site (France) and the results of the magnetotelluric sounding performed there. It is shown that the relative accuracy of the predictions depends significantly on the relationship between the depth of the drilled borehole and the target depth of the prediction. For instance, for a

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Bierkens, M. F. P., and L. P. H. van Beek. "Seasonal Predictability of European Discharge: NAO and Hydrological Response Time." Journal of Hydrometeorology 10, no. 4 (2009): 953–68. http://dx.doi.org/10.1175/2009jhm1034.1.

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Abstract In this paper the skill of seasonal prediction of river discharge and how this skill varies between the branches of European rivers across Europe is assessed. A prediction system of seasonal (winter and summer) discharge is evaluated using 1) predictions of the average North Atlantic Oscillation (NAO) index for the coming winter based on May SST anomalies of the North Atlantic; 2) a global-scale hydrological model; and 3) 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. The skill of seasonal discharge predictions is investigated with a numerical expe

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Kareem Mhalhal, Nabaa, and Suhad Faisal Behadili. "Mobility Prediction Based on LSTM Multi-Layer Using GPS Phone Data." Iraqi Journal for Electrical and Electronic Engineering 21, no. 2 (2025): 284–92. https://doi.org/10.37917/ijeee.21.2.25.

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Precise Prediction of activity location is an essential element in numerous mobility applications and is especially necessary for the development of tailored sustainable transportation systems. Next-location prediction, which involves predicting a user's future position based on their past movement patterns, has significant implications in various domains, including urban planning, geo-marketing, disease transmission, Performance wireless network, Recommender Systems, and many other areas. In recent years, various predictors have been suggested to tackle this issue, including state-of-the-art

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Siswanto, Joko, Danny Manongga, Irwan Sembiring, and Sutarto Wijono. "Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators." Jurnal Online Informatika 9, no. 1 (2024): 18–28. http://dx.doi.org/10.15575/join.v9i1.1245.

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The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM based on deep learning is proposed to predict passengers for 4 bus public transportation operators (Go Bus, New Zealand Bus, Pavlovich, and Ritchies) which are evaluated by MSLE, MAPE, and SMAPE with variations in epoch, batch size, and neurons. The dataset is a CSV performance report on Auckland Tran

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Shen, Runjie, Ruimin Xing, Yiying Wang, Danqiong Hua, and Ming Ma. "Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation." E3S Web of Conferences 185 (2020): 01052. http://dx.doi.org/10.1051/e3sconf/202018501052.

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As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by s

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Kumari, Sweta, Syed Shahid Raza, Gopal Arora, and Shambhu Bharadwaj. "Exploring machine learning in the context of environmental usage prediction." Multidisciplinary Science Journal 6 (July 26, 2024): 2024ss0503. http://dx.doi.org/10.31893/multiscience.2024ss0503.

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The use of environmental prediction refers to predicting the impact that human activity will have on ecosystems, natural resources and other environmental factors in the future. This strategy looks at historical patterns, present situations and future predictions to hypothesize about the ecological effects of human activities, climate change and other factors. This research suggests machine learning(ML) techniques to predict environmental uses. Prediction accuracy declinesover time and models face challenges due to the need for observable data integration in sectors like agriculture, energy an

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Ansah, Kwabena, Ismail Wafaa Denwar, and Justice Kwame Appati. "Intelligent Models for Stock Price Prediction." Journal of Information Technology Research 15, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/jitr.298616.

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Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price

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Lertampaiporn, Supatcha, Sirapop Nuannimnoi, Tayvich Vorapreeda, Nipa Chokesajjawatee, Wonnop Visessanguan, and Chinae Thammarongtham. "PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins." BioMed Research International 2019 (November 19, 2019): 1–11. http://dx.doi.org/10.1155/2019/5617153.

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Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategies, and computational machine learning algorithms. In some cases, these tools may yield inconsistent and conflicting prediction results. It is important to consider such conflicting or contradictory predictions from multiple prediction programs during protein annotation, especially in the case of a multiclass classification problem such as subcellul

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Hu, Min, and Peng Cheng. "Long-Distance Shield Tunnelling Performance Prediction Based on Informer." Applied Sciences 15, no. 3 (2025): 1674. https://doi.org/10.3390/app15031674.

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Shield performance prediction plays a critical role in construction decision-making. However, current models suffer from significant performance degradation in long-distance prediction. To address this gap, we propose a novel Long-Distance Shield Performance Prediction model (LSPP), which leverages the long-term prediction capabilities of Informer. The LSPP model incorporates conventional monitoring data, tunnelling parameters, and stratigraphic spatial information and is optimized using a ProbSparse self-attention mechanism and dynamic decoding techniques. A series of experiments demonstrate

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Venikar, Isha, Jaai Joshi, Harsh Jalnekar, and Shital Raut. "Stock Market Prediction Using LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 920–24. http://dx.doi.org/10.22214/ijraset.2022.47967.

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Abstract: This study proposes a model which will make use of an LSTM model for predicting stock prices. The stock prices will be predicted on the basis of past information. Stacked LSTM will be employed for the prediction because it utilizes the historic data, therefore, making the predictions more accurate since it is able to learn long term dependencies in data, which makes LSTM an ideal technique for stock market prediction due to its dynamic as well as complex nature. After training the model its accuracy will be checked by using the test data and then using the model the stock prices for

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Jin, Xuebo, Nianxiang Yang, Xiaoyi Wang, Yuting Bai, Tingli Su, and Jianlei Kong. "Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction." Applied Sciences 9, no. 21 (2019): 4533. http://dx.doi.org/10.3390/app9214533.

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It is crucial to predict PM2.5 concentration for early warning regarding and the control of air pollution. However, accurate PM2.5 prediction has been challenging, especially in long-term prediction. PM2.5 monitoring data comprise a complex time series that contains multiple components with different characteristics; therefore, it is difficult to obtain an accurate prediction by a single model. In this study, an integrated predictor is proposed, in which the original data are decomposed into three components, that is, trend, period, and residual components, and then different sub-predictors in

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Assrani, Dwika, Pahala Sirait, and Andri Andri. "Pembobotan Kriteria Dalam Prediksi Meningitis Tuberkulosis Menggunakan Metode SWARA dan Nearest Neighbor." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (2021): 1453. http://dx.doi.org/10.30865/mib.v5i4.3276.

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Weights greatly affect the value and results of decisions or predictions of a test data, a problem that often occurs in the results of the prediction process is the weighting of symptom attributes which is less certain of the value of the weight, thus affecting the prediction results and the level of accuracy of a prediction itself. This study predicts a data using the Nearest Neighbor method where in the process of predicting the attribute weight value does not yet have a definite value for testing. Then we need an attribute weighting for each test attribute to get a definite weight value res

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Soewono, Eddy Bambang, Maisevli Harika, Cahya Ramadhan, and Muhammad Reyhan Soeharto. "Model ARIMA Terbaik Prediksi Latitude dan Longitude Kegiatan Kapal Imigran Ilegal." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (2021): 1729. http://dx.doi.org/10.30865/mib.v5i4.3301.

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The migration of a person to another country without following the law is illegal immigration. Many problems are caused by this activity, ranging from population problems to increased crime. Predicting the emergence of ships carrying illegal immigrants can assist border patrols in planning patrols to planning defense equipment. Time series forecasting to predict the latitude and longitude of boats carrying illegal immigrants is the Autoregressive Integrated Moving Average (ARIMA) model. The case studies for this research are the Straits of Malacca and the Riau Islands. The prediction range is

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