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Journal articles on the topic 'Prediction algoritm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Cherepanov, Anatoliy, and Stepan Manuylov. "ALGORITM STAGED LIFE PREDICTION OF VESSEL." Modern Technologies and Scientific and Technological Progress 2020, no. 1 (2020): 87–88. http://dx.doi.org/10.36629/2686-9896-2020-1-87-88.

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The forecasting algorithm is considered using the method of step-by-step determination of the initial resource during manufacture, and then-the remaining resource after a certain period of operation of pressure vessel

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Aqham, Ahmad Ashifuddin, and Kristoko Dwi Hartomo. "Data Mining untuk Nasabah Bank Telemarketing Menggunakan kombinasi Algoritm Naïve Bayes Dan Algoritma Genetik." InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) 4, no. 1 (2019): 47–56. http://dx.doi.org/10.30743/infotekjar.v4i1.1574.

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The strategy used for telemarketing by conducting promotional media, this strategy is a marketing method used by banks, in offering products to customers, banks, one of the products that will be offered is time deposits, the bank has difficulty in knowing the obstacles experienced by customers in making a decision to make deposits against the bank, so that later it will have the effect of a financial crisis at the bank. Telemarketing banks must have targets for customers, where customers have the potential to join one of the bank's products, namely deposits by looking at existing customer data

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Suyash S. Tambe, Vivek Ambetkar, and Prathamesh Ambovkar. "Potential Customer Prediction." International Journal of Engineering and Management Research 11, no. 2 (2021): 100–102. http://dx.doi.org/10.31033/ijemr.11.2.14.

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Major industries today are dealing with large amount of data even small shops are no oblivion for the huge pile of customer data. To gain profit in competitive marketing is imperative that the useful information shall be extracted out of this data. The processing of this huge pile of data becomes monotonous task and the different types of software and algorithms are developed to process and acquire result out of this data. This project deals with same kind of problem of dealing with data. Taking customer purchase history as an input our system using "Apriori Algoritm" classifies these customer

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Komara, Eki, Muhammad Faiz Nugraha, Widya Utama, and Sherly Ardhya Garini. "Lithology Prediction Using Convolutional Neural Network Algoritm Study Case Poseidon Field Australia Basin." IOP Conference Series: Earth and Environmental Science 1458, no. 1 (2025): 012038. https://doi.org/10.1088/1755-1315/1458/1/012038.

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Abstract Convolutional Neural Network (CNN) method is one of a machine learning algorithm that is adapted from the the way of human brain works and is used to facilitate the processing of large amounts of data. The CNN method predicts the lithology of a well with well log data, there are Gamma-Ray Logs, Density Logs, Neutron Porosity Logs, and DT Logs. The CNN model is formed by several layers, such as 2 Convolutional Layers, 2 Dense Layers, 0.5 Dropout Layers, and 256 Dense Nodes. Contrary outcomes are observed in predictions that were previously generated utilizing 10 input parameters from P

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Fahraini, Bacharuddin, Wuryanto Hadi, Yuliza, and Nugraha Beny. "Optimum Work Frequency for Marine Monitoring Based on Genetic Algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1551–59. https://doi.org/10.12928/TELKOMNIKA.v16i4.7328.

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The communication using of HF (High Frequency) is a system that depends on wave propagation using sky waves reflected by the earth's ionosphere layer so that it is highly effective for long distance communication, but highly dependent on varying ionospheric conditions from day and night (time after time) as well as the location of the transmitter and receiver radio. Currently, there is only one main frequency channel and one reserve frequency channel so that there are frequency constraints unable to communicate due to ionosphere changes. This research will predicted allocation of HF freque

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Chen, Zhenpeng, Yuanjie Zheng, Xiaojie Li, et al. "Interactive Trimap Generation for Digital Matting Based on Single-Sample Learning." Electronics 9, no. 4 (2020): 659. http://dx.doi.org/10.3390/electronics9040659.

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Image matting refers to the task of estimating the foreground of images, which is an important problem in image processing. Recently, trimap generation has attracted considerable attention because designing a trimap for every image is labor-intensive. In this paper, a two-step algorithm is proposed to generate trimaps. To use the proposed algorithm, users must only provide some clicks (foreground clicks and background clicks), which are employed as the input to generate a binary mask. One-shot learning technique achieves remarkable progress on semantic segmentation, we extend this technique to

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Herdatullah, Rizki, Syaiful Bukhori, and Windi Eka Yulia Retnani. "OPTIMASI PERSEDIAAN MATERIAL TRANSFORMATOR MENGGUNAKAN METODE JARINGAN SYARAF TIRUAN DAN ANT COLONY OPTIMIZATION DI PT. PLN (PERSERO) AREA JEMBER." INFORMAL: Informatics Journal 4, no. 1 (2019): 25. http://dx.doi.org/10.19184/isj.v4i1.12892.

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   Optimization comes from basic words optimal which mean the best, highest, most beneficial, make the best, and do optimizing (make the best, highest, etc.). Forecasting is an attempt to predict the future. Prediction can be done by studying the pattern of historical data to find a model that can show future data. This methoed is called time series data forecasting. One of many algorithm that can builds model from historical data is Artificial Neural Networks (ANN). The algoritm mimics the human neuron system so that is can solve non-linear problems, such as the forecasting

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Nilson, Tiit, Mattias Rennel, Andres Luhamaa, Maris Hordo, Aire Olesk, and Mait Lang. "MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation / MERIS’e GPP/NPP tulem Eesti jaoks: I. Algoritm ja mudelarvutuste esialgsed tulemused." Forestry Studies 56, no. 1 (2012): 56–78. http://dx.doi.org/10.2478/v10132-012-0005-5.

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Abstract. A light use efficiency (LUE) type model named EST_PP to simulate the yearly gross primary production (GPP) and net primary production (NPP) of Estonian land on a 1 km2 grid is described. The model is based on MERIS (MEdium Resolution Imaging Spectrometer) satellite images to describe the fraction of photosynthetically active radiation (fAPAR) and leaf area index (LAI) as well as meteorological reanalysis datasets on 11 km2 grid produced by Estonian Meteorological Institute (EMHI) and Tartu University (TU) by means of the HIRLAM (High Resolution Limited Area Model) numerical weather p

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Dmytro, Zubov. "BRAIN Journal - Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 6, no. 1-2 (2015): 43–53. https://doi.org/10.5281/zenodo.1044160.

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ABSTRACT In this paper, a forecasting of the heat/cold waves is discussed as a subsystem of the smart city concept using the non-anticipative analog method. The prediction algorithm is described by two paradigms. First one (short range) uses quantum computing formalism. D-Wave adiabatic quantum computing Ising model is employed and evaluated for the forecasting of positive extremes of daily mean air temperature. Forecast models are designed with two to five qubits, which represent 2-, 3-, 4-, and 5-day historical data, respectively. Ising model’s real-valued weights and dimensionless coefficie

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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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Ali, S. Al-Khalid, and S. Omran Safaa. "Hybrid branch prediction for pipelined MIPS processor." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3476–82. https://doi.org/10.11591/ijece.v10i4.pp3476-3482.

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In the modern microprocessors that designed with pipeline stages, the performance of these types of processors will be affected when executing branch instructions, because in this case there will be stalls in the pipeline. In turn this causes in reducing the Cycle Per Instruction (CPI) of the processor. In the case of executing a branch instruction, the processor needs an extra clocks to know if that branch will happen (Taken) or not (Not Taken) and also it requires calculating the new address in the case of the branch is Taken. The prediction that the branch is T / NT is an important stage in

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Nurahman, Nurahman, and Nindi Ernawati. "Analisis Algoritma C45 dan Regresi Linear untuk Memprediksi Hasil Panen Kelapa Sawit." Journal of Computer System and Informatics (JoSYC) 5, no. 4 (2024): 1155–63. https://doi.org/10.47065/josyc.v5i4.5828.

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Indonesia, as one of the main producers of palm oil in the world, has an agricultural sector that is very influential on the national economy, especially through palm oil exports. Prediction of oil palm yields is crucial to improve efficiency in planning and resource management. This study was conducted to compare the performance of two prediction methods, namely the C45 Algorithm and Linear Regression, in predicting oil palm yields. The formulation of the problems raised in this study includes: (1) How does the performance of the C45 Algorithm and Linear Regression compare in predicting oil p

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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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Williams-Riquer, Francisco, Alexander Chmelnizkij, Diaa Alkateeb, and Jürgen Grabe. "Prediction of induced soil vibration during pile vibrodriving using Dynamic Mode Decomposition (DMD)." Journal of Physics: Conference Series 2909, no. 1 (2024): 012002. https://doi.org/10.1088/1742-6596/2909/1/012002.

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Abstract This study investigates using the Dynamic Mode Decomposition (DMD) algorithm to perform approximations and time-ahead prediction of soil vibrations during the vibrodriving process. Geotechnical applications face challenges in modeling and predicting soil vibrations due to the soil’s heterogeneous nature. This study addresses this issue using a purely data-driven approach. Geophone data collected during pile installation using a vibrodriver were used to demonstrate the feasibility of the DMD algorithm. The research reveals that both the standard DMD and augmented DMD, which incorporate

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Dong, Hanyu, Shangyi Shen, and Guangxun Qin. "A Short-term Load Curve Intelligent Prediction Method Based on FFT and GA Composite Algorithm and Its Application." Journal of Physics: Conference Series 2650, no. 1 (2023): 012027. http://dx.doi.org/10.1088/1742-6596/2650/1/012027.

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Abstract In order to reduce the system operation cost of predicting short-term load curves and improve the feasibility of artificial intelligence prediction, the FFT and GA composite algorithm is designed as an artificial intelligence algorithm with the goals of low time complexity, small required sample data, and high flexibility. The algorithm uses mature Fast Fourier Transform and Genetic Algorithm, and improves the flexibility of the prediction method by dynamically adding kernel functions. It can be used for load curve prediction of 96 data points or smaller granularity, and can also reus

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Falah, Miftahul, Dian Palupi Rini, and Iwan Pahendra. "Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 1 (2021): 90. http://dx.doi.org/10.30865/mib.v5i1.2597.

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Predicting disease is usually done based on the experience and knowledge of the doctor. Diagnosis of such a disease is traditionally less effective. The development of medical diagnosis based on machine learning in terms of disease prediction provides a more accurate diagnosis than the traditional way. In terms of predicting disease can use artificial neural networks. The artificial neural network consists of various algorithms, one of which is the Backpropagation Algorithm. In this paper it is proposed that disease prediction systems use the Backpropagation algorithm. Backpropagation algorith

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Sofian Wira Hadi, Ibnu Alfarobi, and Irmawati. "Implementation of Logistic Regression Algorithm in Predicting Tsunami Potential on Earthquake Data Parameters." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 1217–24. https://doi.org/10.59934/jaiea.v4i2.871.

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This study presents the evaluation and testing of a logistic regression model for predicting earthquake-related features, including earthquake depth, magnitude, and tsunami potential. The model achieved high accuracy in predicting earthquake depth categories (99.82%) and earthquake magnitude (99.84%), but faced challenges with low recall for tsunami prediction (50%) due to class imbalance. Evaluation results showed that the model struggled to predict tsunami occurrence accurately, as the dataset contained a disproportionate number of 'no tsunami' instances. Despite these limitations, the model

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Jiang, Feng, Xingyu Han, Wenya Zhang, and Guici Chen. "Atmospheric PM2.5 Prediction Using DeepAR Optimized by Sparrow Search Algorithm with Opposition-Based and Fitness-Based Learning." Atmosphere 12, no. 7 (2021): 894. http://dx.doi.org/10.3390/atmos12070894.

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There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, atmospheric concentration prediction is a challenging topic. In this paper, we propose a novel hybrid learning method to make point and interval predictions of PM2.5 concentration simultaneously. Firstly, we optimize Sparrow Search Algorithm (SSA) by opposition-based learning, fitness-based learning, and Lévy flight. The experiments show that the improved Sparrow Search Algorithm (FOSSA) outperforms SSA-based algorithms. In addition

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Goudar, Ananya Divakar, Hema K S, Inchara T R, and Meghana Kalmat. "PREDICTING STOCK MARKET TRENDS USING MACHINE LEARNING AND DEEP LEARNING ALGORITHM." International Research Journal of Computer Science 9, no. 8 (2022): 281–85. http://dx.doi.org/10.26562/irjcs.2022.v0908.25.

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Intention of StockMarket Prediction is to forecast future worth of a company's monetary stocks. utilising machine learning produces forecasts based on the values of current stock market indices Using their prior values as training data is a new development in stock market prediction technology. Predicting the performance of the stock market is one of the most challenging tasks.Prediction involves a huge number of variables, such as the distinction between physical and psychological factors, rational and irrational conduct, and more. Share prices are unpredictable and challenging to forecast ac

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Zhu, Sicong, LiSian Tey, and Luis Ferreira. "Genetic Algorithm Based Microscale Vehicle Emissions Modelling." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/178490.

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There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2are proposed. A genetic algorithm approa

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Faulina, Ria, Nuramaliyah Nuramaliyah, and Emeylia Safitri. "Air Temperature Prediction System Using Long Short-Term Memory Algorithm." Rekayasa 17, no. 3 (2024): 463–73. https://doi.org/10.21107/rekayasa.v17i3.28229.

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Air temperature is a highly essential parameter in weather forecasting methods and a critical variable for predicting future weather patterns. An accurate temperature prediction system can assist individuals and organizations in preparing for activities heavily influenced by weather conditions. Therefore, developing a precise temperature prediction model requires a reliable and effective algorithm. In this study, the Long Short-Term Memory (LSTM) algorithm, a type of artificial neural network (Recurrent Neural Network - RNN), is implemented with time series data decomposition for variable inpu

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Rao, Kanakam Maruthi Narasimha, Konda Saiteja, Kamini Vasu, Dr Kumar P, Dr K. S. Ramanujam, and Dr V. B. Ganapthy. "Ransom Prediction Using ML Algorithm." International Journal of Research Publication and Reviews 6, no. 4 (2025): 8152–59. https://doi.org/10.55248/gengpi.6.0425.1517.

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Wang, Tong, and Tao Xi. "Residual Life Prediction of Rolling Bearing based on AOA-LSTM." Scientific Journal of Technology 5, no. 1 (2023): 21–30. http://dx.doi.org/10.54691/sjt.v5i1.3523.

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Aiming at the problem of predicting the remaining life of rolling bearings, a method for predicting the remaining life of bearings based on the arithmetic optimization algorithm (AOA) and the long-short-term memory network (LSTM) fusion algorithm is proposed. First, use the random forest algorithm to analyze the importance of the extracted time-domain and frequency-domain feature indicators, and build a degradation feature decision table; then, use the AOA optimization algorithm to optimize the hyperparameters in the LSTM, and select the optimal hyperparameters to establish predictions model;

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Zhang, Chenjun, Fuqian Zhang, Fuyang Gou, and Wensi Cao. "Study on Short-Term Electricity Load Forecasting Based on the Modified Simplex Approach Sparrow Search Algorithm Mixed with a Bidirectional Long- and Short-Term Memory Network." Processes 12, no. 9 (2024): 1796. http://dx.doi.org/10.3390/pr12091796.

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In order to balance power supply and demand, which is crucial for the safe and effective functioning of power systems, short-term power load forecasting is a crucial component of power system planning and operation. This paper aims to address the issue of low prediction accuracy resulting from power load volatility and nonlinearity. It suggests optimizing the number of hidden layer nodes, number of iterations, and learning rate of bi-directional long- and short-term memory networks using the improved sparrow search algorithm, and predicting the actual load data using the load prediction model.

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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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Nugraha, Yoga Estu Nugraha, Ishak Ariawan, and Willdan Aprizal Arifin. "WEATHER FORECAST FROM TIME SERIES DATA USING LSTM ALGORITHM." JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI 14, no. 1 (2023): 144–52. http://dx.doi.org/10.51903/jtikp.v14i1.531.

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Accurate weather forecasts play an important role in today's world as various sectors such as marine, navigation, agriculture and industry are basically dependent on weather conditions. Weather forecasts are also used to predict the occurrence of natural disasters. Weather forecasting determines the exact value of weather parameters and then predicts future weather conditions. In this study the parameters used are. Different weather parameters were collected from the Serang Maritime Meteorological Station and then analyzed using a neural network-based algorithm, namely Long-short term memory (

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Neeru, Singh, and Priyabadini Panda Supriya. "Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4090–98. https://doi.org/10.11591/ijece.v12i4.pp4090-4098.

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A multitude of research has been rising for predicting the behavior of different real-world problems through machine learning models. An erratic nature occurs due to the augmented behavior and inadequacy of the prerequisite dataset for the prediction of water level over different fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed for improvised back-propagation algorithm using parallel computing for groundwater level prediction on graphical processing unit (GPU) for the Faridabad region, Haryana, India. This paper aims to propose the n

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Bozorg-Haddad, Omid, Mohammad Delpasand, and Hugo A. Loáiciga. "Self-optimizer data-mining method for aquifer level prediction." Water Supply 20, no. 2 (2019): 724–36. http://dx.doi.org/10.2166/ws.2019.204.

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Abstract Groundwater management requires accurate methods for simulating and predicting groundwater processes. Data-based methods can be applied to serve this purpose. Support vector regression (SVR) is a novel and powerful data-based method for predicting time series. This study proposes the genetic algorithm (GA)–SVR hybrid algorithm that combines the GA for parameter calibration and the SVR method for the simulation and prediction of groundwater levels. The GA–SVR algorithm is applied to three observation wells in the Karaj plain aquifer, a strategic water source for municipal water supply

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Torhino, Rizal, and Pulung Nurtantio Andono. "Penerapan Algoritma Random Forest dalam Prediksi Curah Hujan untuk Mendukung Analisis Cuaca." Building of Informatics, Technology and Science (BITS) 6, no. 3 (2024): 1688–99. https://doi.org/10.47065/bits.v6i3.6404.

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Indonesia's climate diversity leads to different rainfall patterns in each region. This condition presents a major challenge in the effort to produce accurate rainfall predictions, which are important to support effective infrastructure planning and disaster mitigation. The purpose of this research is to analyze the rainfall potential in Purwodadi Sub-district using Random Forest algorithm. In this analysis, several weather parameters such as air pressure, temperature, humidity, and wind speed are used, while rainfall becomes the target variable in the prediction process. The dataset used in t

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MARTINEZ GARRIZ, IÑAKI, PILAR HERRERA PLAZA, and MAIALEN LARRETXEA URRUTIA. "N-BIR: A NUMERIC OPTIMIZATION APPROACH FOR POWER ELECTRONIC CONVERTER BURN-IN TESTING TIME REDUCTION." DYNA 99, no. 2 (2024): 201–7. http://dx.doi.org/10.6036/10866.

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Burn-in testing is an effective method for detecting early faults in electronic products before they reach the market. This test has a high cost due to lengthy test time on a test bench. In this paper, we propose N-BIR (Numeric optimization approach for power electronic Burn-In testing time Reduction), an algorithm capable of predicting the burn-in (BI) test temperature of power electronic converters, intending to shorten the duration of such tests. This algorithm optimizes by least squares a theoretical model of the system, using as data a fraction of the total burn-in test. Moreover, not onl

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Wu, Jianwei, Jiaqi Wang, and Huanguo Chen. "A Method for Predicting Tool Remaining Useful Life: Utilizing BiLSTM Optimized by an Enhanced NGO Algorithm." Mathematics 12, no. 15 (2024): 2404. http://dx.doi.org/10.3390/math12152404.

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Predicting remaining useful life (RUL) is crucial for tool condition monitoring (TCM) systems. Inaccurate predictions can lead to premature tool replacements or excessive usage, resulting in resource wastage and potential equipment failures. This study introduces a novel tool RUL prediction method that integrates the enhanced northern goshawk optimization (MSANGO) algorithm with a bidirectional long short-term memory (BiLSTM) network. Initially, key statistical features are extracted from collected signal data using multivariate variational mode decomposition. This is followed by effective fea

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Liu, Xiqing. "Credit Risk Classification and Prediction Based on Deep Neural Network Algorithm." Advances in Economics, Management and Political Sciences 88, no. 1 (2024): 235–41. http://dx.doi.org/10.54254/2754-1169/88/20241007.

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This study predicts whether a user has defaulted based on correlation analysis and deep neural network algorithms. The results of the study show that the occurrence of default by a user is positively correlated with age, family, years of employment, and credit length, and negatively correlated with income, amount, rate, status, and percentage of income. After model training and testing, the prediction accuracy was 81.68% on the training set and 81.68% on the test set. Specifically, there were 2858 correct predictions and 641 incorrect predictions in the training set, of which 469 incorrectly p

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Agnes Putria Mukti and Tri Widodo. "Prediction of Tempe Raw Material Needs for Home Industry Using Tsukamoto Fuzzy Logic Algorithm." Journal of Humanities and Social Sciences Studies 6, no. 11 (2024): 65–76. http://dx.doi.org/10.32996/jhsss.2024.6.11.6.

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The home tempeh industry is faced with the challenge of accurately predicting raw material needs. Implementing an effective prediction system can help estimate the amount of tempeh production in the future, so as to avoid stock shortages of raw materials. In addition, when tempeh producers have accurate predictions, they can manage soybean stocks better, especially when there is a stock shortage, so that production is not affected by rising soybean prices. This research aims to develop a Tsukamoto Fuzzy Logic based prediction system to overcome this problem. Research methods include collecting

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Zhao, Tianming, Wei Li, and Albert Y. Zomaya. "Uniform Machine Scheduling with Predictions." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 413–22. http://dx.doi.org/10.1609/icaps.v32i1.19827.

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The revival in learning theory has provided us with improved capabilities for accurate predictions. This work contributes to an emerging research agenda of online scheduling with predictions by studying the makespan minimization in uniformly related machine non-clairvoyant scheduling with job size predictions. Our task is to design online algorithms that effectively use predictions and have performance guarantees with varying prediction quality. We first propose a simple algorithm-independent prediction error measurement to quantify prediction quality. To effectively use the predicted job size

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A. Gokul, R. Manoj Kumar, and A. Jayasurya. "Machine Learning Algorithm for Stock Market Prediction – A Comparison." December 2023 5, no. 4 (2023): 521–33. http://dx.doi.org/10.36548/jaicn.2023.4.005.

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The prediction about the stock market serves as an effort to forecast the value of the market, an individual stock, or a particular industrial sector. The prediction or forecast is usually done by using several approaches and analyzing the fundamental or technical details of an industry, an economy, or both. Predicting stock markets is very essential, as successful prediction can help in proper decision-making as well as in increasing the profit of the business. As prediction of the stock market is a bit complicated and challenging, conventional methods do not consistently forecast the changes

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Carlsson, Leo S., Mikael Vejdemo-Johansson, Gunnar Carlsson, and Pär G. Jönsson. "Fibers of Failure: Classifying Errors in Predictive Processes." Algorithms 13, no. 6 (2020): 150. http://dx.doi.org/10.3390/a13060150.

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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

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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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Singh, Neeru, and Supriya Priyabadini Panda. "Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4090. http://dx.doi.org/10.11591/ijece.v12i4.pp4090-4098.

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<p>A multitude of research has been rising for predicting the behavior of different real-world problems through machine learning models. An erratic nature occurs due to the augmented behavior and inadequacy of the prerequisite dataset for the prediction of water level over different fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed for improvised back-propagation algorithm using parallel computing for groundwater level prediction on graphical processing unit (GPU) for the Faridabad region, Haryana, India. This paper aims to prop

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Lei, Jianmei, Yulan Chen, Qingwen Han, Lingqiu Zeng, and Guangyan He. "Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN." Applied Sciences 15, no. 14 (2025): 8104. https://doi.org/10.3390/app15148104.

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Accurate bus travel time prediction is crucial for improving travel experience, especially in transfer journeys. This study introduces a novel multi-route bus travel time prediction system-based PMLNet, a partition and combination prediction framework, addressing the gap in accurate prediction models by incorporating macro and local impact factors. The system employs a pre-processing algorithm for constructing travel chains, partitions travel time into four components, utilizes LSTM along with the newly proposed MDARNN model for predicting each component, and applies four real-time traffic imp

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Nababan, Darsono, and Eric Alexander. "IMPLEMENTASI METODE FUZZY TIME SERIES DENGAN MODEL ALGORITMA CHEN UNTUK MEMPREDIKSI HARGA EMAS." JURNAL TEKNIK INFORMATIKA 13, no. 1 (2020): 71–78. http://dx.doi.org/10.15408/jti.v13i1.15516.

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Gold is one of the people's preferred forms of investment and is considered the safest (save -heaven). Gold risk which is considered small is the main attraction because in general Indonesian people are not yet familiar with capital market investments such as stocks and mutual funds. But the price of gold is very volatile as for the factors that affect the fluctuations of gold are consumption demand, volatility and market uncertainty, protection of low-interest rates, and the US dollar. Predicting the movement of the gold price and knowing where the direction of the exchange rate moves and det

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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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x, Shweta, Riya x, and Abhay Kumar. "Cancer Prediction Using Machine Learning Algorithm." International Journal of Science and Research (IJSR) 11, no. 5 (2022): 873–75. http://dx.doi.org/10.21275/mr22511160749.

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Parihah, Nur Isnaini, Sari Hartini, and Juarni Siregar. "Prediksi Angka Kelahiran Bayi Pada Desa Tridaya Sakti Dengan Menggunakan Algoritma Naive Bayes." Journal of Students‘ Research in Computer Science 1, no. 2 (2020): 77–88. http://dx.doi.org/10.31599/jsrcs.v1i2.423.

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The birth rate is something that can affect the increase in population growth. Large population is a burden for development. According to Malthus's Theory which states that a large population growth is not the welfare that is obtained but rather poverty will be encountered if the population is not well controlled. The number of baby births in Tridaya Sakti Village is increasing every year. Therefore Data Mining using the Naive Bayes algorithm can help in the calculation of predicting infant birth rates in Tridaya Sakti Village. Data Mining in predicting the number of infant birth rates aims to

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Mujahidin, Irfan, and Fikri Arif Rakhman. "Application of Optimization Algorithm to Machine Learning Model for Solar Panel Output Power Prediction: A Review." Jurnal Informatika: Jurnal Pengembangan IT 9, no. 2 (2024): 180–87. http://dx.doi.org/10.30591/jpit.v9i2.7051.

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Solar panels have become a popular source of renewable energy due to their sustainability and environmental friendliness. Accurate predictions of solar panel output are crucial for various applications, such as energy system optimization, power grid management, and economic planning. Many important factors pose challenges in predicting the output of solar panels, such as weather conditions that can change at any time, geographical factors, data quality, and the duration of data collection. Machine learning (ML) models show promising performance in this prediction; there are many types of machi

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Kaledin, A. P. "Prediction the number of hunting animal populations in the Yaroslavl region based on matrix verified models." Glavnyj zootehnik (Head of Animal Breeding), no. 7 (June 20, 2022): 46–64. http://dx.doi.org/10.33920/sel-03-2207-06.

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Prediction the dynamics of the level and structure of regional hunting resources is relevant from the standpoint of their rational use. Matrix models are widely used to make predictions on the dynamics of hunting animal populations. The algorithm of the modified P. H. Leslie matrix model with a correction matrix is used. The accuracy of predictions on the dynamics of hunting animal populations based on matrix models is improved by their verification. In the proposed study, model verification is considered not only as a method for determining the correspondence of the model to the corresponding

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Jiang, Chang-Long, Yi-Kuang Tsai, Zhen-En Shao, Shih-Hsiung Lee, Cheng-Che Hsueh, and Ko-Wei Huang. "Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting." Applied Sciences 14, no. 23 (2024): 11380. https://doi.org/10.3390/app142311380.

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This study presents a hybrid crow search algorithm–long short-term memory (CSLSTM) system for forecasting stock prices. This system allows investors to effectively avoid risks and enhance profits by predicting the closing price the following day. This method utilizes a stacking ensemble of long short-term memory (LSTM) networks, with the crow search algorithm (CSA) optimizing the weights assigned to the predictions from multiple LSTM models. To improve the overall accuracy, this system leverages three distinct datasets: technical analysis indicators; price fluctuation limits; and variation mod

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Ridha, Meirina Suci, and Hani Harafani. "Implementasi Algoritma Genetika Pada Perancangan Aplikasi Android Untuk Memprediksi Buta Warna." Jurnal Teknik Komputer 5, no. 1 (2019): 77–86. http://dx.doi.org/10.31294/jtk.v5i1.4697.

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Color blindness is one of the decreasing diseases which is very difficult to determine its deterioration, whether a family member will suffer from color blindness or not, especially with the prediction of color blindness which has been using manual methods by calculating the inheritance formula. Genetic Algorithms have advantages over other traditional optimization algorithms. To implement a computerized method in predicting color blindness that can be used by many people, it is necessary to have a user-friendly operating system like the Android operating system. The test results show that the

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Wei, Chih-Chiang, and Wei-Jen Kao. "Establishing a Real-Time Prediction System for Fine Particulate Matter Concentration Using Machine-Learning Models." Atmosphere 14, no. 12 (2023): 1817. http://dx.doi.org/10.3390/atmos14121817.

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With the rapid urbanization and industrialization in Taiwan, pollutants generated from industrial processes, coal combustion, and vehicle emissions have led to severe air pollution issues. This study focuses on predicting the fine particulate matter (PM2.5) concentration. This enables individuals to be aware of their immediate surroundings in advance, reducing their exposure to high concentrations of fine particulate matter. The research area includes Keelung City and Xizhi District in New Taipei City, located in northern Taiwan. This study establishes five fine prediction models based on mach

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Dong, Peilin, Xiaoyu Wang, and Zhouhao Shi. "Financial market trend prediction model based on LSTM neural network algorithm." Journal of Computational Methods in Sciences and Engineering 24, no. 2 (2024): 745–55. http://dx.doi.org/10.3233/jcm-237097.

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The financial market has randomness, and the prediction of the financial market is an important task in the financial market. In traditional financial market prediction models, the prediction results are often unsatisfactory. So it needs to introduce new models for financial analysis. To solve this problem, this paper analyzed a financial market trend prediction model based on LSTM (Long Short-Term Memory) NN (Neural Network) algorithm, and conducted an empirical analysis on the Shanghai stock index dataset. This paper first introduced the LSTM NN algorithm, and then divided it into training s

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Lin, Chuan, Yun Zou, Xiaohe Lai, Xiangyu Wang, and Yan Su. "Variation Trend Prediction of Dam Displacement in the Short-Term Using a Hybrid Model Based on Clustering Methods." Applied Sciences 13, no. 19 (2023): 10827. http://dx.doi.org/10.3390/app131910827.

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The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam

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