Recurrent Unit Model Research Articles

Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods

The lunar calendar is often overlooked in time-series data modeling despite its importance in understanding seasonal patterns, as well as economics, natural phenomena, and consumer behavior. This study aimed to investigate the effectiveness of the lunar calendar in modeling and forecasting rainfall levels using various machine learning methods. The methods employed included long short-term memory (LSTM) and gated recurrent unit (GRU) models to test the accuracy of rainfall forecasts based on the lunar calendar compared to those based on the Gregorian calendar. The results indicated that machine learning models incorporating the lunar calendar generally provided greater accuracy in forecasting for periods of 3, 4, 6, and 12 months compared to models using the Gregorian calendar. The lunar calendar model demonstrated higher accuracy in its prediction, exhibiting smaller errors (MAPE and MBE values), whereas the Gregorian calendar model yielded somewhat larger errors and tended to underestimate the values. These findings contributed to the advancement of forecasting techniques, machine learning, and the adaptation to non-Gregorian calendar systems while also opening new opportunities for further research into lunar calendar applications across various domains.

A novel Z-scheme visible light driven photocatalyst AgZnFe2O4/V2O5/RGO for boosting the degradation of tetracycline antibiotics: Gated Recurrent Unit (GRU) and Response Surface Methodology (RSM) models, toxicity and degradation pathways

A novel Z-scheme visible light driven photocatalyst AgZnFe2O4/V2O5/RGO for boosting the degradation of tetracycline antibiotics: Gated Recurrent Unit (GRU) and Response Surface Methodology (RSM) models, toxicity and degradation pathways

E-mail Classifications Based on Deep Learning Techniques

Email types sorting is one of the most important tasks in current information systems with the purpose to improve the security of messages, allowing for their sorting into different types. This paper aims at studying the Convolution Neural Network and Long Short-Term Memory (CNN-LSTM), Convolution Neural Network and Gated Recurrent Unit (CNN-GRU) and Long Short-Term Memory (LSTM) deep learning models for the classification of emails into categories such as “Normal”, “Fraudulent”, “Harassment” and “Suspicious”. The architecture of each model is discussed and the results of the models’ performance by testing on labelled emails are presented. Evaluation outcomes show substantial gains in precision and throughput to conventional approaches hence inferring to the efficiency of these proposed models for automated email filtration and content evaluation. Last but not the least, the performance of the classification algorithms is evaluated with the help of parameters like Accuracy, precision, recall and F1-Score. From the experiment, the models found out that CNN-LSTM, together with the Term Frequency and Inverse Document Frequency (TF-IDF) feature extraction yielded the highest accuracy. The accuracy, precision, recall and f1-score values are 99. 348%, 99. 5%, 99. 3%, and 99. 2%, respectively.

Spontaneous Coal Combustion Temperature Prediction Based on an Improved Grey Wolf Optimizer-Gated Recurrent Unit Model

Predicting the spontaneous combustion temperature of coal is crucial for early warning systems, and is significantly influenced by changes in temperature and gas concentration. Based on programmed temperature rise experimental data from the Dongtan Coal Mine, we employed Spearman’s rank correlation coefficient analysis and coal oxidation pyrolysis composite reaction to identify six gas indicators closely related to coal temperature. An Improved Grey Wolf Optimizer (IGWO) was proposed and combined with a Gated Recurrent Unit (GRU) model to predict spontaneous coal combustion temperature. Model improvements include enhancing the global exploration capability using chaotic mapping, improving the local search accuracy with a nonlinear convergence factor, and introducing an optimal memory retention strategy to optimize convergence speed and stability. Compared with eight common heuristic optimization algorithms, the significant capability and adaptability of IGWO were verified. The GRU model adopts a double-layered structure, with each layer containing 20 GRU units. To further improve the prediction accuracy, three hyperparameters—number of hidden units, initial learning rate, and maximum number of training epochs—were optimized (85, 0.165451, and 66, respectively). Finally, the dataset was randomly split into a training and a test set (7:3). The IGWO-GRU model was compared with 13 reference models and achieved an R2 of 0.9510, an RMSE value of 16.0503, and a maximum relative error of 0.06 on the test samples, significantly outperforming the other models. Finally, the IGWO-GRU prediction model was applied to a programmed temperature rise experiment for spontaneous coal combustion, demonstrating its superior prediction accuracy, and practical application potential.

Implementation of Recurrent Neural Network Gated Recurrent Unit (GRU) Model for Predicting Top-Tier Bitcoin

Implementation of Recurrent Neural Network Gated Recurrent Unit (GRU) Model for Predicting Top-Tier Bitcoin

Nature-Inspired Optimization with Deep Learning-Enabled Sustainable Predictive Model for Intelligent Systems

Solar energy can be considered as an alternate solution to conventional energy sources. Short-term Photovoltaic (PV) Power Generation (PVPG) prediction methods are essential stabilize power integration among PV and smart grids. The PVPG generation process is highly dependent on climatic conditions and therefore high intermittent. Highly accurate PVPG prediction of PVPG acts based on the generation, transmission and dispersion of electricity, confirming the stability and dependability of power system. The current progress of Machine Learning (ML) and Deep Learning (DL) approaches enables for designing of accurate PVPG prediction models. In this view, this paper develops a new Badger Optimization with Deep Learning Enabled PV Power Generation Predictive (HBODL-PVPGP) model. The presented HBODL-PVPGP model enables to forecast of the PVPG process. To accomplish this, the HBODL-PVPGP model initially investigates the features depending upon intrinsic characteristics earlier in the learning process. In addition, the Bidirectional Gated Recurrent Unit (BiGRU) model is implemented for forecasting process. The performance of the BiGRU model can be improvised by the design of HBO-based hyperparameter tuning procedure. For ensuring the enhanced performance of the HBODL-PVPGP model, an extensive range of experimental study was effectuated and the results were investigated under various factors. The result highlighted the precipitated performance of the HBODL-PVPGP procedure on the current algorithms.

Enhancing IoT-Based Environmental Monitoring and Power Forecasting: A Comparative Analysis of AI Models for Real-Time Applications

The Internet of Things (IoT) is transforming industries by integrating sensors and connectivity into everyday objects, enabling enhanced monitoring, management, and automation through Machine-to-Machine (M2M) communication. Despite these advancements, the IoT faces limitations in accurately predicting environmental conditions and power consumption. This study proposes an advanced IoT platform that combines real-time data collection with secure transmission and forecasting using a hybrid Long Short-Term Memory (LSTM)–Gated Recurrent Unit (GRU) model. The hybrid architecture addresses the computational inefficiencies of LSTM and the short-term dependency challenges of GRU, achieving improved accuracy and efficiency in time-series forecasting. For all prediction use cases, the model achieves a Maximum Mean Absolute Error (MAE) of 3.78%, Root Mean Square Error (RMSE) of 8.15%, and a minimum R2 score of 82.04%, the showing proposed model’s superiority for real-life use cases. Furthermore, a comparative analysis also shows the performance of the proposed model outperforms standalone LSTM and GRU models, enhancing the IoT’s reliability in real-time environmental and power forecasting.

Electric Vehicle Charging Load Forecasting Based on K-Means++-GRU-KSVR

The accurate short-term forecasting of an electric vehicle (EV) load is crucial for the reliable operation of a power grid and for effectively reducing energy consumption. Due to the fluctuations in EV charging loads, particularly the significant load variation between commercial and non-commercial areas, global models often suffer from prediction errors when forecasting loads. To address this issue, this paper proposes a regional forecasting method based on K-means++ clustering and deep learning algorithms. First, the K-means++ algorithm was used to partition the data into different regions, and an independent load-forecasting model was established for each region. Then, a combination of kernel support vector regression (KSVR) and gated recurrent unit (GRU) models was used to handle nonlinear features and time-dependent data, where particle swarm optimization (PSO) further optimized the model parameters to improve the forecasting accuracy. Finally, a weighted summation method was used to integrate the forecast results from each region, resulting in a more accurate overall load forecast. The experimental results show that the proposed model provided better prediction performance by capturing the spatiotemporal characteristics of the EV charging load, effectively addressing the challenges posed by regional differences, and outperforming the single-model forecasts.

Research on a Bearing Fault Diagnosis Method Based on a CNN-LSTM-GRU Model

In view of the problem of the insufficient performance of deep learning models in time series prediction and poor comprehensive space–time feature extraction, this paper proposes a diagnostic method (CNN-LSTM-GRU) that integrates convolutional neural network (CNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) models. In this study, a convolutional neural network (CNN) model is used to process two-dimensional image data in both time and frequency domains, and a convolutional core attention mechanism is introduced to extract spatial features, such as peaks, cliffs, and waveforms, from the samples. A long short-term memory (LSTM) network is embedded in the output processing of the convolutional neural network (CNN) to analyze the long-sequence variation characteristics of rolling bearing vibration signals and enable long-term time series prediction by capturing long-term dependencies in the sequence. In addition, a gated recurrent unit (GRU) is used to refine long-term time series predictions, providing local fine-tuning and improving the accuracy of fault diagnosis. Using a dataset obtained from Case Western Reserve University (CWRU), the average accuracy of CNN-LSTM-GRU fault vibration is greater than 99%, and its superior performance in a noisy environment is demonstrated.

Predictive Abilities of Machine Learning and Deep Learning Approaches for Exchange Rate Prediction

This study evaluates the efficacy of forecasting models in predicting USD/TRY exchange rate fluctuations. We assess Support Vector Machine (SVM), XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models with 96 and 21 feature sets. Data from 01.01.2010 to 30.04.2024 were sourced from Bloomberg, CBRT, and BDDK. Findings indicate that LSTM and GRU models outperform traditional models, with GRU showing the highest predictive accuracy. SVM performs poorly with highdimensional data, while XGBoost offers moderate predictive power but lacks in capturing intricate patterns. This study highlights the importance of model and feature selection in financial time series forecasting and underscores the advantages of advanced neural networks. The results provide valuable insights for analysts and policymakers in developing robust economic forecasting models.

Integrating Artificial Intelligence for Enhanced Fault Detection in Power Transmission Systems: A Smart Grid Approach

Modern electrical systems rely heavily on sensors and relays for fault detection in three-phase transmission lines and distribution transformers. However, these devices often need more time complexity and are prone to false alarms (erroneous signals). The study was guided by the PRISMA guidelines and methodology. The study's objective was to compare the levels of accuracy of fault detection presented by different models studied between 2023 and 2024. The general objective was further divided into sub-objectives, which included determining types of faults in power transmission systems, examining fault detection machine-learning models in power transmission systems, and assessing levels of accuracy of fault detection techniques in power transmission systems. The study used a qualitative research design, which entailed a systematic literature review that involved identifying scholarly articles from respectable international journal websites that aligned with examining how the deep-learning model enhanced fault detection capabilities in a three-phase transmission line simulated dataset. The inclusion criteria were that the study used journals published between 2023 and 2024. The study also included journals that tested Artificial Intelligence systems' accuracy in fault detection in three-phase transmission lines and distribution transformers. The study excluded sources that were older than 2023. The sources of information were journals written by professionals and scholars in the field of Artificial intelligence and engineering. The sourced journals used simulated and real databases to test fault detection accuracy in three-phase transmission lines and distribution transformers. The source of data for review was obtained from the internet via Google search with various credible academic journal websites being searched by the researcher. A total of 12 sources were searched. To assess the risk of bias in the included studies, the researcher used the Robvis methods that visualized risk-of-bias during the inclusion criteria for sources used in the systematic review. The techniques used to present the findings were narratives, graphs, and tables. The method used to synthesize results was comparative data analysis. The study included two studies, the sample size from the three possible journals the researcher identified. The characteristics of the studies were that they both informed the study's general and specific sub-objectives. The two studies also used the same methods to arrive at their findings, which made them ideal for comparative analysis. Finally, both studies compared older systems to new systems regarding Artificial Intelligence accuracy in fault detection. The study's findings were as follows: The study ranked the Novel glass box-based model as the best artificial intelligence machine-learning approach for accurately detecting electric power transmission lines and systems faults. The Novel glass box-based approach's accuracy was 99%, followed by the Convolution Neural Network model's accuracy of 98%, the Gated Recurrent Unit model's accuracy of 92%, the Random Forest model's accuracy of 90%, the Logistic Regression model's accuracy of 74%, and the Support Vector Classifier model's accuracy of 63%. The review was adequate to conclude that the Novel glass-box-based proposed approach was the most advanced and suitable model for detecting faults in electric power transmission lines. The study had one limitation: it reported the results based on two sources, which made the exclusion criteria a point of bias. The survey results implied that builders of new Artificial intelligence systems for fault detection in electric line transmitters should aim to produce 99% accurate systems to meet the recommendations and fill the gap that this study has reported. This study was funded by the researcher using family contributions. The study's author is Nijat Taghizad (ORCID ID: 0009-0005-0505-8767).

Prediction of Drivers’ Red-Light Running Behaviour in Connected Vehicle Environments Using Deep Recurrent Neural Networks

Red-light running at signalised intersections poses a significant safety risk, necessitating advanced predictive technologies to predict red-light violation behaviour, especially for advanced red-light warning (ARLW) systems. This research leverages Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to forecast the red-light running and stopping behaviours of drivers in connected vehicles. We utilised data from the Ipswich Connected Vehicle Pilot (ICVP) in Queensland, Australia, which gathered naturalistic driving data from 355 connected vehicles at 29 signalised intersections. These vehicles broadcast Cooperative Awareness Messages (CAM) within the Cooperative Intelligent Transport Systems (C-ITS), providing kinematic inputs such as vehicle speed, speed limits, longitudinal and lateral accelerations, and yaw rate. These variables were monitored at 100-millisecond intervals for durations from 1 to 4 s before reaching various distances from the stop line. Our results indicate that the LSTM model outperforms the GRU in predicting both red-light running and stopping behaviours with high accuracy. However, the pre-trained GRU model performs better in predicting red-light running specifically, making it valuable in applications requiring early violation prediction. Implementing these models can enhance red-light violation countermeasures, such as dynamic all-red extension (DARE), decreasing the likelihood of severe collisions and enhancing road users’ safety.

A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer

ABSTRACTReal‐time monitoring, prediction, and early warning of operating status during intelligent mining are the key to ensuring stable production. To solve the problem of lag in determining the operating status of a shearer, this study proposes a new method for predicting and warning the real‐time operating status of the shearer involving the Storm framework, based on parallel optimization of data processing and the gated recurrent unit (GRU) model based on hyperparameter optimization. First, the GRU model is optimized through hyperparameter optimization to achieve adaptive and accurate prediction and early warning of multidimensional state parameters of the shearer. Second, a virtual machine is constructed to host the Storm framework, parallel optimized real‐time processing of data is performed on the Storm framework, and real‐time data flow patterns are constructed to speed up data processing and retrieval, ensuring each tuple is fully processed through the topology structure. Finally, the optimized GRU model is embedded into the optimized Storm framework to achieve real‐time prediction and early warning of different dimensional data of the shearer. The prediction accuracy, early warning accuracy, and processing efficiency of the Storm platform are used as evaluation indicators to analyze and evaluate the model, verifying the efficiency and applicability of the proposed model. Experimental results show that the model has a prediction accuracy of 93%, an early warning accuracy of 93.05%, and consumes 10 s. It can achieve high performance, low latency, and high precision in predicting and providing early warnings for the shearer's state parameters, greatly improving the efficiency of predicting and early warning the operating status parameters of the shearer. This model realizes real‐time prediction and early warning of the shearer's operating status, providing technical support for intelligent mining in coal mines.

Modeling nonlinear behavior of piezoelectric actuators using improved WT-GRU neural network

The coupling of hysteresis and low damping vibration in a piezoelectric actuator results in low modeling accuracy and adversely affects output motion precision. To mitigate this problem, this paper proposes an improved wavelet transform gated recurrent unit (WT-GRU) model. This model integrates a convolutional layer with WT-GRU to enhance its capability to express complex relationships. Firstly, the input voltage sequence undergoes wavelet transform to decompose it into a set of frequency subsequences. Then, using the feature extraction and representation ability of the convolutional layer, significant features are extracted from subsequences to construct a time series feature vector. Finally, a gated recurrent unit is trained to predict the output displacement sequence accurately. Statistical metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination ( R2) were utilized to evaluate the model performance. Additionally, experimental tests were conducted with frequency excitation signals at 80–120 Hz. Experimental results show that the proposed model achieves a MAE of 0.0102 mm, an RMSE of 0.0148 mm, and an R2 value of 0.9478. This model exhibits a significant advantage in accurately predicting the output displacement of piezoelectric actuators, thereby providing a reliable foundation for designing piezoelectric actuator control systems.

Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction

With the development of information technology, massive traffic data-driven short-term traffic situation analysis of urban road networks has become a research hotspot in urban traffic management. Accurate vehicle trajectory and traffic flow prediction can provide technical support for vehicle path planning and road congestion warning. Unlike most studies that use GPS data to predict vehicle trajectories, this paper combines the broad coverage, high reliability, and lighter weight of traffic checkpoint data to propose a method that uses trajectory prediction technology to forecast the traffic flow in urban road networks accurately. The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. Firstly, transforming the checkpoint data into daily vehicle trajectories with time series characteristics, realizing the vehicle trajectory travel chain division. Secondly, the adjacency matrix is established by using the spatial relationship of each checkpoint, and the feature matrix of the vehicle’s driving trajectory over time is established, which is used as the input of GCN to learn the spatial characteristics of the vehicle while driving on the road network, and then GRU is added to further process the data after GCN training, constructing a GCN-GRU vehicle trajectory prediction model for vehicle trajectory prediction. Finally, the traffic flow of each checkpoint is calculated based on the prediction result of vehicle trajectory and compared with the real checkpoint flow. This paper conducts many experiments on the Qingdao City Shinan district checkpoint dataset. The results show that compared with the single models GCN, GRU, BiGRU, and BiLSTM, the GCN-GRU model has reduced the MAE by 0.75, 0.46, 0.52, and 0.57, and the RMSE by 0.76, 0.52, 0.58, and 0.68, respectively, demonstrating stronger spatial and temporal correlation characteristics and higher prediction accuracy. The MAPE between the forecasted flow and the real flow is 0.18, which verifies the reliability of the proposed method.

Machine Learning Based Localization of LoRa Mobile Wireless Nodes Using a Novel Sectorization Method

Indoor localization of wireless nodes is a relevant task for wireless sensor networks with mobile nodes using mobile robots. Despite the fact that outdoor localization is successfully performed by Global Positioning System (GPS) technology, indoor environments face several challenges due to multipath signal propagation, reflections from walls and objects, along with noise and interference. This results in the need for the development of new localization techniques. In this paper, Long-Range Wide-Area Network (LoRaWAN) technology is employed to address localization problems. A novel approach is proposed, based on the preliminary division of the room into sectors using a Received Signal Strength Indicator (RSSI) fingerprinting technique combined with machine learning (ML). Among various ML methods, the Gated Recurrent Unit (GRU) model reached the most accurate results, achieving localization accuracies of 94.54%, 91.02%, and 85.12% across three scenarios with a division into 256 sectors. Analysis of the cumulative error distribution function revealed the average localization error of 0.384 m, while the mean absolute error reached 0.246 m. These results demonstrate that the proposed sectorization method effectively mitigates the effects of noise and nonlinear signal propagation, ensuring precise localization of mobile nodes indoors.

Development of a Deep Learning-based Arabic Speech Recognition System for Automatons

The latest developments in voice recognition have achieved amazing results that are on par with those of human transcribers. However, this significant efficiency may not apply to all languages, nor Arabic. Arabic is the native language of 22 countries and is spoken by approximately 400 million individuals. Verbal difficulties have become a growing problem in recent decades, especially among children, and data samples on Arabic phonetic recognition are limited. For Arabic pronunciation, Artificial Intelligence (AI) techniques show encouraging results. Some devices, such as the Servox Digital Electro-Larynx (EL), can produce voice for such individuals. This study presents a Deep Learning-based Arabic speech recognition system for automatons to recognize captured sounds from the Servox Digital EL. The proposed system employs an autoencoder using a mix of Long-Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models. The proposed approach has three main stages: de-noising, feature extraction, and Arabic pronunciation. The experimental findings demonstrate that the proposed model was 95.31% accurate for Arabic speech recognition. The evaluation shows that the use of GRU in both the encoding and decoding structures improves efficiency. The proposed model had a Word Error Rate (WER) of 4.69%. The test results demonstrate that the proposed model can be used to create a real-time application to recognize commonly spoken Arabic words.

Analysis and prediction of battery temperature in thermal management system coupled SiC foam-composite phase change material and air

Lithium-ion batteries crucially rely on an effective battery thermal management system (BTMS) to sustain their temperatures within an optimal range, thereby maximizing operational efficiency. Incorporating bio-based composite phase change material (CPCM) into BTMS enhances efficiency and sustainability. This study commences by blending lauric acid and myristic acid in a 7: 3 mass ratio to synthesize a bio-based CPCM, following which the thermophysical properties of this CPCM are tested. Subsequently, the CPCM is integrated into a SiC foam and coupled with air to develop a novel BTMS. Then the effects of air velocity and initial temperature on the temperature of the battery pack are analyzed. Finally, the RIME-Convolutional Neural Network (CNN)- Self-Attention (SA)- Gated Recurrent Unit (GRU) model is established to predict the temperature of the battery in this BTMS. The results indicate that the latent heat of the CPCM is 97.98 J/g, melting at 35 °C upon heating. The incorporation of SiC foam-CPCM effectively reduces battery temperatures. When the air velocity is set at 3 m/s, the battery temperature remains below 40 °C during a discharge rate of 2C. Notably, the CPCM melts at higher initial temperatures, effectively mitigating the temperature rise in the battery pack. The RIME-CNN-SA-GRU model exhibits remarkable accuracy, with a maximum prediction error of 0.56 °C and a RMSE of 0.23, precisely capturing the trend of a sharp temperature rise at the end of discharge. This study presents an efficient solution for lithium-ion battery thermal management and temperature prediction.

Forecasting the Metal Ores Industry Index on the Tehran Stock Exchange: A Gated Recurrent Unit (GRU) Approach

This research offers an in-depth examination of predicting the closing prices of the metal ores industry index on the Tehran Stock Exchange (TSE) using a Gated Recurrent Unit (GRU) model. The GRU, a type of recurrent neural network, shows great promise for tasks involving time series forecasting. The historical daily price data from October 2017 to October 2022, was used in the study after carefully preprocessing it for further analysis. The study begins with a univariate analysis to reveal distribution characteristics and the relationships between essential variables. A customized GRU model that is trained on 70% of the time series data, with its performance assessed through metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and the R-squared (R2) score is used for prediction. The results indicate that the GRU model provides accurate predictions for the metal ores industry index, outperforming traditional forecasting techniques. The model's recurrent nature enables it to capture both short-term and long-term temporal dependencies within the data. This research highlights the significant potential of GRU networks in the realm of financial forecasting. Future improvements will focus on hyperparameter optimization and further integrating additional input variables to enhance predictive accuracy.

Prediction of ROI Achievements and Potential Maximum Profit on Spot Bitcoin Rupiah Trading Using K-means Clustering and Patterned Dataset Model

Since Satoshi Nakamoto first proposed the idea of bitcoin in 2009, the cryptocurrency and prediction methods for it have grown and changed exceptionally quickly. The Patterned Dataset Model was a valuable tool in earlier studies to explain how changes in the price of Bitcoin affect the movements of other cryptocurrencies in a digital trading market. Three different kinds of datasets are generated by this model: patterned datasets under full conditions, patterned datasets under dropping prices (Crash), and patterned datasets under rising prices (Moon). The K-means approach was then used to cluster these three datasets. Specifically, each dataset was split into two clusters, and the clustering score was determined by utilizing eight unique clustering metrics. Consequently, the best clustering score was found in the patterned dataset in the crash situation. Additionally, from 2022 to 2024, the raw data from this crash-condition-patterned dataset is used to determine the possibility of reaching maximum profit and return on investment (ROI) daily and monthly. According to the calculation results, the range computed over the course of a whole month (30 to 31 days) is significantly larger than the daily range (24 hours multiplied by one month), which represents the most significant profit and ROI attained before the emergence of the first diamond crash level. This research also covers the application of a deep learning model to forecast patterned datasets for crash scenarios that may occur many days in advance. The ConvLSTM2D Model performs better in predicting pattern dataset values for the subsequent crash scenario, according to the hyperparameter comparison between the Gated Recurrent Unit (GRU) Model and the 2D Convolutional Long Short-Term Memory Model.