Intelligent Prediction System Research Articles

Roll force prediction using hybrid genetic algorithm with semi-supervised support vector regression

Roll force prediction plays a significant role in rolling schedule and optimization. For a specific steel grade, the roll force can be determined by the aid of several factors: rolling speed, initial thickness, ratio of thickness reduction, the starting temperature of the strip, and the friction coefficient in the contact region. Roll force prediction mathematical models are sometimes rare and inaccurate. This paper presents a new approach to predict roll separating force using semi-supervised support vector regression (SSSVR). The parameters affecting the sensitivity of the SSSVR were optimized using the genetic algorithm to maximize the r-squared accuracy score. The intelligent system is evaluated using two quality metrics: the root mean square error (RMSE) and the mean absolute error calculated between the measured force from the industrial rolling field and the predicted force using the proposed system from one side, and the measured force and the calculated force from another side. Obtained results show the improvement while using the intelligent predictive system. The reduction in RMSE was achieved by the proposed system by 66.9% and 32.1% for oval and round shape passes, respectively in comparison to the conventional calculation method.

Internet of things and optimized knn based intelligent transportation system for traffic flow prediction in smart cities

The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595.

Comparison of Regression Methods to Predict the First Spike Latency in Response to an External Stimulus in Intracellular Recordings for Cerebellar Cells.

The significance of intracellular recording in neurophysiology is emphasized in this article, with considering the functions of neurons, particularly the role of first spike latency in response to external stimuli. The study employs advanced machine learning techniques to predict first spike latency from whole cell patch recording data. Experiments were conducted on Control (Salin) and Experiment (Harmaline) groups, generating a dataset for developing predictive models. Because the dataset has a limited number of samples, we utilized models that are effective with small datasets. Among different groups of regression models (linear, ensemble, and tree models), the ensemble models, specifically the LGB method, can achieve better performance. The results demonstrate accurate prediction of first spike latency, with an average mean squared error of 0.0002 and mean absolute error of 0.01 in 10-fold cross-validation. The research suggests the potential of machine learning in forecasting the first spike latency, allowing reliable estimation without the need for extensive animal testing. This intelligent predictive system facilitates efficient analysis of first spike latency changes in both healthy and unhealthy brain cells, streamlining experimentation and providing more detailed insights into the captured signals.

Enhanced Cardiovascular Disease Prediction Modelling using Machine Learning Techniques: A Focus on CardioVitalnet

ABSTRACT Aiming at early detection and accurate prediction of cardiovascular disease (CVD) to reduce mortality rates, this study focuses on the development of an intelligent predictive system to identify individuals at risk of CVD. The primary objective of the proposed system is to combine deep learning models with advanced data mining techniques to facilitate informed decision-making and precise CVD prediction. This approach involves several essential steps, including the preprocessing of acquired data, optimized feature selection, and disease classification, all aimed at enhancing the effectiveness of the system. The chosen optimal features are fed as input to the disease classification models and into some Machine Learning (ML) algorithms for improved performance in CVD classification. The experiment was simulated in the Python platform and the evaluation metrics such as accuracy, sensitivity, and F1_score were employed to assess the models’ performances. The ML models (Extra Trees (ET), Random Forest (RF), AdaBoost, and XG-Boost) classifiers achieved high accuracies of 94.35%, 97.87%, 96.44%, and 99.00%, respectively, on the test set, while the proposed CardioVitalNet (CVN) achieved 87.45% accuracy. These results offer valuable insights into the process of selecting models for medical data analysis, ultimately enhancing the ability to make more accurate diagnoses and predictions.

Optimising building heat load prediction using advanced control strategies and Artificial Intelligence for HVAC system

Amid the dynamic challenges posed by the COVID-19 era, this study offers a nuanced exploration, delving into the complexities of optimising building heat load prediction. This study addresses the imperative challenge of optimising building heat load prediction by implementing advanced control strategies and integrating Artificial Intelligence (AI) into air conditioning systems. The study emphasises the need to design HVAC devices for minimal energy consumption without compromising comfort conditions. The study introduces an intelligent predictive adaptive neuro-fuzzy inference system (ANFIS) model that proves highly capable of accurately predicting HVAC performance outcomes while contributing to the development of a comprehensive dataset. The uncertainty percentage is evaluated across various membership functions, with the trapezoidal membership exhibiting the lowest error rate, followed by the Gaussian membership function. Weather parameters crucial to ventilation efficiency and heat load are examined, leading to the identification of an optimal combination involving 45 % relative humidity, 13 °C dry bulb temperature, and 5 km/h wind speed. The current system demonstrates significant efficiency improvements, with ventilation and heat load rates reaching 93 % and 97 %, respectively, compared to pre-COVID-19 conditions. The findings underscore the importance of considering these parameters in future HVAC designs, particularly in the context of COVID-19 guidelines (75 % and 79 %, respectively).

Edge-Centric Intelligent Early Warning System for Residual Soil Stability Prediction in Slope

Edge-Centric Intelligent Early Warning System for Residual Soil Stability Prediction in Slope

Developing an intelligent prediction system for successful aging based on artificial neural networks.

Due to the growing number of disabilities in elderly, Attention to this period of life is essential to be considered. Few studies focused on the physical, mental, disabilities, and disorders affecting the quality of life in elderly people. SA1 is related to various factors influencing the elderly's life. So, the objective of the current study is to build an intelligent system for SA prediction through ANN2 algorithms to investigate better all factors affecting the elderly life and promote them. This study was performed on 1156 SA and non-SA cases. We applied statistical feature reduction method to obtain the best factors predicting the SA. Two models of ANNs with 5, 10, 15, and 20 neurons in hidden layers were used for model construction. Finally, the best ANN configuration was obtained for predicting the SA using sensitivity, specificity, accuracy, and cross-entropy loss function. The study showed that 25 factors correlated with SA at the statistical level of P < 0.05. Assessing all ANN structures resulted in FF-BP3 algorithm having the configuration of 25-15-1 with accuracy-train of 0.92, accuracy-test of 0.86, and accuracy-validation of 0.87 gaining the best performance over other ANN algorithms. Developing the CDSS for predicting SA has crucial role to effectively inform geriatrics and health care policymakers decision making.

An intelligent agriculture management system for rainfall prediction and fruit health monitoring

Contrary to popular belief, agriculture is becoming more data-driven with artificial intelligence and Internet-of-Things (IoT) playing crucial roles. In this paper, the integrated processing executed by various sensors combined as an IoT pack and driving an intelligent agriculture management system designed for rainfall prediction and fruit health monitoring have been included. The proposed system based on an AI aided model makes use of a Convolutional Neural Network (CNN) with long short-term memory (LSTM) layer for rainfall prediction and a CNN with SoftMax layer along with a few deep learning pre-trained models for fruit health monitoring. Another model that works as a combined rainfall predictor and fruit health recognizer is designed using a CNN + LSTM and a multi-head self-attention mechanism which proves to be effective. The entire system is cloud resident and available for use through an application.

Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning on Telecom Industries

Intelligent System for Customer Churn Prediction (CCP) relates to a system or application that controls advanced artificial intelligence (AI), data analysis, and machine learning (ML) methods for anticipating and predicting customer churn in business or service. CCP approach utilizes various data sources comprising customer behavior and historical data, to create predictive method able of categorizing customers who are potential to leave or stop their engagement. By employing intelligent method, this system supports businesses in proactively addressing customer retention and executing manners to decrease churn, ultimately enhancing revenue retention and customer satisfaction. It connects wide data sources, comprising customer behavior and historical information, to progress difficult methods that can identify customers at risk of leaving or discontinuing their service or subscription. By leveraging deep learning (DL) method, this intelligent system enhances the efficiency and accuracy of customer churn prediction, allowing businesses to take proactive measures to maintain customers, maintain revenue, and develop customer satisfaction. This article presents an Intelligent System for Customer Churn Prediction using Dipper Throat Optimization with Deep Learning (ISCCP-DTODL) methodology in Telecom Industries. The purpose of the ISCCP-DTODL system focuses on the design of intelligent systems for the effective prediction of customer churners and non-churners. To accomplish this, the ISCCP-DTODL system performs Z-score data normalization to preprocess the data. For feature selection and to reduce high dimensionality of features, the ISCCP-DTODL technique uses DTO algorithm. Besides, the ISCCP-DTODL technique makes use of hybrid CNN-BiLSTM model for churn prediction. At last, jellyfish optimization (JFO) based hyperparameter tuning approach can be employed to pick hyperparameters connected to CNN-BiLSTM technique. To display enhanced performance of ISCCP-DTODL technique, a widespread set of simulations was performed. The extensive results stated that ISCCP-DTODL model illustrates improved results than its current techniques in terms of dissimilar measures.

HAWKFOG-an enhanced deep learning framework for the Fog-IoT environment.

Cardiac disease is considered as the one of the deadliest diseases that constantly increases the globe's mortality rate. Since a lot of expertise is required for an accurate prediction of heart disease, designing an intelligent predictive system for cardiac diseases remains to be complex and tricky. Internet of Things based health regulation systems are a relatively recent technology. In addition, novel Edge and Fog device concepts are presented to advance prediction results. However, the main problem with the current systems is that they are unable to meet the demands of effective diagnosis systems due to their poor prediction capabilities. To overcome this problem, this research proposes a novel framework called HAWKFOGS which innovatively integrates the deep learning for a practical diagnosis of cardiac problems using edge and fog computing devices. The current datasets were gathered from different subjects using IoT devices interfaced with the electrocardiography and blood pressure sensors. The data are then predicted as normal and abnormal using the Logistic Chaos based Harris Hawk Optimized Enhanced Gated Recurrent Neural Networks. The ablation experiments are carried out using IoT nodes interfaced with medical sensors and fog gateways based on Embedded Jetson Nano devices. The suggested algorithm's performance is measured. Additionally, Model Building Time is computed to validate the suggested model's response. Compared to the other algorithms, the suggested model yielded the best results in terms of accuracy (99.7%), precision (99.65%), recall (99.7%), specificity (99.7%). F1-score (99.69%) and used the least amount of Model Building Time (1.16 s) to predict cardiac diseases.

An intelligent prediction system for predicting the types of joints on extended endplate long bolted joint using fuzzy rules

An intelligent prediction system for predicting the types of joints on extended endplate long bolted joint using fuzzy rules

Predicting the catalytic activities of transition metal (Cr, Fe, Co, Ni) complexes towards ethylene polymerization by machine learning.

The study aims to execute machine learning (ML) method for building an intelligent prediction system for catalytic activities of a relatively big dataset of 1056 transition metal complex precatalysts in ethylene polymerization. Among 14 different algorithms, the CatBoost ensemble model provides the best prediction with the correlation coefficient (R2 ) values of 0.999 for training set and 0.834 for external test set. The interpretation of the obtained model indicates that the catalytic activity is highly correlated with number of atom, conjugated degree in the ligand framework, and charge distributions. Correspondingly, 10 novel complexes are designed and predicted with higher catalytic activities. This work shows the potential application of the ML method as a high-precision tool for designing advanced catalysts for ethylene polymerization.

Conceptual foundations of a corporate intelligent risk-based system for analysis, prediction and prevention of occupational and work-related health disorders of workers

The relevance of the study is determined by the strategic interests of the Russian Federation dictating the need to achieve a national priority, which is the development of human potential. Given the ongoing reduction in labor force against a background of depopulation processes, finding a solution to the problem seems especially relevant. The purpose of this study is to develop a scientific conceptual framework for an intelligent risk-based innovative system for analysis, prediction and prevention of occupational and work-related health disorders of workers. This will help substantiate keynote sanitary-hygienic and medical-preventive measures aimed at reducing losses in healthy life expectancy and increasing occupational longevity. The main theoretical idea is based on a unique information-intellectual innovative system for analyzing and predicting cause-and-effect relationships between the effects of harmful and hazardous occupational factors and the resulting health problems of workers, occupational diseases included. The developed predictive digital neural network models, trained on retrospective or actual data on working conditions, health status, socioeconomic conditions and lifestyle factors, are the information and analytical basis for carrying out calculations and assessing the evolution of personal and group (occupation, age, and work records) health risks caused by occupational or work-related diseases in workers. This provides solid grounds for making a forecast of a prevented period of reduction in occupational working capacity associated with working conditions, specifically for each examined occupation, age, and work records. Such forecasts are an eligible information basis for developing and making well-grounded managerial decisions including those concerning sanitary, hygienic and medical preventive measures aimed at preserving occupational longevity. This will significantly increase effectiveness of corporate health-preserving policies.

Концептуальные основы корпоративной интеллектуальной риск-ориентированной системы анализа, прогноза и профилактики профессиональных и производственно-обусловленных нарушений здоровья работников

The relevance of the study is determined by the strategic interests of the Russian Federation dictating the need to achieve a national priority, which is the development of human potential. Given the ongoing reduction in labor force against a background of depopulation processes, finding a solution to the problem seems especially relevant. The purpose of this study is to develop a scientific conceptual framework for an intelligent risk-based innovative system for analysis, prediction and prevention of occupational and work-related health disorders of workers. This will help substantiate keynote sanitary-hygienic and medical-preventive measures aimed at reducing losses in healthy life expectancy and increasing occupational longevity. The main theoretical idea is based on a unique information-intellectual innovative system for analyzing and predicting cause-and-effect relationships between the effects of harmful and hazardous occupational factors and the resulting health problems of workers, occupational diseases included. The developed predictive digital neural network models, trained on retrospective or actual data on working conditions, health status, socioeconomic conditions and lifestyle factors, are the information and analytical basis for carrying out calculations and assessing the evolution of personal and group (occupation, age, and work records) health risks caused by occupational or work-related diseases in workers. This provides solid grounds for making a forecast of a prevented period of reduction in occupational working capacity associated with working conditions, specifically for each examined occupation, age, and work records. Such forecasts are an eligible information basis for developing and making well-grounded managerial decisions including those concerning sanitary, hygienic and medical preventive measures aimed at preserving occupational longevity. This will significantly increase effectiveness of corporate health-preserving policies.

Machine Learning based Intelligent System for Breast Cancer Prediction (MLISBCP)

Risks of death from Breast Cancer (BC) are drastically rising in recent years. The diagnosis of breast cancer is time-consuming due to the limited availability of diagnostic systems such as dynamic MRI, X-rays etc. Early detection and diagnosis of breast cancer significantly impacts life expectancy as current medical technologies are not advanced enough to treat patients in later stages effectively. Even though researchers have created many expert systems for early detection of BC such as WNBC, AR + NN system, AdaBoost ELM etc., but still most expert systems frequently lack adequate handling of the class imbalance problem, proper data pre-processing, and systematic feature selection. To overcome these limitations, this work proposes an expert system named “Machine Learning Based Intelligent System for Breast Cancer Prediction (MLISBCP)” for better prediction of breast cancer using machine learning analytics. The suggested system utilises the ‘K-Means SMOTE’ oversampling method to handle the class imbalance problem and ‘Boruta’ feature selection technique to select the most relevant features of the BC dataset. To understand the effectiveness of the proposed model – MLISBCP, its performance is compared with various single classifier based models, ensemble models and various models present in literature in terms of performance metrics- accuracy, precision, recall, F1-score and RoC AUC Score. The results reveal that the MLISBCP obtained the highest accuracy of 97.53 % with respect to existing models present in the literature.

FTM-GCN: A novel technique for gas concentration predicting in space with sensor nodes

Accurate and real-time prediction of gas concentrations is a critical component of intelligent prediction systems and holds significant importance for production safety and quality of life. However, due to the constraints of the network topology in gas sensor networks and the temporal nature of gas concentration variations, concentration prediction has always been considered a significant challenge. To simultaneously capture the spatiotemporal correlations in gas concentration data with spatiotemporal characteristics, this study proposes a fully-connected temporal multilayer graph convolutional network (FTM-GCN). This method combines multiple graph convolutional layers (MGC), fully connected layers, and gated recurrent units (GRU). FTM-GCN exhibits three prominent features: (1) It leverages MGC to learn the topology of gas sensor networks, enhancing the model's ability to graph data and capture spatial characteristics. MGC is incorporated to capture spatial features in the data. (2) GRU is employed to capture the dynamic changes in sensor network data and the temporal characteristics of the data. (3) Continuous fully-connected layers are introduced to enhance the model's performance. Experimental results demonstrate that FTM-GCN effectively captures the spatiotemporal correlations in spatiotemporal data across various prediction perspectives and outperforms GCN, GRU, T-GCN and STGCN.

Intelligent Prediction and Real-time Monitoring System for Gas Flow Distribution at the Top of Blast Furnace

Intelligent Prediction and Real-time Monitoring System for Gas Flow Distribution at the Top of Blast Furnace

An efficient hybrid optimization algorithm for detecting heart disease using adaptive stacked residual convolutional neural networks

Heart disease remains a leading global cause of mortality. Cardiovascular disease is the most common vascular disorder contributing to heart-related issues. Critical factors impacting cardiovascular health include blood pressure, glucose control, and cholesterol management. The escalating mortality rates associated with heart disease underscore the urgency for precise and secure diagnoses to facilitate timely intervention and prevention. Incorrect diagnoses of cardiovascular disease can have devastating consequences, highlighting the critical need for accurate assessments. In this study, we introduce an intelligent system for heart disease prediction based on the integration of the ACLS-RCNN and ICSOA techniques. The workflow begins with the collection and preprocessing of patient information. Data preprocessing focuses on addressing missing values, employing IQR-RS scaling, and handling imbalanced data using the ROS technique. Next, features are extracted from the pre-processed data utilizing kernel-based linear discriminate analysis. To reduce the dimensionality of the dataset, hybrid optimization (CI-AO + GI-SMO) is employed to select informative attributes. Disease prognoses are then generated through the application of the ACLS-RCNN model, with further enhancement achieved by the Improved Cuttlefish-Swarm Optimisation Algorithm (ICSOA). Through extensive Python-based simulations, we evaluate the recommended system's performance in terms of accuracy, precision, recall, and f1-score, comparing it against established methodologies. Our software demonstrates remarkable accuracy in clinical data prediction and diagnosis, offering a promising tool for enhancing heart disease prognosis and early detection.

An Intelligent Decision Support System for Crop Yield Prediction Using Machine Learning and Deep Learning Algorithms

Agriculture is crucial to economic growth and development. Crop yield forecasting is critical for food production which includes vegetables, fruits, flowers, and cattle. Artificial Intelligence (AI) is rising in agriculture, providing farmers with real-time or long-term insights about their fields. It allows us to identify the areas that require irrigation, fertilization, or pesticide treatment. Statistical models struggle to track complex relationships in crop yields due to numerous factors. Machine Learning (ML) and Deep Learning (DL) algorithms can solve this problem by training themselves in these relationships, enabling accurate predictions in agricultural yield prediction methods. Predicting product performance in agriculture is challenging due to various factors, but profit forecasting improves decision-making, production, economics, and food safety. The present study focuses on the use of ML and DL algorithms to suggest a novel decision support system for crop yield prediction with the objectives to develop a robust, accurate model, investigate algorithm effectiveness, and create a user-friendly system for informed crop production decisions. According to the results, the developed system is capable of making precise predictions, which can support farmers in making better decisions about how to manage their crops. The simulation results demonstrate that the intelligent decision support system proposed for crop yield prediction using ML and DL algorithms is capable of achieving high accuracy and precision. The system can be used to help farmers make better decisions about crop planting and management, which can lead to increased crop yields and profits. The results of our experiment show that our model is better than the others and it achieves an accuracy of 99.82 %. Additionally, we utilized ML to condense the input space while preserving high accuracy.

Predicting different components of blast-induced ground vibration using earthworm optimisation-based adaptive neuro-fuzzy inference system

ABSTRACT This study focuses on addressing the complexity inherent in various amplitude components of blast-induced ground vibration (BIGV), encompassing vertical, radial, transversal, and the vectoral sum of PPVs of particle velocity. It takes into account their nonlinearity across diverse quarry environments, and aims to present an enhanced nonlinear intelligent system for accurate prediction of these components. Multiple artificial intelligence models were explored and developed for this purpose, including a support vector machine (SVM), an adaptive neural network based on the fuzzy inference system (ANFIS), and a novel hybrid model that combines earthworm optimisation (EO) and ANFIS (EO-ANFIS). The study also leverages the empirical model offered by the United States Bureau of Mines. The outcomes highlighted that the predictions of the three individual components prove to be more accurate compared to the vectoral sum of PPVs of particle velocity. However, the latter remains a valuable metric for evaluating the magnitude of BIGV in open-pit mines. Notably, the hybrid EO-ANFIS model emerges as the most accurate, achieving an impressive ~ 75% accuracy across 10 quarries characterised by distinct geological conditions.