SARM: Salah Activities Recognition Model Based on Smartphone

Ahmad, Nafees; Han, Lei; Iqbal, Khalid; Ahmad, Rashid; Abid, Muhammad Adil; Iqbal, Naeem

doi:10.3390/electronics8080881

Cited by 19 publications

(15 citation statements)

References 44 publications

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“…It is evident that the age group (31)(32)(33)(34)(35)(36)(37)(38)(39)(40) has the highest percentage (%) value of 33.71% as compared to all other defined age groups. It can also be observed that the age group (41)(42)(43)(44)(45)(46)(47)(48)(49)(50) has the second-highest rental frequency percentage (%) value of 22.34%.…”

Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 95%

“…The proposed architecture model of the library rental book is illustrated in figure 14. In this paper, the following ML regression models, such as DNN, SVR, and RF, have implemented in python to predict and recommend future book details [11], [49], [50]. In this section, we survey the most generally utilized metrics for the regression problem and the basic working flow of the regression models used in this paper.…”

Section: Predictive Analysis Of Rental Book Using Machine Learninmentioning

confidence: 99%

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Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

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Large scale data and predictive analytics are the most challenging tasks in the field of academic data mining. Academic libraries are a great source of information and knowledge to provide a wide range of services to meet end-user requirements. Due to the rapid changes in the educational environment and availability of huge library rental book data, it is required to utilize data mining and machine learning techniques in the context of the academic library to extract and analyze underlying knowledge from rental book data, which is important to facilitate library administration to drive better future decisions to improve and manage library resources effectively. These are the following resources, such as managing future demands of the library books, selection and arrangement of the books, operational efficiency, and also improve the quality of interaction between the library and end-users, etc. This work uses and analyzes a real dataset collected from the library of Jeju National University, the Republic of Korea. The dataset contains 2,211,413 rental book records including 173671 unique book records, 57203 unique number of the rental user, and 78 data parameters. In this paper, we propose a novel model to analyze and predict library rental book data to facilitates library administration in order to plan and manage library resources effectively and provide better services to end-users. The proposed model consists of two different modules; library data analysis and prediction modules. Firstly, we use data mining techniques to analyze and extract useful underlying patterns from library rental book data, which can lead to plan and manage library resources effectively. Secondly, a novel prediction model is proposed based on Deep Neural Network (DNN), Support Vector Regressor (SVR), and Random Forest (RF) to predict future usage of the academic libraries rental books. The performance results of the implemented regression models are evaluated in terms of MAE, MSE, and RMSE. In this paper, it is found that the DNN model performs significantly better than SVR and RF. The experimentation results show that the proposed model improves the future usage of library books to facilitate library administration to plan and manage library resources effectively. Based on the proposed model results, the academic library administration can easily plan and manage resources effectively to provide quality services to end-users. INDEX TERMS Data mining, academic libraries, big data, machine learning, predictive analysis.

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Section: ) Rental Book Analysis Based On Rental User Age Groupmentioning

confidence: 95%

Section: Predictive Analysis Of Rental Book Using Machine Learninmentioning

confidence: 99%

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

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“…Recently, machine learning (ML) algorithms are widely used by the researchers to build intelligent inference models based on discovered characteristics using DM techniques to forecast time series data. ML algorithms are employed by many researchers to develop intelligent models in various domains, such as computer vision [9], business intelligence, pattern recognition [10], navigation systems [11], decision-making systems [12], etc. MLbased techniques are robust to build intelligent inference models based on historical electric consumption data and discover underlying characteristics to derive a conclusion.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

et al. 2021

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With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

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“…All these powerful techniques have facilitated improvement in the prediction performance of complex time-series data. ML techniques have been widely utilized in many areas, such as regression [20], classification [21], [22], patterns mining [23], [24], decision-making systems [25], [26], to name of a few. ML-based approaches tend to produce more robust predictions than conventional methods due to their ability to limit uncertainty concerning input variables having various nonlinear dependencies to generate accurate and reliable predictions.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

et al. 2021

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Drilling data for groundwater extraction incur changes over time due to variations in hydrogeological and weather conditions. At any time, if there is a need to deploy a change in drilling operations, drilling companies keep monitoring the time-series drilling data to make sure it is not introducing any changes or new errors. Therefore, a solution is needed to predict groundwater levels (GWL) and detect a change in boreholes data to improve drilling efficiency. The proposed study presents an ensemble GWL prediction (E-GWLP) model using boosting and bagging models based on stacking techniques to predict GWL for enhancing hydraulic resource management and planning. The proposed research study consists of two modules; descriptive analysis of boreholes data and GWL prediction model using ensemble model based on stacking. First, descriptive analysis techniques, such as correlation analysis and difference mechanisms, are applied to investigate boreholes log data for extracting underlying characteristics, which is critical for enhancing hydraulic resource management. Second, an ensemble prediction model is developed based on multiple hydrological patterns using robust machine learning (ML) techniques to predict GWL for enhancing drilling efficiency and water resource management. The architecture of the proposed ensemble model involves three boosting algorithms as base models (level-0) and a bagging algorithm as a meta-model that combines the base models predictions (level-1). The base models consist of the following boosting algorithms; eXtreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB). The meta-model includes Random Forest (RF) as a bagging algorithm referred to as a level-1 model. Furthermore, different evaluation metrics are used, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score. The performance of the proposed E-GWLP model is compared with existing ensemble and baseline models. The experimental results reveal that the proposed model performed accurately in respect of MAE, MSE, and RMSE of 0.340, 0.564, and 0.751, respectively. The MAPE and R2 score of our proposed approach is 12.658 and 0.976, respectively, which signifies the importance of our work. Moreover, experimental results suggest that E-GWLP model is suitable for sustainable water resource management and improves reservoir engineering.

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SARM: Salah Activities Recognition Model Based on Smartphone

Cited by 19 publications

References 44 publications

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

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