Modelling the Demand for Bank Loans by Private Business Sector in Pakistan

The importance of studying demand for bank loan by private business sector stems from the fact the money supply is ‘credit-driven’ and demand-determined and at the rate of interest determined by the central bank the money supply function is horizontal as illustrated by Moore and Threadgold (1985), Coghlan, (1981), Moore (1979, 1983). The analysis of the demand for bank loan by private business sector is important for understating the monetary transmission mechanism and formulation of the effective monetary policy to achieve macroeconomic objectives. The study aimed to model the demand for bank loan by private business sector in Pakistan. We use Hylleberg, et al., (1990) seasonal unit root test for investigation of properties of data. The dynamic Autoregressive Distributed Lag (ARDL) model is used for long rung and the short run analysis of demand for bank loans by the business sector. For the testing long run relationship among the variables we used bounds test proposed by Pasaran and Shin (1995). Real rate of return on advances, economic activity, expectations about future state of economy, macroeconomic risk, inflation and foreign demand pressure are taken as the determinants of demand for bank loan by private business sector. Economic activity, real rate of interest, macroeconomic risk and inflation were found significant in affecting demand for bank loans while the estimated equation do not provide evidence for the role of foreign demand pressure and expectations about future state of economy in effecting demand for bank loans. The sign of the coefficients of real rate of return on advances, inflation and macroeconomic risk is negative whereas economic activity is directly related to demand for bank loans by private business sector. The short run model shows that the speed of adjustment is 8.5% quarterly. Therefore it takes three years to go back to the long run equilibrium level. In the short run change in rate of inflation, RRA and economic activity have negative impact. The short run equation explores that change in real rate of return (RRA) does not affect RDBL. It implies that in very short run business cannot change their demand for bank credit in response to changes in real interest rate. Changing in macroeconomic risk appears in the model in form against a priori expectations. Foreign demand pressure (FDP) has no long run effect and in short run has the coefficient having low value. The demand for bank loan by private business sector was found interest elastic and gives the provision to central bank to control credit in the economy through variations of interest rate

Modelling the Demand for Bank Loans by Private Business Sector in Pakistan

The importance of studying demand for bank loan by private business sector stems from the fact the money supply is ‘credit-driven’ and demand-determined and at the rate of interest determined by the central bank the money supply function is horizontal as illustrated by Moore and Threadgold (1985), Coghlan, (1981), Moore (1979, 1983). The analysis of the demand for bank loan by private business sector is important for understating the monetary transmission mechanism and formulation of the effective monetary policy to achieve macroeconomic objectives. The study aimed to model the demand for bank loan by private business sector in Pakistan. We use Hylleberg, et al., (1990) seasonal unit root test for investigation of properties of data. The dynamic Autoregressive Distributed Lag (ARDL) model is used for long rung and the short run analysis of demand for bank loans by the business sector. For the testing long run relationship among the variables we used bounds test proposed by Pasaran and Shin (1995). Real rate of return on advances, economic activity, expectations about future state of economy, macroeconomic risk, inflation and foreign demand pressure are taken as the determinants of demand for bank loan by private business sector. Economic activity, real rate of interest, macroeconomic risk and inflation were found significant in affecting demand for bank loans while the estimated equation do not provide evidence for the role of foreign demand pressure and expectations about future state of economy in effecting demand for bank loans. The sign of the coefficients of real rate of return on advances, inflation and macroeconomic risk is negative whereas economic activity is directly related to demand for bank loans by private business sector. The short run model shows that the speed of adjustment is 8.5% quarterly. Therefore it takes three years to go back to the long run equilibrium level. In the short run change in rate of inflation, RRA and economic activity have negative impact. The short run equation explores that change in real rate of return (RRA) does not affect RDBL. It implies that in very short run business cannot change their demand for bank credit in response to changes in real interest rate. Changing in macroeconomic risk appears in the model in form against a priori expectations. Foreign demand pressure (FDP) has no long run effect and in short run has the coefficient having low value. The demand for bank loan by private business sector was found interest elastic and gives the provision to central bank to control credit in the economy through variations of interest rate

Towards Potential Content-Based Features Evaluation to Tackle Meaningful Citations

The scientific community has presented various citation classification models to refute the concept of pure quantitative citation analysis systems wherein all citations are treated equally. However, a small number of benchmark datasets exist, which makes the asymmetric citation data-driven modeling quite complex. These models classify citations for varying reasons, mostly harnessing metadata and content-based features derived from research papers. Presently, researchers are more inclined toward binary citation classification with the belief that exploiting the datasets of incomplete nature in the best possible way is adequate to address the issue. We argue that contemporary ML citation classification models overlook essential aspects while selecting the appropriate features that hinder elutriating the asymmetric citation data. This study presents a novel binary citation classification model exploiting a list of potential natural language processing (NLP) based features. Machine learning classifiers, including SVM, KLR, and RF, are harnessed to classify citations into important and non-important classes. The evaluation is performed using two benchmark data sets containing a corpus of around 953 paper-citation pairs annotated by the citing authors and domain experts. The study outcomes exhibit that the proposed model outperformed the contemporary approaches by attaining a precision of 0.88

Wildfire risk exploration:leveraging SHAP and TabNet for precise factor analysis

Background: Understanding the intricacies of wildfire impact across diverse geographical landscapes necessitates a nuanced comprehension of fire dynamics and areas of vulnerability, particularly in regions prone to high wildfire risks. Machine learning (ML) stands as a formidable ally in addressing the complexities associated with predicting and mapping these risks, offering advanced analytical capabilities. Nevertheless, the reliability of such ML approaches is heavily contingent on the integrity of data and the robustness of training protocols. The scientific community has raised concerns about the transparency and interpretability of ML models in the context of wildfire management, recognizing the need for these models to be both accurate and understandable. The often-opaque nature of complex ML algorithms can obscure the rationale behind their outputs, making it imperative to prioritize clarity and interpretability to ensure that model predictions are not only precise but also actionable. Furthermore, a thorough evaluation of model performance must account for multiple critical factors to ensure the utility and dependability of the results in practical wildfire suppression and management strategies. Results: This study unveils a sophisticated spatial deep learning framework grounded in TabNet technology, tailored specifically for delineating areas susceptible to wildfires. To elucidate the predictive interplay between the model’s outputs and the contributing variables across a spectrum of inputs, we embark on an exhaustive analysis using SHapley Additive exPlanations (SHAP). This approach affords a granular understanding of how individual features sway the model’s predictions. Furthermore, the robustness of the predictive model is rigorously validated through 5-fold cross-validation techniques, ensuring the dependability of the findings. The research meticulously investigates the spatial heterogeneity of wildfire susceptibility within the designated study locale, unearthing pivotal insights into the nuanced fabric of fire risk that is distinctly local in nature. Conclusion: Utilizing SHapley Additive exPlanations (SHAP) visualizations, this research meticulously identifies key variables, quantifies their importance, and demystifies the decision-making mechanics of the model. Critical factors, including temperature, elevation, the Normalized Difference Vegetation Index (NDVI), aspect, and wind speed, are discerned to have significant sway over the predictions of wildfire susceptibility. The findings of this study accentuate the criticality of transparency in modeling, which facilitates a deeper understanding of wildfire risk factors. By shedding light on the significant predictors within the models, this work enhances our ability to interpret complex predictive models and drives forward the field of wildfire risk management, ultimately contributing to the development of more effective prevention and mitigation strategies.</p

A Novel Hybrid Ensemble Clustering Technique for Student Performance Prediction

Educational Data Mining (EDM) is a branch of data mining that focuses on extraction of useful knowledge from data generated through academic activities at school, college or at university level. The extracted knowledge can help to perform the academic activities in a better way, so it is useful for students, parents and institutions themselves. One common activity in EDM is students grade prediction with an aim to identify weak or at-risk students. An early identification of such students helps to take supportive measures that may help students to improve. Among a vast number of approaches available in this field, this study mainly focuses on generating a smarter dataset through reduced feature set without compromising the number of records in it and then producing an approach which combines the strengths of classification and clustering for better prediction results. In this study it has been identified that individual features have distinct effect and that removing misclassified data can affect the overall results. Backward selection is adopted using Pearson correlation as a metric to produce smarter dataset with lesser attributes and better accuracy in prediction. After feature set selection, we have applied EMT (Ensemble Meta-Based Tree Model) classification on it to identify best performing classifiers from five families of classifiers. In hybrid approach, first the ensemble clustering is applied on smart dataset and then EMT classification is applied to reevaluate the un-clustered data, which gives a boost in performance and provides us an accuracy of 93%

Enhanced PDR-BLE Compensation Mechanism Based on HMM and AWCLA for Improving Indoor Localization

This paper presents an enhanced PDR-BLE compensation mechanism for improving indoor localization, which is considerably resilient against variant uncertainties. The proposed method of ePDR-BLE compensation mechanism (EPBCM) takes advantage of the non-requirement of linearization of the system around its current state in an unscented Kalman filter (UKF) and Kalman filter (KF) in smoothing of received signal strength indicator (RSSI) values. In this paper, a fusion of conflicting information and the activity detection approach of an object in an indoor environment contemplates varying magnitude of accelerometer values based on the hidden Markov model (HMM). On the estimated orientation, the proposed approach remunerates the inadvertent body acceleration and magnetic distortion sensor data. Moreover, EPBCM can precisely calculate the velocity and position by reducing the position drift, which gives rise to a fault in zero-velocity and heading error. The developed EPBCM localization algorithm using Bluetooth low energy beacons (BLE) was applied and analyzed in an indoor environment. The experiments conducted in an indoor scenario shows the results of various activities performed by the object and achieves better orientation estimation, zero velocity measurements, and high position accuracy than other methods in the literature

Antidepressant and Anxiolytic Effects of Cod Liver Oil in Rats

Cod-liver oil is a rich source of omega 3 fatty acids and has been widely used as omega 3 fatty acids supplementation. Regarding omega-3 fatty acid beneficial effects in humans, this study was designed to investigate the effect of repeated administration of cod-liver oil on the locomotion and behaviors of rats, including depression, anxiety and the 5-Hydroxy tryptamine (5-HT) metabolism. After four weeks oral administration of cod-liver oil, open field test was used to measure the locomotor and exploratory activity. Elevated plus maze test was used to measure anxiety. Cod-liver oil significantly increased locomotion and produced anxiolytic effects in rats. Antidepressant effect of cod-liver oil was monitored by forced swim test (FST) in which struggling time of test animals was increased significantly. 5-HT turnover also increased significantly following the oral repeated administration of cod liver oil in test animals. The results suggest that cod-liver oil has antidepressant and anti-anxiety effects