Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618.

Pengaruh Strategi Pembelajaran berbasis E-Learning dan Motivasi Belajar terhadap Prestasi Mata Pelajaran Sejarah Kebudayaan Islam (SKI)

The purposes of this study are 1) to determine the effect of e-learning learning strategies on the achievement of SKI subjects; 2) to determine the effect of learning motivation on the achievement of SKI subjects; 3) to determine the effect of e-learning based learning strategies and learning motivation on the achievement of SKI subjects. The study used a quantitative approach with a population of 78 students. Data were taken through a questionnaire with validity and reliability tests. Multiple regression techniques analyzed the research data. The research object is MA NU Gesi Sragen students academic year 2020/2021. The results showed that e-learning-based learning strategies and learning motivation positively influenced student achievement in SKI subjects for MA NU Gesi Sragen students in the academic year 2020/2021.

Information needs and delivery channels: Experimental evidence from Cambodian smallholders

Despite numerous policy interventions, poverty still exists. Those most harshly affected are people living in rural areas of low-income countries, regions that are often characterized by information asymmetries leading to market failure. The widespread growth of information and communications technologies (ICTs) in remote areas across the world holds immense potential for lifting the information barriers of the rural poor. However, there is little evidence of the effectiveness of delivery channels, which might be one reason why digital advice differs in its impact. Seeking to ascertain how smallholders can best be served by ICT, the authors investigated information needs and effective ICT delivery channels. Sociodemographic and ICT-related data was collected and a framed field experiment was conducted with smallholders in Cambodia; they were asked to build an object while using various delivery channels for instruction. Employing different regression techniques and matching algorithms, the experiment reveals that multisensory instructions trump all others.

Using Toe Framework to Analyze Factors Influencing Social Media Usage by Micro, Small And Medium Enterprises

This study aims to evaluate the factors that influence the use of social media in micro, small and medium enterprises. By using a mixed qualitative and quantitative approach, the study examines the influence of factors based on the TOE (Technology-Organization-Environment) Framework theory. The data collection process was carried out by means of interviews and questionnaires distributed online. From the quantitative analysis conducted on 100 samples using multiple linear regression techniques, it is known simultaneously that the factors in the context of the TOE Framework have a significant effect on the use of social media in MSMEs. In the context of technology, the factors that have a significant effect on the use of social media are the interactivity of social media and the suitability of the technology that has been previously owned. Data analysis was carried out using SPSS 25. The results of the study concluded that the suitability factor, inter-activity and entrepreneurial orientation had a significant effect on the use of social media in MSMEs.

Identifying Skeletal Maturity from X-rays using Deep Neural Networks

Skeletal maturity estimation is routinely evaluated by pediatrics and radiologists to assess growth and hormonal disorders. Methods integrated with regression techniques are incompatible with low-resolution digital samples and generate bias, when the evaluation protocols are implemented for feature assessment on coarse X-Ray hand images. This paper proposes a comparative analysis between two deep neural network architectures, with the base models such as Inception-ResNet-V2 and Xception-pre-trained networks. Based on 12,611 hand X-Ray images of RSNA Bone Age database, Inception-ResNet-V2 and Xception models have achieved R-Squared value of 0.935 and 0.942 respectively. Further, in the same order, the MAE accomplished by the two models are 12.583 and 13.299 respectively, when subjected to very few training instances with negligible chances of overfitting.

Characterization and Assessment of Stormwater Runoff Quality from Automobile Workshops in Nigeria Using Multivariate Linear Regression

An investigation into the pollution of stormwater runoff from automobile workshops in Nigeria was performed. Also, multivariate regression was used to predict the pH, oil, and grease (O&G) as well as the electrical conductivity (EC) in relation to the characteristics of the solids and metals pollutants of the untreated automobile workshop stormwater. The results indicated that automobile workshops contributed notable amounts of pollutants to stormwater runoff. Results were compared with Nigerian and USEPA standards. It was found that most of the parameters had mean value ranges far greater than standard limits. The multivariate regression showed variations in the results obtained from different automobile workshops. These variations could be due to the influence of factors such as the volume of automobile servicing activities and the waste generated from these activities that flow in the stormwater runoff. However, the bulk of the EC and pH of the stormwater were associated with the concentrations of the total dissolved solids and copper while the bulk of the O&G concentration was associated with the concentrations of lead and cadmium. It is recommended to treat automobile workshop stormwater to prevent detrimental effects in aquatic systems. Future research is aimed at modeling such treatment using multivariate regression techniques is warranted.