A Facial Feature Extraction and Classification Model for Loan-Default-Detection

The purpose of this paper is to make a new trial to explore the influence factors of loan default in Internet finance loan business. A facial feature extraction and classification model is proposed. The optimal facial feature extraction algorithm is obtained by comparing four commonly used facial feature extraction algorithms and certain facial features based on physiognomy are selected and classified in 117, 507face images. An experimental study with the help of the proposed model is conducted to explore the correlations between loan defaults and Internet finance loan users’ classified facial features based on physiognomy. The findings are as follows: among male Internet finance loan users, short eyebrows are related to default and eye angle, nose height-to-width ratio (nHWR), lip thickness and facial width-to-height ratio (fWHR) are positively related to default behavior and the mouth length is negatively related to default; among female Internet finance loan users, eyebrows angle, eyes angle, lip thickness and facial width-to-height ratio are positively correlated to default and mouth length is negatively correlated with default. Additionally, the conclusion of that male fWHR is positively related to default of the proposed study is echoed with the research results of [1] and [2]

The applicability of resource-based theory to the interpretation of strategic management in Jardine Matheson : uncertainty, relationships and capabilities

Abstract available: p. 2-3

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

Real Time Crime Prediction Using Social Media

There is no doubt that crime is on the increase and has a detrimental influence on a nation's economy despite several attempts of studies on crime prediction to minimise crime rates. Historically, data mining techniques for crime prediction models often rely on historical information and its mostly country specific. In fact, only a few of the earlier studies on crime prediction follow standard data mining procedure. Hence, considering the current worldwide crime trend in which criminals routinely publish their criminal intent on social media and ask others to see and/or engage in different crimes, an alternative, and more dynamic strategy is needed. The goal of this research is to improve the performance of crime prediction models. Thus, this thesis explores the potential of using information on social media (Twitter) for crime prediction in combination with historical crime data. It also figures out, using data mining techniques, the most relevant feature engineering needed for United Kingdom dataset which could improve crime prediction model performance. Additionally, this study presents a function that could be used by every state in the United Kingdom for data cleansing, pre-processing and feature engineering. A shinny App was also use to display the tweets sentiment trends to prevent crime in near-real time.Exploratory analysis is essential for revealing the necessary data pre-processing and feature engineering needed prior to feeding the data into the machine learning model for efficient result. Based on earlier documented studies available, this is the first research to do a full exploratory analysis of historical British crime statistics using stop and search historical dataset. Also, based on the findings from the exploratory study, an algorithm was created to clean the data, and prepare it for further analysis and model creation. This is an enormous success because it provides a perfect dataset for future research, particularly for non-experts to utilise in constructing models to forecast crime or conducting investigations in around 32 police districts of the United Kingdom.Moreover, this study is the first study to present a complete collection of geo-spatial parameters for training a crime prediction model by combining demographic data from the same source in the United Kingdom with hourly sentiment polarity that was not restricted to Twitter keyword search. Six unique base models that were frequently mentioned in the previous literature was selected and used to train stop-and-search historical crime dataset and evaluated on test data and finally validated with dataset from London and Kent crime datasets.Two different datasets were created from twitter and historical data (historical crime data with twitter sentiment score and historical data without twitter sentiment score). Six of the most prevalent machine learning classifiers (Random Forest, Decision Tree, K-nearest model, support vector machine, neural network and naïve bayes) were trained and tested on these datasets. Additionally, hyperparameters of each of the six models developed were tweaked using random grid search. Voting classifiers and logistic regression stacked ensemble of different models were also trained and tested on the same datasets to enhance the individual model performance.In addition, two combinations of stack ensembles of multiple models were constructed to enhance and choose the most suitable models for crime prediction, and based on their performance, the appropriate prediction model for the UK dataset would be selected. In terms of how the research may be interpreted, it differs from most earlier studies that employed Twitter data in that several methodologies were used to show how each attribute contributed to the construction of the model, and the findings were discussed and interpreted in the context of the study. Further, a shiny app visualisation tool was designed to display the tweets’ sentiment score, the text, the users’ screen name, and the tweets’ vicinity which allows the investigation of any criminal actions in near-real time. The evaluation of the models revealed that Random Forest, Decision Tree, and K nearest neighbour outperformed other models. However, decision trees and Random Forests perform better consistently when evaluated on test data

Marketing in the Digital Environment

The textbook contains provisions that reveal the main points of marketing in the digital environment and the basic tools necessary for a marketer to successfully implement a variety of projects on the Internet. In particular, the types of Internet business, business models and characteristics of creating a business in the Internet environment, Internet marketing, development and promotion of web resources. It provides analysis of the practical aspects that illustrate the theoretical positions of marketing in the digital environment. The publication contains a series of practical exercises, cases and tests to assess the level of knowledge. It is recommended for students of economics and specialities in the field of Internet business, marketers, teachers, graduate students, as well as a wide range of readers interested in marketing in the digital environment

Sharia and Democracy: Efforts to Synergize the Demands of Faith with the legal System in Indonesia

Since the fall of the New Order's authoritarian regime, Indonesia as a country with the largest Muslim population in the world is often praised as a country that has proven that Islam, democracy and modernity can grow and develop together. However, democracy in Indonesia does not escape the challenges associated with the return of the spirit of religion in political life. The problem is the return of religion to politics – and to public life in general – is a serious challenge to the rule of democratically enacted law and the civil liberties that go with it. Islamic activism or Islamism although they use freedom provided by democracy, actually rejects the principles of democracy and human rights which they see as contrary to the sharia and the absolute sovereignty of God. In the past thirteen years there has been a tendency for rising aspirations for Indonesia to be regulated by sharia law. The purpose of this research is to look for the meaning of sharia and democracy for Muslims, the theological foundations for Muslim to support democracy, and the challenges and alternative solutions that can be offered so that sharia can be transformed to Indonesia legal system. By assuming that sharia has a purpose and that Islamic law can change, evolve in line with developments and challenges of the times, the author argues that the synergy between sharia and democracy can occur in Indonesia as long as Muslims in Indonesia can accept plurality in understanding the sharia and are not bound to one model in understanding sharia. The author believes that sharia can be applied in democratic countries such as Indonesia, because the purpose of the sharia and the purpose of the state are the same, namely the achievement of social justice for all without discrimination

Publishing Activities of Shiites and Democratization of Islamic Thought in Indonesia

This paper examines the pattern of publication in a mass Islamic organization that is a minority in Indonesia, namely those originating from the Shia Islamic School. The publication process itself is inseparable from the position of an organization which is one of the centers of Shia community activities in Indonesia in giving and receiving knowledge and information. The study on the Indonesian Ahlulbait Jamaat Association (IJABI) which was founded in Bandung uses qualitative methods with data collection techniques through observation, interviews, documentation studies, and literature studies. The results of the study show that there is a model of publication activity which is characterized by the presence of managers, participants, and supporters of publication activities based on the role of communication among the very dominant Shia citizens. This needs to be exemplified by other organizations, in order to strengthen the character, intelligence and skills of the community in facing the fast, effective and efficient development of the ag

BGSU 1981-1982-1983 Undergraduate Catalog

Bowling Green State University undergraduate catalog for 1981-1982-1983.https://scholarworks.bgsu.edu/catalogs/1020/thumbnail.jp