Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion

Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature

Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification

To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps(ts)

A Quantitative Study on Push, Pull and Personal Factors Affecting Employees’ Turnover Intentions: A Case of Nationalized Commercial Banks (NCBs) in Bangladesh

‘Employee turnover’ as a term is widely discussed subject matter in business sphere. Organizations nowadays spend a lot of money to retain their valuable employees. But still employees leave their organizations and join new ones. There are various reasons for which employees leave their jobs. The purpose of this paper is to explore the relationship among the factors and their contributions in turnover intentions of employees of NCBs in Bangladesh. A 5 point Likert scale format questionnaire was used to collect primary data. A total of 175 questionnaires were distributed to the employees of NCBs, of which 152 were found flawless to yield a response rate of almost 87%. A pilot study was conducted to test the questionnaire. The questionnaire had a Cronbach alpha coefficient of α = 0.936 suggesting that the instrument was reliable. Different factors i.e. personal, pull and push factors were considered as independent variables whereas the dependent variable was employees’ turnover intentions. There were number of facets for every independent variable. Pearson Correlation was used to find out the relationship between dependent and independent variables. On the other hand, Regression tests were applied to determine the contribution of each independent variable in employees’ turnover intentions. The results show that there is strong statistical positive correlation between dependent and independent variables. Besides this, all the factors have significant contributions in employees’ turnover intentions. However, the most significant factor is the push factors (30.1% contributions) due to which employees intend to quit a job. Finally it is recommended that NCBs can give more emphasis on the push factors followed by pull factors and lastly personal factors to retain their valuable employees. Keywords: Employee Turnover, NCBs, Tacit Knowledge, Personal Factors, Pull Factors, Push Factors

Robust Diagnostics and Estimation in Heteroscedastic Regression Model in the Presence of Outliers

The violation of the assumption of homoscedasticity in OLS method, usually called heteroscedasticity, gravely misleads the inferential statistics. The current study has considered the situation when outliers occur in heteroscedastic data. Hence, the main focus of this research is to take remedial measures on the violation of the assumption of homoscedasticity in the presence of outliers. This thesis also concerns on the normality assumption of the errors of regression model in the presence of outliers. It is now evident that outliers have great impact on the existing normality tests, heteroscedasticity tests, and the estimators for heteroscedastic model. We propose the Robust Rescaled Moment (RRM) test for testing the normality of the regression residuals when there is an evidence of outlier(s). The results of the study signify that the RRM test offers substantial improvements over other existing tests in the presence of outliers. For the detection of heteroscedasticity in the presence of outliers, a modified version of the classical Goldfeld-Quandt (MGQ) test is proposed which is most powerful than the classical tests of heteroscedasticity. Most statistics practitioners assume that the forms of the heteroscedastic error structures are known which may lead to inefficient estimates if it is not correctly specified. In this respect, a Leverage Based Near-Neighbor (LBNN) method is proposed, where prior information on the structure of the heteroscedastic error is not required. The findings indicate that the LBNN is very efficient for correcting the problem of heteroscedastic errors with unknown structure. We also examine the effect of outliers on the existing remedial measures of heteroscedasticity. Hence, in this thesis, a one step M-type of Robust Weighted Least Squares Method (RWLS) and the Two-Step Robust Weighted Least Squares (TSRWLS) are developed. Finally, the new robust wild bootstrap techniques which are resistant to outliers are proposed. The proposed techniques are based on the weighted residuals which incorporated the MM estimator, robust location, robust scale and the bootstrap sampling schemes of Wu (1986) and Liu (1988). All procedures, in this thesis, are examined by using real data and Monte Carlo simulation studies. The comparative studies among the classical and proposed robust methods reveal that all the proposed robust methods outperform the classical methods

A critical analysis of deportation as crimes against the humanity

The Myanmar government has used deportation as an oppressive instrument against the Rohingya minority, resulting in forced displacement, loss of identity, and significant trauma. As the host country, Bangladesh has faced unprecedented hurdles in dealing with the enormous flood of refugees, but it has also been accused of forced repatriation. The Rohingya Muslim minority in Bangladesh has been subjected to systematic and widespread deportation, resulting in serious human rights violations. This critical analysis investigates deportation as a crime against humanity and its consequences for the afflicted community. This study delves into the legal frameworks surrounding crimes against humanity, focusing on international human rights conventions and the Rome Statute. The analysis demonstrates that the deportation of the Rohingya people constitutes a violation of fundamental human rights, including the right to life, liberty, and security of person, as well as the prohibition of torture and persecution. The article also examines how states, international organizations, and the general public can confront and prevent these crimes. It evaluates the responses of many parties critically, focusing emphasis on the necessity of accountability, justice, and victim support. Keywords: crimes against the humanity, Rohingya crisis DOI: 10.7176/JLPG/137-06 Publication date:October 31st 202

GreMuTRRR: A Novel Genetic Algorithm to Solve Distance Geometry Problem for Protein Structures

Nuclear Magnetic Resonance (NMR) Spectroscopy is a widely used technique to predict the native structure of proteins. However, NMR machines are only able to report approximate and partial distances between pair of atoms. To build the protein structure one has to solve the Euclidean distance geometry problem given the incomplete interval distance data produced by NMR machines. In this paper, we propose a new genetic algorithm for solving the Euclidean distance geometry problem for protein structure prediction given sparse NMR data. Our genetic algorithm uses a greedy mutation operator to intensify the search, a twin removal technique for diversification in the population and a random restart method to recover stagnation. On a standard set of benchmark dataset, our algorithm significantly outperforms standard genetic algorithms.Comment: Accepted for publication in the 8th International Conference on Electrical and Computer Engineering (ICECE 2014

Impact of garment industries on road safety in metropolitan Dhaka

There are about 4,000 garment industries in Bangladesh, most of them are clustered in and around the capital city. Together they account for 75 percent of the country's export earnings and employ around 1.8 million people which is almost one half of the total industrial workforce of the country. Though it is the most important economy sector of Bangladesh, unplanned and haphazardly built garment factories are also inducing many social, housing and most importantly urban transportation problems which are a great cause of concern. This study investigates the impact of garment industries on transportation, in particular road safety issues of garment workers. Data is collected to identify the locational problems of garment factories, spatial distribution of worker residences, and their travel pattern as well as to assess their walking and road crossing problems. Finally, recommendations are put forward to tackle transport problems arising from these unplanned establishments of export oriented garments industries in Dhaka Metropolitan City

A New Landscape of Energy Efficiency: A Comprehensive Study on Various Electricity Consumption Related Smartphone Applications

With the recent emergence of mobile platforms capable of executing various complex software there exists a variety of applications those provide services in the diverse field of interests A public utility is one of the most interesting and demanding fields of interest in mobile platforms In this article we present a comprehensive study of various mobile applications that provide nudging for public utility their acceptance their pros and cons and other research aspects of those applications In this article we primarily focused on electricity consumption related to various public utility mobile applications We present our findings based on mobile application s user ratings number of installations and most importantly user feedbacks in terms of comments on those application

Solar System Battery Backups for Reactor Coolant Pumps During Electricity Outages Resulting from Natural Disasters

In a nuclear power plant, its coolant system is major safety equipment. Coolant system failure causes several accidents in nuclear history. There are so many causes for coolant system failure. One of them is lack of electric power for coolant pumps. In typically NPP there is backup system for power redundancy. In this article, focus on reactor coolant system and its backup power when main grid lines failure. Here discuss about solar backup power for batteries and increases a safety lines for reactor coolant pumps. So, our main goal is providing a battery backup from reliable natural source and ensuring electricity for coolant pumps

K. Ali Flour Mills relationship with it’s retailers

This internship report is submitted in a partial fulfillment of the requirements for the degree of Master of Business Administration, 2015.Cataloged from PDF version of Internship report.Includes bibliographical references (page 32).“Relationship marketing is a strategy designed to foster customer loyalty, interaction and long-term engagement. It is designed to develop strong connections with customers by providing them with information directly suited to their needs and interests and by promoting open communication.” Relationship marketing differs from other forms of marketing in that it recognizes the long term value of customer relationships and extends communication beyond intrusive advertising and sales promotional messages. One of the things of most value to our company is its relationships with customers, employees, suppliers, distributors, dealers, and retailers. Our relationship capital is the sum of the knowledge, experience, and trust we have with our customers, employees, suppliers, and distribution partners. These relationships are often worth more than the physical assets of our company. Relationships determine the future value of the firm. Any slips in these relationships will hurt our performance. We keep a relationship score-card that describes the strengths, weaknesses, opportunities, and threats in regard to the relationships. K. Ali flour mills is a company which produces high quality flour ( atta, moida, suji and vushi). We always try to have better relationship with our customers. Customer Relationship Management is an upright concept or strategy to solidify relations with our customers and at the same time reducing cost and enhancing productivity and profitability in business. A CRM system is a centralized collection of all data sources of our organization and provides an atomistic real time vision of customer information. A CRM system is vast and significant, but it can be implemented for our business. The main goal is to assist the customers efficiently. Creating and nurturing a strong relationship with our customers is the key to the ongoing success of our business. A strong customer relationship not only means that our clients are likely to keep doing business with us over the long-term, it also means that the chances of that customer recommending us and our products to others are greatly enhanced.Md. Sohel RanaM. Business Administratio