Imran abdullah forex

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Predicting unknowns, discovering patterns, and revealing useful insights from data excites me the most. Springer, Cham, Leveraging the power of deep learning, transformer models have created a disrupting impact in the domain of natural language processing. However, the benefits of such advancements are still inscribed between few highly resourced languages such as English, German, and French.

Low-resourced language such as Khmer is still deprived of utilizing these advancements due to lack of technical support for this language. In this study, our objective is to apply the state-of-the-art language models within two empirical use cases such as Sentiment Analysis and News Classification in the Khmer language. A large text corpus including over , news articles has been used for pre-training the transformer model, BERT.

The outcome of our experiment shows that in both of the use cases, a pre-trained and fine-tuned BERT model produces the outperforming results. Leveraging the state-of-the-art SOTA techniques of NLP such as transformer models, this problem domain has achieved a considerable advancement. However, this progress is unfortunately only bound to the well-resourced languages like English, French, and German.

Under-resourced language like Bangla is yet to leverage such SOTA techniques to make a breakthrough in this domain. Using the pre-trained weights of these models we have performed fine-tuning and tackled the task of authorship attribution of 16 prominent Bangla writers. Inderscience Publishers, Abstract: The garment industry is one of the key examples of the industrial globalisation of this modern era.

It is a highly labour-intensive industry with lots of manual processes. Satisfying the huge global demand for garment products is mostly dependent on the production and delivery performance of the employees in the garment manufacturing companies. So, it is highly desirable among the decision makers in the garments industry to track, analyse and predict the productivity performance of the working teams in their factories.

This study explores the application of state-of-the-art data mining techniques for analysing industrial data, revealing meaningful insights and predicting the productivity performance of the working teams in a garment company. As part of our exploration, we have applied eight different data mining techniques with six evaluation metrics.

Our experimental results show that the tree ensemble model and gradient boosted tree model are the best performing models in the application scenario. Springer International Publishing, Abstract: The rapid growth of taking loans and digitizing the financial sector is increasing the rate of loan charge-offs as well as the volume of data that represents customer behavior. Nowadays, Machine Learning ML technology is helping financial institutions utilize this huge amount of data and build some black-box prediction models for predicting loan charge-offs with decent accuracy.

Yet, the amount of risk involved in such financial decisions is very high and should not be taken only based on an opaque decision of a black-box model. In this study, we propose a system for building accurate models using interpretable state-of-the-art SOTA ML algorithms as well as utilizing the Explainable AI XAI techniques to explain individual instances for supporting business decisions.

Abstract: Misleading and fake news in rapidly increasing online news portals in Bangladesh has become a major concern to both the government and public lately, as a substantial amount of incidents have taken place in different cities due to unwarranted rumors over the last couple of years. However, the overall progress of research and innovation in detecting fake and satire Bangla news is yet unsatisfactory considering the prospects it would bring to the decision-makers of Bangladesh.

In this study, we have amalgamated both fake and real Bangla news from quite a pool of online news portals and applied a total of seven prominent machine learning algorithms to identify real and fake Bangla news, proposing a Deep Neural Network DNN architecture. Riazur Rahman. ACM, January 10, Abstract: Every language has its own root, form, and grammar, and so does Bengali.

Bengali language has two core forms: "Sadhu-bhasha" and "Cholito-bhasha" which have been widely used from regular communication to literary publications. At present, Sadhu-bhasha can be only found in old books and literary publications, whereas Cholito-bhasha is mostly used everywhere.

However, so many Bengali linguists are still researching on these two forms to preserve its root, understand and develop Bengali, and also extract knowledge from the historical publications which were mainly written in Sadhu-bhasha. Unfortunately, till now they do not have any digital tool that can assist their research by automatically identifying these core forms of Bengali from the large archive of Bengali literature.

This study aims to build such an automatic intelligent system that can accurately identify these two language forms by harnessing the power of Natural Language Processing NLP. In this study, we have applied advanced NLP techniques and six Supervised learning algorithms to classify "Sadhu-bhasha" and "Cholito-bhasha" from text corpora. Results of this study show that all the six models yielded very promising results, however, the Multinomial Naive Bayes outperformed all the models with Additionally, this study also performs qualitative analysis using t-SNE algorithm to visualize the difference between Sadhu-bhasha and Cholito-bhasha.

Elsevier BV, Abstract: Electronic Retailing E-tailing is one of the most impactful technology trends of recent times. This industry has dramatically enhanced the quality of human lives allowing people to shop online while having the comfort of their homes.

In developing countries like Bangladesh, this industry is still rising and creating a significant economic impact. However, there exist a lot of challenges such as the return of orders that affects the growth of an E-tailer and causes revenue losses. This study addresses this most common business challenge in the E-tail industry and performs predictive modeling using 4 different state-of-the-art data mining techniques to help the industry smoothen its curve of growth.

Along with predictive modeling, this study also aims to find out the most important features that influence the return of orders. Al Imran, Abdullah, Md. Rifatul Islam Rifat, and Rafeed Mohammad. Springer Singapore, July 4, Abstract: Lower Back Pain LBP is one of the leading causes of disability around the world that affects several important parts of the human body such as the muscles, nerves, and bones of the back.

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Nowadays, Machine Learning ML technology is helping financial institutions utilize this huge amount of data and build some black-box prediction models for predicting loan charge-offs with decent accuracy. Yet, the amount of risk involved in such financial decisions is very high and should not be taken only based on an opaque decision of a black-box model.

In this study, we propose a system for building accurate models using interpretable state-of-the-art SOTA ML algorithms as well as utilizing the Explainable AI XAI techniques to explain individual instances for supporting business decisions. Abstract: Misleading and fake news in rapidly increasing online news portals in Bangladesh has become a major concern to both the government and public lately, as a substantial amount of incidents have taken place in different cities due to unwarranted rumors over the last couple of years.

However, the overall progress of research and innovation in detecting fake and satire Bangla news is yet unsatisfactory considering the prospects it would bring to the decision-makers of Bangladesh. In this study, we have amalgamated both fake and real Bangla news from quite a pool of online news portals and applied a total of seven prominent machine learning algorithms to identify real and fake Bangla news, proposing a Deep Neural Network DNN architecture. Riazur Rahman. ACM, January 10, Abstract: Every language has its own root, form, and grammar, and so does Bengali.

Bengali language has two core forms: "Sadhu-bhasha" and "Cholito-bhasha" which have been widely used from regular communication to literary publications. At present, Sadhu-bhasha can be only found in old books and literary publications, whereas Cholito-bhasha is mostly used everywhere.

However, so many Bengali linguists are still researching on these two forms to preserve its root, understand and develop Bengali, and also extract knowledge from the historical publications which were mainly written in Sadhu-bhasha. Unfortunately, till now they do not have any digital tool that can assist their research by automatically identifying these core forms of Bengali from the large archive of Bengali literature.

This study aims to build such an automatic intelligent system that can accurately identify these two language forms by harnessing the power of Natural Language Processing NLP. In this study, we have applied advanced NLP techniques and six Supervised learning algorithms to classify "Sadhu-bhasha" and "Cholito-bhasha" from text corpora. Results of this study show that all the six models yielded very promising results, however, the Multinomial Naive Bayes outperformed all the models with Additionally, this study also performs qualitative analysis using t-SNE algorithm to visualize the difference between Sadhu-bhasha and Cholito-bhasha.

Elsevier BV, Abstract: Electronic Retailing E-tailing is one of the most impactful technology trends of recent times. This industry has dramatically enhanced the quality of human lives allowing people to shop online while having the comfort of their homes. In developing countries like Bangladesh, this industry is still rising and creating a significant economic impact.

However, there exist a lot of challenges such as the return of orders that affects the growth of an E-tailer and causes revenue losses. This study addresses this most common business challenge in the E-tail industry and performs predictive modeling using 4 different state-of-the-art data mining techniques to help the industry smoothen its curve of growth.

Along with predictive modeling, this study also aims to find out the most important features that influence the return of orders. Al Imran, Abdullah, Md. Rifatul Islam Rifat, and Rafeed Mohammad. Springer Singapore, July 4, Abstract: Lower Back Pain LBP is one of the leading causes of disability around the world that affects several important parts of the human body such as the muscles, nerves, and bones of the back.

The aim of this study is to enhance the classification performance of LBP by identifying the most relevant feature subset from a broader feature space of an LBP dataset. To serve the aim, we have proposed a Genetic Algorithm GA -based feature selection approach that has been proved to significantly improve the classification performance of LBP. After applying our proposed GA-based feature selection approach along with the base classifiers, we have obtained a significant average increment in accuracy, precision, recall, f1-score, and AUC score by 3.

Abstract: Educational data mining EDM is an emerging interdisciplinary research area concerned with analyzing and studying data from academic databases to better understand the students and the educational settings. In most of the Asian countries, it is a challenging task to perform EDM due to the diverse characteristics of the educational data. To validate our proposed framework, we have also conducted extensive experiments on a real-world dataset that has been prepared by the transcript data of the students from the Marketing department of a renowned university in Bangladesh.

We have applied six state-of-the-art classification algorithms on our dataset for the prediction task where the Random Forest model outperforms the other models with accuracy For pattern analysis, a tree diagram has been generated from the Decision Tree model.

IEEE, May Abstract: Educational Data Mining EDM is an emerging research field concerned with the application of data mining, machine learning, and statistics in the discipline of education. Many researchers have already focused on EDM and exploring the educational data using several traditional data mining techniques to improve the educational performance of the students by extracting the concealed patterns and predicting the final outcome.

In this study, we aim to propose a Deep Neural Network DNN based model to predict the final CGPA of the undergraduate business students with a minimal error than the traditional approaches. We have considered the performance of a decision tree model as the baseline performance.

IEEE, April Abstract: The garment industry is one of the most dominating industries in this era of industrial globalization. It is a highly labor-intensive industry that requires a large number of human resources to produce its goods and fill up the global demand for garment products. Because of the dependency on human labor, the production of a garment company comprehensively relies on the productivity of the employees who are working in different departments of the company.

A common problem in this industry is that the actual productivity of the garment employees sometimes does not meet the targeted productivity that was set for them by the authorities to meet the production goals in due time. When the productivity gap occurs, the company faces a huge loss in production.

This study aims to solve this problem by predicting the actual productivity of the employees. To achieve this aim, a Deep Neural Network DNN model has been proposed to predict the actual productivity of the employees. Such prediction performance can indisputably help the manufacturers to set an accurate target, minimize the production loss and maximize the profit.

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Anda boleh melakukan urusniaga ini di mana-mana sahaja di dunia ini asalkan undeinem mempunyai sambungan Internet dan Computer tidak kiralah Samada di rumah, pejabat, Hotel mahupun semasa berbulan madu. Anda boleh melakukan binsnes ini 24 jam sehari dan 5 hari kecuali hari Sabtu dan Ahad kerana Markt tutup Pada hari tersebut.

Modal urusniaga forex ditetapkan oleh undeinem sendiri. Anda boleh bermula dengan 10usd, USD atau usd. Makin banyak modal, Makin banyak juga Untung boleh di jana dan Makin besar jugalah kerugian Yang Bakal undeinem tanggung sekiranya Rugi. Yasudah tentulah undeinem inginkan keuntungan Yang berlipat kali ganda bila melakukan sebarang urusniaga. Sekiranya undeinem sudah mahir dalam urusniaga ini, setakat RM10, sehari itu memang Menjadi kebiasaan.

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