The growing number of web applications in a developing country like Bangladesh has led to an increase in cybercrime activities. This study focuses on measuring the vulnerabilities present in financial and government websites of Bangladesh to address the rising security concerns. We reviewed related works on web application vulnerability scanners, comparative studies on web application security parameters, surveys on web application penetration testing methodologies and tools, and security analyses of government and financial websites in Bangladesh. Existing studies in the context of developing countries have provided limited insight into web application vulnerabilities and their solutions. These studies have focused on specific vulnerabilities, lacked comprehensive evaluations of security parameters, and offered a limited comparative analysis of vulnerability scanners. Our study aims to address these gaps by conducting an in-depth analysis using the OWASP ZAP tool to scan and analyze risk alerts, including risk levels such as high, medium, low, and informational. Our investigation unveiled eight key vulnerabilities, including Hash Disclosure, SQL injection (SQLi), Cross-Site Request Forgery (CSRF), missing Content Security Policy (CSP) headers, Cross-Domain JavaScript File Inclusion, absence of X-Content-Type-Options headers, Cache-related concerns, and potential Cross-Site Scripting (XSS), which can lead to revealing hidden information, enabling malicious code, and failing to protect against specific types of attacks. In essence, this study does not only reveal major security weaknesses but also provides guidance on how to mitigate them, thereby playing a vital role in promoting enhanced cybersecurity practices within the nation.

News categorization from various newspapers is important as readers want to read the news by category. But, the readers face difficulty if the news from different categories is presented without any order. This study aims to determine the category of news from online Bangla newspapers. In this context Bangla news headlines data, along with its categories, were collected from various online newspapers through scrapping. Eight categories of news are considered for this work and the headlines of the news are used for categorization. The input data is modeled by the LSTM and GRU neural networks, and the predicted category is compared with the actual category. For LSTM model, the result gives an accuracy of 82.74% and GRU model, The result gives an accuracy of 87.48%. GRU accuracy is higher than LSTM.

Because, GRU training performance is faster than that of LSTM. In GRU 64 units used and in LSTM 128 units used for this research. For this reason, it also suggests that the GRU model gives better results than that of LSTM. 

Stock market prediction is a process of trying to decide the stock trends based on the analysis of historical data. However, the stock market is subject to rapid changes. It is very difficult to predict because of its dynamic & unpredictable nature. The main goal of this paper is to present a model that can predict stock market trend. The model is implemented with the help of machine learning algorithms using eleven technical indicators. The model is trained and tested by the published stock data obtained from DSE (Dhaka Stock Exchange, Bangladesh). The empirical result reveals the effectiveness of machine learning techniques with a maximum accuracy of 86.67%, 64.13% and 69.21% for “today”, “tomorrow” and “day_after_tomorrow”.

Stock market trend can be predicted with the help of machine learning techniques. However, the stock market changes is uncertain. So it is very difficult and challenging to forecast stock price trend. The main goal of this paper is to implement a model for stock value trend prediction using share market news by machine learning techniques. Although this kind of work is implemented for the stock markets of various developed countries, it is not so common to observe such kind of analysis for the stock markets of underdeveloped countries. The model for this work is built on published stock data obtained from DSE (Dhaka Stock Exchange, Bangladesh), a representative stock market of an underdeveloped country. The empirical result reveals the effectiveness of Convolutional Neural Networks with LSTM model.

Text-to-speech (TTS) synthesis is a rapidly growing field of research. Speech synthesis systems are applicable to several areas such as robotics, education and embedded systems. The implementation of such TTS system increases the correctness and efficiency of an application. Though Bangla is the seventh most spoken language all over the world, uses of TTS system in applications are difficult to find for Bangla language because of lacking simplicity and lightweightness in TTS systems. Therefore, in this paper, we propose a simple and lightweight TTS system for Bangla language. We converted Bangla text to Romanized text based on Bangla graphemes set and by developing a bunch of romanization rules. Besides, an xml-based data representation is developed as a feature of the system. It gives the flexibility to modify the data representation, parsing data and create speech based on one’s own dialect. Our proposed system is very lightweight which takes less processing time and produces a good understandable speech.

Spam SMSes are unsolicited messages to users, which are disturbing and sometimes harmful. There are a lot of survey papers available on email spam detection techniques. But, SMS spam detection is comparatively a new area and systematic literature review on this area is insufficient. In this paper, we perform a systematic literature review on SMS spam detection techniques. For that purpose, we consider the available published research works from 2006 to 2016. We choose 17 papers for our study and reviewed their used techniques, approaches and algorithms, their advantages and disadvantages, evaluation measures, discussion on datasets and finally result comparison of the studies. Although, the SMS spam detection techniques are more challenging than email spam detection techniques because of the regional contents, use of abbreviated words, unfortunately none of the existing research addresses these challenges. There is a huge scope of future research in this area and this survey can act as a reference point for the future direction of research.

Spell checkers check whether a word is misspelled and provide suggestions to correct it. Detection and correction of spelling errors in Bangla language which is the seventh most spoken native language in the world, is very onerous because of the complex rules of Bangla spelling. There is no systematic literature review on this research topic. In this paper, we present a systematic literature review on checking and correcting spelling errors in Bangla language. We investigate the current methods used for spell checking and find out what challenges are addressed by those methods. We also report the limitations of those methods. Recent relevant studies are selected based on a set of significant criteria. Our results indicate that there are research gaps in this research topic and has a potential for further investigation.

Placement of methods within classes is one of the most important design activities for any object oriented application to optimize software modularization. To enhance interactions among modularized components, recommendation of move method refactorings plays a significant role through grouping similar behaviors of methods. It is also used as a refactoring technique of feature envy code smell by placing methods into correct classes from incorrect ones. Due to this code smell and inefficient modularization, an application will be tightly coupled and loosely cohesive which reflect poor design. Hence development and maintenance effort, time and cost will be increased. Existing techniques deals with only non-static methods for refactoring the code smell and so are not generalized for all types of methods (static and non-static). This paper proposes an approach which recommends 'move method' refactoring to remove the code smell as well as enrich modularization. The approach is based on conceptual similarity (which can be referred as similar behavior of methods) between a source method and methods of target classes of an application. The conceptual similarity relies on both static and non-static entities (method calls and used attributes) which differ the paper from others. In addition, it compares the similarity of used entities by the source method with used entities by methods in probable target classes. The results of a preliminary empirical evaluation indicate that the proposed approach provides better results with average precision of 65% and recall of 63% after running it on five well-known open projects than JDeodorant tool (a popular eclipse plugin for refactorings).

To cope up with the pace of digitalization all over the world, like developed countries, developing countries are also offering services to its citizens through various online portals, web applications and web sites. Unfortunately, due to the lack of consideration on vulnerability issues during the development phase, many of those web based services are suffering from serious security threats. For these developing countries, vulnerability statistics are required to have insight about the current security status of the provided web services. That statistical data can assist the stakeholders to take appropriate actions against cyberattacks. In this work, we conduct a survey to observe the responses of web based services against four most commonly found web attacks called Man in the Middle, SQL Injection, Cross Site Scripting and Denial of Service. We carry out the survey for 30 websites (applications) of Bangladesh as the country has been focusing on digitalization of government services for the last few years and has already been offering various online services to its citizens. Among the 30 websites of several categories, result shows that approximately 77% sites are vulnerable to Man in the Middle attack whereas 3% are vulnerable to SQL Injection and Cross Site Scripting.