Fokrul Alom Mazarbhuiya

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• Doctor of Philosophy
• Associate Professor at Assam Don Bosco University

Finding statistical parameters in fuzzy data is a popular topic among researchers. Most of the real-life applications in Medical Science, Engineering and Technology involves fuzziness. However, unlike crisp data, computing statistical parameters is not so straightforward for fuzzy sets. In this article, a novel method is introduced to find the corr...

Fuzziness has gained ground in every area of human understanding because of its capacity to handle the most realistic problems. It has several theoretical and real-world applications in fields including the medicine, the security, and the stock market, among others. One of the methods for replicating human mind is generally recognised as fuzzy set....

There are many applications of anomaly detection in the Internet of Things domain. IoT technology consists of a large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to the Internet, they frequently meet with the challenges of illeg...

The study of differential equation theory has come a long way, with applications in various fields. In 1961, Zygmund and Calderón introduced the notion of derivatives to metric , which proved to be better in applications than approximate derivatives. However, most of the studies available are on Fuzzy Set Theory. In view of this, intuitionistic fuz...

The retail industry is being significantly impacted by the two key technologies in the sector, IoT and big data. Businesses now have a wealth of new opportunities to get to know their customers and provide them with specific customer journeys that include product recommendations and tailored experiences based on their past preferences. Finding all...

There are many applications of anomaly detection in IoT domain. IoT technology consists of large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to Internet, they frequently meet with the challenges of illegitimate accesses in the f...

IoT technology has significantly contributed in the improvement of quality of life by facilitating various real-life smart applications. IoT consists of large number of interconnecting digital devices which generates the large amount of data and makes computations. However, IoT domain often encounters the issue of anomalies, non-integrity, illegiti...

Clustering is the process of assembling abstract objects that share alike characteristics. Clustering has been commonly used in several arenas such as market research, pattern recognition, data analysis, and image processing. Document clustering is also called text clustering, an extension to traditional clustering used to analyze textual documents...

Study of differential equation theory has come a long way with applications in the various fields. In 1961, Zygmund and Calderón introduced the notion of derivatives on metric Lr which proved to be better in applications than approximate derivatives. But most of the studies involved are on fuzzy set theory, so it seems likely that intuitionistic fu...

In general, the characteristics of false news are difficult to distinguish from those of legitimate news. Even if it is wrong, people can make money by spreading false information. A long time ago, there were fake news stories, including the one about "Bat-men on the moon" in 1835. A mechanism for fact-checking statements must be put in place, part...

The social media has significantly changed how we communicate and exchange information throughout time. Along with it comes the issue of fake news' quick spread, which may have detrimental effects on both people and society. Fake news has been surfacing often and in enormous quantities online for a variety of political and economic goals. To in...

Finding real-time anomalies in any network system is recognized as one of the most challenging studies in the field of information security. It has so many applications, such as IoT and Stock Markets. In any IoT system, the data generated is real-time and temporal in nature. Due to the extreme exposure to the Internet and interconnectivity of the d...

Finding anomalies in the real-time system is recognized as one of most challenging study in information security. It has so many applications like IoT, and Stock-Market. In any IoT system the data generated are real-time, and temporal in nature. Since due to the extreme exposure to Internet and interconnectivity of devices, the IoT systems often fa...

The challenging issues of computer networks and databases are not only the intrusion detection but also the reduction of false positives and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior, which are assumed...

The challenging issues of Computer Network and Databases are not only the intrusion detection but also the reduction of false positive and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior which are assumed to...

Anomaly detection in real-time data is accepted as a vital area of research. Clustering techniques have effectively been applied for the detection of anomalies several times. As the datasets are real time, the time of data generation is important. Most of the existing clustering-based methods ei-ther follow a partitioning or a hierarchical approach...

Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectively been tried for this purpose. As the datasets are real time, the time of generating of the data is also important. In this article, we introduce a mixture of partitioning and agglomerative hierarchical approach to detect anomalies from such datasets....

A vital field of research is network anomaly detection. Several research papers in this topic have previously been published, using various methodologies or algorithms. The classification-based approach is intriguing. The majority of previous techniques assumed that the datasets under investigation were numeric. Nonetheless, up until recently, some...

Finding periodicity of the patterns from super-market data has been found to be an important data mining problem which many researchers encounter often. Such patterns reflect the buying nature of the customers in the super-market. There may be yearly, half-yearly, quarterly, monthly, daily, hourly or any other type of periodicity. In such patterns,...

Nowadays, anomaly detection in data is an important field of research. A couple of methods have already been developed for this purpose, among which clustering-based anomaly detection remains an important one. Most methods using clustering approaches consider either numeric or categorical attributes of the data instance, but not both. However, in r...

Cancer is a disease very common to both rural and urban peoples. It is the abnormal growth of some body cells which then destroy the normal functioning of surrounding cells. Cancer has different stages and can be cured easily if diagnosed earlier. Breast Cancer is widespread among the women of different age groups which results untimely death of so...

: The problem of mining sequential patterns from medical data has received a lot of attention as it aims to discover the causal relationship between different diseases or symptoms that are present in the patient's body. Medical data contains the records pertaining to the information of the diseases or the symptoms of the patients besides the patien...

Sequential patterns mining is a well stated data mining problem and has been applied in DNA sequencing, signal processing, speech analysis etc. Nevertheless, this paper implements and evaluates algorithm for finding sequential patterns of disease from medical dataset. This paper implements and evaluates an existing algorithm for discovering sequenc...

Intrusion detection is becoming a hot topic of research for the information security people. There are mainly two classes of intrusion detection techniques namely anomaly detection techniques and signature recognition techniques. Anomaly detection techniques are gaining popularity among the researchers and new techniques and algorithms are developi...

Data Mining is an analytic process designed to find out data in search of harmonious patterns and methodical relationships between variables, and then to validate the extractive by applying the detected patterns to new subsets of data. The data mining is defined as the procedure of extracting information from enormous sets of data. In other words,...

Since, the last three or four years, the field of “big data” has appeared as the new frontier in the wide spectrum of IT-enabled innovations and favorable time allowed by the information revolution. Today, there is a raise necessity to analyses very huge datasets, that have been coined big data, and in need of uniqueness storage and processing infr...

Two probability laws can be root of a possibility law. Considering two probability densities over two disjoint ranges, we can define the fuzzy standard deviation of a fuzzy variable with the help of the standard deviation two random variables in two disjoint spaces.

Two probability laws can be root of a possibility law. Considering two probability densities over two disjoint ranges, we can define the fuzzy standard deviation of a fuzzy variable with the help of the standard deviation two random variables in two disjoint spaces.

Mining patterns from large dataset is an interested data mining problem. Many methods have been developed for this purpose till today. Most of the methods considered the time attributes as one of the attributes like others. However taking the time attribute into account separately the patterns can be extracted which cannot be extracted by normal me...

The retrace patterns from supermarket datasets is an interesting data mining issue. The hourly fuzzy pattern is an example of such patterns where the pattern holds in some fuzzy time interval in each hour. The issue involves first frequent set mining and then deduces association rules from the frequent sets. We call it hourly fuzzy patterns. In the...

The study of discovering frequent patterns in a dataset is a well defined data mining problem. There are many approaches to resolve this problem including. Clustering is one of the common data mining approaches which is used for discovering data distribution and patterns in a dataset. Many algorithms have been proposed for finding clusters among fr...

Carving association rules from any available set is a pre-defined problem and there are a variety of methods available for the extraction of association rules. Almost in all the cases the major emphasis is given to the generating most occurring itemsets rather than the extraction of association rules. Only a few numbers of researchers have through...

In this special session we meet a set of projects in computer science and engineering education at a university in Saudi Arabia. They are the product of a pedagogical development course ran in collaboration with a Swedish university during the academic year 2013/2014. The projects reflect the local situation, with its possibilities and challenges,...

Welcome to the first AlBaha University-Uppsala University Collaborative Symposium on Quality in Computing Education (ABU3QCE), held in AlBaha, Saudi Arabia, 24-25 February 2015. ABU3QCE 2015 is a local symposium dedicated to the exchange of research and practice focusing on enhancing quality in computing education. Contributions cover a broad spect...

Conjunction of two probability laws can give rise to a possibility law. Using two probability densities over two disjoint ranges, we can define the fuzzy mean of a fuzzy variable with the help of means two random variables in two disjoint spaces.

The problem of finding association rules from a dataset is to find all possible associations that hold among the items, given a minimum support value and a minimum confidence. This involves finding frequent sets first and then the association rules that hold within the items in the frequent sets. The problem of mining temporal association rules fro...

Most of the known rule mining algorithms try to find global patterns in the underlying dataset. Algorithms are designed to find set of items that are frequent in the whole dataset and then find associations that hold among the frequent items. But local patterns are also interesting and may provide useful information in the decision making process....

Most of the known rule mining algorithms try to find global patterns in the underlying dataset. Algorithms are designed to find set of items that are frequent in the whole dataset and then find associations that hold among the frequent items. But local patterns are also interesting and may provide useful information in the decision making process....

Technology and education have wandered many separate but rarely intersecting paths throughout the 20th Century. In the 21st Century, the convergence of cost effective computing and networking products, methodologies, and services is finally enabling more researchers and practitioners than ever before to explore innovative ways to use computer techn...

If sample realizations are intervals, if the upper and the lower boundaries of such intervals are realizations of two independently distributed random variables, the two probability laws together lead to some interesting assertions. In this article, we shall attempt to remove certain confusions regarding the relationship between probability theory...

Frequent pattern mining from super-market transaction datasets is a well-stated data mining problem and consequently there are number of approaches including association rule mining to deal with this problem. However, super-market transaction datasets are generally temporal in the sense that, when a transaction happens in a super-market, the time o...

Some real life data are associated with duration of events instead of point events. The most common example of such data is data of cellular industry where each transaction is associated with a time interval. Mining maximal fuzzy intervals from such data allows the user to group the transactions with similar behavior together. Earlier works were de...

Fuzzy equations were solved by using different standard methods. One of the well-known methods is the method of -cut. The method of superimposition of sets has been used to define arithmetic operations of fuzzy numbers. In this article, it has been shown that the fuzzy equation AX B   , where A, X, B are fuzzy numbers can be solved by using the...

The problem of mining temporal association rules from temporal dataset is to find association between items that hold within certain time intervals but not throughout the dataset. This involves finding frequent sets that are frequent at certain time intervals and then association rules among the items present in the frequent sets. In fuzzy temporal...

In this paper, we present a novel approach for finding association rules from locally frequent itemsets using rough set and boolean reasoning. The rules mined so are termed as local association rules. The efficacy of the proposed approach is established through experiment over retail dataset that contains retail market basket data from an anonymous...

Mining patterns in a market-basket dataset is a well-stated problem. There are a number of approaches to deal with this problem. Different types of patterns may be present in a dataset. An interesting one is patterns that hold seasonally, which are called calendar-based patterns. Earlier methods require periods to be specified by the user. We prese...

The problem of finding association rules from a dataset is to find all possible associations that hold among the items, given a minimum support and confidence. This involves finding frequent sets first and then the association rules that hold within the items in the frequent sets. In temporal datasets as the time in which a transaction takes place...

The set of fuzzy numbers cannot form a group with respect to addition as well as multiplication of fuzzy numbers in the customary sense. Defining an equivalence relation on the set of all fuzzy numbers, it is found that the set of all equivalence classes of fuzzy numbers can form a group with respect to a suitably defined addition and a multiplicat...

Conjunction of two probability laws can give rise to a possibility law. Bifurcating a fuzzy number into two disjoint spaces and using two random variables over the two disjoint ranges, we can define a possibilistic variable as an interval-valued variable whose two boundaries will give two random variables in the two disjoint spaces. Using the above...

Fuzzy equations were solved by using different standard methods. One of the well-known methods is the method of -cut. The method of superimposition of sets has been used to define arithmetic operations of fuzzy numbers. In this article, it has been shown that the fuzzy equation A X B   , where A, X, B are fuzzy numbers can be solved by using the...