Dr. Nazneen Ansari
@sfit.ac.in
Associate Professor, Department of Information Technology
St. Francis Institute of Technology
Scopus Publications
Scopus Publications
Ashwini P. Parkar, Madhuri N. Gedam, Nazneen Ansari, and Shanthi Therese
Springer Singapore
Nazneen Ansari, Siddhi Martal, Namratha Bhat, and Sohan Pawar
Springer Singapore
From past decades, agriculture is remaining as a primary source of food and raw materials for human lives. Recently, the agriculture field is greatly influenced by technologies like big data and automated decision-making systems to deploy an efficient way to farm. Most of the agriculture-related data come from diverse varieties of information sources and networks. The objective of the system is to aid farmers and agriculture experts through a user-friendly website. The data is processed using the Hadoop framework, the results of which are displayed on the website by using a Tableau visualization tool. The ideology consists of data about farming and related aspects. The system has been designed by considering agriculture in India.
Nazneen Ansari, Anjali B. Singh, Bina D. Trivedi, and Priti B. Nandankar
IEEE
Data mining is the knowledge discovery method by examining the huge bulks of information from numerous perspectives and summarizing it into valuable data; data mining has become an important component in numerous fields. It is used to recognize hidden patterns in a huge data set. In this paper, we are using three techniques of Data mining i.e. classification, clustering, and regression. The paper helps users to identify the best algorithm suitable for their dataset along with their advantages and disadvantages. It also shows the accuracy of the best four algorithms for classification and regression, and for clustering, it shows the number of clusters for different clustering algorithms. The performance of algorithms depends on the size of datasets. As the size of the dataset increases, the performance will also increase. This will reduce the work of users finding the best algorithm using different tools like weka, orange, TPOT, etc. The advantages and disadvantages can help users identify the single best algorithm for further analysis. This system also helps the user to identify target columns from the dataset and various dispensable columns in the dataset.
Umang Mavani, Vivian Brian Lobo, Aditi Pednekar, Naomi Christianne Pereira, Rupesh Mishra, and Nazneen Ansari
Springer Singapore
Data mining (DM) is used to analyze and classify data and identify hidden patterns stored in a data warehouse in an attempt to predict future trends, which are quintessential to knowledge discovery and provide tremendous support not only to the world of business but also to that of academia. There are various open-source and freely available software tools such as Weka, R, and Orange as well as programming languages like Python used for DM. This study focuses on comparing the performance of these tools by performing Naive Bayes classification on student placement data. Percentage of marks scored by students in S.S.C. and H.S.C. examinations and their engineering aggregate were inputs to the tools. Moreover, the tools were trained and tested to decide whether a student would be placed or not. Comparative analyses of the tools were done to determine which tool was able to provide the highest prediction accuracy on student placement data.
Hycinta Andrat and Nazneen Ansari
Springer Singapore
The popularity of computer games has led to tremendous generation of gaming data. Such gaming data consists of gamer’s personal information along with the game genres played and the time spent by them on a particular game. This gaming data can be utilized by the gaming industry for the purpose of extracting the knowledge needed to monitor the stickiness of the games. The raw data related to computer games can be refined, which could provide game developers the number of the gamers attracted towards a particular game. If the count of the gamers for a specific game decreases as the time passes by, then game developers need to improve the game, in order to retain the gamers. As gaming industry adds to our country’s revenue to a great extent, certain technological advancements are required. Therefore, this study aims to use a data mining approach, i.e., clustering, for monitoring computer games stickiness.
Aruna Pavate, Pranav Nerurkar, Nazneen Ansari, and Rajesh Bansode
Springer Singapore
A diabetes proliferation gives growth to many health issues. Diabetes complications can be avoided and or prevented with the recommended lifestyle and treatments. Analysis and administration of diabetes mellitus are the inspiring problems for the present-day exploration of risk classification. Diabetes mellitus is a chronic condition noticeable by higher levels of blood glucose. The objective of this work is to expect the major five complications which arise because of diabetes mellitus. In the proposed model, fuzzy logic is used to classify the risk level of major five complications like Vision Loss, Kidney failure, Neuropathy, Stroke diseases, Heart Problem. The correct classification of a rate of FL measured 92.5% for prediction of disease.
Vivian Brian Lobo and Nazneen Ansari
IEEE
Twitter, a well-liked online social networking site, facilitates millions of users on a daily basis to dispatch and orate quick 140-character notes named tweets. Nowadays, Twitter is cogitated as the fastest and popular intermediate of communication and is used to follow latest events. Tweets pertaining to a specific event can be effortlessly found using keyword matching, but there are numerous tweets that are likely to contain information that is semantically identical. Moreover, there exist many systems for recapitulating tweets related to a particular event, but they have numerous limitations and are unable to provide accurate results. This work aims to overcome the limitations of existing systems by developing a system for recapitulating tweets using graph-based clustering.
Vivian Brian Lobo, Nazneen Ansari, and Rajkumar K. Shende
IEEE
Twitter, a well-liked online social networking site, facilitates millions of users on a daily basis to dispatch and orate quick 140-character notes named tweets. Nowadays, twitter is cogitated as the fastest and popular intermediate of communication and is used to follow latest events. Tweets pertaining to a specific event can be effortlessly found using keyword matching, but there are numerous tweets that are likely to contain information that is semantically identical. Moreover, there exist many systems for recapitulating tweets related to a particular event, but they have numerous limitations and are unable to provide accurate results. This study aims to overcome the limitations of existing systems by proposing a system for recapitulating tweets using graph-based clustering.
Nazneen Ansari and G. T. Thampi
IEEE
Application integration (AI) is the process of creating impartially designed application systems that operate collectively. On the whole, integration is not an easy task because developers have to combine disparate structural designs that include different data, process, and object models. Developers must also make a solution operate across several operating systems, middleware technologies, and databases. Integration is essential for any new technology solution. This study discusses different approaches, feasible solutions, and estimates for disparate enterprise solution synchronization. We also discuss influential forces that have made enterprise AI precarious and summarize inspection areas when selecting a method for individual enterprise requirements. Moreover, we provide important aspects of AI that should be considered before planning and creating an integration solution.
Hycinta Andrat and Nazneen Ansari
IEEE
Playing computer games for many years has led to a large volume of gaming data that consist of gamers' likings and their playing behavior. Such data can be used by game creators to extract knowledge for enhancing games. Mining computer game data is a new data mining approach that can help in developing games as per a gamer's requirements and his/her area of interest. Since the gaming industry has been contributing to the countries' revenue on a large scale, so improvement in this industry becomes vital. This study aims to apply data mining techniques such as association, classification, and clustering for improving game design, game marketing, and game stickiness monitoring, respectively, to enrich game quality.
Vivian Brian Lobo, Blety Babu Alengadan, Sehba Siddiqui, Annies Minu, and Nazneen Ansari
IEEE
Traveling salesman problem (TSP) is studied as a combinatorial optimization problem—a problem that attempts to determine an optimal object from a finite set of objects—which is simple to state but difficult to solve. It is a nondeterministic polynomial-time hard problem, hence, exploration on developing algorithms for the TSP has focused on approximate methods above and beyond exact methods. The mission in the TSP is to determine the shortest (optimal) tour when a salesman travels across many cites. A major challenge is that the salesman must be able to minimize entire tour length. The solution to the TSP experiences eclectic applicability in various fields and thus advances the need for an effectual solution. There have been exertions heretofore to provide time efficient solutions (i.e., exact as well as approximate) for the TSP. Dynamic programming is an effective and powerful method that could be used to solve the TSP. Generally, for solving the TSP, a unidirectional path is provided (i.e., whether the salesman travels from city A to B or city B to A) in any input graph, and so, it becomes easier in determining the shortest tour. However, in our study, we have considered a situation where no directions are specified (i.e., the salesman can travel both from city A to B and from city B to A) in an input graph, and for such a graph (i.e., a bidirectional graph), we will determine the shortest tour using dynamic programming.
Aruna Pavate and Nazneen Ansari
IEEE
Diabetes has become the fourth leading cause of death in developed countries. By the endurance and hasty spread of diabetes, with increased number of ill condition, complications in the disease all over the world, several methodologies have been developed to predict and prevent this chronic disease. An early diagnosis of disease helps patients and medical experts to reduce the problem, risk and cost of medications. This paper presented an efficient system to predict diabetes and further complications with risk level. In this system, methods including genetic algorithm, nearest neighbor, and fuzzy rule-based system have been used in order to provide an accurate prediction system to prepare for presence of diabetes and complications. In this system, 235 individual's data were collected. The best subsets of features generated by the implemented algorithm include the most common risk factors such as age, family history, BMI, weight, smoking habit, alcohol habit and also factors related to the presence of other diabetes complications considered for predication of disease. The proposed system was prejudiced and the results showed to be more suitable by selecting best subset of features selected using variations of genetic algorithm depending on the types of nearest neighbor. The succeeded results produced 95.83% sensitivity, 95.50% accuracy and 86.95% specificity on impenetrable data which support the effectiveness of the system to predict the disease.
Nazneen Ansari and G. T. Thampi
IEEE
Software engineering principles form the foundation of methods, techniques, and tools. In this study, we identify software engineering principles introduced by several authors in various ways and describe software engineering procedures. Moreover, we create a template with 16 steps for merging two disparate software solutions.
Vivian Brian Lobo and Nazneen Ansari
IEEE
The complex construction of online educational systems lies within three key activities, i.e., design, execution, and appropriate post-implementation assessment. However, there is inadequate knowledge with regard to these activities. Effective execution of these three activities demands the use of design and educational models to obtain time proficiency, cost, and high educational quality. The use of online educational systems would benefit from an organized approach to design, execution, and evaluation of students. Therefore, this study proposes a general design of both a model and framework for improving online educational systems for teachers as well as students by taking into consideration accurate assessment and effective evaluation of the learning process. In this study, we use a local area network-based connection for creating a virtual classroom that comprises both audio and video conferencing, which can be used by students as well as teachers. Moreover, we include other features such as moderated online chat between students and a teacher, resource sharing, questioning, survey, feedback, and query posting by students during the unavailability of a teacher. Moreover, we implement audio and video conferencing-based e-learning via real-time transport protocol.
Nazneen Ansari, Maahi Talreja, and Vaishali Desai
Springer India
For thousands of years, people have been playing games of chance or wagering on the outcomes of various games and events. As a medium, the computer game is currently in a period of rapid development. From a design point of view, video games are becoming more complex and they are rapidly spreading to new platforms such as mobile phones, pocket computers, and websites. This paper aims to determine whether Association mining algorithm applied to an online social games database would provide the game designers with meaningful rules that would help improve the design of the game. A data set of online social gamer profile was created. The database contains various aspects of social games. The rules generated from association analysis would be of tremendous benefit to the gaming industry, as they can then use them to optimize game design features.