SK Ahammad Fahad

Singapore Institute of Technology, Doctor of Engineering, Graduate Student

AlMadinah International University, Faculty of Computer Science and Information Technology, Alumnus

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I am SK Ahammad Fahad, a seasoned Data Scientist with a profound passion for harnessing data-driven strategies to excel in inventory management, foster customer engagement, and facilitate strategic decision-making. My academic journey has recently expanded, including moving to Singapore to join the Singapore Institute of Technology for my Industrial Doctor of Engineering. I work at the SIT-Nvidia Joint Center to provide AI solutions to renowned businesses. This new role signifies my continual commitment to advancing in my field and allows me to apply cutting-edge AI research in practical, impactful ways.

Previously, I earned an M.Sc. in Information and Communication Technology, a solid foundation that I have tested in dynamic roles at Chaldal Limited, Bangladesh's most significant online grocery platform, and ACI Logistics Limited, the country's largest retail chain. These experiences have honed my expertise in crafting predictive models, spearheading pricing strategies, and delivering innovative data-powered solutions.

My contributions extend to the academic realm, where I have cultivated a notable research portfolio comprising 10 publications and an h-index of 6. I am equally dedicated to serving the educational community, demonstrated by my roles as a session chairman and reviewer for prestigious conferences.

In addition to my recent academic and professional ventures, I hold a diverse range of professional certificates and have completed specialization and certification programs in data science and analytics from renowned institutions such as Stanford University, Wharton School of the University of Pennsylvania, and the University of Michigan. These qualifications underscore my commitment to staying at the forefront of my field and applying the latest techniques and methodologies to drive results.
Supervisors: Dr. Daniel Wang, Dr. Wael Mohamed Shaher Yafooz, Dr. Najeeb Abbas Al-Sammarraie, and Md. Mahbub Alam
Phone: +6598810566
Address: 86 PUNGGOL CENTRAL #05-16 PARC CENTROS
SINGAPORE 828720

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Papers by SK Ahammad Fahad

Big Data Visualization: Allotting by R and Python with GUI Tools

A tremendous amount of data comes with a vast amount of knowledge. Decent use of the persistent i... more A tremendous amount of data comes with a vast amount of knowledge. Decent use of the persistent information can assist to overcome provocations and support to establish further sophisticated judgment. Data visualization techniques are authenticated scientifically as thousand times reliable rather than textual representation. The premature data visualization system met some difficulties and there has some solution for handle this kind of big quantity of data. Data science used two distinct languages Python and R to visualize big data undeviatingly. There also have a lot of tools in operating business. This paper is focused on the visualization technique of Python and R. R appears including the extraordinary visualization library alike ggplot2, leaflet, and lattice to defeat the provocation of the extensive volume. Python has several particular libraries for data visualization. Commonly they are Bokeh, Seaborn, Altair, ggplot and Pygal. Also, with most modern, secure and powerful zero coding GUI's accessories to describe big data visualization for genuine recognition with practical determination. Method and process of visual description of data are significant to recover specific knowledge from the large-scale dataset.

Business Intelligence Through Big Data Analytics, Data Mining and Machine Learning

Advances in intelligent systems and computing, Sep 25, 2019

There is a huge amount of data creating during the fourth industry revaluation and the data are g... more There is a huge amount of data creating during the fourth industry revaluation and the data are generating explosively by various fields of the Internet of Things (IoT). The organizations are producing and storing the huge amount of data into the data servers every moment. This data comes from social media, sensors, tracking, website, and online news articles. The Google, Facebook, Walmart, and Taobao are the most remarkable organizations are generating most of the data in the web servers. Data comes into three forms as structured (text/numeric), semi structured (audio, video, and image) and unstructured (XML and RSS feeds). A business makes revenue from the analysis of 20% of such data, which is a structured form while 80% of data is unstructured. Therefore, unstructured data contains valuable information that can help the organization to improve the business productive, better decision-making, extract the insights, new products and services and understand the market conditions in various fields such as shopping, finance, education, manufacturing, and healthcare. The unstructured data are needed to be analyzed and distribute in a structured manner, that is required information’s are to be gathered through the data mining techniques are used to mining the data. In this paper, expose the importance of data analytics and data management for beneficial usage of business intelligence, big data, data mining and machine and data management. In addition, the different techniques that can be used to discover the knowledge and useful information from such data been analyzed. This can be beneficial for numerous users concern on text mining and convert complex data into meaningful information for researchers, analyst, data scientist, and business decision makers as well.

Inflectional Review of Deep Learning on Natural Language Processing

In the age of knowledge, Natural Language Processing (NLP) express its demand by a huge range of ... more In the age of knowledge, Natural Language Processing (NLP) express its demand by a huge range of utilization. Previously NLP was dealing with statically data. Contemporary time NLP is doing considerably with the corpus, lexicon database, pattern reorganization. Considering Deep Learning (DL) method recognize artificial Neural Network (NN) to nonlinear process, NLP tools become increasingly accurate and efficient that begin a debacle. Multi-Layer Neural Network obtaining the importance of the NLP for its capability including standard speed and resolute output. Hierarchical designs of data operate recurring processing layers to learn and with this arrangement of DL methods manage several practices. In this paper, this resumed striving to reach a review of the tools and the necessary methodology to present a clear understanding of the association of NLP and DL for truly understand in the training. Efficiency and execution both are improved in NLP by Part of speech tagging (POST), Morphological Analysis, Named Entity Recognition (NER), Semantic Role Labeling (SRL), Syntactic Parsing, and Coreference resolution. Artificial Neural Networks (ANN), Time Delay Neural Networks (TDNN), Recurrent Neural Network (RNN), Convolution Neural Networks (CNN), and Long-Short-Term-Memory (LSTM) dealings among Dense Vector (DV), Windows Approach (WA), and Multitask learning (MTL) as a characteristic of Deep Learning. After statically methods, when DL communicate the influence of NLP, the individual form of the NLP process and DL rule collaboration was started a fundamental connection.

Managing Textual Data Semantically In Relational Databases

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2018

the massive volume of data in databases, web pages, and document files usually causes information... more the massive volume of data in databases, web pages, and document files usually causes information to be disorganized and unclear for the user. Therefore, information in such an environment can be classified into three forms: structured, semistructured, or unstructured. Structured information is the best form of information because it facilitates the acquisition and comprehension of knowledge. Relational Database Management System (RDBMS) has a robust structure that manages, organizes and retrieves data. There are many attempts have been made in order to deal with such data. These attempts can be categorized into three groups: within a database schema, by a developed data model within the database, or by query-based techniques in database. Nonetheless, RDBMS contain massive amount of unstructured data such as textual data.. This paper proposed Textual Virtual Schema Model (TVSM). TVSM is conducted to perform semantic textual data linking and clustering and is embedded in the relational database structure (schema). In addition, linking and converting the unstructured information to structured data. Quality improvement of textual data clusters. Achievement of high query processing efficiency in retrieving data clusters. TVSM initially developed to assist researchers, developers, and database administrators who are concerned on unstructured information management, information extraction, multi-document clustering, information retrieval, query processing efficiency, personal information management, question answering, information integration, news tracking, and news summarization

FlexiDC:A Flexible Platform for Database Conversion

2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2018

Database conversion is a process to transfer data from one database to another along with its str... more Database conversion is a process to transfer data from one database to another along with its structure. Since there are many database systems created by organization or individuals, such systems can be in diverse types such as Access, Oracle and MySql. The progression in technology requires some systems to be upgraded to a newer system (e.g., adding new records' structures, changing platform) or migrated (e.g., adapting to a newer version of a system). In order to work with different types of database, a common platform is needed to do data integration or conversion due to their heterogeneity and platform diversity. This paper presents a computerized tool, namely FlexiDC, which is implemented using Java programming language to provide a single platform for database conversion. This platform uses Oracle as a working platform that allows records from various formats and types of databases to be integrated and manipulated before producing a single or multiple databases. Novelties of this work are column level conversion and flexible changing of data type. Therefore, the cost and time to deal with any database enhancement, migration, integration, conversion and new development can be reduced in order to accommodate with the changing requirements in the existing databases.

Design and Develop Semantic Textual Document Clustering Model

Journal of Computer Science and Information Technology, 2017

The utilization of textual documents is spontaneously increasing over the internet, email, web pa... more The utilization of textual documents is spontaneously increasing over the internet, email, web pages, reports, journals, articles and they stored in the electronic database format. It is challenging to find and access these documents without proper classification mechanisms. To overcome such difficulties we proposed a semantic document clustering model and develop this model. The document pre-processing steps, semantic information from WordNet help us to be bioavailable the semantic relation from raw text. By reminding the limitation of traditional clustering algorithms on the natural language, we consider semantic clustering by COBWEB conceptual clustering. Clustering quality and high accuracy were one of the most important aims of our research, and we chose F-Measure evaluation for ensuring the purity of clustering. However, there still exist many challenges, like the word, high spatial property, extracting core linguistics from texts, and assignment adequate description for the generated clusters. By the help of Word Net database, we eliminate those issues. In this research paper, there have a proposed framework and describe our development evaluation with evaluation.

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