11institutetext: Toronto Metropolitan University, School of Information
Technology Management, Toronto, Canada
11email<EMAIL_ADDRESS>22institutetext: Metropolitan University,
Department of Computer Science and Engineering, Bateshwar, Sylhet, Bangladesh
22email<EMAIL_ADDRESS>
# A Hypergraph-Based Approach to Recommend Online Resources in a Library

Debashish Roy 11 0000-1111-2222-3333 Rajarshi Roy Chowdhury 22
0000-0002-1174-3868

###### Abstract

When users in a digital library read or browse online resources, it generates
an immense amount of data. If the underlying system can recommend items, such
as books and journals, to the users, it will help them to find the related
items. In this research analyzes a digital library’s usage data to recommend
items to its users, and it uses different clustering algorithms to design the
recommender system. We have used content-based clustering, including
hierarchical, expectation maximization (EM), K-mean, FarthestFirst and
density-based clustering algorithms, and user access pattern-based clustering,
which uses a hypergraph-based approach to generate the clusters. This research
shows that the recommender system designed using the hypergraph algorithm
generates the most accurate recommendation model compared to those designed
using the content-based clustering approaches.

###### Keywords:

Library Analytics hMETIS Term Frequency–Inverse Document Frequency (TF-IDF)
Recommender Hypergraph.

##  1 Introduction

In this big data era, organizations are investing millions of dollars in
understanding the hidden pattern in the dataset. Identifying the hidden
pattern helps the organization make profitable decisions for them. According
to [1], Web Analytics uses analytic tools to mine the web usage data retrieved
from web servers. Most websites store users’ browsing and interaction
information in the web server logs to apply web analytics. First, the analytic
tools are used to understand user browsing patterns better. Later these
patterns are analyzed to increase the usability of the websites.

An immense amount of data is collected by digital libraries when the users of
the library interact with the different online resources the library offers.
Therefore, the libraries apply different data analytics tools to analyze the
library usage data. By analyzing the usage data, a library can determine the
document sets that are least browsed; they can identify the users who use the
library the most or the users who do not use the library resources, etc. In
this research work, we are analyzing a web server’s log. The server logs what
items are browsed and saves the resource item’s title. Most of the time, we do
not have access to the full description of the online resource because of
subscriptions and copyright issues. Hence, we have limited text for each
server log entry. So, in the recommender system, we have only included the
resource item title. The recommender system uses the online resource items of
a university library to recommend interesting items to the users of the
library. Various content-based and user access pattern-based clustering
algorithms are used to design this recommender system. Association rule mining
(ARM) is used to generate the user access pattern. We have used a hypergraph-
based partitioning algorithm hMETIS to generate clusters from the association
rules. We compared each user’s browsing history with each generated cluster
and calculated Precision, Recall, and F1-Score for each cluster. The result
shows that the recommender system generated using the hypergraph partitioning
provides the most accurate recommendation.

Furthermore, a careful investigation of the recommended results from the
hypergraph-based recommender shows one or more recommended items are content-
wise, not similar. But, they are recommended because the recommender uses user
access patterns to complete the recommendation. The recommender systems
designed using the content-based clustering methods only recommend content-
wise similar items. The hypergraph-based recommender system recommends items
frequently browsed together to a different user, which is not possible in a
content-based recommender system. The targets of this research work are:

  1. 1.

To identify an effective clustering-based recommendation approach for online
resources of a library. Here, we compare different clustering algorithms to
run the comparison process.

  2. 2.

To determine a recommendation approach that will recommend items that are
content-wise not similar. Here, the items are content-wise not similar, but
they are related because of user access patterns.

The rest of the research paper is structured as follows: Section 1.2 focuses
on the related works to analyze library dataset, Section 1.3 describes the
research methodology, Section 1.4 discusses the experiment results, and at the
end, Section 1.5 provides a result summary, and a future direction of this
research work.

##  2 Related Work

In recent years, library analytics has been used to help librarians and the
users of libraries. Many researchers have proposed different recommender
approaches using different characteristics of these datasets, including user
access patterns, URL data, and web usage data, for improving the quality of
online-based services.

The research work in [2] discusses the use of library usage data to find
library usage patterns by undergraduate students. The authors in [3] have used
library analytics to correlate library material usage with students’ academic
performance. In reference [4], the authors use association rule-based
clustering to cluster the e-resource items of a digital library. Using library
usage data, researchers can identify students not studying at the university.
In reference [5], the author is able to group the students who do not study by
comparing different undergraduate academic disciplines and library use.
Library analytics is used to predict the requirements of future library users.
The researchers in reference [6] describe an approach to predict the future
usage of a library. In reference [7], analytics tools are used to provide
attractive site development ideas. Standard web analytics tools are not enough
to analyze library usage data because the uniform resource locator (URL)
pattern for library data is unique.

Different research works have used different clustering algorithms to group
con-tent-wise similar items. Unsupervised classification of the documents is
done using clustering algorithms, and different groups are created for similar
documents. The authors in reference [8] use the K-means clustering algorithm
on a term frequency-inverse document frequency (TF-IDF) matrix that represents
the resource contents to classify the documents. In their research, they have
shown that TF-IDF based clustering provides better results. In reference [9],
a text-based clustering algorithm is proposed. In their approach, frequent
items are used as terms. Here, frequent itemsets are generated, and later the
itemsets are used for association rule mining. The mutual overlaps of the
frequent itemsets are used for clustering, and the itemsets are divided into
k-partitions.

In reference [10], the authors have compared expectation maximization (EM)
clustering, and CFuzzy means for web usage data. Their results show that EM
performs 5 to 8 percent better. The authors in reference [11] have used the
density-based spatial clustering of applications with noise (DBSCAN)
clustering algorithm to classify visitor groups with common behavior. They
have mined web usage data to ﬁnd similar users. In reference [12], the authors
have used association rule mining (ARM) based clustering. From the frequent
itemsets, they generated diﬀerent association rules. They considered each
single association rule as an edge of a hypergraph. So, with all the
association rules, a hypergraph is generated. Then for clustering, they apply
a hypergraph partitioning algorithm known as hMETIS.

The authors in reference [13] have applied ARM-based clustering for pattern
discovery. Here, association rules are formed depending on the frequently
browsed pages. To ﬁnd the clusters, they apply the DBSCAN algorithm. ARM-based
clustering is also applied to web images to ﬁnd similar types of images [14].
The authors generated a hypergraph using all the association rules, and then
they applied a hypergraph partitioning algorithm, hMETIS, to generate the
clusters. ARM-based clustering is also used to cluster text documents [15].
The authors in [15] have used the K-means clustering algorithm to cluster the
association rules. The authors in reference [16] have applied hierarchical
clustering to design a decision support system. In reference [17], library
analytics is applied to ﬁgure out the eﬀect of library usage for successful
students. It has been found that students who do poorly in the exam do not use
the library resources that much compared to those who do well in the exams.

Many ongoing research projects are based on library analytics, but none of the
projects has become a standard. Researchers from the University of
Pennsylvania are designing a data integration framework, Metridoc [18], to
assist libraries with data ingestion and normalization. The ongoing research
works provide usage statistics of different online resource items but does not
apply any technique to recommend items based on the clustering technique. This
research applies clustering algorithms to recommend online resources to a
library user and compares content-based clustering algorithms with usage-based
clustering to design a recommender system. For content-based clustering
algorithms, we have used density-based clustering, hierarchical clustering, EM
clustering, etc. For usage-based clustering, we have used a hypergraph-based
association rule clustering algorithm.

Figure 1: An abstract design of the experimental process.

##  3 Experiment Design

This section describes the experiment design step-by-step, as presented in
Fig. 1. At first, server logs are collected from the EZproxy server (it allows
libraries to deliver e-resources securely over the network to its authorized
users), whilst in this experiment we only collect access logs for analysis.
Subsequently, the basic step of machine learning approach, pre-processing, is
applied to the collected library server logs to remove the stop-words and
unnecessary terms. After data cleaning, a URL categorization is applied based
on the users’ data and then context matrix and association rules are generated
for further processing. Finally, the clustering algorithms are applied to
design the recommender system. Fig. 2 shows the overall design of the library
log analyzer.

Figure 2: A library Log Analyzer.

###  3.1 Dataset Preparation

When a user browses the library website, the browsing information is saved in
the server log. Several server logs are available such as access-based logs,
error-handling logs, agent-handling logs, and referrer logs. We have worked
with the access log ﬁles of the library. The library uses the EZproxy server
to collect the access logs. EZproxy is a middle-ware solution that the
libraries use to provide access to its users who access the library resources
outside the regular library network. It stores usage information using a
standard web server log ﬁle [19]. In our case, EZproxy collects usage
information for the university using a common log format. EZproxy records
usage information using a common log format. EZproxy server for the library
uses the following log format:

%h %u %l %ezproxy-session i %t ”%r” %s %b

Here, %h is the host accessing the EZproxy server and it is always an IP
address, %u is the username used to log into the EZproxy server, %l is the
remote username but it is always a -, %ezproxy-session i is EZproxy’s
identifier for the user’s current session, %t captures the timestamp, %r gets
the complete request with server information, %s collects the status codes
returned by the server and %b provides a statistic number of bytes
transferred. Here is an example log entry created by the EZproxy server:

$\backslash$10.0.0.1 X2bFdM1R3txwlkv -
13d8f72f08d1a4e1c418a7cb8fc31437[01/Jun/2014: 00:47:10-0500] $\backslash$GET
http:// site.ebrary.com:80/lib/oculryerson/docDetail.
action?docID=10251051 HTTP/1.1” 200 29732”

The entry says user ‘X2bFdM1R3txwlkv’ from IP address ’10.0.0.1’ having a
session-id‘13d8f72f08d1a4e1c418a7cb8fc31437’ requested to read a document
having id ’10251051’ on the library server at the given time stamp and the
status code 200 says, the requested resource was found on the server so 29732
bytes were transferred to the user.

EZproxy creates a log ﬁle entry each time an e-resource item is accessed
through the EZproxy server. EZproxy logs each ﬁle downloaded by a user. It
also logs the search queries made against the databases. When we browse a web
page, a hypertext markup language (HTML) ﬁle and diﬀerent images, scripts,
etc., are downloaded. Requests are sent to the server to download each item.
As the server works to serve the request, it also creates an entry in the log
ﬁle. Although the user downloads only one ﬁle, multiple lines of entries are
written to the server log. In the cleaning process, we must remove all
additional requests the users do not request. To clean the log ﬁles, we
removed all the URLs with suﬃxes like jpeg, jpg, gif, CSS, etc. We have also
worked with the status codes returned by the servers to do the cleaning. We
can determine whether the submitted request is successful by examining the
status code. As the status code of successful requests ranges from 200-299
[20], we have removed all the entries with status codes outside 200-299.

###  3.2 User and Session Identification with URL Extraction

After cleaning the log ﬁle, we start the process of user identiﬁcation. Using
the EZproxy log, we can identify a user by the internet protocol (IP) address
and user id. In our architecture, we have identiﬁed the user using both IP
address and user id. After user identiﬁcation, the next step is to identify
the sessions created by diﬀerent users. User session identiﬁcation from an
EZproxy server log is relatively easy because the log ﬁle stores the session
id. So, using the time stamp associated with the session id, we can calculate
the session length. A university library has subscriptions to several research
databases. The databases have their own URL patterns to access resources such
as journals, articles, e-books, etc. Following is an example of a resource
request

URL: site.ebrary.com:80/lib/oculryerson/docDetail.action?do-cID=10017060.

Diﬀerent vendors have different naming patterns for these URLs, but using our
system, we can retrieve article information such as journal id, page numbers,
volume numbers, etc., from diﬀerent resource request URLs.

###  3.3 Recommender System using Online Resource Items

This experiment uses words from each document title to form a dictionary.
Then, a matrix of inverse document frequency (IDF) values is constructed for
each word of the individual title. For IDF calculation, we have used the
following:

$Idf_{k}=log(\frac{N}{df_{k}})$ (1)

Here, $df_{k}$ denotes the number of documents that consist of the $k^{t}h$
term, and $N$ is used for the total number of documents in the dictionary.

Now that the content matrix is ready, content-based clustering algorithms are
applied to design the recommender system. Next, to apply hypergraph-based
clustering, we generate the association rules. We use association rules to
identify which online items are accessed together by a user in a session. We
use this information to understand better how the library resources are being
used and how the resources are correlated through users’ eyes. Association
rule is expressed by the following:

$(X1,X2)=\Rightarrow X3)$ (2)

This rule means X1 and X2 imply X3, i.e., if X1, X2, and X3 are three
articles, then any user who reads articles X1 and X2 has a good chance that
the user will read article X3. To measure the chance of the appearance of
article X3, association rules provide two important metrics: support and
conﬁdence. Support and conﬁdence are two user deﬁned parameters. The user sets
minimum support and minimum conﬁdence to generate the most frequent itemsets.
For example, if the support of an association rule is 10%, it means 10% of the
total records in the database contain this rule. The association rules that
have lower support are not frequent in the database, so rules having lower
support could be ignored by the users. Another important parameter for
association rule mining is conﬁdence. For example, if the conﬁdence for the
association rule ${(X1,X2)\Rightarrow X3}$ is 90%, then it means 90 percent of
the records that contain (X1, X2) also contain X3. In our research, we have
set support to a very low value of 1% because we want to ﬁnd out surprise
relationships among diﬀerent documents. And to get the strong rules, we have
set conﬁdence to 80%. All the association rules are used to generate a
hypergraph. In a hypergraph, there are vertices and hyperedges connecting the
vertices. In this experiment, we have used each association rule to create a
hyperedge. The hyperedge vertices are mapped to the documents of the library.
hMETIS is a hypergraph partitioning tool that can generate balanced
k-partitions. So, for hypergraph partitioning, we have used the standalone
tool hMETIS.

###  3.4 Tools Used

This experiment uses a machine-learning tool WEKA [21] to generate the
association rules. WEKA is a tool that houses a set of machine-learning
algorithms to solve different data mining problems, including classification
[22], [23], [24], [27], [30],[31], [32], [34], clustering [25], [28], [29],
and association [26], citeNaajim tasks. This state-of-the-art software can be
used either using command line interface (CLI) or graphical user interface
(GUI) interfaces. hMETIS is a standalone application used to partition the
hypergraph. The experiment is implemented using the programming language Java.

##  4 Results Analysis

This experiment applies different clustering algorithms to design a
recommender system. For result analysis, we have compared the recommendation
accuracy for the recommender systems generated using the clustering
algorithms. To start the clustering process, first, we selected the top 254
browsed documents. Here, we selected the documents that were browsed at least
ten times. Next, we apply the clustering algorithms to generate seventeen
clusters. Here, we have chosen seventeen because, with the librarian’s help,
we manually grouped the titles into a total of seventeen (17) groups.

To continue with the design of the recommender system, we extracted 353 user
profiles, where a profile consists of the browsed documents by the users.
Here, to select the 353 users, we have selected the users who have at least
browsed fifteen (15) items. Next, we compare the user profiles with each of
the seventeen clusters generated by the algorithms. For each comparison, we
calculate the Precision (P), Recall (R), and F1-Score. These metrics quantify
the effectiveness of the proposed recommender system using a different set of
machine learning algorithms (ML). We have used the following to calculate
Precision, Recall, and F1-Score:

$P=\frac{Number~{}of~{}correctly~{}classified~{}items~{}that~{}belong~{}to~{}the~{}positive~{}class}{Total~{}number~{}of~{}items~{}classified~{}that~{}belong~{}to~{}the~{}positive~{}class}$
(3)

$R=\frac{Number~{}of~{}correctly~{}classified~{}items~{}that~{}belong~{}to~{}the~{}positive~{}class}{Total~{}number~{}of~{}elements~{}that~{}actually~{}belong~{}to~{}the~{}positive~{}class}$
(4)

$F1-Score=\frac{2\times Precision\times Recall}{Precision+Recall}$ (5)

Each user profile is compared with seventeen clusters, so one user profile
generates seventeen sets of Precision, Recall, F1-Score. We calculate the
highest of the seven-teen scores to get a single set of Precision, Recall,
F1-Score for each user. In this way, for each clustering algorithm, we get 353
sets of precision, recall, F1-Score. Finally, we average them to get a single
set of Precision, Recall, F1-Score for each clustering algorithm. Table 1
shows the results of the experiment for different clustering algorithms.

Table 1: Precision, Recall and F1-Score for the clustering algorithms. Algorithm | Precision | Recall | F1-Score
---|---|---|---
EM | 0.6 | 0.34 | 0.43
Filtered | 0.56 | 0.4 | 0.47
K-Mean | 0.65 | 0.45 | 0.53
FarthestFirst | 0.4 | 0.33 | 0.36
Density | 0.52 | 0.38 | 0.44
Hierarchical | 0.45 | 0.32 | 0.37
Hypergraph | 0.75 | 0.6 | 0.67

Table 1 illustrates that the hypergraph-based recommender system generates the
most accurate recommendations compared to other algorithms. For example, the
hypergraph-based recommender recommends items with 75% Precision, and also it
has the highest F1-Score. Furthermore, out of the content-based clustering
algorithms, the recommender performs best with the K-mean clustering algorithm
with a precision of 65% and an F1-Score of 0.53.

This section has analyzed the experiment results. We have used different
clustering algorithms to design the recommender system. We have found that the
hypergraph-based recommender system generates the most accurate
recommendations from all the clustering algorithms.

##  5 Conclusion and Future Work

In this research work, we have worked with library usage data retrieved from
the web server log. We have designed a recommender system that uses library
usage data. The recommender system is designed based on the documents’ titles
and users’ resource access patterns. The experiment result shows that the
hypergraph-based recommender system outperforms (precision 75%) all other
recommender systems generated using the content-based clustering algorithms,
whilst the FarthestFirst algorithm gains the lowest performances compared to
all other algorithms. In terms of the content-based clustering approach, the
K-mean algorithm shows high accuracy.

In the future, we aim to compare this hypergraph-based recommender system with
matrix factorization and deep neural network-based recommender systems. So, we
will change our experiment design to include matrix-factorization and deep
neural networks.

Acknowledgement
The authors are profoundly grateful to the Toronto Metropolitan University,
Ontario, Canada, and the Metropolitan University (MU), Pirer bazar, Bateshwar,
Sylhet-3104, Bangladesh, for supporting this research work.

Conflict of Interest
There are no conflicts of interest from the authors.

## References

  * [1] Initiative, L. E. 7 Things you should know about ANALYTICS.URL: https://library.educause.edu/-/media/files/library/2010/4/eli7059-pdf.pdf (2010).
  * [2] Collins, E. & Stone, G. Understanding Patterns of Library Use Among Under-graduate Students from Different Disciplines. Evidence-Based Library and Information Practice. 9(3) pp. 51-67 (2014).
  * [3] Wong, S. & Webb, T. Uncovering meaningful correlation between student academic performance and library material usage. Association of College & Research Libraries. (2010).
  * [4] Roy, D., Ding, C., Jin, L. & Thomas, D. Association rule-based clustering of electronic resources in University Digital Library. In Digital Libraries for Open Knowledge: 22nd International Conference on Theory and Practice of Digital Libraries. (2018).
  * [5] Bridges, L. Who is not using the library? A comparison of undergraduate academic disciplines and library use. The Johns Hopkins University Press, Portal: Libraries and the Academy. 8(2) pp. 187-196 (2008).
  * [6] Wilson, M. Understanding the needs of tomorrow’s library user: Rethinking library services for the new age. Australasian Public Libraries and Information Services. 13(2) p. 81 (2000).
  * [7] Arendt, J. & Wagner, C. Beyond Description: Converting Web Site Usage Statistics into Concrete Site Improvement Ideas. Journal of Web Librarianship. 4(1) p. 3754 (2000).
  * [8] Singh, V.K., Tiwari, N. & Garg, S. Document Clustering using K-Means, Heuristic K-Means and Fuzzy C-Means. International Conference on Computational Intelligence and Communication Networks (CICN). pp. 297-301 (2011).
  * [9] Beil, F., Ester, M. & Xu, X. Frequent term-based text clustering. In the Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 436-442 (2002).
  * [10] Poongothai, K., Parimala, M. & Sathiyabama, S. Efficient Web Usage Mining with Clustering. International Journal of Computer Science. 8(6) pp. 203-209 (2011).
  * [11] Langhnoja, S. G., Mehul, P. B. & Darshak, B. M. Web Usage Mining to Discover Visitor Group with Common Behavior Using DBSCAN Clustering Algorithm. International Journal of Engineering and Innovative Technology (IJEIT). 2 pp. 169-173 (2013).
  * [12] Han, E., Karypis, G. & Mobasher, B. Clustering Based On Association Rule Hypergraphs. Workshop on Research Issues on Data Mining and Knowledge Discovery. pp. 9-13 (1997).
  * [13] Langhnoja, S. G., Mehul, P. B. & Darshak, B. M. Web Usage Mining Using Association Rule Mining on Clustered Data for Pattern Discovery. International Journal of Data Mining Techniques and Applications. 2(1) pp. 141-150 (2013).
  * [14] Malik, H. & Kender R. Clustering Web Images using Association Rules, Interestingness Measures, and Hypergraph Partitions. Proceedings of the 6th International Conference on Web Engineering, ACM. pp. 48-55 (2006).
  * [15] Liu, G., Huang, S., Lu, C. & Du, Y. An improved K-Means Algorithm Based on Association Rules. International Journal of Computer Theory and Engineering (IJCTE). 6(2), pp. 146-149 (2014).
  * [16] Gerardo, B. D., Byun, Y. & Tanguilig III, B. Hierarchical Clustering and Association Rule Discovery Process for Efficient Decision Support System. Communication and Networking, Communications in Computer and Information Science. 266 pp. 239-247 (2011).
  * [17] Jager, K. d. Successful students: does the library make a difference? Performance Measurement and Metrics. 3(3) pp. 140-144 (2002).
  * [18] Metridoc. Penn Library DataFarm: Metridoc Project. University of Pennsylvania. URL: https://metridoc.library.upenn.edu/ (2023).
  * [19] OCLC. EZproxy Conﬁguration: LogFormat. URL: https://www.oclc.org/support/services/ezproxy/documentation/cfg/logformat.en.html
(2023).

  * [20] Fielding, R. & Adobe, E. Hypertext Transfer Protocol (HTTP/1.1): Semantics and Content. Internet Engineering Task Force. URL: https://www.rfc-editor.org/rfc/rfc7231#page-47 (2023).
  * [21] Frank, E., Hall, M. A. & Witten, I. H. The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, 4th ed. Morgan Kaufmann. URL: https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf (2023).
  * [22] Chowdhury, R. R. & Abas, P. E. A survey on device fingerprinting approach for resource-constraint IoT devices: Comparative study and research challenges. Internet of Things (Netherlands). 20 (2022).
  * [23] Chowdhury, R. R., Idris, A. C. & Abas, P. E. A Deep Learning Approach for Classifying Network Connected IoT Devices Using Communication Traffic Characteristics. Journal of Network and Systems Management. 31(1) p. 26 (2023).
  * [24] Chowdhury, R. R., Idris, A. C. & Abas, P. E. Device identification using optimized digital footprints. IAES International Journal of Artificial Intelligence. 12(1) pp. 232–240 (2023).
  * [25] Roy, D., Chowdhury, R. R., Nasser, A. B., Azim, A. & Babaeianjelodar, M. Item recommendation using user feedback data and item profile. AIP Conference Proceedings. 2643 (2023).
  * [26] Mohamed, K. & Bayraktar, Ü. A. Artificial Intelligence in Public Relations and Association Rule Mining as a Decision Support Tool. International Journal of Humanities and Social Science. 9(3) pp. 23-32 (2022).
  * [27] Chowdhury, R. R., Idris, A. C. & Abas, P. E. Identifying SH-IoT devices from network traffic characteristics using random forest classifier. Wireless Networks. (2023).
  * [28] Dol S. M. & Jawandhiya P. M. Classification Technique and its Combination with Clustering and Association Rule Mining in Educational Data Mining — A survey. Engineering Applications of Artificial Intelligence. 122 p. 106071 (2023).
  * [29] Saputra D., Haryani, Junaidi A., Baidawi T. & Surniandari A. Application of K-mean clustering algorithm in grouping data prospective new students. 2ND INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION SCIENTIFIC DEVELOPMENT (ICAISD) 2021: Innovating Scientific Learning for Deep Communication. (2021).
  * [30] Ortega, J. H. J. C., Resureccion, M. R., Natividad, L. R. Q., Bantug, E. T., Lagman, A. C. & Lopez, S. R. An Analysis of Classification of Breast Cancer Dataset Using J48 Algorithm. International Journal of Advanced Trends in Computer Science and Engineering. 9(1.3) (2020).
  * [31] Chowdhury, R. R., Idris, A. C. & Abas, P. E. Packet-level and IEEE 802.11 MAC frame-level analysis for IoT device identification. Turkish Journal of Electrical Engineering and Computer Sciences. 30(5) pp. 1941–1961 (2022).
  * [32] Aneja, S., Bhargava, B. K., Aneja, N. & Chowdhury, R. R. Device Fingerprinting using Deep Convolutional Neural Networks. International Journal of Communication Networks and Distributed Systems. 28(2) pp. 171–198 (2022).
  * [33] Naajim, M., Vickramkarthick, Radhakrishnan, Jatain, A. Association Rules Generation for Injuries in National Football League (NFL). Proceedings of International Conference on Advanced Communications and Machine Intelligence. MICA 2022. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. (2023).
  * [34] Chowdhury, R. R., Idris, A. C. & Abas, P. E. Internet of Things Device Classification using Transport and Network Layers Communication Traffic Traces. International Journal of Computing and Digital Systems. 12(1) pp. 2210–142 (2022).