Shelbi Joseph

Cochin University of Science and Technology

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Jiby J. Puthiyidam

Cochin University of Science and Technology

Uma Narayanan

Rajagiri Hospital

Varghese Paul

Rajagiri Hospital

N. Sheena

Cochin University of Science and Technology

Kishor Krishnan Nair

Council for Scientific and Industrial Research

P V Akhil

Cochin University of Science and Technology

Bharat Bhushan

Jawaharlal Nehru Medical College

D. Harikrishnan

Cochin University of Science and Technology

Seetha Parameswaran

Cochin University of Science and Technology

S. Shailesh

Cochin University of Science and Technology

Cooperative Institutions

Cochin University of Science and Technology

Rajagiri Hospital

Modibbo Adama University of Technology

Sharda University

The Ohio State University

Sacred Heart College

Aditya Birla (India)

Chitkara University

Jawaharlal Nehru Technological University, Hyderabad

Mahatma Gandhi Institute of Technology

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Different analytical techniques for big data analysis: A review

Uma Narayanan Varghese Paul Shelbi Joseph

Big data refers to any collection of data so large and complex that it exceeds the processing capability of conventional data management system and techniques. Big data is the hot topic of research now a day. The paper gives some ideas about the research area to look into. It has been done as a part of literature survey for the P.hd work. The paper can be taken as the literature review of the existing paper in the area of Security, Machine learning, Health care, Neural Network, Rough Set Theory and Fuzzy Logic. The Security and privacy are the hot topic of research in Big data. Major contribution to this paper comes from Healthcare sector. The Machine learning too has its own role to in the big data analytics. This paper also covers the rough set theory and the neural network contributions to the big data. It also includes the challenges and the scope for research for the each paper. It also gives some insights to all the research paper in the area of big data, helpful for those who are doing research in big data.

Scope (computer science)

10.1109/icecds.2017.8390139

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Citations (10)

Enhanced authentication security for IoT client nodes through T-ECDSA integrated into MQTT broker

The Journal of Supercomputing (2023)

Jiby J. Puthiyidam Shelbi Joseph Bharat Bhushan

MQTT

10.1007/s11227-023-05789-w

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Citations (5)

Temporal ECDSA: A timestamp and signature mask enabled ECDSA algorithm for IoT client node authentication

Computer Communications (2024)

Jiby J. Puthiyidam Shelbi Joseph Bharat Bhushan

Digital Signature Algorithm

10.1016/j.comcom.2024.01.016

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Citations (5)

A Novel Approach for Handwriting Recognition in Malayalam Manuscripts using Contour Detection and Convolutional Neural Nets

Dhanya Sudarsan Shelbi Joseph

Neural Networks is a hot area of research for various kind of pattern recognition. Hand writing recognition is a domain coming under the field of pattern recognition which had captured a high research interest during the last 10 years. It is a complex process mainly due to the huge character set, complexity and similarity of Malayalam letters. Our project aims to digitalize Malayalam handwritten text from Malayalam manuscripts like palm leafs, official documents in Government offices etc. An intensive literature survey was conducted on the various methods of handwriting recognition in various languages to find a best suitable approach for digitalizing Malayalam language. A novel approach to digitalize Malayalam manuscripts using contour detection for segmentation and Convolutional Neural Network for classification was proposed. The system was trained using the samples collected from museums and a neural network is constructed for character identification. According to the results obtained the system is proved to have less overhead without compromising the accuracy.

Malayalam

Handwriting

Optical character recognition

Handwriting Recognition

10.1109/icacci.2018.8554592

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Citations (10)

Lightweight Encryption Algorithms for Resource-constrained Devices for Internet-of-Things Applications

CRC Press eBooks (2024)

N. Sheena Shelbi Joseph S. Shailesh

The Internet-of-Things (IoT) is an emerging and evolving technology that connects the virtual and physical worlds. The interconnection of resource-constrained heterogeneous devices in the IoT through communication protocols enables them to exchange any data. Because of this massive data flow, the system is vulnerable to security attacks. The only way to protect data from security breaches is through encryption. Conventional cryptographic algorithms are unsuitable for IoT devices due to their lack of computing power, communication, energy efficiency, and memory capability. This leads to lightweight protocols, which should provide high security and performance at a low cost. This study investigates lightweight cryptographic protocols for resource-constrained devices commonly utilized in IoT applications. There are four types of lightweight cryptographic protocols: normal, ultra, hybrid, and multilevel lightweight algorithms. It also describes the different attacks and how lightweight protocols may protect against critical assaults. The chapter identifies some issues and challenges and suggests a lightweight cryptographic algorithm for data transfer at the IoT application layer.

10.1201/9781003474111-2

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Citations (2)

Automatic Extraction of Hypernym & Meronym Relations in English Sentences Using Dependency Parser

Procedia Computer Science (2016)

N. Sheena Smitha M. Jasmine Shelbi Joseph

Relation Extraction is the process of finding relationship between named entities in a text. Automatic extraction of semantic relation between pair of nouns is an important task with many potential applications like information retrieval, information extraction, text summarization, machine translation, question answering, thesaurus construction and word sense disambiguation. This paper describes an approach that extracts hypernym and meronym relation between proper nouns in sentences of a given text. Here machine learning techniques are used to automatically extract proper nouns with a given relationship from a text corpus. This work is based on dependency parsing with ADTree and Naive Bayes classifier. The approach is based on the analysis of the paths between noun pairs in the dependency parse trees of the sentences.

Dependency grammar

Relationship extraction

Proper noun

10.1016/j.procs.2016.07.269

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Citations (12)

IoT Service Search using MQTT Protocol

International Journal of Computer Sciences and Engineering (2019)

Nihaala Najeeb Shelbi Joseph

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Bridge (graph theory)

Publication

10.26438/ijcse/v7i7.292297

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Citations (0)

A survey on various supervised classification algorithms

Uma Narayanan Athira Unnikrishnan Varghese Paul Shelbi Joseph

Machine learning is concerned with the development, the analysis and the applications of algorithms that allow computers to learn. Supervised learning is the machine learning task of inferring a function from labelled training data. In this paper, we present a survey of various supervised classification techniques. The goal of this survey is to provide an inclusive review of different supervised classification techniques such as decision tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbour, Neural Network.

Statistical classification

Supervised Learning

One-class classification

10.1109/icecds.2017.8389824

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Citations (45)

Internet of Things Network Performance: Impact of Message and Client sizes and Reliability Levels

ECTI Transactions on Electrical Engineering Electronics and Communications (2024)

Jiby J. Puthiyidam Shelbi Joseph

Consistent and timely message transmission is pivotal in the success of Internet of Things (IoT) communications. IoT applications typically involve resourceconstrained devices with limited processing power, memory capacity, and battery life. These characteristics necessitate the use of specialized communication protocols for IoT applications. Message Queue Telemetry Transport (MQTT) is a widely adopted protocol within the IoT ecosystem. Ensuring the appropriate message size and reliability level is crucial for achieving successful MQTT communication. This article empirically assesses MQTT protocol performance across various Quality of Service (QoS) levels while considering different message and client sizes. Initially, we conducted a comparative analysis of message transfer performance between HTTP and MQTT protocols. Subsequently, we delved into the MQTT protocol’s performance across three distinct configurations: one publisher-one subscriber, multiple publishers-one subscriber, and multiple publishersmultiple subscribers. We also examined how message and client sizes impact message transfer latency. When employing a 100-byte payload, we observed that the time delay in a network comprising 150 clients is 60.71% greater than in a network with 50 clients. Similarly, with a message size of 2500 bytes, a network with 50 clients requires 96% less time to deliver than a network with 150 clients.

10.37936/ecti-eec.2024221.252941

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Citations (0)

A Study on the Performance of Supervised Algorithms for Classification in Sentiment Analysis

TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) (2019)

P. Sunitha Shelbi Joseph P V Akhil

Sentiment Analysis has gained a lot of popularity in the last few years. It is the processes of identifying the underlying sentiment in a comment, review or in a document. Sentiment of a document or sentence generally falls under three categories namely positive, neutral or negative. This paper has surveyed literature relating to supervised algorithms for classification in sentiment analysis. The contribution of the paper includes the extensive survey and classification of literature related to sentiment analysis based on classification algorithm used and application area. We have then performed a comparative study of five popular supervised classification algorithms namely Naive Bayes, Random Forest, Support Vector Machine (SVM), Decision Tree and K Nearest Neighbors (KNN) and evaluated the performance of each of these algorithms in sentiment analysis. The study has been performed on two different data sets to nullify the bias that might happen in the comparative study when focusing on a single data set. This paper has also proposed a novel method with advanced preprocessing(AP) which offers improved performance of about 8% over all the methods taken into study in the paper.

Sentiment Analysis

Statistical classification

10.1109/tencon.2019.8929530

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Citations (7)