Accepted Papers


Vegetation Typification Integrated With Time Series Using Google Earth Engine

K.Rohith1, T.Pranoom1, V.Hari Vamsi1, G.JayaLakshmi4, 1Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India, 2VelagapudiRamakrishna Siddhartha Engineering College, Vijayawada,Andhra Pradesh, India.

ABSTRACT

Using the Google Earth Engine platform, we surveyed all vegetation types using information on remote areas. Specifically, we use Landsat images and Sentinel2A data for conversion. Our aim is to improve the quality of vegetation by typification of vegetation using the various capabilities of the Landsat images. We use state-of-art image processing and machine learning algorithms to accurately classify different plant species in selected study areas. We also track temporal changes in vegetation using Sentinel2A imagery, making it possible to analyze land cover changes over time. Our approach to facility monitoring and change detection is broad because it combines the unique and rich nature of Sentinel2A. change the world. Where we used the starch based mechanism for the plant based classification scientifically and then make them to classified (ndvi) index ranges.

KEYWORDS

Vegetation types, Remote sensing techniques, Multispectral, capabilities, Categorization, Land management decisions, Robust methodology.


Heart Disease Prediction Using Data Mining Classification Algorithms

Deepanshu Sharma and Dr. Siddhartha Chauhan, Department of Computer Science and Engineering, NIT Hamirpur, H.P., India.

ABSTRACT

Heart diseases, also referred to as "cardiovascular diseases," are a group of disorders that affect the heart. This illness can cause a heart attack, stroke, and other symptoms. After examining a few research papers on the subject, it became clear that the majority of them used a single machine learning algorithm to predict heart disease. A few of them state that they are unable to enhance their models performance through optimization techniques. As a result of these findings, they encountered some difficulties in effectively predicting heart disease using their suggested method. In an earlier study PCA was also used, but it failed to provide considerable accuracy for such a sensitive research area, i.e., medical diagnosis. Data for this method was gathered from the "Heart Disease UCI" UCI repository, which was accessible on Kaggle. Working upon the given dataset we used various dimensionality reduction techniques, using various classifiers and found out their effectiveness. Thus, we were able to get considerably higher accuracy (98%) by using certain techniques to de-noise data (checking correlations, outliers, removing them etc.), using the MLP classifier.

KEYWORDS

PCA, MLP, cardiovascular diseases, ML, Data mining.


Multi-faceted Question Complexity Estimation Targeting Topic Domain-specificity

Sujay R1, Suki Perumal1, Yash Nagraj1, Anushka Ghei2 and Srinivas K S1, 1Department of CSE(AI & ML), PES University, Karnataka, India, 2Department of CSE, PES University, Karnataka, India

ABSTRACT

Question difficulty estimation remains a multifaceted challenge in educational and assessment settings. Traditional approaches often focus on surface-level linguistic features or learner comprehension levels, neglecting the intricate interplay of factors contributing to question complexity. This paper presents a novel framework for domain-specific question difficulty estimation, leveraging a suite of NLP techniques and knowledge graph analysis. We introduce four key parameters: Topic Retrieval Cost, Topic Salience, Topic Coherence, and Topic Superficiality, each capturing a distinct facet of question complexity within a given subject domain. These parameters are operationalized through topic modelling, knowledge graph analysis, and information retrieval techniques. A model trained on these features demonstrates the efficacy of our approach in predicting question difficulty. By operationalizing these parameters, our framework offers a novel approach to question complexity estimation, paving the way for more effective question generation, assessment design, and adaptive learning systems across diverse academic disciplines.

KEYWORDS

Question difficulty estimation, knowledge graph analysis, BERT, Domain specific metrics, Topic modelling, Natural Language Processing, Question Answering, Cognitive Load, Learning Analytics.


Resume Analyzer

Vinayak Subray Hegde and Premalatha H M, Department of Computer Applications, PES University, Bengaluru.

ABSTRACT

The “Resume Analyzer” is the advanced web application which provides the solutions for both Recruiters and Applicants by using the Natural Language Processing (NLP) technology. Its main motto is analysing the uploaded resume and providing the prediction, suggestions or advice to the both Job seekers and recruiters. For candidates they’ll upload the resume in pdf format, and the web application provides the basic information, experience level, predicted job role, existing and recommended skills, course recommendation according to the predicted job role, YouTube links for interview and resume tips and ideas. And for recruiters it’ll analyse the resume and provides the basic information, existing and recommended skills, parsed information of whoever using the tool (for better recruiting process) and downloadable parsed information.

KEYWORDS

Recruitment, Resume Analysis, Natural Language Processing (NLP), Candidate Selection, Career Development, User-Friendly Experience.


Security Concerns in Iot Light Bulbs: Investigating Covert Channels

Janvi Panwar and Ravisha Rohilla, Indira Gandhi Delhi Technical University for Women, India

ABSTRACT

The proliferation of Internet of Things (IoT) devices has raised significant concerns regarding their security vulnerabilities. This paper explores the security risks associated with smart light systems, focusing on covert communication channels. Drawing upon previous research highlighting vulnerabilities in communication protocols and encryption flaws, the study investigates the potential for exploiting smart light systems for covert data transmission. Specifically, the paper replicates and analyzes an attack method introduced by Ronen and Shamir, which utilizes the Philips Hue White lighting system to create a covert channel through visible light communication (VLC). Experimental results demonstrate the feasibility of transmitting data covertly through subtle variations in brightness levels, leveraging the inherent functionality of smart light bulbs. Despite limitations imposed by device constraints and communication protocols, the study underscores the need for heightened awareness and security measures in IoT environments. Ultimately, the findings emphasize the importance of implementing robust security practices and exercising caution when deploying networked IoT devices in sensitive environments.

KEYWORDS

Internet of Things (IoT), Security vulnerabilities, Smart light systems, ZigBee Light Link (ZLL) protocol, Denial-of-Service (DoS) attacks, Firmware manipulation, Covert communication channels, Visible light communication (VLC), Data exfiltration, Air-gapped networks, Smart LED bulbs, PWM modulation, Orthogonal Frequency-Division Multiplexing (OFDM), Light sensor, Signal processing.


Teachers Experiences of Integrating Digital Technologies in Elt: A Digcompedu Perspective

Dammar Singh Saud, Far Western University, Nepal

ABSTRACT

Incorporating digital technologies into English Language Teaching (ELT) has become crucial for enriching the teaching and learning processes. However, in Nepal, a developing nation marked by challenging terrain, despite the widespread adoption of technology, there is a significant gap in our knowledge regarding the practices of teacher educators, especially those working in remote areas. To bridge this knowledge gap, this hermeneutic phenomenological study delves into the experiences of four English language teacher educators situated in Darchula, a remote district in Nepals far-western region, concerning the integration of digital technologies into English language teaching. Through semi-structured interviews and thematic analysis guided by the DigCompEdu Framework, this research uncovers teacher educators’ lived experiences of the incorporation of digital technologies into ELT. They use digital tools to enhance teaching and learning experiences, promote student engagement, enhance access to learning materials, and create dynamic and interactive learning environments. Nonetheless, the study emphasizes the necessity of addressing technical challenges and adopting a balanced approach when utilizing online resources to maximize benefits while mitigating drawbacks. By furnishing teacher educators and policymakers in Nepal with a deeper understanding of the importance of digital technologies and the potential offered by the DigCompEdu framework, this article strives to facilitate a more efficient integration of digital technologies within ELT classrooms.

KEYWORDS

Digital Technologies; English Language Teaching; DigCompEdu Framework; Teacher Educators, Digital Competence.


Hardware-backed IOT Sensor Information Tracking on the Cardano Blockchain

A. Adhikari, M. Ramu, R. Thomas, H. Su, and B. Ramesh, International Centre for Neuromorphic Systems, The MARCS Institute, Western Sydney University, New South Wales, Australia

ABSTRACT

The tracking of sensor information has advanced significantly with the rise of the Internet of Things (IoT) and cloud computing, replacing local storage and records. However, it faces challenges such as data leakage, compromised privacy, data tampering, and origin misrepresentation due to mutable data storage and central points of failure. Blockchain-based solutions have been proposed, but they often suffer from high costs, limited scalability, and vulnerability to data tampering in cloud-based processes. This paper introduces a novel approach using Extended Unspent Transaction Output (eUTXO) blockchains, which offer better scalability, lower transaction costs, higher throughput, enhanced privacy, along with a tamper-resistant log. Our hardware-backed integration on the Cardano blockchain achieves decentralized edge IoT nodes and sensors, enhancing security and reliability in sensor data tracking. Our framework overcomes limitations of traditional blockchain methods and centralized cloud systems. By adopting this hardware-backed approach, IoT-based sensor tracking attains new levels of integrity and privacy. Comprehensive evaluations demonstrate the effectiveness and practicality of our system. The proposed framework addresses sensor tracking challenges and advances hardware-backed IoT solutions. We envision its application in secure and reliable industrial IoT systems, benefiting various industries with critical data tracking requirements.

KEYWORDS

Cardano Blockchain, Hardware-Backed Integration, Industrial IoT (IIoT), Sensor Information Tracking.