Enhancing Voting Security and Efficiency

Ranjan, Nikhil

doi:10.57159/gadl.jcmm.2.3.23065

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…These measures provide important information on how well biometric authentication systems work [30][31][32][33][34][35]. These help to design and test systems to understand how security and ease of use can be balanced.…”

Section: False Rejection Rate (Frr)mentioning

confidence: 99%

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Balachandra,

K V

et al. 2024

JMC

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In the ever-evolving realm of technology, the identification of human activities using intelligent devices such as smartwatches, fitness bands, and smartphones has emerged as a crucial area of study. These devices, equipped with inertial sensors, gather a wealth of data and provide insights into users' movements and behaviors. These data not only serve practical purposes, but also hold significant implications for domains such as healthcare and fitness tracking. Traditionally, these devices have been employed to monitor various health metrics such as step counts, calorie expenditure, and real-time blood pressure monitoring. However, recent research has shifted its focus to leveraging the data collected by these sensors for user authentication purposes. This innovative approach involves the utilization of Machine Learning (ML) models to analyze the routine data captured by sensors in smart devices employing ML algorithms, which can recognize and authenticate users based on their unique movement patterns and behaviors. This introduces a paradigm shift from traditional one-time authentication methods to continuous authentication, adding an extra layer of security to protect users against potential threats. Continuous authentication offers several advantages over its conventional counterparts. First, it enhances security by constantly verifying a user's identity through their interaction with the device, thereby mitigating the risk of unauthorized access. Second, it provides a seamless and nonintrusive user experience, eliminating the need for repetitive authentication prompts. Moreover, it offers robust protection against various threats such as identity theft, unauthorized access, and device tampering. The application of continuous authentication extends beyond individual devices and encompasses interconnected systems and networks. This holistic approach ensures a comprehensive security across digital platforms and services. The experiments demonstrate that the logistic regression model achieves an accuracy of 82.32% on the test dataset, highlighting its robustness for binary classification tasks. Additionally, the random forest model outperforms with a 92.18% accuracy, emphasizing its superior capability in handling complex feature interactions. In the study, the sequential neural network achieved an accuracy of 92% on the HAR dataset, outperforming traditional machine learning models by a significant margin. The model also demonstrated robust generalization capabilities with a minimal drop in performance across various cross-validation folds.

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Section: False Rejection Rate (Frr)mentioning

confidence: 99%

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Balachandra,

K V

et al. 2024

JMC

View full text Add to dashboard Cite

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Impact of Effective Word Vectors on Deep Learning Based Subjective Classification of Online Reviews

et al. 2024

JMC

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Sentiment Analysis tasks are made considerably simpler by extracting subjective statements from online reviews, thereby reducing the overhead of the classifiers. The review dataset encompasses both subjective and objective sentences, where subjective writing expresses the author's opinions, and objective text presents factual information. Assessing the subjectivity of review statements involves categorizing them as objective or subjective. The effectiveness of word vectors plays a crucial role in this process, as they capture the semantics and contextual cues of a subjective language. This study investigates the significance of employing sophisticated word vector representations to enhance the detection of subjective reviews. Several methodologies for generating word vectors have been investigated, encompassing both conventional approaches, such as Word2Vec and Global Vectors for word representation, and recent innovations, such as like Bidirectional Encoder Representations from Transformers (BERT), ALBERT, and Embeddings from Language Models. These neural word embeddings were applied using Keras and Scikit-Learn. The analysis focuses on Cornell subjectivity review data within the restaurant domain, and metrics evaluating performance, such as accuracy, F1-score, recall, and precision, are assessed on a dataset containing subjective reviews. A wide range of conventional vector models and deep learning-based word embeddings are utilized for subjective review classification, frequently in combination with deep learning architectures like Long Short-Term Memory (LSTM). Notably, pre-trained BERT-base word embeddings exhibited exceptional accuracy of 96.4%, surpassing the performance of all other models considered in this study. It has been observed that BERT-base is expensive because of its larger structure.

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Enhancing Voting Security and Efficiency

Cited by 2 publications

References 23 publications

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Using Behavioural Biometrics and Machine Learning in Smart Gadgets for Continuous User Authentication Purposes

Impact of Effective Word Vectors on Deep Learning Based Subjective Classification of Online Reviews

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