Deep Learning-Based Object Detection in Diverse Weather Conditions

Ravinder, M.; Jaiswal, Arunima; Gulati, Shivani

doi:10.4018/ijiit.296236

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…In this section, we present the experimental results and analysis conducted to validate the effectiveness of the proposed model for fake news detection. The evaluation was performed on all the datasets mentioned in Section III, using standard evaluation metrics including accuracy [57], precision [58], recall [59], and F1 score [60]. To conduct our experiments, we divided the entire dataset into two parts: a training dataset and a testing dataset.…”

Section: Resultsmentioning

confidence: 99%

WITHDRAWN: A Meta-Heuristic Based Approach for Optimal Fake News Detection Using Supervised Learning

Jaiswal,

Verma,

Sachdeva

et al. 2023

Preprint

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The rapid dissemination of fake news in the digital age has raised concernsregarding the authenticity and credibility of online information. In this study, we propose an innovative approach for detecting fake news by combining the power of RoBERTa, a state-of-the-art language model, with the Firefly Optimization Algorithm (FOA) and Long Short-Term Memory (LSTM) neural networks. Our model leverages RoBERTa’s contextual understanding of text to extract informative features from news articles, including content, user engagement, and source characteristics. The FOA is employed to optimize the selection of these features, enhancing the model’s ability to discern between genuine and deceptive news. The selected features are then used as inputs to an LSTM network, which learns the temporal dependencies within the data for accurate classification. To validate the effectiveness of our approach, we conduct experiments using benchmark datasets such as ISOT and fakenewsnet specifically designed for fake news detection tasks. The experimental results demonstrate that our proposed model outperforms existing methods in terms of accuracy and other performance metrics. By integrating RoBERTa, FOA, and LSTM, our model effectively combines contextual understanding, feature selection optimization, and sequence modeling, enabling accurate identification of fake news articles. This research contributes to the advancement of fake news detection techniques and offers a promising solution to address the challenges associated with online misinformation, ultimately fostering trust and reliability in the digital information landscape.

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Section: Resultsmentioning

confidence: 99%

WITHDRAWN: A Meta-Heuristic Based Approach for Optimal Fake News Detection Using Supervised Learning

Jaiswal,

Verma,

Sachdeva

et al. 2023

Preprint

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Knowledge distillation-based approach for object detection in thermal images during adverse weather conditions

Pahwa,

Yadav,

Saumya

et al. 2024

Int. j. inf. tecnol.

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Classification and security assessment of android apps

Caushaj,

Sugumaran

2023

Discov Internet Things

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Current mobile platforms pose many privacy risks for the users. Android applications (apps) request access to device resources and data, such as storage, GPS location, camera, microphone, SMS, phone identity, and network information. Legitimate mobile apps, advertisements (ads), and malware all require access to mobile resources and data to function properly. Therefore, it is difficult for the user to make informed decisions that effectively balance their privacy and app functionality. This study analyzes the Android application permissions, ad networks and the impact on end-user’s privacy. Dangerous combinations of app permissions, and ad networks are used as features in our prediction models to understand the behavior of apps. Our models have a high classification accuracy of 95.9% considering the imbalance in real life between benign and malicious apps. Our assumption that certain app permissions can be a potential threat to the privacy of end users is confirmed to be one of the most impactful features of our prediction models. Since our study considers the impact of ad networks and malware permissions, it will help end-users make more informed decision about the app permissions they grant and understand that the app permissions open doors to more vulnerabilities, and at some point, benign apps can behave maliciously.

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Deep Learning-Based Object Detection in Diverse Weather Conditions

Cited by 7 publications

References 29 publications

WITHDRAWN: A Meta-Heuristic Based Approach for Optimal Fake News Detection Using Supervised Learning

WITHDRAWN: A Meta-Heuristic Based Approach for Optimal Fake News Detection Using Supervised Learning

Knowledge distillation-based approach for object detection in thermal images during adverse weather conditions

Classification and security assessment of android apps

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