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A Deep Learning Model to Detect Fake News about COVID-19

Recent Advances in computer science and Communications 2023年第9期16卷 58-66页

作者： Shanmugavel, Selva Birunda Rangaswamy, Kanniga Devi Muthukannan, Muthiah Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu Chennai India Department of Civil Engineering KCG College of Technology Chennai India

Aims/Background: Twitter has rapidly become a go-to source for current events coverage. The more people rely on it, the more important it is to provide accurate data. Twitter makes it easy to spread misinformation, which can have a significant impact on how people feel, especially if false information spreads around COVID-19. Methodology: Unfortunately, twitter was also used to spread myths and misinformation about the illness and its preventative immunization. So, it is crucial to identify false information before its spread gets out of hand. In this research, we look into the efficacy of several different types of deep neural networks in automatically classifying and identifying fake news content posted on social media platforms in relation to the COVID-19 pandemic. These networks include long short-term memory (LSTM), bi-directional LSTM, convolutional-neural-networks (CNN), and a hybrid of CNN-LSTM networks. Results: The "COVID-19 Fake News" dataset includes 42,280, actual and fake news cases for the COVID-19 pandemic and associated vaccines and has been used to train and test these deep neural networks. Conclusion: The proposed models are executed and compared to other deep neural networks, the CNN model was found to have the highest accuracy at 95.6%. © 2023 Bentham science Publishers.

关键词： COVID-19

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Instructor-inspired Machine Learning for Robust Molecular Property Prediction 38

Instructor-inspired Machine Learning for Robust Molecular Pr...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Wu, Fang Jin, Shuting Li, Siyuan Li, Stan Z. Computer Science Department Stanford University United States School of Computer Science and Technology Wuhan University of Science and Technology China School of Engineering Westlake University China

Machine learning catalyzes a revolution in chemical and biological science. However, its efficacy heavily depends on the availability of labeled data, and annotating biochemical data is extremely laborious. To surmount this data sparsity challenge, we present an instructive learning algorithm named InstructMol to measure pseudo-labels' reliability and help the target model leverage large-scale unlabeled data. InstructMol does not require transferring knowledge between multiple domains, which avoids the potential gap between the pretraining and fine-tuning stages. We demonstrated the high accuracy of InstructMol on several real-world molecular datasets and out-of-distribution (OOD) benchmarks. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep Neural Network and GAN-Based Reversible Data Hiding in Encrypted Images: A Privacy-Preserving Approach

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SN computer science 2024年第1期5卷 45页

作者： Nalavade, Jagannath E. Patil, Akshta Buchade, Amar Jadhav, Nagesh Department of Computer Science and Engineering MIT Art Design and Technology University School of Computing Pune India Department of Computer Engineering Rajarambapu Institute of Technology Sakharale Sangli India

In light of recent incidents involving the leakage of private photographs of Hollywood celebrities from iCloud, the need for robust methods to safeguard image content has gained paramount importance. This paper addresses this concern by introducing a novel framework for reversible image editing (RIT) supported by reversible data hiding with encrypted images (RDH-EI) techniques. Unlike traditional approaches vulnerable to hacking, this framework ensures both efficient and secure data embedding while maintaining the original image’s privacy. The framework leverages two established methods: secret writing and knowledge activity. While secret writing is susceptible to hacking due to the complex nature of cipher languages, RDH-EI-supported RIT adopts a more secure approach. It replaces the linguistic content of the original image with the semantics of a different image, rendering the encrypted image visually indistinguishable from a plaintext image. This novel substitution prevents cloud servers from detecting encrypted data, enabling the adoption of reversible data hiding (RDH) methods designed for plaintext images. The proposed framework offers several distinct advantages. Firstly, it ensures the confidentiality of sensitive information by concealing the linguistic content of the original image. Secondly, it supports reversible image editing, enabling the restoration of the original image from the encrypted version without any loss of data. Lastly, the integration of RDH techniques designed for plaintext images empowers the cloud server to embed supplementary data while preserving image quality. Incorporating convolutional neural network (CNN) and generative adversarial network (GAN) models, the framework ensures accurate data extraction and high-quality image restoration. The applications of this concealed knowledge are vast, spanning law enforcement, medical data privacy, and military communication. By addressing limitations of previous methods, it opens new avenues

关键词： Deep hiding Deep neural network GAN model Reversible data hiding (RDH)

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A Machine Learning Approach for the Identification and Classification of Cardiovascular Diseases Using ECG Signals and Sensor data

A Machine Learning Approach for the Identification and Class...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Shaik, Nazeer Dharani, M. Kumar, Bura Vijay Gupta, Vikas Singh, Jagendra Pal, Yogesh SRIT-Autonomous Department of Computer Science Engineering Anantapur India K S R Institute for Engineering and Technology School of Electronics and Communication Engineering Tiruchengode India SR University School of Computer Science and Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350394412

Cardiovascular diseases (CVDs) continue to be a major concern in the medical field to date. Among the many diagnostic tools, electrocardiogram (ECG) remains one of the main ways with which to detect cardiac abnormalities. Although classic ECG interpretation is powerful, it is a laborious and time-demanding task and tends to be onerous for physicians. This study suggests a means of effective machine learning to increase the accuracy and speed of diagnosing CVDs based on ECG signals and other sensor data. On the other hand, we are concerned with the application of Frequency Domain Measures in deep-learning methodologies of image classifiers. Other models include the raw ECG sent to a 1-dimensional convolutional model together with the XGBoost model for the time-series feature-based prediction. Preliminary assessment reveals that Frequency Domain Measures provide good atrial fibrillation prediction but are quite limited for other types of arrhythmias. Computational results for the XGBoost model are, however, efficient for predictions on long data but proceed at the cost of extended inference times, considering the computational demand of the pre-processing stage. This paper opens up the scope of the potential for machine learning in revolutionizing ECG-based CVD detection, and we have to keep on the protocols of refinement and research toward this usage. © 2024 IEEE.

关键词： Frequency domain analysis

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Profit Demand Delegated Proof of Stake: Hypothetical Design Concept for Blockchain

Profit Demand Delegated Proof of Stake: Hypothetical Design ...

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Gupta, Udayan Das Haque, Md. Mokammel Reza, Ahmed Wasif Arefin, Mohammad Shamsul Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh Sciences University of Adelaide School of Computer and Mathematical Adelaide Australia East West University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Delegated Proof of Stake (DPoS) is indeed a fascinating consensus algorithm used on various blockchain platforms. It was designed to address some of the scalability and energy consumption issues associated with Proof of Work (PoW) while maintaining decentralization to a certain extent. But, DPoS has some disadvantages in the distribution of profit share for stake forwarding nodes to delegate nodes. The profit share is controlled by the delegate nodes rather than the staking nodes or token holder voting nodes. Here, in Profit Demand Delegated Proof of Stake (PD-DPoS), we introduced a new mechanism to facilitate the token holder voting nodes to demand a rate of profit/profit share. PD-DPoS will encourage the delegate nodes to invest in infrastructure for high connectivity and fast computing crypto systems. As the system builds targeting fast and reliable computing by extending the opportunity of demanding profit share for token holder voter nodes will experimentally perform and prove better than the DPoS. © 2024 IEEE.

关键词： Blockchain

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Enhancing sarcasm detection through grasshopper optimization with deep learning based sentiment analysis on social media

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1785-1791页

作者： Srinu, Nidamanuri Sivaraman, K. Sriram, M. School of Computing Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai 600073 India

Detecting sarcasm in social media presents challenges in natural language processing (NLP) due to the informal language, contextual complexities, and nuanced expression of sentiment. Integrating sentiment analysis (SA) with sarcasm detection enhances the understanding of text meaning. Deep learning (DL), utilizing neural networks to grasp lexical and contextual features, offers a method for sarcasm detection. However, current DL-based sarcasm detection methods often overlook sentiment semantics, a crucial aspect for improving detection outcomes. Therefore, this study develops a new sarcasm detection using grasshopper optimization algorithm with DL (SD-GOADL) technique. The SD-GOADL technique aims to explore the patterns that exist in social media data and detect sarcasm. To obtain this, the SD-GOADL technique undergoes data pre-processing and Glove based word embedding technique. Next, the classification of sarcasm takes place using deep belief network (DBN) system. For enhancing the detection results of the DBN approach, the SD-GOADL technique uses GOA for hyperparameter selection process. The stimulation outcome of the SD-GOADL technique is tested on a sarcasm dataset and the results highlight the significant performance of the SD-GOADL technique compared to recent models. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep learning Grasshopper optimization algorithm Natural language processing Sarcasm detection Sentiment analysis

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Major Software Security Risks in Banking Industry: Design Phase Perspective 3

Major Software Security Risks in Banking Industry: Design Ph...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Srivastava, Archana Singh, Nilu Suhail, Kamran Ybn University Department of Computer Science and Engineering Ranchi India Klep University Department of Computer Science and Engineering India School of Management Sciences Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350336238

As the globe transitions to the internet age, software has emerged as the factor primarily essential to the success of the digital realm. Software now permeates every aspect of daily existence in the age of computers. It is relatively simple to administer software in any business for employees, employers, and other components of the organization. Our daily lives are made more convenient and hassle-free by this software. Shopping, news, processing sales data, architectural, medical, banking, and other aspects of our lives are all handled digitally. One of the most significant fields today is online banking, which also relies on software. Nevertheless, software security is also a significant concern. Any program, especially banking software, that has security flaws can cause us problems. The development of secure software becomes extremely difficult if there is a compromise at the design stage. Therefore, it must be avoided throughout the design phase. Software security risks are software flaws that allow harmful activities to occur accidentally. Consequently, it is a matter that must be properly managed during the design phase. The majority of Developers rely more on their knowledge and experience in risk management than on risk management frameworks. From the Common Weakness Enumeration (CWE), the authors have compiled a list of potential vulnerabilities that could be discovered during software design. © 2023 IEEE.

关键词： Risk management

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Curve Monitoring and Alert system for Smart Transportation 1

Curve Monitoring and Alert System for Smart Transportation

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Natarajan, Yuvaraj Sri Preethaa K, R. Viswanathan, Shrinithi Jayarathinam, Sandhiya Balasubramaniam, Vikas Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kyungpook National University Department of Robot and Smart System Engineering Daegu Korea Republic of

ISBN: (纸本)9798350372748

The proposed IoT-based Smart Curve Monitoring and Alert system is designed to improve road safety through the fusion of embedded systems, machine learning and edge computing technologies. The proposed system is primarily powered by ESP32 microcontroller, continuously gathers data from an array of sensors, including accelerometers, gyroscopes and a potentiometer to set the speed limit. This collected data is then subjected to analysis by a Convolutional Neural Network (CNN) model, which has been trained to recognize driving patterns, especially when navigating curves. Here, CNN classifies the vehicle's behavior as safe and risky, and if a risky behavior is identified, it triggers an alert and dispatches relevant data to a NodeMCU device. The NodeMCU plays a crucial role in the system by monitoring the communication tasks. It receives the alert from the ESP32, validates the vehicle's speed against the preset limit, and, if necessary, activates a GSM module to send an immediate notification to designated guardian. To ensure real-time monitoring and awareness, all pertinent information will be displayed on the LCD screen, including the vehicle's behavior classification, current speed, speed limit and alert status. By addressing reckless driving behaviors, particularly on curves, the system contributes significantly to safer roads and reinforces the importance of responsible driving practices. © 2024 IEEE.

关键词： Convolution

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Sentirec - A Sentiment Analysis with Recommendation system 8

Sentirec - A Sentiment Analysis with Recommendation System

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8th International Conference on Communication and Electronics systems, ICCES 2023

作者： Oswalt Manoj, S. Catherine Chandralekha, T. Dharshini, R.M. Hemadharsini, P. Sri Krishna College of Engineering and Technology Department of Computer Science and Business System Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350396638

The standard language is assessed, and the feelings transmitted by the individual are brought up. The purpose of sentiment analysis is to determine the polarity of a person's textual opinion. Most of the people use social media to market ourselves and share all knowledge, thoughts, ideas, and experiences with the rest of the world. Sentiment analysis has been studied by several internet companies that offer materials based on human emotions expressed through social media. Many people use social media to post and share their thoughts and feelings. The sentiment analysis standard assorts a sentence with all forms of emotions, each of which has an intensity. This study includes a sentiment analysis of diverse perspectives on social topics that are trending on social media, along with a recommendation system. This method recommends related content to viewers based on their current statement or issue. The algorithm also suggests customized video sets to users based on their social media activity, such as diverse perspectives on a particular social problem about which they actively express their thoughts on social media. NLP algorithms are utilized for sentiment analysis and recommendation systems. © 2023 IEEE.

关键词： Recommender systems

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Algorithmic Innovations in Machine Learning for Drug Discovery and Pharmaceutical Innovations 2

Algorithmic Innovations in Machine Learning for Drug Discove...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sangeetha, M. Harin Fernandez, F. Mary Ganesh Ramachandran, A. Saravanan, S.K. Bhanumathi, M. University of Technology and Applied Sciences Department of Computer Engineering Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats BigData and Network Security Department of Computers Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

Algorithmic advancements in machine learning have transformed drug discovery and pharmaceutical innovation. This study examines how machine learning might speed medication development and pharmaceutical innovation. Data from genomes, chemistry, and clinical trials is collected to start the research. Careful data cleansing, normalization, and feature engineering prepare the data for model building. Random Forest, SVM, NN, Decision Tree, and Logistic Regression are tested for prediction. ROC AUC results show that Random Forest is the most accurate model at 86%. Ensemble approaches have drug discovery potential. Beyond accuracy, models are assessed for precision, false positive, and false negative rates. Drug discovery relies on precision to measure positive predictions. The Random Forest model makes the most accurate positive predictions at 0.87. However, false positives and negatives also matter in drug discovery. The Decision Tree model contains the falsest positives (35), underlining the need to reduce them to save resources. The Neural Network approach reduces false negatives to 12, ensuring good medication candidates are not overlooked. The results show that different machine learning models have trade-offs, underscoring the necessity for a personalized strategy depending on objectives and restrictions. Algorithmic breakthroughs in pharmaceutical innovation reduce medication development time by 40% and cost by 25%, according to the study. © 2024 IEEE.

关键词： Drug discovery

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