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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Anil Kumar Pal, Gyanendra Kumar Rajput, Kapil Bhatnagar, Sonika Government Polytechnic Baheri Department of Computer Science and Engineering Uttar Pradesh Bareilly India Unsiet Vbs Purvanchal University Department of Information Technology Uttar Pradesh Jaunpur India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350371055

In recent years, 3-D printing has gained sizable interest in the production enterprise due to its capacity to supply complex designs and prototypes. but the high degree of automation and complexity concerned with the manner also pose demanding situations in detecting and resolving ability faults. To cope with this issue, researchers have turned to device-getting-to-know techniques to broaden automated fault detection systems for 3-D printing. This paper evaluates the numerous gadget mastering procedures in fault detection for 3D printing, including supervised, unsupervised, and semi-supervised mastering methods. The effectiveness and limitations of every process are mentioned at the side of their applications in one-of-a-kind tiers of the 3-D printing method. The analysis shows promising consequences for using gadgets to gain knowledge of fault detection, highlighting the potential for enhancing the general first-class performance of 3-D printing. However, similarly studies and developments are had to deal with demanding situations along with restricted data availability and information heterogeneity. © 2024 IEEE.

关键词： 3D printing Additive manufacturing Anomaly detection Predictive maintenance Quality control

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Emotion Analysis of English-Translated Tamil Literature 15

Emotion Analysis of English-Translated Tamil Literature

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swathi, K. Kamalam, G.K. Suganya Baby, N. Aadhishri, A. Kawethaa Shree, D.K. Kongu Engineering College Department of Computer Science and Design Tamil Nadu India Kongu Engineering College Department of Information Technology Tamil Nadu India Kongu Engineering College Department of Mathematics Tamil Nadu India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu India Ssm College of Engineering Department of Management Studies Tamil Nadu India

ISBN: (纸本)9798350370249

Focus of this study is to investigate the nuanced world of emotions in English-translated Tamil literature through the lens of sentiment analysis. The dataset consists of poems expressing a range of emotions including joy, sadness, love, anger, pride, courage, and fear. To effectively analyze and classify these emotions, we utilized cutting-edge transformer-based models, like BERT, DistilBERT, and RoBERTa, to conduct our analysis. The outcomes of our study showcase the exceptional capabilities of these models in effectively analyzing emotions within poetic expressions. RoBERTa, in particular, emerged as a standout choice, consistently achieving high accuracy, precision, recall, and F1 scores across all emotion categories. Its extensive pre-training and dynamic masking techniques allowed itto excel in capturing the intricate emotional nuancesembedded in the translated poems. DistilBERT demonstrated commendable efficiency without compromising accuracy, making it a practical option for real-time sentiment analysis. BERT, although a strong performer, showcased marginally reduced efficiency compared to RoBERTa and DistilBERT. © 2024 IEEE.

关键词： BERT DistilBERT Emotion Analysis Natural Language Processing poem RoBERTa

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Advanced Machine Learning Algorithms for 1Predictive Analytics in Healthcare to Enhance Patient Outcomes with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Pandiarajan, Rajkumar Jagannathan, J. Ramesh, P.S. Ponmalar, A. Sudha School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

This research aims to explore the use of modern complex defensive machine learning algorithms in the provision of predictive analytics for health improvement. Incorporating electronic health records, medical image information, and genomic data the work proposes and assesses multiple machine learning models including deep learning, ensemble learning, and reinforcement learning. The main findings shown very high performance in disease prediction, in chronic illness and in the application of efficiency improvements in hospitals as a whole. Overall, such threats as data quality, data privacy, and model interpretability have been discussed in the research and emphasize the importance of the machine learning approach in healthcare. It is highlighted that further development and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and machine learning models can be urgent to increase the acceptance among the healthcare staff. This paper enhances the literature on applying machine learning in healthcare systems adding to the repertoire of knowledge for improved patient care. © 2024 IEEE.

关键词： Electronic health record

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Analysis of Urbanization Impact on Land Surface Temperature Variability by Using Landsat Imagery

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SN computer Science 2024年第7期5卷 863页

作者： Mishra, Vikash Kumar Verma, Kamlesh Kumar Pant, Triloki Upadhyay, Govind Murari Singh, Pangambam Sendash Soni, Pramod Kumar Department of Electrical Engineering University of Cape Town Private Bag X3 Rondebosch 7700 South Africa Department of Information Technology Indian Institute of Information Technology Allahabad UP Prayagraj 211015 India Department of Computer Applications Manipal University Jaipur Rajasthan Dehmi Kalan Jaipur 303007 India

Urbanization is one of the most important human-dominated activities that has impacted the Earth’s ecosystem, biodiversity, and regional climate. With the advent of COVID-19 in 2020, the imposition of complete lockdown in India significantly altered urban activities in India in terms of industrial activities, economic development, import–export and means of transport. Environmental experts anticipate substantial alterations in various environmental conditions due to the lockdown measures. With this view, this paper investigates the influence of different levels of urban activities on land surface temperature (LST) in major Indian cities: Bangalore, Delhi, Kolkata, and Mumbai. LST variations are analyzed across pre-COVID and COVID lockdown periods to understand the impact of urban dynamics on surface temperature. A total of 54 images from Landsat 8 sensor data, spanning 2018–2020, are utilized to capture changes in LST amidst varying levels of urban activities. Results indicate higher LST in 2018 and 2019, followed by a reduction in 2020 attributed to COVID-19 related restrictions. The observed decrease in LST during the lockdown underlines the influence of urban activities on surface temperature dynamics. This study underscores the importance of considering urban dynamics in environmental assessments and emphasizes the need for sustainable urban planning and extreme climate mitigation strategies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Land surface emissivity Land surface temperature Landsat Normalized difference vegetation index Thermal images Urbanization

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Large-scale real-world radio signal recognition with deep learning

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Chinese Journal of Aeronautics 2022年第9期35卷 35-48页

作者： Ya TU Yun LIN Haoran ZHA Ju ZHANG Yu WANG Guan GUI Shiwen MAO College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China College of Electronic Science and Engineering National University of Defense TechnologyChangsha 410073China College of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China Department of Electrical and Computer Engineering Auburn UniversityAuburn 36849USA

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems(6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system-Automatic Dependent Surveillance-Broadcast(ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning ***, we conclude this paper with a discussion of open problems in this area.

关键词： Signal recognition Radio signal dataset Automatic Dependent Surveillance-Broadcast(ADS-B) Deep learning Recognition benchmark

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Assessing a BERT-based model for analyzing subjectivity and classifying academic articles

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Multimedia Tools and Applications 2024年第42期83卷 90511-90532页

作者： Mehmood, Atif Shahid, Farah Khan, Rizwan Ahmed, Shahzad Ibrahim, Mostafa M. Zheng, Zhonglong School of Computer Science and Technology Zhejiang Normal University Jinhua321002 China Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt

The metaverse concept extends beyond virtual worlds and can be applied to collaborative analysis environments. Data analysts worldwide may read academic article extracts in real-time in a shared digital workplace to mine and analyze data using the metaverse. Furthermore, a semantic metaverse in natural language processing might also involve creating a digital environment with linguistic and semantic connections. Many dedicated researchers navigating the complexities of natural language processing in the metaverse era have spent considerable time searching for relevant papers. However, online reviews and evaluations of articles are helpful for their assistance and may save the researcher time. In this work, human specialists manually produced a dataset from four conferences and evaluated subjectively using rule-based techniques. Subsequently, we aim to evaluate the effectiveness of pre-trained word embeddings and pre-trained BERT models seamlessly integrated with convolutional neural networks. This endeavor focuses on the subjective analysis and classification of contributions and previous work sentences extracted from academic literature. For comparison, various deep learning architectures were systematically employed, including long short-term memory-GloVe and bi-directional long short-term memory-GloVe, alongside classical machine learning methods. Our findings show that the proposed BERT model achieved state-of-the-art performance in classification and subjective analysis tasks with an accuracy of 91.50% and F1 score of 91.00%. Finally, we plan to utilize sentence similarity to identify contributions within abstracts, thus outlining potential avenues for future research. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Natural language processing systems

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Time- Dependent Eikonal Solution Using Physics-Informed Neural Networks 3

Time- Dependent Eikonal Solution Using Physics-Informed Neur...

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Das, Indrajit Das, Debjit Bhattacharya, Abheepsa Debnath, Papiya Nath, Subhrapratim Chanda, Manash Meghnad Saha Institute of Technology Department of Information Technology Kolkata India Techno International New Town Department of Basic Science & Humanities Kolkata India Meghnad Saha Institute of Technology Department of Computer Science & Engineering Kolkata India Saha Institute of Technology Department of Electronics & Communication Engineering Meghnad Kolkata India

ISBN: (纸本)9798350374643

Many scientific and technical fields employ the eikonal equation. Multiple numerical strategies have been devised throughout the years to solve the eikonal problem. Nevertheless, these techniques necessitate significant alterations to accommodate supplementary principles of physics, such as anisotropy, and may even fail to function properly for some intricate variations of the eikonal equation, necessitating the use of approximation methods. Massive 3D situations that require repeated calculations of velocity variations and initial positions face a processing challenge. This paper introduces a method to address the eikonal equation by utilizing PINNs, a relatively new strategy in the realm of physical science. By decreasing a loss function derived from the eikonal equation, the neural network is instructed to construct path durations that align with the underlying partial differential formula. This work employs an adaptive stimulation factor and flexible scaling of the loss factor variables to enhance velocity of convergence and reaction effectiveness. This additionally shows the technique's adaptability in incorporating moderate distortion and topography of surfaces without boundaries, in contrast to traditional approaches that necessitate substantial changes to the algorithm. © 2024 IEEE.

关键词： Differentiation (calculus)

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Advanced Multi-Spectral Image Processing Techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

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Optimizing Satellite-Based Entanglement Distribution in Quantum Networks via Quantum-Assisted Approaches 1

Optimizing Satellite-Based Entanglement Distribution in Quan...

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： Wei, Xinliang Fan, Lei Guo, Yuanxiong Han, Zhu Wang, Yu Temple University Department of Computer and Information Sciences PhiladelphiaPA United States University of Houston Department of Engineering Technology HoustonTX United States University of Houston Department of Electrical and Computer Engineering HoustonTX United States University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX United States

ISBN: (纸本)9798350366778

Satellite-based quantum networks leverage the advantageous properties of optical signals from satellites to ground stations, to enable the distribution of high-fidelity quantum entanglements over vast distances, thus circumventing the limitations of traditional terrestrial-based systems. However, the satellite-based entanglement distribution coupled with terrestrial quantum swapping in the space-terrestrial integrated network becomes very complex when considering the joint optimization with satellite assignment, resource allocation, and path selections. To tackle this issue, we introduce a hybrid quantum-classical Dantzig-Wolfe decomposition technique by leveraging the strengths of both quantum and classical computing, to solve the joint optimization problem. Through a series of experiments, the paper demonstrates the efficiency and robustness of the proposed methods in addressing large-scale network optimization and balancing qubit usage. The insights generated by this work offer valuable guidance for the design and implementation of satellite-based entanglement distribution for quantum networks, thereby laying the groundwork for the realization of a secure quantum communication infrastructure on a global scale. © 2024 IEEE.

关键词： Quantum entanglement

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Speech-to-text Emotion Detection System using SVM, CNN, and BERT 1

Speech-to-text Emotion Detection System using SVM, CNN, and ...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Yadav, Uma Bondre, Shweta Thakre, Bhakti Likhar, Kiran Shri Ramdeobaba College of Engineering and Management Nagpur India Shri Ramdeobaba College of Engineering and Management Information Technology Department Nagpur India St. Vincent Pallotti College of Engineering and Technology Cyber Security Department Nagpur India Computer Science & Engineering G H Raisoni Institute of Engineering and Technology Nagpur India

ISBN: (纸本)9798350377002

Emotions have a tremendous impact on human experiences and decision-making processes. People are frequently motivated to repeat happy behaviors while avoiding depressing ones. Emotions may spread like wildfire in the modern digital era due to the quick broadcast of information and the heavy use of emotive language in communication. The identification of these emotional undercurrents in a variety of literary forms has been made easier by the analysis of indigenous languages, allowing for prompt interventions when needed. This study presents an algorithm that can analyze statements and then categorize them into sentiments in order to determine a person's emotional state. An analysis of the algorithm's performance shows that BERT performs better than SVM and CNN in a variety of emotional categories, showing better recall, accuracy, and F1-scores in the identification of emotions including Joy, Sadness, Fear, and Anger. For example, BERT outperforms SVM and CNN by large margins, achieving an average F1-score of 0.85 across all emotions. CNN performs competitively, especially when it comes to identifying emotions such as Fear and Sadness, but SVM performs somewhat worse overall than BERT and CNN. Interestingly, all models are good at recognizing Neutral emotions, with BERT and CNN doing somewhat better than SVM. As a result, BERT stands out as the best model for classifying emotions, demonstrating its effectiveness in identifying complex emotional states. © 2024 IEEE.

关键词： Convolutional neural networks

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