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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Padmanabhan, P. Bhuvana, J. Naval, Preeti Faujdar, Pramod Kumar Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mechanical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

the automobile industry is an unexpectedly changing sector that has embraced the modern era to take advantage of competitive benefits. Natural language processing (NLP) performs a crucial position in information patron sentiment, as it can hit upon diffused nuances in textual content that people can also leave out or misinterpret. this paper explores how NLP can be used to tune and analyze client sentiment inside the automotive industry. We describe the exclusive types of sentiment analysis processes currently being applied in this enterprise, along with sentiment dictionaries, device learning-primarily based methods, and deep getting-to-know-primarily based strategies. We then offer numerous examples of how sentiment evaluation may be applied in the car industry, such as enhancing customer service, detecting and responding to patron reviews, and growing marketing campaigns. Finally, we speak of some of the challenges associated with sentiment evaluation inside the automotive enterprise and provide pointers for overcoming those demanding situations. Sentiment-primarily based Natural Language processing (NLP) is a modern utility of NLP techniques for extracting sentiment from text-based resources. the utility of NLP for sentiment evaluation can provide automotive enterprise stakeholders with critical insights into customer sentiment concerning their emblem, competition, product lines, and different marketplace factors. Using sentiment-based NLP, automotive industry stakeholders can benefit from essential insights into patron sentiment and leverage them to serve their customers better. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Analysis and the cause of corrosion and materials loss at the base of a three-phase separator in petroleum-based processing 5

Analysis and the cause of corrosion and materials loss at th...

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5th international conference on Mechanical Engineering and Materials, ICMEM 2024

作者： Ding, H. Li, H.B. Qi, D.T. Zhang, Z. Qi, G.Q. Wei, B. Cai, X.H. Tubular Goods Research Institute China National Petroleum Corporation State Key Laboratory for Performance and Structure Safety of Petroleum Tubular Goods and Equipment Materials Shaanxi Xi'an710065 China Department of Applied Chemistry School of Science Northwestern Polytechnical University Xi'an710072 China

ISBN: (数字)9781510688971

ISBN: (纸本)9781510688964

the three-phase separator was perforated after four years' service. the perforation position is at the bottom of the vessel under the baffle plate in an oil transfer station. the cause of perforation was investigated by chemical analysis using a direct-reading spectrometer, microstructure examined by optical microscope(OM), and lab simulated corrosion experiments by electrochemical workstation, and the corrosion products examined with scanning electron microscopy(SEM), energy dispersive spectrometer(EDS) in this paper. the analyses indicated that the coating at the base of the pressure vessel failed and this led to the direct exposure of the carbon steel plate to the corrosive medium. the electrical contact between the carbon steel and the stainless steel, sets up a galvanic cell that accelerated the corrosion of the pressure vessel which eventually led leads to the perforation. © 2025 SPIE.

关键词： Galvanic corrosion

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Directing Natural Language processing Text Similarity Challenges in Social Media with AI Techniques 5th

Directing Natural Language Processing Text Similarity Challe...

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Madhan, N. Dheva Rajan, S. Jain, Madhuri University of Technology and Applied Sciences Al Musannah Muladdah Oman Banasthali Vidyapith Rajasthan Sikar India

ISBN: (纸本)9789819761050

Text similarity is important in natural language processing (NLP), including information retrieval, text categorization, and plagiarism detection. this study investigates the field of NLP, especially addressing the growing problem of plagiarism enabled by social networking (SN) platforms. Plagiarism, a global issue, damages academic and professional integrity, with social media serving as a fertile ground for uncredited material duplication. the investigation thoroughly assesses a variety of text similarity metrics, including Jaccard, Cosine, Euclidean, and Hamming distances, as well as novel techniques such as Minhash, and N-gram analysis. the approaches are used to find and measure similarities between documents, which adds to the arsenal of plagiarism detection algorithms. this study identifies essential variables in constructing effective plagiarism detectors, such as proper text representation, similarity metric selection, parameter tuning, and ensemble approaches. the study stresses the significance of strong algorithms in fighting plagiarism, given the huge terrain of digital information. the findings demonstrate limits in existing metrics, with each technique highlighting strengths and flaws. the Jaccard and Cosine similarity emphasizes exact overlaps but ignores semantic subtleties, whereas Euclidean distance and Hamming distance struggle with variable-length texts. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hamming distance

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Character Recognition Using Convolutional Neural Networks Based on CUDA

Character Recognition Using Convolutional Neural Networks Ba...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Set Paing Tun Applied Mathematics and Artificial Intelligence Department National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

the article explores character recognition using convolutional neural networks (CNNs) optimized with the CUDA platform to enhance computational efficiency. It outlines the CNN architecture, methods for leveraging GPU-based parallel data processing, and presents experimental results derived from the MNIST dataset. the study highlights that implementing CUDA drastically reduces processing time while maintaining a high level of predictive accuracy. the findings emphasize the potential of GPU acceleration in handling intensive computational tasks, making it a promising approach for real-time applications in image recognition and machine learning.

关键词： Deep learning Training Energy consumption Graphics processing units parallel processing Real-time systems Energy efficiency Convolutional neural networks Character recognition Optimization

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Special issue on advances in techniques for assessment performance portability of HPC applications

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Future Generation Computer Systems 2025年 171卷

作者： Marowka, Ami Stpiczyński, Przemyslaw Wyrzykowski, Roman Parallel Research Lab Petach Tikva49729 Israel Institute of Computer Science Maria Curie-Sklodowska University Lublin20-033 Poland Department of Computer Science Czestochowa University of Technology Czestochowa42-201 Poland

this special issue aims to present new developments and advances in techniques for assessment performance portability of high performance computing applications. It contains revised and extended versions of selected papers presented at the 10th Workshop on Language-Based parallel Programming Models, WLPP 2024, which was a part of 15th international conference on parallel processing and applied mathematics, PPAM 2024, held on September 8–11, 2024, in Ostrava, Czech Republic. © 2025

关键词： HPC applications parallel programming languages Performance portability

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parallel processing and applied mathematics : 4th international conference, PPAM 2001, Nałęczów, Po

Parallel processing and applied mathematics : 4th Internatio...

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2002年

作者： PPAM 2001

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Enhancing Multi-crop Disease Prediction Using Convolutional Neural Networks: A Global Perspective 5th

Enhancing Multi-crop Disease Prediction Using Convolutional ...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Garigipati, Nagababu Nivrithi, Bathini ShreshtaReddy, Dachireddy Eesha, Mummaneni Nikitha, Patlavath G. Narayanamma Institute of Technology and Science Telangana Hyderabad India

ISBN: (纸本)9789819780426

Given the recent rate of population expansion, it is anticipated that by 2050, global crop productivity will need to double. Getting this production result is significantly hampered by pests and diseases. It is essential to offer efficient methods for automatically diagnosing, classifying, and foretelling diseases and pests in agricultural crops. Machine learning techniques can be applied to the data being worked on to extract information and relationships in order to automate this process. Deep learning techniques are employed in the study, which focuses on the categorization, detection, and pest and disease prediction tasks in the agricultural industry. We seek to advance precision agriculture and smart farming by supporting the development of techniques that will allow farmers to use less pesticides and chemicals while protecting and improving the quality and yield of their crops. We’ll employ image classification method, in which the user uploads a photo of the diseased plant or crop, and the system uses deep learning techniques to identify the disease type. And this disease identification will offer advice on how to treat or avoid that crop. the goal is to create a uniform approach for all features and deliver results for all crops throughout the majority of regions in India that are as accurate as possible. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional Neural Networks Deep learning Image processing Plant diseases Prediction

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Optimal Control for Stochastic Multi-agent Systems With the Use of parallel Hybrid Genetic Algorithm 4th

Optimal Control for Stochastic Multi-agent Systems With the...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Akopov, Andranik S. Beklaryan, Armen L. Central Economics and Mathematics Institute of RAS Moscow Russia Moscow Institute of Physics and Technology Moscow Russia HSE University Moscow Russia

ISBN: (纸本)9783031812408

In modern times, stochastic large-scale multi-agent systems (MAS) aimed at supporting socio-economic planning are being developed. there is a well known problem of a high computational complexity task of an optimal control for multiple agents’ behaviour in models of random interactions. In particular, agents (such as sellers and buyers) should make individualised decisions on establishing interconnections to exchange products, money or information at each moment of time. Such decisions affect the values of agents’ utility functions that, as a rule, should be maximised. In fact, each agent forms the set of individual states that define whether or not the interaction with other agents is allowed at moments of time. As a result, the dynamic programming method should be applied at the individual level of each agent maximising its own utility function, that is the extremely complex task. To overcome appropriate difficulties and seek suboptimal individual decisions in such MAS, a novel parallel hybrid real-coded genetic algorithm has been developed. the proposed hybrid method combines the use of the real-coded genetic algorithm (RCGA) for an evolutionary search, particle swarm optimisation for reducing the necessary number of model recalculations and periodically engaging ANN-based surrogate models for the fitness-function approximation. the approach allows researchers to significantly improve the time-efficiency of seeking optimal individualised decisions in MAS while keeping up their quality. Moreover, the distribution type can be one of the decision variables used in RCGAs for maximising the utility function. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stochastic systems

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Optimizing Concurrent Data Structures in Rust

Optimizing Concurrent Data Structures in Rust

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Xin Zhao Collage of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Lock- Free data structures are a key approach in concurrent programming that allows for thread-safe data manipulation without the need for traditional locking methods. they can diminish the overhead of context switching and lock contention in multi-threaded scenarios, which in turn boosts program performance. this paper investigates the efficient construction of lock-free concurrent data structures, harnessing Rust's type system and the commutativity of operations to attain a considerable performance enhancement compared to traditional atomic data types.

关键词： Concurrent computing Costs Commutation Instruction sets Memory management Switches Programming parallel processing Data structures Safety

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Private Computation of Boolean Functions Using Single Qubits 15th

Private Computation of Boolean Functions Using Single Qubit...

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15th international conference on parallel processing and applied mathematics, PPAM 2024

作者： Rahmani, Zeinab Pinto, Armando N. Barbosa, Luis S. Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Instituto de Telecomunicações Aveiro Portugal International Iberian Nanotechnology Laboratory Braga Portugal Department of Computer Science University of Minho Braga Portugal Institute of Systems and Computer Engineering Technology and Science Braga Portugal

ISBN: (纸本)9783031856990

Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. this paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

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