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Enhancing Product Review System: Leveraging CUDA-BB Algorithm for Sentiment Classification

Enhancing Product Review System: Leveraging CUDA-BB Algorith...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： S Ramanjaneyulu Bhvs Ramakrishnam Raju D Rajya Lakshmi Department of CSE Geethanjali College of Engineering and Technology Keesara(M) Medchal Dist. Telangana INDIA Dept of IT SRKR Engineering College Bhimavaram AP India Department of CSE JNTU Vizianagaram Vizianagaram AP India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Sentiment analysis is an important method in natural language processing (NLP) for detecting emotions and categorizing text as positive, negative, or neutral. It is invaluable for evaluating customer feedback, which is becoming increasingly accessible due to the massive amounts of data published on sites like as Facebook, Twitter, Amazon, and Flipkart. Businesses rely heavily on feedback to improve product quality and maintain a competitive advantage. To improve sentiment analysis, the CUDABB technique is proposed, which uses GPU-based parallel computing to forecast overall smart-phones star ratings. this strategy reduces bias and variation, which improves forecast accuracy and operating efficiency. the approach excels in forecasting smart-phones ratings and demonstrates the value of GPU computing (parallel processing) in sentiment analysis. With a $95.5 \%$ accuracy rate, the proposed classifier outperformed other base models and provided insightful customer feedback to support corporate decision-making.

关键词： Sentiment analysis Accuracy Social networking (online) Reviews Stars Graphics processing units parallel processing Product design Quality assessment Forecasting

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Mobile Game Development Using Unity Engine 13th

Mobile Game Development Using Unity Engine

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13th international conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, mis4TEL 2023

作者： Sapundzhi, Fatima Kitanov, Anton Lazarova, Meglena Georgiev, Slavi Department of Communication and Computer Engineering Faculty of Engineering South-West University "Neofit Rilski" 66 Ivan Mihailov Street Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Mathematics and Infromatics Bulgarian Academy of Sciences 8 Acad. Georgi Bonchev Street Sofia1113 Bulgaria Department of Applied Mathematics and Statistics University of Ruse "Angel Kanchev" 8 Studentska Street Ruse7004 Bulgaria

ISBN: (纸本)9783031421334

Unity is a popular game development platform. Various industries are inspired by it and this can be a positive impact on the learning motivation, career growth and job opportunities. the aim of this paper is to develop and implement a 3D game application for education promoting through innovation and digital skills. the game consists of two parts. the first part of the developed application is a maze that the player must overcome. In this labyrinth the player has to collect a certain number of specific bags that contain pieces of the needed puzzle. the second part is a puzzle that the player begins to arrange. If the puzzle is arranged correctly, then the player can read an interesting fact about what is seen on the picture. the theme given on the picture is an educational theme and it main aim is to develop the educational and mental abilities of the playersp who may also be students. the game is suitable for a wide range of potential players as it can be interesting and educational for children, teenagers and even adults. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image processing

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Research on the Joint Optimization of Traditional Chinese Medicine Pulse Signals Based on BP Neural Networks and Adaptive Filters

Research on the Joint Optimization of Traditional Chinese Me...

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Advanced Algorithms and Control Engineering (ICAACE), international conference on

作者： Mengcong Liu Bo Feng Department of Mathematics and Computer Science Yan'an University Yanan China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

TCM (Traditional Chinese Medicine) pulse diagnosis is an important part of traditional Chinese medical diagnosis, carrying rich clinical information. To address the randomness and complex non-stationarity of pulse signal processing, a new technique for the joint optimization of TCM pulse signals based on BP neural networks and adaptive filters (LMS) is proposed. Traditional signal processing methods often fail to effectively denoise and classify signals, limiting the accuracy of pulse analysis. the adaptive filtering algorithm, combined with the advantages of neural networks, can dynamically adjust the filter parameters in real time to adapt to the dynamic changes of pulse signals. this study explores its applications in pulse signal denoising, feature extraction, and classification, thereby improving the accuracy and efficiency of signal processing. It also provides more reliable support for TCM diagnosis, advancing the modernization of TCM.

关键词： Adaptation models Accuracy Noise reduction Adaptive filters Signal processing algorithms Signal processing Feature extraction Classification algorithms Reliability Optimization

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parallel gEUD Models for Accelerated IMRT Planning on Modern HPC Platforms 1

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14th international conference on parallel processing and applied mathematics, PPAM 2022

作者： Moreno, Juan José Miroforidis, Janusz Kaliszewski, Ignacy Martín Garzón, Gracia Ester Informatics Department ceiA3 University of Almería Almería Spain Systems Research Institute Polish Academy of Sciences Warsaw Poland

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

Radiotherapy treatments apply high doses of radiation to tumorous cells to break the structure of cancer DNA, trying at the same time to minimize radiation doses absorbed by healthy cells. the personalized design of radiotherapy plans has been a relevant challenge since the beginning of these therapies. A wide set of models have been defined to translate complex clinical prescriptions into optimization problems. the model based on the generalized equivalent uniform dose, gEUD, is very relevant for IMRT radiotherapy planning in clinical practice. this way, the expert physicists can tune plans near the prescriptions, solving the optimization problem based on gEUD in a trial-and-error process. the gradient descent methods can be applied for solving these models personalized for every patient. However, their computational requirements are huge. So, to facilitate their use in clinical practice it is necessary to apply HPC techniques to implement such models. In this work, we have developed two parallel implementations of an gEUD model for IMRT planning on multi-core and GPU architectures, as they are increasingly available in clinical settings. Both implementations are evaluated with two Head &Neck clinical tumor cases on modern GPU and multi-core CPU platforms. Our implementations are very useful since they help expert physicists obtain fast plans that can satisfy all the prescriptions. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Enhanced 16 × 16 Dadda Multiplier Using Kung-Brent Adder for Superior parallel processing Performance

Enhanced 16 × 16 Dadda Multiplier Using Kung-Brent Adder fo...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： K Karthigadevi M Sakthimohan S Tamilselvan Bhuvaneswari thiyagarajan G Elizabeth Rani V Sanjeevi Kumar Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of ECE Kalaignar Karunanidhi Institute of Technology Coimbatore Tamil Nadu India Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of ECE Dr. NGP Institute of Technology Coimbatore Tamil Nadu India Department of AI&DS Kalaignar Karunanidhi Institute of Technology Coimbatore Tamil Nadu India Department of CSBS KGiSL Institute of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

this study presents a refined 16x16 Dadda multiplier optimized for parallel processing applications, integrating the Kung-Brent adder to enhance performance. By combining the Dadda multiplier’s efficient partial product reduction with the Kung-Brent adder’s reduced delay and area overhead, the design achieves notable improvements in power efficiency and speed. the use of parallel processing at multiple stages further accelerates computation, ensuring high throughput and low latency. Our thorough simulation and synthesis results demonstrate that this optimized multiplier surpasses traditional designs in key performance indicators, including speed, area, and efficiency. the enhanced design is particularly effective for high-demand parallel processing tasks and offers significant advancements in digital circuit design. this development not only provides a robust solution for efficient arithmetic operations but also paves the way for future innovations in digital multiplier technologies.

关键词： Technological innovation Power demand Multicore processing Scalability parallel processing throughput Delays Low latency communication Adders Arithmetic

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Multi-core parallel processing technique to prepare the time series data for the early detection of DDoS Flooding Attacks 8

Multi-core parallel processing technique to prepare the time...

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8th international conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Kumar, S. Ratan Kumari, V. Valli Raju, K.V.S.V.N. Anil Neerukonda Institute of Technology and Sciences Dept. of Computer Sc. and Engineering Visakhapatnam India Andhra University Dept. of Computer Sc. and Systems Engineering Visakhapatnam India Gayatri Vidya Parishad College for Degree and PG Courses Dept. of Computer Sc. and Engineering Visakhapatnam India

ISBN: (纸本)9789380544434

Distributed Denial of Service (DDoS) attacks pose a considerable threat to Cloud Computing, Internet of things (IoT) and other services offered on the Internet. the victim server receives terabytes of data per second during the DDoS attack. It may take hours to examine them to detect a potential threat, leading to denial of service to legitimate users. processing vast volumes of traffic to mitigate the attack is a challenging task for network administrators. High-performance techniques are more suited for processing DDoS attack traffic compared to Sequential processing Techniques. this paper proposes a Multi-Core parallel processing Technique to prepare the time series data for the early detection of DDoS flooding attacks. Different time series analysis methods are suggested to detect the attack early on. Producing time series data using parallel processing saves time and further speeds up the detection of the attack. the proposed method is applied to the benchmark data set CICDDoS2019 for generating four different time series to detect TCP-based flooding attacks, namely TCP-SYN, TCP-SYN-ACK, TCP-ACK, and TCP-RST. the implementation results show that the proposed method can give a speedup of 2.3 times for processing attack traffic compared to sequential processing © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Denial-of-service attack

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Emission Evaluation for Diesel Vehicles under Typical Operating Conditions 8

Emission Evaluation for Diesel Vehicles under Typical Operat...

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8th IEEE international conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Li, Jiaren Xu, Zhenyi Cao, Yang Xia, Xiushan Kang, Yu Anhui University AHU-IAI AI Joint Laboratory Hefei230601 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China University of Science and Technology of China Department of Automation Hefei230026 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China

ISBN: (纸本)9798350300178

China's ambient air quality has improved significantly as a result of the formulation and implementation of the Action Plan for the Prevention and Control of Air Pollution and the three-Year Action Plan for Winning the Blue Sky Defense War, but the air pollution problem in key regions remains severe, diesel truck pollution has not been effectively addressed, and mobile sources are an important source of nitrogen oxide emissions. the existing diesel vehicle emission model is primarily divided based on average vehicle speed and VSP binning methods, and it lacks an instantaneous vehicle speed model. this paper collected data from 10 diesel vehicles in order to accurately evaluate driving conditions and promote energy saving and emission reduction of heavy-duty diesel vehicles. then we extracted 51,925 kinematic fragment data after cleaning abnormal data and built a kinematic feature evaluation system of diesel vehicles closely related to NOx emission. the sample data of kinematic segments were clustered into two groups of low-speed conditions and medium-speed conditions using the standardized mutual information to extract the characteristics with strong correlation with NOx and the vehicle driving conditions assessment model of cluster analysis. From the clustering results, the high NOx emission is completely classified into the low-speed working condition, which matches the conclusion that the high NOx emission region appears in the low-speed acceleration region reached by previous researchers through experimental studies, indicating that the kinematic feature evaluation model of diesel vehicles established in this paper is reasonable and effective. © 2023 IEEE.

关键词： Cluster analysis

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parallel Identification of Unique Sequences in Nuclear Structure Calculations 1

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14th international conference on parallel processing and applied mathematics, PPAM 2022

作者： Langr, Daniel Dytrych, Tomáš Faculty of Information Technology Czech Technical University in Prague Thákurova 9 Praha16000 Czech Republic Nuclear Physics Institute Czech Academy of Sciences Řež 130 Řež25068 Czech Republic Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

Reducing the set of sequences into the set of sequences that are unique can save a lot of memory space in computer programs. We study this problem on the symmetry-adapted no-core shell model (SA-NCSM) nuclear structure calculations, where duplicated sequences of different kinds naturally emerge in the data of the basis of the Hilbert space physically relevant to a given nucleus. For a fast solution of this problem on multicore architectures, we propose and present a multithreaded algorithm suitable for high performance computing (HPC) environments. Furthermore, we provide an experimental evaluation of this algorithm and show that, in practice, it can significantly reduce the time required to identify unique sequences in a real-world application. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Software architecture

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8th international conference on Variable Neighborhood Search, ICVNS 2021

8th International Conference on Variable Neighborhood Search...

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8th international conference on Variable Neighborhood Search, ICVNS 2021

ISBN: (纸本)9783030696245

the proceedings contain 14 papers. the special focus in this conference is on Variable Neighborhood Search. the topics include: Scheduling of Patients in Emergency Departments with a Variable Neighborhood Search;A GRASP/VND Heuristic for the Heterogeneous Fleet Vehicle Routing Problem with Time Windows;using K-Means and Variable Neighborhood Search for Automatic Summarization of Scientific Articles;BVNS Approach for the Order processing in parallel Picking Workstations;variable Neighborhood Descent Branching applied to the Green Electric Vehicle Routing Problem with Time Window and Mixed Fleet;a Variable Neighborhood Heuristic for Facility Locations in Fog Computing;A GRASP/VND Heuristic for the Generalized Steiner Problem with Node-Connectivity Constraints and Hostile Reliability;Max-Diversity Orthogonal Regrouping of MBA Students Using a GRASP/VND Heuristic;scheduling in parallel Machines with Two Servers: the Restrictive Case;reduced Variable Neighbourhood Search for the Generation of Controlled Circular Data;sequential and parallel Scattered Variable Neighborhood Search for Solving Nurikabe;A Hybrid VNS for the Multi-product Maritime Inventory Routing Problem.

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Transformers for COVID-19 Misinformation Detection on Twitter: A South African Case Study 1

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8th Annual international conference on Machine Learning, Optimization and Data science (LOD)

作者： Strydom, Irene Francesca Grobler, Jacomine Stellenbosch Univ Dept Ind Engn Stellenbosch Western Cape South Africa

ISBN: (数字)9783031255991

ISBN: (纸本)9783031255984;9783031255991

the aim of this paper is to investigate the use of transformerbased neural network classifiers for the detection of misinformation on South African Twitter. Twitter COVID-19 misinformation data from four publicly available datasets are used for training. Four different transformer-based embedding methods are used, namely: BERT, CTBERT, ELECTRA, and LAMBERT. A neural network classifier is trained for each embedding method, and the architectures are optimized with the hyperband optimization algorithm. the model using the LAMBERT embedding method attains the highest F1-score (0.899) on the test data. the model does not generalize well to the South African context however, since it fails to reliably distinguish between general Tweets and COVID-19 misinformation Tweets when applied to the unlabeled South African data. the classifier does detect instances of misinformation that are consistent with known COVID-19 misinformation spread in South Africa, but these are in the minority. It is therefore recommended that misinformation datasets specific to the South African context be curated in order to facilitate future research efforts dedicated to misinformation detection on South African Twitter.

关键词： Misinformation Transformers Social media Twitter Transfer learning Natural language processing

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