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检索条件"任意字段=Proceedings of the 7th International Conference on Algorithms, Computing and Systems"

共 3437 条记录，以下是931-940 订阅

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E-Hailing from Service Quality Perspective: A Malaysian Based Study 7

E-Hailing from Service Quality Perspective: A Malaysian Base...

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7th international conference on Software Engineering and Computer systems and 4th international conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Mohamed Razi, Mohamed Jalaldeen Tamrin, Mohd Izzuddin Mohd Nor, Rizal Mohd University of Kelaniya Dept of Commerce and Financial Management Kelaniya Sri Lanka International Islamic University Malaysia Dept of Information Systems Kuala Lumpur Malaysia International Islamic University Malaysia Dept of Computer Science Kuala Lumpur Malaysia

ISBN: (纸本)9781665414074

On-demand vehicle acquisition with a driver on internet-based platform (e-hailing) has become one of the standard transport modes nowadays. Accordingly, different stakeholders have shown high interest in this concept, including the research community;thus, several types of research have been carried out from different perspectives. the current researchers investigate this phenomenon from Service Quality Perspective. Finding the antecedents of user/passenger use intention is the main objective of this study. A conceptual framework was developed mainly based on RECSA model. Affordability, Reliability, Safety, and Service were considered as the antecedents of customer intention to use. the proposed model was tested using data collected from 352 Malaysian university students. the outcome of the path analysis performed using SMART PLS (3.0) proved that all four factors considered in this study are statistically significant antecedence of user intention. © 2021 IEEE.

关键词： Quality of service

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2nd international Workshop on Industrial Recommendation systems (IRS) 21

2nd International Workshop on Industrial Recommendation Syst...

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27th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD)

作者： Xu, Jianpeng Wu, Lingfei Pang, Xiaolin Sharma, Mohit Yin, Dawei Karypis, George Basilico, Justin Yu, Philip S. WalmartLabs San Bruno CA 94066 USA JD Com Beijing Peoples R China Google Mountain View CA 94043 USA Baidu Beijing Peoples R China Univ Minnesota Twin Cities Minneapolis MN USA Netflix Los Gatos CA USA Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9781450383325

Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms and social networks, to provide suggestions that a user will most likely consume or connect;thus, improving the user experience [1]. this motivates people in both industry and research organizations to focus on personalization or recommendation algorithms, which has resulted in a plethora of research papers [2, 3]. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment. these key factors include but are not limited to: business metric definition and evaluation, recommendation quality control, data and model scalability, model interpretability, model robustness and fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. the gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions for industrial recommendation systems. this workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue through which academic researchers become aware of the additional factors that may affect the adoption of an algorithm into real production systems, and how well it will perform if deployed. Industrial researchers will also benefit from sharing the practical insights, approaches, and frameworks as well.

关键词： Industrial Recommendation systems

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26th international conference on Tools and algorithms for the Construction and Analysis of systems, TACAS 2020, held as part of the European Joint conferences on theory and Practice of Software, ETAPS 2020

26th International Conference on Tools and Algorithms for th...

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26th international conference on Tools and algorithms for the Construction and Analysis of systems, TACAS 2020, held as part of the European Joint conferences on theory and Practice of Software, ETAPS 2020

ISBN: (纸本)9783030452360

the proceedings contain 60 papers. the special focus in this conference is on Tools and algorithms for the Construction and Analysis of systems. the topics include: What’s Decidable About Program Verification Modulo Axioms?;formalized Proofs of the Infinity and Normal Form Predicates in the First-Order theory of Rewriting;fold/Unfold Transformations for Fixpoint Logic;Verifying OpenJDK’s LinkedList using KeY;analysing installation scenarios of Debian packages;endicheck: Dynamic Analysis for Detecting Endianness Bugs;describing and Simulating Concurrent Quantum systems;EMTST: Engineering the Meta-theory of Session Types;solving Mean-Payoff Games via Quasi Dominions;partial-Order Reduction for Parity Games with an Application on Parameterised Boolean Equation systems;verifying Quantum Communication Protocols with Ground Bisimulation;polynomial Identification of ω-Automata;Advances in Automatic Software Verification: SV-COMP 2020;2LS: Heap Analysis and Memory Safety: (Competition Contribution);COASTAL: Combining Concolic and Fuzzing for Java (Competition Contribution);dartagnan: Bounded Model Checking for Weak Memory Models (Competition Contribution);veriAbs: Verification by Abstraction and Test Generation (Competition Contribution);GACAL: Conjecture-Based Verification: (Competition Contribution);Java Ranger at SV-COMP 2020 (Competition Contribution);JDart: Dynamic Symbolic Execution for Java Bytecode (Competition Contribution);map2Check: Using Symbolic Execution and Fuzzing: (Competition Contribution);deciding the Bisimilarity of Context-Free Session Types;PredatorHP Revamped (Not Only) for Interval-Sized Memory Regions and Memory Reallocation (Competition Contribution);symbiotic 7: Integration of Predator and More: (Competition Contribution);ultimate Taipan with Symbolic Interpretation and Fluid Abstractions: (Competition Contribution);sharp Congruences Adequate with Temporal Logics Combining Weak and Strong Modalities;how Many Bits Does it Take to Quantize Your Neur

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Artificial Intelligence in the Banking Sector in Uzbekistan: Exploring the Impacts and Opportunities 23

Artificial Intelligence in the Banking Sector in Uzbekistan:...

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proceedings of the 7th international conference on Future Networks and Distributed systems

作者： Marina Sagatovna Abdurashidova Muhammad Eid Balbaa Department of International Economics Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9798400709036

the use of artificial intelligence (AI) in the banking sector has been a rapidly growing trend globally, and Uzbekistan is no exception. this research article explores the impact of AI on the banking sector in Uzbekistan, including its benefits and challenges. the study employed a mixed-method approach, including surveys and interviews with banking professionals in Uzbekistan. the results show that AI has already made some significant contributions to the banking sector in Uzbekistan, such as improving customer service, enhancing fraud detection, and enabling more accurate credit scoring. However, there are still some challenges associated with the implementation of AI in the banking sector, such as data privacy concerns, lack of skilled professionals, and regulatory issues. the findings of this research suggest that the banking sector in Uzbekistan could benefit greatly from the continued development and implementation of AI technologies. this research article provides insights and recommendations that could guide the banking sector in Uzbekistan in embracing and leveraging the benefits of AI.

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Exploration of Topic Relevance in Summaries for Re-summarizing using LLM in Crisis Chronology

Exploration of Topic Relevance in Summaries for Re-summarizi...

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international conference on computing and Networking Technology (ICCNT)

作者： Shrikant Manikarao Mahindrakar Amol Dhakne Tamal Mondal Sirshendu Arosh D.Y. Patil International University Pune Maharashtra India Symbiosis Centre for Information Technology Symbiosis International (Deemed University) Pune Maharashtra India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the current work proposes an exploration over the topical relevance of five extractive micro-blog summary techniques i.e. LexRank, LSA, T5, ALBERT & DistilBERT in crisis timeline for situational tweets of Turkey Earthquake 2023. Here, the topic model i.e. LDA is utilized to extract the crisis topics from generated summary using each technique in crisis timeline. then a topical relevance has been formulated a) between incoming corpus (situational tweets) and generated summaries and b) among the summary techniques at distinct time frames t in crisis timeline. the behavioral patterns of each technique at distinct time frame in terms of topical correlation are observed for $6 \boldsymbol \& 7$ February, 2023 to interpret the utility of single or multiple summary algorithms in terms of capturing crisis topics at various time frames t of the crisis. Finally, the summaries of the most suitable techniques are provided as a prompt at every time frame t to Google’s LLM i.e. Gemini AI for re-summarizing at crisis timeline. It is evident from the results that the selection of multiple summaries as a prompt and providing it to LLM has improved the lexical & syntactic relevance of crisis summaries at crisis timeline.

关键词： Manifolds Correlation Earthquakes Syntactics Real-time systems Artificial intelligence

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Message from Safethings 2023 Chairs: 7th ACM/IEEE Workshop on the Internet of Safe things (Safethings 2023)

ACM International Conference Proceeding Series

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ACM international conference Proceeding Series 2023年 IX页

作者： Celik, Z. Berkay Zonouz, Saman Tian, Dave Tian, Yuan Purdue University United States Georgia Institute of Technology United States University of California Los Angeles United States

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Challenges’ Strategies in Virtual Robocup Standard Platform League

Challenges’ Strategies in Virtual Robocup Standard Platform...

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Artificial Intelligence Technology (ACAIT), Asian conference on

作者： Zijian Wang Hongjiang Liu Pingchuan Chen Yinuo He Lingyun Zhu Liangjiang International College ChongQing University of Technology ChongQing China College Of Computer Science And Engineering ChongQing University of Technology ChongQing China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

the RoboCup, an international competition, necessitated an innovative approach in 2022 due to its transition to an online format in China, prompted by an epidemic. this shift demanded the development of more specialized algorithms for the competition. In response to this, we devised a unique algorithm for each challenge. For Challenge 1, we implemented a streamlined greedy algorithm, which rounded the target points to minimize the total time expenditure. In Challenge 2, we adopted an automated obstacle avoidance strategy. For Challenge 3, we calculated the kick angle in real-time to enhance the synergy between the two robots, employing the Sector-angle method to select the optimal shot or pass angle. Our algorithm showed excellent results in the competition, ranking first in the country. In the practical test, our cumulative scores for the three challenges remained consistent at 163.18, 33.65, and 4.8, significantly improving from the pre-optimization scores of 196.2, 51.78, and 3.5, respectively. this indicates the precise and reliable nature of our algorithms. In particular, the sector angle method was notably influential in Challenge 3, swiftly calculating the ideal angle with exceptional accuracy. Before improvements, the algorithm in this project only managed to score 3.5, which was substantially improved post-optimization. Moving forward, these algorithms can be further optimized and adapted to meet the demands of future scenarios.

关键词： Heuristic algorithms Robot sensing systems Real-time systems Reliability Collision avoidance Time complexity Optimization

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Database Querying Optimization via Genetic Algorithm for Biomedical Research 22

Database Querying Optimization via Genetic Algorithm for Bio...

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proceedings of the 2022 7th international conference on systems, Control and Communications

作者： Nguyen thanh Huong Le Minh Hoang School of Electrical and Electronic Engineering Hanoi University of Science and Technology Vietnam and International Research Institute MICA Hanoi University of Science and Technology Vietnam

ISBN: (纸本)9781450397247

thanks to the skyscraping development of hardware and software technologies, the data solutions have become an urgent trend to deal with vast amount of data, especially in biomedical research, human genome and healthcare systems. the healthcare research has always demanded close association with biomedical data to produce personalized medicine and deliver suitable cure and treatments. Nevertheless, coping with huge amount of information from biomedical data requires bulky solutions. In the light of data science, the solution for this issue can change from a theoretical approach to a data-driven approach. Database stores a huge amount of information and particular sets of data can be accessed via queries which are written in specific interface language. In order to manage this amount of data, database optimization is implemented to maximize the speed and efficiency with data retrieval or reduce database system response time. Query optimization is one of the major functionalities in database management systems. the purpose of the query optimization is to determine the most efficient and effective way to execute a particular query by considering several query plans. In this article, genetic algorithm (GA) strategy is utilized for biomedical database systems to execute the query plan. Genetic algorithms are extensively using to solve constrained and unconstrained optimization problems. Based on three main types of rules of GA such as selection, crossover and mutation, the querying can be optimized for solving database problem.

关键词： database querying genetic algorithm biomedical research optimization

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IoT Revolutionization using Federated Learning and Decentralized AI on Edge Devices

IoT Revolutionization using Federated Learning and Decentral...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Diwakar Chaudhary S K Verma Sumit Pundir S Ranganathan V. Umadevi K Srinivasan Department of Management Mangalmay Institute of Management & Technology Greater Noida Uttar Pradesh India School of Business Management Noida International University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Nehru Memorial College (Autonomous) Affiliated to Bharathidasan University Trichy Tamil Nadu India Department of Commerce General Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

this research proposes a novel strategy for addressing the limitations of centralized architectures in IoT data processing. Traditional systems experience significant bandwidth use, privacy difficulties, and scalability issues. To address these difficulties, the proposed approach combines Federated Learning (FL) with Decentralized AI on Edge Devices (DAI-ED). By decentralizing processing duties and utilizing FL methods, data is kept local to edge devices, lowering latency and improving privacy. Deployment entails choosing AI-capable edge devices and configuring them for seamless integration. Secure communication techniques provide privacy throughout FL operations. Model optimization approaches help to improve efficiency even further. Compared to centralized systems, the results show higher model accuracy, decreased communication overhead, and increased resource utilization. the proposed system has an overall accuracy of $\mathbf{9 5 \%}$, with edge device accuracy at $\mathbf{9 4. 5 \%}$ and central server accuracy at $\mathbf{9 6 \%}$. Communication overhead during the training and inference phases is greatly minimized, with the proposed system requiring 150 MB per device against $\mathbf{4 0 0} \mathrm{MB}$ in centralized systems. the proposed system has low memory consumption and computational complexity, but it is very scalable.

关键词： Training Privacy Data privacy Accuracy Protocols Federated learning Data processing Artificial intelligence Optimization Load modeling

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the Relationship Among Production Errors and Working Hours for three Shifts: A Case Study from a Metal Coating Business

The Relationship Among Production Errors and Working Hours f...

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conference of Recent Trends and Applications of Soft computing in Engineering, RTASCE 2024

作者： Duraković, Benjamin International University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9783031828805

the optimal number of working hours in the labor-intensive industry is important to identify the best productivity rate and decrease errors. this research examines the impact of the working hours on the error rate and productivity for three shifts and variability among the shifts in a surface protection manufacturing process. the measurement was taken from the real process and data were analyzed using statistical tools. A strong correlation was found among three-shift productivities. the results showed that there is a slight increase in productivity after the lunch break for all three shifts. A significant difference in productivity has been observed between the first hour and the rest of the day for each shift. the best productivity yield was observed between the 2nd and 7th hour, indicating that longer working hours are less productive and the error rate is increasing. the error rate has a strong positive correlation in the first shift, moderate in the second shift, and weak in the third shift. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Error statistics

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