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检索条件"任意字段=IEEE International Conference and Workshops on Engineering of Computer Based Systems"

共 89038 条记录，以下是41-50 订阅

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Generative Artificial Intelligence based Biomedical Applications for Pharmaceutical Industry

Generative Artificial Intelligence Based Biomedical Applicat...

引用

2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bhatt, Stuti Sharma, Sachin Uttaranchal University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331512965

The quick advancement in AI has led to the development of refined large language models (LLM). While LLM is a specific type of generative AI focused on generative text generative AI is a broader category encompassing various AI systems that can generate new content including texts, images, music, and more generation models. Generative AI is transforming various fields, like education, finance, marketing, fashion, gaming, medical area or healthcare. Generative AI finds its applications in improving drug discovery, biomedical instrument design, and healthcare delivery within the pharmaceutical industry. In this study, we aim to find generative artificial intelligence-based biomedical applications for the pharmaceutical industry. © 2025 ieee.

关键词： Generative adversarial networks

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SMT-based Layout Synthesis for Silicon-based Quantum Computing with Crossbar Architecture 24

SMT-based Layout Synthesis for Silicon-based Quantum Computi...

引用

43rd international conference on computer-Aided Design, ICCAD 2024

作者： Huang, Sheng-Tan Jiang, Ying-Jie Fang, Shao-Yun Cheng, Chung-Kuan Department of Electrical Engineering National Taiwan University of Science and Technology Taiwan Department of Computer Science and Engineering University of California San Diego United States

ISBN: (纸本)9798400710773

With the announcement of the most advanced silicon spin quantum-bit (qubit) chip by Intel, silicon-based quantum circuit manufacturing technology has shown the superior potential to realize quantum computing to other technologies because of the mature semiconductor manufacturing technologies and the compatibility with electronics. Due to the bottleneck in scalability caused by interconnections in silicon-based quantum circuits, crossbar architectures serve as one of the most promising solutions for circuitry implementation. This paper proposes the first satisfiability modulo theories (SMT) formulation to optimally solve the layout synthesis problem by simultaneously performing scheduling, mapping, and routing for silicon-based quantum systems with a specific crossbar architecture. The experiments demonstrate that the proposed method can effectively generate layout synthesis results with minimal usage of swap and shuttle gates within the shortest circuit depths, and the solutions greatly outperform those derived from a state-of-the-art work. © 2024 Copyright is held by the owner/author(s).

关键词： Qubits

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Framework for Cloud-based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

引用

2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 ieee.

关键词： Cloud platforms

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Utilizing Ontologies for Combinatorial Testing 18

Utilizing Ontologies for Combinatorial Testing

引用

18th ieee international conference on Software Testing, Verification and Validation workshops, ICSTW 2025

作者： Wotawa, Franz Graz University of Technology Institute of Software Engineering and Artificial Intelligence Graz Austria

ISBN: (纸本)9798331534677

Test case generation using combinatorial testing requires an input model comprising parameters and their ranges of possible values, i.e., their domains, which might be appropriate for ordinary applications but not for more sophisticated application domains like autonomous systems. Therefore, we discuss the use of ontologies for modeling and its utilization for combinatorial testing. We focus on ontologies describing the interface between the system under test and its environment, discuss the rationale behind such an approach, and introduce the basic algorithm for mapping ontologies to input models. We further consider modeling challenges and provide some initial modeling principles. © 2025 ieee.

关键词： Ontology

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MapFormer: Attention-based multi-DNN Manager for Throughout & Power Co-Optimization on Embedded Devices 24

MapFormer: Attention-based multi-DNN Manager for Throughout ...

引用

43rd international conference on computer-Aided Design, ICCAD 2024

作者： Karatzas, Andreas Anagnostopoulos, Iraklis School of Electrical Computer and Biomedical Engineering Southern Illinois University Carbondale United States

ISBN: (纸本)9798400710773

In the context of modern services that use multiple Deep Neural Networks (DNNs), managing workloads on embedded devices presents unique challenges. These devices often incorporate diverse architectures, necessitating advanced management solutions to efficiently deploy multi-DNN workloads. Traditionally, the focus has been on improving throughput, while power optimization has received less attention. This paper presents MapFormer, a new manager that uses attention-based mechanisms to enhance both throughput and power efficiency. MapFormer intelligently assigns multi-DNN workloads to different computing components of embedded systems—CPU, GPU, and DLA—and adjusts operational frequencies to optimize power use. Experimental results show that MapFormer significantly improves average throughput under set power budgets by 90.8%, offering a promising approach for managing complex workloads on heterogeneous embedded systems. © 2024 Copyright is held by the owner/author(s).

关键词： Budget control

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Dynamic Dictionary Design for Localization in Automotive Radar systems

Dynamic Dictionary Design for Localization in Automotive Rad...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bishe, Farhan Saif, Mohammed Li, Jun Valaee, Shahrokh Department of Electrical & Computer Engineering University of Toronto Toronto ON CA NXP Semiconductors San Jose CA USA

ISBN: (纸本)9798350368741

This paper proposes a dynamic orthogonal matching pursuit (OMP)-based localization for automotive radar systems. At each time instant, three dictionaries are designed based on prior information on targets' approximate locations. We use mutual coherence as the criterion for designing each dictionary. The mutual coherence minimization problem over each dictionary is developed as a selection problem in the binary domain. Afterward, the OMP algorithm is used to perform the direction of arrival (DoA) estimation over each dictionary, and the estimated DoAs are fused together through averaging to obtain the final DoA estimation. We show that the proposed method significantly outperforms the uniform grid dictionary and improves localization accuracy. © 2025 ieee.

关键词： automotive radar DoA estimation orthogonal matching pursuit sparse recovery

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CNN-DBO: Dung Beetle Optimization based Urban Area Change Detection Using Satellite Images

CNN-DBO: Dung Beetle Optimization Based Urban Area Change De...

引用

2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pujari, Jambukeshwar Wasim, Javed Tripathi, Aprna Mangalayatan University Department of Computer Engineering and Applications Aligarh20221385 India Mangalayatan University Department of Computer Engineering and Applications Aligarh India Manipal University Department of Data Science Engineering Jaipur India

ISBN: (纸本)9798331512965

In recent years, Geographic Information systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective. © 2025 ieee.

关键词： Convolutional neural networks

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A Comprehensive Study of Case-based Reasoning in Information Retrieval: An Artificial Intelligence Approach

A Comprehensive Study of Case-Based Reasoning in Information...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Solanki, Surabhi Verma, Seema Biswas, Prolay Roy, Pulakesh Singh, Aparna Sharma, Surbhi Computer Science & Engineering Banasthali Vidyapith Rajasthan Tonk India Scset Bennett University Greater Noida India Education National Institute of Technical Teachers' Training and Research Department of Electrical & Electronics Engineering Bhopal India Computer Science & Engineering Dayananda Sagar University Ramanagara India Computer Science& Engg Brainware University Kolkata India

ISBN: (纸本)9798331508685

Information Retrieval (IR) systems which provide accurate and contextually relevant information to healthcare and law etc. where accuracy is very crucial. Traditional information retrieval methods fundamentally based on keyword matching and statistical models usually difficult to achieve the semantic meaning and contextual nuances of queries. Case-based Reasoning (CBR), as an artificial intelligence technique, provide advance solutions to enhance IR systems based on a set of past cases through adaptive context-supporting retrieval. By taking advantage of past cases data, CBR allows IR systems to interpret complex context-sensitive queries and offer personalized results. The paper provides an overview of traditional IR models, examines the CBR integration into these systems, and evaluates the impact of CBR on retrieval effectiveness and personalization. And finally, we elaborate on the challenges involved with scaling and adapting cases towards real-time retrieval and provide some future directions for research, including integrating machine learning techniques and hybrid models to enhance the relevance, interpretability, and scalability of CBR-based IR systems. © 2025 ieee.

关键词： Case based reasoning

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Effectiveness of the Programmed Visual Contents Comparison Method for Two Phase Collaborative Learning in computer Programming Education: A Case Study 10th

Effectiveness of the Programmed Visual Contents Comparison M...

引用

29th international conference on Database systems for Advanced Applications (DASFAA)

作者： Thanh Ha Nguyen Sun, Yi Nishida, Takeshi Wang, Xiaonan Ohtsuki, Kazuhiro Kiyomitsu, Hidenari Kobe Univ Kobe Hyogo Japan Kobe Inst Comp Kobe Hyogo Japan

ISBN: (纸本)9789819609130;9789819609147

This paper introduces the Programmed Visual Content Comparison (PVCC) for collaborative programming learning. The PVCC is based on comparing two displayed visual objects produced by programming. This feature enables candidates to take the tests in a lighthearted and fun manner because they only judge "which is more difficult." The test based on the PVCC method works well to assess a candidate's programming abilities related to Panoramic Understanding of Programming (PUP). We conducted a collaborative learning experiment with students in a programming class using the PVCC method. This experiment method contains two phases of activity. First, each candidate answers a problem with the reason for the judge. In the second (collaborative phase), candidates discuss their answers and reasons in a group and then make the group answer with its explanation. The result from the experiment suggests a potential that collaborative learning facilitated by the PVCC method is beneficial for students in programming education by improving understanding and problem-solving skills in programming.

关键词： computer science education programming training collaborative learning software engineering

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A Comprehensive Hybrid Model for Movie Recommendation: Integrating CNN and Traditional Algorithms

A Comprehensive Hybrid Model for Movie Recommendation: Integ...

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2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Venkatesam, Anne Sreyas Karnakoti, Nandini Sri, Ramoji Sai Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Kukreja, Vinay Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Hyderabad India Chitkara University Centre for Research Impact & Outcome Punjab Rajpura India

ISBN: (纸本)9798331529833

In recent times, a significant portion of entertainment consumption comes from watching movies, prompting movie creation and researchers to seek expert systems that can accurately predict a movie's success probability before production. These days people are used to these things that they want all the recommendations, even though it is not that difficult to choose movies based on own preferences. Many users often rely on search engines for specific movie-related information, highlighting the importance of recommendation systems that analyze past search behaviors and interests. This study develops a movie recommendation system using publicly available Internet Movie Database (IMDB) data, employing a hybrid model approach that integrates Collaborative Filtering (KNN), Latent Factor Modeling (SVD), Content-based Filtering (TF-IDF), and Sentiment Analysis (CNN). It further presents an analysis of Collaborative and Content-based recommender systems to assist in selecting suitable machine learning algorithms based on specific requirements. The model achieves an impressive accuracy of 0.9239 with the CNN model, surpassing all benchmark models. These findings underscore the significant potential of predictive and prescriptive data analytics in information systems, providing valuable insights to support industry decision-making in the movie recommendation domain. Additionally, the system effectively handles edge cases, such as non-existent user IDs or movie titles, and ensures that recommendations exclude already rated movies to maintain relevance. © 2025 ieee.

关键词： Expert systems

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