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检索条件"任意字段=7th International Conference on Algorithms, Computing and Systems, ICACS 2023"

共 249 条记录，以下是241-250 订阅

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Formal Modeling and Analysis of Transaction Scheduling and Concurrency Control in Grid Databases

Formal Modeling and Analysis of Transaction Scheduling and C...

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Asia Simulation conference 2008/the 7th international conference on System Simulation and Scientific computing（ICSC‘2008）

作者： Yaojun Han College of international Business,Shanghai international Studies University,Shanghai,200083 CHINA Changjun Jiang Department of Computer Science & Engineering,Tongji University,Shanghai,200092,CHINA Xumei Luo College of international Finance and Trade,Shanghai international Studies University,Shanghai,200083 CHINA

Transaction scheduling and concurrency control are two important problems in a distributed environment. A lot of research has been focused on the problems recently, and many models and measures have been proposed. However, some of them didn't fit for grid environment, as grid architecture is a new and distinguished from conventional distributed computing by its heterogeneous, dynamic and autonomous properties. In this paper, we propose a three-level transaction scheduling model for grid database, which not only shows the autonomy of grid but also lightens the pressure of communication and grid scheduler. We consider communication time of a transaction, user's priority and the number of DB sites accessed by the transaction as a priority of the transaction, and give scheduling algorithms for concurrency transaction, which not only supports correctness of the data, but also improves the effectiveness of the system. Petri nets are promising tools for modeling and analysis information processing systems that are characterized as being concurrent, parallel and distributed. We use a dynamic time colored Petri net (DTCPN) to model each level's transaction scheduling and get a DTCPN model for three-level transaction scheduling model of grid database using sharing composition. Finally, we get some important results such as miss rate of transactions by analyzing reachability of Petri nets.

关键词： net Petri Formal Modeling and Analysis of Transaction Scheduling and Concurrency Control in Grid Databases

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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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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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Impact of Machine Learning on Applying the Best Worst Method for Social Sustainability in Manufacturing Supply Chains

Impact of Machine Learning on Applying the Best Worst Method...

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international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Kottala Sri Yogi Dankan Gowda V Atul Kumar Sahu Madan Mohanrao Jagtap Ved Srinivas Ibrahim Abdullah Department of Operations Symbiosis Institute of Business Management Hyderabad Symbiosis International University Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Industrial and Production Engineering School of Studies in Engineering & Technology Guru Ghasidas Vishwavidyalaya (A Central University) Koni Bilaspur Chattisgarh India Department of Operations Management Symbiosis Institute of Operations Management Nashik Campus Constituent of Symbiosis International (Deemed University) Pune Maharashtra India Department of Human Resources Thiagarajar School of Management Madurai Tamil Nadu India Department of Computer Science Al-Turath University College Baghdad Iraq

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

this research is carried out merging ML with BWM to intensify decision making in manufacturing supply chain using social sustainability under light. As companies become more focused on environmental sustainability, the capability of machine learning to effectively assess and prioritize sustainability criteria should be highly valued. through a team of domain experts, the research clearly identifies the most vital social sustainability factors and, afterwards, deploys ML algorithms to analyze and optimize the provided decision-making framework by BWM. the selected approach gives possibility to dynamic weighting of the criteria which is based on real-time data. this way we are able to come up with the more precise and effective sustainability strategies. the results show that the hybrid ML-BWM model is not only an effective tool to enhance the validity and accuracy of evaluations but also one that provides the prudent directions that help the manufacturing sector shape its choice and responses. this paper analyzes the possible outcome of the integration of AI and traditional approach to decision-making process on social aspects of sustainability within supply chain.

关键词： Adaptation models Supply chain management Shape Supply chains Decision making Machine learning Real-time systems

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Improved YOLOv7 UAV image small target detection algorithm 23

Improved YOLOv7 UAV image small target detection algorithm

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Proceedings of the 2023 7th international conference on Computer Science and Artificial Intelligence

作者： Wang Ying Bo Liu Zhi Sanya University China School of Software Liaoning Technical University China

ISBN: (纸本)9798400708688

In order to improve the detection efficiency of small targets in UAV images in traffic systems, a small target detection algorithm for UAV images in traffic systems based on improved YOLOv7 is proposed. In the feature fusion network, the small target detection layer is added to extract feature information of different scales to improve the perception ability and positioning accuracy of small targets. Deep separable convolution ( DSConv ) is introduced into the feature extraction and feature fusion network of the model, and feature extraction is performed on each channel without introducing additional parameters, which reduces the computational complexity and memory occupation of the network and enhances the ability of the network to extract target features. the nearest upsampling module of the original model is replaced by the lightweight operator Carafe, which expands the receptive field of the model and improves the quality and accuracy of the feature map. In the feature extraction and feature fusion connection part of the model, the CBAM hybrid attention mechanism is introduced to dynamically adjust the channel and spatial information in the feature map, and improve the model 's ability to capture the important features of small targets in the traffic system under complex background. the experimental results show that compared with the original algorithm, the improved YOLOv7 algorithm improves the mAP by 4.5 percentage points and the recall rate by 5 %. Compared with the current mainstream algorithms, the improved algorithm can effectively improve the detection accuracy of small targets in UAV images in traffic systems, and significantly improve the false detection and missed detection of small target images in complex backgrounds.

关键词： CBAM Carafe DSConv Drone imagery Small target detection layer Yolov7 small object

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Optimized Campus Navigation Using Mobile App: Network Analysis for Effective Route Planning

Optimized Campus Navigation Using Mobile App: Network Analys...

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international conference on Network Applications Protocols and Services (NETAPPS)

作者： Nur Suhaili Mansor Hapini Awang Nur Faridatul Jamalia Radzali Anna Bluszcz Amirulikhsan Zolkipli Pee Foo Yee Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia School of Education and Human Sciences Albukhary International University Kedah Malaysia Faculty of Mining Safety Engineering and Industrial Automation Silesian University of Technology Poland School of Computing Universiti Utara Malaysia Sintok Malaysia

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

the scholarly article examines prevalent difficulties encountered by students in maneuvering through campus by proposing a solution grounded in network analysis. the investigation underscores the creation of a Route Planner mobile application, which utilizes Dijkstra's algorithm to facilitate optimized navigation and shortest-path routing. this methodology guarantees that students are provided with the most effective routes, thereby minimizing travel duration and reducing detours, thus enhancing their overall campus experience. the application was designed employing an Agile software development life cycle, which permitted adaptability and iterative enhancements informed by user feedback. Usability assessments were pivotal in refining the application's functionality and user interface, ensuring it addressed the practical requirements of students. the findings indicated that 70% of participants agreed that the Route Planner would be user-friendly. In addition to its technical execution, the Route Planner advocates for sustainable transportation practices on campus by promoting efficient route planning and minimizing resource wastage. the application also encourages inclusivity by facilitating more straightforward navigation for all students, particularly newcomers, fostering their confidence in traversing campus. this advancement exemplifies the potential of amalgamating network analysis with mobile navigation to devise practical, socially advantageous tools within academic settings. Ultimately, the research underscores the application's significance in enhancing campus exploration, augmenting usability, and contributing to the disciplines of computer networks and mobile navigation systems.

关键词： Legged locomotion Navigation Software algorithms Refining Network analyzers Routing Routing protocols Mobile applications Iterative methods Usability

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Comparison Between Explainable AI algorithms for Alzheimer’s Disease Prediction Using EfficientNet Models 16th

Comparison Between Explainable AI Algorithms for Alzheimer...

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16th international conference on Brain Informatics, BI 2023

作者： Jahan, Sobhana Saif Adib, Md. Rawnak Mahmud, Mufti Kaiser, M. Shamim Department of Computer Science and Engineering Bangladesh University of Professionals Dhaka Bangladesh Department of Information and Communication Technology Bangladesh University of Professionals Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Institute of Information and Technology Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9783031430749

Alzheimer’s disease (AD) is a common form of dementia that affects brain regions that control cognition, memory, and language. Globally, more than 55 million people are suffering from dementia. Given these troubling statistics, predicting AD is critical for future medications and treatment. Mild Cognitive Impairment (MCI) is a vital stage for patients because from this stage a majority of patients turn into AD patients. Early MCI (EMCI) and Late MCI (LMCI) are vital two stages of MCI. Successful prediction of Cognitive Normal (CN), AD, MCI, EMCI, and LMCI stages is a big challenge. Although there are models for predicting these stages but all models are not near to accurate. One major concern for these lacking is not having sufficient datasets to train the model. Data augmentation can be a solution to create an abundance of MRI data. One major issue regarding Machine Learning (ML) is the black box nature. Due to this limitation, user satisfaction as well as trust in the model’s prediction is missing. Explainable Artificial Intelligence (XAI) is the torchbearer approach and through this, the reason behind every decision can be observed by the user. this paper proposes a comparison between four XAI models named Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, Score-weighted CAM (Score-CAM), and Faster Score-CAM. For performing the five classes (CN, AD, EMCI, MCI, and LMCI) prediction, EfficientNet models are used because these models have performed well on the augmented dataset. Among EfficientNet models (B0-B7), EfficientNetB7 has performed the best. the testing accuracy and loss are 96.34% and 0.12, respectively. After that, the last layer of the EfficientNetB7 model is passed to the XAI models. Comparing the four XAI models it is observed that Grad-CAM++ and Score-CAM are better performing than others. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Colored ICP: Advancing 2D to 3D Shape Registration for Enhanced Accuracy and Visualization 23

Colored ICP: Advancing 2D to 3D Shape Registration for Enhan...

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Proceedings of the 7th international conference on algorithms, computing and systems

作者： Alexandros Vrochidis Stelios Krinidis Dimitrios Tzovaras Information Technologies Institute Center for Research and Technology Hellas Greece and Department of Management Science & Technology International Hellenic University Greece Information Technologies Institute Center for Research and Technology Hellas Greece

ISBN: (纸本)9798400709098

Robust and accurate registration is a fundamental capability for numerous computer vision and robotics applications. this paper proposes a novel colored 2D to 3D registration method that exploits keypoints from Scale-Invariant Feature Transform (SIFT) and Intrinsic Shape Signatures (ISS). the proposed method works on multiple models without utilizing a neural network or requiring a training dataset. After detecting keypoints and their colors in the L*a*b* color space, a modified Iterative Closest Point (ICP) algorithm is utilized to find the correspondences between the image and 3D model points and the transformation matrix. Additionally, there is an experimental evaluation using publicly available colored models for 3D-3D and 2D-3D registration to show that color information can increase accuracy in both tasks. On 3D-3D registration, the use of color information decreased Relative Root Mean Square Error (RRMSE) by 85.69% when the k-d tree was used and 92.63% when color was utilized in combination with the geometric information. For the 2D-3D registration, the mean distance decreased by 25.52% for the k-d tree and 28.76% for the weighted colored ICP, proving that color can efficiently enhance accuracy in this task.

关键词： Colored 2D to 3D Registration Colored ICP Point Cloud Registration Shape Alignment

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7th international Workshop on Mental Health and Well-being: Sensing and Intervention

7th International Workshop on Mental Health and Well-being: ...

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Adjunct Proceedings of the 2022 ACM international Joint conference on Pervasive and Ubiquitous computing and the 2022 ACM international Symposium on Wearable Computers

作者： Varun Mishra Akane Sano Sahiti Kunchay Saeed Abdullah Jakob E. Bardram Elizabeth Murnane Tanzeem Choudhury Mirco Musolesi Giovanna Nunes Vilaza Rajalakshmi Nandakumar Tauhidur Rahman Xuhai Xu King Zach Manasa Kalanadhabhatta Daniel A. Adler Rony Krell Northeastern University United States Department of Electrical and Computer Engineering Rice University United States Pennsylvania State University United States Information Sciences and Technology Pennsylvania State University United States Department of Health Technology Technical University of Denmark Denmark Thayer School of Engineering Dartmouth College United States Cornell University United States Department of Computer Science University College London United Kingdom Cornell Tech United States Manning College of Information and Computer Sciences University of Massachusetts Amherst United States Information School University of Washington United States Rice University United States Information Science Cornell Tech United States Optum Labs United States

ISBN: (纸本)9781450394239

Mental health issues affect a significant portion of the world’s population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. Toward this, ubiquitous systems can play a central role in revealing and tracking clinically relevant behaviors, contexts, and symptoms. Further, such systems can passively detect relapse onset and enable the opportune delivery of effective intervention strategies. However, despite their clear potential, the uptake of ubiquitous technologies into clinical mental healthcare is slow, and a number of challenges still face the overall efficacy of such technology-based solutions. the goal of this workshop is to bring together researchers interested in identifying, articulating, and addressing such issues and opportunities. Following the success of this workshop for the last five years, we aim to continue facilitating the UbiComp community in developing a holistic approach for sensing and intervention in the context of mental health.

关键词： Predictive Modeling Behavioral Intervention mHealth Mobile Sensing Mental Health

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