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An Optimized Approach for Goods Based Delivery Network in Metropolitan Cities

An Optimized Approach for Goods Based Delivery Network in Me...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Avik Khanna Shivam Singh Panwar Avni Verma Preeti Dubey Department of Computer Science & Engineering Sharda University

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

In the field of modern urban logistics, optimizing the delivery networks for goods in large, sprawling cities is a difficult but urgent task. the present study conducts a comprehensive analysis of this complex issue, exploring its diverse aspects and subtleties. the research weaves its way through the complexities of improving the efficacy and efficiency of these networks while emphasizing novel solutions. through careful investigation and consideration, the study benefits from the use of cutting edge technologies like ***, ***, and Flutter for solving issues like load balancing, real time tracking, and secure payment deals. the later sections of the paper present some empirical data and case studies which show how the suggestions make for practical gains in terms of transaction security, user experience, and system responsiveness. this comprehensive investigation reaches the conclusion that a successful delivery application must be an endeavor where it is necessary to join the frontend and backend technologies smoothly to deliver applications that are reliable, flexible and user centered, with zenith attention to urban requirements. Finally, the information provided in this study provides priceless methods and insights for streamlining goods based delivery networks, ensuring their continued applicability and efficiency in the dynamic surroundings of emerging urban logistics.

关键词： Technological innovation machine learning algorithms Scalability Urban areas Load management Real-time systems Routing protocols Security Optimization Logistics

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Node Localization in Range-Free 3D-WSNs Using New DV-Hop Algorithm Based machine learning Techniques 22

Node Localization in Range-Free 3D-WSNs Using New DV-Hop Alg...

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proceedings of the 2022 4th international conference on Software Engineering and Development

作者： Oumaima Liouane Smain Femmam Toufik Bakir Abdessalem Ben Abdelali University of Monastir National Engineering School of Monastir Tunisia Department of Networks and Communications Haute-Alsace University France Bourgogne University France University of Monastir ISIMM of Monastirr Tunisia

ISBN: (纸本)9781450397940

In many Wireless Sensor Network (WSN) applications, location is critical. Another intriguing aspect of the acquired data is the ability to obtain exact information about sensors' locations. In order to localize multi-hop WSNs, based connectivity algorithms use their benefits, such as simplicity and acceptable accuracy, to do so. However, the localization accuracy may be limited due to the two- or three-dimensional environment (2D or 3D). Range-Free 3D-WSNs can benefit from an analytic model for hop-size quantization and an Extreme learning machine (ELM) method for localization to reduce localization errors. the additional third dimension greatly affects the accuracy of localization. Since many applications require 3D localization, it is important to develop efficient self-localization algorithms for 3D WSNs. In this paper, for a uniform distribution of sensor nodes, a new probabilistic quantization of hop size in 3D WSNs is proposed. Moreover, the extreme learning machine (ELM) which represents a new approach to WSN localization is exploited combining a conventional method (probabilistic approach) with a non-conventional method (machine learning). For a variety of conditions, our algorithms have been tested through simulation in isotropic settings. the performance of the localization model was assessed using the average localization error (LE). When compared to previous 3D-DV-Hop heuristics, the suggested localization algorithm's performance in terms of accuracy is clearly demonstrated by simulation data. With the help of the predicted hop quantization for hop-size estimation and the ELM was used for position estimation, our localization method for 3D-WSNs lowers the average localization error of nodes and has a greater location accuracy compared to its rivals.

关键词： machine learning Localization hop-quantization 3D Wireless Sensors Network

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application of Chaos theory and Data Mining to Seizure Detection of Epilepsy

Application of Chaos Theory and Data Mining to Seizure Detec...

引用

the 2012 4th international conference on machine learning and Computing（ICMLC 2012）

作者： Chien-Liang Chen Jenn-Long Liu Jhih-Yu Syu Dept.of Information Management Fortune Institute TechnologyKaohsiung 831Taiwan Dept.of Information Engineering I-Shou UniversityKaohsiung 840Taiwan Dept.of Information Management I-Shou UniversityKaohsiung 840Taiwan

this study applies Chaos theory to investigate the seizure detection of epilepsy with three groups of data （Groups H,S and E）,showing the electroencephalography （EEG） changes of *** three groups,Group H （normal state）,Group S （during seizures） and Group E （after seizures）,contain 100 series of EEG signals *** detected data are processed by chaotic theory to transform the signals to four parameters with Delay Time （τ）,Embedding Dimension （dm）,Correlation Dimension （CD） value,and Largest Lyapunov Exponent （LLE）.Furthermore,well-known classification software for data mining,termed See5 based on entropy theory,is used to find out the classification rules for the EEG signals of epilepsy patients.

关键词： Chaos theory Epilepsy Decision Tree Classification Rule

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Self-Supervised Superpixel Correspondences For Video Object Segmentation 22

Self-Supervised Superpixel Correspondences For Video Object ...

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proceedings of the 2022 4th international conference on Video, Signal and Image Processing

作者： Clement Tan Chai Kiat Yeo Cheston Tan Basura Fernando School of Computer Science and Engineering Nanyang Technological University Singapore and Agency for Science Technology and Research (A*STAR) Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (纸本)9781450397810

Prior self-supervised video object segmentation models directly find pixel correspondences between pairs of frames. Instead, we propose a novel approach of employing superpixel features for learning visual correspondences between frames of a video sequence. With an attention mechanism, we train the model to reconstruct the next frame using superpixel features between adjacent frames of videos trained in a self-supervised manner. As superpixels are able to group pixels based on color and proximity, they reduce repetitive and noisy pixels that may confuse the model during tracking. In addition, the structural information provided by the superpixel features can help improve the segmentation of objects. We benchmark our method (S3CNet) against existing self-supervised pixel correspondence frameworks such as [12, 22, 24] and show that our self-supervised superpixel correspondence method performs better and is a viable alternative for visual tracking on the commonly-used DAVIS 2017 video segmentation benchmark.

关键词： Video Object Segmentation Self-Supervised learning Superpixels

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A comparative study on fetal head circumference measurement from ultrasound images using deep learning models 22

A comparative study on fetal head circumference measurement ...

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proceedings of the 2022 4th international conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Yu Wang Yudong Yao College of Medicine and Biological Information Engineering Northeastern University China Department of Electrical and Computer Engineering Stevens Institute of Technology USA

ISBN: (纸本)9781450398343

Ultrasound imaging is the most commonly used imaging modality for the prenatal examination of pregnant women, with real-time imaging and no radiation characteristics. through a ultrasound image of the fetal head, doctors can measure the fetal head circumference (HC) to evaluate fetal growth and potential delivery mode. In practice, fetal HC is usually measured manually by doctors based on ultrasound images. Manual measurement of fetal HC is subjective and time-consuming, which has a negative impact on measurement accuracy and efficiency. At present, deep learning is widely investigated in the medical field. Many researchers apply deep learning to measuring fetal HC to assist doctors to accurately and quickly completing the measurement of fetal HC. In this paper, we compare the performance of eight deep learning models (U-Net, Attention U-Net, GINet, global reasoning unit (GloRe), SegFormer, Segmenter, BiSeNet V2, and short-term dense concatenate network (STDC)) on two fetal HC measurement datasets. SegFormer achieves the best results in Dice similarity coefficient (DSC), Hausdorff distance (HD), and absolute Difference (ADF). the performance of Attention U-Net is slightly worse than that of SegFormer.

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Maximizing Privacy in Reinforcement learning with Federated Approaches

Maximizing Privacy in Reinforcement Learning with Federated ...

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Intelligent Technologies (CONIT), international conference on

作者： Shiva Mehta Sumeet Singh Sarpal Centre for Research Impact &Outcome Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Data protection issues and security in the age of technological advancement have become problematic, mainly because these methods are increasingly widespread. this paper covers critical problems of Federated Reinforcement learning (FRL) and shows how this technique could provide a viable solution to the issue. We are attempting the use case scenario of the Federation Q-learning in the Labyrinth difficulties here. It ensures that technological requirements are adapted at all levels and keeps the information secret. A trial has been made to test the application of IRL on AI training, enabling the scientists to acquire actual proof of the advantages of FRL against the theoretical approaches in training, which are the majority. the main quantitative facet of this case uses pretty scientific statistics. Research results from conventional Q-learning and our variation, codel bean Q-learning, are significant in drawing conclusions and gaining insights. the federated q-learning presented scores around 25% higher than the q-learning with an agent, which is one more advantage than the q-learning using an agent. Just as Federated Q-learning obtains fast policy optimization by reducing convergence time by 2 5%, parallelism also brings about quicker and more effective exploration of the Q-table. Federated Q-learning is no exception to the general rule that this model has an advantage in ensuring data privacy, yielding an evident four-point improvement in the data privacy score. this shows that this technology quickly becomes a trusted ally and can anonymize data without compromising users’ privacy and security. Moreover, in the final stage of our work, the implementation of the model pretty soon caused an improvement in computer efficiency by 40%. the results showed that the current traditional AIL techniques suddenly faced five times more risks concerning privacy and security.

关键词： Training Privacy Q-learning Statistical analysis Data protection Parallel processing Privacy breach

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A Data Commercialization Evaluation Model Based on Fuzzy Multi-attribute Group Decision Making 23

A Data Commercialization Evaluation Model Based on Fuzzy Mul...

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proceedings of the 2023 4th international conference on Big Data Economy and Information Management

作者： Bin Zhao Li Cao Yilong Gao Peng Zhai School of Information Science and Engineering Linyi University China School of Mathematics and Computer Application Technology Jining University China

ISBN: (纸本)9798400716669

the core purpose of data commoditization transformation is to lower the threshold of data utilization and facilitate data analysis and mining at the user end. In this paper, we propose a data commoditization degree evaluation model for data trading websites. First, the evaluation index system of data commercialization is constructed, combining the abstract indexes reflecting data commonality and industry-specific data commercialization standards to assess the degree of data commercialization; second, the fuzzy multi-attribute group decision-making method is utilized to conduct fuzzy evaluation of the indexes in response to the issue of user evaluation preference; and the fuzzy function corresponding to the fuzzy evaluation is calculated based on the fuzzy structured element afterwards. the degree of superiority of the program is calculated by fuzzy compromise group decision-making method. Finally, the simplicity, convenience, and effectiveness of the data commoditization assessment model are demonstrated through a speech data case study.

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application of fuzzy clustering based on improved whitening weight function in vocational education evaluation 23

Application of fuzzy clustering based on improved whitening ...

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proceedings of the 2023 4th international conference on computer Science and Management Technology

作者： Yijun Feng Wanning Zhu Lei Hong Youfeng Li Shaoyu Tang School of Software Engineering Jinling Institue of Technology China

ISBN: (纸本)9798400709517

In recent years, the importance of vocational education development has increased significantly. the key to improving vocational education lies in addressing the evaluation problem of vocational education schools. However, previous solutions to this problem suffered from issues like subjective evaluation methods and distorted data. this paper presents a vocational education evaluation method that combines gray clustering and fuzzy clustering, while also improving the form of the whitening weight function. the initial membership degree is calculated using the whitening weight function, followed by optimization using fuzzy C-means clustering. Experimental results demonstrate that this algorithm effectively combines the advantages of gray clustering and fuzzy C-means clustering, enabling more accurate clustering of vocational education schools in the evaluation process. this approach facilitates more objective evaluation and provides a quantitative basis for vocational education evaluation.

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QuACK: accelerating gradient-based quantum optimization with koopman operator learning 23

QuACK: accelerating gradient-based quantum optimization with...

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proceedings of the 37th international conference on Neural Information Processing Systems

作者： Di Luo Jiayu Shen Rumen Dangovski Marin Soljačić Center for Theoretical Physics Massachusetts Institute of Technology Cambridge MA and Department of Physics Harvard University Cambridge MA and The NSF AI Institute for Artificial Intelligence and Fundamental Interactions Department of Physics University of Illinois Urbana-Champaign Urbana IL and Illinois Quantum Information Science and Technology Center Illinois Center for Advanced Studies of the Universe Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Department of Physics Massachusetts Institute of Technology Cambridge MA

Quantum optimization, a key application of quantum computing, has traditionally been stymied by the linearly increasing complexity of gradient calculations with an increasing number of parameters. this work bridges the gap between Koopman operator theory, which has found utility in applications because it allows for a linear representation of nonlinear dynamical systems, and natural gradient methods in quantum optimization, leading to a significant acceleration of gradient-based quantum optimization. We present Quantum-circuit Alternating Controlled Koopman learning (QuACK), a novel framework that leverages an alternating algorithm for efficient prediction of gradient dynamics on quantum computers. We demonstrate QuACK's remarkable ability to accelerate gradient-based optimization across a range of applications in quantum optimization and machine learning. In fact, our empirical studies, spanning quantum chemistry, quantum condensed matter, quantum machine learning, and noisy environments, have shown accelerations of more than 200x speedup in the overparameterized regime, 10x speedup in the smooth regime, and 3x speedup in the non-smooth regime. With QuACK, we offer a robust advancement that harnesses the advantage of gradient-based quantum optimization for practical benefits.

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Lexicon-matched Word Injection for Chinese NER 22

Lexicon-matched Word Injection for Chinese NER

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proceedings of the 2022 5th international conference on machine learning and machine Intelligence

作者： Qingyu Wang Guokai Sun Jianguo Sun Yuan Zhuang Lu Li Tianyi Gao School of Computer Science and Technology Harbin Engineering University China and Wuhan Sanjiang Aerospace Network Communication Co. LTD China School of Computer Science and Technology Harbin Engineering University China Cyberspace Security Lab Xidian University China Network Security Department Defence Industry Secrecy Examination and Certification Center China Network Security Technology Application Center Wuhan Sanjiang Aerospace Network Communication Co. LTD China

ISBN: (纸本)9781450397551

Recently, Chinese named entity recognition has attracted a lot of attention. Most of the work utilizes words matching with lexicon which integrates potential word information with lattice structure or graph structure. Although existing approaches have been proved to be effective for exploiting abundant word boundary information, it is difficult to model global semantic interactions due to the inherent one-way sequential nature of the DAG structure. Meanwhile, more interfering lexicon words have been introduced leading to word boundary *** address the above issues, this paper proposes a knowledge fusion method based on lexicon matching word injection, which captures sentence context features through a pre-trained learning model and then injects lexicon knowledge into each character. With the power of Transformer and well-designed encoding, it becomes easier to obtain accurate word information using the character encoding vector of transformer encoder model. Besides, the self-attention module integrating characters with different words is fully leveraged to improve recognition accuracy. Experiments on three Chinese public datasets show that the proposed method outperformed other lexicon-based methods in performance and efficiency.

关键词： Chinese named entity recognition Word injection Multi-headed self-attention

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