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Revisiting the K-nn algorithm for obstetric image segmentation

International Journal of Hybrid Intelligent systems 2021年第1-2期17卷 1-13页

作者： Salgado, P. Azevedo Perdicoúlis, T.-P. CITAB-Department de Engenharias Ect Utad Quinta de Prados Vila Real Portugal Department of Electrical and Computer Engineering Ect Utad of Institute of Systems and Robotics University of Coimbra Coimbra Portugal

Medical image techniques are used to examine and determine the well-being of the foetus during pregnancy. Digital image processing (DIP) is essential to extract valuable information embedded in most biomedical signals. Afterwords, intelligent segmentation methods, based on classifier algorithms, must be applied to identify structures and relevant features from previous data. The success of both is essential for helping doctors to identify adverse health conditions from the medical images. To obtain easy and reliable DIP methods for foetus images in real-time, at different gestational ages, aware pre-processing needs to be applied to the images. Thence, some data features are extracted that are meant to be used as input to the segmentation algorithms presented in this work. Due to the high dimension of the problems in question, assemblage of the data is also desired. The segmentation of the images is done by revisiting the K-nn algorithm that is a conventional nonparametric classifier. Besides its simplicity, its power to accomplish high classification results in medical applications has been demonstrated. In this work two versions of this algorithm are presented (i) an enhancement of the standard version by aggregating the data apriori and (ii) an iterative version of the same method where the training set (TS) is not static. The procedure is demonstrated in two experiments, where two images of different technologies were selected: a magnetic resonance image and an ultrasound image, respectively. The results were assessed by comparison with the K-means clustering algorithm, a well-known and robust method for this type of task. Both described versions showed results close to 100% matching with the ones obtained by the validation method, although the iterative version displays much higher reliability in the classification. © 2021 - IOS Press. All rights reserved.

关键词： Feature extraction image segmentation K-nn algorithm magnetic resonance image ultrasound image

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Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

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computers, Materials & Continua 2021年第5期67卷 2449-2462页

作者： Nabeela Awan Salman Khan Mohammad Khalid Imam Rahmani Muhammad Tahir Nur Alam MD Ryan Alturki Ihsan Ullah Department of Computer Science Government Girls Degree College MardanKPK23200Pakistan Department of Electrical Energy System UETPeshawar25000Pakistan Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityKSA Department of Smart Computing Kyungdong UniversityGlobal CampusGosung24764South Korea Department of Information Sciences College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Robotics Engineering Daegu Gyeongbuk Institute of Science and TechnologySouth Korea

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy *** smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart *** complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research *** challenges can be resolved through various machine learning techniques and data *** this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart *** proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution *** the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm *** simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

关键词： PSO IBR machine learning IoT smart cities CDBR

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Evaluation of resource arbitration methods for multi-core real-time systems

Evaluation of resource arbitration methods for multi-core re...

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13th International Workshop on Worst-Case Execution time Analysis, WCET 2013

作者： Kelter, Timon Harde, Tim Marwedel, Peter Falk, Heiko Department of Computer Science TU Dortmund Otto-Hahn-Straße 16 D-44227 Dortmund Germany Institute of Embedded Systems/Real-Time Systems Ulm University James-Franck-Ring D-89081 Ulm Germany

ISBN: (纸本)9783939897545

Multi-core systems have become prevalent in the last years, because of their favorable properties in terms of energy consumption, computing power and design complexity. First attempts have been made to devise WCET analyses for multi-core processors, which have to deal with the problem that the cores may experience interferences during accesses to shared resources. To limit these interferences, the vast amount of previous work is proposing a strict TDMA (time division multiple access) schedule for arbitrating shared resources. Though this type of arbitration yields a high predictability, this advantage is paid for with a poor resource utilization. In this work, we compare different arbitration methods with respect to their predictability and average case performance. We show how known WCET analysis techniques can be extended to work with the presented arbitration strategies and perform an evaluation of the resulting ACETs and WCETs on an extensive set of realworld benchmarks. Results show that there are cases when TDMA is not the best strategy, especially when predictability and performance are equally important. © Timon Kelter, Tim Harde, Peter Marwedel, and Heiko Falk.

关键词： real time systems

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Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches 21

Privacy-preserving Biometric-driven Data for Student Identit...

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Fidas, Christos Belk, Marios Portugal, David Pitsillides, Andreas University of Patras Department of Electrical and Computer Engineering Greece Cognitive Ux GmbH Germany University of Coimbra Institute of Systems and Robotics Portugal University of Cyprus Department of Computer Science Cyprus

ISBN: (纸本)9781450383677

Biometric technologies are being considered lately for student identity management in Higher Education Institutions, as they provide several advantages over the traditional knowledge-based and token-based authentication methods, i.e., biometrics provide high security entropies, convenience and a sense of technological modernity to the end-users. While biometric technologies have many benefits from both a security and usability point of view, still there is a need for innovative user identity management solutions that continuously identify and authenticate students during academic and teaching activities. In addition, biometrics entail several threats and weaknesses with regards to the privacy of data stored about the user, which negatively affect the user acceptance and the wider adoption of biometrics due to regulatory and legal issues. In this paper, we refer to our ongoing research on intelligent and continuous online student identity management for improving security and trust in European Higher Education Institutions. We further highlight based on the literature, existing challenges, threats and state-of-the-art approaches with regards to preserving the privacy of biometric-driven data. © 2021 Owner/Author.

关键词： Blockchain

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Speculative execution in a real-time system 2

Speculative execution in a real-time system

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2nd IEEE Workshop real-time Applications, RTA 1994

作者： Younis, Mohamed F. Marlowe, Thomas J. Stoyenko, Alexander D. Real-Time Computing Laboratory Department of Computer and Information Science New Jersey Institute of Technology NewarkNJ07102 United States Department of Mathematics and Computer Science Seton Hall University South OrangeNJ07079 United States

ISBN: (纸本)0818663758

Compiler techniques can help enhance average case execution and extract parallelism opportunities in distributed real time systems. We show that there are situations in which speculative execution with rollback can improve the performance of a hard real time system, either by improving average performance while not affecting the worst-case, or by actually decreasing worst-case execution time. We also show how related strategies for partial or total precomputation can lead to improved performance. We briefly discuss possible practical compiler transformation tools to detect chances of profitable speculative execution in real-time applications. © 1994 IEEE.

关键词： real time systems

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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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Personality Trait Detection via Transfer Learning

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computers, Materials & Continua 2024年第2期78卷 1933-1956页

作者： Bashar Alshouha Jesus Serrano-Guerrero Francisco Chiclana Francisco P.Romero Jose A.Olivas Department of Information Technologies and Systems University of Castilla-La ManchaCiudad Real13071Spain Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityThe GatewayLeicesterLE19BHUK

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human ***-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their *** study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality *** models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term *** makes them suitable to classify multi-label personality traits from reviews while mitigating computational *** focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from ***,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook *** status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality *** test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted *** results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of *** findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality *** findings represent substantial a

关键词： Personality trait detection pre-trained language model big five model transfer learning

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Compiler transformations for speculative execution in a real-time system

Compiler transformations for speculative execution in a real...

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15th IEEE real-time systems Symposium, RTSS 1994

作者： Younis, Mohamed F. Marlowet, Thomas J. Stoyenko, Alexander D. Real-Time Computing Laboratory Department of Computer and Information Science New Jersey Institute of Technology Newark NJ 07102 United States Department of Mathematics and Computer Science Seton Hall University South Orange NJ 07079 United States

ISBN: (纸本)0818666005

Deterministic worst-case execution to satisfy hard real-time constraints, and speculative execution with rollback to improve average-case throughput, appear to lie on opposite ends of a spectrum of performance requirements and strategies. Nonetheless, we show there are situations in which speculative execution can probably improve the performance of a hard real-time system, either by improving average performance while not affecting the worst case, or by actually decreasing worst-case execution time. We also show how related strategies for partial or total precomputation can lead to improved performance. Finally, we discuss possible compiler transformations to detect chances of profitable speculative execution. © 1994 IEEE.

关键词： real time systems

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Corrigendum to “Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems” Appl. Soft Comput. 132 (2023) 109909

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Applied Soft Computing 2025年 169卷

作者： Hafiz Tayyab Rauf Jiechao Gao Ahmad Almadhor Ali Haider Yu-Dong Zhang Fadi Al-Turjman Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Department of Computer Science University of Virginia USA Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia Department of Computer Science University of Gujrat Gujrat Pakistan School of Informatics University of Leicester Leicester LE1 7RH UK Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin 10 Turkey

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Generating Manipulation Sequences using Reinforcement Learning and Behavior Trees for Peg-In-Hole Task

Generating Manipulation Sequences using Reinforcement Learni...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Xu, Jiahua Lin, Yunhan Zhou, Haotian Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Reinforcement Learning (RL), a method of learning skills through trial-and-error, has been successfully used in many robotics applications in recent years. We combine manipulation primitives (MPs), behavior trees (BTs), and reinforcement learning to propose an algorithm for peg-in-hole tasks, which speeds up the convergence of the RL model and enhance the adaptability of the dynamic environment. Manipulation primitives are used as actions for RL, which can reduce the gap between control instruction and robotic actions and speed up the convergence of the RL model. Behavior trees are used as robot behavior control, which makes robots can actively adapt to the changes in the environment. In experiments, RL-BT, from the combination of RL and BT, is designed for the peg-in-hole task in the Gazebo simulation environment by using UR5 as the actuator. The experiments are conducted on a simple peg and a complex multi-hole peg by three aspects, which include convergence speed verification experiment, adaptability of dynamic environment experiment, and algorithm robustness experiment. The experiment result proves that our RL-BT can speed up the convergence and adapt to the changes in the environment. © 2022 IEEE.

关键词： Reinforcement learning

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