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A clinical decision support system for polycystic ovarian syndrome using red deer algorithm and random forest classifier

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Healthcare Analytics 2022年 2卷 100102-100102页

作者： Sreejith, S. Khanna Nehemiah, H. Kannan, A. Ramanujan Computing Centre Anna University Tamil Nadu Chennai 600025 India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

This study develops a clinical decision support system to assist physicians with monitoring Polycystic Ovarian Syndrome (PCOS). The proposed method uses a classification model with optimization techniques and machine learning algorithms. The existence of irrelevant features can deteriorate the performance of any classifiers. This work analyses the impact of such features on classification accuracy and automatically identifies and removes the irrelevant features from the dataset. A wrapper approach is employed for feature selection. It uses Red Deer Algorithm (RDA) to find the optimal features and a random forest (RF) classifier to evaluate them. RDA is an optimization algorithm inspired by red deer's mating and roaring behaviour. Experiments were carried out on the PCOS dataset, which is available in the Kaggle dataset repository. The optimal dataset is trained and tested using an RF classifier with 50 estimators. RDA's improved exploration and exploitation capabilities are the rationales behind using it as the search method. The novelty of the proposed method lies in the novel fitness function used, which considers both accuracy of the classification and the optimized number of features. The proposed CDSS displays accuracy of 89.81%. The results of the proposed work were compared with the performance of competing methods in the literature. Logistic Regression classifier, k-Nearest Neighbour (k-NN), Decision tree, Naïve Bayes, Support Vector Machine (SVM) classifier, RF-PSO, RF-Ant Colony Optimization, and RF-Genetic Algorithm and are found to be superior in terms of accuracy, sensitivity, and specificity. The statistical significance of the suggested feature selection approach is confirmed using McNamer's test. © 2022 The Author(s)

关键词： Clinical data analytics Decision support systems Feature selection Optimization

Towards a Theoretical Framework of Autonomous Systems Underpinned by Intelligence and Systems sciences

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 52-63页

作者： Yingxu Wang Ming Hou Konstantinos NPlataniotis Sam Kwong Henry Leung Edward Tunstel Imre JRudas Ljiljana Trajkovic International Institute of Cognitive Informatics and Cognitive Computing Department of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of CalgaryCalgary T2N 1N4Canada Toronto Research Centre Defence Research&Development CanadaToronto M3K 2C9Canada Department of Electrical&Computer Engineering University of TorontoToronto M5S3G4Canada Department of Computer Science City University of Hong KongHong KongChina Department of Electrical and Computer Engineering Schulich School of EngineeringUniversity of CalgaryCalgary T2N 1N4Canada Autonomous&Intelligent Systems Department United Technologies Research CenterCT 06108 USA University Research&Innovation Center(EKIK) Óbuda UniversityBudapest H-1034Hungary School of Engineering Science Simon Fraser UniversityBurnaby V5A 1S6Canada

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems *** paper explores the intelligent and mathematical foundations of autonomous *** focuses on structural and behavioral properties that constitute the intelligent power of autonomous *** explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive *** properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems *** paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are *** in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

关键词： Autonomous systems(AS) cognitive systems computational intelligence engineering paradigms intelligence science intelligent mathematics

Efficient Estimation and Sequential Optimization of Cost Functions in Variational Quantum Algorithms

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arXiv 2024年

作者： Umer, Muhammad Mastorakis, Eleftherios Angelakis, Dimitris G. Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece AngelQ Quantum Computing 531A Upper Cross Street #04-95 Hong Lim Complex 051531 Singapore

Classical optimization is a cornerstone of the success of variational quantum algorithms, which often require determining the derivatives of the cost function relative to variational parameters. The computation of the cost function and its derivatives, coupled with their effective utilization, facilitates faster convergence by enabling smooth navigation through complex landscapes, ensuring the algorithm's success in addressing challenging variational problems. In this work, we introduce a novel optimization methodology that conceptualizes the parameterized quantum circuit as a weighted sum of distinct unitary operators, enabling the cost function to be expressed as a sum of multiple terms. This representation facilitates the efficient evaluation of nonlocal characteristics of cost functions, as well as their arbitrary derivatives. The optimization protocol then utilizes the nonlocal information on the cost function to facilitate a more efficient navigation process, ultimately enhancing the performance in the pursuit of optimal solutions. We utilize this methodology for two distinct cost functions. The first is the squared residual of the variational state relative to a target state, which is subsequently employed to examine the nonlinear dynamics of fluid configurations governed by the one-dimensional Burgers' equation. The second cost function is the expectation value of an observable, which is later utilized to approximate the ground state of the nonlinear Schrödinger equation. Our findings reveal substantial enhancements in convergence speed and accuracy relative to traditional optimization methods, even within complex, high-dimensional landscapes. Our work contributes to the advancement of optimization strategies for variational quantum algorithms, establishing a robust framework for addressing a range of computationally intensive problems across numerous applications. © 2024, CC BY.

关键词： Cost functions

Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring

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Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained M...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shitong Fan Feiyang Xiao Wenbo Wang Shuhan Qi Qiaoxi Zhu Wenwu Wang Jian Guan Group of Intelligent Signal Processing Harbin Engineering University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Acoustics Lab University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge’s Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10.

关键词： Location awareness Smart cities Signal processing Feature extraction Acoustics Data models Microphone arrays Speech processing Monitoring Faces

Probing the limits of variational quantum algorithms for nonlinear ground states on real quantum hardware: The effects of noise

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arXiv 2024年

作者： Umer, Muhammad Mastorakis, Eleftherios Evangelou, Sofia Angelakis, Dimitris G. Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore117543 Singapore School of Electrical and Computer Engineering Technical University of Crete Chania73100 Greece AngelQ Quantum Computing 531A Upper Cross Street #04-95 Hong Lim Complex 051531 Singapore

A recently proposed variational quantum algorithm has expanded the horizon of variational quantum computing to nonlinear physics and fluid dynamics. In this work, we probe the ability of such approaches to capture the ground state of the nonlinear Schrödinger equation for a range of parameters on real superconducting quantum processors. Specifically, we study the expressivity of real-amplitude, hardware-efficient ansatz to capture the ground state of this nonlinear system across various interaction regimes and implement different noise scenarios in both simulators and cloud processors. Our investigation reveals that although quantum hardware noise impairs the evaluation of the energy cost function, certain small instances of the problem consistently converge to the ground state. We test for a variety of cases on IBM Q superconducting devices and analyze the discrepancies in the energy cost function evaluation due to quantum hardware noise. These discrepancies are absent in the state fidelity estimation because of the shallow state preparation circuit. Our comprehensive analysis offers valuable insights into the practical implementation and advancement of the variational algorithms for nonlinear problems. © 2024, CC BY.

关键词： Cost functions

How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies

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arXiv 2022年

作者： Small, Edward A. Shao, Wei Zhang, Zeliang Liu, Peihan Chan, Jeffrey Sokol, Kacper Salim, Flora D. ARC Centre of Excellence for Automated Decision-Making and Society School of Computing Technologies RMIT University Australia Data61 CSIRO Australia School of Computing Technologies RMIT University Australia Department of Mathematics University of Michigan United States Department of Computer Science ETH Zurich Switzerland ARC Centre of Excellence for Automated Decision-Making and Society School of Computer Science and Engineering UNSW Sydney Australia

With the introduction of machine learning in high stakes decision-making, ensuring algorithmic fairness has become an increasingly important task. To this end, many mathematical definitions of fairness have been proposed, and a variety of optimisation techniques have been developed, all designed to maximise a given notion of fairness. Fair solutions, however, tend to rely on the quality of training data, and can be highly sensitive to noise. Recent studies have shown that robustness of many such fairness strategies – i.e., their ability to perform well on unseen data – is not a given and requires careful consideration. To address this challenge, we propose robustness ratio, which is a novel criterion to measure the robustness of diverse fairness optimisation strategies. We support our analysis with multiple extensive experiments on five benchmark fairness data sets, using three prominent fairness strategies, in view of four of the most popular definitions of fairness. Our experiments show that while fairness methods that rely on threshold optimisation (post-processing) mostly outperform other techniques, they are acutely sensitive to noise. This is in contrast to two other methods – correlation remover (pre-processing) and exponentiated gradient descent (in-processing) – which become increasingly fairer as the random noise injected into the data becomes larger. Our findings offer a comprehensive overview of fairness strategies that proves invaluable when tasked with choosing the most suitable method for the task at hand. To the best of our knowledge, we are the first to quantitatively evaluate the robustness of fairness optimisation strategies. © 2022, CC BY-NC-ND.

关键词： Optimization

Using Euler's Elastica Regularization to Reconstruct 3d Surface from Point Clouds

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SSRN

SSRN 2023年

作者： Song, Jintao Pan, Huizhu Lu, Wenqi Ding, Jieyu Wei, Weibo Zhu, Haotian Liu, Wanquan Duan, Jinmng Pan, Zhenkuan College of Computer Science and Technology Qingdao University Qingdao266071 China School of Electrical Engineering Mathematical Science and Computing Curtin University PerthWA6102 Australia Tissue Image Analytics Centre Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom School of Arts and Sciences University of Pennsylvania Pennsylvania19104 United States School of Intelligent Systems Engineering Sun Yat-Sen university Shenzhen510275 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

The Euler’s elastica energy regularizer, initially employed in mathematical and physical systems, has recently garnered much attention in image processing and computer vision tasks. While implicit surface reconstruction methods generally demonstrate superior robustness and ffexibility, the traditional methods rely on initialization and can easily become trapped in local minima. The Euler’s elastica term, on the other hand, has been found to share the advantages of both the TV regularization term and the curvature regularization term. In this paper, we aim to address the problems of missing details and complex computation in implicit 3D reconstruction by efffciently using the Euler’s elastica term. We propose a surface reconstruction model with the Euler’s elastica energy regularizer as well as a new dual method to simplify the model’s solution. Experimental results show that the proposed model can maintain the robustness and accuracy of existing variational surface reconstruction models while improving efffciency and model usability. © 2023, The Authors. All rights reserved.

关键词： Surface reconstruction

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources

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arXiv 2023年

作者： Zheng, Haotian Wang, Qizhou Fang, Zhen Xia, Xiaobo Liu, Feng Liu, Tongliang Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong School of Electronic Engineering Xidian University China Australian Artificial Intelligence Institute University of Technology Sydney Australia Sydney AI Centre The University of Sydney Australia School of Computing and Information Systems The University of Melbourne Australia

Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open-world classification. However, it is typically hard to collect real out-of-distribution (OOD) data for training a predictor capable of discerning ID and OOD patterns. This obstacle gives rise to data generation-based learning methods, synthesizing OOD data via data generators for predictor training without requiring any real OOD data. Related methods typically pre-train a generator on ID data and adopt various selection procedures to find those data likely to be the OOD cases. However, generated data may still coincide with ID semantics, i.e., mistaken OOD generation remains, confusing the predictor between ID and OOD data. To this end, we suggest that generated data (with mistaken OOD generation) can be used to devise an auxiliary OOD detection task to facilitate real OOD detection. Specifically, we can ensure that learning from such an auxiliary task is beneficial if the ID and the OOD parts have disjoint supports, with the help of a well-designed training procedure for the predictor. Accordingly, we propose a powerful data generation-based learning method named Auxiliary Task-based OOD Learning (ATOL) that can relieve the mistaken OOD generation. We conduct extensive experiments under various OOD detection setups, demonstrating the effectiveness of our method against its advanced counterparts. The code is publicly available at: https://***/tmlr-group/ATOL. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

Adoption of Internet of Things for Smart Monitoring of Aquaponics

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Adoption of Internet of Things for Smart Monitoring of Aquap...

International Conference on computing and Networking Technology (ICCNT)

作者： Kapil Joshi Priya Priya Vishal Kaushik Harishchander Anandaram Arati Mohapatro Anil Kumar Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India School of Agriculture Graphic Era Hill University Dehradun Uttarakhand India School of Computer Science UPES Dehradun India Centre for Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India Dept of Computer Science Indian Academy Degree College(Autonomous) Bengaluru India School of Computing DIT University Dehradun India

Aquaponics is a cutting-edge, clever, and ecological technology for agriculture that combines both aquaculture and hydroponics to produce vegetable products prototype. This method promises to be environmentally friendly and viable due to its high-water consumption efficacy, lack of use of pesticides, and decreased fertilizer use. The main task is to make aquaponics viable and dependable at a business scale because interest in it is growing. By recognizing, being listed, and thoroughly describing each of the characteristics detected in aquaponics, as well as the smart platforms and Internet of Things (IoT) devices in the examined literature, this article hopes to support study towards a feasible business aquaponics option. The suggested study also identifies future efforts needed for mechanized aquaponics and possible voids in the existing literature. This is anticipated the fact that the system for aquaponics backed by smart control units will benefit in terms of profitability, intelligence, accuracy, and effectiveness within the context of the research studies examined in this paper.

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