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1st International Conference on Advances in computing Research, ACR’23

作者： Bekhit, Mahmoud Fathalla, Ahmed Eldesouky, Esraa Salah, Ahmad Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Kent Institute Australia Melbourne Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia College of Computers and Informatics Zagazig University Zagazig Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9783031337420

With the rising acceptance of virtual network functions (VNFs) as a replacement for traditional network functions, the optimal placement of VNFs has become a crucial task for ensuring constant performance within constrained resources. This research investigates the challenge of mapping and scheduling VNFs as a multi-objective optimization problem. The state-of-the-art methods have focused on optimizing the mapping or scheduling of VNFs while considering one or two objectives continuously. In this study, we aim to optimize two objectives: minimizing the link capacity cost and maximizing the average resource utilization of each virtual machine. To address this problem, an adaptation of the NSGA-III optimization algorithm is proposed and thoroughly evaluated through a set of experiments. In terms of objective values, the results outlined that the proposed method outperformed the other methods of comparison by a significant margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mapping

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Exploring the Potential of Recurrent Neural Networks for Medical Image Segmentation

Exploring the Potential of Recurrent Neural Networks for Med...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Aaditya Jain Sanjeev Kumar Mandal Monika Abrol Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Applied Science Sanskriti University Mathura Uttar Pradesh India

Recurrent Neural Networks (RNNs) are a modern-day state-of-the-art algorithm that is brand new modern getting used for clinical picture segmentation. RNNs are, in particular, nicely applicable for this undertaking due to the fact they can be skilled to bear in mind patterns over long sequences brand new information. This enables them to perceive structural patterns in an image and carry out sophisticated segmentation obligations together with tumor or organ boundary identification. similarly, RNNs have the ability to contain earlier know-how from different pics and medical data, as well as contextual know-how from external resources such as electronic fitness information. This paper critiques the contemporary in RNNs for medical picture segmentation, outlining the key methods and programs contemporary RNNs inside the field. We discuss both the successes and demanding situations of trendy RNN-based procedures and provide destiny studies directions for the improvement of modern-day extra correct and efficient segmentation equipment.

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Density, Kinematic Viscosity and Cetane Number Prediction of Biofuel Feedstocks from Fatty Acid Compositions 1

Density, Kinematic Viscosity and Cetane Number Prediction of...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Beeravalli, Vijayalaxmi Ashwath, Nanjappa Rasul, Mohammad Khan, Masud Capareda, Sergio CQUniversity School of Health Medical and Applied Sciences Rockhampton Australia CQUniversity School of Engineering and Technology Rockhampton Australia Auckland University of Technology School of Engineering Computing and Mathematical Sciences Auckland New Zealand Texas A&M University Department of Agricultural Engineering College StationTX United States

ISBN: (纸本)9781665450461

Stringent emission laws and the depletion of fossil fuels have made biodiesel one of the promising renewable and environmentally friendly fuel alternatives of the future. To get dependable engine performance and safe emission characteristics, it is imperative to know the Cetane number, kinematics viscosity and density of any fuel. While it is experimentally expensive and requires a continued concerted effort to know the actual properties of a fuel feedstock, recent Machine Learning methods offer wide possibilities to predict with close proximity certain characteristics of biomaterials within well-defined confidence levels. The present paper uses six different machine learning algorithms to predict three basic properties of fifty different biodiesel feedstocks from their fatty acid compositions. The issue of sparsity and anomaly in the data is dealt with through tested methods, following which properties for each feedstock are predicted within good accuracy levels. It is observed that the percentage error is very closely related to the coefficient of variation of a property, which will allow scientists to set variability bounds of predicted values. © 2022 IEEE.

关键词： Feedstocks

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Enhancing Medical Image Segmentation Through Knowledge Transfer

Enhancing Medical Image Segmentation Through Knowledge Trans...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： R Raghavendra Monika Abrol Ramesh Chandra Tripathi Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Applied Science Sanskriti University Mathura Uttar Pradesh India Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India

Clinical image segmentation is a challenge that identifies a selected organ or anatomical structure in a given scientific image which can be of radiographic or other modality. Expertise switch is an essential side of clinical image segmentation as medical practitioners may additionally possess information about the patient's anatomy or physiology that could contribute to the segmentation process. Understanding transfer to a deep mastering version, such as a convolution neural community, can allow the model to perform correct segmentation compared to other tactics. In this paper, we explore strategies and packages of expertise switches for scientific photograph segmentation, discuss its blessings and challenges, and eventually advise a singular method based totally on understanding fusion. We increase a two-step framework to fuse photo-degree features and segmentation labels with medical data before segmentation. The proposed approach demonstrates advanced segmentation accuracy in assessing the present strategies…

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A Comparative Study of Recent 2D Human Pose Estimation Methods

A Comparative Study of Recent 2D Human Pose Estimation Metho...

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computing and Informatics (ICCI), International Conference on

作者： Nada E. Elshami Ahmad Salah Heba Mohsen Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computers and Informatics Zagazig University Zagazig Egypt

Human Pose Estimation is a computer vision technique utilized in various fields such as healthcare, security, and sports to detect the pose of single or multi-person utilizing various machine learning and deep learning methodologies. These methodologies can be applied in a variety of ways, including top-down and bottom-up approaches. Furthermore, this technique can be used with images or videos, as well as in 2D or 3D domains. This paper delves into recent advancements in deep learning methods used for estimating the pose, covering commonly employed datasets, evaluation metrics, and the challenges encountered in this field. It aims to assist new researchers in getting familiar with this technique based on work published between 2019 and 2023, with a spotlight on 2D and omitting 3D.

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Energy performance of Internet of Things (IoT) networks for pipeline monitoring

Energy performance of Internet of Things (IoT) networks for ...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Godlove Suila Kuaban Tadeusz Czachórski Erol Gelenbe Piotr Pecka Valery Nkemeni Piotr Czekalski Polish Academy of Sciences (IITiS PAN) Institute of Theoretical and Applied Informatics Gliwice Poland Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology University of Buea Cameroon Faculty of Automatic Control Electronics and Informatics Silesian University of Technology Gliwice Poland

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Pipelines are the most convenient ways to transport fluids (e.g., water, oil, and gas). However, leakage of fluids into the environment results in resource wastage (primarily water, which is becoming a scarce resource) and environmental pollution (in the case of leakage of toxic fluids like oil and gas). Emerging technologies like the Internet of Things (IoT), Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), distributed computing, and cloud computing enable continuous monitoring of pipelines to detect leakages and corrosion on the pipeline. The main challenge with using battery-powered sensor nodes to monitor pipelines is the energy constraint, necessitating frequent battery replacement. Thus, there is a need to develop energy-saving mechanisms to prolong the lifetime of these sensor nodes. In this paper, we use the diffusion approximation modelling framework in which the data from the experimental testbed are used to model the dynamics of the battery’s energy content and to estimate the mean and variance of the device’s lifetime. The novelty in the proposed diffusion model of the battery of an IoT node is the introduction of multiple energy thresholds that split the energy state-space of the battery into multiple energy-saving regimes. As the battery discharges, the node gradually transitions into energy-saving regimes by reconfiguring some of its parameters to reduce energy consumption (sometimes at the cost of trading off some performance metrics). We investigate the impact of energy-saving regimes or the number of thresholds on the node’s lifetime.

关键词： Wireless sensor networks Fluids Pipelines Data models Water pollution Batteries Sensors

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Chernobyl Disaster Optimizer Algorithm for Feature Selection Problems

Chernobyl Disaster Optimizer Algorithm for Feature Selection...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Hisham A. Shehadeh Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Al-Huson Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The rapid growth of high-dimensional data in Machine Learning (ML) applications presents essential computational challenges. This phenomenon manifests primarily through redundant feature space dimensions, requiring robust dimensionality reduction approaches such as Feature Selection (FS). This research introduces an investigation using the Chernobyl Disaster Optimizer (CDO), a physics-inspired metaheuristic optimization algorithm for solving feature selection problems. We propose a CDO algorithm that utilizes dynamic radiation dispersion mechanisms for wrapper-based feature selection, where solution fitness is evaluated through accuracy metrics obtained from a K-nearest-neighbor (KNN) classifier. Comprehensive empirical analysis across eight UCI repository benchmark datasets shows a satisfactory performance of CDO in classification accuracy and feature selection efficiency with an average of 45.8% compared to competing metaheuristic approaches. Friedman tests, with p-value = 0.0302, confirm the algorithm's significant performance advantages. The results demonstrate that CDO is a reliable optimization algorithm for feature selection tasks, particularly effective in high-dimensional classification problems.

关键词： Dimensionality reduction Measurement Accuracy Heuristic algorithms Disasters Metaheuristics Nearest neighbor methods Feature extraction Classification algorithms Reliability

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Study on the structure, elasticity, and thermal conductivity of orthocarbonate Sr₂CO₄

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Results in Physics 2024年 61卷

作者： Liu, Zi-Jiang Ju, Jia-Qi Dou, Xi-Long Jin, Neng-Zhi Zhang, Cai-Rong Wang, Xiao-Yun Wu, Liang School of Mathematics and Physics Lanzhou Jiaotong University Lanzhou 730070 China Key Laboratory of Cloud Computing of Gansu Province Gansu Computing Center Lanzhou 730030 China Department of Applied Physics Lanzhou University of Technology Lanzhou 730050 China

Orthocarbonate Sr2CO4 is a recently discovered Sr-carbonate that plays a crucial role in understanding the global long-term carbon cycle. In this work, the structure, equation of state, elasticity, and thermal conduct... 详细信息

Orthocarbonate Sr₂CO₄ is a recently discovered Sr-carbonate that plays a crucial role in understanding the global long-term carbon cycle. In this work, the structure, equation of state, elasticity, and thermal conductivity of Sr₂CO₄ were studied by first-principles calculations. The results show that the surfaces of the SrO₉ and SrO₁₁ polyhedrons, which make up Sr₂CO₄, are composed of triangles and quadrangles. By comparison with other alkaline earth metal orthocarbonates, SrCO₃ polymorphs, and the Preliminary Reference Earth Model (PREM), Sr₂CO₄ is found to be a carbonate with high density, low wave velocity, low anisotropy, and low thermal conductivity in the entire Earth's mantle. This study may help to understand the high-pressure behavior of alkaline earth metal orthocarbonates in the Earth's interior. © 2024 The Author(s)

关键词： Anisotropy Elasticity Sr2CO4 Structure Thermal conductivity

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Efficient Deterministic Algorithms for Maximizing Symmetric Submodular Functions

arXiv

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arXiv 2024年

作者： Wan, Zongqi Zhang, Jialin Sun, Xiaoming Zhang, Zhijie State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Center for Applied Mathematics of Fujian Province School of Mathematics and Statistics Fuzhou University China

Symmetric submodular maximization is an important class of combinatorial optimization problems, including MAX-CUT on graphs and hyper-graphs. The state-of-the-art algorithm for the problem over general constraints has an approximation ratio of 0.432 [16]. The algorithm applies the canonical continuous greedy technique that involves a sampling process. It, therefore, suffers from high query complexity and is inherently randomized. In this paper, we present several efficient deterministic algorithms for maximizing a symmetric submodular function under various constraints. Specifically, for the cardinality constraint, we design a deterministic algorithm that attains a 0.432 ratio and uses O(kn) queries. Previously, the best deterministic algorithm attains a 0.385 − ǫ ratio and uses O ( kn(109ǫ ) 20 9ǫ −1) queries [12]. For the matroid constraint, we design a deterministic algorithm that attains a 1/3 − ǫ ratio and uses O(kn log ǫ−1) queries. Previously, the best deterministic algorithm can also attains 1/3 − ǫ ratio but it uses much larger O(ǫ−1n4) queries [24]. For the packing constraints with a large width, we design a deterministic algorithm that attains a 0.432 − ǫ ratio and uses O(n2) queries. To the best of our knowledge, there is no deterministic algorithm for the constraint previously. The last algorithm can be adapted to attain a 0.432 ratio for single knapsack constraint using O(n4) queries. Previously, the best deterministic algorithm attains a 0.316 − ǫ ratio and uses Oe(n3) queries [2]. Copyright © 2024, The Authors. All rights reserved.

关键词： Approximation algorithms

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Deriving a Power-Law Model for Aggregating Data with Head Based Clustering

Deriving a Power-Law Model for Aggregating Data with Head Ba...

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Optimization computing and Wireless Communication (ICOCWC), International Conference on

作者： Vipin Solanki Ajay Rastogi R Raghavendra Department of Applied Science Sanskriti University Mathura Uttar Pradesh India Department of Computing Sciences and Information Technology Teetthanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

Head-based Totally Clustering is a technique of grouping facts and factors with similar traits using a rooted tree structure. The research paper Deriving an electricity-law version for Aggregating facts with Head primarily based Clustering outlines an energy-law model for creating clusters of facts points in a head-based totally hierarchy that may be used to efficiently mixture groups of related information. The authors used a topological distance technique to generate the clusters and implemented the method on an objective global dataset of web pages. The proposed model is designed to lessen the complexity of incorporating records from a pre-described tree shape with electricity regulation. The effects confirmed a widespread discount in computational time and aid utilization. Additionally, the approach could increase aggregated facts' accuracy by incorporating numerous information points in a head-based hierarchy. The look gives a similar understanding of the potential application of a strength-regulation model for aggregating statistics with head-primarily based Clustering.

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