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Addressing the traveling salesperson problem with frequency fitness assignment and hybrid algorithms

Soft computing 2024年第17-18期28卷 9495-9508页

作者： Liang, Tianyu Wu, Zhize Lässig, Jörg Berg, Daan van den Thomson, Sarah L. Weise, Thomas Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Jinxiu Dadao 99 Anhui Hefei230601 China Fakultät Elektrotechnik und Informatik Hochschule Zittau/Görlitz Brückenstraße 1 Saxony Görlitz02826 Germany Abteilung Kognitive Energiesysteme Fraunhofer IOSB-AST Wilhelmsplatz 11 Saxony Görlitz02826 Germany Department of Computer Science Vrije Universiteit Amsterdam De Boelelaan 1111 Amsterdam1081 HV Netherlands School of Computing Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Jiulong Road 111 Anhui Hefei230601 China

The traveling salesperson problem (TSP) is one of the most iconic hard optimization tasks. With frequency fitness assignment (FFA), a new approach to optimization has recently been proposed: instead of directing the search towards better solutions, the optimization process prefers those with rarely encountered objective values. FFA can be plugged into existing algorithms and can improve their performance on some problems that are hard for them. However, problems like the TSP, where the number of possible objective values (here: tour lengths) is high, cause the performance of FFA-based algorithms to deteriorate. We choose 56 symmetric instances from TSPLib as a testbed for our experiments. We plug FFA both into the (1+1) EA and simulated annealing (SA) and obtain the FEA and FSA, respectively. We find that the resulting FEA substantially outperforms the (1+1) EA and that FSA can reach the global optima more often than SA within our computational budget. We propose two ways to hybridize FFA and traditional optimization in a round-robin scheme where objective function evaluations are alternatingly assigned to both components. The FFA-based parts of the algorithms sometimes send a solution over to the traditional-optimization-based ones. This can lead to further performance improvements. In particular, the hybrids combining simulated annealing with the FEA solve most problems and find close-to-optimal solutions on other instances. Even for several instances with many possible different objective values, which are therefore not suited to FFA, hybrids combining FFA and traditional objective-guided optimization algorithms can offer substantial performance improvements. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Simulated annealing

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Leveraging Consumer Technology for Healthcare Systems Using Blockchain Based Bio-Sensor Devices

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Shabaz, Mohammad Rahman, Mohammad Zia Ur Alsaadi, Mahmood Raparthi, Mohan Maaliw, Renato R. Keshta, Ismail Soni, Mukesh Patni, Jagdish Chandra Byeon, Haewon Model Institute of Engineering and Technology Jammu India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P. India Department of computer science Al-Maarif University College Al Anbar Iraq Dallas Texas College of Engineering Southern Luzon State University Lucban Quezon Philippines Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune India School of advance Computing Alliance University Bengaluru India Department of Digital Anti-Aging Healthcare Inje University Gimhae Republic of Korea

Wireless biosensor devices have significantly enhanced level of convenience associated with patient treatment. Presently, most uncertified aggregate signature methods struggle to withstand key attacks that employ only specific keys. This paper presents an uncertified parallel key-isolated aggregate signature framework that utilizes blockchain technology in wireless medical sensor networks and biosensor devices for consumer electronics to tackle the challenges. The proximity of the signature verification and aggregation processes to end users is increased by integrating a bio-sensor device for consumer electronics into the cutting-edge framework. This action simultaneously enhances the safeguarding of patient confidentiality and mitigates computational burden on primary cloud server. By integrating the advantages of key-isolated and uncertified technologies, the proposed solution circumvents exposure concerns, difficult certificate administration, and key storage. This study demonstrates that the proposed method is resistant to Type I, Type II, and wholly selected key attacks when implemented in a random oracle model. This scheme shows a reduction in calculation overhead of 82.97%, 74.03%, 84.58%, and 86.79% correspondingly when compared to other schemes. Analysis of performance indicates that in comparison to pertinent uncertified signature systems, this solution can reduce communication overhead by a minimum of 25% and computational cost by a minimum of 74.03%. IEEE

关键词： Blockchain

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Tuberculosis Detection from Chest Radiographs with Pre-trained Deep Learning Scheme: A Study 31

Tuberculosis Detection from Chest Radiographs with Pre-train...

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31st International Conference on computer Theory and Applications, ICCTA 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Chandrasekar, A. Nandhini, A. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9781665478540

In human, the abnormality in lung causes a severe respiratory problem and breathing difficulties. Tuberculosis (TB) is one of the common lung abnormality caused due a bacterium named Mycobacterium tuberculosis. TB infection will cause harsh breathing issues and untreated TB will lead to death. Detection of the TB using Chest X-ray is one of the common techniques and in this research work;TB detection using Deep-Learning-Technique (DLT) is demonstrated. The various phases of this research involve;(i) Image collection and resizing, (ii) Deep-Feature (DF) extraction with chosen technique, (iii) Classification using SoftMax classifier and (iv) Performance evaluation with existing technique. The proposed work employs a 5-fold cross validation and the best value is considered as the result. The outcome of this study confirms that, the classification accuracy achieved with ResNetl8 and K-Nearest Neighbor (ResNet+KNN) offered better outcome >97% compared to other DLT of this study. © 2021 IEEE.

关键词： Deep learning

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Sentiment Analysis: Amazon Electronics Reviews Using BERT and Textblob

Sentiment Analysis: Amazon Electronics Reviews Using BERT an...

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Language Engineering (ESOLEC), International Conference on

作者： Abdulrahman Mahgoub Hesham Atef Abdulrahman Nasser Mohamed Yasser Walaa M. Medhat M. Saeed Darweesh Passent M. El-Kafrawy Computing and Information Systems Research Center (CIS) School of Information Technology and Computer Science Nile University Egypt Faculty of Computer and AI Benha University Cairo Egypt Wireless Intelligent Networks Center (WINC) School of Engineering and Applied Sciences Nile University Egypt Computer Science Department Effat College of Engineering SA

ISBN: (纸本)9781665453233

The market needs a deeper and more comprehensive grasp of its insight, where the analytics world and methodologies such as “Sentiment Analysis” come in. These methods can assist people especially “business owners” in gaining live insights into their businesses and determining wheatear customers are satisfied or not. This paper plans to provide indicators by gathering real world Amazon reviews from Egyptian customers. By applying both Bidirectional Encoder Representations from Transformers “Bert” and “Text Blob” sentiment analysis methods. The processes shall determine the overall satisfaction of Egyptian customers in the electronics department - in order to focus on a specific domain. The two methods will be compared for both the Arabic and English languages. The results show that people in *** are mostly satisfied with the percentage of 47%. For the performance, BERT outperformed Textblob indicating that word embedding model BERT is more superior than rule-based model Textblob with a difference of 15% - 25%.

关键词： Sentiment analysis Bit error rate Transformers Consumer electronics Business

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Twitter Sentiment Analysis in Covid-19 Pandemic 12

Twitter Sentiment Analysis in Covid-19 Pandemic

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12th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Madanian, Samaneh Airehrour, David Samsuri, Nabilah Ahmad Cherrington, Marianne Auckland University of Technology Department of Computer Science and Software Engineering Auckland New Zealand School of Applied Business Unitec Institute of Technology Auckland New Zealand Auckland University of Technology Department of Mathematical Sciences Auckland New Zealand School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (纸本)9781665400664

We have yet to realise the full capability of social media as an innovative information platform during emergencies and crisis response and management. Sentiment analysis can systematically identify, extract, and scrutinise emotional states and subjective information in social media data. Exploring reactions and perceptions to response messaging is invaluable and proved especially useful for a pandemic response as it can demonstrate general population reaction to the pandemic and governments response actions. This can be further analysed to identify the gap between government response actions and communications and citizens' perceptions. In this paper, an analysis of Twitter data explores population reaction towards COVID-19 health messaging. A Natural Language Processing Python tool is known as TextBlob was used to discover general data sentiment. Data were divided into three sentiments and text extraction of health messages was conducted to explore subsequent tweets in predefined categories. Our findings show the outcome of Tweets analysis could help us to identify the general population concerns and their reactions to COVID-19 to give a better understanding of the situation to governments and support them in implementing appropriate policies. © 2021 IEEE.

关键词： Sentiment analysis

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Effective Surrogate Models for Docking Scores Prediction of Candidate Drug Molecules on SARS-CoV-2 Protein Targets

Effective Surrogate Models for Docking Scores Prediction of ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenqi Shi Mio Murakoso Xiaoyan Guo Linxi Xiong Matthew Chen May D. Wang Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Applied Life Science Kyoto University Kyoto Japan College of Computing Georgia Institute of Technology Atlanta USA Tanwei College Tsinghua University Beijing China Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta USA

Emerging infectious diseases, such as coronavirus disease 2019 (COVID-19), pose a major threat to public health and present a critical challenge for drug discovery. Due to the cost- and time-consuming process of new drug development, virtual pre-screening methods such as protein-ligand docking prediction have become essential tools in enhancing drug refurbishment and repurposing. In this study, we propose a machine learning-based surrogate model for docking score prediction of drug candidates on SARS-CoV-2 protein targets via deep feature concatenation. We investigate 14 different combinations of rule-based and data-driven fingerprinting methods to identify the optimal representation of candidate drug molecules. Extensive experiments on docking scores of 270,000 molecules across 18 different SARS-CoV-2 protein targets demonstrate the effectiveness of the proposed surrogate models. In addition to unseen drugs, we further investigate the generalization of the proposed framework for unseen protein targets. This study may provide an instrumental and generalizable framework for exploring ligand-protein interaction, serving as a useful tool to facilitate rapid drug pre-screening during emerging public health crises.

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Classification of Soil Images using Convolution Neural Networks

Classification of Soil Images using Convolution Neural Netwo...

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2021 IEEE International Conference on Communications, computing, Cybersecurity and Informatics, CCCI 2021

作者： Chatterjee, Kajal Obaidat, Mohammad S. Samanta, Debabrata Sadoun, Balqies Islam, Sk Hafizul Chatterjee, Rajdeep University Department of Computer Science Karnataka Bangalore India University of Sharjah College of Computing and Informatics Sharjah27272 United Arab Emirates Al-Balqa' Applied University College of Engineering Al Salt Jordan Indian Institute of Information Technology Kalyani Department of CSE West Bengal 741235 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9781665432085

Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. © 2021 IEEE.

关键词： Soils

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Detection of Diabetic Macular Edema in Retinal OCT Images with Firefly-Algorithm Selected Features 31

Detection of Diabetic Macular Edema in Retinal OCT Images wi...

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31st International Conference on computer Theory and Applications, ICCTA 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Jerry, Jiny Mariam Gifta, R. Carolin Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9781665478540

In modern hospitals, computer algorithms are widely implemented to diagnose the disease in patients using the medical data collected using a recommended protocol. Recently, the diabetes is human group is gradually rising and the early stage can be predicted by screening the eye. Diabetic-Macular-Edema (DME) is one of the harsh diseases and the untreated disease will initiate various eye abnormalities including the vision loss. In clinics, the retinal image recorded with a chosen imaging system is considered to inspect the disease. In this work, Optical Coherence Tomography (OCT) images are considered for the assessment of the DME in the OCT images. The proposed scheme consist the following stages;(i) Image collection and resizing, (ii) Retinal section extraction using joint thresholding and segmentation, (iii) Feature extraction and dominant feature selection with Firefly-Algorithm, (iv) Binary classification and validation. In this work, 500 numbers of Normal/DME class images with pixel dimension 256 × 256 ×1 pixels are considered and a binary classification with a 5-fold cross validation is implemented. The proposed technique helped to get a detection accuracy of 95% with K-Nearest Neighbors (KNN) classifier. © 2021 IEEE.

关键词： Optical tomography

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Automatic Detection of Breast Cancer in Thermal Images using Optimal Handcrafted Features 31

Automatic Detection of Breast Cancer in Thermal Images using...

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31st International Conference on computer Theory and Applications, ICCTA 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Dasari, Susmitha Suvathi, T. Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9781665478540

Breast Cancer (BC) is one of the harsh illnesses which widely distress the women population and premature recognition is always preferred to reduce its harshness. BC is normally detected in hospitals using a chosen non-invasive medical imaging technique. The proposed work aims to develop and execute the machine learning supported automatic detection of the BC using breast Thermal Images (TI). In TI, examination of thermal silhouette helps to get the necessary information about the normal/cancer infected section. The developed system consist the following phases;(i) Image collection and pre-processing, (ii) Image enhancement and handcrafted feature extraction, (iii) Feature selection with Bat Algorithm, and (iv) Binary classification and validation. In this work, a 5-fold cross validation is employed to classify the test images and the detected accuracy of Fine KNN is better (>92%) compared to the alternatives. The proposed work is tested using benchmark breast TI and the outcome of this research confirms its clinical significance. © 2021 IEEE.

关键词： Image enhancement

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On error-based step size control for discontinuous Galerkin methods for compressible fluid dynamics

arXiv

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arXiv 2022年

作者： Ranocha, Hendrik Winters, Andrew R. Castro, Hugo Guillermo Dalcin, Lisandro Schlottke-Lakemper, Michael Gassner, Gregor J. Parsani, Matteo Applied Mathematics University of Hamburg Germany Department of Mathematics Applied Mathematics Linköping University Sweden Thuwal23955-6900 Saudi Arabia Applied and Computational Mathematics RWTH Aachen University Germany High-Performance Computing Center Stuttgart University of Stuttgart Germany Department of Mathematics and Computer Science University of Cologne Germany Center for Data and Simulation Science University of Cologne Germany

We study temporal step size control of explicit Runge-Kutta methods for compressible computational fluid dynamics (CFD), including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler equations. We demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy (CFL) number. Our numerical examples show that error-based step size control is easy to use, robust, and efficient, e.g., for (initial) transient periods, complex geometries, nonlinear shock capturing approaches, and schemes that use nonlinear entropy projections. We demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases, the open source Julia packages *** with *** and the C/Fortran code SSDC based on *** Codes 65L06, 65M20, 65M70, 76M10, 76M22, 76N99, 35L50 Copyright © 2022, The Authors. All rights reserved.

关键词： Errors

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