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A hybrid optimization algorithm-based feature selection for thyroid disease classifier with rough type-2 fuzzy support vector machine

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EXPERT SYSTEMS 2022年第1期39卷 e12811-e12811页

作者： Sureshkumar, Vidhushavarshini Balasubramaniam, Sathiyabhama Ravi, Vinayakumar Arunachalam, Ajay Sona Coll Technol Comp Sci & Engn Salem 636005 India Prince Mohammad Bin Fahd Univ Ctr Artificial Intelligence Khobar Saudi Arabia Orebro Univ Ctr Appl Autonomous Sensor Syst Orebro Sweden

Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population;thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm-based feature selection design for thyroid disease classifier with rough type-2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top-n features. The proposed hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well-known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed-kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO-RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.

关键词： classification clinical trial clustering algorithm feature selection fuzzy sets hormone machine learning optimization support vector machines thyroid disease

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Research on Temporal and Spatial Distribution Pattern of the Night-Time Economy Supported by Multi-Source Data-A Case Study of Xiamen City, Fujian Province, China

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REMOTE SENSING 2024年第2期16卷 360页

作者： Zeng, Leixin Liu, Tao Du, Ping Ling, Zhenfei Wang, Yong Chinese Acad Surveying & Mapping Beijing 100830 Peoples R China Surveying & Mapping Inst Lands & Resource Dept Guangdong Prov Guangzhou 510700 Peoples R China Lanzhou Jiao Tong Univ Fac Geomat Lanzhou 730070 Peoples R China Gansu Prov Engn Lab Natl Geog State Monitoring Lanzhou 730070 Peoples R China Natl Local Joint Engn Res Ctr Technol & Applicat N Lanzhou 730070 Peoples R China

The increasing proportion of night consumption in the total daily consumption means the night economy has become an indispensable part of national economic development. However, early social science research on alcohol in the night economy and social science research on the night leisure industry dominated our understanding of the night city. Few researchers have built effective mathematical statistical models to explore the spatio-temporal distribution and regional interactions of the night economy. This paper presents a method to analyze the spatial and temporal distribution pattern of the night economy based on multi-source data. Firstly, K-Means++ and DBSCAN were used to cluster OD points to identify the gathering areas of night activities. Then, the local L-function in "flow space" was used to extract the aggregated flow of each aggregation area and analyze the regional interactions. Finally, the correlation between night activity and night service facilities was calculated by using geographic detector, and night activity and lighting were coupled by the profit and loss value. The research shows that this method can identify the main areas of night activity, dig out the interrelationships at the community level, and find the new night activity gathering points and night economic growth areas in the future. This study extends the current situation that the night economy is limited to theoretical research and research in central urban areas, provides the temporal and spatial distribution of night activities and night lighting supplies from the perspective of big data, and provides a basis for future night economy research, urban planning, and relevant policy issuance.

关键词： night-time economy multi source data clustering algorithm temporal and spatial distribution Xiamen city

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On representative day selection for capacity expansion planning of power under extreme conditions

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 137卷 107697-107697页

作者： Li, Can Conejo, Antonio J. Siirola, John D. Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Ohio State Univ Dept Integrated Syst Engn 1971 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn 2015 Neil Ave Columbus OH 43210 USA Sandia Natl Labs Ctr Res Comp PO 5800 Albuquerque NM 87185 USA

Capacity expansion planning (CEP) of power systems determines the optimal future generation mix and/or transmission lines. Due to the increasing penetration of renewables, CEP has to capture the hourly variations of renewable generator outputs and load demand. Since CEP problems typically involve planning horizons of several years, solving the fullspace models where the operating decisions corresponding to all the days is intractable. Therefore, some "representative days"are selected as a surrogate to the fullspace model. We present an input-based and a cost-based approach in combination with the k-means and the k-medoids clustering algorithms for representative day selection. The mathematical properties of the proposed algorithms are analyzed, including an approach to calculate the "optimality gap"of the investment decisions obtained from the representative day model to the fullspace model, and the relationship between the clustering error and the optimality gap. To capture the extreme operating conditions, two novel approaches, i.e., a "load shedding cost'' approach and a "highest cost'' approach, are proposed to identify the "extreme days". We conclude with a case study based on the Electric Reliability Council of Texas (ERCOT) region, which compares the different approaches and the effects of adding the extreme days.

关键词： Power systems Capacity expansion planning Renewable units Representative day selection clustering algorithm Extreme operating conditions

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Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2020年第7期16卷 1-18页

作者： Agbehadji, Israel Edem Frimpong, Samuel Ofori Millham, Richard C. Fong, Simon James Jung, Jason J. Durban Univ Technol Dept Informat Technol ICT & Soc Res Grp Ritson Campus ZA-4001 Durban South Africa Univ Macau Dept Comp & Informat Sci ICT & Soc Res Grp Taipa Macau Peoples R China Durban Univ Technol Durban South Africa Chung Ang Univ Seoul South Korea

The current dispensation of big data analytics requires innovative ways of data capturing and transmission. One of the innovative approaches is the use of a sensor device. However, the challenge with a sensor network is how to balance the energy load of wireless sensor networks, which can be achieved by selecting sensor nodes with an adequate amount of energy from a cluster. The clustering technique is one of the approaches to solve this challenge because it optimizes energy in order to increase the lifetime of the sensor network. In this article, a novel bio-inspired clustering algorithm was proposed for a heterogeneous energy environment. The proposed algorithm (referred to as DEEC-KSA) was integrated with a distributed energy-efficient clustering algorithm to ensure efficient energy optimization and was evaluated through simulation and compared with benchmarked clustering algorithms. During the simulation, the dynamic nature of the proposed DEEC-KSA was observed using different parameters, which were expressed in percentages as 0.1%, 4.5%, 11.3%, and 34% while the percentage of the parameter for comparative algorithms was 10%. The simulation result showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime, and network throughput. In addition, the proposed DEEC-KSA has the optimal time (in seconds) to send a higher number of packets to the base station successfully. The advantage of the proposed bio-inspired technique is that it utilizes random encircling and half-life period to quickly adapt to different rounds of iteration and jumps out of any local optimum that might not lead to an ideal cluster formation and better network performance.

关键词： Edge computing wireless sensor network clustering algorithm Kestrel-based search algorithm heterogeneous energy environment Internet of things

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A New B-Scan Interpretation Model in Complex Environments

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Dai, Qianqi Li, Yibing Jiang, Tao Sun, Qian Harbin Engn Univ Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin 150001 Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

In the scenario where the echo hyperbolas of different targets intersect or the opening structure of the hyperbola is destroyed, the current B-scan image interpretation model has the problems of missing detection and unable to make full use of the hyperbolic information. The hyperbola needs to be identified manually or using neural network methods. The recognition accuracy is difficult to guarantee. To solve the above problem, a new ground-penetrating radar (GPR) B-scan interpretation model based on the hyperbolic trend is proposed in this article. The model consists of four parts, including preprocessing method, hyperbolic trend clustering algorithm (HTCA), restricted algebraic distance fitting and cluster center (RADFCC) algorithm, and hierarchical filtered back-projection (HFBP). First, the GPR B-scan image is converted into a binarized image based on the ratio of the main lobe and sidelobe of the echo. Second, the points in the binarized image are classified into hyperbolic column segment clusters based on the hyperbolic trend, and further denoised according to the main lobe width of the Ricker wave. Third, by analyzing the characteristics of the GPR in the ground coupling mode, the restricted hyperbola fitting algorithm is used to fit the classified hyperbola column segment clusters to speed up the convergence of the fit and avoid the result from falling into a local optimum. Fourth, the target's location is preliminarily estimated using the parameters of the hyperbola and the cluster center is established through the location and radius of the target to obtain the accurate estimation of the target and the dielectric constant of the underground. Finally, an imaging algorithm is applied to invert the subsurface image based on the dielectric constant of the subsurface medium. The effectiveness of the proposed model has been demonstrated on both the simulated and real datasets.

关键词： clustering algorithms Fitting Market research Classification algorithms Image edge detection Pipelines Information filters Buried object detection clustering algorithm data processing ground-penetrating radar (GPR) image processing

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Energy and task completion time minimization algorithm for UAVs-empowered MEC SYSTEM

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2022年 35卷

作者： Asim, Muhammad Mashwani, Wali Khan Abd El-Latif, Ahmed A. Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Kohat Univ Sci & Technol Inst Numer Sci Kohat Pakistan Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci Lab Riyadh 11586 Saudi Arabia Menoufia Univ Fac Sci Dept Math & Comp Sci Al Minufiyah 32511 Egypt

This paper presents an energy and task completion time minimization scheme for the unmanned aerial vehicles (UAVs)-empowered mobile edge computing (MEC) system, where several UAVs are deployed to serve large-scale users' equipment (UEs). The aim is to minimize the weighted sum of energy consumption and task completion time of the system by planning the trajectories of UAVs. The problem is NP-hard, non-convex, non-linear, and mixed-decision variables. Therefore, it is very challenging to be solved by conventional optimization techniques. To handle this problem, this paper proposes an energy and task completion time minimization algorithm (ETCTMA) that solves the above problem in three steps. In the first step, the deployment updation of stop points (SPs) is handled by adopting a differential evolution algorithm with a variable population size. Then, in the second step, the association between SPs and UAVs is determined. Specifically, a clustering algorithm is proposed to associate SPs with UAVs. Finally, in the third step, a low-complexity tabu search algorithm is adopted to construct the trajectories of all UAVs. The performance of the proposed ETCTMA is tested on seven instances with up to 700 UEs. The results reveal that our proposed algorithm ETCTMA outperforms other variants in terms of minimizing the weighted sum of energy consumption and task completion time of the system.

关键词： Mobile edge computing Unmanned aerial vehicle Differential evolution clustering algorithm Tabu search algorithm

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Power output optimization of electric vehicles smart charging hubs using deep reinforcement learning

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷

作者： Bertolini, Andrea Martins, Miguel S. E. Vieira, Susana M. Sousa, Joao M. C. Univ Lisbon IDMEC Inst Super Tecn 1 Ave Rovisco Pais P-1049001 Lisbon Portugal

Since most branches of the distribution grid may already be close to their maximum capacity, smart management when charging electric vehicles (EVs) is becoming more and more crucial. In fact, office buildings might not be able to handle several transactions at the same time, especially considering the next generation of fast chargers which are very power expensive. Thus, an efficient charging policy needs to be found. This paper proposes the scheduling of real-time EVs charging through deep reinforcement learning (DRL) techniques. DRL has been chosen because it can adaptively learn from interacting with the surrounding environment. The focus of the optimization is to ensure the completion of the charging transactions in a timely manner, while shifting the load from the times of peak demand. The novelty of the proposed approach lies in its innovative framework: pools of electric vehicles with different characteristics are categorized using a clustering algorithm, a tree-based classifier has been developed to sort new instances of EVs, and a multilayer perceptron artificial deep neural network has been trained to predict the expected duration of each charging session. These features are used as inputs to the DRL agent, and are mapped into actions that adjust the maximum power associated to each charging station. The model has been compared to a traditional charging algorithm and increasingly challenging scenarios have been considered. Results have shown that the developed algorithm fails less than the baseline, with a reduction of the load due to EVs charging of 80% during peak times.

关键词： Reinforcement learning Electric vehicles Real-time charging scheduling Neural network clustering algorithm

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Quantum walks-based classification model with resistance for cloud computing attacks

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期232卷

作者： Wu, Xiaodong Jin, Zhigang Zhou, Junyi Duan, Chenxu Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tianjin Univ Int Engn Inst Tianjin 300072 Peoples R China State Grid Tianjin Elect Power Co State Grid Tianjin Elect Power Co Informat & Commu Tianjin 300140 Peoples R China Tianjin Univ Sch Elect & Informat Engn Dept Commun Engn Tianjin 300072 Peoples R China

Cloud computing is considerably investigable and adoptable in both industry and academia, and Software Defined Networking (SDN) has been applied in cloud computing. Although SDN mitigates some security issues in cloud computing, new security issues related to its own architecture are also introduced. In this paper, we propose a quantum walks-based classification model which is available for intrusion detection in cloud computing. The proposed model concentrates feature information of data via Principal Component Analysis, and then aggregates the concentrated data in the way of quantum walks by a training-free clustering algorithm. The clustering algorithm constructs coin transformation and conditional shift transformation based on transition probabilities to move similar data toward each other. To enhance the usability of the proposed model in cloud computing security, we propose a new cloud architecture which adds security layer in SDN to ponder the protection of cloud computing fundamentally, and simplify transition probabilities equations of clustering algorithm without affecting clustering accuracy, decreasing the time complexity from O(nk2) to O(nk). The experimental results on popular datasets (Accuracy: 99.4% on InSDN, 95.8% on NSL-KDD, 98% on UNSW-NB15 and 96.4% on CSE-CIC-IDS2018) revealed that the proposed model is effective dealing with attacks on SDN-based cloud computing, and is able to maintain stable and excellent attack identification ability under different traffic intensities.

关键词： Classification model Quantum walks Cloud computing security Software defined networking clustering algorithm Intrusion detection

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Parameterization of channel characteristics based on statistical analysis at refuge chamber

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PHYSICAL COMMUNICATION 2021年 48卷 101429-101429页

作者： Li, Shiyin Sun, Junchang Ma, Shuai Zhou, Hui China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Xian Univ Posts & Telecommun Shanxi Key Lab Informat Commun Network & Secur Xian 710121 Peoples R China

In view of the wireless channel complexity in refuge chamber environments, we parameterize the channel characteristics for reliable signal propagation under line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. According to the Saleh-Valenzuela channel model, the multipath components (MPCs) arrive at the receiver in cluster form. Therefore, clustering algorithms are required to identify clusters. However, the drawback of conventional KMeans and KPowerMeans algorithms is their limitation to an a priori cluster number. In this paper, we propose a clustering algorithm that uses a fitted curve to intersect the slope curve of the normalized power delay profile (PDP). The effectiveness of the proposed algorithm is verified based on the statistical measurement data by comparison with the conventional KMeans and KPowerMeans algorithms. Based on the clustering results, we explore the distribution of the cluster arrival delay and cluster power. We then analyze the probability density function (PDF) of the first MPC arrival delay in each cluster by comparison with conventional models. Finally, we investigate the path loss exponent (PLE) and shadow fading factor of the large-scale path loss. The outcomes of this paper is in the provision of theoretical support for mobile communications in refuge chamber environments. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Wireless channel characteristics clustering algorithm Cluster arrival delay Clusters power Multipath components arrival delay Path loss

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Improving Chip Seal Construction Using Laser Intensity Data

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JOURNAL OF TRANSPORTATION ENGINEERING PART B-PAVEMENTS 2020年第3期146卷

作者： Gurganus, Charles F. Chang, Shi Ravipati, Deepika Goehl, Darlene Gharaibeh, Nasir G. Texas A&M Transportat Inst Pavement & Mat Div 402 Harvey Mitchell Pkwy South College Stn TX 77845 USA Texas A&M Univ Zachry Dept Civil Engn 3136 TAMU College Stn TX 77843 USA Texas A&M Univ Dept Elect & Comp Engn 4302 Coll Main St College Stn TX 77801 USA

Chip seal is a common pavement preservation technique used to extend pavement life and improve its functional performance thoughout the United States. The need to improve chip seal design and construction is an active area of research. One area for improvement in chip seal construction is the adjustment of binder application rate based on the pavement surface condition. This paper provides a method for adjusting the binder application rate based on analysis of reflected laser signal intensity from the existing (preseal) pavement surface, converted to red, green, and blue (RGB). Using the k-means clustering algorithm as an initial step to reduce the RGB scale, a subsequent grouping step is performed before visualizing the data as a directed acyclic graph (DAG). Structuring the data into a DAG allows for the systematic description of the pavement surface condition. With the ability to accurately describe the pavement surface condition, binder rate adjustments were suggested depending on how the existing surface compares with an as-expected condition. The results of applying the method to two roadways in Texas were presented. The methodology provides binder rate adjustment for each wheel path and the area between wheel paths at 30.5-m (100-ft) intervals. (C) 2020 American Society of Civil Engineers.

关键词： Chip seal Mobile light detection and ranging (LiDAR) clustering algorithm Pavement maintenance

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