Sangyun Lee¹, Junekoo Kang², Hyunki Hong^2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2705-2725, 2023, DOI:10.32604/cmc.2023.034136

Abstract In feature-based visual localization for small-scale scenes, local descriptors are used to estimate the camera pose of a query image. For large and ambiguous environments, learning-based hierarchical networks that employ local as well as global descriptors to reduce the search space of database images into a smaller set of reference views have been introduced. However, since global descriptors are generated using visual features, reference images with some of these features may be erroneously selected. In order to address this limitation, this paper proposes two clustering methods based on how often features appear as well as their covisibility. For both approaches,… More >

P. Kavitha Rani¹, Hee-Kwon Chae², Yunyoung Nam^2,*, Mohamed Abouhawwash^3,4

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2591-2605, 2023, DOI:10.32604/iasc.2023.033697

Abstract Wireless sensor networks (WSNs) are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data. WSNs use sensor nodes (SNs) to collect and transmit data. However, the power supplied by the sensor network is restricted. Thus, SNs must store energy as often as to extend the lifespan of the network. In the proposed study, effective clustering and longer network lifetimes are achieved using multi-swarm optimization (MSO) and game theory based on locust search (LS-II). In this research, MSO is used to improve the optimum routing, while the LS-II… More >

Jiaqi Shao^1,#, Shuwen Chen^1,2,3,#,*, Jin Zhou^1,#, Huisheng Zhu¹, Ziyi Wang¹, Mackenzie Brown^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2173-2219, 2023, DOI:10.32604/cmes.2023.025499

Abstract As a mainstream research direction in the field of image segmentation, medical image segmentation plays a key role in the quantification of lesions, three-dimensional reconstruction, region of interest extraction and so on. Compared with natural images, medical images have a variety of modes. Besides, the emphasis of information which is conveyed by images of different modes is quite different. Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors. Therefore, large quantities of automated medical image segmentation methods have been developed. However, until now, researchers have not developed a universal method for all… More >

Jipeng Gu¹, Weijie Zhang¹, Youbing Zhang^1,*, Binjie Wang¹, Wei Lou², Mingkang Ye³, Linhai Wang³, Tao Liu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2221-2236, 2023, DOI:10.32604/cmes.2023.025396

Abstract An improved fuzzy time series algorithm based on clustering is designed in this paper. The algorithm is successfully applied to short-term load forecasting in the distribution stations. Firstly, the K-means clustering method is used to cluster the data, and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division. On this basis, the data is fuzzed to form a fuzzy time series. Secondly, a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load, which is used to predict the short-term trend change of load in the… More >

Jinrui Tang^*, Ganheng Ge, Jianchao Liu, Honghui Yang

Energy Engineering, Vol.120, No.5, pp. 1107-1132, 2023, DOI:10.32604/ee.2023.025666

Abstract Electric vehicle (EV) charging load is greatly affected by many traffic factors, such as road congestion. Accurate ultra short-term load forecasting (STLF) results for regional EV charging load are important to the scheduling plan of regional charging load, which can be derived to realize the optimal vehicle to grid benefit. In this paper, a regional-level EV ultra STLF method is proposed and discussed. The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles, and then constructed by our collected EV charging transaction data in the field. Secondly, these usage degrees… More >

Sungjin Lee¹, Ahyoung Lee², Min Hong^3,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2543-2554, 2023, DOI:10.32604/csse.2023.037055

Abstract Specific medical data has limitations in that there are not many numbers and it is not standardized. to solve these limitations, it is necessary to study how to efficiently process these limited amounts of data. In this paper, deep learning methods for automatically determining cardiovascular diseases are described, and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted. The cardiac CT images include several parts of the body such as the heart, lungs, spine, and ribs. The preprocessing step proposed in this paper divided CT image data into regions… More >

Pham Huy Thong^1,2,3, Florentin Smarandache⁴, Phung The Huan⁵, Tran Manh Tuan⁶, Tran Thi Ngan^6,*, Vu Duc Thai⁵, Nguyen Long Giang², Le Hoang Son³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1981-1997, 2023, DOI:10.32604/csse.2023.035692

Abstract Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees… More >

R. Bala Krishnan¹, D. Yuvaraj², P. Suthanthira Devi³, Varghese S. Chooralil⁴, N. Rajesh Kumar¹, B. Karthikeyan⁵, G. Manikandan^5,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1563-1576, 2023, DOI:10.32604/csse.2023.035307

Abstract With the improvement of current online communication schemes, it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate. Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission. Both of the techniques mentioned above aid in the confidentiality of feature content. The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red, Green, and Blue. The private Content originated from a medical client and was forwarded to a medical practitioner… More >

Xiangwei Chen¹, Zhijin Zhao^1,2,*, Xueyi Ye¹, Shilian Zheng², Caiyi Lou², Xiaoniu Yang²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1385-1400, 2023, DOI:10.32604/csse.2023.034543

Abstract Interference signals recognition plays an important role in anti-jamming communication. With the development of deep learning, many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms. However, there is no unsupervised interference signals recognition algorithm at present. In this paper, an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering (DDCC) is proposed. Specifically, in the first phase, four data augmentation strategies for interference signals are used in data-augmentation-based (DA-based) contrastive learning. In the second phase, the original dataset’s k-nearest neighbor set (KNNset) is designed… More >

M. F. Mridha¹, Zabir Mohammad², Muhammad Mohsin Kabir², Aklima Akter Lima², Sujoy Chandra Das², Md Rashedul Islam^3,*, Yutaka Watanobe⁴

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2059-2073, 2023, DOI:10.32604/csse.2023.032977

Abstract The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes… More >