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Journal articles on the topic 'Concatenation method'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Auni, Ahmad Ramadhan, and Endang Sugiharti. "Optimization of Mango Plant Leaf Disease Classification Using Concatenation Method of MobileNetV2 and DenseNet201 CNN Architectures." Scientific Journal of Informatics 11, no. 4 (2025): 1023–34. https://doi.org/10.15294/sji.v11i4.15169.

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Purpose: Mango production can be severely impacted by diseases affecting mango plants. By leveraging artificial intelligence, the agricultural sector can automate the analysis of mango leaves to monitor plant health. The goal of this research is to improve the early detection of diseases in mango leaves to allow early treatment to minimize damage to the crops. Methods: This study employs an approach of combining two pre-trained CNN architectures, namely MobileNetV2 and DenseNet201 through concatenation method. To enhance the model’s generalization ability, various image augmentation techniques were applied during the training phase. Result: The model developed in this study achieved great performance in classifying mango leaf diseases with a testing accuracy of 99.25%. This result indicates the effectiveness of the concatenation method by outperforming the accuracy of either MobileNetV2 or DenseNet201 when implemented separately. Novelty: This research introduces a novel strategy by concatenating two pre-trained CNN architectures for mango leaf disease classification, a method not previously explored in this context. The model developed from this study has the potential to serve as a tool for the early detection and treatment of mango leaf diseases.

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Kim, Songeun, and Soon-Yong Park. "Expandable Spherical Projection and Feature Concatenation Methods for Real-Time Road Object Detection Using Fisheye Image." Applied Sciences 12, no. 5 (2022): 2403. http://dx.doi.org/10.3390/app12052403.

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Fisheye lens cameras are widely used in such applications where a large field of view (FOV) is necessary. A large FOV can provide an enhanced understanding of the surrounding environment and can be an effective solution for detecting the objects in automotive applications. However, this comes with the cost of strong radial distortions and irregular size of objects depending on the location in an image. Therefore, we propose a new fisheye image warping method called Expandable Spherical Projection to expand the center and boundary regions in which smaller objects are mostly located. The proposed method produces undistorted objects especially in the image boundary and a less unwanted background in the bounding boxes. Additionally, we propose three multi-scale feature concatenation methods and provide the analysis of the influence from the three concatenation methods in a real-time object detector. Multiple fisheye image datasets are employed to demonstrate the effectiveness of the proposed projection and feature concatenation methods. From the experimental results, we find that the proposed Expandable Spherical projection and the LCat feature concatenation yield the best AP performance, which is up to 4.7% improvement compared to the original fisheye image datasets and the baseline model.

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Maria Fayyaz, NaumanRiaz Chaudhry, and Reema Choudhary. "CLASSIFICATION OF ISOCITRATE DEHYDROGENASE (IDH) MUTATION STATUS IN GLIOMAS USING TRANSFER LEARNING." Pakistan Journal of Scientific Research 3, no. 2 (2024): 224–33. http://dx.doi.org/10.57041/pjosr.v3i2.1069.

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Isocitrate Dehydrogenase (IDH) mutation is a significant genetic alteration that is found in brain tumors. Its diagnosis is vital for the prognosis of low-grade Glioma and secondary grade Glioma patients. Physicians used invasive methods to diagnose the Gliomas, which was an unsafe method but now advanced magnetic resonance imaging techniques are being used for tumor visualization and treatments. Classical machine learning and deep learning methods have been used by some studies for the problem of IDH mutation status detection using magnetic resonance images. Recent studies have used concatenation of deep and handcrafted features to achieve superior performance. This study has used the concatenations of the deep features extracted through pre-trained convolution neural networks (CNNs) for the task of IDH mutation status detection using magnetic resonance images. To select the pre-trained CNNs, five top accuracies on the ImageNet dataset were considered. Magnetic resonance images were acquired from The Cancer Genome Atlas Glioblastoma Multiforme and The Cancer Genome Atlas Low-Grade Glioma. All experiments (performed using features extracted from each CNN and their concatenation) were compared with each other and state-of-the-art. The proposed technique achieved 99% accuracy while being efficient in terms of data and computational resources.

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Hlaing, Chaw Su, and Aye Thida. "Phoneme Concatenation Method for Myanmar Speech Synthesis System." International Journal of Information Technology and Computer Science 10, no. 12 (2018): 13–20. http://dx.doi.org/10.5815/ijitcs.2018.12.02.

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Velička, Deivydas, Gilija Bernotienė, Kristina Poderienė, Alfonsas Vainoras, and Jonas Poderys. "DIFFERENTIAL TRAINING TASKS TRIGGER THE DECREASE OF CONCATENATION BETWEEN CARDIOVASCULAR PARAMETERS." Baltic Journal of Sport and Health Sciences 4, no. 103 (2016): 38–44. http://dx.doi.org/10.33607/bjshs.v4i103.71.

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Background. A lot of studies have shown the Differential Training (DT) is more effective than the traditional training based on repetition at constant conditions. The aim of this study was to find the changes in dynamical concatenation between ECG parameters during the DT task. Methods. Participants (13 healthy adult males) performed two balance tests standing on LIBRA balance board. Continuous ECG registration during both balance tests and during the rest was 3 minutes. The changeable parametric interactions and its dynamics during exercise tasks while monitoring ECG parameters and its data sequences analysis based on mathematical method based on matrix theory were applied, the concatenation between ECG parameters were analysed. Results. Results obtained during the study showed that there were no drastic differences between ECG parameters while performing both balance tests. The concatenation between analysed ECG parameters increased or there was no significant change. The decrease of concatenation was observed during the second task while the participants performed the squats standing on the balance board. This type of change in concatenation was found during the analysis of all ECG parameters, i.e. between duration of RR intervals and QRS, between JT and RR intervals and between QRS and JT intervals. Conclusion. DT tasks trigger the decrease of concatenation between cardiovascular parameters that allows discussing about the hypothesis that decrease of concatenation between systemic regulatory commands and cardiac metabolic changes could be one of possible chain activating and enhancing the efficiency of long-term adaptation at conditions of DT.

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S, Chaitra, Rekha V, M. Harisha A, et al. "A comprehensive review of parallel concatenation of LDPC code techniques." Indian Journal of Science and Technology 14, no. 5 (2021): 432–44. https://doi.org/10.17485/IJST/v14i5.2171.

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Abstract Objective: In the code theory, parallel concatenation of codes becomes more popular after the introduction of turbo codes. In recent years, the Low Density Parity Check (LDPC) code has found remarkable advancement and has seen them outshine turbo codes in terms of performance especially in the error floor and higher code rate. The main objective of this paper is to address the various techniques of a parallel concatenation of LDPC code in code theory. Method/Finding: To reduce the complexity of encoding and decoding for longer codes various parallel concatenation of LDPC coding methods were introduced and the performance was compared with other work. Novelty: When a longer block length is used, the parallel LDPC decoder is suffered from the complexity of prohibitive implementation. To overcome this issue and to achieve the best performance for longer codes, the different methods for parallel concatenation of LDPC codes were introduced with reduced complexity. This will helps to break the long and complex LDPC code into less complex and smaller LDPC to distribute the decoding and encoding load. Also, this will provides scalability and scope for improving the performance of LDPC codes in practical delay-sensitive and energy-aware applications. Keywords: Parallel concatenation; LowDensity ParityCheck (LDPC); turbo codes; Parallel Concatenated Gallager Codes (PCGC)

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Buskies, Christoph. "Method and device for coarticulated concatenation of audio segments." Journal of the Acoustical Society of America 122, no. 3 (2007): 1320. http://dx.doi.org/10.1121/1.2781455.

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Tao, Jianhua, Le Xin, and Panrong Yin. "Realistic Visual Speech Synthesis Based on Hybrid Concatenation Method." IEEE Transactions on Audio, Speech, and Language Processing 17, no. 3 (2009): 469–77. http://dx.doi.org/10.1109/tasl.2008.2011538.

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Bahubalendruni, MVA Raju, and Bibhuti Bhusan Biswal. "A novel concatenation method for generating optimal robotic assembly sequences." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 10 (2015): 1966–77. http://dx.doi.org/10.1177/0954406215623813.

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Selection of optimized assembly sequence is significantly essential to achieve cost-effective manufacturing process. This paper presents a novel efficient methodology to generate cost-effective feasible robotic assembly sequences though concatenation of parts. Part concatenation process will be followed with liaison predicate test and feasibility predicate test. A unique method called bounding box method is described to test the feasibility predicate efficiently in the computer-aided design environment. Assembly indexing technique is proposed to filter the redundant assembly subsets with high energy in order to minimize the computational time. The cost of collision free assembling operation is considered by the weight and distance traveled by the part in the assembly environment to join with the mating part. The method is successful in finding feasible optimal assembly sequence without ignoring any possible assembly sequence and found to be efficient in solving computer-aided assembly sequence generation. The correctness of the methodology is illustrated with an example.

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Liu Xu, Chen Lin, and Sun Xiao-Han. "Equivalent rectangle approximation-staircase concatenation method for wedge shaped fiber." Acta Physica Sinica 57, no. 10 (2008): 6430. http://dx.doi.org/10.7498/aps.57.6430.

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Zhai, Zhenyu, Qiantong Wang, Zongxu Pan, Zhentong Gao, and Wenlong Hu. "Muti-Frame Point Cloud Feature Fusion Based on Attention Mechanisms for 3D Object Detection." Sensors 22, no. 19 (2022): 7473. http://dx.doi.org/10.3390/s22197473.

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Continuous frames of point-cloud-based object detection is a new research direction. Currently, most research studies fuse multi-frame point clouds using concatenation-based methods. The method aligns different frames by using information on GPS, IMU, etc. However, this fusion method can only align static objects and not moving objects. In this paper, we proposed a non-local-based multi-scale feature fusion method, which can handle both moving and static objects without GPS- and IMU-based registrations. Considering that non-local methods are resource-consuming, we proposed a novel simplified non-local block based on the sparsity of the point cloud. By filtering out empty units, memory consumption decreased by 99.93%. In addition, triple attention is adopted to enhance the key information on the object and suppresses background noise, further benefiting non-local-based feature fusion methods. Finally, we verify the method based on PointPillars and CenterPoint. Experimental results show that the mAP of the proposed method improved by 3.9% and 4.1% in mAP compared with concatenation-based fusion modules, PointPillars-2 and CenterPoint-2, respectively. In addition, the proposed network outperforms powerful 3D-VID by 1.2% in mAP.

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Chettri, Bhusan, and Krishna Bikram Shah. "Nepali Text to Speech Synthesis System using ESNOLA Method of Concatenation." International Journal of Computer Applications 62, no. 2 (2013): 24–28. http://dx.doi.org/10.5120/10053-4909.

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Weng, Zhenyu, and Yuesheng Zhu. "Concatenation hashing: A relative position preserving method for learning binary codes." Pattern Recognition 100 (April 2020): 107151. http://dx.doi.org/10.1016/j.patcog.2019.107151.

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Pan, Feng, Lin Wang, Yansha Zhang, and Jie Wang. "Cross-Domain Person Re-identification Combining Feature Concatenation and Attention." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 153–60. http://dx.doi.org/10.54097/hset.v56i.10099.

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To improve the insufficient generalization and poor cross-domain capability of the existing direct cross-dataset person re-identification methods, a cross-domain person re-identification method combining feature concatenation and attention (FCANet) is proposed. The deep features of the network are concatenated to complement the feature information and obtain discriminatively feature, and the position attention module is introduced to enhance the data feature representation capability of the cross-domain task, using the joint training network of label smooth cross-entropy loss and triplet loss, model training in the source domain, and directly deploy to the target domain for testing. To verify the performance of the proposed method, it was experimented on three public datasets of Market1501, DukeMTMC-reID and MSMT17, which mAP and Rank1can reach 51.4% and 62.7% on Market1501. The results show that the proposed method has good performance in improving the generalization of cross-domain tasks, and the recognition accuracy outperforms the domain generalization algorithms of comparison.

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Tulitbaeva, Galina Fanilevna, and Regina Nagimovna Khaidarshina. "Study of the Relationship Between Loneliness and Suicidal Risks among the Adolescents and the Elderly." Психолог, no. 6 (June 2023): 66–79. http://dx.doi.org/10.25136/2409-8701.2023.6.39455.

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The article presents the results of the study which identifies the concatenation between loneliness and the degree of suicidal risk among teenagers and the elderly. Subject being examined: loneliness and suicidal risk from the point of view of their concatenation among teenagers and the elderly. The methods which were used are the Differential Questionnaire showing the Experience of Loneliness (E. N. Osin, D. A. Leontiev), the method of diagnosing the level of subjective feeling of loneliness (D. Russell, M. Ferguson), the method «Map of the risk of suicides» (modified by L. B. Schneider), the SAD PERSONS Scale (Patterson et al.), Hopelessness Inventory (A. Beck), mathematical and statistical analysis. The study is novel in that it is for the first time when the system of concatenation between the experience of loneliness and suicidal tendencies among teenagers and the elderly was developed. As part of the study, there was established the concatenation between the experience of loneliness and the level of suicidal risk in both groups among those being examined. The research shows that the subjective understanding of the level of one's real loneliness as high, the negative attitude towards one's loneliness and the dependence on communication are associated with a high level of suicidal risk, while the positive attitude towards loneliness and a low degree of experiencing real loneliness are associated with a low level of suicidal risk. The results obtained prove that there is a way to overcome the negative aspects of loneliness and reduce the number of suicides, which is connected with a change of one's attitude towards loneliness, the ability to self-control, the ability to find and use the internal and external resources of the individual. The conclusions reached will be useful for both psychologists and professionals working with teenagers and elderly citizens.

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YALÇIN, Sercan. "Hybrid Convolutional Neural Network Method for Robust Brain Stroke Diagnosis and Segmentation." Balkan Journal of Electrical and Computer Engineering 10, no. 4 (2022): 410–18. http://dx.doi.org/10.17694/bajece.1129233.

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Artificial intelligence with deep learning methods have been employed by a majority of researchers in medical image classification and segmentation applications for many years. In this study, hybrid convolutional neural network (CNN) model has been proposed for diagnosing of brain stroke from the dataset consisting of the computed tomography (CT) brain images. The model inspired from C-Net consists of multiple concatenation layers of the networks, and prevents the concatenation of convolutional feature maps to evince the mapping process. The structures of the convolutional index and residual shortcuts of the INet model are also integrated into the proposed CNN model. In output layer of the model, it is split into two classes as whether there is a stroke or not in a brain image, and then the region of the stroke in the image is segmented. Tremendous analyzes have been conducted in terms of many benchmarks using Python programming. The proposed method shows better performances rather than some other current CNN-based methods by 99.54% accuracy and 99.1% Matthews correlation coefficient (MCC) in the diagnosis of brain stroke. The proposed method can alleviate the work of most medical staffs and facilitate the process of the patient’s remedy.

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Thornton, William A. "Design of Base Plates for Wide Flange Columns: A Concatenation of Methods." Engineering Journal 27, no. 4 (1990): 173–75. http://dx.doi.org/10.62913/engj.v27i4.559.

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The Murray Stockwell (MS) method for design of base plates has the capability of producing very thin, and hence economical, small base plates for lightly loaded columns. The method has two problems, however, which make its incorporation into a general method for all base plates, i.e., large as well as small plates, and heavily as well as lightly loaded plates, difficult. First, the boundary between lightly and heavily loaded plates is not defined. Application of the MS method to a particular situation can lead to a base plate thicker than would be required by the AISC 8th Ed. Manual method, or can lead to a numerical failure of the method as indicated by mathematically imaginary solutions in terms of complex numbers. This problem has been demonstrated by Ahmed and Kreps. Second, the MS method assumes a peak bearing pressure of Fp over an H-shaped region adjacent to the column crosssection whereas the conventional assumption (i.e., AISC Manual 8th Ed. and AISC LRFD Manual 1st Ed.) is a uniform pressure fp over the entire contact area between the plate and the concrete. This problem led the author to propose a yield line method (referred to as Model 2 in Ref. 5) to replace the MS method for small base plates. Ref. 5 demonstrates that Model 2 coupled with the cantilever method for large base plates yields plate thicknesses equal to or less than the AISC 8th Ed. method. However, Model 2 yields thicker plates than the MS method when the plate is lightly loaded. In order to maintain the benefit of the MS method for lightly loaded plates, to define the boundary between lightly and heavily loaded plates, and to effect a merger of the two different pressure distributions and three methods into a single method, consider the following analysis.

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Yang, Jian, Shuming Ma, Dongdong Zhang, ShuangZhi Wu, Zhoujun Li, and Ming Zhou. "Alternating Language Modeling for Cross-Lingual Pre-Training." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9386–93. http://dx.doi.org/10.1609/aaai.v34i05.6480.

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Language model pre-training has achieved success in many natural language processing tasks. Existing methods for cross-lingual pre-training adopt Translation Language Model to predict masked words with the concatenation of the source sentence and its target equivalent. In this work, we introduce a novel cross-lingual pre-training method, called Alternating Language Modeling (ALM). It code-switches sentences of different languages rather than simple concatenation, hoping to capture the rich cross-lingual context of words and phrases. More specifically, we randomly substitute source phrases with target translations to create code-switched sentences. Then, we use these code-switched data to train ALM model to learn to predict words of different languages. We evaluate our pre-training ALM on the downstream tasks of machine translation and cross-lingual classification. Experiments show that ALM can outperform the previous pre-training methods on three benchmarks.1

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Iwаszkiewicz, Jаn, Аdаm Muc, and Аgаtа Bieleckа. "Simulation studies of concatenation as the simplest way of multi-phase inverter control." Renewable Energy and Power Quality Journal 20 (September 2022): 811–16. http://dx.doi.org/10.24084/repqj20.445.

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The paper presents simulation studies of potentially thinkable method of multi-phase inverter control. The method consists in concatenation of consecutive voltage space vectors. Useful mathematical tools, suitable to study the inverter control as well as selected results of simulation studies are also included.

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Helfert, S. F. "The Method of Lines in the time domain." Advances in Radio Science 11 (July 4, 2013): 15–21. http://dx.doi.org/10.5194/ars-11-15-2013.

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Abstract. The Method of Lines (MoL) is a semi-analytical numerical algorithm that has been used in the past to solve Maxwell's equations for waveguide problems. It is mainly used in the frequency domain. In this paper it is shown how the MoL can be used to solve initial value problems in the time domain. The required expressions are derived for one-dimensional structures, where the materials may be dispersive. The algorithm is verified with numerical results for homogeneous structures, and for the concatenation of standard dielectric and left handed materials.

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Liu, Ziyi, Lei Li, Mengxuan Zhang, Wen Hua, and Xiaofang Zhou. "FHL-cube." Proceedings of the VLDB Endowment 15, no. 11 (2022): 3112–25. http://dx.doi.org/10.14778/3551793.3551856.

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Multi-Constraint Shortest Path ( MCSP ) generalizes the classic shortest path from single to multiple criteria such that more personalized needs can be satisfied. However, MCSP query is essentially a high-dimensional skyline problem and thus time-consuming to answer. Although the current Forest Hop Labeling (FHL) index can answer MCSP efficiently, it takes a long time to construct and lacks the flexibility to handle arbitrary criteria combinations. In this paper, we propose a skyline-cube-based FHL index that can handle the flexible MCSP efficiently. Firstly, we analyze the relation between low and high-dimensional skyline paths theoretically and use a cube to organize them hierarchically. After that, we propose methods to derive the high-dimensional path from the lower ones, which can adapt to the flexible scenario naturally and reduce the expensive high dimensional path concatenation. Then we introduce efficient methods for both single and multi-hop cube concatenations and propose pruning methods to further alleviate the computation. Finally, we improve the FHL structure with lower height for faster construction and query. Experiments on real-life road networks demonstrate the superiority of our method over the state-of-the-art.

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Wang, Zheng, Yuexin Wu, Yang Bao, Jing Yu, and Xiaohui Wang. "Fusing Node Embeddings and Incomplete Attributes by Complement-Based Concatenation." Wireless Communications and Mobile Computing 2021 (February 25, 2021): 1–10. http://dx.doi.org/10.1155/2021/6654349.

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Network embedding that learns representations of network nodes plays a critical role in network analysis, since it enables many downstream learning tasks. Although various network embedding methods have been proposed, they are mainly designed for a single network scenario. This paper considers a “multiple network” scenario by studying the problem of fusing the node embeddings and incomplete attributes from two different networks. To address this problem, we propose to complement the incomplete attributes, so as to conduct data fusion via concatenation. Specifically, we first propose a simple inductive method, in which attributes are defined as a parametric function of the given node embedding vectors. We then propose its transductive variant by adaptively learning an adjacency graph to approximate the original network structure. Additionally, we also provide a light version of this transductive variant. Experimental results on four datasets demonstrate the superiority of our methods.

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Deng, Xiuqin, Yifei Zhang, and Fangqing Gu. "Fusion and Enhancement of Consensus Matrix for Multi-View Subspace Clustering." Mathematics 11, no. 6 (2023): 1509. http://dx.doi.org/10.3390/math11061509.

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Multi-view subspace clustering is an effective method that has been successfully applied to many applications and has attracted the attention of scholars. Existing multi-view subspace clustering seeks to learn multiple representations from different views, then gets a consistent matrix. Until now, most of the existing efforts only consider the multi-view information and ignore the feature concatenation. It may fail to explore their high correlation. Consequently, this paper proposes a multi-view subspace clustering algorithm with a novel consensus matrix construction strategy. It learns a consensus matrix by fusing the different information from multiple views and is enhanced by the information contained in the original feature direct linkage of the data. The error matrix of the feature concatenation data is reconstructed by regularization constraints and the sparse structure of the multi-view subspace. The feature concatenation data are simultaneously used to fuse the individual views and learn the consensus matrix. Finally, the data is clustered by using spectral clustering according to the consensus matrix. We compare the proposed algorithm with its counterparts on six datasets. Experimental results verify the effectiveness of the proposed algorithm.

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Huang, Xin, Yu Fang, Mingming Lu, Fengqi Yan, Jun Yang, and Yilu Xu. "Dual-Ray Net: Automatic Diagnosis of Thoracic Diseases Using Frontal and Lateral Chest X-rays." Journal of Medical Imaging and Health Informatics 10, no. 2 (2020): 348–55. http://dx.doi.org/10.1166/jmihi.2020.2901.

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Computer-aided diagnosis (CAD) is an important work which can improve the working efficiency of physicians. With the availability of large-scale data sets, several methods have been proposed to classify pathology on chest X-ray images. However, most methods report performance based on a frontal chest radiograph, ignoring the effect of the lateral chest radiography on the diagnosis. This paper puts forward a kind of model, Dual-Ray Net, of a deep convolutional neural network which can deal with the front and lateral chest radiography at the same time by referring the method of using lateral chest radiography to assist diagnose during the diagnosis used by radiologists. Firstly, we evaluated the performance of parameter migration to small data after pre-training for large datasets. The data sets for pre-training are chest X-ray 14 and ImageNet respectively. The results showed that pre-training with chest X-ray 14 performed better than with the generic dataset ImageNet. Secondly, We evaluated the performance of the Frontal and lateral chest radiographs in different modes of input model for the diagnosis of assisted chest disease. Finally, by comparing different feature fusion methods of addition and concatenation, we found that the fusion effect of concatenation is better, which average AUC reached 0.778. The comparison results show that whether it is a public or a non-public dataset, our Dual-Ray Net (concatenation) architecture shows improved performance in recognizing findings in CXR images when compared to applying separate baseline frontal and lateral classes.

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Barion, Antonio, Martijn J. H. Anthonissen, Jan H. M. ten Thije Boonkkamp, and Wilbert L. IJzerman. "Computation of aberration coefficients for plane-symmetric reflective optical systems using Lie algebraic methods." EPJ Web of Conferences 266 (2022): 02002. http://dx.doi.org/10.1051/epjconf/202226602002.

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The Lie algebraic method offers a systematic way to find aberration coefficients of any order for plane-symmetric reflective optical systems. The coefficients derived from the Lie method are in closed form and solely depend on the geometry of the optical system. We investigate and verify the results for a single reflector. The concatenation of multiple mirrors follows from the mathematical framework.

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Parshukova, N. B. "CRYPTOGRAPHIC ALGORITHMS IN SPREADSHEETS." Informatics in school, no. 8 (November 9, 2019): 51–55. http://dx.doi.org/10.32517/2221-1993-2019-18-8-51-55.

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The article examines three well known cryptographic algorithm — Skytale, Caesar's cipher, Vigenere's cipher and the method of their implementation using spreadsheets. Functions on work with strings, such as calculation of string length, search of a substring position in a string, substring selection, concatenation are considered.

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Hlaing, Chaw Su, and Aye Thida. "Phoneme concatenation method considering half vowel sound for the Myanmar speech synthesis system." International Journal of Advanced Computer Research 9, no. 41 (2019): 81–93. http://dx.doi.org/10.19101/ijacr.2018.839001.

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Cui, Lingli, Xin Wang, Huaqing Wang, and Na Wu. "Improved Fault Size Estimation Method for Rolling Element Bearings Based on Concatenation Dictionary." IEEE Access 7 (2019): 22710–18. http://dx.doi.org/10.1109/access.2019.2899036.

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Yu, Jian, and Jianhua Tao. "A novel prosody adaptation method for Mandarin concatenation-based text-to-speech system." Acoustical Science and Technology 30, no. 1 (2009): 33–41. http://dx.doi.org/10.1250/ast.30.33.

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Iwakura, Tomoya, Hiroya Takamura, and Manabu Okumura. "A Named Entity Recognition Method Based on Decomposition and Concatenation of Word Chunks." ACM Transactions on Asian Language Information Processing 12, no. 3 (2013): 1–18. http://dx.doi.org/10.1145/2499955.2499958.

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Saqué, Valentin, Martino Vaglio, Christian Funck-Brentano, et al. "Fast, accurate and easy-to-teach QT interval assessment: The triplicate concatenation method." Archives of Cardiovascular Diseases 110, no. 8-9 (2017): 475–81. http://dx.doi.org/10.1016/j.acvd.2016.12.011.

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RAJU BAHUBALENDRUNI, M. V. A., and BIBHUTI BHUSAN BISWAL. "Liaison concatenation – A method to obtain feasible assembly sequences from 3D-CAD product." Sadhana 41, no. 1 (2016): 67–74. http://dx.doi.org/10.1007/s12046-015-0453-8.

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Han, Xiaoxu. "Infer Species Phylogenies Using Self-Organizing Maps." International Journal of Knowledge Discovery in Bioinformatics 1, no. 2 (2010): 29–49. http://dx.doi.org/10.4018/jkdb.2010040103.

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With rapid advances in genomics, phylogenetics has turned to phylogenomics due to the availability of large amounts of sequence and genome data. However, incongruence between species trees and gene trees remains a challenge in molecular phylogenetics for its biological and algorithmic complexities. A state-of-the-art gene concatenation approach was proposed to resolve this problem by inferring the species phylogeny using a random combination of widely distributed orthologous genes screened from genomes. However, such an approach may not be a robust solution to this problem because it ignores the fact that some genes are more informative than others in species inference. This paper presents a self-organizing map (SOM) based phylogeny inference method to overcome its weakness. The author’s proposed algorithm not only demonstrates its superiority to the original gene concatenation method by using same datasets, but also shows its advantages in generalization. This paper illustrates that data missing may not play a negative role in phylogeny inferring. This study presents a method to cluster multispecies genes, estimate multispecies gene entropy and visualize the species patterns through the self-organizing map mining.

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Song, Zhehan, Zhihai Xu, Jing Wang, Huajun Feng, and Qi Li. "Dual-Branch Feature Fusion Network for Salient Object Detection." Photonics 9, no. 1 (2022): 44. http://dx.doi.org/10.3390/photonics9010044.

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Proper features matter for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present the dual-branch feature fusion network (DBFFNet), a simple effective framework mainly composed of three modules: global information perception module, local information concatenation module and refinement fusion module. The local information of a salient object is extracted from the local information concatenation module. The global information perception module exploits the U-Net structure to transmit the global information layer by layer. By employing the refinement fusion module, our approach is able to refine features from two branches and detect salient objects with final details without any post-processing. Experiments on standard benchmarks demonstrate that our method outperforms almost all of the state-of-the-art methods in terms of accuracy, and achieves the best performance in terms of speed under fair settings. Moreover, we design a wide-field optical system and combine with DBFFNet to achieve salient object detection with large field of view.

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Saragih, Jopinus. "THE EFFECT OF COMMUNICATION, MOTIVATION AND WORK ENVIRONMENT ON JOB SATISFACTION OF SERVICE EMPLOYEES TRANSPORTATION TO THE CITY OF PEMATANG SIANTAR." International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) 1, no. 9 (2022): 465–74. http://dx.doi.org/10.54443/ijset.v1i9.54.

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This study directs to influence the chain reaction of communication, motive and elbow grease surrounding on employment fulfilment of staff member of the division of Transportation of Pematang Siantar City, the inclination inoffensive pre-owned is a appropriate the measurement of method. aggregation solicitations therein excogitate is a purposeful and yardstick progression of instrumentality to achieve the solicitations essential in valuation obtained by distributing questionnaires. The consequences showed that the consequences of SPSS 25 processing showed that the depletion of tcount < ttable, it could be completed that the rudimentary possibleness was accepted, import that communication had an concatenation response on utilisation satisfaction. From the consequences of SPSS 25 processing make evident that the depletion of tcount > ttable, it channel mark be completed that the endorsement possibleness is accepted, import that motivation has an concatenation response on utilisation satisfaction. From the consequences of SPSS 25 processing make evident that the depletion of tcount > t table, it channel mark be completed that the third possibleness is accepted, import that the cubitus grunge neighbourhood deposit occupation of utilisation satisfaction. The quartern possibleness is accepted, import that the communication iridescent, cubitus grunge motivation iridescent, exertion neigh bourhood iridescent methamphetamine up a considerable concatenation response simultaneously on the utilisation fulfilment iridescent.

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Sun, Kai, Jiangshe Zhang, Junmin Liu, Shuang Xu, Xiangyong Cao, and Rongrong Fei. "Modified Dynamic Routing Convolutional Neural Network for Pan-Sharpening." Remote Sensing 15, no. 11 (2023): 2869. http://dx.doi.org/10.3390/rs15112869.

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Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchromatic (PAN) images, which may cause a loss of detailed information. To tackle this issue, inspired by capsule networks, we propose a plug-and-play layer named modified dynamic routing layer (MDRL), which modifies the information transmission mode of capsules to effectively fuse LRMS images and PAN images. Concretely, the lower-level capsules are generated by applying transform operation to the features of LRMS images and PAN images, which preserve the spatial location information. Then, the dynamic routing algorithm is modified to adaptively select the lower-level capsules to generate the higher-level capsule features to represent the fusion of LRMS images and PAN images, which can effectively avoid the loss of detailed information. In addition, the previous addition and concatenation operations are illustrated as special cases of our MDRL. Based on MIPSM with addition operations and DRPNN with concatenation operations, two modified dynamic routing models named MDR–MIPSM and MDR–DRPNN are further proposed for pan-sharpening. Extensive experimental results demonstrate that the proposed method can achieve remarkable spectral and spatial quality.

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Bartlett, Christopher W., Brett G. Klamer, Steven Buyske, Stephen A. Petrill, and William C. Ray. "Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features." Genes 10, no. 9 (2019): 727. http://dx.doi.org/10.3390/genes10090727.

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Informatics researchers often need to combine data from many different sources to increase statistical power and study subtle or complicated effects. Perfect overlap of measurements across academic studies is rare since virtually every dataset is collected for a unique purpose and without coordination across parties not-at-hand (i.e., informatics researchers in the future). Thus, incomplete concordance of measurements across datasets poses a major challenge for researchers seeking to combine public databases. In any given field, some measurements are fairly standard, but every organization collecting data makes unique decisions on instruments, protocols, and methods of processing the data. This typically denies literal concatenation of the raw data since constituent cohorts do not have the same measurements (i.e., columns of data). When measurements across datasets are similar prima facie, there is a desire to combine the data to increase power, but mixing non-identical measurements could greatly reduce the sensitivity of the downstream analysis. Here, we discuss a statistical method that is applicable when certain patterns of missing data are found; namely, it is possible to combine datasets that measure the same underlying constructs (or latent traits) when there is only partial overlap of measurements across the constituent datasets. Our method, ROSETTA empirically derives a set of common latent trait metrics for each related measurement domain using a novel variation of factor analysis to ensure equivalence across the constituent datasets. The advantage of combining datasets this way is the simplicity, statistical power, and modeling flexibility of a single joint analysis of all the data. Three simulation studies show the performance of ROSETTA on datasets with only partially overlapping measurements (i.e., systematically missing information), benchmarked to a condition of perfectly overlapped data (i.e., full information). The first study examined a range of correlations, while the second study was modeled after the observed correlations in a well-characterized clinical, behavioral cohort. Both studies consistently show significant correlations >0.94, often >0.96, indicating the robustness of the method and validating the general approach. The third study varied within and between domain correlations and compared ROSETTA to multiple imputation and meta-analysis as two commonly used methods that ostensibly solve the same data integration problem. We provide one alternative to meta-analysis and multiple imputation by developing a method that statistically equates similar but distinct manifest metrics into a set of empirically derived metrics that can be used for analysis across all datasets.

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R.R., Ayalapogu,, Pabboju, S., and Ramisetty, R.R. "Pathological Analysis of Brain Tumor in MRI Images Using CAR-UNET Model Architecture." CARDIOMETRY, no. 24 (November 30, 2022): 1086–97. http://dx.doi.org/10.18137/cardiometry.2022.24.10861097.

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The most dangerous type of organ failure in humans is a brain tumor. A brain tumor’s incorrect segmentation and classification are critical for treatment and early diagnosis. Several Deep neural network-based architectures have recently been developed to improve brain tumor classification performance. However, brain tumor classification performance must be improved, which is a difficult area of research. The goal of this study is to analyze different types of brain tumors and how to classify them to increase the survival rate of people with brain tumors. The CAR-U-Net (Concatenation and Residual) image classification method is proposed in this paper to help with brain tumor segmentation and classification research. The baseline U-Net architecture employs concatenation and residual connections. The changes in the network help in discovering varied features by expanding the specific receptive field. We consider two factors for a better diagnosis system: finding missing feature maps and eliminating unfeatured feature maps. The residual connection can solve the over learn or Null feature map problem, while the concatenation connection can solve the missing feature maps problems. This model has been tested on the BraTS2017 Challenge datasets. The network’s concatenation and residual connections, which are used for better deep supervision and tumor differentiation, are accurate. The accuracy, sensitivity, specificity, and dice score were used to compare the performance quantitatively. The proposed system achieved 94.12% Accuracy and 97.16% sensitivity which is higher than the existing systems such as U-net, Residual U-net and, Bayesian SVNN. Also, the proposed system got the dice score coefficient of 90.32. which is higher compared to conventional U-net and Residual U-net. The proposed CAR-UNET model outperformed current best-practice techniques. The high-performance capacity of this model can help bioinformatics, medicine, and early diagnosis researchers.

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Wang, Yuanming, Yuanjie Li, Chao Dong, and Kai Niu. "Gray mapping method of QAM constellation for trellis shaping." Journal of Physics: Conference Series 2849, no. 1 (2024): 012116. http://dx.doi.org/10.1088/1742-6596/2849/1/012116.

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Abstract A novel method to construct quadrature amplitude modulation (QAM) constellation for trellis shaping is proposed. To overcome the non-gray mapping of set-partitioning constellation used in trellis shaping, we proposed a Gray mapping constellation. This constellation can get the same shaping gain as the set-partitioning constellation while the demodulation bit error ratio (BER) performance is better than the latter, which makes the trellis shaping connect with the forward error correction (FEC) code to form an FEC-shaping concatenation system to improve the error correction performance. For 64-QAM and 256-QAM, based on the BER results, the system can get 0.5-0.8 dB gain with the non-shaping system at BER 10−3.

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Beliavsky, Danilova, and Ougolnitsky. "Calculation of Probability of the Exit of a Stochastic Process from a Band by Monte-Carlo Method: A Wiener-Hopf Factorization." Mathematics 7, no. 7 (2019): 581. http://dx.doi.org/10.3390/math7070581.

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This paper considers a method of the calculation of probability of the exit from a band of the solution of a stochastic differential equation. The method is based on the approximation of the solution of the considered equation by a process which is received as a concatenation of Gauss processes, random partition of the interval, Girsanov transform and Wiener-Hopf factorization, and the Monte-Carlo method. The errors of approximation are estimated. The proposed method is illustrated by numerical examples.

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Xu, Tiantian, Xiangjun Dong, Jianliang Xu, and Xue Dong. "Mining High Utility Sequential Patterns with Negative Item Values." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (2017): 1750035. http://dx.doi.org/10.1142/s0218001417500355.

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High utility sequential patterns (HUSP) refer to those sequential patterns with high utility (such as profit), which play a crucial role in many real-life applications. Relevant studies of HUSP only consider positive values of sequence utility. In some applications, however, a sequence consists of items with negative values (NIV). For example, a supermarket sells a cartridge with negative profit in a package with a printer at higher positive return. Although a few methods have been proposed to mine high utility itemsets (HUI) with NIV, they are not suitable for mining HUSP with NIV because an item may occur more than once in a sequence and its utility may have multiple values. In this paper, we propose a novel method High Utility Sequential Patterns with Negative Item Values (HUSP-NIV) to efficiently mine HUSP with NIV from sequential utility-based databases. HUSP-NIV works as follows: (1) using the lexicographic quantitative sequence tree (LQS-tree) to extract the complete set of high utility sequences and using I-Concatenation and S-Concatenation mechanisms to generate newly concatenated sequences; (2) using three pruning methods to reduce the search space in the LQS-tree; (3) traversing LQS-tree and outputting all the high utility sequential patterns. To the best of our knowledge, HUSP-NIV is the first method to mine HUSP with NIV, which is shown efficient on both synthetic and real datasets.

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Hopfmueller, Florian, Maxime Tremblay, Philippe St-Jean, Baptiste Royer, and Marc-Antoine Lemonde. "Bosonic Pauli+: Efficient Simulation of Concatenated Gottesman-Kitaev-Preskill Codes." Quantum 8 (November 26, 2024): 1539. http://dx.doi.org/10.22331/q-2024-11-26-1539.

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A promising route towards fault-tolerant quantum error correction is the concatenation of a Gottesman-Kitaev-Preskill (GKP) code with a qubit code. Development of such concatenated codes requires simulation tools which realistically model noise, while being able to simulate the dynamics of many modes. However, so far, large-scale simulation tools for concatenated GKP codes have been limited to idealized noise models and GKP code implementations. Here, we introduce the Bosonic Pauli+ model (BP+), which can be simulated efficiently for a large number of modes, while capturing the rich dynamics in the bosonic multi-mode Hilbert space. We demonstrate the method by simulating a hybrid surface code, where the data qubits are finite-energy GKP qubits stabilized using the small-Big-small (sBs) protocol, and the syndrome qubits are standard two-level systems. Using BP+, we present logical error rates of such an implementation. Confidence in the accuracy of the method is gained by comparing its predictions with full time evolution simulations for several relevant quantum circuits. While developed specifically for GKP qubits stabilized using the sBs protocol, the mathematical structure of BP+ is generic and may be applicable also to the simulation of concatenations using other bosonic codes.

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Sigel, Erwin, Kuldeep H. Kaur, Benjamin P. Lüscher та Roland Baur. "Use of concatamers to study GABAA receptor architecture and function: application to δ-subunit-containing receptors and possible pitfalls". Biochemical Society Transactions 37, № 6 (2009): 1338–42. http://dx.doi.org/10.1042/bst0371338.

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Many membrane proteins, including the GABAA [GABA (γ-aminobutyric acid) type A] receptors, are oligomers often built from different subunits. As an example, the major adult isoform of the GABAA receptor is a pentamer built from three different subunits. Theoretically, co-expression of three subunits may result in many different receptor pentamers. Subunit concatenation allows us to pre-define the relative arrangement of the subunits. This method may thus be used to study receptor architecture, but also the nature of binding sites. Indeed, it made possible the discovery of a novel benzodiazepine site. We use here subunit concatenation to study δ-subunit-containing GABAA receptors. We provide evidence for the formation of different functional subunit arrangements in recombinant α1β3δ and α6β3δ receptors. As with all valuable techniques, subunit concatenation has also some pitfalls. Most of these can be avoided by carefully titrating and minimizing the length of the linker sequences joining the two linked subunits and avoiding inclusion of the signal sequence of all but the N-terminal subunit of a multi-subunit construct. Maybe the most common error found in the literature is that low expression can be overcome by simply overloading the expression system with genetic information. As some concatenated constructs result by themselves in a low level of expression, this erroneous assembly leading to receptor function may be promoted by overloading the expression system and leads to wrong conclusions.

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M., Ramana Reddy. "A Deep Learning Model Based on Concatenation Approach for Classification of MRI Images." Journals of Advancement in Electronics Design 5, no. 3 (2022): 1–9. https://doi.org/10.5281/zenodo.7494513.

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The heterogeneity of the tumor cells makes it difficult for radiologists to classify brain tumors, a potentially fatal condition. As an assistive technology, computer-aided diagnosis-based systems have recently promised to use magnetic resonance imaging (MRI) to diagnose brain tumors. Typically, features are extracted from bottom layers in recent applications of pre-trained models, which are distinct from natural and medical images. This study proposes a method for the early diagnosis of brain tumors using multi-level feature extraction and concatenation to solve this issue. This model is valid thanks to the two pretrained deep learning models Inception-v3 and DensNet201. Two distinct scenarios for the detection and classification of brain tumors were evaluated with the assistance of these two models. For brain tumor classification, the features from various Inception modules were first extracted from the pre-trained Inception-v3 model and combined. The softmax classifier was then used to classify the brain tumor using these features. Second, features were extracted from various DensNet blocks using pre-trained DensNet201. The softmax classifier was then used to classify the brain tumor after these features were concatenated. A publicly accessible three-class brain tumor dataset was used to evaluate both scenarios. With Inception-v3 and DensNet201 on testing samples, the proposed method produced 99.34% and 99.51% testing accuracies, respectively, and performed best in the detection of brain tumors. For brain tumor classification, the results indicated that the proposed method based on features concatenation and using pre-trained models performed better than the current state-of-the-art deep learning and machine learning methods.

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Dudakov, Sergey Mikhailovich. "On Undecidability of Finite Subsets Theory for Torsion Abelian Groups." Mathematics 10, no. 3 (2022): 533. http://dx.doi.org/10.3390/math10030533.

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Let M be a commutative cancellative monoid with an element of infinite order. The binary operation can be extended to all finite subsets of M by the pointwise definition. So, we can consider the theory of finite subsets of M. Earlier, we have proved the following result: in the theory of finite subsets of M elementary arithmetic can be interpreted. In particular, this theory is undecidable. For example, the free monoid (the sets of all words with concatenation) has this property, the corresponding algebra of finite subsets is the theory of all finite languages with concatenation. Another example is an arbitrary Abelian group that is not a torsion group. But the method of proof significantly used an element of infinite order, hence, it can’t be immediately generalized to torsion groups. In this paper we prove the given theorem for Abelian torsion groups that have elements of unbounded order: for such group, the theory of finite subsets allows interpreting the elementary arithmetic.

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Biswas, Anjan, Jose Vega-Guzman, Abdul H. Kara, et al. "Optical Solitons and Conservation Laws for the Concatenation Model: Undetermined Coefficients and Multipliers Approach." Universe 9, no. 1 (2022): 15. http://dx.doi.org/10.3390/universe9010015.

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This paper retrieves an optical 1–soliton solution to a model that is written as a concatenation of the Lakshmanan–Porsezian–Daniel model and Sasa–Satsuma equation. The method of undetermined coefficients obtains a full spectrum of 1–soliton solutions. The multiplier approach yields the conserved densities, which subsequently lead to the conserved quantities from the bright 1–soliton solution.

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Luo, Jie, Junhao Chen, Wenlei Guo, Zhengfu Yang, Kean-Jin Lim, and Zhengjia Wang. "Reassessment of Annamocarya sinesis (Carya sinensis) Taxonomy through Concatenation and Coalescence Phylogenetic Analysis." Plants 11, no. 1 (2021): 52. http://dx.doi.org/10.3390/plants11010052.

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Due to its peculiar morphological characteristics, there is dispute as to whether the genus of Annamocarya sinensis, a species of Juglandaceae, is Annamocarya or Carya. Most morphologists believe it should be distinguished from the Carya genus while genomicists suggest that A. sinensis belongs to the Carya genus. To explore the taxonomic status of A. sinensis using chloroplast genes, we collected chloroplast genomes of 16 plant species and assembled chloroplast genomes of 10 unpublished Carya species. We analyzed all 26 species’ chloroplast genomes through two analytical approaches (concatenation and coalescence), using the entire and unique chloroplast coding sequence (CDS) and entire and protein sequences. Our results indicate that the analysis of the CDS and protein sequences or unique CDS and unique protein sequence of chloroplast genomes shows that A. sinensis indeed belongs to the Carya genus. In addition, our analysis shows that, compared to single chloroplast genes, the phylogeny trees constructed using numerous genes showed higher consistency. Moreover, the phylogenetic analysis calculated with the coalescence method and unique gene sequences was more robust than that done with the concatenation method, particularly for analyzing phylogenetically controversial species. Through the analysis, our results concluded that A. sinensis should be called C. sinensis.

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Zou, Hangtao, and Shibing Zhou. "Deep Multi-View Clustering Optimized by Long Short-Term Memory Network." Symmetry 17, no. 2 (2025): 161. https://doi.org/10.3390/sym17020161.

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Long short-term memory (LSTM) networks have shown great promise in sequential data analysis, especially in time-series and natural language processing. However, their potential for multi-view clustering has been largely underexplored. In this paper, we introduce a novel approach called deep multi-view clustering optimized by long short-term memory network (DMVC-LSTM), which leverages the sequential modeling capability of LSTM to effectively integrate multi-view data. By capturing complex interdependencies and nonlinear relationships between views, DMVC-LSTM improves clustering accuracy and robustness. The method includes three feature fusion techniques—concatenation, averaging, and attention-based fusion—with concatenation as the primary method. Notably, DMVC-LSTM is well suited for datasets that exhibit symmetry, as it can effectively handle symmetrical relationships between views while preserving the underlying structures. Extensive experiments demonstrate that DMVC-LSTM outperforms existing multi-view clustering algorithms, particularly in high-dimensional and complex datasets, achieving superior performance in datasets like 20 Newsgroups and Wikipedia Articles. This paper presents the first application of LSTM in multi-view clustering, marking a significant step forward in both clustering performance and the application of LSTM in multi-view data analysis.

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Mensouri, Mohammed, and Mustapha Eddahibi. "New Structure of Channel Coding: Serial Concatenation of Polar Codes." International Journal of Wireless & Mobile Networks 15, no. 5 (2023): 01–11. http://dx.doi.org/10.5121/ijwmn.2023.15501.

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In this paper, we introduce a new coding and decoding structure for enhancing the reliability and performance of polar codes, specifically at low error rates. We achieve this by concatenating two polar codes in series to create robust error-correcting codes. The primary objective here is to optimize the behavior of individual elementary codes within polar codes. In this structure, we incorporate interleaving, a technique that rearranges bits to maximize the separation between originally neighboring symbols. This rearrangement is instrumental in converting error clusters into distributed errors across the entire sequence. To evaluate their performance, we proposed to model a communication system with seven components: an information source, a channel encoder, a modulator, a channel, a demodulator, a channel decoder, and a destination. This work focuses on evaluating the bit error rate (BER) of codes for different block lengths and code rates. Next, we compare the bit error rate (BER) performance between our proposed method and polar codes.

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Biswas, Anjan, Jose Vega-Guzman, Yakup Yıldırım, Luminita Moraru, Catalina Iticescu, and Abdulah A. Alghamdi. "Optical Solitons for the Concatenation Model with Differential Group Delay: Undetermined Coefficients." Mathematics 11, no. 9 (2023): 2012. http://dx.doi.org/10.3390/math11092012.

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In the current study, the concatenation model of birefringent fibers is explored for the first time, and we present optical soliton solutions to the model. The integration algorithm used to achieve this retrieval is the method of undetermined coefficients, which yields a wide range of soliton solutions. The parameter constraints arise naturally during the derivation of the soliton solutions, which are essential for such solitons to exist.

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