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Li H, Wang S, Zeng Q, Chen C, Lv X, Ma M, Su H, Ma B, Chen C, Fang J. Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer. Photodiagnosis Photodyn Ther 2022; 40:103115. [PMID: 36096439 DOI: 10.1016/j.pdpdt.2022.103115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 09/03/2022] [Accepted: 09/07/2022] [Indexed: 12/14/2022]
Abstract
Breast cancer is a malignant tumor with the highest incidence rate in women. Current diagnostic methods are time-consuming, costly, and dependent on physician experience. In this study, we used serum Raman spectroscopy combined with multiple classification algorithms to implement an auxiliary diagnosis method for breast cancer, which will help in the early diagnosis of breast cancer patients. We analyzed the serum Raman spectra of 171 invasive ductal carcinoma (IDC) and 100 healthy volunteers; The analysis showed differences in nucleic acids, carotenoids, amino acids, and lipid concentrations in their blood. These differences provide a theoretical basis for this experiment. First, we used adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky-Golay (SG) for baseline correction and smoothing denoising to remove the effect of noise on the experiment. Then, the Principal component analysis (PCA) algorithm was used to extract features. Finally, we built four classification models: support vector machine (SVM), decision tree (DT), linear discriminant analysis (LDA), and Neural Network Language Model (NNLM). The LDA, SVM, and NNLM achieve 100% accuracy. As supplementary, we added the classification experiment of the raw data. By comparing the experimental results of the two groups, We concluded that the NNLM was the best model. The results show the reliability of the combination of serum Raman spectroscopy and classification models under large sample conditions.
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Affiliation(s)
- Hongtao Li
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| | | | - Qinggang Zeng
- College of Software, Xinjiang University, Urumqi 830046, China; College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Chen Chen
- College of Information Science and Engineering Xinjiang University, Urumqi 830046, China; Xinjiang Cloud Computing Application Laboratory, Karamay 834099, China
| | - Xiaoyi Lv
- College of Software, Xinjiang University, Urumqi 830046, China; College of Information Science and Engineering Xinjiang University, Urumqi 830046, China
| | - Mingrui Ma
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
| | - Haihua Su
- Hospital of Xinjiang Production and Construction Corps, Urumqi 830092, China
| | - Binlin Ma
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China.
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi 830046, China.
| | - Jingjing Fang
- Department of Breast, Head and Neck Surgery, Xinjiang Medical University Affiliated Tumor Hospital, Urumqi, China
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Chai G, Wen Q. An Interactive English–Chinese Translation System Based on GLA Algorithm. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2022. [DOI: 10.1142/s0219649222400147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In view of the longtime interactive English–Chinese translation system, an interactive English–Chinese translation system based on Griffin–Lim algorithm (GLA) is proposed. The hardware design of the system is completed by the hardware structure design, the interactive English–Chinese translation memory design and the interactive English–Chinese translation retrieval system design. Through analysing the semantic characteristics of interactive English–Chinese translation, constructing interactive English–Chinese translation database and designing interactive English–Chinese translation process, the system software design is completed and interactive English–Chinese translation is realised. The results of the system test show that the interactive English–Chinese translation system based on the GLA algorithm cannot only shorten the time of interactive English–Chinese translation, but also accelerate the response speed of the translation system, and greatly improve the overall performance of the interactive English–Chinese translation system.
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Affiliation(s)
- Guoxi Chai
- School of Foreign Language, Xijing University, Xi’an 710123, P. R. China
| | - Qiaozhi Wen
- School of Foreign Language, Xijing University, Xi’an 710123, P. R. China
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Jiménez P, Corchuelo R. An Experimental Study of Neural Approaches to Multi-Hop Inference in Question Answering. Int J Neural Syst 2022; 32:2250011. [PMID: 35172705 DOI: 10.1142/s0129065722500113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Question answering aims at computing the answer to a question given a context with facts. Many proposals focus on questions whose answer is explicit in the context; lately, there has been an increasing interest in questions whose answer is not explicit and requires multi-hop inference to be computed. Our analysis of the literature reveals that there is a seminal proposal with increasingly complex follow-ups. Unfortunately, they were presented without an extensive study of their hyper-parameters, the experimental studies focused exclusively on English, and no statistical analysis to sustain the conclusions was ever performed. In this paper, we report on our experience devising a very simple neural approach to address the problem, on our extensive grid search over the space of hyper-parameters, on the results attained with English, Spanish, Hindi, and Portuguese, and sustain our conclusions with statistically sound analyses. Our findings prove that it is possible to beat many of the proposals in the literature with a very simple approach that was likely overlooked due to the difficulty to perform an extensive grid search, that the language does not have a statistically significant impact on the results, and that the empirical differences found among some existing proposals are not statistically significant.
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Affiliation(s)
- Patricia Jiménez
- Universidad de Sevilla, ETSI Informática, Avda. de la Reina Mercedes, s/n. Sevilla E-41012, Spain
| | - Rafael Corchuelo
- Universidad de Sevilla, ETSI Informática, Avda. de la Reina Mercedes, s/n. Sevilla E-41012, Spain
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Zhang J, Li D, Wang L, Zhang L. One-Shot Neural Architecture Search by Dynamically Pruning Supernet in Hierarchical Order. Int J Neural Syst 2021; 31:2150029. [PMID: 34128778 DOI: 10.1142/s0129065721500295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Neural Architecture Search (NAS), which aims at automatically designing neural architectures, recently draw a growing research interest. Different from conventional NAS methods, in which a large number of neural architectures need to be trained for evaluation, the one-shot NAS methods only have to train one supernet which synthesizes all the possible candidate architectures. As a result, the search efficiency could be significantly improved by sharing the supernet's weights during the candidate architectures' evaluation. This strategy could greatly speed up the search process but suffer a challenge that the evaluation based on sharing weights is not predictive enough. Recently, pruning the supernet during the search has been proven to be an efficient way to alleviate this problem. However, the pruning direction in complex-structured search space remains unexplored. In this paper, we revisited the role of path dropout strategy, which drops the neural operations instead of the neurons, in supernet training, and several interesting characters of the supernet trained with dropout are found. Based on the observations, a Hierarchically-Ordered Pruning Neural Architecture Search (HOPNAS) algorithm is proposed by dynamically pruning the supernet with a proper pruning direction. Experimental results indicate that our method is competitive with state-of-the-art approaches on CIFAR10 and ImageNet.
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Affiliation(s)
- Jianwei Zhang
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
| | - Dong Li
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
| | - Lituan Wang
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
| | - Lei Zhang
- College of Computer Science, Sichuan University, Section 4, Southern 1st Ring Rd, Chengdu, Sichuan 610065, P. R. China
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Zamora-Martínez FJ, España-Boquera S, Castro-Bleda MJ, Palacios-Corella A. Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training. PLoS One 2018; 13:e0200884. [PMID: 30048480 PMCID: PMC6062053 DOI: 10.1371/journal.pone.0200884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 07/04/2018] [Indexed: 11/18/2022] Open
Abstract
This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different length in order to ease the use of a fallback mechanism together with the precomputation of softmax normalization constants for these inputs. The proposed approach is empirically validated, showing their capability to emulate lower order N-grams with a single Neural Network. A machine translation task shows that the proposed model constitutes a good solution to the normalization cost of the output softmax layer of Neural Networks, for some practical cases, without a significant impact in performance while improving the system speed.
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