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Multi-file dynamic compression method based on classification algorithm in DNA storage

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第12期62卷 3623-3635页

作者： Bi, Kun Xu, Qi Lai, Xin Zhao, Xiangwei Lu, Zuhong Southeast Univ Sch Biol Sci & Med Engn State Key Lab Digital Med Engn Nanjing 210096 Peoples R China Southeast Univ Monash Univ Joint Grad Sch Suzhou 215123 Peoples R China

The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis and sequencing impeded its development. Pre-compressing the data is recognized as one of the most effective approaches for reducing storage costs. However, different compression methods yield varying compression ratios for the same file, and compressing a large number of files with a single method may not achieve the maximum compression ratio. This study proposes a multi-file dynamic compression method based on machine learning classification algorithms that selects the appropriate compression method for each file to minimize the amount of data stored into DNA as much as possible. Firstly, four different compression methods are applied to the collected files. Subsequently, the optimal compression method is selected as a label, as well as the file type and size are used as features, which are put into seven machine learning classification algorithms for training. The results demonstrate that k-nearest neighbor outperforms other machine learning algorithms on the validation set and test set most of the time, achieving an accuracy rate of over 85% and showing less volatility. Additionally, the compression rate of 30.85% can be achieved according to k-nearest neighbor model, more than 4.5% compared to the traditional single compression method, resulting in significant cost savings for DNA storage in the range of $0.48 to 3 billion/TB. In comparison to the traditional compression method, the multi-file dynamic compression method demonstrates a more significant compression effect when compressing multiple files. Therefore, it can considerably decrease the cost of DNA storage and facilitate the widespread implementation of DNA storage *** AbstractFile compression is an important step in DNA storage, and different types of files may have varyin

关键词： classification algorithm Data compression DNA storage Compression rate

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Defect Detection and classification algorithm of Metal Nanomaterials Based on Deep Learning

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INTEGRATED FERROELECTRICS 2022年第1期226卷 277-292页

作者： Xue, Bin Wu, Zhisheng Taiyuan Univ Sci & Technol Sch Mat Sci & Engn Taiyuan Shanxi Peoples R China Qingdao Binhai Univ Sch Mech & Elect Engn Qingdao Shandong Peoples R China

With the rapid development of nanotechnology, researchers can prepare nanomaterials by various methods. The rapid development of nanotechnology has greatly promoted the wide application of nano materials in the field of detection and catalysis. Especially the metal nanomolecules have good biocompatibility, absorption of plasma surface resonance, enhanced Raman surface and other properties. Metal nanomolecules have good biocompatibility, absorption of plasma surface resonance and enhanced Raman surface Rice inspection and catalytic application have attracted wide attention. This paper mainly studies the defect detection and classification algorithm of metal nanomaterials based on deep learning. Through the experimental phenomena, we can understand and master the defect detection methods of metal nanomaterials, and review the problems and future development direction of the preparation of metal nanomaterials. Deep learning algorithm the first mock exam deep learning network model, metal defect multi-mode detection method and the classification of metal surface defects are investigated. The defect detection of metal nano materials based on deep learning is realized. The whole defect and the quantitative detection of metal defects are realized. The detection range of traditional single mode nondestructive testing technology is overcome. The deficiency of accurate quantitative detection is difficult. The results show that there are five characteristic parameters for metal nano surface defect detection. In the deep learning based defect detection and classification algorithm, the big data technology is used to analyze the complete defect data, environmental data and working intensity data to complete the prediction of the future development trend of defects, which can play an important role in the maintenance of materials It is also of great significance to the development of metal nano detection technology.

关键词： deep learning metal nanomaterial defect detection classification algorithm multimodal detection method

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Development of COI classification algorithm for chemical terrorism

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KOREAN JOURNAL OF CHEMICAL ENGINEERING 2013年第3期30卷 559-562页

作者： Park, Jaedeuk Lee, Younghee Yoon, Yi Kim, Jinkyung Cho, Jae Hyun Moon, Il Yonsei Univ Dept Chem & Biomol Engn Seoul 120749 South Korea GS Engn & Construct Seoul 100722 South Korea Natl Inst Environm Res Inchon 404170 South Korea Changwon Natl Univ Dept Chem Engn Chang Won 641773 South Korea

This study focused on a novel approach for classifying hazardous chemicals to be used for chemical terrorism. We developed a novel algorithm to classify nationally customized chemicals of interest (COI) out of 325 COI in USA. The proposed COI classification algorithm aims to identify a key set of factors that reflect nation-wide uniqueness: intentional use, objectives, toxicity, related laws (CWC, ITF-25, CAA, etc.) and responsive counter-actions to terrorism. Although the U.S. has managed 325 COI to prevent terrorism, there are some nations in which the management and control of all the hazardous chemicals are beyond their capability. Based upon the outcome of this study the Ministry of the Environment of Korea has made appropriate revisions on relevant law. As a result, the Korean government has officially added a new set of 13 chemical species to the list of existing hazardous chemicals. This work is worthwhile to contribute to protecting the people's lives and property from possible chemical accidents including tenor by chemicals.

关键词： Chemicals of Interest Chemical Terrorism classification algorithm Hazardous Chemicals

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Machine learning classification algorithm screening for the main controlling factors of heavy oil CO_(2)huff and puff

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Petroleum Research 2024年第4期9卷 541-552页

作者： Peng-xiang Diwu Beichen Zhao Hangxiangpan Wang Chao Wen Siwei Nie Wenjing Wei A-qiao Li Jingjie Xu Fengyuan Zhang College of Science China University of Petroleum(Beijing)Beijing102249China State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum(Beijing)Beijing102249China

CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut ***,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads to a high-dimensional and small-sample(HDSS)*** is difficult for conventional techniques that identify key factors that influence CO_(2)huff and puff effects,such as fuzzy mathematics,to manage HDSS datasets,which often contain nonlinear and irremovable abnormal *** accurately pinpoint the primary control factors for heavy oil CO_(2)huff and puff,four machine learning classification algorithms were *** algorithms were selected to align with the characteristics of HDSS datasets,taking into account algorithmic principles and an analysis of key control *** results demonstrated that logistic regression encounters difficulties when dealing with nonlinear data,whereas the extreme gradient boosting and gradient boosting decision tree algorithms exhibit greater sensitivity to abnormal *** contrast,the random forest algorithm proved to be insensitive to outliers and provided a reliable ranking of factors that influence CO_(2)huff and puff *** top five control factors identified were the distance between parallel wells,cumulative gas injection volume,liquid production rate of parallel wells,huff and puff timing,and heterogeneous Lorentz *** research find-ings not only contribute to the precise implementation of heavy oil CO_(2)huff and puff but also offer valuable insights into selecting classification algorithms for typical HDSS data.

关键词： classification algorithm algorithm screening Heavy oil CO_(2)huff and puff Main control factors

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Study and Implementation of Web Mining classification algorithm Based on Building Tree of Detection Class Threshold

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Journal of Beijing Institute of Technology 2005年第2期14卷 126-129页

作者：陈俊杰宋瀚涛陆玉昌 School of Information Science and Technology Beijing Institute of Technology Beijing100081 China State

A new classification algorithm for web mining is proposed on the basis of general classification algorithm for data mining in order to implement personalized information services. The building tree method of detecting class threshold is used for construction of decision tree according to the concept of user expectation so as to find classification rules in different layers. Compared with the traditional C4.5 algorithm, the disadvantage of excessive adaptation in C4.5 has been improved so that classification results not only have much higher accuracy but also statistic meaning.

关键词： data mining classification algorithm class threshold induced concept

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An empirical study of classification algorithm evaluation for financial risk prediction

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APPLIED SOFT COMPUTING 2011年第2期11卷 2906-2915页

作者： Peng, Yi Wang, Guoxun Kou, Gang Shi, Yong Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China CAS Res Ctr Fictitious Econ & Data Sci Beijing 100080 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

A wide range of classification methods have been used for the early detection of financial risks in recent years. How to select an adequate classifier (or set of classifiers) for a given dataset is an important task in financial risk prediction. Previous studies indicate that classifiers' performances in financial risk prediction may vary using different performance measures and under different circumstances. The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for financial risk prediction. It constructs a performance score to measure the performance of classification algorithms and introduces three multiple criteria decision making (MCDM) methods (i.e., TOPSIS, PROMETHEE, and VIKOR) to provide a final ranking of classifiers. An empirical study is designed to assess various classification algorithms over seven real-life credit risk and fraud risk datasets from six countries. The results show that linear logistic, Bayesian Network, and ensemble methods are ranked as the top-three classifiers by TOPSIS, PROMETHEE, and VIKOR. In addition, this work discusses the construction of a knowledge-rich financial risk management process to increase the usefulness of classification results in financial risk detection. (C) 2010 Elsevier B.V. All rights reserved.

关键词： classification algorithm Multiple criteria decision making (MCDM) Financial risk prediction Knowledge-rich financial risk analysis

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Development of classification algorithm for Epileptic Seizures Using Electrocardiogram Signal

Development of Classification Algorithm for Epileptic Seizur...

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IEEE Annual India Conference (INDICON)

作者： Valke, Nikita S. Karthikeyan, B. R. MS Ramaiah Univ Appl Sci Dept Elect & Commun Engn Bengaluru India

ISBN: (纸本)9781509036462

An epilepsy classification system using electrocardiogram (ECG) data will ease the process of diagnosis. In epileptic patients, the seizures affect Heart Rate Variability (HRV). This emphasizes the importance of autonomic function changes in diagnosing epilepsy. The present work proposes an algorithm that classifies a person as epileptic or nonepileptic using ECG signal. Time Domain Features (TDF) and Frequency Domain Features (FDF), derived from the R-R Intervals (RRI) of ECG signal are utilized. In addition, Statistical Features (SF) are derived from extracted TDF and FDF. The Support Vector Machines (SVM) classifier is used to classify the ECG signal as epileptic or nonepileptic based on the extracted TDF, FDF and SF. The classification accuracy of the proposed method exhibits 97.5%. Analysis on clinical data shows that the proposed combination of TDF, FDF and statistical HRV features gives excellent classification accuracy. These results indicate that the proposed method can be applied to wearable heart rate measuring devices for diagnostic purpose.

关键词： biomedical signal processing classification algorithm epilepsy feature extraction

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MACHINE LEARNING-BASED HUMAN RESOURCE MANAGEMENT INFORMATION RETRIEVAL AND classification algorithm

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第6期25卷 5431-5440页

作者： Li, Wen Zhou, Xiukao Fuzhou Technol & Business Univ Sch Management Fuzhou 35071 Fujian Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350108 Fujian Peoples R China

Efficient human resource management (HRM) is essential for company achievement in today's fast-paced corporate world. Businesses must find effective ways to retrieve and categorise the ever-growing amount of HR-related knowledge. This work presents a new method for retrieving and classifying HRM data using machine learning. Modern natural language processing (NLP) and CNN methods are used by the algorithm we developed to handle unorganized human resources (HR) information, including worker records, job postings, and certificates. HR decision-making is facilitated by the system's ability to derive insightful information from the information using sophisticated text mining and machine learning *** two main parts of the method are classification and data extraction. HR workers can more easily obtain the required knowledge thanks to information retrieval, which makes it possible to search HR data quickly and accurately. Contrarily, categorisation optimises the division of human resources information into pertinent classifications, including job positions, competencies, and achievement grades. We assess our algorithm's effectiveness on various datasets from actual HR datasets. The outcomes show how well our strategy works to streamline HRM procedures, cut down on manual labour, and increase the precision of decisions. Furthermore, our technology is compatible with corporate human resources offices and educational settings because it complies with worldwide university *** study belongs to the growing body of knowledge in HRM. It provides a useful tool for businesses looking to improve employee relations, simplify HR procedures, and attract and retain talent. The suggested method is a valuable resource for academics and businesses alike because of its versatility and compliance with global educational norms.

关键词： Machine learning human resource management information retrieval classification algorithm

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A Hybrid classification algorithm Approach for Breast Cancer Diagnosis

A Hybrid Classification Algorithm Approach for Breast Cancer...

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IEEE Conference on Industrial Electronics and Applications

作者： Abed, Baraa M. Shaker, Khalid Jalab, Hamid A. Shaker, Hothefa Mansoor, Ali Mohammed Alwan, Ahmad F. Al-Gburi, Ihsan Salman Univ Anbar Coll Comp Sci & Informat Technol Ramadi Iraq Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Modern Coll Business & Sci Muscat Oman Univ Nizwa Coll Arts & Sci Dept Math & Phys Sci Nizwa Oman Istanbul Kemerburgaz Univ Grad Sch Sci & Engn Istanbul Turkey

ISBN: (纸本)9781509009251

Early diagnosis of Breast Cancer is significantly important to treat the disease easily therefore it is necessary to develop techniques that can help physicians to get accurate diagnosis. This study suggests a hybrid classification algorithm which is based upon Genetic algorithm (GA) and k Nearest neighbor algorithm (kNN). GA algorithm has been used for its primary purpose as an optimization technique for kNN by selecting best features as well as optimization of the k value, while the kNN is used for classification purpose. The planned algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases using different datasets in which the first is Wisconsin Breast Cancer Database (WBCD) and the second one is Wisconsin Diagnosis Breast Cancer (WDBC) which has changes in the number of attributes and number of instances. The proposed algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved 99% accuracy.

关键词： Breast Cancer Diagnosis classification algorithm Genetic algorithm and k Nearest Neighbor algorithm

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New classification algorithm for electromyography-based computer cursor control system

New classification algorithm for electromyography-based comp...

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IEEE SoutheastCon 2005

作者： Chin, C Barreto, A Zhai, J Li, C Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA

ISBN: (纸本)0780388658

At present, a three-input Electromyography (EMG) system has been created to provide real-time, hands-free cursor control. The system uses the real-time spectral analysis of three EMG signals to produce the following five cursor actions: i) LEFT, ii) RIGHT, iii) UP, iv) DOWN, v) LEFT-CLICK. The three EMG signals are obtained from two surface electrodes placed on the left and right temples of the head and one electrode placed in the forehead region. The present system for translating EMG activity into cursor actions does not always discriminate between up and down EMG activity efficiently. To resolve this problem it was proposed that the three-electrode system be converted into a four-electrode system, using two electrodes in the forehead of the user, instead of one. This paper compares the effectiveness of the four-electrode system to that of the three-electrode system in classifying EMG activity into cursor actions through the use of Matlab simulations. It will be shown that the new four-electrode system produces significant improvements in classification performance.

关键词： keyboards mouse controllers (computers) electromyography signal classification spectral analysis handicapped aids human computer interaction classification algorithm electromyography-based computer cursor control system three-input electromyography system EMG real-time hands-free cursor control real-time spectral analysis EMG signals cursor actions cursor left motion cursor right motion cursor up motion cursor down motion cursor left-click surface electrodes forehead region temple electrodes EMG activity translation activity discrimination four-electrode system Matlab simulations classification performance

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