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Prediction of protein phenotype based on protein interaction network by coupling genetic algorithm and K-nearest neighbor algorithm

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ANALYTICAL METHODS 2014年第14期6卷 5281-5289页

作者： Li, Zhanchao Chen, Lili Lai, Yanhua Xie, Yun Dai, Zong Zou, Xiaoyong Guangdong Pharmaceut Univ Sch Chem & Chem Engn Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Sch Chem & Chem Engn Guangzhou 510275 Guangdong Peoples R China

Quick and accurate identification of protein phenotype is a key step for understanding life at the molecular level, and has a significant impact in the fields of biomedicine and pharmacy. However, as a result of genome and other sequencing projects, there is a huge gap between the number of discovered proteins and the number of phenotype annotated proteins. Therefore, it is indispensable to develop an automated and reliable method for predicting protein phenotype. In this paper, a novel method is proposed and used to identify protein phenotype. It is featured by coupling the genetic algorithm and K-nearest neighbor algorithm, and the feature vector is introduced to take into account the information of the protein and the neighboring proteins in the protein interaction network. As a demonstration, a fivefold cross-validation test and independent test set are performed, and the results indicate that the current method may serve as an important complementary tool for other existing algorithms in this area. The source code of MATLAB is freely available on request from the authors.

关键词： Phenotype K-nearest neighbor algorithm Eigenvector Protein Interaction Maps TEST SETS Protein nearest neighbor algorithm Genetic algorithm (GA)

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Classification of Knee Joint Vibroarthrographic Signals Using k-nearest neighbor algorithm 27

Classification of Knee Joint Vibroarthrographic Signals Usin...

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IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Liu, Kaizhi Luo, Xin Yang, Shanshan Cai, Suxian Zheng, Fang Wu, Yunfeng Xiamen Univ Sch Informat Sci & Technol Xiamen 361005 Fujian Peoples R China

ISBN: (纸本)9781479930999

The pathological condition in a degenerative knee joint may be assessed by analyzing the knee joint vibroarthrographic signals. With the severity level of the knee joint disorders evaluated by the computational methods, unnecessary imaging examination or open surgery can be prevented. In the present study, we used the k-nearest neighbor (k-NN) algorithm, a type of lazy learning approach, to classify the knee joint vibroarthrographic signals collected from healthy subjects and symptomatic patients with knee joint disorders. With the representative features of form factor and variance of the mean-square values, the k-NN algorithm is able to correctly discriminate 80% signals with the sensitivity of 0.71 and the specificity of 0.85, which is superior to the total accurate rate of 77% (sensitivity: 0.64, specificity: 0.85) provided by the Fisher's linear discriminant analysis.

关键词： Knee Joint K-nearest neighbor algorithm Joint Diseases signals nearest neighbor algorithm

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nearest neighbor empirical processes

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BERNOULLI 2025年第1期31卷 312-332页

作者： Portier, Francois Univ Rennes Ensai CREST Rennes France

In the regression framework, the empirical measure based on the responses resulting from the nearest neighbors, among the covariates, to a given point x is introduced and studied as a central statistical quantity. First, the associated empirical process is shown to satisfy a uniform central limit theorem under a local bracketing entropy condition on the underlying class of functions reflecting the localizing nature of the nearest neighbor algorithm. Second a uniform non-asymptotic bound is established under a well-known condition, often referred to as VapnikChervonenkis, on the uniform entropy numbers. The covariance of the Gaussian limit obtained in the uniform central limit theorem is simply equal to the conditional covariance operator given the covariate value. This suggests the possibility of using standard formulas to estimate the variance by using only the nearest neighbors instead of the full data. This is illustrated on two problems: the estimation of the conditional cumulative distribution function and local linear regression.

关键词： Concentration inequality empirical process theory nearest neighbor algorithm weak convergence

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Real-time travel time prediction using multi-level k-nearest neighbor algorithm and data fusion method

Real-time travel time prediction using multi-level k-nearest...

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2014 International Conference on Computing in Civil and Building Engineering

作者： Tak, Sehyun Kim, Sunghoon Jang, Kiate Yeo, Hwasoo Smart Transportation System Laboratory Department of Civil and Environmental Engineering KAIST 291 Daehak-ro Yuseong-gu Daejeon Korea Republic of School for Green Transportation KAIST 291 Daehak-ro Yuseong-gu Daejeon Korea Republic of

ISBN: (纸本)9780784413616

Estimating and predicting the travel time on freeways with reasonable accuracy is essential for successful implementation of an intelligent transportation system. However, the related previous studies have some problems. The statistics-based methods have problems in accuracy, and some others are limited to predicting the travel time only during short time intervals. Another challenging matter is that the existing road sensors have some limitations in being directly utilized because data from road sensors have a lot of errors. In this study, we propose a new algorithm called multi-level k-nearest neighbor (k-NN), which is designed for predicting travel time with higher computational efficiency and prediction accuracy. The algorithm consists of three parts: classification, global matching, and local matching. As a part of the proposed algorithm, in order to overcome the problems of data errors, we provide a data fusion method that combines the traffic data from ILDs and DSRC. The results show that the proposed multi-level k-NN with the data fusion can effectively predict the future travel time within less than 5% error range, even in congested traffic situations. © ASCE 2014.

关键词： Travel time Data fusion data integration Traffic data nearest neighbor algorithm K-nearest neighbor algorithm fusion method Freeway error range Time intervals Computational efficiency

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Exploring logistics transport route optimization: An algorithmic study based on RFID technology

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INTERNATIONAL JOURNAL OF RF TECHNOLOGIES-RESEARCH AND APPLICATIONS 2024年第2期14卷 107-124页

作者： Guo, Jing Wang, Yalan Guo, Ying Dai, Shuaijun Yan, Ruyu Shi, Zaijie Henan Polytech Zhengzhou 450000 Henan Peoples R China Henan Univ Engn Zhengzhou Henan Peoples R China Fourth Geol Brigade Henan Nonferrous Met Geol & Mi Zhengzhou Henan Peoples R China Henan Polytech Univ Jiaozuo Henan Peoples R China HongHao Informat Technol Co Ltd Zhengzhou Henan Peoples R China

With the rapid growth of e-commerce, logistics companies face challenges in efficient routing and scheduling to meet dynamic delivery demands. This paper proposes a novel logistics scheduling model to optimize vehicle routing using Radio Frequency Identification (RFID) technology. A vehicle scheduling model is developed. The random customer demand and service time are solved using an adaptive taboo search algorithm combined with a nearest neighbor algorithm. Comparative experiments demonstrate the performance of the improved method in completing tasks and reducing queueing time compared to other methods. A case study of route optimization for a logistics company shows the model can recommend optimized routes that reduce total transportation cost by over 25% compared to using RFID alone. The results highlight the potential of the proposed technique to enhance logistics efficiency. Limitations and

关键词： RFID technology logistics adaptive taboo search algorithm nearest neighbor algorithm scheduling

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Clustering mining method of large-scale network abnormal data based on selective collaborative learning

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2023年第1期23卷 9-21页

作者： Zhang, Hai'ou Jilin Prov Econ Management Cadre Coll Informat & Media Dept Changchun 130012 Jilin Peoples R China

In order to improve the accuracy and recall rate of the clustering mining process of large-scale network abnormal data and shorten the time of clustering mining, in this study, a large-scale network anomaly data clustering mining method based on selective collaborative learning is proposed. Through cooperative training and selective ensemble learning, a machine learning anomaly detection model and a strong classifier for large-scale network data are designed, and the correlation variable analysis method is used to obtain the dissimilarity measure of data. The network anomaly data is processed by fuzzy fusion, and the nearest neighbor algorithm is used to realize the clustering mining of large scale network anomaly data. The data clustering mining accuracy of this method reaches 98.16%, the time of data clustering mining is only 2.5 s, and the recall rate of data clustering mining is up to 98.38%, indicating that this method can improve the effect of large-scale network anomaly data clustering mining.

关键词： Selective ensemble learning hybrid weighted block matching nearest neighbor algorithm cluster mining

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Using Genetic algorithm in Inner Product to Resist Modular Exponentiation From Higher Order DPA Attacks

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IEEE ACCESS 2022年 10卷 3238-3251页

作者： Mahanta, Hridoy Jyoti Nath, Keshab Roy, Amit Kumar Kotecha, Ketan Varadaranjan, Vijayakumar Assam Don Bosco Univ Dept Comp Sci & Engn Gauhati 781017 India Indian Inst Informat Technol Dept Comp Sci & Engn Kottayam 686635 Kerala India Natl Inst Technol Mizoram Dept Comp Sci & Engn Aizawl 796012 India Symbiosis Ctr Appl Artificial Intelligence Mulshi 412115 India Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

Evolutionary computation techniques have always provided fascinating results in all the fields of science and engineering. However in the area of computer security, their contribution has been comparatively very less. More specifically if we consider the side-channel attacks, use of these nature based techniques have been very nominal. Therefore, we proposed a secure protocol in this paper to combat against the Higher Order Differential Power Analysis attacks on modular exponentiation based cryptosystems using one of the popular evolutionary computation techniques. The proposed work first uses Genetic algorithm for splitting the huge exponent within the modular exponentiation into multiple non-uniform shares. Then, this shares are randomly chosen for computing individual modular exponentiation with the help of nearest neighbor algorithm. Using Genetic algorithm, our proposed protocol can generate reasonable number of shares which exposes secret exponent at the least. As a result, it provides significant resistance to Higher Order Differential Power Analysis attacks. Moreover, randomization in computing individual modular exponentiation secures the cryptosystem from generic power analysis attacks like SPA and DPA.

关键词： Cryptography Genetic algorithms Protocols Resists Elliptic curve cryptography Ciphers IP networks Power analysis attacks modular exponentiation genetic algorithm nearest neighbor algorithm mutual information analysis

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Improving the Running Time of the nearest neighbor algorithm

Improving the Running Time of the Nearest Neighbor Algorithm

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17th International Computer Science and Engineering Conference (ICSEC)

作者： Chompupatipong, Nattakon Jearanaitanakij, Kietikul King Mongkuts Inst Technol Ladkrabang Fac Engn Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781467353229

nearest neighbor algorithm is a well-known method, in pattern recognition, for classifying objects based on the nearest examples in the feature space. However, it's major drawback is the sequential search operation which calculates the distance between the probing object and the entire set of the training instances. In this paper, we propose a novel method to accelerate the searching operation in the nearest neighbor algorithm. Our method consists of two main steps;creating the reference table and searching the nearest neighbor. Reference table of the training instances is created once in the initial phase and referred periodically by the searching step. Surprisingly, this reference table can drastically reduce the searching time of the nearest neighbor algorithm on any feature space. The experimental results on five real-world datasets from the UCI repository show a remarkable improvement on the searching time while the accuracy is still preserved.

关键词： nearest neighbor algorithm Euclidean Distance Running Time Reference Table Geometric Measurement

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X-Ray medical image super-resolution via self-organization neural networks and geometric directional gradient

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IET IMAGE PROCESSING 2022年第14期16卷 3910-3928页

作者： Ahmadian, Khodabakhsh Reza-Alikhani, Hamid-reza Islamic Azad Univ Mahshahr Branch Dept Elect & Comp Engn Mahshahr Iran Natl Univ Tafresh Branch Dept Elect & Comp Engn Tafresh *** Iran

Nowadays, due to the rapid development of the Internet and communication networks, remote disease diagnosis by physicians utilizing medical images such as X-rays have increased. However, the transmitted images lose their true quality and resolutions due to several factors, including noise and image compression techniques used to conserve memory space. This paper presents a novel clinical X-ray image enhancement method based on self-organizing neural networks, the ability of first-order derivative operators to detect edges, and improved classification methods by enhancing image features and extending the nearest neighbor algorithms selection of the best similarity feature vector among all sample feature vectors. The proposed method employs first-order image derivation operators to extract edge details and features from images. The feature vectors were preprocessed and segmented into various classes using self-organizing neural networks. The best matching and most similar vectors among the feature vectors are retrieved via the nearest neighbor algorithm. In contrast to conventional and contemporary image super-resolution techniques, the proposed method does not rely on the cost function. It is independent of error backpropagation methods because the competitive nature of the neural network is used to update the weight vectors rather than the gradient descent method. In addition, the performance and accuracy of the proposed image super-resolution method were improved by increasing the number of feature components in the feature vectors through the use of the two-dimensional image gradient. Furthermore, numerous image quality measurement criteria, such as structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), were used to compare the reconstructed images of the proposed algorithm with those of other image enhancement algorithms. The results show that the proposed image enhancement algorithms' high-resolution and high-quality reconstructed image signif

关键词： nearest neighbor algorithms selection image super-resolution method neural network clinical X-ray image enhancement method two-dimensional image gradient image reconstruction feature components self-organization neural networks nearest neighbor algorithm classification methods medical image enhancement algorithms image features backpropagation contemporary image super-resolution techniques most similar vectors data compression edge detection medical images sample feature vectors first-order image derivation operators gradient methods similarity feature vector weight vectors X-ray imaging feature extraction communication networks image segmentation geometric directional gradient Ray medical image super-resolution image enhancement conventional image super-resolution techniques reconstructed images image compression techniques medical image processing image coding error backpropagation methods remote disease diagnosis gradient descent method first-order derivative operators numerous image quality measurement criteria transmitted images image resolution diseases

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The vehicle routing problem in the dairy sector: a case study

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INTERNATIONAL JOURNAL OF FOOD ENGINEERING 2022年第3期18卷 239-252页

作者： Rinaldi, Marta Bottani, Eleonora Solari, Federico Montanari, Roberto Univ Campania Luigi Vanvitelli Dept Engn Via Roma 29 I-81031 Aversa Italy Univ Parma Dept Engn & Architecture Viale GP Usberti 181-A I-43124 Parma Italy

The vehicle routing problem is one of the most studied NP-hard combinatorial problem. In the food sector, the complexity of the issue grows because of the presence of strict constraints. Taking into account the variability and the restrictions typical of the dairy sector, the aim of this paper is to provide a practical tool for solving the milk collection problem in real scenarios. A heuristic approach has been proposed to determine a feasible solution for a real-life problem, including capacity and time constraints. Two different applications of the nearest neighbor algorithm have been modelled and compared with the current system. Different tests have been implemented for evaluating the suitability of the outcomes. Results show that the greedy approach allows for involving less vehicles and reducing the travel time. Moreover, the tool has been proved to be flexible, able to solve routing problems with stochastic times and high supply variability.

关键词： dairy transportation food nearest neighbor algorithm practical tool vehicle routing problem

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