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13th International Joint Conference on Biomedical Engineering Systems and Technologies

作者： Ahmad, Rushda Basir Khan, Nadeem Ahmad Lahore Univ Management Sci Dept Elect Engn Lahore Pakistan

ISBN: (纸本)9789897583988

Ambulatory electroencephalogram (EEG), allows collection of patients data over extended periods of time. However, as a small recording requires large memory for storage, and this makes EEG data storage an arduous task. Moreover, classification of EEG for extraction of relevant information is relatively challenging, and selective data retrieval depends on task at hand. Consequently, EEG data storage and classification need to be computationally efficient. This paper presents a combined scheme, for the simultaneous compression and classification of EEG data, which not only decreases the overall computational effort, but also allows selective archiving and retrieval of data. Huffman and Arithmetic coding techniques are employed on CHB-MIT scalp EEG database and the results are presented in form of compression ratio (CR) and percentage root mean square distortion (PDR). For classification, Intelligent Neurologist Support System (INSS), has been used. The classifier output apart from being stored as data, is also used for intelligent data reduction, when only specific information is required, resulting in increased CR and decreased PDR, which is desired. Hence, the results show intelligent compression and reduction of data results in efficient management of EEG data. The signal undergoes state-of-the-art compression such that on reconstruction it almost maintains the same classification accuracy as the original one.

关键词： Compression binary encoding Epilepsy EEG

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UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2019年 184卷 28-43页

作者： Cui, Xiaowen Yu, Zhaomin Yu, Bin Wang, Minghui Tian, Baoguang Ma, Qin Qingdao Univ Sci & Technol Coll Math & Phys Qingdao 266061 Peoples R China Qingdao Univ Sci & Technol Artificial Intelligence & Biomed Big Data Res Ctr Qingdao 266061 Peoples R China Univ Sci & Technol China Sch Life Sci Hefei 230027 Anhui Peoples R China Univ Calgary Dept Biochem & Mol Biol Med Genet & Oncol Calgary AB T2N 4N1 Canada Ohio State Univ Coll Med Dept Biomed Informat Columbus OH 43210 USA

Ubiquitination is an essential process in protein post-translational modification, which plays a crucial role in cell life activities, such as proteasomal degradation, transcriptional regulation, and DNA damage repair. Therefore, recognition of ubiquitination sites is a crucial step to understand the molecular mechanisms of ubiquitination. However, the experimental verification of numerous ubiquitination sites is time-consuming and costly. To alleviate these issues, a computational approach is needed to predict ubiquitination sites. This paper proposes a new method called UbiSitePred for predicting ubiquitination sites combined least absolute shrinkage and selection operator (LASSO) feature selection and support vector machine. First, we use binary encoding (BE), pseudo-amino acid composition (PseAAC), the composition of k-spaced amino acid pairs (CKSAAP), position-specific propensity matrices (PSPM) to extract the sequence feature information;thus, the initial feature space is obtained. Secondly, LASSO is applied to remove the feature redundancy information and selects the optimal feature subset. Finally, the optimal feature subset is input into the support vector machine (SVM) to predict the ubiquitination sites. Five-fold cross-validation shows that UbiSitePred model can achieve a better prediction performance compared with other methods, the AUC values for Set1, Set2, and Set3 are 0.9998, 0.8887, and 0.8481, respectively. Notably, the UbiSitePred has overall accuracy rates of 98.33%, 81.12%, and 76.90%, respectively. The results demonstrate that the proposed method is significantly superior to other state-of-the-art prediction methods and provide a new idea for the prediction of other post-translational modification sites of proteins. The source code and all datasets are available at https://***/QUST-AIBBDRC/UbiSitePred/.

关键词： Ubiquitination sites binary encoding Pseudo-amino acid composition Composition of k-spaced amino acid pairs Position-specific propensity matrices Least absolute shrinkage and selection operator

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Sparse representation based image super-resolution on the KNN based dictionaries

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OPTICS AND LASER TECHNOLOGY 2019年 110卷 135-144页

作者： Liu, Ning Xu, Xing Li, Yujie Zhu, Anna Univ Elect Sci & Technol China Chinese Acad Sci Natl Lab Pattern Recognit Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Future Media Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Yangzhou Univ Yangzhou Jiangsu Peoples R China Wuhan Univ Technol Wuhan Hubei Peoples R China

This paper addresses the problem of single image super-resolution (SR). In recent years, sparse representation based SR methods have been proposed and achieved great success. Traditional sparse representation based SR methods learn a unified high-resolution (HR) and low-resolution (LR) dictionary pair. All LR patches share the same dictionary pair to conduct reconstruction to get their corresponding HR patches. The reconstruction process introduces reconstruction error, which limits the performance of SR results. In this paper, to minimize the reconstruction error, we utilize a K-Nearest-Neighbours (KNN) based dictionary pair for each LR patch instead of the unified dictionary pair to conduct the reconstruction process. The KNN based dictionaries are selected among a huge dictionary which contains hundreds of millions of patch pairs. To speed up the KNN retrieval process, we adopt a binary encoding method which preserves local information for the LR patches, and retrieve the KNN of each LR patch in the Hamming space. Besides, since a large patch contains more structured information than a small patch, we utilize large patches instead of small ones as the atoms of dictionaries, which further improves the SR results. Experimental results demonstrate that our method outperforms the existed state-of-the-art methods, especially when the magnification factor is large or the image is blurred. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Super resolution Sparse representations K-Nearest-Neighbours binary encoding

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binary encoding Differential Evolution with Application to Combinatorial Optimization Problem

Binary Encoding Differential Evolution with Application to C...

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2013年中国智能自动化学术会议

作者： Changshou Deng Bingyan Zhao Yanlin Yang Hai Zhang School of Information Science and Technology Jiujiang University

Differential Evolution algorithm is a new competitive heuristic optimization algorithm in the continuous *** operators in the original Differential Evolution are simple;however,these operators make it impossible to use the Differential Evolution in the binary space *** on the analysis of problems led by the mutation operator of the original Differential Evolution in the binary space,a new mutation operator was proposed to enable this optimization technique can be used in binary *** new mutation operator,which is called semi-probability mutation operator,is a combination of the original mutation operator and a new probability-based defined *** experimental results of two different combinatorial optimization problems show its effectiveness and validity.

关键词： Combinatorial optimization problem Differential evolution binary encoding Semi-probability mutation operator

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VISUALISING AND SOLVING A MAZE USING AN ARTIFICIAL INTELLIGENCE TECHNIQUE

VISUALISING AND SOLVING A MAZE USING AN ARTIFICIAL INTELLIGE...

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IEEE AFRICON Conference - Powering Africa s Sustainable Energy for AD Agenda - The Role ofICT and Engineering.

作者： Sagming, M. N. Heymann, R. Hurwitz, E. Univ Johannesburg Dept Elect & Elect Engn Sci POB 524 ZA-2006 Auckland Pk South Africa

ISBN: (纸本)9781728132891

This paper describes the implementation of an artificial intelligence (AI) technique known as Genetic Algorithm (GA), used to solve randomly generated mazes that are of varying sizes and complexity. To evaluate the effectiveness of the GA, several non-AI techniques are implemented such as Depth-First Search (DFS), Breadth-First Search (BFS), A-Star Algorithm (A*), Dijkstra Algorithm (DA), and Greedy Best-First Search (GBFS). Genetic algorithm is a method for solving both constrained and unconstrained problems based on a natural selection process that mimics biological evolution. The non-AI algorithms make use of graph concepts together with underlying data structures to solve randomly generated mazes. This paper also shows the results obtained from five different experiments after implementing the complete system and executing each of the non-AI algorithms as well as the GA. The strongest results after executing each algorithm, that is, the number of steps and the time taken to solve the maze were recorded and graphs were plotted to compare the performance of the GA compared to the non-AI algorithms. The GA always found the shortest path but becomes slower than the non-AI algorithms for dimensions greater than 10 x 10.

关键词： Artificial intelligence (AI) genetic algorithm (GA) breadth-first search (BFS) A-star (A*) depth-first search (DFS) dijkstra algorithm (DA) greedy best-first search (GBFS) mutation crossover population generation selection individual elitism count fitness function heuristic cost queue stack priority queue binary encoding and smart agent

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Arc Consistency Revisited 16th

Arc Consistency Revisited

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16th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Wang, Ruiwei Yap, Roland H. C. Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9783030192129;9783030192112

binary constraints are a general representation for constraints and is used in Constraint Satisfaction Problems (CSPs). However, many problems are more easily modelled with non-binary constraints (constraints with arity >2). Several well-known binary encoding methods can be used to transform non-binary CSPs to binary CSPs. Historically, work on constraint satisfaction began with binary CSPs with many algorithms proposed to maintain Arc Consistency (AC) on binary constraints. In more recent times, research has focused on non-binary constraints and efficient Generalized Arc Consistency (GAC) algorithms for non-binary constraints. Existing results and "folklore" suggest that AC algorithms on the binary encoding of a non-binary CSP do not compete with GAC algorithms on the original problem. We propose new algorithms to enforce AC on binary encoded instances. Preliminary experiments show that our AC algorithm on the binary encoded instances is competitive to state-of-the-art GAC algorithms on the original non-binary instances and faster in some instances. This result is surprising and is contrary to the "folklore" on AC versus GAC algorithms. We believe our results can lead to a revival of AC algorithms as binary constraints and resulting algorithms are simpler than the non-binary ones.

关键词： binary constraint binary encoding Arc Consistency Generalized Arc Consistency CSP

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DYNAMIC TEXTURE RECOGNITION USING 3D RANDOM FEATURES 44

DYNAMIC TEXTURE RECOGNITION USING 3D RANDOM FEATURES

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhao, Xiaochao Lin, Yaping Liu, Li Hunan Univ Hunan Prov Key Lab Trusted Syst & Network Changsha Hunan Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland Natl Univ Def Technol Coll Syst Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781479981311

In this paper, we present a novel, simple but effective approach for dynamic texture recognition using 3D random features. Compared with the existing dynamic texture recognition approaches using carefully designed features for high performance, our method use only a few 3D random filters to extract spatio-temporal features from local dynamic texture blocks, which are further encoded into a low-dimensional feature vector. To explore the representative power of the 3D random features, we use two different encoding schemes, the learning-based Fisher vector encoding and the learning-free binary encoding. The proposed method is tested on the UCLA and DynTex databases with various evaluation protocols. Experimental results demonstrate the high performance of our method for dynamic texture recognition.

关键词： Dynamic texture recognition random features Fisher vector encoding binary encoding

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Performance Evaluation of Different Feature encoding Schemes on Cybersecurity Logs

Performance Evaluation of Different Feature Encoding Schemes...

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IEEE SoutheastCon Conference

作者： Jackson, Eric Agrawal, Rajeev Engn Reserch & Dev Ctr USA Informat Technol Lab Vicksburg MS 39810 USA

ISBN: (纸本)9781728101378

Many cybersecurity logs contain a substantial volume of textual data regarding security events. This data needs to be converted to numerical types before any machine learning (ML) algorithms can be applied. Feature encoding is the process of transforming textual data into numerical values so they may be applied to ML algorithms, resulting in improved model accuracy. Researchers have used many approaches to convert textual data into numerical values such as, "Label encoding", "One Hot encoding", and "binary encoding". These approaches are useful encoding schemes for dealing with large scale text data. We examine the application of these methods to cybersecurity datasets to determine which encoding scheme performs the best when used with a classification ML algorithm in identifying intrusion detections. Experimental results show that label encoding performed the best, whereas one hot encoding was least effective.

关键词： cybersecurity feature engineering machine learning Label encoding One Hot encoding binary encoding

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encoding Web-based Data for Efficient Storage in Machine Learning Applications 15

Encoding Web-based Data for Efficient Storage in Machine Lea...

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15th International Conference on Information Processing (ICINPRO) - Internet of Things

作者： Aich, Animikh Krishna, Akshay Akhilesh, V Hegde, Chetana Wobot Intelligence Delhi 110030 India Univ Nebraska Med Ctr Omaha NE 68198 USA Flux Auto Bangalore 560097 Karnataka India Manipal ProLearn Bangalore 560100 Karnataka India

ISBN: (纸本)9781728138077

With the advent of the information era, we have seen a huge boom in the amount of data produced over the years, which is primarily the result of the Internet and its billions of users worldwide. The internet is a storehouse of all kinds of data text, videos, and images. But most of this data is not suitable for learning algorithms directly. There is a need for the processing of the data prior to being applied to various learning algorithms. Deep learning algorithms using neural networks require efficient and proper datasets to yield better results for predictive analysis. As we need to deal with big data over the internet, efficient storage is a challenge. encoding the dataset in a convenient and efficient form and then storing it is of immense importance. Here, we compare various encoding algorithms to store pre-processed text data. Using Huffman encoding, the simulation results, for a random sample of 8000 English words, have indicated that the storage space (memory) requirement dropped to just 0.1% in comparison with the more traditional One-Hot encoding technique.

关键词： binary encoding Data Mining Deep Learning Huffman encoding Neural Networks One-Hot encoding Porter Stemmer Algorithm Tokenization Vectorization Web Scraping

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Feature Selection Method Based on Hybrid SA-GA and Random Forests

Feature Selection Method Based on Hybrid SA-GA and Random Fo...

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作者： Zibo Zhou Yunfan Wang Man Li School of Information Science and Technology Jinan University

We propose a feature selection algorithm based on hybrid simulated annealing(SA)-genetic algorithm(GA)and random forests,whose procedure can be described as the following ***,set an initial temperature and create an initial solution as current solution by binary encoding of the ***,generate a new potential solution according to the principle of crossover and mutation in the genetic ***,construct random forests and calculate the fitness degree of the new potential solution by using out-of-bag(OOB) *** acceptance of new state is based on Metropolis criterion,and the steps are iterated until a predefined lowest temperature is ***,a contrast experiment has done to show that the hybrid algorithm we proposed has better classification and feature selection performance than the methods from the existing literature.

关键词： simulated annealing genetic algorithm random forests feature selection binary encoding

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