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IEEE Signal Processing and Communications Applications (SIU)

作者： Elnaz Pashaei Elham Pashaei Istanbul Aydin University Istanbul Turkey Istanbul Gelisim University Istanbul Turkey

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

This paper proposes a new hybrid approach for solving gene selection problems in cancer microarray data, which is one of the most challenging tasks in bioinformatics. Minimum-redundancy-maximum-relevance (mRMR) filter approach is combined with the binary black hole optimization algorithm (BBHA) to pick out extremely discriminative genes from cancer datasets. The support vector machine (SVM) is employed as a fitness function to accurately diagnose cancer. The experimental results prove that the suggested method exhibits better classification accuracy with the smallest gene subset compared to existing state-of-art methods.

关键词： Support vector machines classification algorithms Optimization Colon Filtering algorithms Feature extraction Breast cancer

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Incorporated preprocessing and physical layout analysis of a binary document image using a two stage classification

Incorporated preprocessing and physical layout analysis of a...

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International Conference on Computer and Communication Engineering, ICCCE

作者： Hamed Behin Afshin Ebrahimi Sepideh Ebrahimi Faculty of Electrical Engineering Sahand University of Technology Tabriz Iran School of Electrical and Computer University of Tehran Tabriz Iran

Before the image of a document enter an OCR module, it should undergo Preprocessing and Document Layout Analysis steps. Document layout analysis usually comes after preprocessing. Noise removal and skew correction are two major preprocessing operations. Document layout analysis itself is divided into physical and logical layout analysis. Physical layout analysis decomposes the image of a document into homogenous regions such as "text", "graphics", and "lines". In physical layout analysis, first, the image is segmented to homogenous regions, and then each homogenous region is classified into one of the present classes. On the other hand, logical layout analysis tries to assign functional labels (such as "title", "author", and "footnote") to some of the classified regions to find relationship between some regions, and to discover reading order of different parts of a document. This article presents an innovative method for preprocessing and physical layout analysis of binary documents. Although, most of the present systems give the result of preprocessing to document layout analysis; state-of-the-art algorithms try to postpone the processing operations as much as possible in order to prevent irreparable mistakes. These two steps are incorporated in our approach. This is achieved through using segmentation results for noise removal. One reason of effectiveness of this approach is appropriate arrangement of procedures. Also, a neural classifier is so trained that the output is robust to the skew. A two stage classification is used for determining pixel classes. In the first step, the Haar wavelet transform is computed on resized and gray leveled image. The coefficients are normalized, and then 10% of them are picked up randomly. Selected coefficients are clustered into 4 groups using Kmeans. A novel algorithm is introduced for assigning these 4 clusters to a background, a vertical, and two horizontal classes. Other wavelet coefficients are also classified to one of thes

关键词： Layout classification algorithms Pixel Noise Algorithm design and analysis Image segmentation Feature extraction

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classification of multiple targets based on disaggregation of Micro-Doppler signatures

Classification of multiple targets based on disaggregation o...

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Asia-Pacific Conference on Microwave

作者： Shelly Vishwakarma Shobha Sundar Ram Indraprastha Institute of Information Technology Delhi New Delhi Delhi IN

ISBN: (纸本)9781509015931

Micro-Doppler signatures of dynamic indoor targets (such as humans and fans) serve as a useful tool for classification. However, all the current classification methods are limited by the assumption that only a single target is present in the channel. In this work, we propose a method to classify multiple targets that are simultaneously present in the channel on the basis of a single channel source separation technique. We apply sparse coding based dictionary learning (DL) algorithms for disaggregating micro-Doppler returns from multiple targets into its constituent signals. The classification is subsequently carried out on the disaggregated signals. We have tested the performance of the proposed algorithm on simulated human and fan data.

关键词： Fans Dictionaries classification algorithms Radar Legged locomotion Spectrogram Blades

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Fast Nearest Neighbor classification using Class-Based Clustering

Fast Nearest Neighbor Classification using Class-Based Clust...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Tung-Shou Chen Yung-Hsing Chiu Chih-Chiang Lin Graduate School of Computer Science and Information Technology National Taichung Institute of Technology Taichung Taiwan Department of Computer Science National Chung Hsing University Taichung Taiwan

Nearest neighbor rule (NNR) is a parameter-free classifier which is easy to implement, simple to operate and with high accuracy. However, it is time and memory consuming for large datasets. This study proposed a parameter-free method to accelerate NNR. This method employs a class-based clustering algorithm to divide the training data to several clusters with respective members belonging to the same class. Cluster representations are extracted clustering border data based on the nearest neighbors between the different class clusters. Since the cluster representations are the clustering border data rather than the clustering centers, the predicting accuracy will not be affected by removing a cluster's internal data. In the predicting phase, the nearest neighbor search area is narrowed down by referring to a distance between a testing data and its nearest cluster. Thus the predicting process is speeded up. In this paper, the performance of the proposed method was evaluated and compared with NNR, K-NNR, and LIBSVM by using 5 benchmark datasets. Experimental results show that the proposed parameter-free classification algorithm is very easy to operate and gives consideration to speed and accuracy.

关键词： Nearest neighbor searches Training data Clustering algorithms Machine learning Cybernetics Computer science Acceleration Data mining Accuracy classification algorithms

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Hybrid Optimization for Enhanced Retinal Image classification Using Neural Networks

Hybrid Optimization for Enhanced Retinal Image Classificatio...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Anwar Basha H Deepak R Babu M Shaik Thasleem Bhanu B Amutha R. Madhavan Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamilnadu India Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Bengaluru Karnataka India Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai Tamilnadu India Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Science and Business Systems Jeppiaar Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The present study proposes a hybrid optimization algorithm that involves the integration of Neural Networks (NN), Genetic algorithms(GA), and Particle Swarm Optimization(PSO) to improve the accuracy and efficiency of retinal image analysis. First, a wide range of features are extracted from retinal images and normalized. Then a NN is constructed with its weights and biases initialized to process these features. The fitness of each feature subset is measured with the help of a network's output, quantified by a Mean Squared Error(MSE)-based fitness function. Receiving fitness values, the GA component picks out the most important subsets of features, which are refined with crossover and mutation operations to retain diversity and improve the search process. In a parallel process, PSO dynamically adjusts the position of particles representing subsets of features through modification of velocity and position vectors under the influences of cognition and society. With the proposed integrated approach, iteratively, an optimal feature subset is generated by minimizing error and maximizing classification performance. Finally, after convergence, this last optimized subset of features is used in the final tuning of the NN to achieve a far-reaching improvement in the accuracy of classification. This method takes advantage of the complementary strengths of NN, GA, and PSO; therefore, it is able to provide an effective solution for the optimization of features within medical image analysis.

关键词： Accuracy Artificial neural networks Retina Feature extraction Vectors classification algorithms Particle swarm optimization Optimization Tuning Genetic algorithms

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Design and Development of Tomato Leaf classification Framework Using CNN

Design and Development of Tomato Leaf Classification Framewo...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Sayed Abu Lais Ezaz Ahmed Abhishek Kumar Ranjit Singh Department of Computer Science and Engineering Chandigarh University Gharuan Punjab India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Crop production loss in agriculture is twenty-two percent because of biotic and abiotic stress/disease and living and non-living organisms. Producers must be able to identify these requirements from the beginning. Computer vision can help discover early illness patterns and clusters. Recent Deep learning-based computer vision advancements can recognise single or multi-biotic stress in tomato plant leaves. This investigation will use the PlantVillage dataset to segment objects. The model was trained with tagged, enriched, and augmented data. The Hybrid-CNN model was used to segment sick tomato plants. In object detection, the proposed Hybrid CNN outperforms the U-Net architecture and Seg-Net when paired with instance segmentation. This study used semantic segmentation data to identify and classify single-and multiple-leaf illnesses. The Hybrid-CNN algorithm's predicted output corresponds to that of the adapted U-Net, M -SegN et, and adapted U -Net models based on state-of-art evaluation parameters with other evaluation parameters such as Intersection over Union (IoU) and Mean IoU. The proposed model outperformed competitors, processing 1004 pictures in under 30 nanoseconds. With an accuracy of 98.580/0, the suggested model outperforms modified segmentation models.

关键词： Instance segmentation Adaptation models Computer vision Accuracy Computational modeling Semantic segmentation Biological system modeling Data models classification algorithms Lesions

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Sentiment analysis in a hybrid hierarchical classification process

Sentiment analysis in a hybrid hierarchical classification p...

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International Conference on Digital Information Management (ICDIM)

作者： Wei Wei Jon Atle Gulla Department of Computer and Information Science Norwegian University of Science and Technology Trondheim Norway

As more and more reviews are generated online, sentiment analysis has been widely studied and developed both in academia and industry recently. In this paper, we propose a novel approach to tackle two complementary sub-tasks of sentiment analysis on review texts, i.e., the Attribute Detection (AD) task and the Sentiment Orientation (SO) task, in a Hybrid Hierarchical classification Process (HHCP). Specifically, the HHCP approach employs a linear Fisher classifier to achieve the AD task in an ontology-based hierarchical classification process. As evidences show that common statistical classifiers that have superior performances on semantic classifications do not necessarily work well on classifying sentiment information, we did not continue to use the linear Fisher classifier in the SO task. Instead, we turn to a rule-based heuristic classification method on performing sentiment orientation for attributes identified from the AD task. The proposed HHCP approach is empirically analyzed in extensive experiments. Experiments conducted for performance comparison not only show that our proposed HHCP approach outperforms the other three baseline methods, but also address all the concerns raised before experiments. Further experiments on analyzing the impact of dimensionality d of the input vector space confirm that the conclusions drawn from performance comparison hold very well as d varies. Experiments of studying computational efficiency demonstrate that compared with the existing HL-SOT approach our proposed HHCP approach is more efficient in computation.

关键词： classification algorithms

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Comparison Of Multinomial Naive Bayes Algorithm And Logistic Regression For Intent classification In Chatbot

Comparison Of Multinomial Naive Bayes Algorithm And Logistic...

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International Conference on Applied Engineering (ICAE)

作者： Muhammad Yusril Helmi Setyawan Rolly Maulana Awangga Safif Rafi Efendi Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia

ISBN: (纸本)9781538680674

Chatbot is software that communicates using natural language. chatbots such as machine conversation systems, Chatterbot, virtual agents, and dialogue systems. This software enables to simulate human conversations. In this research, the chatbot system that will be created must be able to understand the natural language of what is entered by the user, and the chatbot will answer according to what the user is expecting. The researcher proposes a classification method to identify intent rather than user input or called intent classification on the chatbot system; the researcher also wants to know the level of accuracy, precision, and recall on the evaluation results of both methods. The classification method applied in this research is the Naive Bayes method and compared with the Logistic Regression method to determine the class intention. The evaluation results show the level of accuracy precision and recall in the Logistic Regression model is higher than the Naive Bayes model.

关键词： Logistics Training data Bayes methods Mathematical model Data models classification algorithms Natural languages

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Cognitive State classification using Optimized Feature Selection Approach

Cognitive State Classification using Optimized Feature Selec...

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IEEE India Conference (INDICON)

作者： J. Siva Ramakrishna M. S. Ramaiah University of Applied Sciences Bangalore India

ISBN: (纸本)9781665441766

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique where it is possible to capture neural activity in human brain regions when subjected to different stimuli. However, due to the fMRI dataset’s high dimensional and sparse nature, the selection of appropriate features plays a crucial role in achieving the best classification accuracy. In this work, the stable features are selected from the fMRI dataset by combining Fast Fourier Transform (FFT) with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Then, the machine learning classifiers such as Support Vector Machine (SVM), Gaussian NB, and XGboost have been trained using these features. StarPlus fMRI dataset is used to examine the performance of the proposed feature selection framework. The experimental results reveal that the proposed feature selection algorithm resulted in optimum features with better classification accuracy. Comparison of the proposed scheme with state of the art models show that it performs better, and as a result, can be used for the pattern recognition of brain responses in multisubject fMRI data.

关键词： Support vector machines Neural activity Functional magnetic resonance imaging Feature extraction Computational efficiency classification algorithms Planning

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The Use of Information sets in Decoding Cyclic Codes

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IRE Transactions on Information Theory 1962年第5期8卷 5-9页

作者： Prange, Eugene AF Cambridge Research Laboratories United States Air Force Bedford Massachusetts United States

A class of decoding algorithms using encoding-and-comparison is considered for error-correcting code spaces. Code words, each of which agrees on some information set for the code with the word r to be decoded, are constructed and compared with r. An operationally simple algorithm of this type is studied for cyclic code spaces A. Let A have length n, dimension k over some finite field, and minimal Hamming distance m. The construction of fewer than n /2 code words is required in decoding a word r. The procedure seems to be most efficient for small minimal distance m, but somewhat paradoxically it is suggested on operational grounds that it may prove most useful in those cases where m is relatively large with respect to the code length n. © 1962, IEEE. All rights reserved.

关键词： Decoding Galois fields classification algorithms Laboratories Hamming distance Concrete Hamming weight Equations

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