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International Conference on Intelligent Computing and Control Systems (ICCS)

作者： Sinha, Ankita Sahoo, Bhaswati Rautaray, Siddharth Swamp Pandey, Manjusha KIIT Deemed Univ Sch Comp Engn Bhubaneswar Odisha India

ISBN: (纸本)9781538681138

Data Mining is applicable for pulling up some new information by analyzing the database. It is also used for prediction based on real and actual current data. Breast cancer is a very harmful disease which effects badly to ones social, physical life and also effects mentally. This paper focuses on the attribute filtering techniques i.e frequent itemsets mining with the intention to find the essential and relevant attribute from the Wisconsin breast cancer dataset and classification algorithmic program like SVM, Naive Bayes, k-NN, Decision Tree comparison is done with attribute filtering. SVM produces beat result among all the classifier with attribute filtering.

关键词： Data mining Breast cancer Z-score normalization Frequent itemsets mining classification algorithm R analytical tool Prediction

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Data Dimension Reduction in Training Strategy for Face Recognition System

Data Dimension Reduction in Training Strategy for Face Recog...

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21st International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Loderer, Marek Pavlovicova, Jarmila Feder, Matej Oravec, Milos Slovak Univ Technol Bratislava Fac Elect Engn & Informat Technol Ilkovicova 3 Bratislava 81219 Slovakia

ISBN: (纸本)9789531841917

In this paper, we propose a training strategy for an automatic face recognition system. Our strategy is based on cascade reduction of data dimensionality using LBP and PCA algorithms. This method is able to achieve higher recognition accuracy in comparison with simple LBP or PCA and it is suitable in the case of adding a new user to the face recognition system. We provide a comparative study of our proposed algorithm and several standard algorithms that reduce dimensionality of input data. Dimension reduction is important also in the case of storage and computational complexity reduction. We propose an overview of selected strategies and we compare their performance using CMU PIE face database. Our results in testing of clustering algorithms indicate that SOM and K-means algorithms are suitable for an automatic selection of training samples for a recognition system. According to achieved results we propose a part of the face recognition system suitable for example for the next-generation of hybrid broadcast broadband television.

关键词： Face recognition Histogram equalization Feature extraction LBP LDA PCA Clustering algorithms SOM DBSCAN K-means classification algorithm SVM

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Analysis of Student Performance to Predict Career Specialization using Random Forest Data Mining Technique 24

Analysis of Student Performance to Predict Career Specializa...

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9th International Conference on Intelligent Information Technology (ICIIT)

作者： Hermogenes, Mary Grace G. Repaso, Jennifer Anne A. Perez, Joann G. Bulacan State Univ Malolos Philippines

ISBN: (纸本)9798400716713

Career specialization has emerged as a critical factor for students to attain success in today's highly competitive job market. Therefore, analyzing students' academic performance data to predict their career specialization can be a valuable tool for educational institutions. This study uses Random Forest to forecast graduates' job specialties. The study employed 360 records of IT graduates and the related grades from their technical/major subjects. The conducted tracer research was used to determine the graduates' present occupations. The model made use of 16 attributes in total. An accuracy rate of 87.19% was obtained from the prediction, which is quite good. The collected data also revealed that while students who perform fairly well in most of the major topics may have another relevant vocation, those who achieve very well will likely obtain a job as a developer.

关键词： Career Prediction classification algorithm Learning Analytics Prediction Random Forest

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Polyadic Quantum Classifier

Polyadic Quantum Classifier

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IEEE International Conference on Quantum Computing and Engineering (QCE)

作者： Cappelletti, William Erbanni, Rebecca Keller, Joaquin Entrop Labs Singapore Singapore

ISBN: (纸本)9781728189697

We introduce here a supervised quantum machine learning algorithm for multi-class classification on NISQ architectures. A parametric quantum circuit is trained to output a specific bit string corresponding to the class of the input datapoint. We train and test it on an IBMq 5-qubit quantum computer and the algorithm shows good accuracy -compared to a classical machine learning model- for ternary classification of the Iris dataset and an extension of the XOR problem. Furthermore, we evaluate with simulations how the algorithm fares for a binary and a quaternary classification on resp. a known binary dataset and a synthetic dataset.

关键词： quantum machine learning variational quantum algorithm NISQ architecture classification algorithm supervised learning

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A load balance service based on probabilistic neural network

A load balance service based on probabilistic neural network

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International Conference on Machine Learning and Cybernetics

作者： Jia, Y Sun, JZ Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)0780378652

For widely supporting the group communication in the WAN environment, client/server cluster architecture is introduced in our protocol. In order to balance the loads on servers, a novel method for characterizing the workload is presented to replace the traditional workload descriptor - the CPU queue length. The combination of the CPU queue length and other five workload indices are used to determine the load levels. A modified Probabilistic Neural Network is adopted to classify the server load states into six types with the index combination as the input vector. The experiment proves that the shorter mean response time can be obtained in the new method. Based on it, load balancing policy is executed in the system to balance loads on servers.

关键词： classification algorithm probabilistic neural network workload description load balance

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Head-Mounted Sensory Augmentation Device: Comparing Haptic and Audio Modality 5th

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5th International Conference on Biomimetic and Biohybrid Systems (Living Machines)

作者： Kerdegari, Hamideh Kim, Yeongmi Prescott, Tony J. Univ Sheffield Sheffield Robot Sheffield S Yorkshire England MCI Dept Mech Innsbruck Austria

ISBN: (纸本)9783319424170;9783319424163

This paper investigates and compares the effectiveness of haptic and audio modality for navigation in low visibility environment using a sensory augmentation device. A second generation head-mounted vibrotactile interface as a sensory augmentation prototype was developed to help users to navigate in such environments. In our experiment, a subject navigates along a wall relying on the haptic or audio feedbacks as navigation commands. Haptic/audio feedback is presented to the subjects according to the information measured from the walls to a set of 12 ultrasound sensors placed around a helmet and a classification algorithm by using multilayer perceptron neural network. Results showed the haptic modality leads to significantly lower route deviation in navigation compared to auditory feedback. Furthermore, the NASA TLX questionnaire showed that subjects reported lower cognitive workload with haptic modality although both modalities were able to navigate the users along the wall.

关键词： Sensory augmentation Haptic feedback Audio feedback classification algorithm

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Three-Fold Cross-Validation of Parkinsonian Brain Patterns

Three-Fold Cross-Validation of Parkinsonian Brain Patterns

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Spetsieris, Phoebe G. Dhawan, Vijay Eidelberg, David N Shore LIJ Hlth Syst Ctr Neurosci Feinstein Inst Med Res Manhasset NY 11030 USA

ISBN: (纸本)9781424441242

Abnormal physiological networks of brain areas in disease can be identified by applying specialized multivariate computational algorithms based on principal component analysis to functional image data. Here we demonstrate the reproducibility of network patterns derived using positron emission tomography (PET) data in independent populations of parkinsonian patients for a large, clinically validated data set comprised of subjects with idiopathic Parkinson's disease (iPD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). Correlation of voxel values of network patterns derived for the same condition in different data sets was high. To further illustrate the validity of these networks, we performed single subject differential diagnosis of prospective test subjects to determine the most probable case based on a subject's network scores expressed for each of these distinct parkinsonian syndromes. Three-fold cross-validation was performed to determine accuracy and positive predictive rates based on networks derived in separate folds of the composite data set. A logistic regression based classification algorithm was used to train in each fold and test in the remaining two folds. Combined accuracy for each of the three folds ranged from 82% to 93% in the training set and was approximately 81% for prospective test subjects.

关键词： abnormal physiological networks brain classification algorithm diseases idiopathic Parkinson disease image classification logistic regression medical image processing multiple system atrophy network pattern voxel values neural nets neurophysiology parkinsonian brain patterns parkinsonian syndromes positron emission tomography positron emission tomography progressive supranuclear palsy regression analysis single subject differential diagnosis

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On the enumeration and classification of σ-LCD codes over finite commutative chain rings

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DISCRETE MATHEMATICS 2022年第8期345卷

作者： Yadav, Monika Sharma, Anuradha IIIT Delhi Dept Math New Delhi 110020 India

Let R-e denote a finite commutative chain ring with the maximal ideal (u) and the nilpotency index e, and let (R) over bar (e) = R-e/(u) be the residue field of R-e. Let sigma(0) be an automorphism of R-e, and let (sigma) over bar (0) be the corresponding automorphism of the residue field (R) over bar (e) of R-e, defined as sigma(0)(a + (u)) = sigma(0)(a) + (u) for all a + (u) is an element of(R) over bar (e). Let sigma be an automorphism of R-e(n) corresponding to the automorphism sigma(0) of R-e, defined as sigma (v(1), v(2), . . . , v(n)) = (sigma(0)(v(1)), sigma(0)(v(2)), . . . , sigma(0)(v(n))) for all (v(1), v(2), ... , v(n)) is an element of R-e(n). In this paper, we obtain explicit enumeration formulae for all sigma-LCD codes of an arbitrary length over the chain ring R-e when (sigma) over bar (2)(0) is the identity automorphism of (R) over bar (e). With the help of these enumeration formulae and by applying the classification algorithm, we classify all Euclidean LCD codes of lengths 2, 3, 4 and 5 over the chain ring F-2[u]/(u(2)) and of lengths 2, 3 and 4 over the chain ring F-3[u]/(u(2)), and all sigma-LCD codes of lengths 2, 3 and 4 over the chain ring F-4[u]/(u(2)), where sigma(0) is an automorphism of F-4[u]/((u2)) such that the corresponding automorphism sigma 0 of the residue field F4 has order 2. Besides this, we show that the class of sigma-LCD codes over Re is asymptotically good, and that every free linear [n, k, d]-code over Re is equivalent to a sigma-LCD [n, k, d]-code over Re when |(R) over bar (e)| > 4. We also explicitly determine all inequivalent sigma-LCD [n, 1, d]-codes and [n, n - 1, d]-codes over R-e for 1 <= d <= n. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Galois rings classification algorithm Witt decomposition

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Feature Selection and Comparison of Machine Learning algorithms in classification of Grazing and Rumination Behaviour in Sheep

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SENSORS 2018年第10期18卷 3532-3532页

作者： Mansbridge, Nicola Mitsch, Jurgen Bollard, Nicola Ellis, Keith Miguel-Pacheco, Giuliana G. Dottorini, Tania Kaler, Jasmeet Univ Nottingham Sch Vet Med & Sci Sutton Bonington Campus Loughborough LE12 5RD Leics England Univ Nottingham Sch Comp Sci Jubilee Campus Nottingham NG8 1BB England Univ Nottingham Adv Data Anal Ctr Nottingham NG8 1BB England Intel Labs Internet Things Syst Res Leixlip W23 CX68 Ireland

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy;this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

关键词： sheep behaviour grazing rumination behaviour classification algorithm accelerometer and gyroscope sensor machine learning precision livestock monitoring

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Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning

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BRIEFINGS IN BIOINFORMATICS 2021年第5期22卷 bbab097-bbab097页

作者： Qu, Chiwen Zhang, Lupeng Li, Jinlong Deng, Fang Tang, Yifan Zeng, Xiaomin Peng, Xiaoning Hunan Normal Univ Sch Med Dept Pathol & Pathophysiol Changsha 410081 Peoples R China Hunan Normal Univ Coll Math & Stat Changsha Peoples R China Baise Univ Sch Informat Engn Baise Peoples R China Jishou Univ Sch Med Dept Pathol & Pathophysiol Jishou Peoples R China Cent South Univ Dept Epidemiol & Hlth Stat Xiangya Publ Hlth Sch Changsha Peoples R China

Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discriminative feature genes, and existing methods fail to deal with this problem efficiently. This article proposes a novel metaheuristic approach for gene feature extraction, called variable neighborhood learning Harris Hawks optimizer (VNLHHO). First, the F-score is used for a primary selection of the genes in gene expression data to narrow down the selection range of the feature genes. Subsequently, a variable neighborhood learning strategy is constructed to balance the global exploration and local exploitation of the Harris Hawks optimization. Finally, mutation operations are employed to increase the diversity of the population, so as to prevent the algorithm from falling into a local optimum. In addition, a novel activation function is used to convert the continuous solution of the VNLHHO into binary values, and a naive Bayesian classifier is utilized as a fitness function to select feature genes that can help classify biological tissues of binary and multi-class cancers. An experiment is conducted on gene expression profile data of eight types of tumors. The results show that the classification accuracy of the VNLHHO is greater than 96.128% for tumors in the colon, nervous system and lungs and 100% for the rest. We compare seven other algorithms and demonstrate the superiority of the VNLHHO in terms of the classification accuracy, fitness value and AUC value in feature selection for gene expression data.

关键词： gene feature selection variable neighborhood learning Harris Hawks algorithm classification algorithm

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