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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

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machine learning Methods Analysis For Calories Measuremnt of Fruits and Vegetables

Machine Learning Methods Analysis For Calories Measuremnt of...

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IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Manisha Mittal Gittaly Dhingra Vinay Kumar Thapar Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781728139890

As people around the globe are becoming conscious about their weight, consume healthy and low-calorie food and keep away from obesity, it's an urge to establish a reliable system with high accuracy and efficiency for calorie and nutrition measurement in fruit/vegetable. the proposed model is developed to assist patients and dieticians to compute daily intake of calories. In this approach, 5 different machine learning models are used to predict classification accuracy. Our system includes camera and intelligent mat to capture the picture of the fruit/vegetable, in order to calculate the consumption of calorie. the proposed model achieves 88% accuracy with different testing-training cross validation dataset.

关键词： food products learning (artificial intelligence) medical computing pattern classification medical computing Calorific value Pattern recognition Food low energy food Nutritionists Artificial Intelligence machine learning Trustworthy System Vegetable Consciousness Fruits and vegetables Globe

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A Review on Automatic signal Classification Techniques for Software Defined Radios

A Review on Automatic Signal Classification Techniques for S...

引用

IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Ghunsar Manisha Marriwala Nikhil University Institute of Engineering and Technology Kurukshetra University Kurukshetra India

ISBN: (纸本)9781728139890

In today's generation automatic modulation recognition for software defined radio (SDR) is a hot topic of research. their ability like re-configurability, flexibility, high capacity, improved bandwidth efficiency, rapid up-gradation of technology etc., has made it popular and widely acceptable. this paper represents a review about SDR infrastructure, modulation recognition problem and various existing modulation recognition techniques. this paper summarizes the work and contribution of various researchers in this field. this paper also holds discussion about various machine learning tools and their advantages and disadvantages. this paper helps the reader to have brief knowledge about development of SDR and automatic modulation recognition technique.

关键词： learning (artificial intelligence) signal classification software radio telecommunication computing Software radio telecommunication computing signal classification SDPR gene automatic signal bandwidth efficiency Recognition (Psychology)

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learning from Software defect datasets

Learning from Software defect datasets

引用

IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Pradeep Singh NIT Raipur Chhattisgarh India

ISBN: (纸本)9781728139890

Development of high-quality software is very much essential now-a-days. Prediction of good quality software in the early phase during the development reduces the cost of the testing resources. Various data mining and machine learning algorithms are developed to predict the quality of the software. In the real-life scenarios while dealing with software modules many of the time the underline datasets are imbalanced. In order to increase the efficiency of the prediction or classification algorithms balancing algorithms are implemented as a preprocessing stage before the prediction phase. In this paper we conduct extensive experiments to explore the effect of imbalance learning including undersampling, oversampling and hybrid methods and its interaction with different types of classifiers on various projects. We evaluate six imbalance learning methods with six classifiers on 12 data sets. the imbalance learning methods used are Random Oversampling (ROS), Random under-sampling, SMOTE, TOMEK, SMOTE+TOMEK, and SMOTE+ENN. this study reveals that the appropriate combination of imbalanced method and classifier can improve the accuracy of software fault prediction models.

关键词： Software Support vector machines learning systems Training conferences signal processing Classification algorithms

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Hybrid RBFN Based Encrypted SSH Traffic Classification 5

Hybrid RBFN Based Encrypted SSH Traffic Classification

引用

5th international conference on signal processing and Integrated Networks (SPIN)

作者： Pradhan, Ayush Behera, Sidharth Dash, Ratnakar Natl Inst Technol Dept Comp Sci & Engn Rourkela India

ISBN: (纸本)9781538630457

this paper presents a hybrid Radial Basis Function Network (RBFN) based machine learning approach for encrypted data traffic classification. We concentrate on classifying the SSH-based encrypted traffic from the non SSH traffic. the hybrid RBFN network is employed with k-means clustering and gradient descent learning technique for the binary classification. the suggested technique is experimented on standard datasets including AMP, MAWI, DARPAA99, and NIMS with k-fold cross-validation. Simulation results show that RBFN network is able to provide flow accuracy of more than 99% for all the datasets. Comparative analysis is made to show the efficacy of the proposed scheme with other competent techniques.

关键词： machine learning Encrypted Clustering Gradient descent Binary Classification

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Dataset of Raw and Pre-processed Speech signals, Mel Frequency Cepstral Coefficients of Speech and Heart Rate Measurements

Dataset of Raw and Pre-processed Speech Signals, Mel Frequen...

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IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Mohammed Usman Zeeshan Ahmad Mohd Wajid King Khalid University Abha Saudi Arabia ZHCET Aligarh Muslim University Aligrah India

ISBN: (纸本)9781728139890

Heart rate is an important vital sign used in the diagnosis of many medical conditions. Conventionally, heart rate is measured using a medical device such as pulse oximeter. Physiological parameters such as heart rate bear a correlation to speech characteristics of an individual. Hence, there is a possibility to measure heart rate from speech signals using machine learning and deep learning, which would also allow noninvasive, non-contact based and remote monitoring of patients. However, to design such a scheme and verify its accuracy, it is necessary to collect speech recordings along with heart rates measured using a medical device, simultaneously during the recording. this article provides a dataset as well as the procedure used to create the dataset which could be used to facilitate research in developing techniques to estimate heart rate accurately by observing speech signal.

关键词： cardiology cepstral analysis feature extraction learning (artificial intelligence) medical signal processing neural nets patient monitoring speech processing Patient Monitoring Cepstral analysis Mel frequency cepstral coefficient Heart Rate medical signal processing Dataset Speech processing Neural network Cardiology Speech Feature extraction Vital Signs Medical device immatures Heart rate measurement

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A Novel machine learning Approach to Detect Phishing Websites 5

A Novel Machine Learning Approach to Detect Phishing Website...

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5th international conference on signal processing and Integrated Networks (SPIN)

作者： Tyagi, Ishant Shad, Jatin Sharma, Shubham Gaur, Siddharth Kaur, Gagandeep Jaypee Inst Informat Technol Dept CSE&IT Noida India

ISBN: (纸本)9781538630457

Phishing can be described as a way by which someone may try to steal some personal and important information like login id's, passwords, and details of credit/debit cards, for wrong reasons, by appearing as a trusted body. Many websites, which look perfectly legitimate to us, can be phishing and could well be the reason for various online frauds. these phishing websites may try to obtain our important information through many ways, for example: phone calls, messages, and pop up windows. So, the need of the hour is to secure information that is sent online and one concrete way of doing so is by countering these phishing attacks. this paper is focused on various machine learning algorithms aimed at predicting whether a website is phishing or legitimate. machine learning solutions are able to detect zero hour phishing attacks and they are better at handling new types of phishing attacks, so they are preferred. In our implementation, we managed an accuracy of 98.4% in prediction a website to be phishing or legitimate.

关键词： phishing R machine learning algorithms

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Investigating Deep CNNs Models Applied in Kinship Verification through Facial Images

Investigating Deep CNNs Models Applied in Kinship Verificati...

引用

international conference on Frontiers of signal processing (ICFSP)

作者： Abdelhakim Chergui Salim Ouchtati Sebastien Mavromatis Salah Eddine Bekhouche Jean Sequeira Electronics Research Laboratory of Skikda (LRES) Skikda University Algeria LIS Laboratory (UMR CNRS 7020) Aix Marseille University France Center for Machine Vision and Signal Analysis University of Oulu Finland

the kinship verification through facial images is ana ctive research topic due to its potential applications. In this paper, we propose an approach which takes two images as input then give kinship result (kinship / No-kinship) as an output. our approach based on the deep learning model (ResNet) for the feature extraction step, alongside with our proposed pair feature representation function and RankFeatures (Ttest) for feature selection to reduce the number of features finally we use the SVM classifier for the decision of kinship verification. the approach contains three steps which are: (1) face preprocessing, (2) deep features extraction and pair features representation (3) Classification. Experiments are conducted on five public databases. the experimental results show that our approach is comparable with existed approaches.

关键词： Databases Feature extraction Face Deep learning Computer architecture Support vector machines Measurement

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Centroid Selection in Kernel Extreme learning machine using K-means 5

Centroid Selection in Kernel Extreme Learning Machine using ...

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5th international conference on signal processing and Integrated Networks (SPIN)

作者： Singhal, Mona Shukla, Sanyam Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal 462003 Madhya Pradesh India

ISBN: (纸本)9781538630457

Kernel Extreme learning machine (KELM) is used for classification, regression, clustering and feature selection with the help of kernel functions. Conventional KELM uses all training instances as centroids for classification problem while reduced KELM uses randomly choosen training instances as centroids. Furthermore, reduced KELM is used for reducing the computational complexity of conventional KELM. To further improve the computational complexity of KELM, K-means clustering algorithm for centroid selection in KELM is proposed in this paper. In this proposed approach, number of centroids are selected as 1/10 or 5/10 of the total number of training instances and then centroids are computed by using K-means algorithm. Experiments have been carried out by using 15 data sets to illustrate the effectiveness of the proposed method. the results obtained show the reduction in computational time and increment in G-mean which verify the proposed method as an efficient approach in comparison to earlier works.

关键词： Kernel Extreme learning machine Classification Clustering K-means Centroid Selection Radial Basis Function G-mean

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Modulation Recognition Based on IQ-eyes Diagrams and Deep learning

Modulation Recognition Based on IQ-eyes Diagrams and Deep Le...

引用

IEEE international conference on Computer and Communications

作者： Zhuolun Li Xiong Zha Nion Digi Syse Engineering nd Technoogic Reserch CenerNion Digi Syse Engineering nd Technoogic Reserch Cener

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

this article presents our initial results in deep learning for modulation recognition with the signal IQ-eyes diagrams. we design a representative convolution neural network to distinguish between eight modulation classes which shows more promising when compared to more traditional likelihood-based or feature-based techniques. Furthermore, we discuss the impact of frequency offset and influence of network parameters on recognition performance though the recognition accuracy. Moreover, the method based on multi-terminal processing can mine the intrinsic characteristics of the signal more completely, which reduces the requirement of network and makes the network more robust.

关键词： conferences Modulation Convolution Neural networks machine learning signal to noise ratio Systems architecture

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Breast Cancer Classification Using AdaBoost- Extreme learning machine

Breast Cancer Classification Using AdaBoost- Extreme Learnin...

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international conference of signal processing and Intelligent Systems (ICSPIS)

作者： Mahboobe Sharifmoghadam Hamid Jazayeriy Babol Noshirvani University of Technology Babol Iran

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

Extreme learning machine (ELM) is a new learning algorithm with single-hidden layer feed-forward neural network (SLFN). In this study, an ensemble of ELMs is used to predict breast cancers. To this end, an AdaBoost-based algorithm is proposed to adapt ELM in ensemble learning. A threshold value is defined for reweighting part of data that is misclassified. Based on the threshold-value, a tuning parameter is used. this parameter is defined as the inverse of the number of neurons in ELM's hidden-layer. then, an experiment is conducted for tuning the number of neuron in hidden-layer. the results indicate that the proposed ensemble learning can effectively improves the recall and precision of the classification.

关键词： Classification algorithms signal processing algorithms Neurons Biological neural networks Breast cancer Training Prediction algorithms

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