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Daphnet Freezing Recognition with Gait Data by Using machine learning Algorithms

Daphnet Freezing Recognition with Gait Data by Using Machine...

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international conference on Telecommunications and signal processing (TSP)

作者： selda GÜNEY Büşra BÖLÜK Electrical-Electronics Engineering Faculty of Engineering Başkent Üniversity Ankara Türkiye

ISBN: (数字)9781728163765

ISBN: (纸本)9781728163772

The aim of this study was to test the success of the data set obtained by a wearable health assistant developed for the symptom of freezing (FOG) in gait of Parkinson's patients and to increase the success of the system. The system was tested with different machine learning methods to measure the success of the wearable health assistant system. For all patients (ten patients), the highest success value was obtained and the mean sensitivity and specificity values of the system were calculated and compared with the results obtained in the literature review. In the literature, mean sensitivity and specificity were 73.1% and 81.6%, respectively; In this study, mean sensitivity and specificity were 91.9% and 71.14%, respectively. In order to better analyze the success of the system, two patients with successful and unsuccessful results were selected for the data set in line with the results obtained in the literature review. The success of the system was tested by using different machine learning methods on the data sets of two patients. Finally, the successes obtained by feature extraction methods were tried to be increased. Among the different machine learning methods on the data sets used for patient 8 and patient 3, the most successful method was obtained by combining the models (ensemble). The highest achievement value obtained by attribute extraction methods was obtained when PCA was applied. However, the success value obtained with raw data could not be increased. All results are tabulated and presented.

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Demystifying the Rumbling Noise Generated from the Elevator machine Room

Demystifying the Rumbling Noise Generated from the Elevator ...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Alex Shi Shanghai High School International Division Shanghai China

ISBN: (数字)9781728188232

ISBN: (纸本)9781728188249

When an elevator is operating, there is often a cacophonous rumbling sound emanating from its machine room. Commonly the elevator companies claim no responsibility, citing that the overall sound pressure level (SPL) is legally compliant. However, clearly, SPL alone cannot fully represent the perceivable sound quality of the noise from the machine room. A smart phone has been used to record this noise, and the audio clip has been converted into time-domain signals. These digitalized signals have then been comprehensively analyzed by tools such as, but not limited to Fast Fourier Transformation (FFT), Short-time Fourier Transformation (STFT), and Wavelet functions. This visualized noise analysis in turn points to the fact that it is the tonality and fluctuations in the noise signal that make the acoustic noise of elevator machine so jarring to the ears despite its acceptable SPL level, prompting the negative psychoacoustic reactions of the residents. The resulting analysis demystifies the noise and has convinced elevator companies to replace their machines, paving a new path to be used for victims globally suffering from discomforting elevator noise. Furthermore, elevator companies should use similar analysis to improve its designs and manufacturing so as to improve overall perceived sound quality.

关键词： Elevators Time-frequency analysis Fluctuations Continuous wavelet transforms Smart phones Companies Buildings

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Classification of Durian Fruits based on Ripening with machine learning Techniques

Classification of Durian Fruits based on Ripening with Machi...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： Muthulakshmi. A P. N. Renjith Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Eating fruits is good for health and an excellent source of essential vitamins and minerals. But fruits are unfit for intake if they are not fully ripened. Ripening of fruits is an important phase in Pomology based on which the fruits are categorized. In this context, various machine learning techniques were proposed to detect the classification of fruits. This paper provides a special architecture to identify the features of different classes of Durian fruits. Durian, popularly given the name, "king of fruits", is one of the rarest and sweetest fruits grown in South Asia. The model of image processing helps to divide the classification into two parts of the feature extraction part and classification part of the fruit used. In this article, the system is evaluated using the datasets of the Durian Fruit. The datasets is divided into two parts as training and testing. By applying the Edge Detection and color extraction the features of the durian are properly measured. Finally, the performance is measured using Non-Destructive machine learning techniques such as SVM, GNB, Random Forest. The results obtained provide the best accuracy of 89.3 % using the SVM technique and 84.3% using Random Forest.

关键词： Image color analysis Support vector machines Image edge detection Feature extraction Classification algorithms machine learning algorithms Random forests

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Named Entity Recognition for Russian Historical Texts 2019

Named Entity Recognition for Russian Historical Texts

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Proceedings of the 2019 3rd international conference on Computer Science and Artificial Intelligence

作者： Evgenia Bogacheva Antonina Puchkovskaia Ivan Smetannikov ITMO University St.Petersburg Russia

ISBN: (纸本)9781450376273

With the raise of big data, machine learning and crowdsourcing, the volume of existing datasets for different machine learning problems have greatly increased. The natural language processing field is not an exception; so, as a result, most of the researches have transitioned into investigating and applying different deep architectures for it. One of the main issues of this trend is as follows: it is hard to adopt such approaches for somewhat poorly studied languages, which do not have training data enough as for natural language processing perspective. In this paper, we investigate some modern approaches to named entity recognition as for Russian language and show that for historical texts their results are much lower than for general ones. In addition, we propose our own algorithm that improves the results of for these historical texts.

关键词： named entity recognition Russian language historical texts Natural language processing

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Classifying Alcoholics and Control Patients Using Deep learning and Peak Visualization Method 2019

Classifying Alcoholics and Control Patients Using Deep Learn...

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Proceedings of the 3rd international conference on Vision, Image and signal processing

作者： Asim Fayyaz Muaz Maqbool Mehreen Saeed FAST NUCES Lahore Pakistan

ISBN: (纸本)9781450376259

A lot of advancements have been made in the field of Brain Computer Interfaces (BCI) using machine and deep learning. This paper presents a novel preprocessing technique to process Electroencephalography (EEG) signals in time domain. The proposed methodology, (Peak Visualization Method) (PVM) is based on selecting peaks with distinctive width and height range in order to perform better classification. PVM uses multiple machine learning techniques such as Random Forest, Logistic Regression and Support Vector machine (SVM), Naive Bayes in order to find the most discriminating ranges. Moreover, selected range peaks are further used to compute features like indices of peaks, prominence of peak, contour heights, relative maxima, relative minima, local maxima and local minima. The extracted features were used as training and test data for a competitive 5-fold cross-validated analysis with Long Short Term Memory (LSTM) network. A publicly available EEG dataset for alcoholic and non-alcoholic classification was used to compare the proposed technique with state of the art EEG-NET deep learning model. In order to visualize the generalized performance of proposed system we use award winning dimensionality reduction technique t-Distributed Stochastic Neighbor Embedding (t-SNE) on the features extracted by EEGLSTM and show how our model's activations are classified in alcoholic and non-alcoholic categories. The reduced features are visualized into two *** extracted using PVM gives an average accuracy of 90% on 5 folds beating the current state of the art EEG-NET, which manages to achieve an average accuracy of 88% on this dataset.

关键词： EEG classification preprocessing technique signal processing TSNE visualization deep neural networks

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Investigating the Use of Wearables for Monitoring Circadian Rhythms: A Feasibility Study 4th

Investigating the Use of Wearables for Monitoring Circadian ...

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international conference on Biomedical and Health Informatics (ICBHI)

作者： Castaldo, Rossana Prati, Marta Montesinos, Luis Kulkarni, Vishwesh Chappell, Micheal Byrne, Helen Innominato, Pasquale Hughes, Stephen Pecchia, Leandro Univ Warwick Sch Engn Lib Rd Coventry W Midlands England Univ Warwick Inst Adv Studies Coventry W Midlands England Univ Roma Sapienza Ingn Informaz Informat & Stat Rome Italy Univ Oxford Math Inst Oxford England Univ Warwick Warwick Med Sch Coventry W Midlands England North Wales Clin Res Ctr Wrexham Wales

ISBN: (纸本)9783030306366;9783030306359

Circadian rhythms are physiological and behavioural processes that typically recur over 24-h periods. Researchers show that circadian disruption, a marked break in normal 24-h cycles of circadian rhythms, can cause serious health problems. It could lead to critical illness, cancer, stress, myocardial infarction, diabetes, hypertension and arrhythmias. Today, circadian rhythms are monitored using blood, salivary and urine hormone tests, such tests are not practical at home and do not provide continuous real-time monitoring. Combining signal processing and artificial intelligence with commercial sensors embedded in smartwatches or clothes that measure physiological and behavioral attributes offers unprecedented and as yet unexplored opportunities to monitor circadian rhythms in real time. This paper presents the initial steps towards the development of a model for real-time monitoring of the circadian rhythms. This model will contribute to transform medicine from primarily intervention-focused to predictive and preventative. Preliminary analysis shows promising results to automatically classify cortisol levels as high or low, based on behavioral and physiological signals monitored by non-invasive wearable sensors.

关键词： Circadian rhythms Biomedical signals Wearable sensors machine learning

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Image Noise Estimation with Using Pre-trained Conventional Neural Network

Image Noise Estimation with Using Pre-trained Conventional N...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Lianming Hu Hajim School of Engineering and Applied Sciences University of Rochester Rochester US

ISBN: (数字)9781728198279

ISBN: (纸本)9781728181585

Noise level estimation becomes a necessary step when the noise level of the white Gaussian noise images is unknown in a realistic situation. machine learning has brought out a series of high-performance convolutional neural networks (CNN) that work well in noise level estimation. However, studies on the pre-trained convolutional neural network are still lacking. As a result, a model based on a pre-trained Visual Geometry Group-16 network with additional regression layers was created in this research. It was then trained with a dataset that contained a series of images with different levels of additive white Gaussian noise. The results showed high performances in all ranges of the noise levels and were relatively competitive among some other algorithms. In all, a pre-trained convolutional network can estimate the noise level as accurately as some classic algorithms, and it even performed better in the high noise level range. The whole processing would be far simpler, compared to the traditional method, since CNN does not require setting parameters once the training is done.

关键词： Noise level Estimation AWGN Mathematical model Training signal processing algorithms Neural networks

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Review of computational approaches to Parkinson’s disease gene prediction

Review of computational approaches to Parkinson’s disease g...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： RHEA MARY JOSI R. I. MINU SRM INSTITUTE OF SCIENCE AND TECHNOLOGY CHENNAI INDIA

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Parkinson's disease is a chronic neuro-degenerative disease that affects the central nervous system. Since the causes of the disease are still unknown, both genetic and environmental factors are believed to be involved. Therefore, the identification and prediction of this genetic disorder plays an important role in early detection and treatment. The genomic data is extremely large and distorted, so the use of R tools can make the analysis and pre- processing of data easier. There are several computational approaches to the prediction of this disorder which varies with its accuracy and performance.

关键词： Diseases Prediction algorithms Support vector machines Genetics Classification algorithms Cancer machine learning algorithms

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SPAR-2: A SIMD Processor Array for machine learning in IoT Devices

SPAR-2: A SIMD Processor Array for Machine Learning in IoT D...

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international conference on Data Intelligence and Security (ICDIS)

作者： Suhail Basalama Atiyehsadat Panahi Ange-Thierry Ishimwe David Andrews University of Arkansas Fayetteville Arkansas

ISBN: (数字)9781728193793

ISBN: (纸本)9781728193809

In this paper, SPAR-2, a SIMD processor array for migrating machine learning applications into FPGA IoT edge devices is presented. SPAR-2 is a second-generation processorin/ near-memory architecture developed as a programmable overlay for modern FPGAs. In contrast to point designs, SPAR-2 can be programmed to implement different classes of machine learning algorithms. The overlay architecture is based on Processor-In-Memory (PIM) tiles, which integrate bit-serial ALUs with distributed Block RAMs (BRAMs). Forming PIM tiles increases the size of the multiply-accumulate array and on-chip storage capacity. User-visible inference latencies are reduced by exploiting concurrent accesses to weights and partial results stored throughout the distributed BRAMs. The sizing and performance analysis of SPAR-2 running a standard Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model is provided. Results show that our approach enables packing up to 16,384 processing elements in a Virtex-7 FPGA. Runtime performance comparisons show that SPAR-2 achieves up to 24.51x speedup compared to a High-Level Synthesis (HLS) equivalent design and 1.75x speedup over a previous custom-tuned design.

关键词： Field programmable gate arrays Registers Parallel processing Computer architecture Random access memory Standards Arrays

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Metro Pedestrian Detection Based on Mask R-CNN and Spatial-temporal Feature

Metro Pedestrian Detection Based on Mask R-CNN and Spatial-t...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Guochen Shen Faezeh Jamshidi Decun Dong Rei ZhG Department of Traffic Information and Control Engineering Tongji University Shanghai China MOE International Joint Lab of Trustworthy Software East China Normal University Shanghai China Department of Traffic Information and Control Engineering Tongji University China Country

ISBN: (数字)9781728188232

ISBN: (纸本)9781728188249

In this paper, we introduce the Mask R-CNN, an object detection method based on deep learning networks, to detect the number of pedestrians from surveillance video in the metro train carriage and on the metro station platform, and introduce the fusion of the multi-frame processing result to reduce the detection error. In order to apply and analyze the detection result, we establish a spatial-temporal model of the number of pedestrians in the carriage and on the platform. The experiment shows the efficient result of our method. The average accuracy of the single-frame detection is 73.43%. By fusing the detection result of frames in time series, the average accuracy is 88.85%, which increases 21%. The data of pedestrians' numbers produced by our method can be helpful for metro management, pedestrian guidance, emergency management and so on.

关键词： Feature extraction Surveillance Safety Object detection Public transportation Control engineering Urban areas

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