Heart diseases are a global leading cause of death, affecting nations universally. Early detection andmachinelearning assistance can mitigate mortality despite medical complexities. Hence, timely diagnosis is crucia...
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The proceedings contain 153 papers. The topics discussed include: designing of a handbag for women safety;a web based four-tier architecture using reduced feature based neural network approach for prediction of studen...
ISBN:
(纸本)9781665415767
The proceedings contain 153 papers. The topics discussed include: designing of a handbag for women safety;a web based four-tier architecture using reduced feature based neural network approach for prediction of student performance;voice activated portable braille with audio feedback;transient stability enhancement of power system by virtual synchronous generator control of battery;blockchain-based peer-to-peer sustainable energy trading in microgrid using smart contracts;treatment of water algae by water surface discharge plasma;nicotine sensing by photonic crystal fiber in THz regime;electronic medical record data sharing through authentication and integrity management;improved machinelearning based classification model for early autism detection;and an adaptive graph cut algorithm for spammer group detection from weighted one mode projection of bipartite graph.
Many people in the world are affected by the Alzheimer disease leading to the dysfunctionality of the hand. In one side, this symptom is not the most important of this disease and not much attention is given to this o...
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Many people in the world are affected by the Alzheimer disease leading to the dysfunctionality of the hand. In one side, this symptom is not the most important of this disease and not much attention is given to this one. In the other side, the literrature provides two main solutions such as computer vision and data glove allowing to recognize hand gestures for virtual reality or robotic applications. From this finding and need, we decided to developed our own data glove prototype allowing to monitor the evolution of the dysfunctionality of the hand by recognizing objects in basic daily activities. Our approach is simple, cheap (similar to 220$) and efficient (similar to 100% of correct predictions) considering that we are abstracting all the theory about the gesture recognition. Also, we can access directly and easily to the raw data. Finally, the proposed prototype is described in a way that researchers can reproduce it. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the conference Program Chairs.
The motive of Multimedia signalprocessing (MSP) is to integrate text, sound, image, and video information into a single communications channel, and to get high-quality communication. It also aims to provide an easy-t...
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In order to solve the problem of low classification accuracy for frequency hopping signals under low signal-to-noise ratio, a classification method of frequency hopping signal is presented. By using STFT to gain time-...
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The recognition and classification are important to the research contents like underwater acoustic signalprocessing. Generally, the state-of-the-art signal recognition systems depend on feature extraction that is bas...
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ISBN:
(纸本)9781450366069
The recognition and classification are important to the research contents like underwater acoustic signalprocessing. Generally, the state-of-the-art signal recognition systems depend on feature extraction that is based on the knowledge or experience of experts, so that, it can efficiently represent target signatures. In contrast, this paper introduces a new framework based on deep learning methods to preprocess signals and extract features for the recognition and classification of underwater acoustic signals, which present the targets of ships and torpedoes. In our framework, signals are firstly transformed to spectral images of LOFAR, so that, those images can be extracted and classified by convolutional neural networks (CNN). The experimental results show that our deep learning based framework can obtain high classification accuracy in underwater acoustic signals case with the transformation to LOFAR spectrum. The accuracy of our best version reaches 97.22%, higher than those that use other networks, and achieved the expected objectives for real applications.
This manuscript develops a framework to deliver prediction of diabetes using the Ensemble-based machinelearning techniques on Pima Indian Diabetes Dataset (PIDD). We investigate the performance measures of four disti...
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In this study, we suggested an approximation multiplier that employs an approximate 4-2 compressor and is energy-efficient. When compared to the current designs, the suggested compressor has a small area. The results ...
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Nowadays, with the birth of 5G and the Internet of Things, more and more business signals have emerged. The identification of signal service types has become a hot research topic. Whether it is suitable for daily life...
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ISBN:
(纸本)9781538692981
Nowadays, with the birth of 5G and the Internet of Things, more and more business signals have emerged. The identification of signal service types has become a hot research topic. Whether it is suitable for daily life in the military field, there is a wide application prospect. Using the power spectrum data of the wireless signal, the characteristics of the power spectrum waveform are captured to identify the type of traffic of the wireless signal. This paper proposes the use of convolutional neural networks to extract and classify the wireless signal power spectrum data. After hundreds of iterative training, it can achieve an ideal recognition effect.
Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG record...
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ISBN:
(纸本)9798331540661;9798331540678
Electroencephalography (EEG) is a well-established method in neuroscience and bioengineering that offers valuable information about brain function. Recent technological advancements have led to more complex EEG recordings, which necessitate more advanced analysis techniques. machinelearning has emerged as an asset in EEG signalprocessing, enabling novel approaches to comprehension and utilization of brain activity. This article provides an in-depth analysis of the intersection of machinelearning and EEG signalprocessing in bioengineering applications. This text presents a comprehensive overview of EEG data analysis techniques, focusing on the key steps of acquisition, preprocessing, and feature extraction. It highlights the challenges and strategies used to extract valuable information from raw EEG recordings. Additionally, it surveys various machinelearning algorithms, including classic and modern deep learning methods, demonstrating their effectiveness in analyzing EEG signals and opening new frontiers in the field of bioengineering. This paper explores the growing connection between machinelearning and EEG signalprocessing, examining how they work together to enhance healthcare, neurotechnology, and our knowledge of the brain. By studying existing research and cutting-edge developments, this study intends to focus on this synergistic relationship and its importance in these fields.
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