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Pole-2p: Improved consensus algorithm based on Proof of learning 2

Pole-2p: Improved consensus algorithm based on Proof of Lear...

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2nd international conference on Digital signal and Computer Communications, DSCC 2022

作者： Zhang, Biying Zhang, Bowen Sun, Jianliang School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China Heilongjiang College of Business and Technology Harbin150025 China

ISBN: (纸本)9781510656734

PoW(Proof of Work) consensus algorithm has the advantages of simple algorithm, easy to use, allowing 50% of the nodes in the whole network to fail, but it still has some problems, such as low efficiency, mining waste a lot of resources. PoUW(Proof of Useful Work) based on PoW uses the wasted resources of PoW to carry out meaningful calculation, while PoLe(Proof of learning) uses the wasted resources to train the machine learning model. However, it has some problems, such as unreasonable reward mechanism, only one model training, failed to exert all the computing power of nodes. Therefore, the PoLe consensus algorithm is implemented and improved on the consortium blockchain in this paper. Based on the limited access characteristics of the consortium blockchain, some security is sacrificed in exchange for the increase of block speed. © 2022 SPIE.

关键词： Blockchain

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Teat and Udder Disease Detection on Cattle using machine learning 2

Teat and Udder Disease Detection on Cattle using Machine Lea...

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2nd international conference on signal and Information processing, IConSIP 2022

作者： Srivalli, M.R. Vishnu, N.K. Kanchana, V. Amrita School of Arts and Sciences Amrita Vishwa Vidyapeetham Mysuru Campus Department of Computer Science Mysuru India

ISBN: (纸本)9781728168852

In high milk yielding cattle udder and teat diseases are common. Due to this the farmers are facing more problem in production and quality of milk. In agriculture, research of automatic cow disease is essential in monitoring large population of cows, and If symptoms appear on the udder or teat, the disease should be automatically detected immediately. There is a need for automatic system where the Cow Teat and Udder diseases are detected, the effect of disease on milk yield and what medicines should be used for its cure. The aim of this work is to solve the problem of detecting and preventing diseases of different breed of cows. To determine the optimal architecture for deep learning, several models are considered. As a source of the training data, the cow teat and udder images of cow in villages are used. For this approach automatic classifier Convolutional Neural Networks (CNN) model will be used for training the model based on learning with some training samples. The developed model is then considered for classification using Support Vector machine (SVM) deployed as a web Application which can detect 3 types of diseases among cows viz. Mastitis, Udder score and Frozen teats also classifying healthy cows among the datasets. Validation Accuracy Results show that the system can show the accuracy 93.50 % and Training Accuracy is approximately 97.87% © 2022 IEEE.

关键词： Convolutional neural networks

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Sign Language Recognition using Spiking Neural Networks 2

Sign Language Recognition using Spiking Neural Networks

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2nd international conference on machine learning and Data Engineering, ICMLDE 2023

作者： Chaudhari, Pranav Vicente-Sola, Alex Basu, Amlan Manna, Davide L. Kirkland, Paul Caterina, Gaetano Di Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow United Kingdom

In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages. © 2024 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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Pedestrian Detection in Thermal Images by Enhancing the Brightness of Pedestrians 2

Pedestrian Detection in Thermal Images by Enhancing the Brig...

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2nd IEEE international conference on Information Communication and Software Engineering (ICICSE) / 7th international conference on Mathematics and Artificial Intelligence (ICMAI)

作者： Cui, Han Wu, Kewei Zhu, Xiaoping Wang, Haiying Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Zhuoshizhitong Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781665482202

Thermal image, which is not affected by visibility, has attracted significant attention in pedestrian detection. However, thermal images have poorer image quality and lack color and texture features compared with RGB images. Furthermore, pedestrian detectors based on deep learning often rely heavily on feature extraction networks. As a result, the performance of the detectors tends to decrease when directly applied to thermal images. To solve this problem, we design a pre-processing network to fully use the feature that pedestrians have higher brightness in thermal images. The pre-processing network can enhance the brightness of pedestrians in thermal images. Then we filter out the brightest area from the processed image by increasing the contrast, and input the filtered result into the detector together with the original image to help the detector find pedestrians. In addition, we found that an overly complex feature extraction network is redundant for thermal images and will have a negative impact. On this basis, we simplify the feature extraction network of YOLOv3. After simplification, the accuracy and running speed are improved, and the memory usage of the model is reduced. Through sufficient experiments on the KAIST dataset, it is proved that our method can significantly improve the performance of pedestrian detection.

关键词： thermal pedestrian detection feature extraction pre-processing network simplification

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SEGR and SEB Analysis of SJVDMOS using SiO2/Si3N4 as Gate Dielectric with Buffer layer

SEGR and SEB Analysis of SJVDMOS using SiO2/Si3N4 as Gate Di...

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international conference on the Paradigm Shifts in Communication, Embedded Systems, machine learning and signal processing (PCEMS)

作者： Shivam Singh Sanjeev Ranjan Alok Naugarhiya Electronics & Comm. Engg. National Institute of Technology Raipur India

This work presents SJ-VDMOS with buffer and gate stacking of silicon nitride. The proposed engineering (Sio2/Si 3 N 4 gate stack) improves the Si/Sio2 interface Electric field nearly by 42%. In addition, buffer layer in between the epitaxial layer and $\mathrm{N}^{+}$ substrate layer protects the device from the parasitic components during irradiation. The proposed device with both the engineering techniques improves overall pre/post radiation performances. These techniques also show better susceptibility against the SEGR and SEB.

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machine learning Approach for Estimation of High Acquity ECG Monitoring System 2

Machine Learning Approach for Estimation of High Acquity ECG...

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2nd international conference on signal and Information processing, IConSIP 2022

作者： Nikhare, Shiwani Chaskar, Uttam Misar, Surabhi College of Engineering Department of Instrumentation & Control Engineering Pune India

ISBN: (纸本)9781728168852

Electrocardiogram (ECGs) are simple, painless tests that are used to measure the electrical activity of the heart. Various cardiovascular conditions are early diagnosed using this test. It is also useful for detecting heart-related disorders such as arrhythmia, and heart valve complications. In this paper, a threelead monitoring system is designed using Arduino UNO and an ECG sensor AD8232. The ECG signals of a patient are collected by the AD8232 ECG sensor. Then acquired signals are further processed by an Arduino Uno, which digitizes the collected data and displays the continuous ECG on an OLED screen to make the system portable. These OLEDs are used to obtain, process, and display the amplitudes and intervals of some critical components. We approach a method to estimate P, QRS Complex, and T amplitudes as well as time durations (PR, RR, QRS, and QT). It is used to diagnose the heart condition, whether it functions normal or abnormal. In order to classify the data samples, supervised machine learning algorithms namely, Support Vector machines(SVM) and Logistic Regression are applied, resulting in an accuracy of 92.5 % and 87.5 %, respectively. © 2022 IEEE.

关键词： Electrocardiograms

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Bengali Text Classification Based on Probability Measure 1

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2nd international conference on signal and Data processing, ICSDP 2022

作者： Ghosh, Soumya Sankar Mal, Sandip SASL VIT Bhopal University Bhopal India SCSE VIT Bhopal University Bhopal India

ISBN: (数字)9789819914104

ISBN: (纸本)9789819914098

Text is divided into categories based on its relevance. Understanding the meaning of a text frequently requires a subjective component. Linguistic advances, on the other hand, can reveal vital details about what drives text categorization in general and topic identification in particular. Theoretical underpinnings from formal linguistics and systemic functional linguistics, in particular, may provide vital insights into how difficulties can be addressed. Classification of a text is the most popular topic in recent research on the English language. Several powerful machine learning techniques are used in the field of Natural Language processing or Computational Linguistics to classify a text. This paper describes a system to classify one text based on probability measures. Data with four different types of news (political, sports, entertainment, and a mix of these three) has been collected from a popular Bengali newspaper Anandabazar Patrika. The model is tested using Bengali news but it can be used to classify a text of any language. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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proceedings of 2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

Proceedings of 2nd International Conference on Innovations i...

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2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

ISBN: (纸本)9781728107448

The proceedings contain 60 papers. The topics discussed include: modeling a H-H neuron based spiking neural network incorporating multiple pre-synaptic inputs;design of memristor - CMOS based logic gates and logic circuits;voltage controlled stimulator for nerve conduction study;a review on artifacts removal techniques for electroencephalogram signals;a non-intrusive opto-mechanical technique for the measurement of liquid level;and improved shuf?ed frog leaping algorithm for path planning of multiple mobile-robot.

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2nd international conference on Electronic Systems and Intelligent Computing, ESIC 2021

2nd International Conference on Electronic Systems and Intel...

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2nd international conference on Electronic Systems and Intelligent Computing, ESIC 2021

ISBN: (纸本)9789811694875

The proceedings contain 73 papers. The special focus in this conference is on Electronic Systems and Intelligent Computing. The topics include: Fake News Predictor: A Random Forest-Based Web Application for the Prediction of Fake News on Social Media;a New Single-Phase Asymmetrical Multilevel Inverter Topology with Reduced Switching Devices for Different Pulse Width Modulation Techniques;study of Filters for Improving the Output of Cascaded Seven-Level Inverter;a Design of Fork-Shaped Wearable Printed Antenna for Wireless Applications;performance Improvement of S-shaped for Wireless Communication;component-Wise Scrutiny of Existing Rule-Based Punjabi Grammar System and Implication for Accuracy Determination;role of Innovation in Education and Development;validation of Adaptive Neuro-Fuzzy Technique for Performance Analysis of Induction machine;detection of Stress with Deep learning and Health Parameters Monitoring Using Raspberry Pi;energy-Efficient Automated Guided Vehicle for Warehouse Management System;a Crow Search Algorithm-Based machine learning Model for Heart Disease and Cervical Cancer Diagnosis;Design of an Automated Stethoscope Using AI, IoT and signal processing;maximum Likelihood Estimation for Bangla–Odia Word Alignment;performance Analysis of Video Streaming Methods;intelligent Blockchain Services for Smart City Economy Using Emotion Analysis;decision-Making in Fuzzy Environment;dealing with Imbalanced Data;physical Design of a Multibit D FlipFlop-Based Linear Feedback Shift Register;queuing Theory-Based Analysis of Berth Allocation and Management in Paradip Port for Container Ships;A Deep learning Model for Classification of COVID-19 Related Fake News;a Relative Study on machine learning-Based Algorithm Used to Develop Recommender Systems;secure Watermarking to Protect Colour Images on Social Media from Misuse.

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Graph Unseen Node Embedding learning Network for Drug Target Affinity Prediction 17

Graph Unseen Node Embedding Learning Network for Drug Target...

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17th IEEE international conference on signal processing, ICSP 2024

作者： Xu, Xiaoqian Jiang, Wenzong Liu, Weifeng Liu, Baodi Wang, Yanjiang College of Control Science and Engineering Qing Dao China

ISBN: (纸本)9798350387384

The procedure of drug development is both financially burdensome and time-demanding. In order to accelerate the pace of drug discovery and development, it is worthwhile to understand how closely a drug binds to a specific target, known as drug-target binding affinity (DTA). Traditional experimental approaches to determine drug-target binding affinity usually require a considerable amount of time, so treating the task of combining target affinity with drug confirmation as a regression task captivates people's interest. As machine learning progresses, the utilization of deep models for the prediction of drug-target binding affinity is becoming more prevalentand, people take advantage of deep learning frameworks to learn representation of ligand chemical compounds and protein sequences. Recent methods use graph structures to represent drug molecules and then use graph convolutional networks for characteristic encoding. Traditional graph convolutional networks require global structural information when training embeddings and cannot naturally generalize to unseen nodes. In an effort to find solutions to the aforementioned issues, we proposed the graph unseen node embedding learning network for drug target affinity prediction (GUNE-DTA), using SAGEConv, an improved network of graph convolution network, to perform representation learning on drugs. SAGEConv introduces a framework GraphSAGE, which proposes an unsupervised node representation learning method that can use node attribute data to effectively produce node embeddings for previously unknown data, and obtain node embeddings through neighbor sampling and aggregation. It has good scalability and cross-network generalization capabilities. We study the model's performance on two benchmark datasets. The findings suggest that GUNE-DTA surpasses established test domain models in terms of all performance metrics. © 2024 IEEE.

关键词： Drug target affinity graph convolutional network unseen node embedding

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