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High-Dimensional Signal Processing using Classical-Quantum machine learning Pipelines with the TensorFlow Stack, Cirq-NISQ, and Vertica 3

High-Dimensional Signal Processing using Classical-Quantum M...

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3rd IEEE international conference on Quantum Computing and Engineering (QCE)

作者： Melvin, Theresa Northcentral Univ Sch Technol Adv Data Sci & Maching Learning Lab Vertica Scottsdale AZ USA

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

Modeling High Dimensional Signal data (HDSD) is challenging due to statistical complexities and an imbalance of noise to signal data, which creates ground truth problems. Likewise, enabling recognition and detection analytics on the information-bearing portion of the noise-obscured HDSD is computationally cost-prohibitive due to the unprecedented size, speed, and scale of HDSD. Consequently, human experts often out-perform Deep learning (DL) algorithms at HDSD recognition. This is problematic due to the spectrum of applications reliant on accurate HDSD detection. These include multi-sensor and biologic signal detection, virus outbreak surveillance, genetic association identification, image, audio, and sonar processing, source separation for speech identification, astrophysical source detection, financial time series, and satellite payload processing. To solve this problem and facilitate favorable DL outcomes, synthetic data possessing enough positive samples is needed to balance the HDSD dataset. Research reveals an entangled Quantum Generative Adversarial Network (QGAN) utilizing parameterized quantum circuits for the QGAN Generator (QGEN) and Discriminator (QDIS) exhibit an exponential advantage over a classical-GAN when generating highly accurate and cost-effective synthetic data. Using this method, an unsupervised TensorFlow-Quantum QGAN is prepared from Cirq Noisy Intermediate-Scale Quantum (NISQ) circuits. The QGEN and QDIS create new statistically consistent synthetic HDSD from real data;thereby solving the ground truth problem. Next, TensorFlow-GPU facilitates classical-DL to train the new synthetic HDSD dataset. Once validated, the TensorFlow model is exported to the production Vertica analytics tier for fast and accurate ML inference, where the HDSD is instantly identified and classified at ingest.

关键词： TensorFlow Quantum Cirq Entangled Quantum Generative Adversarial Network Vertica machine learning High-Dimensional Signal data Processing

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Application of text mining technology in power data prediction

Application of text mining technology in power data predicti...

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CIBDA 2022;3rd international conference on Computer Information and Big data Applications

作者： Jian Guo Wu Guo

ISBN: (纸本)9783800758760

This paper analyses the main research methods of power text mining technology in detail, and discusses the research hotspots of power text named entity recognition and named entity relationship extraction based on machine learning and deep learning. On this basis, the limited Boltzmann machine is further used to predict the power data. The test results show that the analysis of power text features in this paper is accurate, can effectively analyse the key text information, and the prediction results meet the requirements.

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Towards Automated Screening via Two-Stage Deep learning: A Pipeline for Classification and Localization of Bleeding from Wireless Capsule Endoscopy Visuals 3rd

Towards Automated Screening via Two-Stage Deep Learning: A P...

引用

3rd international conference on Advanced Computing and Applications, ICACA 2024

作者： Neogy, Sayan Mazumder, Sabyasachi Chowdhury, Niladri Sur, Tapashri Das, Sahana Budge Budge Institute of Technology West Bengal Kolkata India Ramakrishna Mission Vivekananda Educational and Research Institute West Bengal Howrah India

ISBN: (纸本)9789819747986

This study presents an automated two-stage pipeline utilizing deep learning for gastrointestinal bleeding detection in wireless capsule endoscopy visuals. Through transfer learning, convolutional and YOLOv8 architectures are adapted to demonstrate state-of-the-art performance on an open innovation challenge dataset. The classification model distinguishes between bleeding and non-bleeding frames in a set of 2,618 images with 99.59% accuracy. Subsequently, the object detection model localizes bleeding regions within positive samples, achieving strong average precision of 74.64 and 60.21% at intersection-over-union thresholds of 0.5 and 0.75, respectively. Extensive experimentation validates consistent excellence in generalizing across 30 patient cases exhibiting diverse bleeding manifestations, aided by ample data augmentation. The self-contained solution indicates clinical translation—analyzing vendor-agnostic visuals without parameter tuning needs between datasets. By reliably flagging abnormalities within expansive wireless capsule footage, this system aims to mitigate clinician workload via computer-aided screening, focusing review efforts on critical findings. Our unified recognition model also incorporates localization interpretability via techniques like Eigen CAM to promote practical utility and trust. Progressing automated sensitivity alongside localization precision stands to enhance diagnosis and intervention planning, serving to unlock the abundant visual insights in wireless capsule endoscopy technology towards patient impact. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Endoscopy

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Pest and Disease Detection Based on data Augmentation

Pest and Disease Detection Based on Data Augmentation

引用

data Science and Computer Application (ICDSCA), IEEE international conference on

作者： Wenjun Jia Nan Yang Yiping Lu Peng Deng School of Computer Science Southwest Petroleum University Nanchong China

Pest identification plays an increasingly important role in agricultural production, and effective control measures based on the results can reduce the impact of pests and diseases and ensure higher and more stable yields. Advances in machine learning and deep learning, especially in image recognition, provide a better way to identify pests and diseases. EfficientNet-B3 neural network was used to identify and classify healthy crops and diseased plants with 98% accuracy, and data enhancement technology was used to address the insufficient amount of data.

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An Enhanced Deep learning Approach for Smartphone-Based Human Activity recognition in IoHT 3rd

An Enhanced Deep Learning Approach for Smartphone-Based Hum...

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3rd international conference on machine learning, Image Processing, Network Security and data Sciences, MIND 2021

作者： Soni, Vaibhav Jaiswal, Shashank Semwal, Vijay Bhaskar Roy, Bholanath Choubey, Dilip Kumar Mallick, Dheeresh K. Department of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal Madhya Pradesh Bhopal India Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bihar Bhagalpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

ISBN: (纸本)9789811958670

Human activity recognition (HAR) uses sensor-based technology to predict human activity using sensor-generated time-series data. According to recent studies, researchers have been drawn to the area of HAR as the use of mobile devices with various sensors has increased in several research areas in health care that includes the identification of gait abnormality in brain or neurological disorder subjects, designing techniques for clinical gait analyses of a disabled and elderly person, etc. HAR is especially essential on the Internet of healthcare things (IoHT) because of the increasing growth of the Internet of Things (IoT) technology incorporated in numerous smart products and wearable technology (such as smartwatches and smartphones) that have a significant impact on the life of human. A deep neural network (DNN) comprising a bidirectional gated recurrent unit (BiGRU) and two convolutional layers is proposed in this research. The model used could automatically extract and identify activity features using a few of the architecture parameters. The raw data obtained using smartphone sensors is sent into a two layer BiGRU followed by two CNN layers in the proposed architecture. The model’s performance is assessed using two publicly available datasets (UCI-HAR and WISDM). The observations indicate that the reliability and activity identification capability of the proposed model is better than earlier findings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smartphones

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A data-Driven Retrospective Analysis of Cyberbullying Using machine learning Algorithms 3

A Data-Driven Retrospective Analysis of Cyberbullying Using ...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Tripathi, Ashirwad R. Tyagi, Palki Sivaraman, Himani Kumar, Anuj Jain, Sachin Jigyasa University Department of Cs&it Dhradun India

ISBN: (纸本)9798331542375

Cyberbullying has become one of the main concerns in today's digital world, posing a great risk to everyone being online and effecting their mental health and their online safety. This research explores the application of machine learning using the machine learning algorithm to detect and reduce the cyberbullying behaviours. Here we utilize the data from the dataset of cyberbullying done by people online, preprocessing the data to clean and structure it for analysis. Here we use various machine learning models like Naive Bayes, Logistic Regression and SVM, which are then tested on dataset for accuracy, precision, recall, and overall effectiveness in detecting the harmful words and pattern. Our result highlight that which of the model that we used performed best contributing to detection of Cyberbullying online. Additionally, the research underscores the importance of integrating NPL(Natural Language Processing) for delicate text analysis and sentiment detection. The insights from this research aim to support the development of machine learning models for monitoring and addressing online harassment, making a safer digital environment. © 2025 IEEE.

关键词： Sentiment analysis

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Privacy issues involved in data mining and countermeasures 2

Privacy issues involved in data mining and countermeasures

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2nd international conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology, MEMAT 2022

作者： Liu, Jing Law School Xi'an Jiaotong University 28 Xianning West Road Shaanxi Province Xi'an China

ISBN: (纸本)9783800757619

data mining can extract valuable information from large amounts of data and has matured in recent years as an important resource for productive life. This technology is a decision support process. It is mainly based on artificial intelligence, machine learning, pattern recognition, statistics, database, visualization technology, etc. It is highly automated to analyse a company's data, make inductive inferences and extract potential patterns from it to help decision makers adjust their market strategies, reduce risk and make the right decisions. Technology is neutral and not entirely good or bad. However, with the rapid development of the Internet, the increasing popularity of embedded and location-based technologies and the widespread use of location-based social networks, the amount of personal privacy information has increased dramatically and it is very easy to violate personal privacy if data mining tools are applied directly to the data for comprehensive analysis. Thus, in order to be able to carry out data mining with privacy protection, this article suggests some solutions, including: 1) Establish a perfect data privacy protection assessment mechanism and legal means.;2) Develop a new theoretical framework for privacy protection;and 3) Efficient encryption algorithm. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Decision support systems

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Comprehensive Research on Speaker recognition and its Challenges

Comprehensive Research on Speaker Recognition and its Challe...

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Smart data Intelligence (ICSMDI), international conference on

作者： Venkata Syama Sowmya Sri Hari Arun Kumar Annavarapu Vamsi Shesamsetti Sathwik Nalla Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

The study of speech is a subfield of the measurement of uniquely identifying and measurable patterns in human activity. It communicates details about a person's characteristics. As an illustration, consider gender, age, emotion, and health status. Voice Identification is the process of pointing person purely with the help of vocal pulses. Using voice assistants and smart gadgets as a base, several academics are exploring in this field. This article offers detailed information on speaker recognition. Additionally, a different adversarial technique is described in advance of the well-known machine learning methods. This study provides an overview of the speaker recognition topic by covering its system, modelling approaches, applications, and some underlying theories. The strengths of speaker recognition technologies will be discussed.

关键词： Surveys Deep learning Biometrics (access control) Virtual assistants Biological system modeling Humanoid robots Signal processing

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The Classification of Satellite Galaxies Based on Convolution Neural Networks machine learning Algorithm 3

The Classification of Satellite Galaxies Based on Convolutio...

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3rd international conference for Emerging Technology, INCET 2022

作者： Shetty, Ganesh Srinivasa Raghu, N. Jain University Dept. of Eee Bengaluru India Jain University Bengaluru India

ISBN: (纸本)9781665494991

Human exploration in space science unleashed positive achievements of mankind in understanding universe, the whole universe comprises of galaxies, nebula and black holes need to be identifiable in order to extend future exploration and better understanding of space, to leverage the space technology human intelligence does need a automation support in understanding cosmological formations, evolution's and galactic activities, basic understanding of cosmology by pictorial information is main source of knowledge gathering of macroscopic world, technical application of satellites able to capture high resolution of images of planets and galaxies among which Hubble Space Telescope, Kepler Space Telescope, and Spitzer Space Telescopes embedded various types of imaging mechanisms such as infrared, photometer in capturing astronomical objects while the most advanced imaging techniques been applied in space observation laboratories to classify, understand pattern of shape, motion and temporal characteristics of space objects. In this research the data-set provided by NASA and European Space Agency of Galaxy Zoo challenge exists in Kaggle platform for classification of galaxy images with as the day passes by, the telescopes above the earth capture increasingly images of faraway galaxies. As telescopes bigger in size proceed on to collecting those images, the datasets begin to blow up in the size. For more understanding how the different shapes of galaxies connect to the domain of physics which has created them, such images need to be segregated and categorized using a Convolution Neural Network. © 2022 IEEE.

关键词： Convolution

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A Comparative Study of datasets for Cyber-attacks Detection in Wireless Sensor Networks

A Comparative Study of Datasets for Cyber-attacks Detection ...

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Computing and machine Intelligence (ICMI), international conference on

作者： Shereen Ismail Diana W. Dawoud Hassan Reza School of Electrical Engineering and Computer Science University of North Dakota ND USA College of Engineering and Information Technology University of Dubai Dubai UAE

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

machine learning (ML) is one of the effective security approaches to build cyber-attacks detection systems in Wireless Sensor Networks (WSNs). ML leverages the power of data analysis and pattern recognition to detect and classify various types of cyber-attacks to enhance the security of WSN s. A well-constructed dataset is one of the key factors that significantly impact the performance and generalization capabilities of any ML classifier trained on it. In this paper, we evaluate the effectiveness of two datasets: WSN-DS and WSN-BFSF which are specialized for Denial-of-Service (DoS) attacks targeting WSNs. We compare the two datasets in terms of their key characteristics, dataset quality, and ML classification performance. Mutual Information (MI) and Recursive Feature Elimination (RFE) are used for feature selection. The dataset quality is measured using statistical information calculation. The ML classification performance is investigated for three supervised ensemble techniques: LightGBM, bagging, and stacking using evaluation metrics including probability of detection, probability of false alarm, probability of misdetection, classification accuracy, model size, and processing time.

关键词： Measurement Wireless sensor networks Time division multiple access Stacking Probability Feature extraction pattern recognition

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