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ESSCIRC 2022 - IEEE 48th European Solid State Circuits conference, Proceedings

ESSCIRC 2022 - IEEE 48th European Solid State Circuits Confe...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

ISBN: (纸本)9798350320749

The proceedings contain 128 papers. The topics discussed include: brain tumor segmentation using deep learning techniques: comparative analysis;imperceptibility and robustness study in different transform domain for copyright protection in digital image watermarking in hybrid SVD-domain;optimized detection of continuous gravitational-wave signals using convolutional neural network;automated method for calibration findings of auto slide scanner using automated digital microscopy;implementation of perovskite solar cells using GPVDM;mangoes taste detection system using deep learning;bird species recognition using deep learning;optimization of power and delay in VLSI circuits using hybrid flip-flop circuit;ellipse shaped graphene metamaterial absorber for terahertz applications;design and development of a microgripper for use in pipeline inspection robot;and screening of citrus diseases using deep learning embedders and machine learning techniques.

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3rd international conference on Artificial Intelligence and Knowledge processing, AIKP 2023

3rd International Conference on Artificial Intelligence and ...

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3rd international conference on Artificial Intelligence and Knowledge processing, AIKP 2023

ISBN: (纸本)9783031686160

The proceedings contain 28 papers. The special focus in this conference is on Artificial Intelligence and Knowledge processing. The topics include: How Do Senior Secondary Level Students and Their Teachers Perceive Artificial Intelligence and Its Implementation? An Exploratory Study;anomalous Sound Pattern Detection for machine Health Monitoring;performance Evaluation of Various machine learning Algorithms for Lung Cancer Prediction Using Demographic Data;enhancing Stock Portfolio Optimization Based on a Hybrid Approach Using Artificial Bee Colony Optimization and Firefly Optimization;Enhancing Yarn Quality in the Cotton Industry: AI- Based Nep Detection for Improved Manufacturing Processes;breast Cancer Diagnosis from Ultrasonic Image and Histopathology Image Using Deep learning Approach;Advancing Time Series Forecasting: LSTM Networks with Multiple Attention Mechanisms;trajectory Tracking and Navigation Model for Autonomous Vehicles Using Reinforcement learning;quantum Graph Neural Networks Based Protein-Ligand Classification;comparative Analysis on Speech Driven Gesture Generation;enhancing Deep learning: Leveraging Skip Connections and Memory Efficiency;quality-Based Decision-Making Using Image processing for Supply Chain Management;enhancing Endometrial Tumor Detection: Early Diagnosis with Advanced Vision Transformer Architecture;sweetSight: A Deep Convolutional Neural Network Approach for Automatic Categorization of Bengal Sweets;a Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth;automatic Conversion of Broadcasted Football Match Recordings to Its 2D Top View;measuring the Vehicle-in-Motion, Density and Allocation of Traffic signal Using Transfer learning;Ensemble Model of VGG16, ResNet50, and DenseNet121 for Human Identification Through Gait Features;Performance of Sentiment Analysis APIs on Political Opinion Polling;summarization of Telugu Text Discourses;crowd-Sourced Supervisors for the Automatic Invigilation of Online

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Using machine learning Regression Model to Predict the Optimum Election Algorithm for Parallel and Distributed Computing Systems 3

Using Machine Learning Regression Model to Predict the Optim...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Dhariwal, Naman Vellore Institute of Technology Department of Software Systems Vellore India

ISBN: (纸本)9798350370867

Parallel and distributed computing has become a necessity when it comes to high-performance computing, artificial intelligence, big data analytics, machine learning, deep learning, signal processing and bioengineering. System engineers connect distributed systems, with large number of nodes, that then compute the solution for a problem concurrently. The distributed system has a leader that manages the overall coordination of these connected nodes. This research provides a novel approach to predict the most efficient and time-feasible election algorithm, that must be ran on a distributed system, using linear regression machine learning approach. This research implements the prediction process for three election algorithms and successfully predicts the most efficient and least time-consuming algorithm with 94.98% accuracy. Initially, the dataset for the regression model training was created by executing the three election algorithms parallelly using MPI running 16 parallel processors (cores). The research also presents three regression line equations, trained and modelled on this dataset, that can be used to predict the execution time for the three algorithms for a system with any number of nodes. The results are duly visualized, and regression lines are plotted for comparison. © 2023 IEEE.

关键词： Forecasting

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Enhancing Sign Language Classification through Reservoir Computing and Depth Camera Technology 3

Enhancing Sign Language Classification through Reservoir Com...

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3rd international conference on Image processing and Robotics, ICIPRoB 2024

作者： Thongking, Witchuda Premachandra, Chinthaka Wiranata, Ardi Maeda, Shingo Shibaura Institute of Technology Department of Engineering and Science Tokyo Japan Shibaura Institute of Technology Department of Electronic Engineering Tokyo Japan University of Gadjah Mada Department of Mechanical and Industrial Engineering Yogyakarta Indonesia Tokyo Institute of Technology Department of Mechanical Engineering Tokyo Japan Research Group Tokyo Japan

ISBN: (纸本)9798350374766

The depth camera has emerged as an efficacious tool for facilitating interactions between individuals and machines. Its advantageous features, including a lightweight design, high durability, completeness, superior image quality, and versatility, have prompted researchers to explore its integration into advanced applications. Currently, the prevailing methods for classifying sign language exhibit certain limitations, such as suboptimal accuracy and inefficiency in gesture recognition technologies. In this study, we conduct a comprehensive examination of hand language characteristics rooted in American Sign Language (ASL), utilizing gesture-based interactions and augmenting sensing performance through preprocessing and the application of reservoir computing in the training model. We conduct a comprehensive evaluation of the gesture characteristics captured by the depth camera, elucidating a methodology encompassing preprocessing, machine learning, and the technical prediction of salient gesture features derived from conventional depth camera video footage. This endeavor aims to establish a robust signal model for enhanced understanding and representation of the analyzed gestures. Our machine-learning models adeptly predict American Sign Language signs. Correlation values derived from the depth camera data demonstrate a pronounced alignment with naturally occurring variations in metrics observed within specific gestures. Our methodologies for quantifying gestures with depth cameras contribute to heightened accessibility in quantitative motion analysis. The outcomes of the reservoir computing implementation exhibit the successful classification of three hand language signs, thereby manifesting high precision and recall. The robust performance of the classification is pivotal for practical applications and underscores the efficacy of our approach. © 2024 IEEE.

关键词： machine learning

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EEG mental arithmetic task levels classification using machine learning and deep learning algorithms 3

EEG mental arithmetic task levels classification using machi...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Nirde, Krashana Gunda, Manasa Manthalkar, Ramchandra Gajre, Suhas SRTMU Vishnupuri Nanded Electronics and Telecommunication Engg.SGGSIE&T Nanded India

ISBN: (纸本)9798350320749

EEG signals are often-used modalities for monitoring mental arithmetic performance. In order to learn robust characteristics and predict the level of classification from EEG, we provide a deep neural networks and machine learning architecture. In this research, we considered online public dataset related to mental arithmetic task. EEG dataset recording are stored last 3 minutes of resting period (180 sec before task) and first 1 minute of the mental arithmetic task period (60 sec-while doing task). The task involves series of subtraction performed by 36 participants. The participants were divided among Good (1) and Bad (0) based on task level. We used machine learning and deep learning methods to classify three different task levels. (i.e., no task, normal task, and difficult task). In algorithm 1 we extracted five features like skewness, kurtosis, mean, standard deviation and variance from the EEG signals and applied as input to the classifiers. Here three machine learning classifiers used for three task levels classification like SVM, KNN and Decision Tree. KNN obtained high classification with 91% whereas SVM obtained S9% and Decision Tree obtained 65%. Algorithm 2 uses ANN and LSTM, two deep learning algorithms, to learn directly from raw EEG data. For better performance we used optimization techniques. ANN used rmsprop and obtained the classification accuracy of 96.80% whereas LSTM uses Adam and SGD and obtained the classification accuracy of 94%. Therefore, when raw data applied to deep learning algorithms can perform well when grouping of handmade features given to a machine learning algorithm. © 2023 IEEE.

关键词： learning algorithms

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Efficient Hardware and Software Co-design for EEG signal Classification based on Extreme learning machine 3

Efficient Hardware and Software Co-design for EEG Signal Cla...

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3rd international conference on Electrical, Computer and Communication Engineering, ECCE 2023

作者： Lyu, Songyang Chowdhury, Mehdi Hasan Cheung, Ray C.C. City University of Hong Kong Department of Electrical Engineering Hong Kong Chittagong University of Engineering and Technology Department of Eee Chittagong Bangladesh

ISBN: (纸本)9798350345360

ElectroEncephaloGram (EEG) is associated with multiple functions, including communications with neurons, organic monitoring, and interactions with external stimuli. By decoding EEG signals, certain human activities such as sleeping, brain diseases, motor imagery, movement of limbs, and others can be observed and controlled through the brain-computer interface (BCI). Therefore, it is vital to efficiently process EEG signals with a robust and accurate system to build BCI systems with powerful applications. However, as a weak biosignal, EEG demands a fast-reaction system signal processing with high accuracy and sensitivity. In this work, a hardware/software co-design network based on Extreme learning machine (ELM) is introduced for the classification of certain actions, motor imagery of the human brain. This system is based on an optimized Hierarchical Extreme learning machine (H-ELM) on the software layer. The proposed method has advantages over previous designs with an accuracy of 90.3%. It also improves the training speed by around 25X compared to conventional methods. The software model is also translated into efficient FPGA hardware design to maintain high computation efficiency and reduce power consumption for biomedical applications. © 2023 IEEE.

关键词： Electroencephalography

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Convolutional neural network-based signal discrimination for P300 brain-computer interface 8

Convolutional neural network-based signal discrimination for...

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8th international conference on Intelligent Computing and signal processing, ICSP 2023

作者： Liao, Yongzhi Zhang, Liumei Wang, Yaoxin Xi'an Shiyou University School of Computer Science Xi'an China

ISBN: (纸本)9798350302455

The P300-based brain-machine interface can achieve efficient communication between human brain and computer, and help physically disabled people achieve autonomous control of external devices. In this paper, we propose a convolutional neural network-based signal detection method, which includes signal pre-processing, construction and training of convolutional neural network, and testing and performance evaluation of the model. This paper conducts experimental validation on dataset II of the 3rd BCI competition and compares it with some traditional machine learning methods and deep learning methods. The test accuracy of the experimental results reaches 0.85 and the AUC reaches 0.93, which indicates that the method has significantly improved in classification accuracy and model performance, and has high practicality and application prospects. © 2023 IEEE.

关键词： Convolutional neural networks

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Data-Driven Models for Water Potability: A machine learning Approach 3

Data-Driven Models for Water Potability: A Machine Learning ...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Mishra, Nisha Sangeetha, S. V. Tresa Deepa, K. Venugopal, Vivek Amrita School of Engineering Bengaluru Campus Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham India University of Technology and Applied Sciences Department of Engineering Al Musannah Oman Amrita School of Engineering Bengaluru Campus Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350361186

Water is an essential component in our daily lives. Water is used for different purposes, such as drinking, industrial, and irrigation. Many problems can be seen nowadays because of containment water. Water is being polluted, and as a result, the environment is experiencing many negative effects, such as global warming and different types of diseases spreading in the environment. Different ML models are used to predict water quality because it's very important for the environment and surroundings. Water Potability can be predicted by machine learning using parameters such as PH, Conductivity, Turbidity, Solids, Hardness, silver, nitrates, cadmium, lead, etc. As there is a huge increase in the contamination of water by industries from urban to rural areas, it is affecting the environment and our way of life. So good accessibility and better quality of water are needed. The point of this work is to get goodquality of water by using machine learning techniques. In recent years, numerous contaminants have put water quality at risk. Forecasting and model development for assessing water quality have become essential to control water pollution. The level of quality is measured using variables like water quality indexing (WQI) and water quality categorization. © 2024 IEEE.

关键词： Support vector machines

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Neural Network Approach to Channel estimation in Wireless Communication 10

Neural Network Approach to Channel estimation in Wireless Co...

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10th IEEE international conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Tangudu, Narendranath Durga Kumar, Swaraj Vankayala, Satya Kumar Yoon, Seungil Samsung R&D Inst Stand Res Grp Bengaluru India Samsung R&D Inst Call SW Grp Network Call & OAM Team Bengaluru India Samsung Elect OAM SW R&D Grp Suwon South Korea

ISBN: (纸本)9798350385939;9798350385922

To meet the high throughput and stringent Quality of Service (QoS) requirements of the fifth and the sixth generation (5G and 6G) users, operators have explored technologies such as Multi-Input Multi-Output (MIMO), massive MIMO (mMIMO), Base Station (BS) densification, Non-Orthogonal Multiplex Access (NOMA) etc. The Central Unit (CU) and Distributed Unit (DU) split architectures defined in Open Radio Access Network (ORAN) and 3rd Generation Partnership Project (3GPP), gives the possibilities to explore the software-based methods, which were restricted earlier due to hardware-centric systems. With software defined Radio Access Network (RAN) at cloud and artificial intelligence being a promising domain for solving various problems, these two can be leveraged in exploring to solve or optimize various existing challenges like channel estimation in the wireless receivers, which is very computationally expensive. In this work, we attempted to improve the performance of the compute intensive module channel estimation using machine learning architecture. Convolutional Neural Network (CNN) based image processing architectures, Super Resolution CNN (SRCNN) and Denoising CNN (DnCNN) were implemented for channel estimation module, along with various combinations of spatial data arrangements of the input signal. The spatial data arrangement of complex and real components of input signal outperformed and yielded better accuracy compared to traditional methods such as estimated Minimum Mean Square Error (MMSE) and Approximate Linear Minimum Mean Square Error (ALMMSE).

关键词： Channel estimation convolutional neural network image super resolution image denoising machine learning

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Enhancing Quantum machine learning Algorithms for Optimized Financial Portfolio Management 3

Enhancing Quantum Machine Learning Algorithms for Optimized ...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Bhasin, Narinder Kumar Kadyan, Sunil Santosh, Kathari Hp, Ramya Changala, Ravindra Bala, B Kiran Amity University Zonal Head Indian Institute of Banking and Finance Delhi India School of Management and Commerce Manav Rachna University Haryana Faridabad India Cmr Institute of Technology Department of Mba Bengaluru India Bangalore India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India K.Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum machine learning algorithms for optimal financial portfolio management, presenting a novel approach implemented in Python that outperforms existing methods. The algorithm's capability to generate such substantial returns over time positions it as a groundbreaking tool for portfolio optimization in the dynamic landscape of financial markets. In the pursuit of enhancing quantum machine learning algorithms, this research focuses on the development and optimization of the QSVM algorithm. Leveraging Python for implementation, the study considers critical factors such as quantum circuit optimization, noise mitigation, and the integration of classical and quantum components to achieve superior results. The achieved portfolio performance over time not only underscores the algorithm's efficacy but also signifies a quantum advantage in financial decision-making. The implementation in Python ensures accessibility and applicability, facilitating the integration of this advanced quantum algorithm into existing financial frameworks. This research contributes to the evolving field of quantum finance, showcasing the potential of quantum machine learning in optimizing financial portfolios. The findings not only validate the superior performance of the proposed QSVM but also highlight the broader implications for the future of financial decision support systems, where quantum algorithms could play a transformative role in enhancing portfolio management strategies. The proposed Quantum Support Vector machine (QSVM) demonstrates unparalleled success, with a remarkable Portfolio performance over time of 89.65%. This result significantly surpasses existing quantum algorithms, including Quantum Principal Component Analysis (QPCA), Quantum Boltzmann machines (QBM), and Quantum K

关键词： Python

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