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Machine Learning Application for Classification of Brain Tumors Through MR Images to Detect Malignancy 4

Machine Learning Application for Classification of Brain Tum...

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4th IEEE International conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chanda, Kunal Tiwari, Jay Verdhan Banik, Souvik Ahmed, Washef Advanced Signal Processing Group Centre for Development of Advanced Computing Kolkata India Medical Electronics Ministry of Electronics and Information Technology New Delhi India

ISBN: (纸本)9798350306118

In the present paper classification of Brain Tumours through MRI imaging as Fine, Medium and Coarse textures is proposed. The other methods of classification of brain tumours that are frequently used are based on size, shape and spread of the tumour. The importance of classifying tumours based on texture is that there is correlation between malignancy of the tumour and the texture of the tumour. The other methods normally don't give this information. In this paper relation between texture of the tumour and it's malignancy has been explored. Four methods of feature extraction have been used, in order to obtain texture information of the tumour. They are, Gray Level Co-occurrence Matrix (GLCM), Gradient Matrix (GM), Laplacian Of Gaussian (LoG) and the Run-Length Matrix (RLM). Once the features are extracted, they are passed to Decision Tree classification algorithm. The classification algorithms classified the extracted features as having Fine, Medium or Coarse textures. The result obtained showed correlation between texture of the tumour and its malignancy. If the texture is predicted as Fine, then it is most likely a non-malignant, or benign tumour, and its diagnosis is most likely going to give a Complete response. A Medium texture would most likely give a Partial response, and is not very malignant. This is important, since knowing this information can benefit the patient and clinicians. This improves the diagnostic confidence of the clinicians and allows them to diagnose and report multiple patients that results in lower cost of the examination as well as access to diagnostics to people living in remote areas. © 2023 IEEE.

关键词： Decision trees

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Brain Controlled Robotic Car Using Mind Wave

Brain Controlled Robotic Car Using Mind Wave

引用

Multimedia processing, Communication & Information Technology (MPCIT), International conference on

作者： Shwetha B Sathyanarayana S V Yuvaraj K E Sujith S Sahana M Soppin Sujal G Chinchnekar Dept. of E& CE JNNCE. Shivamogga India Affiliated to VTU Belagavi

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

In this paper, Mind Wave technology based brain-controlled robotic car is implemented. The objective is to create an innovative human machine interface, enabling users to control the cars movements seamlessly through their brain signals and facial movement. The Mind Wave headset, equipped with EEG sensors, captures and interprets the users brainwave pattern, translating them into specific commands for the robotic car. The system integrates real time signal processing algorithms to extract relevant features from the EEG data, mapping them to predefined control commands for the robotic car. A comprehension control architecture is established to facilitate smooth navigation, including forward, backward, left, and right movements Additionally, advanced features such as speed modulation and obstacle avoidance are implemented to enhance the user experience and ensure safety. Furthermore, the work explores the challenges and limitations of the proposed brain-controlled robotic car system, addressing issues related to signal noise,user training,and the adaptability of the *** directions for improvement and expansion of the system are discussed, aiming to refine the interface, increase responsiveness, and broken the range of supported commands.

关键词： Adaptation models signal processing algorithms Robot sensing systems Chatbots Brain modeling Electroencephalography Sensors Safety Automobiles Robots

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PulseSync BP: A Neural Network Framework for Non-Contact Blood Pressure Estimation

PulseSync BP: A Neural Network Framework for Non-Contact Blo...

引用

Advances in Data Engineering and Intelligent Computing Systems (ADICS), International conference on

作者： Tanumay Ghosh Omisha Singal Namit Lodh S Suchitra Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Using a cutting-edge neural network framework, “PulseSync BP” estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advanced signal processing. The suggested framework changes blood pressure monitoring by eliminating cuff-based measures. PulseSync BP's RNN architecture is carefully designed for optimal prediction accuracy. The algorithm effectively estimates blood pressure through extensive experimentation, demonstrating its potential for continuous healthcare applications. This framework's capacity to use various physiological cues shows its adaptability and versatility. PulseSync BP allows real-time, non-intrusive blood pressure monitoring, advancing individualized health tracking solutions. Cuffless blood pressure estimate is advanced here, demonstrating how artificial intelligence could improve healthcare accessibility and patient-centric monitoring. PulseSync BP and other revolutionary technologies can transform blood pressure monitoring in healthcare.

关键词： Neural networks signal processing algorithms Estimation signal processing Blood pressure Real-time systems Biomedical monitoring

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An Enhanced Car Parking Detection Using YOLOv8 Deep Learning Capabilities

An Enhanced Car Parking Detection Using YOLOv8 Deep Learning...

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Electronics, Communication and signal processing (ICECSP), International conference on

作者： Vishnu Shankar Vibhav Singh Vibhash Choudhary Sachin Agrawal Bhaskar Awadhiya Electronics & Communication Engineering National Institute of Technology Delhi

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

This research paper introduces a novel approach for car parking slot detection using YOLOv8, an advanced object detection algorithm renowned for its state-of-the-art performance. The objective of this study is to address the increasing demand for efficient parking management in urban areas, where optimizing parking space utilization is essential to alleviate traffic congestion. The proposed model efficiently uses the power of YOLOv8's single-shot detection. It uses anchor boxes to automatically identify and classify vacant and occupied parking slots in real time. By utilizing YOLOv8's deep learning capabilities, the proposed method achieved impressive accuracy and efficiency in car parking slot detection. The simulation results showcase the system's effectiveness, providing valuable insights for urban mobility planning and management.

关键词： Deep learning Accuracy Simulation Urban areas signal processing algorithms Object detection signal processing Car parking Real-time systems Traffic congestion

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EXTENSION FOR HANDWRITTEN CHARACTER RECOGNITION USING RNN-GRU ALGORITHM

EXTENSION FOR HANDWRITTEN CHARACTER RECOGNITION USING RNN-GR...

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signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International conference on

作者： VS Moulishwar P Karthikevan V Mahendra Kumar A Srihariharan Department of Computer science and Engineering Sri Manakula Vinayagar Engineering College Puducherry India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Recognizing handwritten characters is a complex task in pattern recognition and computer vision, demanding advanced techniques to enable computers to discern and transform handwritten or printed characters into a digital format. The integration of the GRU algorithm within the RNN-CNN hybrid methodology marks a significant advancement in handwritten character recognition. By transitioning from a character-by-character to a line-by-line recognition approach, the system aims to improve the conversion of handwritten text into digital formats. GRU enhances context awareness by predicting subsequent letters in incomplete words, thereby facilitating more accurate recognition. Leveraging the IAM dataset not only provides ample training data but also aids in error detection and correction, enhancing the overall accuracy and reliability of the system. This innovative approach underscores a commitment to advancing OCR capabilities beyond isolated characters and words, toward more comprehensive and context-aware text recognition, thereby paving the way for more effective digitization of handwritten documents.

关键词： Handwriting recognition Accuracy Text recognition signal processing algorithms Training data Transforms signal processing

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Two-Step High Precision Burst Pulse Detection in Low SNR Based on FPGA

Two-Step High Precision Burst Pulse Detection in Low SNR Bas...

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IEEE advanced Information Management,Communicates,Electronic and Automation Control conference (IMCEC)

作者： Jing Chen Jun Hu Zengping Chen Shujian Zheng School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

To address the issue of unsatisfactory burst signal detection and insufficient time-of-arrival estimation accuracy in low SNR environments in modern digital signal receivers, this paper investigates a two-step high precision real-time signal detection method. The first step involves using the Short-Time Fourier Transform and Constant False Alarm Rate detection in the frequency domain to improve the detection of weak signals, and obtain the existence and rough position of signals. By using time-domain autocorrelation detection based on the detection results, the TOA of the signal is accurately estimated. The method is then implemented on the FPGA platform and verified in an actual environment, demonstrating practical value for engineering.

关键词： Image edge detection Frequency-domain analysis Time of arrival estimation signal processing algorithms Real-time systems Autocorrelation Time-domain analysis

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Audio Classification through MFCC features using RNN algorithm

Audio Classification through MFCC features using RNN algorit...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Kilaru Devi Bhargavi V. Deepa I. Harshitha Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Recurrent Neural Network (RNN) algorithm, is renowned for its effectiveness in object localization and classification tasks within the realm of computer vision. This study combines RNN capabilities with the analysis of Mel-Frequency Cepstral Coefficients(MFCC) features, which have demonstrated effectiveness in capturing unique audio signal characteristics. Mel-Frequency Cepstral Coefficients (MFCCs) serve as a compact representation of the spectral content of audio signals, enabling the identification of distinctive features crucial for detecting anomalies introduced during the generation of deepfake audio. The Recurrent Neural Network (RNN) algorithm is applied to these MFCC features. RNN excels in tasks such as image recognition, object detection, and semantic segmentation. Previous research in the field of audio forensics has explored various methodologies for detecting manipulated audio content. While some approaches focus on signal processing techniques, others employ machine learning algorithms. MFCCs have consistently demonstrated their efficacy in capturing audio features relevant to authenticity. The proposed approach enhances the detection of fake audio, a critical challenge in the era of advanced digital media manipulation. By combining the strengths of MFCC features and the RNN algorithm, the model aims to learn and identify intricate patterns associated with genuine audio, enabling accurate differentiation between authentic and manipulated audio sources. By adapting RNN to analyze MFCC features, the model gains the capability to discern intricate patterns associated with genuine audio, thereby distinguishing between authentic and manipulated audio sources.

关键词： Adaptation models Technological innovation Recurrent neural networks Machine learning algorithms Semantic segmentation signal processing algorithms signal processing Feature extraction Classification algorithms Mel frequency cepstral coefficient

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Advancing EEG Brain-Computer Interfaces with Efficient Machine Learning Classifications

Advancing EEG Brain-Computer Interfaces with Efficient Machi...

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Intelligent Technologies (ACOIT), Asian conference on

作者： Ramakrishnan Raman Vikram Kumar C. P. Sanjay Apurv Verma Shailesh Rastogi Dharmendra Pandey Symbiosis International (Deemed University) SRV Media Pvt. Ltd. IT & Business Analytics Sri Balaji University Pune (SBUP) Computer science and Engineering Faculty of Engineering and Technology Parul institute of Technology Parul University Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In recent years, brain-computer interfaces (BCIs) have advanced significantly through the integration of electroencephalogram (EEG) data, though the complexity of EEG signals remains challenging for traditional classification methods. This paper introduces a novel machine learning framework to enhance classification efficiency in EEG-based BCIs. We employ advanced algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to capture temporal and spatial dependencies in EEG data. Our approach leverages a combination of feature extraction techniques and dimensionality reduction to optimize classification, improving accuracy and speed. We performed a comparative analysis using a standardized EEG dataset, revealing our model’s significant improvement in classification accuracy and computational efficiency over traditional classifiers. These results highlight our method’s potential in realtime BCI applications. This study contributes to the theoretical understanding of EEG signal processing and paves the way for more responsive and user-friendly BCI systems.

关键词： Accuracy Recurrent neural networks signal processing algorithms Brain modeling Feature extraction Electroencephalography Brain-computer interfaces Classification algorithms Convolutional neural networks Reliability

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Enhancing Wind Speed Forecasting with Multi-Meteorological Factors LSTM Models and Novel Loss Function Design

Enhancing Wind Speed Forecasting with Multi-Meteorological F...

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International conference on Vision, Image and signal processing (ICVISP)

作者： Xinyi Li Jing Hu Dufu Liu Dongling Wang Feihu Huang Cheng Ji School of Computer Chengdu University of Information Technology Chengdu China Huafeng Meteorological Media Group Co. Ltd. Beijing China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Wind power generation has grown in importance as renewable energy technology has advanced; nonetheless, wind speed forecasting remains a concern. Traditional approaches are inadequate in dealing with the nonlinearity and long-term dependency of wind speed data. The suggested method aims to present a deep learning architecture that incorporates several meteorological elements, with a hybrid loss employed to increase the wind speed prediction effect. The experiment used meteorological data from the Hangzhou Asian Games. Comparative results show that our suggested strategy outperformed ARIMA and Random Forest models in measures by up to 10.86%. Ablation studies demonstrated that including wind speed along with other meteorological data can improve performance, highlighting the importance of wind speed in predicting.

关键词： Adaptation models Visualization Wind speed Deep architecture signal processing algorithms Predictive models Wind power generation signal processing Forecasting Long short term memory

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Innovative Wearable EEG Solution for Stress Assessment and Control

Innovative Wearable EEG Solution for Stress Assessment and C...

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Intelligent Systems and advanced Applications (ICISAA), International conference on

作者： Aviral Patel Prerna Zagade Shravan Ambudkar Aboli Jadhav Bhavna Ambudkar Department of EnTC Symbiosis Institute of Technology Pune India Department of EnTC Symbiosis Professionals and Skills University Pune India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

In this work, wearable electroencephalogram (EEG) technology is investigated as a cutting-edge stress assessment and treatment method. An increasing number of people require simple, practical methods for monitoring and managing their stress levels because of the increasing prevalence of illnesses associated with stress. Wearable electroencephalography (EEG) devices offer a feasible path for the high temporal resolution monitoring of stress circumstances by permitting real-time, noninvasive brain activity measurements. This abstract examines the current status of wearable EEG technology, emphasizing how advancements in sensor technology, hardware downsizing, and signal processing techniques have made it simpler to integrate EEG monitoring into everyday life. The project also investigates the utility of EEG-based Brain-Computer Interfaces (BCIs) for stress detection and control, as well as the potential of EEG signals as biomarkers for stress-related cognitive processes. The paper also covers the methodological aspects of implementing EEG-based stress assessment protocols, including feature extraction, signal processing algorithms, and machine learning techniques for stress prediction. The end of the abstract discusses the therapeutic applications of wearable EEG devices in stress management therapy and points to potential directions for future study in this rapidly emerging field. Because wearable electroencephalography (EEG) technology offers new insights into the neurophysiological foundation of stress and makes individualized therapies based on individual stress profiles possible, it has the potential to completely transform paradigms for stress measurement and regulation.

关键词： Electric potential Medical treatment signal processing algorithms Transforms Electroencephalography Biomedical monitoring Stress Monitoring signal resolution Stress measurement

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