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Transfer Learning Models for MRI-Based Brain Tumor Detection

Transfer Learning Models for MRI-Based Brain Tumor Detection

引用

International conference on advanced Technologies for signal and Image processing (ATSIP)

作者： Moriba Bérété Amira Echtioui Lamia Sellami Ahmed Ben Hamida Advanced Technologies for Medicine and Signals Laboratory ‘ATMS’ National Engineering School of Sfax (ENIS) Sfax University Sfax Tunisia Department IS College of Computer Science King Khaled University ‘KKU’ Abha Kingdom of Saudi Arabia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Human health, especially brain health, is paramount for overall well-being. Magnetic Resonance Imaging (MRI) serves as a vital diagnostic tool for assessing critical organs such as the brain. The abundance of data generated by MRI scans presents a significant opportunity for artificial intelligence (AI) to aid in health decision-making processes. In this paper, we focus on using AI techniques, particularly transfer learning, to classify brain tumors from MRI images. By leveraging transfer learning, we aim to enhance the performance of image processing algorithms in the classification of brain tumors. Our approach involves employing established architectures such as Xception, ResNet50, and DenseNet201, adapted through transfer learning for this specific task. Among the architectures tested, our results highlight the superiority of the ResNet50 model in binary classification of brain tumors using MRI scans. The proposed model achieves remarkable performance metrics, including an accuracy of $99 \%$, precision of $99.5 \%$, recall of $99.5 \%$, and F1 Score of $99 \%$. This research underscores the potential of transfer learning in advancing diagnostic capabilities for brain health assessment.

关键词： Measurement Accuracy Magnetic resonance imaging Image processing Transfer learning Brain modeling Artificial intelligence

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Implementation of the Optical Lock through Visible Light Communication using AES

Implementation of the Optical Lock through Visible Light Com...

引用

Advances in signal processing, Power, Communication, and Computing (ASPCC), IEEE International conference on

作者： K.P. Swain Sumant Kumar Mohapatra Puspanjali Mallik R. K. Dash Rakesh Kumar Lenka Department of ETC Trident Academy of Technology Bhubaneswar India Dept. of CSE Shailabala Women’s College Cuttack India Dept. of Electrical Electronics and Communication Engineering Galgotia University Dept. of Computer Science Central University of Odisha Sunabeda Koraput

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

This study presents the development and implementation of a novel optical lock using visible light communication (VLC), leveraging a custom-built mobile application and flash LED for secure information transmission. The system utilizes an Android application with a user-friendly interface to input and encrypt messages using the AES-128 bit algorithm and Base64 encoding. The encrypted message is transmitted via a smartphone’s flash LED as an OOK signal, received by a specially designed PCB composed of a photodiode and a servo motor. At the heart of the receiving mechanism is a Raspberry Pi, which is responsible for decrypting the message and executing a comparison check with a pre-stored message to determine system unlocking. This innovative approach combines the convenience of mobile technology with advanced encryption, offering a robust solution in optical locking systems.

关键词： Heart signal processing algorithms Information processing Light emitting diodes Mobile applications Encryption Photodiodes Optical signal processing Servomotors Visible light communication

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Speech Enhancement with Overlapped-Frame Information Fusion and Causal Self-Attention

Speech Enhancement with Overlapped-Frame Information Fusion ...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yuewei Zhang Huanbin Zou Jie Zhu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Xiaohongshu Inc. Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For time-frequency (TF) domain speech enhancement (SE) methods, the overlap-and-add operation in the inverse TF transformation inevitably leads to an algorithmic delay equal to the window size. However, typical causal SE systems fail to utilize the future speech information within this inherent delay, thereby limiting SE performance. In this paper, we propose an overlapped-frame information fusion scheme. At each frame index, we construct several pseudo overlapped-frames, fuse them with the original speech frame, and then send the fused results to the SE model. Additionally, we introduce a causal time-frequency-channel attention (TFCA) block to boost the representation capability of the neural network. This block parallelly processes the intermediate feature maps through self-attention-based operations in the time, frequency, and channel dimensions. Experiments demonstrate the superiority of these improvements, and the proposed SE system outperforms the current advanced methods.

关键词： Time-frequency analysis Limiting Fuses Neural networks signal processing algorithms Speech enhancement Acoustics Delays Indexes

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User Modeling Using EEG signals: A Systematic Literature Review

User Modeling Using EEG Signals: A Systematic Literature Rev...

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International conference in Software Engineering Research and Innovation (CONISOFT)

作者： Aridai Ortega-Zenteno Carmen Mezura-Godoy Faculty of Statistics and Informatics University of Veracruz Xalapa Mexico

ISBN: (数字)9798331532116

ISBN: (纸本)9798331532123

Electroencephalography (EEG) has emerged as a critical tool for user modeling in brain-computer interfaces (BCIs), offering unique perspectives into cognitive processes and user behavior. This systematic review explores how EEG signals have been used to model various facets of user behavior, from attention and cognitive load to emotional responses and personality. We analyze transdisciplinary studies incorporating EEG in BCIs, This review highlights advanced signal processing techniques. Additionally, it examines machine learning algorithms that enable sophisticated interpretation of brain signals in real-time. In addition, we observe how these method-ologies are applied in contexts ranging from collaborative gaming to device control in home and clinical settings. Through the integration of key findings, this review identifies trends in the accurate modeling of user behavior and performance of BCIs, as well as the challenges inherent in working with interindividual variability and neurophysiological signals.

关键词： Adaptation models Adaptive systems Machine learning algorithms Accuracy signal processing Brain modeling Market research Electroencephalography Data models Load modeling

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Review of Acoustic Features and Computational Models in Lung Disease Diagnosis

Review of Acoustic Features and Computational Models in Lung...

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Sustainable Expert Systems (ICSES), International conference on

作者： Ashish Pandey Mohammad Shahnawaz Shaikh Pratik Patel Computer Science & Engineering Department PIET Parul University Vadodara Gujarat India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Respiratory diseases are increasingly prevalent, necessitating efficient diagnostic methods for healthcare providers to manage patient care effectively. This review addresses the growing need for non-invasive, cost-effective diagnostic techniques focused on acoustic properties of lung sounds. The primary objective is to explore contemporary methodologies and models used in the acoustic analysis of lung sounds for disease identification and characterization. The review examines and contrasts current signal processing techniques and machine learning algorithms applied to lung sound analysis, highlighting their strengths and limitations. By evaluating these methods, the review aims to identify promising applications for clinical settings and provide insights into how these technologies can be optimized. Additionally, the review offers recommendations for future research and practical applications of lung sound analysis to enhance differential diagnosis and prognosis of respiratory disorders. This work aims to contribute to the development of advanced, accessible diagnostic tools that can improve patient outcomes and streamline respiratory disease management.

关键词： Machine learning algorithms Reviews Lungs Pulmonary diseases Medical services signal processing Acoustics Object recognition Prognostics and health management Expert systems

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Heartbeat Rate Estimation using Convolutional Neural Network

Heartbeat Rate Estimation using Convolutional Neural Network

引用

International conference on Inventive Computation Technologies (ICICT)

作者： J Calvin Abilash D Albert P Jayabharathi Department of CSE St. Joseph’s College of Engineering Chennai Tamil Nadu

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Heartbeat rate estimation using Convolutional Neural Network presents an innovative and non-invasive solution for cardiovascular monitoring. This approach harnesses computer vision and signal processing techniques to extract valuable physiological information from facial video data captured by a standard web camera. By analyzing subtle color variations associated with blood flow, the system provides continuous and instantaneous estimations of the user’s heartbeat rate. The proposed method involves the application of advanced signal processing algorithms, specifically utilizing photoplethysmography (PPG) principles, to the facial video stream. These algorithms track changes in pixel intensity over time, correlating with variations in blood volume during each cardiac cycle. To enhance accuracy and adaptability, machine learning models are employed, allowing the system to account for individual differences in skin tones, lighting conditions, and facial features. The result is a real-time analysis that enables continuous and unobtrusive monitoring of the user’s heart rate. A notable advantage of this approach lies in its accessibility and cost-effectiveness, as it leverages widely available web cameras without requiring specialized hardware. The proposed system’s robustness is evaluated across diverse scenarios, including varying lighting conditions and demographic groups, ensuring its reliability in real-world applications. This real-time heartbeat rate estimation using a web camera holds promise for a range of applications, from personalized health tracking to telehealth, marking a significant advancement in accessible cardiovascular monitoring

关键词： Heart beat Telemedicine Estimation signal processing algorithms Lighting Cameras Real-time systems

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VisionaryX-9: Efficient plant disease prediction model

VisionaryX-9: Efficient plant disease prediction model

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Communication, Computing and signal processing (IICCCS), IEEE International conference on

作者： Ishwari Singh Rajput Kapil Bhardwaj Deepak Sanwal CSE Department Grahpic Era Hill University Haldwani

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Agriculture, which is the foundation of worldwide sustenance, is facing substantial obstacles in the twenty-first century, mainly because of increasing demands for food production and the growing menace of plant diseases. Ensuring the well-being of crops is of utmost importance for the security of global food supply, which requires the timely and precise detection of plant diseases. Conventional approaches are becoming less effective in dealing with the changing agricultural dangers, necessitating the development of new and creative solutions. The incorporation of machine learning and deep learning technologies in recent agricultural practices has significantly transformed plant disease diagnosis, providing remarkable levels of accuracy, efficiency, and scalability. This study utilises advanced algorithms such as ResNet, VGG, and EfficientNet to improve the accuracy of disease identification, thereby revolutionising precision agriculture. The main objective of this article is to enhance the process of plant disease detection with the help of a sophisticated model, VisonaryX-9, that can not only properly identify plant diseases but also classify their symptoms. The objective is to empower farmers by furnishing them with timely information, enabling informed decisions that directly influence agricultural output and, consequently, global food production. Our contributions consist of the creation of VisonaryX-9, a hybrid model that merges the ResNet and VGG architectures. The model is validated with a dataset of having 87,295 real-world plant disease images. The results obtained indicates the the model outperforms other baseline model with an accuracy of 99.12%. Our objective is to reveal profound understandings that will enhance the discussion on sustainable and resilient farming methodologies.

关键词： Plant diseases Accuracy Food manufacturing Computational modeling Scalability signal processing algorithms signal processing Predictive models Telecommunication computing Security

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Deep Learning for Future: Advancements and Prospects in signal Denoising for Cognitive Radio

Deep Learning for Future: Advancements and Prospects in Sign...

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International conference on Optimization and Applications (ICOA)

作者： Noureddine El-haryqy Abdelmouttalib Bousrout Zhour Madini Younes Zouine Dept. electrical and telecommunication Laboratory of Advanced Systems Engineering (ISA) Kenitra Morocco

ISBN: (数字)9798350387353

ISBN: (纸本)9798350387360

Within communication systems, the challenge of noise and interference presents a formidable barrier to reliable data transmission. While classical denoising methods have provided valuable insights, their limitations impede their effectiveness. However, the emergence of deep learning offers a promising solution, especially within the domain of cognitive radio. Deep learning techniques exhibit remarkable efficacy in addressing noise and interference across various signal types. Recent research emphasizes the transformative potential of deep learning in advancing signal denoising methodologies, highlighting its pivotal role in bolstering the performance of cognitive radio systems.

关键词： Deep learning Noise reduction signal processing algorithms Interference Sensors signal denoising Cognitive radio Reliability Optimization signal to noise ratio

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Fast Erasure Decoding of Reed-Solomon Codes Based on Decomposition of Vandermonde Matrix

Fast Erasure Decoding of Reed-Solomon Codes Based on Decompo...

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International conference on Wireless Communications and signal processing (WCSP)

作者： Zebing Lin Jingjie Lv Pingping Li Linqi Song Hui Liang Hanxu Hou School of Computer Science and Technology Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan University of Technology Shenzhen Institute for Advanced Study University of Electronic Science & Technology of China Department of Computer Science City University of Hong Kong City University of Hong Kong Shenzhen Research Institute

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Reed-Solomon (RS) codes are widely applied in storage systems. The main bottleneck of RS codes is that the high decoding complexity defined as the number of multiplications and additions. In this paper, we propose a fast decoding algorithm for RS codes that requires fewer number of multiplications. Our fast decoding algorithm is designed by a decomposition of Vandermonde matrices. We show that our decoding algorithm requires fewer number of multiplications than the existing best known algorithm when the number of parities $T$ is large. When the codeword length $N=16$ , our decoding algorithm reduces the number of multiplications by 7% and 29% when $T=4$ and $T=7$ , respectively, compared with the best known decoding algorithm.

关键词： Reed-Solomon codes Wireless communication Codes Upper bound signal processing algorithms Transforms signal processing Decoding Matrix decomposition Galois fields

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Beamforming Optimization: Extended to Bidirectional Integrated Sensing and Communication System

Beamforming Optimization: Extended to Bidirectional Integrat...

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European signal processing conference (EUSIPCO)

作者： Siyang Guan Zehan Liu Rui Duan Jian Zhang Jun Bao School of Information and Communication Engineering University of Electronic Science and Technology of China Institute of Advanced Millimeter-Wave Technology University of Electronic Science and Technology of China

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

This paper investigates the joint transmit and receive beamforming of the bidirectional integrated sensing and communication (BD-ISAC) system. Through in-band full-duplex (FD), the BD-ISAC system aims to achieve the coexistence of bidirectional communication and radar detection. An optimization problem is proposed with the object of maximizing the weighted sum of communication rates, subject to the constraints of radar performance and interference suppression. To be more specific, the similarity between BD-ISAC beamforming weights and referential ones with ideal radar performance is constrained, therefore taking radar properties into account while optimizing communication rates. Nulling of interference is also introduced to tackle the challenges of self-interference within a node and mutual interference between nodes. An algorithm based on the weighted minimum mean square error (WMMSE) method is developed to tackle the non-convex problem. Numerical results demonstrate the effectiveness of the proposed beamforming method for BD-ISAC systems.

关键词： Array signal processing System performance signal processing algorithms Radar detection Radar Bidirectional control Transforms Integrated sensing and communication Transceivers Optimization

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