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Early Detection of Brain Tumor using ResNet50 and Transfer Learning 3

Early Detection of Brain Tumor using ResNet50 and Transfer L...

引用

3rd international conference on automation, Computing and Renewable Systems, ICACRS 2024

作者： Desai, Sandip Mushrif, Milind Dept. of Electronics and TelecommunicationEngineering Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9798331532420

Brain tumor is a disease which can be cause of death of a patient. In India, every year 40000 new brain tumor cases are diagnosed. Identification and early detection of brain tumor will be helpful in timely treating the patient. Manual process of classification of brain tumor suffers from human error. The computer aided detection (CAD) methods are very helpful to reduce this human intervention in brain tumor classification. This research work presents approach of using concept of transfer learning for brain tumor detection. Here we have compared proposed method with various pre trained deep learning models for the classification of brain tumor. We have observed that ResNet50 with modified top layer outperforms over all the available pre trained CNN models. © 2024 ieee.

关键词： Transfer learning

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MultimodNet: A Multimodal Deep Learning Model for COPD Staging based on Chest X-Ray and Pulmonary Function Tests 3

MultimodNet: A Multimodal Deep Learning Model for COPD Stagi...

引用

3rd international conference on automation, Computing and Renewable Systems, ICACRS 2024

作者： Krishnaveni, A. Rajkumar, G. Vinoth Raj, J. Relin Francis Krishnan, R. Santhana Murali, S. Settu, Imran Javeed Department of Mechanical Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronicsand Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (纸本)9798331532420

Chronic Obstructive Pulmonary Disease (COPD) represents a major global health challenge, with early and accurate diagnosis being essential for improving patient outcomes. Traditional diagnostic approaches often rely on single-modality assessments, which may not fully capture the complexity of the disease. This research introduces MultimodNet, a deep learning framework that integrates both chest X-ray images and Pulmonary Function Test (PFT) data to provide a more comprehensive classification of COPD stages. By combining structural information from chest X-rays with functional lung metrics like FEV1, FVC, and the FEV1/FVC ratio, MultimodNet aims to enhance the accuracy and reliability of COPD diagnosis. The core strength of MultimodNet lies in its multimodal approach, which allows for the processing and fusion of diverse data types to overcome the limitations of using a single modality. This integration helps the model provide more accurate predictions regarding COPD progression, classifying patients into one of four stages: Initial, Progressive, Complicated, and Critical. These stages reflect the disease's clinical trajectory, from the onset of symptoms through to advanced stages where life-threatening complications may arise. By leveraging both imaging and clinical data, MultimodNet offers a powerful diagnostic tool that can assist healthcare providers in making early and precise decisions, leading to better treatment planning and improved patient care. This framework has the potential to not only enhance diagnostic workflows but also contribute to the broader field of AI-driven healthcare solutions, where multimodal data integration plays a key role in advancing disease detection and management. © 2024 ieee.

关键词： Patient treatment

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Dimensional Lifting, Finite-Time Convergence, and Bearing-Only Algorithms for the Fermat-Weber Location Problem 12

Dimensional Lifting, Finite-Time Convergence, and Bearing-On...

引用

12th ieee international conference on Control, automation and Information sciences, ICCAIS 2023

作者： Le-Phan, Nhat-Minh Trinh, Minh Hoang Nguyen, Phuoc Doan Hanoi University of Science and Technology School of Electrical and Electronic Engineering Department of Automation Engineering Hanoi Viet Nam Viettel Aerospace Institute Hanoi Viet Nam Fpt University Quy Nhon AI Campus cBinh Dinh Viet Nam

ISBN: (纸本)9798350328783

In this paper, some novel results on the Fermat Weber Location Problem (FWLP) with stationary beacons are reported. We propose using dimensional lifting to prevent collision between the agent and the beacons and bearing-only control laws to guide an agent to the Fermat-Weber point. To improving convergence rate, finite-time bearing-only control laws are also provided. The stability and robustness of the Fermat-Weber point under the proposed algorithms are asserted from a control approach and supported by numerical simulations. © 2023 ieee.

关键词： Control theory

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SMCS TEAM: Open Course on Cyber Physical Systems Foundation and Design for Unmanned Aerial Vehicles (UAVs)

SMCS TEAM: Open Course on Cyber Physical Systems Foundation ...

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2024 ieee international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wan, Yan Fu, Shengli Xie, Junfei Lu, Kejie The University of Texas at Arlington Electrical Engineering ArlingtonTX75025 United States University of North Texas Electrical Engineering DentonTX United States San Diego State University Department of Electrical and Computer Engineering San DiegoCA92182 United States The University of Puerto Rico at Mayaguez Department of Computer Science and Engineering Mayaguez Puerto Rico

ISBN: (纸本)9781665410205

This abstract describes the project funded by the ieee SMCS on Transforming Educational Assets and Materials (TEAM) in Systems, Man, and Cybernetics. The project develops an open course on Cyber Physical Systems (CPS) Foundation and Design for Unmanned Aerial Vehicles (UAVs). The course will be available to the public and serve the need of researchers, students and professionals who are interested in conducting UAVs related research. The open course contains integrated modules on control, communication and networking, computing, and artificial intelligence (AI) to provide trainees a comprehensive knowledge needed for UAVs. The course is self-paced and contains quizzes in each module for help students assess the quality of learning and also allow course designers to evaluate the effectiveness of the course materials for continuous improvement. The open course promotes CPS which is a SMCS technical field. It will also attract students and professionals to the SMC community. © 2024 ieee.

关键词： Antennas

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Interpretable Roadside 2D Object Detection Based on Axial Attention 24

Interpretable Roadside 2D Object Detection Based on Axial At...

引用

24th ieee/ACIS international conference on computer and Information science, ICIS 2024

作者： Wu, Fan Song, Shaojing Hu, Jinyan Shanghai Polytechnic University Faculty of Intelligent Manufacturing and Control Engineering Shanghai China College of Computer and Informational Engineering Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9798350376647

Environmental perception plays a crucial role in the operation of autonomous vehicles. Recent studies have relied on single vehicle cameras for perception due to the lack of roadside view dataset, but this approach provides limited perception information. To address this limitation, there is a growing interest in studying roadside perception to complement vehicle-side perception information. However, existing methods face challenges in accurately recognizing and locating small objects or objects under occluding conditions in the roadside. And autonomous vehicles require millisecond-level decision-making for safe driving, yet many advanced object detection methods struggle to meet the need for high-speed reasoning. Additionally, deep learning models often make black-box decisions without providing explanations or understanding of their decision-making process. To tackle these issues, this study proposes an interpretable 2D object detection method for roadside based on the YOLO V8 with Axial Attention replacing the CNN network within the network architecture. The trained model is then input into HiResCAM to visualize the decision-making process of the roadside detection model. Experimental results on DAIR-V2X-I and Rope3D demonstrate that our proposed method not only improves mean Average Precision (mAP) but also enhances image processing speed by 1.5 to 2 times compared to existing methods. By visualizing the decision-making process of the roadside detection model, it make autonomous vehicle perception more transparent and credible. © 2024 ieee.

关键词： Deep learning

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Defending Against Wormhole Attacks in Wireless Networks Using the Twofish Algorithm: A Performance Analysis 14

Defending Against Wormhole Attacks in Wireless Networks Usin...

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2024 ieee 14th Annual Computing and Communication Workshop and conference, CCWC 2024

作者： Rathore, Pramod Singh Sarkar, Mrinal Kanti University of Engineering and Management Jaipur India University of Engineering and Management Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350360134

Wireless Sensor Networks (WSNs) are becoming more essential in areas such as environmental monitoring, industrial automation, and healthcare. However, their wireless nature leaves them vulnerable to security threats such as eavesdropping, tampering, and attacks. This paper introduces a robust and secure algorithm for WSNs that enhances their security and reliability by incorporating key distribution, packet timestamping, location-based detection, wormhole detection, packet encryption, and wormhole prevention. Our algorithm utilizes the TwoFish algorithm to establish a shared key, timestamps each data packet using a synchronized network time protocol, monitors hop count, determines the distance between nodes using RSSI or other location-based techniques, exchanges probe packets to detect wormhole attacks, and encrypts data packets with TwoFish. In the event of a wormhole assault, it isolates the affected network segment by obstructing communication in that area. The efficacy of this approach is validated through implementation in a simulated NS-2 network environment, demonstrating significant enhancements in the security and reliability of WSNs against wormhole attacks. © 2024 ieee.

关键词： Wireless sensor networks

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Advancements in Object Detection for Accurate and Efficient Visual Recognition Using Machine Learning 2

Advancements in Object Detection for Accurate and Efficient ...

引用

2nd ieee international conference on Advances in Information Technology, ICAIT 2024

作者： Fernandez, F. Mary Harin Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Chennai India Computer Engineering Department University of Technology and Applied Sciences Nizwa Oman

ISBN: (纸本)9798350383867

Object detection in images and videos is a vital task in computer vision where its application goes beyond the automobile driving and surveillance, to healthcare. The paper introduces an object detection model which is specific and powerful, that is able to recognize visually with machine learning. The framework is constructed on the basis of the recent innovations in deep learning and attentive mechanisms, which not only lead to the best performance in object detection today but also guarantee the widest applicability. Using an extensive experiment which reveals the described model superiority over the current state-of-the-art algorithms by evaluating mAP indices, whereas the latter has a normal complexity. The ablation study uncovers the impact of core parameters including attention mechanisms and semi-supervised techniques, underlining their importance in the growth of detection precision. Moreover, the illustrative comparison of outcomes from detecting objects on the framework calls for attention to the capacity of the networks to objectify in different categories. Our outcome also suggest that although the proposed framework has a human-level performance in task of object detection there is still a scope to enhance their performance above the human level. Summarizing, this work concludes the conversation and can be considered in the progress of research developments of the object detection systems aimed at practical object meeting situations. © 2024 ieee.

关键词： computer Vision Deep Learning Machine Learning Object Detection Visual Recognition

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Data Dimensionality Reduction using Principal Component Analysis: A Case Study 1

Data Dimensionality Reduction using Principal Component Anal...

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1st ieee international conference on Communications and computer science, InCCCS 2024

作者： Ramasubramanian, Srivaths Sowmyarani, C.N. Athreya, Amogh J. Devarajan, Anjali Shankar, Adithya Udaya Kumar, Ramakanth P. Dept. of ECE RV College of Engineering Bangalore India Dept. of CSE RV College of Engineering Bangalore India Dept. of CSE RV College of Engineering Bangalore India

ISBN: (纸本)9798350358858

Training of Machine Learning (ML) models requires huge amounts of data. Usually, in the training of sophisticated models, data-sets can be very computationally intensive on resources. In order to reduce the computational burden of large training data-sets on ML models, the work describes Principal Component Analysis (PCA), a popular dimensionality reduction technique used in ML to reduce the size of training data-sets. This method aims to transform the given set of variables into a new set of components on a lower dimensional subspace. After dimensionality reduction, these components retain the prominent features of data, which leads to reduction in volume and efficient analysis. Inferences on the choice of number of components are presented by analyzing the results obtained from variance contribution plots. © 2024 ieee.

关键词： Principal component analysis

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Enhancing Question-Answering with Knowledge Graph Retrieval and Generation using LLMs

Enhancing Question-Answering with Knowledge Graph Retrieval ...

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2024 international conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Gijre, Supraj Agrawal, Rishi Laddha, Priyash Keswani, Gunjan Shri Ramdeobaba College of Engineering and Management Dept. Computer Science and Engineering Nagpur India School of Computer Science and Engineering Ramdeobaba University Nagpur India

ISBN: (纸本)9798331517953

This paper describes a novel technique to improving Large Language Models (LLMs) for document analysis that employs knowledge graphs and retrieval-augmented generation (RAG). We are working on constructing a chatbot system that can handle and analyze large documents from a variety of fields. Our approach addresses basic LLM issues including context maintenance and hallucination avoidance. The system combines document chunking, vector embedding, and similarity search with graph-based knowledge representation. Users can upload large papers and answer questions accurately. We show that integrating standard information retrieval approaches with graph-based storage and LLM capabilities improves context awareness and response accuracy across a wide range of document genres. This strategy is especially promising for complicated publications such as financial reports. © 2024 ieee.

关键词： Knowledge graph

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General Population Projection Models with Census Data

General Population Projection Models with Census Data

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2024 ieee international conference on Future Machine Learning and Data science, FMLDS 2024

作者： Tsuruga, Takenori Qiao, Haiyan School of Computer Science and Engineering California State University San Bernardino San Bernardino United States

ISBN: (纸本)9798350391213

This paper applies machine learning methods to generate effective population projections using the American Community Survey (ACS) 5-Year Estimate data. To quantify the error margin in the population projection for each city, conformalized quantile regression, a form of conformal prediction, is used. The modeling process encompasses both statistical machine learning models and deep learning models. The results are presented as visualizations, featuring combined plots of selected statistics for Riverside County in California, which serve as the test dataset. © 2024 ieee.

关键词： Adversarial machine learning

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