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Comparative Analysis of vision Transformers and CNN-based Models for Enhanced Brain Tumor Diagnosis

Comparative Analysis of Vision Transformers and CNN-based Mo...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： Asmi Mangesh Kocharekar Satavisha Datta Padmanaban Rajakumar R. Final Year B.Tech Computer Science VIT Chennai Chennai India SCOPE VIT Chennai Chennai India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Patient care quality is directly linked to the accuracy and success of diagnostic radiography, notably in the diagnosis and treatment of brain tumors. Central to both medical treatments used for diagnosis and treatment, segmentation serves as a fundamental precursor. Recent advancements in machine learning aim to improve the effectiveness of diagnosis by transitioning from specialized applications to generalized algorithms. Unsupervised anomaly detection plays a crucial role in identifying possible areas of anomaly, particularly those not observed during instruction. Autoencoders based on convolutional neural networks (CNN) and variational autoencoders have emerged as standard approaches for segmenting anomalies based on reconstruction. However, CNNs' constrained receptive fields impose limitations on their ability to capture global context, thereby hindering semantic understanding when large sections of the image are filled with anomalies. We investigate the possibilities of vision transformers (ViTs) in this research as a viable alternative to CNNs for brain magnetic resonance imaging abnormality segmentation. ViTs depend on self-attention mechanisms to establish relationships between image patches, offering a more comprehensive view of the image. We present a comparative study wherein five transformer-based models are developed to reconstruct coherent and realistic images for anomaly segmentation. Our findings demonstrate that these ViT-based models achieve segmentation effectiveness that is on par with or surpassing cutting-edge CNN-based methods. By harnessing the capabilities of ViTs, we pave the way for improved anomaly detection and segmentation in brain MRI, thereby enhancing diagnostic accuracy and patient care in radiology practice.

关键词： Image segmentation computer vision Accuracy Magnetic resonance imaging Medical treatment Machine learning Radiology Transformers Brain modeling Convolutional neural networks

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SSVEP-based brain-computer interface for bidirectional human-vehicle interaction 3

SSVEP-based brain-computer interface for bidirectional human...

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3rd international conference "Neurotechnologies and Neurointerfaces", CNN 2021

作者： Mironov, Vasiliy Bolshakov, Denis Gulyaev, Vadim Komarov, Alexey Uslugin, Nikolai Kazarin, Petr Zhukov, Sergey Li, August Salikhov, Rustem Kazantsev, Victor The Institute of Living Systems Immanuel Kant Baltic Federal University Kaliningrad Russia Radiophysics Department National Research Lobachevsky State University Nizhny Novgorod Russia Lobachevsky University Engineering Center LLC Nizhny Novgorod Russia

ISBN: (纸本)9781665424745

The loss of attention while driving is considered as one of the reasons for the increase in road accidents’ rate. Car manufacturers equip some models with the visualization systems projecting assistive information on the windshield to solve this problem. Also, technically simpler devices have become widespread, in which the image is formed by an LCD or a cell phone screen lying on the dashboard. However, the solution mentioned removes the problem of loss of attention only partially because of the necessity to interact with peripheral devices. In this paper, an approach providing bidirectional human-vehicle interaction is presented. To create the control flow from a car driver to peripheral devices, the brain-computer interface technology is used. For this purpose, we implemented an additional mode of the head-up display functioning: presentation of flickering visual stimuli. Classification methods of the EEG brain activity determine the stimulus on which the user's attention is focused, and the command associated is then performed. To examine the performance of this approach the test platform was made and the series of tests were carried out. The results obtained validate the efficiency of the proposed method and indicate the direction for further improvement. © 2021 IEEE.

关键词： Liquid crystal displays

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Reliability Analysis of Instrument Power System Based on Monte Carlo-Dynamic Fault Tree 3

Reliability Analysis of Instrument Power System Based on Mon...

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3rd international conference on System Reliability and Safety Engineering, SRSE 2021

作者： Chen, Sipei Wang, Lili Wei, Zhenhua Qiao, Xingyu State Nuclear Electric Power Planning Design Research Institute Co. LTD Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781665401609

The reliability analysis of nuclear power conventional island instrument power supply system is an important part of the reliability research of nuclear power unit. Aiming at the structural characteristics of nuclear power meter power supply system, the reliability analysis method of instrument power supply system based on Monte Carlo-dynamic fault tree is proposed. According to the component composition and connection logic of the instrument power supply system, the dynamic fault tree model is established and Monte Carlo simulation is used to calculate the probability importance index of each component of the system, and suggestions for improving the reliability of the system are proposed. The simulation results show that the method can effectively and accurately quantitatively analyze the reliability of the instrument power supply system, and has the characteristics of strong applicability and high calculation efficiency. It has important guiding significance for the design and transformation of other instrument power supply systems. © 2021 IEEE.

关键词： Reliability analysis

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Input-Output Stability Based Model Reference Adaptive control of a Passive Pneumatic Teleoperation System 3

Input-Output Stability Based Model Reference Adaptive Contro...

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3rd IEEE international Multidisciplinary conference on Engineering Technology, IMCET 2021

作者： Daher, Leen Daher, Naseem American University of Beirut Department of Electrical and Computer Engineering Beirut Lebanon

ISBN: (纸本)9781665405874

The design of an input-output stability based model reference adaptive control system for a passive pneumatic teleoperation system is presented in this paper. The master and slave actuators of the system are connected by electric and pneumatic signals that are transmitted via long transmission lines, which substitute the use of force and pressure sensors. The system consists of a passive connection that reduces the cost and complexity of the teleoperation system as compared to other conventional systems. An adaptive controller is designed due to its ability to cope with parametric uncertainties, given that the system runs under varying operating conditions, while maintaining stability and guaranteeing a near-unity transparency ratio, as demonstrated in the provided simulation results. © 2021 IEEE.

关键词： Electric lines

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DeepLeafNet: Multiclass Classification of Soybean Plant Leaves with ResNet50V2 for Enhanced Crop Monitoring and Disease Detection

DeepLeafNet: Multiclass Classification of Soybean Plant Leav...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Gunjan Sharma Vatsala Anand Sonal Malhotra Sanjeev Kukreti Sheifali Gupta Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The soybean, which is a fundamental component of worldwide agriculture, plays a crucial role in meeting the global need for protein and oil. Nevertheless, the spread of many diseases poses a significant risk to both the quantity and quality of soybean harvests. The timely and accurate categorization of diseases is crucial for the successful implementation of crop management strategies. This study aims to enhance the categorization of soybean diseases by addressing ten different disease classes using the ResNet50V2 deep learning model. In order to accomplish this objective, the Soybean Disease Leaf Image Classification Dataset has been employed for both the purposes of training as well as evaluating the model. The ResNet50V2 model's architectural depth and inclusion of skip connections augment its capacity to grasp complicated patterns present in leaf images, hence bolstering its efficacy in performing robust categorization. The experimental findings provide evidence for the effectiveness of our methodology since the model attains a notable training accuracy of 97% and validation accuracy rate of 96% when diagnosing illnesses in soybean plant leaves with a minimum loss of 0.05 and 0.38. Moreover, this research endeavor makes a valuable contribution to the expanding domain of computer vision in the agricultural sector, facilitating the automation and enhancement of plant disease diagnosis processes.

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Automated Denoising of Diabetic Retinopathy Images for Enhanced Medical Diagnosis

Automated Denoising of Diabetic Retinopathy Images for Enhan...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： G.V. Suresh Gokavarapu Pavani Naga Pranathi Sri Krishna Sai Angidi Tumma Sai Dheeraj Goda Naga Venkata Snehitha Department of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering(Autonomous) Mylavaram India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Globally, diabetic retinopathy (DR) continues to be a major cause of vision impairment. Early diagnosis and detection of DR are essential to preventing blindness and vision loss. But the traditional manual method of screening retinal pictures takes a lot of time and is prone to human mistake. Using image processing to apply automated denoising techniques can increase image quality, make DR lesions more visible, and help with correct diagnosis. In order to improve the quality of images of diabetic retinopathy, this study investigates the ability of four image denoising filters: guided image filtering, Gaussian-bilateral filtering, Gabor filtering, and Haar Wavelet filtering. AGAR300 (IEEE DataPort), Diabetic Retinopathy Detection, Stare, Indian Diabetic Retinopathy Image Dataset, and APTOS 2019 Blindness Detection are the five datasets used to evaluate the efficacy of these filters. The findings demonstrate that noise in DR images may be efficiently reduced by all four filters. The one that shows the biggest overall improvement in lesion visibility and image quality among them is guided image filtering.

关键词： Image quality Diabetic retinopathy Noise reduction Visual impairment Blindness Feature extraction Retina

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Leaf Disease Detection and Crop Prediction Using Machine Learning 3

Leaf Disease Detection and Crop Prediction Using Machine Lea...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Wahal, Asthha Choudhary, Shyam Sagar Singh Singh, Nandika Galgotias University School of Computer Science Engineering UP Greater Noida India

ISBN: (纸本)9781665438117

India as an agricultural country, economy is heavily dependent on increasing agricultural yields and related products for agricultural industries. When plants and plants are affected by pests it affects the country's agricultural products. Farmers or experts often look at plants with the naked eye to detect and diagnose diseases. But this approach can be time-consuming, expensive, and inconvenient. Automatic detection using image processing techniques provides fast and accurate results. Agricultural growth also depends on specific soil parameters, including nitrogen, potassium, crop rotation, soil moisture, soil temperature, and temperature, rainfall, etc. the project provides a Smart Agriculture solution by monitoring the agricultural sector which can assist farmers to a greater extent by increasing productivity. Climate data obtained from the IMD (Indian Metrological Department) such as temperature and rainfall and soil boundary reserves provide an understanding of whether the plants are suitable for planting in a given region. This gives farmers many farming options. Therefore, the project develops a framework by combining data from a variety of sources, data analytics, analytical analysis that can increase crop productivity and improve farmers' profit margins, benefiting them in the long run. © 2021 IEEE.

关键词： Productivity

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A Literature Review: Performance Evaluation of Wearable system with Pill Dispenser Box for Post Covid Elderly Patients 3

A Literature Review: Performance Evaluation of Wearable syst...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Kumar, S. Kalimuthu Manimegalai, R. Rajeswari, A. Deekshita, R. Dhineshkumar, M. Manikandan, G. Kalasalingam Academy of Research and Education Department of Biomedical Engineering Tamil Nadu India Dr. M.G.R Educational and Research Institute Department of Computer Science Engineering Tamil Nadu India

ISBN: (纸本)9781665438117

In today's world, the number of diseases like covid 19, pneumonia is increasing due to ignorance, lethargy, and some medication non-adherence of the individuals. So, there is a need to monitor the vitals of an individual. Especially elder patients are greatly affected because of their age-related complications like Diabetes, Hypertension, or Hypotension. The most common vitals that have to be noticed are heart rate, body temperature and respiratory rate. But the existing techniques proposed either a health monitoring system or pill dispenser. The de-merits of these techniques are the patients have to afford two different systems to take care of their health. So, therefore in this paper, we've discussed a solution to this by proposing an idea of a wearable Health monitoring system along with a Smart pill dispenser box. Therefore, the goal of this survey is to evaluate the different methods of monitoring systems using the technology used and to compare its merits and demerits to help the upcoming researchers to make a system as per the need to overcome the coercions in the existing systems. © 2021 IEEE.

关键词： Dispensers

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Comparative Analysis of Deep-Fake Detection Methods

Comparative Analysis of Deep-Fake Detection Methods

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Disruptive Technologies (ICDT), international conference on

作者： Saiful Islam Rahmani Tarun Pal Vineet Chaudhary Ms. Bhumika Nirmohi Department of Computer Science and Engineering (AI) G.L. Bajaj Institute of Technology and Management Gr. Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The easy availability of deepfakes because of advancements in generative adversarial networks (GANs) and other deep learning models, has led to popularization of extremely realistic fake media which can easily be misused. This review paper gives a comparative analysis of different deepfake detection methods in images as well as videos format, it focusses on advanced methodologies like hybrid deep learning, surface anomaly detection, vision language adoption, and transfer learning. Different methods like CNN-LSTM, GAN based approaches and architectures like CLIP, have different strengths in deepfake detection. Each method is assessed for its effectiveness in different real-world scenarios, generalization capabilities and performance with multiple datasets. We will also look into the shortcomings in every method. This analysis aims to find optimal deployment of these techniques and find which technique is optimal for detecting deepfakes to combat misuse and misinformation caused by such altered images and videos.

关键词： Deep learning Deepfakes Adaptation models Analytical models Reviews Transfer learning Generative adversarial networks Data models Convolutional neural networks Long short term memory

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Analysis and Prediction of Drugs using Machine Learning Techniques 3

Analysis and Prediction of Drugs using Machine Learning Tech...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Dadhwal, Abhay Gupta, Meenu Chandigarh University Department of Computer Science Engineering Punjab India

ISBN: (纸本)9781665438117

In the era of increasing demand of data analytics in drug discovery domain, processing the complex high dimensional drug datasets is a challenging research area. High dimensionality refers to dataset having more number of columns than rows in which there may be irrelevant features that are futile. For reducing high dimensionality issue, feature selection techniques are widely adopted. Feature selection aims to identify relevant subset of features from original feature set before the training the machine learning model. From variety of feature selection techniques available one can select according to his requirement that can include faster processing, low resource consumption, high accuracy etc. In this paper, a high dimensional drug dataset is used to train the machine learning model. The rationale is to analyze and compare the performance of various feature selection techniques namely Pearson Correlation Coefficient, Chisquare test, Lasso, Treebased, Anova-f to produce best predictive performance. When a feature selection method is applied, the results are tested using K fold cross validation with state-of-the art machine learning algorithms. These numerical results of methods are presented and compared to acquire insight about these methods leading to better generalization and accuracy and also in making better future decisions. © 2021 IEEE.

关键词： Feature Selection

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