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Brain Stroke Detection from CT Images using Transfer Learning Method 13

Brain Stroke Detection from CT Images using Transfer Learnin...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Çinar, Necip Kaya, Buket Kaya, Mehmet Dicle University Department of Computer Engineering Diyabakir Turkey Firat University Department of Electronics and Automation Elaziǧ Turkey Firat University Dept. of Computer Engineering Elaziǧ Turkey

Recently, with the production of high-capacity computers, artificial intelligence methods have been used in many areas. In particular, in the field of health, artificial intelligence methods are used to detect diseases and determine the treatments to be applied. Deep learning methods, a sub-branch of artificial intelligence, show a high success in diagnosing many diseases thanks to its deep CNN networks. In this study, firstly, it was tried to determine which deep learning methods are more successful for the detection of brain stroke from computerized tomography images. This study is of great importance in terms of determining which deep learning architectures we will focus on in our future studies on brain stroke. In this study, brain stroke disease was detected from CT images by using the five most common used models in the field of image processing, one of the deep learning methods. The pre-trained ResNetl01, VGG19, EfficientNet-B0, MobileNet-V2 and GoogleNet models were run with the same dataset and same parameters. As a result, the EfficientNet-B0 architecture showed the highest performance with 97.93% accuracy. ResNetl01, VGG19, MobileNet-V2 and GoogleNet models showed 94.32%, 97.28%, 92.30% and 91.61% accuracy rates, respectively. With this study, it was concluded that the EfficientNet architecture is more suitable for the detection of brain stroke disease from CT images. © 2023 IEEE.

关键词： Diagnosis

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Low-cost Smart Glasses for People with Visual Impairments 1

Low-cost Smart Glasses for People with Visual Impairments

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1st IEEE International Conference on Advances in electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Nayana, B.R. Raju N R, Yogesh Velidi, Abhav S M S Ramaiah Univesrity of Applied Sciences Dept. of Computer Science Engineering Karnataka Bangalore India Sir M Visvesvaraya Institute of Technology Dept. of Electrical and Electronics Engineering Karnataka Bangalore India National Institute of Technology Dept. of Electronics and Commuincation Engineering Suratkal Karnataka India

ISBN: (纸本)9798350348057

An efficient visual aid is essential for visually impaired candidate. At the same instance it has to be simple, robust and cost effective. However, regardless of being expensive, it is challenging to incorporate high end artificial intelligence into a compact device to serve the purpose. This paper presents a prototype of low-cost smart glasses for visually impaired individuals. Also, a brief survey on the cost and features of the goggles available in the market are included. In this work a prototype is developed integrating Raspberry Pi module, camera, sensors and a goggles. The captured images are processed using object recognition models and its corresponding text is read aloud using google text to translator. Subsequently, a comparative analysis of object recognition model is explored and discussed. Essential experimental results are incorporated along with cost involved for the prototype development. © 2023 IEEE.

关键词： Object recognition

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Stripe Removal from Hyperspectral Food Images acquired by Handheld Camera using 2,1 Norm Minimization and SSTV Regularization

Stripe Removal from Hyperspectral Food Images acquired by Ha...

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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

作者： Shanthini, K.S. George, Sudhish N George, Sony Devassy, Binu Melit National Institute of Technology Dept. of Electronics and Communication Engineering Calicut India Norwegian University of Science and Technology Dept. of Computer Science Norway

ISBN: (纸本)9798350313635

Hyperspectral imaging offers the capacity to quickly and noninvasively monitor a food product's physical, chemical and morphological properties. Specim IQ is a handheld push broom camera with basic data handling and data analysis capabilities within the camera software. However, the recordings of the Specim IQ camera showed a line pattern (stripes) that was evident in all images. Stripes significantly reduce the visual quality of the images and lower the results of further processing. Hence an efficient destriping model is developed, which specifically addresses this issue. The proposed model uses a spatial gradient term to analyze the directional characteristics and group sparsity to describe the structural characteristics of the stripe component. In addition to this, a spatial spectral total variation regularization is used to ensure piecewise smoothness in the spatial and spectral domains and to remove Gaussian noise. The ensuing optimisation problem is solved using the alternating direction method of multipliers (ADMM). The proposed method is tested in real stripe noise environments, and the findings demonstrate that it outperforms some of the best approaches in terms of visual quality and quantitative evaluations. When compared with the other approaches, the proposed method attained the highest noise reduction (NR) and lowest mean relative deviation (MRD) values (NR=1.67, MRD=1.02%). © 2023 IEEE.

关键词： Noise abatement

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A comparative study for lung, colon and breast cancer diagnosis using different convolutional neural networks 7

A comparative study for lung, colon and breast cancer diagno...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Akar, Hussein Diab, Jacqueline Abou Sbeity, Fatima Ali, Mohamad Abou Kassem, Abdallah Hamawy, Lara Lebanese International University Dept. of Biomedical Engineering Lebanon Lebanese University Dept. of Physics & Electronics Lebanon University of the Basque Country Dept.of Computer Science & Artificial Intelligence Spain Notre Dame University Dept. of Electrical &computer Engineering Lebanon

ISBN: (纸本)9798350325850

In modern times, the prevalence of diseases, particularly cancer, is rising rapidly. Diagnostic errors pose a critical safety concern in healthcare due to the complexity of medical decision-making, disease variability, and human cognitive limitations. This paper aims to compare different convolutional neural networks (CNNs) to find the most accurate model that can classify lung, colon, and breast cancer using histopathological images. A convolutional neural network is a subset of machine learning. It is one of the various types of artificial neural networks which are used for different applications and data types. A CNN is a kind of network architecture for deep learning algorithms and is specifically used for image recognition and tasks that involve the processing of pixel data. We created a baseline model that consists of a basic CNN architecture. It serves as a starting point for comparison with more complicated models. The baseline model's performance is then compared to the performance of various pretrained models (ResNet-50, ResNet-101, VGG-16, VGG-19, Inception V3, and DenseNet-121). The dataset used consists of 25,000 histopathological images of both the lung and colon and 277,000 histopathological of breast images. Results show that the ResNet-50 model is the most efficient among all the other models, scoring a validation accuracy of 98.3%. Additionally, ResNet-101, VGG-16, VGG-19, and DenseNet-121 also demonstrated strong performance, scoring 95.8%, 96.6%, 95.1%, and 92.4%, respectively. Meanwhile, Inception-V3displayed a fair result, scoring a validation accuracy of 73.2%. © 2023 IEEE.

关键词： Convolutional neural networks

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Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications 31

Velocity-aided Channel Estimation for Spatially Selective mm...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Vlachos, Evangelos Mavrokefalidis, Christos Berberidis, Kostas Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (纸本)9789464593600

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A Novel Flow Priority and Continuity Control Mechanism in SDN Network 1

A Novel Flow Priority and Continuity Control Mechanism in SD...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Yaseen, Fouad A. Alkhalidi, Nahlah Abdulrahman Al Wahaibi, Fawziya Nasser University of Baghdad College of Engineering Electronic and Communications Dept Iraq University of Baghdad College of Science Computer Science Dept. Iraq University of Technology and Applied Science College of Engineering Elecrical and Electronics Dept Oman

ISBN: (纸本)9798350348637

In recent days, the escalating need to seamlessly transfer data traffic without discontinuities across the Internet network has exerted immense pressure on the capacity of these networks. Consequently, this surge in demand has resulted in the disruption of traffic flow continuity. Despite the emergence of intelligent networking technologies such as software-defined networking, network cloudification, and network function virtualization, they still need to improve their performance. Our proposal provides a novel solution to tackle traffic flow continuity by controlling the selected packet header bits (Differentiated Services Code Point (DSCP)) that govern the traffic flow priority. By setting the DSCP bits, we can determine the appropriate precedence level for the data traffic flow. The control and modification of the DSCP bits directly impact the priority assigned to data packets, thereby shaping the traffic flow continuity accordingly. The evaluation and performance analysis of the proposed network were conducted using the Mininet simulator and the MATLAB platform. The outcomes of the comprehensive testing demonstrate that the implementation of our novel priority management technique has successfully reduced queuing delay and minimized packet loss. As a result, the overall data traffic continuity has been significantly enhanced. The obtained results demonstrate that the traffic flow continuity, handled by the Software-Defined Networking (SDN) controller with the support of DSCP modification, has increased by approximately 65% when implementing the proposed priority management based on DSCP bits modification in the SDN network. © 2024 IEEE.

关键词： MATLAB

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Graph Convolutional Networks for Predicting State-wise Pandemic Incidence in India 2

Graph Convolutional Networks for Predicting State-wise Pande...

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2nd International Conference on Artificial Intelligence and Signal Processing, AISP 2022

作者： Sriraman, Siddharth Manjunathan, R. Sivakumar, Nethraa Pooja, S. Viswanath, Nikhil Dept. of Computer Science and Engineering SSN College of Engineering Kalavakkam India Dept. of Electronics and Communication Engineering SSN College of Engineering Kalavakkam India

ISBN: (纸本)9781665442909

In this paper, we analyze the performance of graph convolutional networks (GCNs) in predicting COVID-19 incidence in states and union territories (UTs) in India as a semi-supervised learning task. By training the model with data from a small number of states whose incidence is known, we analyze the accuracy in predicting incidence levels in the remaining states and UTs in India. We explore the effect of pre-existing factors such as foreign visitor count, senior citizen population and population density of states in predicting spread. To show the robustness of this model, we introduce a novel method to choose states for training that reduces bias through random sampling in five regions that cover India’s geography. We show that GCNs, on average, produce a 9% improvement in accuracy over the best performing non-graph-based model and discuss if the results are feasible for use in a real-world scenario. © 2022 IEEE.

关键词： Graphic methods

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Utilizing Anomaly Detection Methods for Identifying Fraudulent Activities in Credit Card Transactions 1

Utilizing Anomaly Detection Methods for Identifying Fraudule...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Prasad, N. Siva Rama Krishna Gangopadhyay, Amit Prabakaran, R. Mishra, Nilamadhab Prasad, D. Shyam Kapoor, Sneha Saveetha School of Engineering Dept. of CSE Chennai India Mohan Babu University Dept. of ECE Tirupati India Arignar Anna Institute of Science and Technology Dept. of Computer Application India VIT Bhopal University Dept. of CSE Bhopal India CVR College of Engineering Dept. of Electronics and Instrumentation Engineering Hyderabad India Swami Vivekanand Subharti University Dept of CSE Meerut India

ISBN: (纸本)9798350356816

Anomaly detection involves identifying unusual occurrences that could signal potential issues, such as security breaches, system failures, financial fraud, structural defects, or medical errors. In the context of digital finance, credit card fraud has become a significant concern, with fraudulent transactions often designed to appear legitimate. To address this, we employ the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset during sampling, and use the ARIMA model for time series forecasting to ensure accurate results. This research explores various fraud detection techniques and models, including K-Nearest Neighbors (KNN), Random Forest, Support Vector Classifier (SVC), and Decision Trees. We evaluate and compare the performance of these models to determine which offers the highest accuracy in detecting fraud. © 2024 IEEE.

关键词： Anomaly detection

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An Efficient Authentication System to Access Electric Vehicle Data in The Cloud Based on Identity Role-based Access Control

An Efficient Authentication System to Access Electric Vehicl...

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International electronics Symposium (IES)

作者： Amang Sudarsono Rahardhita Widyatra Sudibyo Idris Winarno Mike Yuliana Dept. of Electrical Engineering and Dept. of Informatics & Computer Engineering Politeknik Elektronika Negeri Surabaya (PENS) Electronics Engineering Polytechnic Institute of Surabaya (EEPIS) Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Security is one of the main considerations in choosing file storage media. Cloud server is one of storage mediums that provides virtual resources to users through third party and it can be accessed through the Internet connection. Cloud computing has several advantages, such as ease to access, ease to control, user-friendly to operate, and flexible capacity. However, these advantages cause potential security problems such as data breach attack. Therefore, a strong security system becomes important to overcome the losses caused by these security problems. A solution to increase the security level is utilizing cryptography algorithms. Due to the discovery of several loopholes in the traditional cryptosystems, the concept of using Computational Deoxyribonucleic Acid (DNA) cryptosystem was identified as a possible more complex cryptosystem to solve. Meanwhile, Role-based Access Control (RBAC) provides efficient and strong ways to manage authorization of the system resources based on user authentication. By cooperating with Identity-based Cryptography (IBC) and DNA cryptosystem, we propose an efficient authentication system to securely access electric vehicle (EV) data stored in the cloud server based on identity-based RBAC.

关键词： Cloud computing System performance DNA Authentication Public key Media Electric vehicles

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ML based Pre-Diagnosis Framework for early detection of Neurodevelopmental Disorders 4

ML based Pre-Diagnosis Framework for early detection of Neur...

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4th International Conference on Advancement in electronics and Communication engineering, AECE 2024

作者： Pandey, Sumit Sharma, Srishti Agarwal, Anant The Northcap University School of Engineering and Technology Dept. of Computer Science Haryana Gurugram122017 India Aligarh Muslim University Zakir Husain College of Engg. and Tech Dept. of Electronics Engineering Uttar Pradesh Aligarh202001 India

ISBN: (纸本)9798350364729

Neurodevelopmental disorders cover a wide range of behavioral and cognitive symptoms mapped to various conditions. Early diagnosis and planned interventions are crucial for improving outcomes and the quality of life for people suffering from such disorders. This research work talks about a detailed framework with the objective of assisting in the early pre-diagnostic phase of neurodevelopmental disorders. The motivation for the development of a ML-based framework in pre-diagnosis of neurodevelopmental disorders is the effective and zero-cost getting diagnosed. This framework will integrate knowledge from diverse domains to systematize and make recognition of early signs and symptoms associated with neurodevelopmental disorders much more efficient. Additionally, the framework emphasizes two very critical parameters: accessibility and cost-effectiveness. The developed pre-diagnostic mechanism can be implemented in a diversified variety of scenarios, especially with limited resources, towards zero cost. The implementation of advanced machine learning techniques can assist in the analysis of the response to a customized questionnaire in identifying patterns that may suggest a risk of neurodevelopmental disorders. This proactive approach not only supports and facilitates early detection and intervention but also seeks to add towards the overall well-being of patients with these disorders. © 2024 IEEE.

关键词： Adversarial machine learning

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