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Fusion of Images to Detect Brain Tumors Using DWT, IDWT and VGG19 Algorithms

Fusion of Images to Detect Brain Tumors Using DWT, IDWT and ...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Noorul Julaiha A G B. Vasudevan Ruhit Krishnan M Suriya M S Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Dept. of Electronics & Communication Engineering St. Joseph's College of Engineering Chennai

Detecting brain tumors from medical images is a complex task in medical image analysis and is crucial for accurate diagnosis and treatment planning. Over the past few years, there has been significant development in the field of medical image processing. One such technique that has been employed is the multi-scale decomposition of brain images, which is achieved through the discrete wavelet transform (DWT). DWT decomposes an image into two different components, structural and textural information, without compressing the image. As a result, it provides high accuracy details and it preserves edge and texture details when reconstructing the image from its original frequency, and reduces problems like blocking and ringing artifacts. The low frequency sub-band coefficients are fused by selecting the coefficient with the maximum spatial frequency, indicating the overall active level of an image, while the high frequency subband coefficients are fused by selecting the coefficient with the maximum code value. Brain tumor detection using wavelet transform involves the use of both CT scan and MRI scan images. CT scan uses X-ray images taken from different angles of a specific part of the human body to provide detailed information about its internal structure, while MRI scan employs strong magnetic fields, radio waves, and field gradients to generate images of the inner human body. Finally, the fused image is reconstructed by taking the inverse Inverse Discrete Wavelet Transformation (IDWT) of two varied frequency sub-bands. The methods such as Visual Geometry Group (VGG19) which is a sub class of Convolutional Neural Network (CNN), watershed algorithm, Procrustes Analysis Algorithm, data argumentation are used in registration, fusion and segmentation process to detect brain tumor. Overall, the application of DWT and the fusion of multi-scale components of brain images has significant potential in improving the accuracy and quality of medical image processing, especially in t

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Parameter based Performance Evaluation of Deep Learning Models for Classification of CoViD and Pneumonia CT Images 4

Parameter based Performance Evaluation of Deep Learning Mode...

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4th IEEE International Conference on Computing, Power and communication Technologies, GUCON 2021

作者： Chilakalapudi, Hari Priyanka Venkatesan, Ramya Kamatham, Yedukondalu Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Chidambaram India CVR College of Engineering Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9781728199511

computer Tomography (CT) scan is one of the widely used techniques to identify Corona Virus Disease-2019. The presence of infection is identified using ground glass opacity in these images. Sometimes, there are very few changes that cannot be identified with the naked eye. Deep learning algorithms can be used to classify such images. Many deep learning algorithms have performed exceptionally well in classifying images. In this paper, we evaluated VGG16 and Xception models and found that the Xception model has a large number of non-trainable parameters. VGG16 model uses a normal convolution and Xception uses a depth wise separable convolution with Batch Normalization. Our results show that VGG16 performs better in classifying CoViD CT Images than the Xception model. We conclude that due to the Batch Normalization Xception model has non-trainable parameters and shows low performance when lesser images are used to train the model. However, VGG16 was found to perform very well even on images with subtle opacity. © 2021 IEEE.

关键词： Learning algorithms

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IoT based Women Safety Bag

IoT based Women Safety Bag

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Innovative Data communication Technologies and Application (ICIDCA), International Conference on

作者： M. Ashok Kumar P. Pradeepa A. Essaki Muthu R. Santhana Krishnan K. Saravanan K. Lakshmi Narayanan Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Computer Science and Engineering Anna University Regional Campus Tirunelveli India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India

Women's safety has become one of the chief concerns in India, since quite a large number of violence takes place against women's like sexual assault, abuse and murder. Women's are worshiped as God in this country yet her safety is at stake. Even when Women's started to equally flourish in every sectors starting from a Taxi Driver to a CEO of the top MNC, there are major safety issues which still prevent them to excel in their sector. Women's of all ages are affected by some sort of problems against their rights. It tends to increase as we let things grow without noticing them. The reports from National Crime Records Bureau (NCRB) which was released in 2021 which clearly states that more than 4, 00,000 cases were registered under crime against women. Hence, there should be a system which helps them to seek help when they are feeling unsecured. Therefore we propose a system which will assist the women to seek help when they are in danger. This system facilitates them to contact their relative when they experience unsecured situations.

关键词： Safety Data communication Public transportation

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An Ensemble Deep Learning Framework for Binary Classification of COVID-19 Cases Using the VGG16 (Visual Geometry Group 16) Architecture: A Comprehensive Analysis and Performance Evaluation

An Ensemble Deep Learning Framework for Binary Classificatio...

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Disruptive Technologies (ICDT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Somesh Mishra Saksham Mittal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

This paper presents a novel approach to deep learning by putting forth a cooperative system that uses the VGG16 architecture to categorise COVID-19 examples into two groups. Our model is distinguished by its remarkable recall metrics and precision, which achieve a careful balance that is essential for accurate categorization. What's more impressive is how well the model performs in the non-COVID category, effectively differentiating between COVID and non-COVID cases. With a remarkable overall accuracy of 96%, the model successfully classifies cases from both groups, demonstrating the potential of our suggested framework as a useful diagnostic tool useful in various clinical contexts. This work clarifies the effectiveness of deep learning techniques, concentrating on the VGG16 architecture in the crucial job of binary classification for COVID-19 identification. Our results open up new avenues for investigation in the field of accurate medical diagnosis in addition to providing insights into the real-world applications of sophisticated machine learning. The study highlights the ensemble approach's encouraging benefits, showing how it may strengthen diagnostic precision and advance clinical decision-making.

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Alzheimer's Disease Prediction Using Convolutional Neural Networks Pre Trained Architecture

Alzheimer's Disease Prediction Using Convolutional Neural Ne...

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Intelligent Signal Processing and Effective communication Technologies (INSPECT), IEEE International Conference on

作者： P Sam Sahil Bhawesh K Sinha Thirumala Akash K Mohammed Riyaz Ahmed Computer Science and Engineering HKBK College of Engineering Bangalore India Data Science and Engineering Swami Vivekananda University Kolkata West Bengal India Analyst 1 Infrastructure Service DXC Technology Bangalore India Electronics and Communication Engineering HKBK College of Engineering Bangalore India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Alzheimer's Disease (AD) is a progressive neu-rodegenerative disorder that significantly impacts memory and cognitive abilities. Accurate early-stage diagnosis is critical for implementing preventive measures before irreversible brain damage occurs. This paper presents a novel approach for AD prediction using convolutional neural networks (CNN) with pre-trained architectures, leveraging transfer learning to enhance classification accuracy. MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were used, categorized into three classes: AD (Alzheimer's Disease), CI (Mild Cognitive Impaired) and CN (Common Normal). The proposed method em-ploys pre-trained models such as VGG16, DenseNet121, Xception, ResNet152V2, NasNetLarge and InceptionResNetV2, fine-tuned for AD detection. Experimental results demonstrate that our approach achieved classification exceeding 90 % for precision and recall, highlighting the effectiveness of CNNs in handling large-scale medical data. These findings underscore the potential of deep learning models for supporting clinical diagnosis, reducing time and cost, and aiding in the early detection of Alzheimer's Disease

关键词： Neuroimaging Deep learning Accuracy Magnetic resonance imaging Transfer learning Signal processing Predictive models Convolutional neural networks Alzheimer's disease Medical diagnostic imaging

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Performance Evaluation of IoT Architecture for Heterogeneous Networks

Performance Evaluation of IoT Architecture for Heterogeneous...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Monika Kashyap Zaineb Naaz Neeti Bansal Vidushi Sharma Department of Computer Science and Engineering Gautam Buddha University Greater Noida India Department of Electronics and Communication Engineering Meerut Institute of Engineering and Technology Meerut India

With the increased deployment of IoT networks in different applications, the need to develop resource-efficient IoT networks arises. The traditional networks based on a single communication and connectivity protocol (either wired or wireless) fail to serve the dynamic requirements of the IoT. Heterogeneous networks combining wired (Ethernet, Power Line communication, etc.) and wireless (ZigBee, Wi-Fi, etc.) protocols are the future networks that will serve the purpose of IoT in the best possible manner. A real-world test bed analysis is essential to analyze and enhance the efficiency of hybrid wired and wireless-based heterogeneous networks. The paper presents an experimental investigation of traditional wired and hybrid (partially wired and wireless) networks based on IoT architectures. The results depict a reduction in the PDR (packet delivery ratio) and an increase in the packet loss rate for hybrid topology compared to a completely wired architecture.

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Performance Evaluation of Triple-Branch Diversity Receivers in Composite Gamma-Shadowed Rician Fading Channels with AMC and Asymmetric SNR Conditions

Performance Evaluation of Triple-Branch Diversity Receivers ...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Deepak Upadhyay Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Devansh Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

This paper provides an in-depth analysis of the performance enhancement resulting from AMC in triple-branch diversity receivers under composite Gamma shadowed Rician fading channels with uneven SNR conditions. This paper was able to show the level of enhancement in average SNR resulting through simulations of AMC on signal quality in several receiver branches, which clearly shows AMC's ability in alleviating fading and shadowing based degradations. The analysis revealed that AMC adequately alleviates degradations by changing modulation schemes and coding rates based on the current quality of the channel. This reinforces AMC's ability to improve the link's reliability and likelihood to optimize the efficiency of the communication channel. This makes AMC a desirable design and optimization feature for wireless communication in highly fading and shadowed environments, which makes this innovation a potential fundamental technology in the realization of robust, resilient, and adaptive communication systems.

关键词： Wireless communication Fading channels Degradation Technological innovation Adaptive systems Rician channels Modulation Receivers Encoding Signal to noise ratio

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Support Vector Regression Based Traffic Prediction Machine Learning Model*

Support Vector Regression Based Traffic Prediction Machine L...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Ansh Srivastava Mrigaannkaa Singh Somesh Nandi Dept. of Computer Science and Engineering RV College of Engineering Bengaluru India Dept. of Electronics and Communication RV College of Engineering Bengaluru India Dept. of AI/ML RV College of Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

To combat the rising issue of urban traffic congestion, optimization of traffic light timings at intersections is crucial. Traditional methods show stagnancy in managing traffic flow dynamically. This study introduces Support Vector Regression (SVR) as an unconventional solution for traffic signal optimization. The model predicts the traffic conditions and alters the green light to turn on and off accordingly. Utilization of a comprehensive dataset consisting of vehicle distributions and critical flow ratios have been implemented to training a machine learning model. The statistics and performance is checked by usage of Mean Squared Error, R Squared, Mean Absolute Error demonstrating that SVR can effectively enhance traffic signal control. This approach pledges significant improvements in reduction in congestion and streamlined traffic flow. Our findings majorly highlight the capability of the SVR Machine Learning model in improving urban traffic management systems and call for further enhancement and collaboration with experts to address dynamic traffic issues.

关键词： Support vector machines Training Computational modeling Urban areas Machine learning Predictive models Vectors Vehicle dynamics Optimization Traffic congestion

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Performance analysis of IEEE 802.11 WLANs with frame aggregation and unsaturated traffic load

Performance analysis of IEEE 802.11 WLANs with frame aggrega...

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International Black Sea Conference on communications and Networking (BlackSeaCom)

作者： Shinnazar Seytnazarov Dong Geun Jeong Wha Sook Jeon Faculty of Computer Science and Engineering Innopolis University Innopolis Russia Department of Electronics Engineering and Applied Communication Research Center Hankuk University of Foreign Studies Yongin-si South Korea Department of Computer Science and Engineering Seoul National University Seoul South Korea

ISBN: (数字)9798350351859

ISBN: (纸本)9798350351866

Existing performance models of modern IEEE 802.11 networks with frame aggregation do not correctly reflect the operations for usual/unsaturated traffic conditions. Some of them assume a predefined service-time distribution. Others model very specific cases of unsaturated traffic load, such as stations defering transmission until $K$ packets are gathered or stations always having at least one packet in their buffers. We propose a new performance model for 802.11 networks with frame aggregation and unsaturated traffic load, where the aggregation size, and the service times are dynamically determined by a number of factors such as the offered traffic load, the random backoff process, the number of stations, etc. Performance evaluations show that the proposed model produces a more accurate throughput for different numbers of nodes and various traffic loads.

关键词： Performance evaluation Solid modeling Analytical models Three-dimensional displays Simulation Telecommunication traffic Throughput

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A Comparative Study of YOLOv5 and YOLOv8 for Helmet Detection Inside ATMs

A Comparative Study of YOLOv5 and YOLOv8 for Helmet Detectio...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Prashant Kumar Sharma Lakshmipriya B Poonguzhali N Jayalakshmy S School of Computer Science & Engineering Vellore Institute of Technology Chennai India School of Engineering Jawaharlal Nehru University New Delhi India School of Computer & System Sciences Jawaharlal Nehru University New Delhi India Electronics & Communication Engineering IFET College of Engineering Villupuram India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Automated helmet detection inside automated teller machine (ATM) cabin for cash dispense can enhance security as well as serves as a means to prevent heist and vandalism. This study presents a comparative study on helmet detection inside ATMs using two prominent object detection you look only once (YOLO) v5 and YOLOv8. The system facilitates detection of helmets from closed circuit television (CCTV) footages to help in identifying the people entering into the ATM by wearing helmets and also in avoiding potential intrusion. In addition to precision and recall, mean average precision (mAP), a standard object detection metric to measure the accuracy of localization is used in this paper to assess the performance of helmet detection models constructed using YOLOv5 and YOLOv8. Experimental evaluation carried out in this study elucidates that YOLOv8 performs better than YOLOv5. By integrating the proposed model with IoT techniques, this model improves the safety and security. In addition, the incorporation of other alert models with this system offers a safer environment for the users inside the ATM cabin.

关键词： YOLO Training Performance evaluation Location awareness Head TV Automated teller machine Safety Security Standards

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