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Brain Tumor Diagnosis and Classification based on AutoML and Traditional Analysis

Brain Tumor Diagnosis and Classification based on AutoML and...

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2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022

作者： Menon, Sindhu P Vaishaali, Kondapalli Sathvik, N G Gollapalli, Siva Prakash Anupam Sadhwani, Subhramanya N Punagin, Vaishnavi A Dayananda Sagar University Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9781665493659

ISBN: (纸本)9781665493659

Analysis of brain tumors is a challenging task. Due to the complexities of the brain structure, the analysis of a brain MRI (Magnetic Resonance Imaging) requires sophisticated tools and vast knowledge in the field of neuroscience. Detecting the presence of Malignant brain tumors can be done by using ML (Machine Learning) techniques. Moreover, recent changes in automation with the advent of tools like AutoML (Automated Machine Learning) have created significant room for research. When training ML models, there are several hyperparameters such as the number of clusters in the case of KNN, or the number of hidden layers in the case of Artificial Neural Networks. Different methods and models are implemented on the brain MRI scan dataset. The images were preprocessed and ran on all the models discussed in the paper individually. The validation accuracies are then compared with the performance results obtained by using AutoML. The comparison of the obtained validation accuracies assists in discerning the most optimal segmentation method along with the corresponding preprocessing technique used. The best model can then be used for detecting the presence of brain tumors in any given MRI scan. © 2022 IEEE.

关键词： Magnetic resonance imaging

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An Implementation of Android Programming Self-learning Topic for Developing Online Database Application Using Firebase 6

An Implementation of Android Programming Self-learning Topic...

引用

6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Syaifudin, Yan Watequlis Rozi, Imam Fahrur Kaswar, Andi Baso Funabiki, Nobuo Fatmawati, Triana Siradjuddin, Indrazno State Polytechnic of Malang Dept. of Information Technology Malang Indonesia State University of Makassar Dept. of Computer Engineering Makassar Indonesia Okayama University Dept. of Elec. and Comm. Engineering Okayama Japan State Polytechnic of Malang Dept. of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350326642

The trend in software development recently has been toward the creation of applications for Android smartphones that impacts the growth of Android application developer demand. The Android Programming Learning Assistance System (APLAS) has been developed to study Android programming independently. Students get automatic guidance and feedback in writing source code by implementing test-driven development (TDD) method using JUnit and Robolectric. This paper presents a new implementation of Online Database topic, as the second topic in third learning stage of APLAS, for developing an Android online database application using Firebase. It provides basic functions to make e-commerce transactions consisting of user registration and login, selling, and buying goods that are interpreted into 9 activities. The topic was applied to 50 students in an Indonesian IT department to test its efficacy, and all of the students correctly completed the assignments with compliments. © 2023 IEEE.

关键词： Students

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Automated Cardiovascular Disease Detection from ECG Images Using Deep Learning

Automated Cardiovascular Disease Detection from ECG Images U...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Rout, Mamatamayee Nayak, Suvendu Chandan Rai, Satyananda Champati Silicon University Dept. of Comp. Sc. & Engg. Bhubaneswar India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350369175

Healthcare is one of the crucial parts of our lives, and currently heart attack is one of the major causes of death worldwide. It is essential to predict the cardiovascular disease at an early stage. The traditional approach of consulting the physician periodically is neglected due to ignorance in the society. Automatic heart disease prediction and detection system would save several lives. The four types of cardiac abnormalities are NP, AH, MI, and HMI. To predict the cardiac abnormalities this paper uses the CNN technique of Deep Learning. The heart disease dataset of 'UCI Cleveland' is used in our proposed model. The dropout technique is used in our proposed model to avoid overfitting. For performance measurement, Various performance parameters like precision, accuracy, F1 score, and Recall are considered. The accuracy of our CNN model is 94%, which shows the significant achievement for the prediction. © 2024 IEEE.

关键词： Convolutional neural networks

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Developing and Implementing An Effective Big Data Classification System 2

Developing and Implementing An Effective Big Data Classifica...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Managuli, Manjunath Pujar, Sagar Managuli, Sangamesh C Krishnamurthy, K.T. Nandalike, Rajesh Kunchur, Pavan Kls Gogte Institute of Technology Dept. of Electronics & Communication Belagavi India Kls Gogte Institute of Technology Dept. of Computer Science Belagavi India Pes University Dept. of Mechanical Engineering Bangalore India B M S College of Engineering Dept. of Electronics & Instrumentation Bangalore India Nitte Meenakshi Institute of Technology Dept. of Electronics & Communication Bangalore India

ISBN: (纸本)9798350317558

Relevant data Technology advancements have led to a massive growth in classifications. Deep learning systems, however, need help handling such a massive volume of data. This work aims to use the Evaluation of different classification algorithms for big data, Comparison of accuracy, precision, recall, and F1 score among various algorithms, and Identification of algorithms suitable for handling large-scale datasets and conventional deep learning to analyze a large amount of data. Classifiers allowed researchers to examine how various random sampling strategies affected the model's performance. Two methods of feature selection were employed in the tests. The Bayesian Network likewise employs a single deep learning classifier. By lowering the dimensionality of the data, this study seeks to maximize model performance. A recording dataset larger than 60 GB was made less dimensional to conduct the trials. The findings demonstrate that, when employing both sampled and non-sample data, overall performance varies between 78% accuracy and 82% accuracy, a minor variation in performance while utilizing both sampled and non-sample (all the data) datasets. The random subset approach and the Bayesian network classifier were employed in the trials, and the overall findings show that the variety experiment had the best performance out of all the combinations of experiments. Test Cradle Experimentation: Variation length, delay variation, and packet loss are used to assess the predicted scheme's efficiency. © 2024 IEEE.

关键词： Bayesian Network deep learning feature classification Feature selection

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Estimating the Price of the Real Estate Properties with the Help of Online Images 1

Estimating the Price of the Real Estate Properties with the ...

引用

1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Ashok, Dasari Lavanya, Chaganti Dept. of Computer Science and Technology Qis College of Engineering and Technology India

ISBN: (纸本)9781607685395

When it comes to property investment pricing, it may be tough for homebuyers to understand and compare. A number of estate evaluation methods have been developed in order to analyse real estate, however they are constrained by the traditional data sources for estate assessment. Potential exists, however, to collect and analyse property-related mobile data when new ways of doing things arise. use estates' geographical dependency to increase the value of a property. "The New York Times" reports that a property's value may be determined by its own characteristics, the value of neighbouring homes in a neighbourhood (individual), and the profitability of its company has to do with (zone). There is a great need for real estate agents these days since they offer easy and fast access to real estate information on the internet, giving clients a broad variety of homes to choose from. As part of our study, we are examining the predictive power of online home forecasts. In the decision-making process of internet users, pictures play a major role in deciding whether or not to visit a website. The development of sophisticated computer vision algorithms has made it possible to analyse visual data. When analysing the findings of a university survey, this research makes use of an RNN. © The Electrochemical Society

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Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography

Comparative Study of Face Tracking Algorithms for Remote Pho...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Jayasinghe, J.A.S.Y. Katsigiannis, Stamos Malasinghe, Lakmini Sri Lanka Institute of Information Technology Dept. of Electrical and Electronic Engineering Malabe Sri Lanka Durham University Dept. of Computer Science Durham United Kingdom

ISBN: (纸本)9798350327816

Remote Photoplethysmography (rPPG) is a non-invasive approach for monitoring Heart Rate (HR) that can be used in various applications in healthcare and biometrics. rPPG measurements acquired using facial videos have become very popular and one of the main steps of this technique is facial tracking and Region of Interest (ROI) extraction. This research paper investigates four widely used face tracking algorithms, namely MediaPipe Face Mesh (MPFM), Haar Cascade, Multi-task Cascaded Convolutional Network (MTCNN), and Dlib, concerning their ROI extraction capabilities for rPPG HR measurements. Using evaluation metrics such as accuracy, processing time, and ease of extracting ROIs, this work also recommends the most suitable face tracking algorithm from those mentioned above for rPPG measurements, and presents a compilation of a prioritization list of ROIs based on their sensitivities for rPPG measurements. Experimental results showed that the MPFM algorithm and cheek ROIs provided the best HR measurements. © 2023 IEEE.

关键词： Face Tracking Machine Learning Remote Photoplethysmography ROI Extraction

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Leveraging Edge Based Deep Neural Networks for Road Damage Detection

Leveraging Edge Based Deep Neural Networks for Road Damage D...

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2022 International Conference on Intelligent Innovations in engineering and Technology, ICIIET 2022

作者： Karpagam, G.R. Guna, M. Lohith Sowmiyan, P.S. Sairam Vaidya, M. PSG College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (数字)9781665456531

ISBN: (纸本)9781665456524

With the increasing number of casualties resulting from road damages, posing a serious threat to every individual and society;imperative action in solving the issue must be given primacy. Over the years, various approaches and solutions have been presented by researchers with a noticeable pattern-every breakthrough solution solves the shortcomings of its predecessor. With the recent advancements in image classification techniques through deep neural networks, it is now possible to classify road damages with high accuracy. However, one disadvantage of employing this technology is the considerable latency associated with running machine learning models in the cloud in real-Time. Considering all these complexities, this paper presents an edge computing framework that runs efficient deep neural networks thereby reducing the latency inherent in the previous approaches. This solution can bring drastic changes to road maintenance by providing crucial information at opportune times thereby substantially reducing road accidents. Using transfer learning-based models, an F1 score of 0.64 was achieved for the RDD2020 dataset. © 2022 IEEE.

关键词： Deep neural networks

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Length Controllable Literature Summarization Using Transformers 7

Length Controllable Literature Summarization Using Transform...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Avinash, V.K. Pattanayak, Sanskriti Raghuraman, Venkatavaradan Prakash, Samyuktha Arya, Arti Pes University Dept. of Computer Sc. Engineering Bangalore India

ISBN: (纸本)9781665421683

This paper defines an approach to implement an output length controllable text summarizer that would help readers and learners understand large chunks of text quickly and efficiently and develop a tool to do the same. It would help and play an important role in revolutionizing today's education. The approach defined here is a way to better leverage the current state-of-the-art (SOTA) transformer-based models to help achieve length-controllable text summaries from text corpora. The paper introduces an algorithm that, when used in unison with existing transformer-based models, enables the user to specify the required extent of summarization to generate the output summary of the desired length. The approach has been tested on a widely accepted text summarization dataset and gives acceptable ROUGE(45.31) and METEOR(22.16) scores when compared to current state-of-the-art models. © 2022 IEEE.

关键词： Text processing

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An Automated System to Calculate Marks from Answer Scripts 13

An Automated System to Calculate Marks from Answer Scripts

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13th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Rizvee, Redwan Ahmed Arefin, Md. Fahim Khan, Md. Rayhun Islam, Md. Nezarul Rabbi, Kh. Fazle Dept. of Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

During evaluating exam scripts, teachers have to write down marks for each question on the top sheet of the answer script and then sum those marks to calculate the total marks - which is a common source of error. This literature has focused on automating the script marks calculation process using multiple handwritten characters' recognition. Traditional solutions for identifying handwritten characters focus on labeling a single character using suggestion based solution. So, existing solutions are not enough to address the problem that this study has focused on. In this regard, the proposed framework has tried to develop a hybrid approach. The proposed solution first identifies the connected components, based on the pixel color as red, through a dept.-first search based solution. Then each connected component is passed through a learning architecture to generate a numeric digit suggestion combining heuristic and machine learning, making a hybrid system. The proposed system is very modular and flexible to be implemented and modified. The extensive analysis presented in the evaluation section shows the merit of the proposed framework in automating the addressed problem. © 2022 IEEE.

关键词： Pixels

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SFLADock: A Memetic Protein-Protein Docking Algorithm

SFLADock: A Memetic Protein-Protein Docking Algorithm

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2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Sunny, Sharon Sreekumar, Gautham Jayaraj, P.B. National Institute of Technology Dept. of Computer Science and Engineering Calicut India

ISBN: (纸本)9781665483162

Protein-protein interactions are biologically significant as they govern many biological systems, including the immune and digestive systems. An abnormal increase in proteins may causes diseases like Alzheimer's disease, for which no cure is found yet. An advanced scientific study on their interactions and functions may shed light on the ways for treating protein-related diseases. Protein-protein docking is a method to study the structure of protein assemblies and hence their functions and characteristics. The proposed method uses the shuffled frog-leaping algorithm to predict the structure of protein complexes. Unlike other evolutionary algorithms that allow the use of information from previous generations only, this algorithm supports the use of all the information available at the moment. The division of the population into memeplexes and submemeplexes allows the searching for optimal solutions in different directions, thereby avoiding premature convergence. The proposed method is tested on the Docking Benchmark v 5. Results show that the method is capable of generating plausible structures of medium and acceptable quality even in the top 10 ranks. It should be noted that the results are independent of the initial position of individual proteins. The use of DFIRE scoring function to rank the poses helps in better throughput. The inclusion of a better selection strategy for conformations may positively change the results of the proposed method. © 2022 IEEE.

关键词： Proteins

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