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Brain Tumor Detection with Ensemble of Convolutional Neural Networks and Vision Transformer 2

Brain Tumor Detection with Ensemble of Convolutional Neural ...

引用

2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Yurdakul, Mustafa Tasdemir, Sakir Kirikkale University Engineering Faculty Computer Engineering Dept. Kirikkale Turkey Selçuk University Technology Faculty Computer Engineering Dept. Konya Turkey

ISBN: (纸本)9798350373363

Brain tumors are recognized as one of the most lethal cancer types worldwide. Detecting brain tumors using medical imaging techniques is a challenging task due to their complex anatomical structures. Traditional methods rely on specialists meticulously examining MRI scan images. However, this approach is not only time-consuming but also carries a significant risk of error. Therefore, there is a need for more effective methods to detect brain tumors from MRI images. In this study, an ensemble model was proposed for classifying tumor types using MRI scans. Initially, sixteen well-known Convolutional Neural Network (CNN) models and four Vision Transformer (ViT) models were trained on the Brain Tumor Dataset, which contains 3264 MRI scan images. Subsequently, by combining the top three high-performing models, we achieved a robust classification performance. Experimental results demonstrate that our proposed model provides a satisfactory performance comparedto existing methods. © 2023 IEEE.

关键词： Convolutional neural networks

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A BrainNet (BrN) based New Approach to Classify Brain Stroke from CT Scan Images

A BrainNet (BrN) based New Approach to Classify Brain Stroke...

引用

2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Tripura, Dhonita Haque, Imdadul Dutta, Mithun Dev, Shaikat Jahan, Tanjila Ghosh, Shomitro Kumar Islam, Md. Ashiqul Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350396485

Worldwide, brain stroke is known as the 2nd leading cause of death, and based on Indian history, three people have suffered every minute. There are mainly two different types of brain stroke: ischemic stroke and Hemorrhagic stroke used to train the proposed models. Ischemic stroke is the most common and it contributes mostly to 80% of the brain stroke and Hemorrhagic stroke contributes mostly to 20% of the brain stroke. In the proposed model, there has been used a hybrid model called BrainNet (BrN) as CNN(Convolutional Neural Network) and SVM(Support Vector Machine)to classify brain stroke disease. After applying the required proposed model, it has produced a smart score of 91.91% accuracy, and compared to the existing model it performs pretty well. The BrainNet (BrN) model is mainly designed based on a deep neural network with dataset collection, preprocessing, and feature extraction with the desired model and make the classification concerning SVM. With compare to the existing model, it is an acceptable performance that belongs to the collected dataset designed with Ischemic stroke and Hemorrhagic stroke disease within the total number of 2515 data. © 2023 IEEE.

关键词： Support vector machines

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Automated Attendance Management System 5

Automated Attendance Management System

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Goyal, Pranshu Nigam, Vishwas Khan, Shahnaw A.J. Singh, Shashi Kant Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering India

ISBN: (纸本)9798350330861

the manual process of recording and managing attendance in educational institutions and organizations is often time-consuming, prone to errors, and inefficient. To address these challenges, there is a growing need for automated attendance management systems. This research paper presents the design and implementation of an Automated Attendance Management System (AAMS) using modern technologies. The AAMS utilizes various technologies such as biometric recognition, RFID (Radio Frequency Identification), and computer vision to accurately and efficiently track attendance. The system aims to simplify the attendance recording process for both students and instructors, reduce administrative burden, and improve overall efficiency. The proposed AAMS incorporates biometric recognition techniques, such as fingerprint scanning or facial recognition, to identify and verify the identity of individuals. © 2023 IEEE.

关键词： Efficiency

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Faulty Classes Prediction in Object-Oriented Programming Using Composed Dagging Technique 3

Faulty Classes Prediction in Object-Oriented Programming Usi...

引用

3rd International Conference on Electrical, computer and Communication Engineering, ECCE 2023

作者： Mahfuz, Nagib Shill, Pintu Chandra North Western University Dept. of Computer Science and Engineering Khulna Bangladesh Khulna University of Engineering and Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350345360

Class is one of the fundamental concepts of the object-oriented paradigm and has been scrutinized since the developers moved on from procedural programming design. In software fault prediction, the legalization of software metrics is essential. As a handful of software metrics suites exist, it is a very hard task to predict the defective classes flawlessly using a particular set of metrics suites. However, it is a rational approach to use only the object-oriented metrics that are directly relatable to the class definitions in the code that helps the developers foresee the errors in defining the classes and minimize the errors as much as possible. This paper utilized twelve object-oriented metrics selected from various metrics suites. The dagging ensemble model is merged with three well-known classification algorithms (Naive Bayes, Multilayer Perceptron, J48 Decision Tree) individually and applied to twelve java projects. The study depicts that the proposed ensemble method gives improved outcomes that are statistically significant when merged with Naive Bayes and Multilayer Perceptron. The proposed ensemble method shows improvements up to 12.5% in accuracy and 15% in F-Score. © 2023 IEEE.

关键词： Object oriented programming

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An Ensemble ML Model to Predict the Wastage of Food: Towards Achieving the Food Sustainability

An Ensemble ML Model to Predict the Wastage of Food: Towards...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Masuk Shopnil, Md Masrur Husna, Asmaul Sultana, Shaheena Islam, Muhammad Nazrul Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh NotreDame University Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Food waste is a major issue in students' hostel or dormitories, where uneaten food often ends up in the garbage, resulting in economic losses as well as reducing the food sus-tainability. Although Machine Leaning (ML) has emerged as a promising tool for predicting future events in several files, but a very few has been focused to generate accurate predictions and reducing food waste by analyzing read data for a particular context or a country. Therefore, the objective of this research is to explore the factors of affecting food waste in students' hostel or dormitories and to develop an ensemble ML-based food waste prediction system for the student hostel or dormitories. In order to attain these objectives, this study firstly find out the 18 key features that ensure a comprehensive understanding of food waste patterns;then collected the data against these features over the last six months from various mess records of a students' hostel of an engineering university situated in Dhaka, Bangladesh. Secondly, six classification models were developed and analyzed their results and found that Random Forest and Decision Tree performed best accurate prediction (75.41%). Thirdly, the all possible combinations of the studies ML models were explored to find out the best possible ensemble combination;as such, the proposed ensemble ML model showed the highest F1-score of 86.88% that considers the Random Forest and AdaBoost;while all other possible combinations showed around 80-82% F1-score. This research thus contributes to the broader goal of reducing food waste and promoting sustainability in students' mess and other food service settings;as well as may assist the management team in making data-driven decisions about food production, purchasing, and resource allocation. © 2023 IEEE.

关键词： Forecasting

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Institutional Accreditation Data Analysis And Decision-Making System 3

Institutional Accreditation Data Analysis And Decision-Makin...

引用

3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Dsouza, Chris Gupta, Rhea Pradhan, Sarah Shaikh, Phiroj Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

This paper highlights the implementation of the National Accreditation System's fifth criteria and its application to ensure quality assurance standards are met by the University. The authors provide an overview of recent advancements in the Institute's Special Faculty Administrative Region and examine how they relate to neighbouring institutes' engineering practices. The paper concludes with the author's observations on the interplay between different institutes in India with regard to accreditation criteria. Specifically, the paper delves into the contents of Self-Assessment Reports, the process of developing them, challenges encountered during their creation, as well as the advantages and features of the Accreditation Data Management © 2023 IEEE.

关键词： Accreditation

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An Implementation of Blockchain technology in Combination with IPFS for Crime Evidence Management System

An Implementation of Blockchain Technology in Combination wi...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shilpa, C. Shanthakumara, A.H. Siddaganga Institute of Technology Dept. of Computer Science & Engineering Tumakuru India

ISBN: (纸本)9798350348217

Crime is an unlawful act and is punished by the authority, in order to prove crime, evidence is necessary. Evidence obtained from crime scene is crucial as it act as proof of crime, Digitalization of evidences is need of hour. During the entire process of investigation heterogenous format of data is generated and integrity of the sensitive data has to be maintained as sensitive data passes through the various levels of intermediaries forming Chain of Evidences (CoE). Evidence needs to be tramper proof and should be protected from any kind of alterations. In order to build strong system with immutability, integrity, and legitimacy features blockchain technology is more suitable. The digital evidence can be transferred in a transparent way between the parties involved without any central authority using blockchain technology. This paper focuses on how blockchain based solutions can help in building a strong secure system. The system is implemented using Ethereum platform to achieve integrity, immutability transparency as well as tampering can be identified by any one at any time. © 2023 IEEE.

关键词： Smart contract

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DeepLeaf-Attentive: Integrating Attention Mechanisms for Crop Leaf Disease Identification 26

DeepLeaf-Attentive: Integrating Attention Mechanisms for Cro...

引用

26th International Conference on computer and Information technology, ICCIT 2023

作者： Ahmed, Tanvir Mou, Farzana Sharmin Engineering Dhaka University of Engineering & Technology Dept. of Computer Science Dhaka Gazipur Bangladesh University of Birmingham Dept. of Computer Science Birmingham United Kingdom

ISBN: (纸本)9798350359015

Crop leaf diseases have a detrimental impact on agricultural production. Visual inspection by organic farmers for disease detection is time-consuming and ineffective. To address this, we propose DeepLeaf-Attentive, a novel deep learning model for classifying crop leaf diseases. It combines MobileNet architecture with additional layers and attention mechanism. It is trained on a dataset of 30,000 images including data augmentation representing 60 classes with 500 images per class. The model achieves outstanding performance, with 97.21% accuracy, 97.72% precision, 97.43% recall, 97.49% F1-score and 18.0 ms inference time. Comparative evaluations against state-of-the-art approaches demonstrate the superiority of DeepLeaf-Attentive in crop leaf disease classification. By providing a reliable and efficient tool, our model will empower farmers to accurately identify and categorize various crop leaf diseases, facilitating informed decision-making and enabling the implementation of targeted and timely treatment strategies. The adoption of DeepLeaf-Attentive can significantly improve disease management practices in agriculture, leading to enhanced crop yields, reduced economic losses, and sustainable farming practices. By revolutionizing crop leaf disease identification, our research contributes to the advancement of precision agriculture, benefiting farmers and supporting global food security. © 2023 IEEE.

关键词： Attention Mechanisms Crop Leaf Disease Identification Deep Learning DeepLeaf-Attentive MobileNet Transfer Learning

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Human Skin Diseases Detection and Classification using CNN 3

Human Skin Diseases Detection and Classification using CNN

引用

3rd International Conference on Electrical, computer and Communication Engineering, ECCE 2023

作者： Ahmed, Shamim Abiduzzaman, M.D. Rajib, Mahedi Hasan Rahaman, Rafat Hussain, Salam Rashid, Hafsha Binta Wadud, Anwar Hussen Amir-Ul-Haque Bhuiya, T.M. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350345360

Recently skin disease has turned into a threat to normal human life when it becomes cancer. If there do any negligible steps during treatment it will become severe and also may cause the reason of human death. So, necessary steps in an early stage of the diagnosis are mandatory to avoid the worst scenario like human death, especially when the disease is Melanoma skin cancer. Because the human death rate of Melanoma skin cancer has become high during this recent era. To detect skin cancer disease there is some common treatment strategy which is done by expert healthcare professional like doctor or dermatologist, as well as these procedures are also very lengthy and time-consuming. Therefore, recently some automated computer vision or image processing with artificial neural network-based methods are introduced but during the implementation level, these methods are highly cost-effective thoughthey provide better accuracy during prediction. In this paper, we proposed a deep learning-based CNN method for classifying skin diseases. To implement our proposed model, we gathered approximately 2102 image datasets from a massive repository of diverse source images, particularly dermatoscopic images of pigmented lesions. Our experiment results show an 89.45 percent accuracy in detecting skin cancer disease. We hope our proposed method will help to diagnose this skin cancer disease more efficient way than other methods that have already been used. © 2023 IEEE.

关键词： Diagnosis

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Smart BAT - Smart Budget Analyzer and Tracker 3

Smart BAT - Smart Budget Analyzer and Tracker

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Shelke, Sahil Shingre, Manish Lebisha, Sahaya Shaikh, Shainila Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Maintaining your daily expenses manually by entering all the details of the items purchased can often feel like an intimidating task. This research discusses the difficulties in budget tracking and suggests a fresh budget tracking program to simplify difficulties such as maintaining the expenses manually using pen and paper, spreadsheet or a diary. Departing from these traditional manual approaches the authors have proposed an innovative solution for this tedious task. In this research paper, we have proposed an automated system that combines Optical Character Recognition and Machine Learning to tackle the challenges associated with transforming physical bills into digital format. The key feature of the application includes optical character recognition which will scan the bill and extract all the items present on the bill and automatically categorize the items into various categories where the item belongs. The application also provides some additional features such as future expense prediction and a detailed report generation. In this study, authors emphasizes the significance of effective budget tracking in the modern world and offers a creative solution by creating a user-centered budget tracking application. We hope that by meeting the needs of the average customer and providing a flawless experience, our application can considerably enhance people's financial well-being. © 2023 IEEE.

关键词： Optical character recognition

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