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Brain Cancer Detection and Classification using Transfer Learning 3

Brain Cancer Detection and Classification using Transfer Lea...

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3rd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2025

作者： Gadaokar, Varad More, Piyush Wakode, Madhuri Kale, Geetanjali Pune Institute of Computer Technology Dept. of Computer Engineering Pune India Pune Institute of Computer Technology Dept. of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798331521691

Brain tumors are abnormal cells that grow inside the brain. If not diagnosed or treated early, such incidents can be fatal. Types of brain tumors include meningioma, glioma, and pituitary tumors. Magnetic resonance imaging (MRI) is an essential resource in diagnosis due to the clear expression of brain tissues, enabling professionals to analyze abnormal developments in the brain. The detection and classification of brain tumors using MRIs can be automated using machine learning techniques that can help to identify and diagnose brain tumors in their early stages, and subsequent treatment may result in a lower death rate. Transfer Learning allows the transfer of knowledge learned from large-scale datasets to other smaller datasets, thus avoiding wide-ranging training requirements with high accuracy and making the system converge much faster. This substantially minimizes computational resources and time in the training process. Transfer Learning also enables the effective MRI image abstraction of complex features, improved accuracy in tumor detection and classification, minimizes overfitting, especially with small amounts of medical data. In this work, we have used Transfer Learning with two pre-trained deep models, VGG16 and ResNet-50 on 3064 images available in the dataset to classify brain tumors. Fine-tuning for VGG16 and ResNet-50, we achieved validation accuracies of 98.85% and 97.72%, respectively. The results show that transfer learning can be used for the classification of brain tumors to enhance efficiency and precision for early detection and treatment. © 2025 IEEE.

关键词： Brain Tumors Convolutional Neural Networks Medical Image Processing ResNet-50 Transfer Learning VGG16

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A Critical Analysis of Object Tracking Using Radial Function Networks

A Critical Analysis of Object Tracking Using Radial Function...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Mishra, Amit Kumar Pandey, Neeraj Kumar Diwakar, Manoj Bahadure, Nilesh Bhaskarrao Dumka, Ankur Patni, Jagdish Chandra Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Dept. of CSE Nagpur Campus Pune India Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India Alliance University Alliance School of Advance Computing Dept. of CSE Karnataka Bengaluru India

ISBN: (纸本)9798350394412

A important method in several fields, including driverless cars, medical imaging, human-computer interaction, and investigation systems, is object tracking. Because of their flexibility to changes in object presence and ambient factors, as well as their capacity to estimated complex, non-linear functions, radial networks have become a potent tool for object stalking in recent years. The article conditions for the analysis of the radical working networking where the algorithm is a tracking segment that aims in moving the object. Object recognition is one of the most basic and centralized factor analyses in determining outputs having relevant values and a strong research background. Radial functioning is a phenomenon that has gained rapid training and desirable properties for improving the presentation of networking sites. radial networks, a form of artificial neural network, utilize radial basis functions as initiation functions. Because of this characteristic, RBF networks are especially well-suited for applications like object tracking that call for high accuracy in function approximation and pattern recognition. We explore the design of RBF networks in detail, emphasizing the functions of the input layer, output layer, and hidden layer using radial basis functions. We examine the mathematical foundations that allow RBF networks to represent intricate connections in the data, emphasizing how well they can generalize and interpolate. © 2024 IEEE.

关键词： Radial basis function networks

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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers 16

Photoplethysmography Signal Quality Assessment Using Neighbo...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Khan, Zahir Manikandan, M. Sabarimalai Mahmud, Mufti Indian Institute of Technology Palakkad Dept. of Computer Science and Engineering Kerala Palakkad678623 India Indian Institute of Technology Palakkad Dept. of Electrical Engineering Kerala Palakkad678623 India Nottingham Trent University Dept. of Computer Science Nottingham Clifton Lane United Kingdom

ISBN: (纸本)9798350371154

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: 99.09%, 95.03%, 96.56 % and 84.63% using the GNB classifier. © 2024 IEEE.

关键词： Decision trees

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TSPvis: A Temperature Sensitive Paint Formulation Visual Design System 6

TSPvis: A Temperature Sensitive Paint Formulation Visual Des...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Liao, Jing Wu, Yadong Wang, Guijuan Wang, Fupan Chen, Huarong Long, Li Southwest University of Science and Technology Dept. Computer Science and Technology Mianyang China Sichuan University of Science and Engineering Dept. Computer Science and Engineering Yibin China

ISBN: (纸本)9798350325485

The composition and proportion parameters of temperature sensitive paint formulation are complex. The current methods have a long development cycle and low efficiency. We design a visual formulation design system for temperature sensitive paint, helps desingers use historical data to improve the efficiency of formulation design. TSPvis provides a visual formulation design function, implements the proportion decision of formulation based on decision tree, and recommends reference for ingredients proportion to the formulation designer. We use the DPC clustering algorithm to quickly obtain the radial basis center, and use the Adam algorithm to improve the weight calculation from the hidden layer to the output layer, to improve RBF neural network. TSPvis use this RBF as a model to predict performance of the formulation which designed before, and feeds back to the designer to improve the formulation designing of temperature sensitive paint. © 2023 IEEE.

关键词： Decision trees

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Relating Muscle Activity and Mouse Sensitivity in FPS Game Players Using Surface EMG Signals 5

Relating Muscle Activity and Mouse Sensitivity in FPS Game P...

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5th International Conference on Biomedical engineering, IBIOMED 2024

作者： Prajapati, Vaibhav Turlapaty, Anish C. Sarma, Himangshu Ghorai, Mrinmoy Indian Institute of Information Technology Dept. of Electronics and Communication Engineering Sri City India Indian Institute of Information Technology Dept. of Computer Science and Engineering Sri City India

ISBN: (纸本)9798350389265

In the domain of first-person shooter (FPS) gaming, mouse sensitivity is crucial for performance. This paper presents an analysis showing the correlation between specific muscle activity and mouse movement. We analyzed mouse movements from twelve subjects with varying sensitivity levels. A new dataset electromyography analysis of human activity database 8 (EMAHA-DB8), is introduced which involved four mouse designs and two game modes: Medium (normal movement) and Hard (aggressive movement). Sensitivity settings were standardized using effective dots per inch (eDPI), calculated as Mouse DPI multiplied by in-game sensitivity. Hand muscle activity was measured using five Noraxon Ultium wireless surface electromyography (sEMG) sensors with Ag/AgCl electrodes. Various regression models, including Random Forest, Support Vector Regressor, K-Nearest Neighbors Regressor, MLP Regressor, and Gradient Boost Regressor, were used to predict eDPI from muscle movement patterns. Sequential feature selection identified the top ten features, highlighting the most significant muscles for predicting sensitivity. The results demonstrate that the Extensor Carpi Radialis (ECR) is the most active muscle, followed by the Extensor Digitorum (ED), while playing the chosen FPS game. These findings give insights for ergonomic and personalized equipment design to enhance gaming experience and performance. © 2024 IEEE.

关键词： Decision trees

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm 9

Plagiarism Detection and Similarity Checking Program using M...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Bhavana, Munugapati Rao, Koppula Srinivas Koduru, Gagan Kumar Vatsal, K.Vijay Krupa Parvathi, S. Sravani, Mogiligidda Mlr Institute of Technology Affiliated to Jntu Dept. of Computer Science & Information Technology Hyderabad India Mlr Institute of Technology Affiliated to Jntu Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Hyderabad India Malla Reddy University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Dept.of Computer Science & Information Technology Hyderabad India Mvsr Engineering College Department of Information Technology India

ISBN: (纸本)9798350377972

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment. © 2024 IEEE.

关键词： Contrastive Learning

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CAM-Based High Efficient Weakly Supervised Multi-Object Multi-Class Detection Method

CAM-Based High Efficient Weakly Supervised Multi-Object Mult...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Chen, Xiu-Zhi Huang, Hsiao-Wei Chen, Yen-Lin Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9798331521165

Two approaches are available for WSOD: MIL and CAM. MIL-based methods are capable of addressing multiple targets and multiple class problems. However, these methods require the input of region proposals. In contrast, CAM-based methods are straightforward and more efficient, yet they are constrained by limited localization accuracy and are likely to be applicable only to single target classes. This study proposes a CAM-based WSOD method that is capable of addressing multi-object, multi-class tasks with high efficiency, which could prove beneficial for the development of specific consumer electronic applications, such as smart cameras and smart home devices. The proposed method was evaluated on two public datasets, demonstrating that it is significantly more efficient than the baseline method. © 2025 IEEE.

关键词： Object detection

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TypeScript: An Open-Source Programming Language with Options for Robust Development and Large-Scale Applications

TypeScript: An Open-Source Programming Language with Options...

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Kushwah, Shreyansh Bansal, Chanchal Rathore, Shreyansh Kate, Vandana Bargal, Divyanshi Vishwakarma, Divesh Acropolis Institute of Technology and Research Dept. of Computer Science and Information Technology Indore India

ISBN: (纸本)9798350388800

JavaScript is widely used for web and application development but is criticized for its dynamic nature. TypeScript, an open-source language developed by Microsoft, addresses theseconcerns by introducing optional static typing. Released in 2012, TypeScript is a syntactical superset of JavaScript, offering features to enhance code quality and support large-scale applications. However, despite its benefits, the adoption of TypeScript is not without challenges. From a research perspective, further investigation is needed to understand the impact of TypeScript on software development practices, its integration with existing JavaScript codebases, and its effectiveness in improving overall software quality and developer productivity. Such research can provide valuable insights into the evolution of programming languages and their adaptation to changing technologies and industry demands. © 2024 IEEE.

关键词： Software quality

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Recommendation System for Research Grants 7

Recommendation System for Research Grants

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Lokhande, Sharayu Tholia, Rakesh Upadhyay, Mukul Choudhary, Sachin Rathore, Kuldeep Pawar, Shilpa Army Institute of Technology Pune Dept. of Computer Engineering Pune India Army Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

This research paper proposes the design and implementation of a recommendation system for researchers to simplify the process of finding research grants. Despite the positive impact that researchers and scientists have on the world through their discoveries and innovations, securing funding for their projects remains a challenging and time-consuming task. This paper objects to address the issue by introducing a recommendation system that leverages the architecture of the system, detailed design, project planning, and requirements to suggest grants that researchers may find interesting based on their platform activities. The proposed system will be beneficial for researchers, students, and scientists seeking grants, as it will provide an efficient and user- friendly way of finding grants that fit their interests and project requirements. © 2023 IEEE.

关键词： decision trees GBDT

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Pre-Harvest to Post-Harvest: A Review of AI and IoT Applications in Smart Agriculture and the Prospects of 6G-Enabled IoT Framework 27

Pre-Harvest to Post-Harvest: A Review of AI and IoT Applicat...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Bhola, Amit Sharma, Himanshu Sagar, Anil Kumar Kumar, Prabhat Sharda Univesity Dept. of Computer Science and Engg Greater Noida India National Institute of Technology Dept. of Computer Science and Engg Bihar Patna India

ISBN: (纸本)9798350392319

Farmers are increasingly adopting Smart farming worldwide, leveraging various advanced technologies. Artificial Intelligence (AI) is instrumental in driving the evolution of smart agriculture applications. Internet of Things (IoT), edge computing, cloud computing, and big data are among the forefront technologies employed in this field. Agricultural activities typically encompass three phases: pre-harvest, during harvest, and postharvest. The pre-harvest phase involves choosing seeds, preparing the farm, and selecting the crops. Harvesting tasks include crop classification, disease analysis, and pathogen detection. Post-harvesting activities involve storage, cooling, and reaping, among others. This study conducted an in-dept. review of activities in each phase, aiming at shortcomings related to AI-based Machine and Deep learning, IoT, security, datasets, and methodologies employed in existing research. This work also proposes a smart farming framework based on 6G technology, utilizing these identified research gaps. Additionally, the work conducted a comparative analysis between this survey and existing studies, revealing that the survey offers a more comprehensive overview in various aspects. © 2024 IEEE.

关键词： Smart agriculture

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