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Detection of Brain Tumor Using a Deep Learning Approach

Detection of Brain Tumor Using a Deep Learning Approach

引用

2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Arun Kumar, S. Anand Kumar, M. Vincent, Robin Rohit Anusha M, S. Hegde, Uttam Upendra Pasha, N Nauman Dept. of Computer Studies Pune India Networks & IoT Lab Presidency University Dept. of Information Science and Engineering Bangaluru India Presidency University Bio-Intelligence Lab Dept. of Computer Science and Engineering Bangaluru India

ISBN: (纸本)9798331527518

This study introduces a deep learning-based method for classifying brain tumors using a pre-trained VGG19 convolutional neural network (CNN). By leveraging transfer learning, we adapted the VGG19 model with custom fully connected layers and trained it on MRI images to differentiate between tumor and non-tumor cases. The model's architecture includes dropout layers to minimize overfitting and enhance generalization while maintaining the robust feature extraction capabilities of the VGG19 base. We developed a Flask web application interface to make the model accessible via web-based user input, allowing users to upload MRI images and receive immediate predictions on tumor presence. Preliminary results show that proposed model can accurately classify brain tumor images with high accuracy, highlighting the potential of deep learning in automating brain tumor diagnostics. This tool could significantly aid clinicians in making rapid and accurate diagnoses, ultimately improving patient outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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A Novel Analysis and Detection of Autism Spectrum Disorder in Artificial Intelligence Using Hybrid Machine Learning

A Novel Analysis and Detection of Autism Spectrum Disorder i...

引用

2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Senthil, G.A. Prabha, R. Nithyashri, J. Suganthi, P. Thamarai, I. Sridevi, S. Agni College of Technology Department of Information Technology Chennai India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Kattankulathur Dept. of Computing Technologies Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus Chennai India Sri Ramachandra Engineering and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397208

Heart Disease or Cardiovascular Disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age and sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include K-Nearest Neighbour (KNN) and decision tree classifier which is compared to yield the desired and efficient output. © 2023 IEEE.

关键词： Heart

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Deepfake Image Detection using Deep Learning

Deepfake Image Detection using Deep Learning

引用

2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Kumar S, Arun Kumar M, Anand Vincent, Robin Rohit Hegde, Uttam Upendra Anusha, M.S. Pasha, N Nauman Dept. of Computer Studies Pune India Presidency University Bio-Intelligence Lab Dept. of Information Science and Engineering Bangaluru India Presidency University Bio-Intelligence Lab Dept. of Computer Science and Engineering Bangaluru India

ISBN: (纸本)9798331527518

Deepfake images are generated by modifying existing visuals and are frequently exploited in harmful ways. When executed proficiently, these images can be almost indistinguishable from authentic ones. The increasing development of deep learning techniques has largely fueled the increase in deepfake content. While numerous techniques are available for creating deepfake images, the most commonly employed are GANs and autoencoders. This paper presents a way to make deepfake detection more accurate by using a combination of the YOLOv8 model and a Recurrent Neural Network (RNN). YOLOv8 extracts spatial details from the images, while the RNN identifies temporal patterns and subtle irregularities across image frames, signaling potential manipulation. This methodology presents an efficient solution for identifying deepfake images and reducing their misuse. © 2025 IEEE.

关键词： Recurrent neural networks

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Automated Traffic Light Control System for Ambulance Using Laser Signals and Raspberry Pi

Automated Traffic Light Control System for Ambulance Using L...

引用

2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Arun Kumar, S. Anand Kumar, M. Vincent, Robin Rohit Anusha M, S. Hegde, Uttam Upendra Pasha, N Nauman Dept. of Computer Studies Pune India Presidency University Networks & IoT Lab Dept. of Information Science and Engineering Bangaluru India Presidency University Networks & IoT Lab Dept. of Computer Science and Engineering Bangaluru India

ISBN: (纸本)9798331527518

Urban traffic congestion poses a major challenge, particularly for emergency services like ambulances, where delays can have life-threatening consequences. This paper proposes a novel, cost-efficient Automated Traffic Light Control System aimed at prioritizing ambulances at busy intersections, thereby reducing delays and ensuring faster response times. The system utilizes Raspberry Pi Zero 2W, Laser Diodes (KY-008), and Light Dependent Resistors (LDRs) to detect ambulances and dynamically control traffic lights. Unlike traditional systems relying on GPS or RFID technologies, this laser-based approach offers greater precision and affordability through the use of modulated laser signals. Experimental results reveal that this system reduces ambulance response times by 35%. The tests were conducted under simulated urban conditions, highlighting the system's capability to outperform conventional solutions. The simplicity of integration and real-time responsiveness underscore its potential for widespread urban adoption. © 2025 IEEE.

关键词： Proportional control systems

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Analyzing Cyberbullying Word Distribution Across Social Media to Develop Lexicon Dictionary

Analyzing Cyberbullying Word Distribution Across Social Medi...

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Network, Multimedia and information Technology (NMITCON) International Conference on

作者： V Sahana K M Anil Kumar Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India Dept. of Computer Science and Engineering JSS Science and Technology University Mysuru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Social media platforms have become essential tools for communication, collaboration, and exchanging information, ideas, and knowledge among users worldwide. Despite their benefits, the anonymity offered by these platforms has unfortunately led to a rise in hate speech and cyberbullying, causing concern across the globe. This issue has drawn the attention of researchers and scholars now focused on devising methods for automatically detecting cyberaggression and hate speech. The goal is to mitigate these harmful behaviors and create safer online environments [1]. This study specifically aims to identify and analyze the usage of cyberbullying language within the English Tweet corpus, focusing on distinguishing between bullying and non-bullying language. This research contributes to developing more effective cyberbullying detection models by examining the frequency and patterns of cyberbullying words. These findings are vital for enhancing the efficacy of cyberbullying detection on social networks in future research efforts. The conclusions of the experiments indicate that the SVM achieved the highest average accuracy, approximately $84 \%$.

关键词： Support vector machines Accuracy Hate speech Text categorization Cyberbullying Focusing Media Safety Multimedia communication Public healthcare

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Lightweight Single-Image dept. Estimation Model

Lightweight Single-Image Depth Estimation Model

引用

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Man-Chen Hsueh Xiu-Zhi Chen Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This paper explores the challenge of maintaining performance in single-image dept. estimation when faced with hardware limitations. While most existing studies concentrate on improving accuracy, hardware efficiency is often disregarded. To tackle this issue, we combine the YOLOv7 and Zoedept. models to create a model that preserves accuracy while reducing the number of parameters. The model was evaluated on the KITTI and NYU dept. v2 datasets, achieving similar accuracy to Zoedept. despite drastically reducing the number of model parameters.

关键词： Performance evaluation Deep learning Accuracy Costs Computational modeling Estimation Hardware

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Disclosure Privacy information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

引用

9th International Conference on information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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Strategies for Helping Anchor-Based Trackers Learn re-ID Features for Smart City Surveillance

Strategies for Helping Anchor-Based Trackers Learn re-ID Fea...

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Xiu-Zhi Chen Mu-Chuan Li Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

Re-identification has become a crucial issue in computer vision today as it allows for tracking objects in both continuous and discontinuous scenarios. Despite achieving perfect detection results, anchor-based trackers encountered difficulties in effectively learning re-identification features, due to various issues. This research proposes strategies aimed at improving the capability of anchor-based trackers to learn high-quality re-identification (re-ID) features. The model developed through our strategies can extract more distinct features and achieve almost 0.57 Multiple Object Tracking Accuracy (MOTA) on MOT20, even under a limited training dataset. This result indicates that our proposed strategies hold potential for improving the performance of anchor-based trackers.

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Adaptive Forward Error Correction for Voice over IP with Random Linear Network Coding

Adaptive Forward Error Correction for Voice over IP with Ran...

引用

Virtual Conference on Communications (VCC), IEEE

作者： Yi-Chiuan Su Quincy Wu Dept. of Computer Science and Information Engineering National Chi Nan University Nantou Taiwan

ISBN: (数字)9798331530099

ISBN: (纸本)9798331530105

This paper proposes the use of Random Linear Network Coding (RLNC) as an erasure code in the forward error correction (FEC) mechanism for Internet telephony. It also designs and implements a forward error correction algorithm applied to the Real-time Transport Protocol (RTP), which we call Adaptive Systematic RLNC (AS-RLNC). The algorithm learns the current packet loss rate of the network through the Real-time Transport Control Protocol (RTCP) and dynamically adjusts the number of redundant packets sent. Compared with traditional forward error correction methods based on block codes, this approach can enhance the reliability of data transmission and improve audio quality by 31%.

关键词： Transport protocols Systematics Adaptive systems Heuristic algorithms Packet loss Forward error correction Network coding Real-time systems Reliability Internet telephony

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Investigating the Effectiveness of Algorithms in Serverless Cloud Computing

Investigating the Effectiveness of Algorithms in Serverless ...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Jindal, Mayank Mimani, Sushant Pillai, Saniaikanth E Vadakkethil Somanathan Jiwani, Nasmin Kiruthiga, T. San Jose CA United States GoDaddy WA United States School of Electrical Engineering and Computer Science University of North Dakota Grand Forks United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Vetri Vinayaha College of Engineering and Technology Dept. of Ece Trichy India

ISBN: (纸本)9798350394399

Server less cloud computing has won vast recognition in current years because of its capability to provide green and price-effective solutions for numerous computing needs. On this model, cloud carriers manage the scaling and provisioning of sources, permitting users to consciousness totally on their application code. As an end result, server less computing gives an appealing opportunity to standard server-primarily based computing, in which customers needed to control the whole infrastructure themselves. However, with the rise of server less computing, there was a growing challenge about the effectiveness of algorithms on these surroundings. Traditional algorithms are designed to run on a set of resources, which may not translate properly to the dynamic and elastic nature of server less structures. This has caused a want for studies in developing algorithms mainly tailor-made for server less computing. One fundamental assignment on this place is the optimization of resources and price inside the server less environment. Since customers simplest pay for the resources they use, it's far critical to layout algorithms that minimize useful resource consumption and value. Previous research has already indicated that conventional algorithms that rely upon pre-determined aid allocation won't carry out nicely within the server less model. Moreover, server less systems additionally introduce new challenges in phrases of useful resource availability, network latency, and processing delays due to the pay-in keeping with-use model. © 2024 IEEE.

关键词： Cloud computing

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