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3rd International Conference for Emerging Technology, INCET 2022

作者： Duvvuri, Kavya Kanisettypalli, Harshitha Jayan, Sarada Amrita School of Engineering Dept. of Computer Science Engineering Bengaluru India Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9781665494991

Brain tumor is a type of cancerous growth that may occur in the brain. Early diagnosis of the disease is crucial for proper treatment. Diagnosis of brain tumors is usually done using images obtained through magnetic resonance imaging (MRI). MRI images can be classified using a Convolutional Neural Network (CNN), which is a technique in deep learning. It is suitable for classifying large image datasets. Support Vector Machine (SVM) is a technique in machine learning that is predominantly used for classification and in various regression problems. In this paper, we classified brain MRI images using pre-trained models like AlexNet, VGG16, InceptionV3, and ResNet50. Finally, a CNN model and an SVM model are trained with the same dataset. Using the results thus obtained a hybrid CNN-SVM model has been built to get better accuracy and prediction results. © 2022 IEEE.

关键词： Support vector machines

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A System to Track Fabricated Content in Social media using Machine Learning Approach

A System to Track Fabricated Content in Social media using M...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Hegde, Ramakrishna Soumyasri, S.M. Shivashankara, S. Kavyashree, M.K. Pemula, Rambabu Harshitha, M. Vidyavardhaka College of Engineering Affiliated to Visvesvaraya Technological University Dept. of Computer Science and Engineering Mysuru India Vidya Vikas Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Applications Mysuru India Kr Pete Krishna Govt. Engg. College Dept. of Computer Science and Engineering Krishnarajapet Mandya India Sjce Jssstu Mysore India Vidya Jyothi Institute of Technology Dept. of Artificial Intelligence Hyderabad India Vidya Vikas Institute of Engineering and Technology Dept.of Information Science and Engg. Mysore India

ISBN: (纸本)9798350388602

In today's society, when the internet is pervasive, everyone gets their news from a variety of online sources. information can swiftly reach millions of users in a short amount of time due to the growing usage of social media sites like Facebook, Twitter, and others. Fake news has far-reaching effects, ranging from swaying election outcomes in favor of particular politicians to fostering prejudiced viewpoints. The primary platforms for disseminating fake news are Instagram, WhatsApp, and numerous other social media platforms. By presenting a machine learning-based fake news detection algorithm, this work provides a remedy. This model needs fictitious data that was gathered from multiple information sources. Data is extracted using web analytics, which is mostly used to build datasets. Real data sets and fake data sets are the two primary categories into which the data is separated. Random forest, logistic regression, decision trees, KNN, and gradient boosting are the classifiers used to classify the data. The data is categorized as true or false data based on the version that was received. The user will be able to determine whether or not the information on the website is fraudulent based on this. © 2024 IEEE.

关键词： Adversarial machine learning

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ECDSA-based tamper detection in medical data using a watermarking technique

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 78-87页

作者： Ch, Rupa Vivek K, Naga Srivastava, Gautam Reddy Gadekallu Department CSE VR Siddhartha Engineering College Andhra Pradesh Vijayawada520 007 India Dept of Math and Computer Science Brandon University MB Canada Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara Punjab Rajpura140401 India Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon Lovely Professional University Division of Research and development Phagwara India Jiaxing University College of Information Science and Engineering Jiaxing China

Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. Digital Watermarking can assist in addressing such issues by incorporating a unique identifier into an image that can be used to authenticate its validity. To tackle these issues, this study proposes a robust digital watermarking approach tailored to brain medical images, combining hashing, the Elliptic Curve Digital Signature Algorithm (ECDSA), and the Integer Wavelet Transform-Discrete Cosine Transform (IWT-DCT). This method utilizes the Secure Hash Algorithm (SHA-256) to first segment the brain's Region of Interest (RoI). Subsequently, the hashed RoI, along with an ECDSA signature, is embedded into the high-frequency sub-bands of the medical image using IWT-DCT. The embedding process strategically alters the coefficients of the high-frequency sub-bands to accommodate the signature while minimizing perceptual distortion. The technique leverages the robustness of transformed-domain image watermarking techniques against various attacks and combines it with SHA-256 for integrity and ECDSA for authentication purposes. The results demonstrate that the suggested approach is robust to a variety of image processing techniques, including noise addition, filtering, and compression while maintaining high levels of imperceptibility. Key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) were used to evaluate performance. The suggested strategy exhibited a substantial improvement over existing methods. The PSNR increased to 68.67, indicating higher image quality, while the MSE reduced to 0.96, demonstrating closer pixel values to the or

关键词： Mean square error

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Tangispeak: A Digital Game Based Language Learning System by Using Augmented Reality Technology and Tangible Objects 12

Tangispeak: A Digital Game Based Language Learning System by...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Ongoro, Catherine Akoth Fanjiang, Yong-Yi Hung, Chi-Huang Lin, Bing-Jun Lee, Mu-En Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan

ISBN: (纸本)9798350340181

In this study, Tangispeak is introduced as a language learning system for preschoolers, incorporating Digital Game-Based learning (DGBL), Augmented Reality (AR), and tangible objects. It aims to enhance English language skills through interactive and fun experiences. Tangispeak utilizes a micro-controller, gameboard, Unity 3D, and Vuforia database platform, offering scrambling and speaking steps for word formation and pronunciation. The system provides rewards, hints, and timing to engage learners while fostering cognitive and innovative skills. © 2023 IEEE.

关键词： Augmented reality

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Vector Model Based information Retrieval System with Word Embedding Transformation 10

Vector Model Based Information Retrieval System with Word Em...

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10th International Conference on Emerging Trends in engineering and Technology - Signal and information Processing, ICETET-SIP-22 2022

作者： Brundha, J. Meera, K.N. Amrita School of Engineering Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9781665467414

Vector based information retrieval system has been one of the trending methods in Natural Language Processing. The embeddings vector generated from a document helps in identifying most relevant document related to the query. There is various approach were embedding vectors can be generated and some of them which have implemented are Word2vec, Glove2vec and Sentence BERT. For information retrieval system also used word embedding transformation like PCA and Factor Analysis to improvise the model's performance. Most of information retrieval system involves getting query from the user, preprocessing of the query and generating most relevant information to the query. Results obtained by post processing methods such as PCA and Factor Analysis shows a comparatively better results with an increase of 2-3% of Mean average precision. © 2022 IEEE.

关键词： Analytical models Machine learning algorithms Bit error rate Machine learning information processing Euclidean distance information retrieval

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International Conference on Computational, Communication and information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. Applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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Doctoral Consortium AIxIA 2023

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CEUR Workshop Proceedings 2024年 3670卷

作者： Poggioni, Valentina Rossi, Silvia University of Perugia Dept. of Mathematics and Computer Science Perugia Italy University of Naples"Federico II" Dept. of Electronic Engineering and Information Technology Naples Italy

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Recognition of Facial Stress System using Machine Learning with an Intelligent Alert System 7

Recognition of Facial Stress System using Machine Learning w...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Raja, S. Kanaga Suba Durai Arumugam, S.S.L. Kumar, R. Praveen Selvakumar, J. Easwari Engineering College Chennai India Dept. of Information Technology Easwari Engineering College Chennai India Dept. of Electronics and Communication Engineering Easwari Engineering College Chennai India Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9781665464086

The primary component in this paper is to investigate the facial emotional states and EEG indicators, especially in pressure, for the duration of the interplay with games. The proposed paper identifies sure precise expressions in game enthusiasts whose facial feelings are segmented frames were separated into special areas, then the mind indicators are classified based on their frequencies, ranges are also analysed, and the signal value is *** on facial features are extracted from the localized areas, used fuzzy c-means class, and directed onto an emotion space. Then the EEG sign price is evaluated with the brink fee. After that, the strain data can be ship thru by using a SMS alert by using GSM module and buzzer alerts the use of Arduino micro controller. The cease results are accurate and robust. © 2023 IEEE.

关键词： C (programming language)

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The Influence of Kanban Agile Methodology on Software Project Management: A Survey Method 10

The Influence of Kanban Agile Methodology on Software Projec...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ahmed, Irfan Munir, Fatima Nafees, Fouzia Mahmood, Jabar Chaudhry, Shehzad Ashraf Javaid, Maryam Batool, Khadija University of Sialkot Dept. of Computer Science Sialkot Pakistan School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University State Key Laboratory of Blockchain Hangzhou China College of Engineering Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates International Institute of Science Arts and Technology Dept. of Computer Sciences Gujranwala Pakistan

ISBN: (纸本)9798331532895

Software project management (SPM) is an important process in the software development (SD) industry, that involves the planning, organizing, and overseeing software application development. Kanban agile (KA) methodology is a systematic project management approach that prioritizes workflow optimization, clear visibility, and continuous delivery in SD. It has become increasingly popular in software development for its ability to adapt to changing requirements and promote continuous delivery. It is examined that kanban affects critical project management factors. Data is gathered through a survey of software professionals who are experienced with kanban. The results indicate that kanban significantly improves team collaboration, workflow transparency, and overall project efficiency. This study investigates the impact of the KA methodology on SPM using a survey-based approach. However, the study also identifies challenges, including the initial learning curve and resistance to change within teams. These findings provide valuable insights for organizations considering kanban and contribute to the broader understanding of its role in SPM. We surveyed several software development companies, and the results show that almost all of them use agile methodology (Kanban), which improves SPM as compared to other agile methodologies like Scrum, extreme programming and dynamic systems development methods. © 2024 IEEE.

关键词： Agile DSDM Kanban Quality Enhancement Scrum SPM XP

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