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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Gowthamy, J. Suleka, C. Pereira, W Kenitawin Helena Kumar, P Monish SRM Institute of Science and Technology Ramapuram Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

The brain tumors detected early and precisely is essential for giving patients a better chance of recovery. Magnetic Resonance Imaging are the leading method for problems abnormalities in the brain, however traditional diagnosis relies on manual analysis by medical professionals, which can diagnose brain tumors from MRIs can be lengthy, rely heavily on individual interpretation, and lead to mistakes. It introduces a new method for automatically finding brain tumors in MRIs using deep learning technology. Deep learning has revolutionized medical image analysis by enabling computers to learn complex patterns directly from data. This research leverages grayscale MRIs and employees deep learning algorithms architecture, Xception, to achieve high-performance computer-aided diagnosis. The Xception utilizes depth-wise separable convolutions, a technique that offers a balance between model accuracy, size, and computational efficiency. It examines the Xception model performs with transfer learning. In transfer learning, a deep learning model trained on a vast amount of data is adapted to a new task. The model is fine-tuned for the detecting brain tumors in MRIs and has the potential to make brain tumor diagnosis faster and more accurate compared to traditional methods. By automating tumor detection and reducing human subjectivity, deep learning-based systems improves early diagnosis and treatment planning for brain tumors. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Chaotic Dynamics of Switched Reluctance Motor Drives: A State-of-the-Art Review

Chaotic Dynamics of Switched Reluctance Motor Drives: A Stat...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Al Aman, Yaheya Sahoo, N.C. School of Electrical and Computer Sciences Indian Institute of Technology Bhubaneswar Department of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9798331540821

Complex nonlinear systems have been very often found to exhibit unpredictable, chaotic behavior as they become extremely sensitive to initial conditions. In electric motor drives, which are highly nonlinear systems, occurrence of chaos is inevitable and often causes undesirable phenomena in the system. This article begins with a structured introduction to chaotic phenomena in nature. Then it focuses on a state-of-the-art review on occurrence of chaos in switched reluctance motor drive, whose main source of nonlinearity is the nonlinear magnetization characteristics due to the doubly-salient structure of the motor. Analysis tools such as phase portraits, bifurcation diagrams, and Lyapunov exponents are also discussed to detect the presence of chaos in the motor. Some applications of chaos have also been reported. Also, some possible future works based on the research gaps in the literature are identified. © 2024 IEEE.

关键词： Reluctance motors

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Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Kasif, Gibran Thondilege, Comganesha University of Westminster London School of Computer Science and Engineering United Kingdom Informatics Institute of Technology Department of Computing Colombo06 Sri Lanka

ISBN: (纸本)9798350341454

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model's performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues. © 2023 IEEE.

关键词： Music

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Big Mart Sales Prediction Using Machine Learning 10

Big Mart Sales Prediction Using Machine Learning

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Mondal, Sagar Debbarma, Anindita Prakash, B. Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai India Srm Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350353068

In today's competitive retail landscape, supermarkets like Big Marts meticulously track the sales data of each product to anticipate consumer demand and optimize inventory management. By analyzing this data, including identifying anomalies and trends through data mining techniques, retailers can develop predictive models using advanced machine learning algorithms. Techniques such as Xgboost, Linear Regression, Polynomial Regression, and Ridge Regression are employed to forecast sales volumes with greater accuracy than traditional methods. Through the application of these models, businesses like Big Mart can make informed decisions to enhance their operational efficiency and strategic planning, ensuring they stay ahead in the dynamic market environment. © 2024 IEEE.

关键词： Sales

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A Stroke Complication Neural Network Model to Predict the Severity of Brain Stroke Using Family History 8th

A Stroke Complication Neural Network Model to Predict the Se...

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8th International Conference on computers, Management and Mathematical Sciences, ICCM 2022

作者： Gangarapu, Puneeth Sine, Nitish Bandi, Vamsi Department of Computer Science and Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Artificial Intelligence Madanapalle Institute of Technology & Science Madanapalle India

ISBN: (纸本)9783031511660

These days, due to technological advancements, the lifestyle of modern humans has changed from being active to sedentary. As computational power is rapidly increasing day by day, we can use concepts like machine learning and neural networks to estimate and monitor the health of people by just using stroke parameters along with family history. The findings in the body of literature are already in existence, which show that brain strokes can be classified without considering the significance of family history. In this work, ischemic, intracerebral, and subarachnoid hemorrhagic brain strokes are the main emphasis. A Stroke Complication Neural Network (SCNN) is proposed. This model works by taking the stroke classification results from machine learning models. Using this stroke complication graph, family history, and a multi-layered perceptron (MLP), an attempt is made to determine the severity of the stroke. The proposed model has obtained decent outcomes with an accuracy of 94.32%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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A Real-Time Framework for Automatic Sarcasm Detection Using Proposed Tensor-DNN-50 Algorithm 4th

A Real-Time Framework for Automatic Sarcasm Detection Using ...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

Social media platforms such as Twitter, Facebook, and Instagram are vast repositories of trending global news. They generate an enormous amount of data, offering a valuable resource for both academic researchers and IT marketing teams. These data can be harnessed for various insights and applications. A prominent and pressing research area within this data is the detection of sarcasm and fake news. The internet is rife with misleading and false information, often causing widespread confusion. Current research primarily categorizes news as either real or fake, using machine learning algorithms. However, this binary approach struggles with news that is partially true yet contains elements that could be deemed fake. To address this issue, our work introduces a real-time framework for sarcasm detection, utilizing an innovative deep learning technique called Tensor-DNN-50 for multiclass classification. The TDNN-50 model not only classifies news articles based on authenticity but also quantifies the level of sarcasm within them. Comparative analysis against leading techniques like SVM, att-RNN, and EANN demonstrates the superiority of our proposed algorithm. It achieves an impressive accuracy of 96.4% for two-class classification, 94.32% for four-class classification, and an astonishing 99.1% for seven-class classification. These results underscore the potential of our Tensor-DNN-50 Model in effectively addressing the intricate challenges of sarcasm detection and news classification with unparalleled accuracy across multiple classes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Eye Disease Detection Through Image Classification Using Federated Learning

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SN computer Science 2023年第6期4卷 1-14页

作者： Kaushal, Vishal Hada, Nishant Singh Sharma, Sangeeta Computer Science and Engineering Department National Institute of Technology Himachal Pradesh Hamirpur 177005 India

In recent years, the fields of artificial intelligence and deep learning have had a significant impact on the field of ocular imaging. One area where deep learning has been particularly useful is in identifying and categorizing different eye diseases through the analysis of retinal photographs, optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA) images. However, traditional methods of training deep learning models often require large and diverse datasets from multiple locations, which can raise concerns about data security and patient privacy. To address these concerns, researchers have begun to explore the use of federated learning, a method of distributed, collaborative learning that allows multiple parties to work together without needing to share sensitive data. This paper implements four popular deep learning algorithms: VGG16, MobileNet, ResNet50, and InceptionV3, in a federated learning environment for ocular imaging datasets. The objective is to explore the effectiveness of these algorithms in terms of accuracy, privacy, and other performance parameters. The results demonstrate that implementing these algorithms in a federated learning environment can lead to significant improvements in performance and privacy compared to traditional centralized learning methods. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks Fundus imaging Ophthalmological disease detection Transfer learning

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Automated Pneumonia Detection Using Manta Ray Optimizer with Deep Learning Approaches 2

Automated Pneumonia Detection Using Manta Ray Optimizer with...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Sabaresh, M.M. Shahin, Fidin Sabri Raja, M Senthil SRM Institute of Science and Technology Computer Science and Engineering CTech Department Chennai India SRM Institute of Science and Technology CTech Department Chennai India

ISBN: (纸本)9798350368109

This new method merging various disciplines into each stage, introduces a novel integration of state-of-the-art neural networks (MobileNet, ResNet, and VGG19) with an adaptive nature based on the Manta Ray scheme within the framework approaches helped by Adam optimizer to significantly improve accuracy across chest X-ray images. Integrating physics-motivated model parameter adaptation, based on the fluid-inspired lenticular lens architecture of manta rays (7), into MRO encourages more rapid convergence during training and allows for flexible manipulation in response to distributed behaviors inherent to medical imaging. This combined action not only accelerates convergence but also enhances the global stability of the model, improving classification accuracy and increasing reliability in diagnostics. Furthermore, the interoperability of these approaches highlights how cross-disciplinary research in healthcare technology can not only be transformational but also provide invaluable adjuncts to clinical practice for high-throughput targeted analyses. In an entirely new way, we want to transform traditional medical imaging analysis by bringing pervasively accurate diagnosis into everyday clinical practice while providing better outcomes for patients and leading "the cutting edge"of healthcare delivery. This ultimate workflow does seem to hold the key which can result in far-reaching improvements in health care, especially after making important progress upon the currently existing slower and less efficient healthcare systems. This approach is interdisciplinary and promises revolutionary progress in medical diagnostics. © 2024 IEEE.

关键词： Clinical research

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An Evaluation of Network Performance through the Integration of SRv6 and MPTCP 13

An Evaluation of Network Performance through the Integration...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Laohakanniyom, Pakorn Ichikawa, Kohei Iida, Hajimu Leelaprute, Pattara Manaskasemsak, Bundit Rungsawang, Arnon Kasetsart University Department of Computer Engineering Bangkok Thailand Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798350368086

The exponential growth in networked systems, such as entertainment streaming services, has necessitated advancements in network performance. Traditional singlepath routing often results in bottlenecks and inefficiencies, whereas multipath routing can significantly enhance bandwidth utilization and accommodate diverse user demands. This study explores the integration of SRv6 (Segment Routing over IPv6) with MPTCP (Multipath TCP) to leverage multiple network routes concurrently. SRv6 offers a scalable routing architecture by embedding routing information within IPv6 headers, while MPTCP distributes traffic across multiple paths, enhancing both network performance and reliability. We conducted experiments using two network topologies: a synthetic topology with 7 nodes and 11 links, and the Internet2 OS3E topology with 34 nodes and 42 links. Throughput measurements were taken under four scenarios: single-path TCP without SRv6, single-path TCP with SRv6, MPTCP without SRv6, and MPTCP with SRv6. The experimental results confirmed that the integration of SRv6 with MPTCP achieved the expected improvements in network performance, demonstrating enhanced throughput and better resource utilization compared to single-path TCP configurations. The implications of this research indicate that SRv6 and MPTCP integration enhances throughput, network reliability, and efficiency. This approach provides additional perspective for further scalable solutions that can be applied across various network topologies, and addressing modern network demands. However, this research also identifies potential challenges such as throughput fluctuations when SRv6 and MPTCP are combined, which may affect the performance of realtime applications. Future research should explore these challenges to optimize the integration of SRv6 and MPTCP. © 2024 IEEE.

关键词： Integration

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A Framework for Video Based Sign Language Interpretation Using Machine Learning and Statistical Methods 4

A Framework for Video Based Sign Language Interpretation Usi...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swathi, D. Susmetha, K. Sreeji, S. Mangairkarasi, S. B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri TamilNadu Chennai600119 India Sathyabama Institute of Science and Technology Semmancheri Department of Computer Science and Engineering TamilNadu Chennai6001109 India

ISBN: (纸本)9798331523923

The Sign Language Recognition System has been designed to capture video input, process it to detect hand gestures, and translate these gestures into readable text. The project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the subsequent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time. © 2025 IEEE.

关键词： Convolutional neural networks

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