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Epilepsy Classification using Multi-Sequence MRI’s: An Ensemble Approach

Epilepsy Classification using Multi-Sequence MRI’s: An Ense...

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International Conference on computing and Networking Technology (ICCNT)

作者： Alamanda Hima Naga Rajasekhar Gurram Kavya Kota Sriya Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Epilepsy is a major threat, when it comes to human health. Classifying brain anomalies on MR images as a pathological clue of Epilepsy is crucial for diagnosis. Electroencephalography (EEG) and Magnetic resonance (MR) imaging are only two of the diagnostic tools that have been proposed for use in screening for epileptic seizures. The diagnosis can be made with the use of many MR images taken at different times by a neuroradiologist. The dataset under consideration is comprised of axial multi-sequences of MR images, and it features both epileptic and nonepileptic aberrant brain structures from a wide range of people. This article performs a comparative study among the models - CNN, MobileNet, InceptionV3, VGG16, VGG19, LSTM, GRU and also the Ensemble models - ‘CNN + MobileNet’, ‘VGG16 + LSTM’, ‘VGG16 + GRU’, ‘VGG19 + LSTM’ and ‘VGG19 + GRU’. The Epilepsy prediction for user upload images is achieved through an ensemble approach. We have demonstrated the effectiveness of these models in correctly detecting epileptic patients based on multi-sequence MRI data by achieving extremely accurate and robust classification findings through extended experimentation. The experiments demonstrated the classification best accuracy of 97.16% for ‘VGG19 + LSTM’, while the lowest score was 69.93 for InceptionV3 among all the twelve models.

关键词： Deep learning Pathology Accuracy Magnetic resonance imaging Computational modeling Epilepsy Brain modeling Electroencephalography Ensemble learning Long short term memory

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Hybrid Optimized A* Algorithm for Dynamic Rerouting in Autonomous Vehicles

Hybrid Optimized A* Algorithm for Dynamic Rerouting in Auton...

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International Conference on computing and Networking Technology (ICCNT)

作者： Tina Choudary Yalamanchili Nakshatra Sree Sreedhar Sreenivas Nouduri P J Amritesh Neethu Mr Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research presents a new approach to adaptive control in autonomous vehicles by optimizing the Hybrid A star algorithm. This algorithm is designed to ensure reliable and efficient operation in complex environments. Leveraging the hybrid A star algorithm, this plan will improve the ability of autonomous vehicles to navigate difficult situations such as crowded cities or residential areas. Optimization of algorithms enables vehicles to plan routes quickly and accurately, increasing travel time and overall transportation efficiency. Additionally, the method further increases reliability by integrating real-time information to update the change process. Numerous simulations prove the superior performance of this algorithm compared to traditional routing technology. Autonomous vehicles, Dynamic routing, Blockchain, Hybrid A star algorithm, Data verification.

关键词： Heuristic algorithms Urban areas Stars Transportation Routing Reliability engineering Real-time systems Vehicle dynamics Autonomous vehicles Optimization

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Comparative Study on Synthetic and Natural Error Analysis with BART & MarianMT

Comparative Study on Synthetic and Natural Error Analysis wi...

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International Conference for Convergence of Technology (I2CT)

作者： Rohit R SA Gandheesh Gayatri Sanjana Sannala Peeta Basa Pati Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Text is essential for communication, information sharing, knowledge acquisition, and analysis. It shapes public opinion, supports education, and drives online content, making it crucial in various domains. While there are various language models utilized for text analysis and text correction, there is little to no survey conducted on these model’s behavior and limitations. This work deals with studying BART and MarianMT language models behavior to an input dataset consisting of two types of errors, Synthetic and Natural. Synthetic errors are efficient to create and test, whereas Natural errors are more common and close to real world occurring errors. The models were trained and tested with the generated data, the results highlighted that BART exhibited consistent outputs towards both Synthetic and Natural errors and hence revealing a break-even point at the vicinity of 26% Synthetic error introduction. Conversely, the performance of MarianMT was comparatively diminished for Synthetic errors in contrast to Natural errors. These findings provide valuable insights into the behavior and capabilities of the models.

关键词： Training Surveys Analytical models Text analysis Error analysis Shape Knowledge acquisition

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Fortifying Digital Security: A Machine Learning and Explainable AI Framework for Password Strength Assessment

Fortifying Digital Security: A Machine Learning and Explaina...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Kuthati Shreya Lasya Priya Divakarla Kanderi Johith Kumar H. N. Vishwas Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

In the current era where almost anything is accessible over the internet, passwords have become most important to ensure security and authentication. To provide authentication many other mechanisms exist like smart locks, biometrics, voice recognition, face recognition, etc. but still, passwords exist as the widely preferred method for authentication because of its ease of use and as many people are familiar with passwords. With the ease of use that it comes with there is a possible threat of guessing the passwords that exists. Usually, the passwords are some patterns, names, important years, etc. These are sometimes repetitive or easily guessed by the attacker because of their simplicity, and having common and weak passwords. The proposed model is a machine-learning algorithms that classifies the passwords as weak, medium, and strong based on their strength. Machine learning models like Random Forest (RF) classifier, Logistic Regression, XGBoost (XGB), and Gradient Boosting Machines (GBM).

关键词： Machine learning algorithms Accuracy Explainable AI Authentication Passwords Predictive models Classification algorithms Random forests Tuning Classification tree analysis

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Unleashing power of Vision Transformers for disease prediction in Chest X ray images

Unleashing power of Vision Transformers for disease predicti...

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International Conference on computing and Networking Technology (ICCNT)

作者： Chava Sriya Chowdary Dovala Ruthwik Kancharla Nititsh Sai Rimjhim Padam Singh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This work develops and evaluates a deep learning model to properly categorise chest X-ray pictures into three categories: normal, pneumonia, and COVID-19. The main idea is to help physicians diagnose illnesses at an early stage and provide accurate treatment, which eventually leads to critical patient care and enhanced healthcare system efficiency. Hence, this research work makes use of various state-of-the-art architectures like Vision Transformers, InceptionNetV3, EfficientNetV2, MobileNetV2, NasnetMobile, ConvNext and DenseNet169, to fully evaluate their performance on the dataset of Chest-Xray-14. These models have shown good results and their usefulness in detecting chronic chest conditions thereby, proving their efficient applicability in the medical field. This study has great reported efficient results with Vision Transformers clearly to be the best model for diagnosing chest diseases like Pneumonia and COVID-19 speedily and effectively.

关键词： Deep learning computer vision Adaptation models Accuracy Computational modeling Predictive models Transformers Data models X-ray imaging Diseases

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Enhancing Medical Imaging: Noise Reduction and Super Resolution with Transfer Learning

Enhancing Medical Imaging: Noise Reduction and Super Resolut...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： K Lokeshwar Reddy G Bharathi Mohan Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

Medical image improvement, including noise reduction and super-resolution, is vital for proper diagnosis and therapy. However, conventional approaches typically fail to generate sufficient results, particularly in the presence of both noise and poor resolution. Leveraging the power of pre-trained models, MAXIM for noise reduction, and ESRGAN for solving large-scale problems, our methods combine their strengths to provide unique results. MAXIM denoiser intelligently reduces noise to provide clean input to ESRGAN super-resolution models. Unlike traditional cascade systems, our integrated system allows for contact and flexibility, allowing for simultaneous optimization. The proposed method has been carefully evaluated on STARE (Structured Analysis of the Retina) and NIH Chest X-ray datasets and has been found to provide significant improvements in image quality compared to separate methods. Quantitative measurements, including PSNR and SSIM, supported the performance of the combined model. Comparative analysis with existing models, including SRGAN, SRCNN, and ESRGAN, demonstrates the effectiveness of our approach. This research is an important contribution to the improvement of medical images in terms of noise reduction and super-resolution to facilitate better evaluation and analysis.

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Real-Time Stress Monitoring using IoT and Machine Learning for Cognitive Wellness

Real-Time Stress Monitoring using IoT and Machine Learning f...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Thangam S Garikipati Karthik Tejaswi Muppala Vinitha Chowdary A J Jesy Janet Kumari Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Stress monitoring and management became some of the most vital requirements for maintaining overall well-being in high-stress environments. This work deals with the challenging issue of real-time stress detection using a fusion of the Internet of Things and machine learning technology implemented on a Raspberry Pi. Using the proposed model, the following physiological signals are collected using a Pulse Sensor for heart rate, a DS18B20 sensor for temperature, a DHT22 sensor for humidity, and a Grove GSR sensor for galvanic skin response (GSR). Based on the collected data, a hybrid model of Support Vector Machines and Random forests was trained, which would ultimately classify stress into different levels. A Snake game dynamics were controlled through a joystick. Using the predicted stress levels, the game difficulty is automatically adjusted, to provide real-time biofeedback for the user's cognitive wellness and stress management. This ensures that the user is calm and focused. The system effectively adapted to the gaming environment based on the stress levels of the player and it will serve as a promising tool to enhance cognitive wellness through game interaction.

关键词： Temperature sensors Temperature measurement Support vector machines Games Real-time systems Skin Vectors Biomedical monitoring Stress Monitoring

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A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling

A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zheng, Jianbo Yang, Chao Zheng, Fangrong Jiang, Bin College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China School of Computational Science and Electronics Hunan Institute of Engineering China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of bearing data, limiting the further improvement of the performance of the fault diagnosis model. This paper proposes a fault diagnosis method using multi-sensor data and periodic sampling to solve the problems above. First, the vibration data of different bearing positions are fused into multi-channel fusion data to improve the defect of insufficient data utilization. Second, based on the sampling length and sampling stride, periodic sampling is carried out for the fusion data to solve the problem of unreasonable sampling. Third, the traditional convolutional neural network is adjusted to extract more detailed fault features and obtain the best recognition effect. Finally, the experimental results verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Failure analysis

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Garment-Specific Style Transfer Using VGG19 and Semantic Segmentation for High-Fidelity Clothing Image Generation

Garment-Specific Style Transfer Using VGG19 and Semantic Seg...

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Data, Computation and Communication (ICDCC), International Conference on

作者： Venkatraman K Logeshwar BS Aadhithyan D Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

This paper will present a new style transfer technique, applied specifically to clothing images, by using the VGG19 neural network together with a semantic segmentation approach. The process involves the creation of a segmenting network where the clothing is properly separated from the background such that this style transfer technique may be more specific and targeted. This targeted approach captures the important garment features, including textures and boundary details of the garments, during the transformation. To further enhance the quality of the results, it introduces loss functions custom-designed to preserve the smoothness of the fabric and the integrity of edges of the garment. In this method, proposed here, through immense experimentation, the performance in output is superbly better than that of conventionally used techniques as the quality of the output visuals will be more accurate with all the structural elements of the garments.

关键词： Visualization Accuracy Image synthesis Semantic segmentation Clothing Neural networks Real-time systems Fabrics

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Enhanced Aerial Vehicle Detection Using a Customized YOLOv8 Model for Real-World Applications

Enhanced Aerial Vehicle Detection Using a Customized YOLOv8 ...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Maanav Thalapilly Ketone Agasti Niranjan S Nambiar Ananthu Suresh Anjali T Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Aerial vehicle detection is a critical function in surveillance, disaster response, and many defense systems. In this paper, a customized model, which extends the YOLOv8 model to detect aerial vehicles with exact precision and efficiency with four classes-categorized as helicopters, airplanes, drones, and UAVs-is developed. The dataset comprises 5,050 labeled images of various sources, augmented for robustness, and formatted in the YOLO-compatible structure for smooth training. The proposed approach integrates optimized depth and width multipliers for reducing complexity of computation at a preserved high detection accuracy. Its efficiency reflects in an mAP of 98.7%, which is superior to YOLOv8 (97.1%) and YOLOv5 (95.8%). Comparison in the two regimes indicates that the work offers superior precision and computational efficiency, hence suitable for real-world scenarios where resources are constrained. Results confirm the proposed approach is efficient in dealing with the cases of small objects, overlapping entities, and complex backgrounds, and its potential could be for further development of aerial surveillance technologies.

关键词： YOLO Training Accuracy Computational modeling Vehicle detection Surveillance Atmospheric modeling Robustness Real-time systems Vehicle dynamics

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