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Diagnosis of Glaucoma Through the Analysis of Saccadic Eye Movements Employing Machine Learning Methods 4

Diagnosis of Glaucoma Through the Analysis of Saccadic Eye M...

引用

4th IEEE International Conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Kumar, R V B S Prasanth Amudha, J. Krishnan, Sajitha Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350306118

This study addresses the significant public health concern of glaucoma, a collection of eye conditions resulting in optic nerve damage and progressive vision loss. To conduct our research, we employed a dataset obtained from collaborative efforts between the Amrita school of engineering in Bangalore and Narayana Nethralaya Hospital during visual field examinations. The dataset, named EXGP, comprises Humphrey Visual Field tests conducted on a sample of 111 patients. Our research investigates the impact of variations in saccadic eye movements on glaucoma performance. This hypothesis is tested through an examination of diverse metrics associated with saccadic eye movements during visual search tasks. The summarizing parameter, Visual Field Index (VFI), is employed. However, for its widespread acceptance, further characterization and comparison with conventional indices used in glaucoma diagnosis, specifically Mean Deviation (MD), are required. Our objective is to define the applicability of VFI in advanced glaucoma cases by contrasting it with MD and the criteria for establishing blindness. Through statistical analysis of confidential eye data, we generated data samples from the provided dataset. Subsequently, a range of machine learning algorithms, including Support Vector Machine (SVM), K nearest neighbors (Knn), and Logistic Regression (LR), were utilized to predict the presence of glaucoma or normality in individuals. Classification accuracy for the various machine learning models was assessed using appropriate evaluation metrics. Among the three models, Logistic Regression demonstrated the highest accuracy of 0.94 in performing the classification task. © 2023 IEEE.

关键词： Support vector machines

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Robust Image Forgery Classification using SqueezeNet Network 1

Robust Image Forgery Classification using SqueezeNet Network

引用

1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Bhuvaneswari, R. Enaganti, Karun Kumar Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350342796

The rapid advancement of digital manipulation tools has made it increasingly challenging to detect image forgery, especially with the emergence of complex and realistic deep imaging forgeries. Traditional techniques often struggle to identify these sophisticated manipulations. This research focuses on leveraging the SqueezeNet deep learning network to detect deep image forgeries. By training the SqueezeNet network on a dataset of real and fake images, distinctive features can be learned to enhance the detection process. The effectiveness of the SqueezeNet model is demonstrated through evaluation on a separate test dataset, although its performance may vary depending on the complexity of the forgeries. This study sheds light on the potential of using the SqueezeNet deep learning network to combat deep picture fraud and improve image analytics systems. The evaluation results with distinguishing forgery and original images with an accuracy of 98% which is 3-10% increase when compared with the other deep learning methods. © 2023 IEEE.

关键词： Deep image forgery detection Deep learning networks Image editing Image forensics Performance evaluation SqueezeNet

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Advancing Robotic Jumping with CVT Enhanced SEA 25th

Advancing Robotic Jumping with CVT Enhanced SEA

引用

25th Annual Conference on Towards Autonomous Robotic systems, TAROS 2024

作者： Sun, Jingcheng Zhou, Chengxu School of Mechanical Engineering University of Leeds Leeds United Kingdom Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031720611

Series-elastic actuation (SEA) is widely employed on hopping robots due to its capability to diminish energy consumption and peak torque requirements. However, scant attention has been given in existing studies to the jump height achieved by robots utilizing SEA. To maximise jump height while retaining SEA’s benefits, a solution to improve the actuator involves having a variable mechanical advantage (MA) gearbox between the elastic element and the end-effector. Continuously variable transmission (CVT) can produce such a precise MA profile with relatively straightforward structures. Although previous studies have incorporated CVT in robotics, with a predominant focus on wheeled robots, the potential advantages of using CVT on jumping robots have been overlooked. In this study, mathematical modelling is utilized to simulate and analyse the advantages of having a CVT-enhanced SEA (C-SEA) in terms of achieving higher jumps and protecting structural integrity for hopping robots. The results indicate that the C-SEA manages to reduce the peak ground reaction force by approximately 50% compared to the regular SEA. Moreover, C-SEA demonstrates superior performance in most cases with higher motor rotor mass and viscous damping coefficient when actuated by a motor with linear behaviour. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： End effectors

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Audio Deepfake Detection Using Deep Learning 12

Audio Deepfake Detection Using Deep Learning

引用

12th International Conference on system Modeling and Advancement in Research Trends, SMART 2023

作者： Anagha, R. Arya, A. Narayan, V. Hari Abhishek, S. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350369861

The capacity to identify real audio recordings from their modified counterparts is essential in the age of sophisticated digital manipulation for maintaining security and trust in a vari- ety of applications, from media forensics to voice authentication systems. This research aims to create a deep learning model that can distinguish between authentic and altered audio files, with an emphasis on identifying audio deepfakes. The study uses Mel spectrogram representations and data augmentation techniques to effectively extract features from the ASV spoof 2019 dataset and train models. Convolutional neural networks (CNNs) comprising a number of layers, including convolutional, pooling, batch normalization, ReLU activation, dropout, global average pooling, and a dense classification layer are used as the foundation of the design. The Adam optimizer is used to optimize the model once it has been trained using binary cross-entropy loss, and a variety of metrics, such as accuracy, F1 score, ROC curve, and AUC, are used to track its performance. By making it easier to identify audio deepfakes, this project will ultimately increase the security and integrity of audio data in the digital world. © 2023 IEEE.

关键词： Feature extraction

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Comparative Evaluation of Various Models for the Classification of Music Genre 5

Comparative Evaluation of Various Models for the Classificat...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Srinivasa Sai, M Shanmukha Sampat, P.V. Praneeth, P. Ullas, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350321425

This project explores music genre differentiation using the GT-ZAN dataset, comprising ten genres. Ten classical machine learning models, including KNN, SVM, and Cross Gradient Booster, are compared using features such as MFCC mean, MFCC variance, genre labels, and root mean square values from the frequency and time domains. Experimental results demonstrate variations in model performance, with specific classifiers achieving higher accuracy and precision for particular genres, while others excel in recall and F1-score. The findings offer valuable insights for future music genre classification and contribute to the development of music genre categorization systems. © 2023 IEEE.

关键词： Support vector machines

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Enhancing Healthcare Data Security in Cloud Computing systems through Advanced Homomorphic Encryption Techniques Integrated with Cuckoo Search Optimization Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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

作者： Sudha, I. Ramesh, P.S. Ponmalar, A. Jagannathan, J. Pandiarajan, Rajkumar Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India

ISBN: (纸本)9798350388602

The studies in this research are focused on the mix of homomorphic encryption mechanisms and the Cuckoo Search Optimization Algorithm to promote the security of the healthcare data in the cloud computing system. I have observed that cloud solutions are now at the center of the contemporary health care systems, and security of patient's data remains a top priority. Earlier used encryption techniques, though secure if implemented, have problems in the way that data is to be processed first it has to be decrypted and then made ready for use - it is in this phase that data can be easily hacked into. Homomorphic encryption provides solution to this issue since it enables one to perform computations on encrypted data while maintaining its confidentiality. However, it takes a lot of computation which presents a lot of problems even in terms of simple performance. The Cuckoo Search Algorithm, a new nature-inspired metaheuristic, is incorporated into the encryption process to provide further optimization, whilst preserving the security benefits of the algorithm. The use of the proposed methodology takes place employing Python and its deployment at the cloud platform with elaborated dataset containing anonymized records extracted from electronic health records. The outcome proves that there are great enhancements in encryption and decryption time, computational complexity, and capacity, and thereby making the system appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancement of adaptive, comprehensive and secure cloud computing systems. © 2024 IEEE.

关键词： Electronic health record

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Optimizing User Data Privacy and Confidentiality in Cloud Storage systems Through Advanced Obfuscrypt Encryption Methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

引用

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

作者： Ramesh, P.S. Sudha, I. Jagannathan, J. Pandiarajan, Rajkumar Ponmalar, A. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy Of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India R.M.K. Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350388602

With a steady increase in the adoption of cloud storage systems, the client's data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today's sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats. © 2024 IEEE.

关键词： Data obfuscation

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Prediction of Road Traffic using an ELM-based Neural Network 3

Prediction of Road Traffic using an ELM-based Neural Network

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Ali Fathima, R Syed Sumathi, R. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering KrishnanKovil India

ISBN: (纸本)9781665464871

This study discusses about traffic prediction, which is possible in intelligent transportation systems. This involves making predictions based on data from the previous year and data from the most recent years, which eventually yields accuracy and mean square error. For those who need to check the current traffic situation, this prediction will be useful. The traffic statistics is based on a 1 hour time gap. From this prediction, live traffic numbers are examined. So, while the user is also driving, this will be simpler to examine. The core objective of this proposed system is to identify the future traffic based on the video analysis. The proposed system uses video analysis and ELM based neural network. The proposed system is also useful for central and state government for maintaining smooth traffic flow. © 2023 IEEE.

关键词： Machine learning

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Medicinal plant classification using attention mechanism driven VGG19 and Resnet101

Medicinal plant classification using attention mechanism dri...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Rohith, N.D. Bhuvaneswari, R. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350308167

The accurate classification of the medicinal plants is essential in various fields such as pharmaceutical research, traditional medicine, and biodiversity conservation. In this study, we focus on advancing the performance of two popular convolutional neural network architectures, VGG19-BN and Resnet101, for the classification of plant species which have medicinal properties. We propose the integration of attention mechanisms, specifically the Channel-wise Attention Module (CBAM) and the Efficient Channel Attention (ECANet), to improve the feature representation and discrimination capabilities of the models. By enhancing the attention to salient regions, the models become more adept at capturing intricate botanical attributes that contribute to accurate classification. Overall, this study contributes to the advancement of state-of-the-art methods for medicinal plant classification through the integration of attention mechanisms. © 2023 IEEE.

关键词： Attention Mechanisms CBAM ECANet Image Classification Medicinal Plants ResNet101 VGG19

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Deep Learning for Facial Expression Recognition using EfficientNetB0

Deep Learning for Facial Expression Recognition using Effici...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Bhuvaneswari, R. Krishna, Chetlapalli Amritha Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350308167

In this study, we present a novel deep learning-based program for Facial Expression Recognition (FER) using the EfficientNetB0 architecture. The program is designed to automatically recognize and classify facial expressions in images, an essential component in fields like human-computer interaction and emotional analysis. We employ two distinct image data stores for training and testing, both sourced from the CK+48 dataset, while also augmenting the data for improved model generalization. An image input layer, a pruned EfficientNetB0 network, an entirely interconnected layer for categorization, a Softmax layer, and a layer for classification make up out model structure. The efficiency of the model is assessed using a different test dataset after it has been developed via random gradient descent with momentum. Notably, we assess model performance using important parameters like accuracy, precision, recall, and the F1 score. The importance of this program rests in its potential to improve human-machine communication by training computers to recognize and react to emotional cues in humans. Its application spans a wide range of domains, including healthcare, marketing, and entertainment, where understanding user emotions is of paramount importance. The results obtained from this FER model can empower applications like virtual assistants and affective computing systems to provide more personalized and context-aware responses. The developed application's robust architecture, training methodology, and evaluation metrics make it a valuable contribution to the area of FER. © 2023 IEEE.

关键词： Affective Computing EfficientNetB0 Emotion Analysis Expression recognition Human computer Interaction

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