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7th International Conference on Image Information Processing, ICIIP 2023

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Chennai India Yale University Section For Biomedical Informatics And Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350371406

Diabetic retinopathy is a serious medical disorder that, if left untreated, can result in visual impairment or blindness. The precise and timely categorization of its severity is critical for appropriate medical management. This research describes an automated method for determining the severity of diabetic retinopathy using deep learning algorithms. The suggested methodology adopts a two-step process for identifying diabetic retinopathy and then categorizing its severity. In this technique, two pre-trained deep learning models, InceptionResNetV2 and DenseNet121, are used. For training and assessment purposes, the 'APTOS 2019 Blindness Detection' dataset which consists of five severity classes is used as the foundation. To overcome class imbalance, data augmentation approaches are used to improve the model's performance. The results of the experiment show that the strategy is effective, with an accuracy of 83.10 % and an F1 score of 82.77 %. This study contributes to the development of automated systems for assessing diabetic retinopathy, perhaps enhancing early detection and treatment. © 2023 IEEE.

关键词： Medical imaging

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation 14

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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14th International Conference on Cloud Computing, data science and Engineering, Confluence 2024

作者： Varshini Devi, I. Oviya, I.R. Raja, Kalpana Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350344837

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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Attribute-Based Access Control Utilizing Verifiable Credentials for Multi-Tenant IoT Systems 4

Attribute-Based Access Control Utilizing Verifiable Credenti...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big data, ICEIB 2024

作者： Kyriakidou, C.D. Nassar Papathanasiou, A.M. Pittaras, I. Fotiou, N. Thomas, Y. Polyzos, G.C. Mobile Multimedia Laboratory Department of Informatics Aueb Greece ExcID P.C Athens Greece School of Data Science CUHK-Shenzhen China

ISBN: (纸本)9798350360721

Many Internet of Things (IoT) applications are considering multi-tenancy to support for multiple entities sharing access to the same IoT devices. The challenge of ensuring IoT security and privacy is exacerbated in multi-tenant environments accommodating 'guest' users, i.e., opportunistic users that the system has not encountered. Thus, there is a need for novel access control mechanisms capable of addressing the complexities introduced by the opportunistic nature of the users who create complex trust relationships within the IoT ecosystem. In this study, we proposed a solution that leverages Verifiable Credentials (VCs) to implement Attribute-Based Access Control (ABA C) for multi-tenant IoT environments and we integrate it with W3C's Web of Things (WoT) standards, enhancing interoperability. Through the utilization of VCs, the solution provides secure verification and efficient revocation of user attributes, enabling access control decisions based on the enclosed attributes. Additionally, the proposed system ensures privacy, since users can selectively disclose the necessary attributes to gain access to resources through the utilization of Zero Knowledge Proofs (ZKPs). Finally, the solution does not require users to have any 'pre-existing' trust relationships with the protected system. © 2024 IEEE.

关键词： Internet of things

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings 15

Predicting Protein-Protein Interactions Using Self-Attention...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Oviya, I.R. Shanmukha Sravya, N. Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350370249

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model's performance is assessed through 5-fold cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of 92.28% and 94.08%, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep Learning Machine Learning Protein Feature Encoding Protein Protein interaction Protein sequence

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STABLE TRAINING OF NORMALIZING FLOWS FOR HIGH-DIMENSIONAL VARIATIONAL INFERENCE

arXiv

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arXiv 2024年

作者： Andrade, Daniel School of Informatics and Data Science Hiroshima University Japan

Variational inference with normalizing flows (NFs) is an increasingly popular alternative to MCMC methods. In particular, NFs based on coupling layers (Real NVPs) are frequently used due to their good empirical performance. In theory, increasing the depth of normalizing flows should lead to more accurate posterior approximations. However, in practice, training deep normalizing flows for approximating high-dimensional posterior distributions is often infeasible due to the high variance of the stochastic gradients. In this work, we show that previous methods for stabilizing the variance of stochastic gradient descent can be insufficient to achieve stable training of Real NVPs. As the source of the problem, we identify that, during training, samples often exhibit unusual high values. As a remedy, we propose a combination of two methods: (1) soft-thresholding of the scale in Real NVPs, and (2) a bijective soft log transformation of the samples. We evaluate these and other previously proposed modification on several challenging target distributions, including a high-dimensional horseshoe logistic regression model. Our experiments show that with our modifications, stable training of Real NVPs for posteriors with several thousand dimensions is possible, allowing for more accurate marginal likelihood estimation via importance sampling. Moreover, we evaluate several common training techniques and architecture choices and provide practical advise for training NFs for high-dimensional variational inference. © 2024, CC BY.

关键词： Importance sampling

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Stroke Prediction Using Machine Learning 5

Stroke Prediction Using Machine Learning

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5th IEEE International Conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Abinandhini, D.M. Kumar, Aman Teja, Gudi Vishnu Sukesh, Mandava Divya, S. Chauhan, Naman Oviya, I.R. Raja, Kalpana Chennai India School of Medicine Yale University New Haven Section for Biomedical Informatics and Data Science CT United States

ISBN: (纸本)9798350305449

A stroke is a serious medical emergency that happens when bleeding or blood clots cut off the blood flow to a part of the brain. With a mortality rate of 5.5 million per year, it ranks as the second leading cause of death globally. Over 15 million individuals experience a stroke each year, and one person dies from one every four minutes. According to the World Health Organization, stroke is the main cause of death and disability worldwide (WHO). Identifying the many stroke warning signs helps lessen the severity of the stroke. A stroke can be avoided in up to 80% of instances because it is typically the result of a poor lifestyle. As a result, stroke prediction becomes important and should be employed to stop it from causing long-term harm. The current study uses a variety of machine learning models, including Gaussian Naive Bayes, Logistic Regression, Support Vector Machine (SVM), KNN and Random Forest to predict stroke. The paper presents the comparison among all machine learning algorithms. Analysis of results revealed that KNN had the least accuracy of 76.32% and Random Forest had the highest accuracy of 94.81%. © 2023 IEEE.

关键词： Logistic regression

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ML Based Phenotype Analysis Using Differential Gene Expression data in Schizophrenia 4

ML Based Phenotype Analysis Using Differential Gene Expressi...

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4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Aadhithyan, D. Shabarirajan, K.J. Maniappan, Varun Shivanesh, B. Divya, S. Praghaadeesh, R. Raja, Kalpana Oviya, I.R. Chennai India School Of Medicine Yale University Section For Biomedical Informatics And Data Science New HavenCT United States

ISBN: (纸本)9798350384628

In this paper, differential gene expression (DGE) data in schizophrenia is analysed using machine learning approaches with the objective of predicting cell phenotypes related to the condition. The dataset contains information on cis eQTNs, gene orientation, probe information, gene expression profiles, and SNP information. When first investigating well-known machine learning methods, a hybrid model combining Random Forests and XGBoost is suggested, however it only offers a little increase. Increases in predicted accuracy of more than 99.12% are made possible by the H2O AutoML package. The results demonstrate the capability of ML-based analysis to unravel the underlying biological mechanisms of schizophrenia and contribute to the design of individualised therapeutic approaches. The paper represents the capability of machine learning to comprehend the relationship between gene expression patterns and complicated illnesses like schizophrenia. © 2023 IEEE.

关键词： Machine learning

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Towards cognitive navigation:A biologically inspired calibration mechanism for the head direction cell network

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Journal of Automation and Intelligence 2023年第1期2卷 31-41页

作者： Zhenshan Bing Dominik Nitschke Genghang Zhuang Kai Huang Alois Knoll Department of Informatics Technical University of MunichMunich85741Germany School of Data and Computer Science Sun Yat-sen UniversityGuangzhou510006China

To derive meaningful navigation strategies,animals have to estimate their directional headings in the ***,this function is achieved by the head direction cells that were found in mammalian brains,whose neural activities encode one’s heading *** is believed that such head direction information is generated by integrating self-motion cues,which also introduces accumulative errors in the long *** eliminate such errors,this paper presents an efficient calibration model that mimics the animals’behavior by exploiting visual cues in a biologically plausible way,and then implements it in robotic navigation *** proposed calibration model allows the agent to associate its head direction and the perceived egocentric direction of a visual cue with its position and orientation,and therefore to calibrate the head direction when the same cue is viewed *** examine the proposed head direction calibration model in extensive simulations and real-world experiments and demonstrate its excellent performance in terms of quick association of information to proximal or distal cues as well as accuracy of calibrating the integration errors of the head *** can be viewed at https://***/hdc-calibration.

关键词： Head direction cells Error calibration Cognitive navigation Neural SLAM

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Ancient Character Recognition: A Novel Image dataset of Shui Manuscript Characters and Classification Model

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Chinese Journal of Electronics 2023年第1期32卷 64-75页

作者： TANG Minli XIE Shaomin LIU Xiangrong Department of Computer Science and Technology School of Informatics Xiamen University School of Big Data Engineering KaiLi University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University

Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu＿C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A,a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiN et-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.

关键词： Training Speech recognition Artificial neural networks Feature extraction Stability analysis Character recognition Machine translation

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