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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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Dimensionality Reduction with DLMNN Technique for Handling Secure Medical data in Healthcare-IoT Model 3

Dimensionality Reduction with DLMNN Technique for Handling S...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Lalithambigai, M. Kalpana, V. Sasi Kumar, A. Uthayakumar, J. Santhosh, J. Mahaveerakannan, R. Sri Krishna Adithya College of Arts and Science Department of Computer Applications Coimbatore641042 India Vel Tech University India Institute of Engineering and Technology Srinivas University Department of Cloud Technology and Data Science Surathkal Karnataka State Mukka574146 India Dhanalakshmi Srinivasan College of Engineering Department of Ai and Ds Coimbatore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

Security and maintaining the integrity of sensitive patient information have emerged as major obstacles for healthcare service applications in the wake of IoT's meteoric rise in the healthcare industry. The diagnostic information included in medical photographs is of particular importance, and this research presents a hybrid security strategy to protect against such breaches. Chronic diseases such as Heart Disease (HD), cancer, and chronic respiratory sickness are the main causes of death worldwide. Those who have a heart attack suddenly have a very poor chance of surviving. As a result, this paper proposes a heart disease monitoring scheme, wherein a Deep Learning Modified Neural Network (DLMNN) centred on the Internet of Things is used to aid in the diagnosis and treatment of HD. There are three stages to implementing this strategy: First, there is encryption;second, a combination of information gain (IG) and principle component analysis (PCA) to reduce dimensionality;and third, a classification system. To begin, the patient is fitted with a wearable Internet of Things (IoT) sensor device, which simultaneously uploads its sensor data to a remote server. The sensor data is encrypted using a hybrid encryption scheme that combines the Advanced Encryption Standard (AES) and the Rivest, Shamir, and Adleman (RSA) algorithms. A DLMNN classifier is then used to classify the data after it has been decrypted. There are two types of classification outcomes. It provides information on the patient's heart and notifies the doctor in case of abnormalities. Estimated study results demonstrate that DLMNN is an improvement over previously used algorithms for HD diagnosis. The proposed AES-RSA used to support safe data transfer also yields the highest level of security, i.e. 94.32%, and does so in the quickest time for encryption and decryption. © 2023 IEEE.

关键词： Principal component analysis

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EUDRL: Explainable Uncertainty-Based Deep Reinforcement Learning for Portfolio Management 15

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Lear...

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

作者： Lina, Jia Banda, Shahab S. Paib, Hao-Ting Rawal, Bharat S. International Graduate School of Artificial Intelligence Yunlin County Douliu City Taiwan Bachelor Program of Big Data Applications National Pingtung University Pingtung Taiwan Department of Computer Science and Digital Technologies University GgramblingLA United States

ISBN: (纸本)9798350370249

Supervised deep learning (SDL) has shown remarkable success in various financial applications, such as stock prediction and fraud detection. However, SDL's reliance on class labels renders it unsuitable for portfolio management (PM) tasks, where such labels are often unavailable. To address this limitation, we propose a novel two-level architecture based on deep reinforcement learning (DRL) for PM, which does not require class labels. Our approach comprises several local agents that provide trading decisions and uncertainty assessments for individual stocks, and a global agent that makes portfolio management decisions based on the outputs of the local agents. Additionally, we incorporate the concept of explainable AI (XAI) into our framework using the SHAP (Shapley additive explanations) method, enhancing the transparency and interpretability of the global agent's decisions. Our experimental results demonstrate that the proposed architecture consistently yields profitable outcomes in the market. ©2024 IEEE.

关键词： Deep reinforcement learning

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Enhanced Framework for MRI Brain Tumor Recognition with Residual Learning and Intuitive Heatmap Visualization

Enhanced Framework for MRI Brain Tumor Recognition with Resi...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Velmurugan, L. Maheswari, R.K. Janani, K. Agalya, K. Poornima, M. Gayathri, K.C. Vit Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore India M.A.M College of Enginnering and Technology Artificial Intelligence and Data Science Tamilnadu Trichy India Manipal institute of Technology Manipal Academy of Higher Education Department of Data science and computer applications Manipal India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India Oasys Institute of Technology Department of Information Technology Tamil Nadu Trichy India Kelambakkam Chennai India

ISBN: (纸本)9798331543617

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet' s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices. © 2024 IEEE.

关键词： Brain mapping

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Fourier neural operator approach to large eddy simulation of three-dimensional turbulence

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Theoretical & Applied Mechanics Letters 2022年第6期12卷 438-444页

作者： Zhijie Li Wenhui Peng Zelong Yuan Jianchun Wang Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) 511458China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and TechnologyShenzhen 518055China Department of Computer Engineering Polytechnique MontrealMontreal H3T1J4Canada

Fourier neural operator(FNO)model is developed for large eddy simulation(LES)of three-dimensional(3D)*** fields of isotropic turbulence generated by direct numerical simulation(DNS)are used for training the FNO model to predict the filtered velocity field at a given *** input of the FNO model is the filtered velocity fields at the previous several time-nodes with large time *** the a posteriori study of LES,the FNO model performs better than the dynamic Smagorinsky model(DSM)and the dynamic mixed model(DMM)in the prediction of the velocity spectrum,probability density functions(PDFs)of vorticity and velocity increments,and the instantaneous flow ***,the proposed model can significantly reduce the computational cost,and can be well generalized to LES of turbulence at higher Taylor-Reynolds numbers.

关键词： Fourier neural operator Large eddy simulation data-driven simulation Incompressible turbulence

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VIKAS: A Multimodal Framework to Aid in Effective Disaster Management 13th

VIKAS: A Multimodal Framework to Aid in Effective Disaster M...

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13th International Conference on applications and Techniques in Information Security, ATIS 2022

作者： Prabhu, Gautham Manuru Gupta, Tanay Srujan, Metta Venkata Soumya, A.R. Palorkar, Anshita Chowdhury, Anurag Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819922635

In the event of a disaster, social media is often used to draw attention to affected areas and distressed people. The massive population and diversity in Indian languages warrant a novel real-time, big-data solution that can increase situational awareness, reduce special forces’ response time, and expedite decision-making. The proposed solution, VIKAS, streams text, images, videos, and audio from posts on microblogging platforms using keywords. It then uses the Google Translate and Transliteration APIs to handle multilingual and macaronic hybrid text, including Hinglish. An Apache Kafka event pipeline processes the sheer volume of posts asynchronously. Duplicate, uninformative, or bot-posted data (checked using the Botometer machine learning algorithm) is discarded. Scraped data is also verified through Google’s FactCheck Explorer API. Audio and video clips are processed leveraging speech-to-text methods. The solution incorporates a BERT pre-trained model and word embeddings for natural language processing tasks, including sentiment analysis and classification of textual data. Image classification and object identification are implemented using a ResNet deep learning model. This multimodal approach pinpoints locations using nearby landmarks, severity, and type of support needed, ranging from humanitarian aid and rescue relief to infrastructure damage. Easy-to-interpret visualizations on an accessible dashboard consolidate many details that can streamline resource distribution and personnel deployment. VIKAS was presented to the National Disaster Response Force at the national-level finals of the Smart India Hackathon 2022. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Improving latent factor models via personalized feature projection for one class recommendation 15

Improving latent factor models via personalized feature proj...

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhao, Tong McAuley, Julian King, Irwin Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications Shenzhen Research Institute Chinese University of Hong Kong Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong Department of Computer Science and Engineering UC San Diego San DiegoCA United States

ISBN: (纸本)9781450337946

Latent Factor models, which transform both users and items into the same latent feature space, are one of the most successful and ubiquitous models in recommender systems. Most existing models in this paradigm define both users' and items' latent factors to be of the same size and use an inner product to represent a user's 'compatibility' with an item. Intuitively, users' factors encode 'preferences' while item factors encode 'properties', so that the inner product encodes how well an item matches a user's preferences. However, a user's opinion of an item may be more complex, for example each dimension of each user's opinion may depend on a combination of multiple item factors simultaneously. Thus it may be better to view each dimension of a user's preference as a personalized projection of an item's properties so that the preference model can capture complex relationships between items' properties and users' preferences. Therefore, in this paper we propose a novel personalized feature projection method to model users' preferences over items. Specifically, for each user, we define a personalized projection matrix, which takes the place of user-specific factors from existing models. This matrix describes a mapping between items' factors and users' preferences in order to build personalized preference models for each user and item. The proposed personalized feature projection method is quite general and existing latent factor models, for example, can be cast as a special case. We present three objective functions to optimize predictions in the form of ranked lists of users' preferences over items, and demonstrate how each can be used to improve one-class recommendation performance. Experiments are conducted on four real-world datasets and our results show that our personalized feature projection method outperforms several state-of-the-art methods on various evaluation metrics. © 2015 ACM.

关键词： Collaborative filtering

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Classification and Discovery of Brain Tumor Using Ensemble Deep Learning 2

Classification and Discovery of Brain Tumor Using Ensemble D...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Srilatha, Kasanagottu Sravani, Chekka Vasudevarao, Mikkilineni Kiran, Ajmeera Chitra, D. Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Department of Data Science Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India Dayananda Sagar Business Academy Department Of Computer Applications Karnataka India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350372892

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor. © 2024 IEEE.

关键词： Convolutional neural networks

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Leveraging social connections to improve personalized ranking for collaborative filtering 14

Leveraging social connections to improve personalized rankin...

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23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

ISBN: (纸本)9781450325981

Recommending products to users means estimating their preferences for certain items over others. This can be cast either as a problem of estimating the rating that each user will give to each item, or as a problem of estimating users' relative preferences in the form of a ranking. Although collaborative-filtering approaches can be used to identify users who rate and rank products similarly, another source of data that informs us about users' preferences is their set of social connections. Both rating- and ranking-based paradigms are important in real-world recommendation settings, though rankings are especially important in settings where explicit feedback in the form of a numerical rating may not be available. Although many existing works have studied how social connections can be used to build better models for rating prediction, few have used social connections as a means to derive more accurate ranking-based models. Using social connections to better estimate users' rankings of products is the task we consider in this paper. We develop a model, SBPR (Social Bayesian Personalized Ranking), based on the simple observation that users tend to assign higher ranks to items that their friends prefer. We perform experiments on four real-world recommendation data sets, and show that SBPR outperforms alternatives in ranking prediction both in warm- and cold-start settings. Copyright 2014 ACM.

关键词： Collaborative filtering

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Does the Fairness of Your Pre-Training Hold Up? Examining the Influence of Pre-Training Techniques on Skin Tone Bias in Skin Lesion Classification

Does the Fairness of Your Pre-Training Hold Up? Examining th...

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IEEE Winter applications and computer Vision Workshops (WACVW)

作者： Pratinav Seth Abhilash K. Pai Department of Data Science & Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Deep Neural Networks (DNNs) have found widespread application in various domains, but the challenge of addressing Algorithmic bias and ensuring fairness in their decision-making processes has emerged as a critical concern, particularly in mission-critical contexts. One of the main reasons for this concern is the inadequate representation of certain groups in the available datasets used for training. Pre-Training is a powerful technique for training DNNs, but it can be affected by pre-existing biases in the dataset. These biases can be transferred to the DNN during Pre-Training, leading to the DNNs making biased decisions, even when trained on unbiased datasets. This study investigates the impact on the fairness of popular Pre-Training methods, such as Masked Image Modeling (MAE, SimMIM) and Self-Supervised Learning (BYOL, MoCo, SimCLR, VICRegL), when used on skin lesion classification datasets with underrepresented demographic groups. The study compares the performance of pre-trained models to supervised learning backbones on two skin lesion datasets (ISIC-2019 and Fitzpatrickl17k) with different skin tone distributions. The findings of this study reveal that Pre-Training improves performance but has a trade-offwith fairness, which can be a potential danger associated with the model when applied in the real world. This study is one of the first to investigate how Self-Supervised Learning and Masked Image Modeling Pre-Training methods affect fairness in both in-distribution and out-of-distribution scenarios. Code is available at https://***/ptnv-s/PretrainingImpactOnSkinBias.

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