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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Sidhant Sharma Md Anas Hasan Abhivansh Bhatnagar Valadri Pardhavan Reddy K. Sornalakshmi Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

IPVM (Interplanetary Virtual Machine) is a versatile protocol, an extension of IPFS capable of handling a diverse range of computations, spanning from small-scale to large-scale tasks. It plays a pivotal role in driving the transition towards open decentralized computing infrastructure, shifting from conventional centralized cloud services. By making use of IPFS content addressing, IPVM streamlines computation processes, facilitating distributed scheduling, caching, memoization, and adaptive optimization. This exploration highlights the emergence of a decentralized ecosystem promising enhanced resilience, scalability, and inclusivity in computing frameworks.

关键词： Processor scheduling Scalability Image processing Ecosystems Streaming media InterPlanetary File System Task analysis

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Anatomization of Neural Networks based models for Semantic Analysis of Tabular dataset

Anatomization of Neural Networks based models for Semantic A...

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International Conference for Convergence of Technology (I2CT)

作者： B Sai Bharath Jahnavi Bollineni Sandeep Preetham Mandala Tania Ganguly Amudha J Nikhil Shukla Raj Joseph Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Intellectyx Data Science PVT LTD

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

The Paper focuses on analysing neural network models that are used for semantically classifying tabular customer datasets. Additionally, we propose a custom neural network architecture to analyze tabular datasets and enable the model to extract and comprehend the underlying semantics within the data. The research focuses on three distinct neural network models: CharCNN, Bi-LSTM, and CNN+Bi-LSTM. Through comprehensive evaluation based on accuracy, and confidence scores of the models’ performance, we determined that Bi-LSTM proved to be the best fit for this approach and dataset. The findings suggest that the custom CHAR-CNN model can be effective in classifying tabular data and can potentially be applied on various datasets considering both computational time and accuracy. This research contributes to the advancement in the field of semantic analysis for tabular dataset, opening avenues for further research on how to handle different kinds of Tabular datasets and enhancement of semantic models in NLP.

关键词： Analytical models Computational modeling Semantics Neural networks Computer architecture data models data mining

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Impact of Application Choice on iTracker Model Performance Evaluation: A Perspective 2

Impact of Application Choice on iTracker Model Performance E...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Moghlebey, Adam Bou Haydar, Hady Moubayed, Abdallah Injadat, MohammadNoor School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan

ISBN: (纸本)9798350373363

Eye tracking is underpinned by the eye-mind hypothesis, which posits that individuals tend to direct their gaze toward the information they are currently cognitively engaged with. This aspect has garnered significant attention within deep learning research due to its versatile applications and research. The utilization of Artificial Intelligence (AI) in conjunction with webcams for tracking eye movements has gained increasing popularity in recent years and is anticipated to continue flourishing. This literature review aims to assess the current state and depth of the research in this domain and its various applications. We examine the different methods that employ eye tracking across a diverse range of applications, and by re-exploring existing research, we are able to filter out any overlooked potential or setbacks associated with the use of AI-webcam based systems, with the ultimate goal of maximizing their potential contributions to humanity. In our study, we analyze and evaluate a pioneering eye tracking algorithm, iTracker trained on a crowd-sourced data-set, GazeCapture within the context of the applications it may be used for. While we acknowledge inherent limitations, this widely adopted model forms a robust foundation for future refinements. Furthermore, our investigation encompasses diverse applications with varying accuracy requirements, making it invaluable for individuals seeking to integrate eye tracking into their projects. © 2023 IEEE.

关键词： Eye tracking

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Secure IoHT data Sharing in the Cloud: Machine Learning with Logistic Regression for Privacy Protection 2

Secure IoHT Data Sharing in the Cloud: Machine Learning with...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Raman, Ramakrishnan Saravanan, K. Gokhale, Atul A Anitha, K. Ishwarya, M.V. Sujatha, S. Maharashtra Pune India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Maharashtra Pune India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350327533

Healthcare delivery and patient monitoring have been transformed by the fast rise of the Internet of Healthcare Things (IoHT). Cloud storage of sensitive health data creates privacy issues. Logistic Regression (LR) is used to ensure IoHT data sharing in this investigation. The cloud architecture allows efficient data exchange while protecting health information. LR is used to predict data sharing risk and make privacy choices. A varied collection of health measures and demographic data trains the model to generalize well to numerous contexts. The suggested system is tested extensively with Logistic Regression, which reliably identifies and mitigates privacy threats. data accessibility and privacy are balanced by the model's high sensitivity and specificity. It also examines the model's interpretability to provide privacy prediction factors. This openness improves user trust and regulatory compliance. This technique is scalable for large-scale IoHT installations. The cloud-based IoHT data sharing challenge has a unique approach. Machine learning, especially Logistic Regression, protects privacy without reducing health data usefulness. This research increases healthcare data security in the connected device age. © 2024 IEEE.

关键词： Health data Security Healthcare Analytics Patient Privacy Predictive Modeling Sensitive Information

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Speech Emotion Recognition Using data Augmentation

Speech Emotion Recognition Using Data Augmentation

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Tanisha Kapoor Arnaja Ganguly D Rajeswari Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Speech emotion recognition (SER) is a vital aspect of human-computer interaction systems, enabling applications such as virtual assistants, affective computing, and mental health monitoring. In this work, deep learning methods to audio data to suggest a fresh approach to SER are applied. While still in its infancy, CNN is capable of accurately identifying emotions in human speech, but it requires careful study in order to produce reliable *** studywas done using a heterogeneous dataset that included recordings of different emotional states from many sources, such as TESS, RAVDESS, SAVEE, CREMAD, and others. A deep neural network model was built with many fully connected layers and ReLU activation functions after preprocessing and feature extraction. The suggested method’ s effectiveness was assessed using common performance indicators like F1-score, recall, accuracy, and precision. Our tests produced encouraging outcomes, with an accuracy of 84% on the test dataset. This demonstrates how well the suggested technique works to discern emotions from speech audio information.

关键词： Emotion recognition Accuracy Virtual assistants Speech recognition Mental health Feature extraction Recording

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PsFuture: A Pseudo-Future-based Zero-Shot Adaptive Policy for Simultaneous Machine Translation

PsFuture: A Pseudo-Future-based Zero-Shot Adaptive Policy fo...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhao, Libo Li, Jing Zeng, Ziqian Shien-Ming Wu School of Intelligent Engineering South China University of Technology China Department of Computing Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761643

Simultaneous Machine Translation (SiMT) requires target tokens to be generated in real-time as streaming source tokens are consumed. Traditional approaches to SiMT typically require sophisticated architectures and extensive parameter configurations for training adaptive read/write policies, which in turn demand considerable computational power and memory. We propose PsFuture, the first zero-shot adaptive read/write policy for SiMT, enabling the translation model to independently determine read/write actions without the necessity for additional training. Furthermore, we introduce a novel training strategy, Prefix-to-Full (P2F), specifically tailored to adjust offline translation models for SiMT applications, exploiting the advantages of the bidirectional attention mechanism inherent in offline models. Experiments across multiple benchmarks demonstrate that our zero-shot policy attains performance on par with strong baselines and the P2F method can further enhance performance, achieving an outstanding trade-off between translation quality and latency. © 2024 Association for Computational Linguistics.

关键词： Machine translation

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Fraud Detection in Financial Transactions: A Machine Learning Approach

Fraud Detection in Financial Transactions: A Machine Learnin...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： Shashank Patel Mudita Pandey Rajeswari D Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Financial fraud presents substantial risks to individuals and financial institutions globally, necessitating efficient detection mechanisms to mitigate probable fatalities. In this study, the development and evaluation of machine learning (ML) models for detecting fraudulent activities in mobile money transactions are investigated. Using a synthetic dataset, realworld transaction scenarios involving a variety of transaction kinds and features are simulated. The effectiveness of many machine learning (ML) methods, such as LGBM, random forests, XGBoost, and logistic regression, in spotting fraudulent transactions is investigated through data preparation, feature engineering, and model-building procedures. Employing techniques such as SMOTE-Tomek resampling and hyperparameter tuning enhances the performance and robustness of our models. Our results reveal that the XGBoost classifier emerges as the top-performing model, exhibiting a remarkable accuracy of 99.95%. The findings underscore the potential of MLbased approaches to bolster security and trust in mobile financial services, contributing to the ongoing advancement of fraud detection methodologies in the financial sector.

关键词： Analytical models Accuracy Computational modeling Finance data models Fraud Security

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Robustness of Explainable AI Algorithms for Disease Biomarker Discovery from Functional Connectivity datasets

Robustness of Explainable AI Algorithms for Disease Biomarke...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Deepank Girish Yi Hao Chan Sukrit Gupta Jing Xia Jagath C. Rajapakse College of Computing and Data Science Nanyang Technological University Singapore Department of Computer Science and Engineering Indian Institute of Technology Ropar India

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Machine learning (ML) models have been used in functional neuroimaging for wide-ranging tasks, ranging from disease diagnosis to disease prognosis. There have been successive functional connectivity-based ML studies focused on improving model performances for disease detection. An increasing number of such studies use their trained models to detect and evaluate salient features that could be potential biomarkers of these neuro-logical conditions. The evaluation of these salient features is often qualitative and limited to cross-referencing existing literature for similar findings. In this study, we present objective quantitative metrics to evaluate the robustness of these salient features. Building upon existing generic evaluation metrics, we propose metrics that capture topological properties known to be characteristic of brain functional connectomes. Using existing and newly proposed measures on a set of baselines and state-of-the-art graph neural networks (GNN) models, we found that when GNNExplainer is used with models that incorporate attention, the scores produced are relatively more robust than other combinations. On datasets of patients with Autism Spectrum Disorder (ASD) or Attention-deficit Hyperactivity Disorder (ADHD), our proposed metrics highlighted that salient features identified in both disorders are highly involved in functional specialization, while salient ASD features expressed stronger functional integration than ADHD. We package these existing and novel metrics together in the RE-CONFIRM framework that holds promise to set the foundations for the quantitative evaluation of salient features detected by future studies.

关键词： Biological system modeling Biomarkers Feature extraction Brain modeling Functional neuroimaging Graph neural networks Robustness Medical diagnosis Prognostics and health management Diseases

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Performance Analysis of Indian States and Union Territories for Covid-19 Management Through DEA and Machine Learning 12th

Performance Analysis of Indian States and Union Territories ...

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12th International Conference on Soft computing for Problem Solving, SocProS 2023

作者： Joshi, Shubham Panwar, Ankita Pant, Millie Deep, Kusum Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Mathematics Indian Institute of Technology Uttarakhand Roorkee247667 India Mehta Family School for Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India

ISBN: (纸本)9789819731794

In this study, an analysis has been performed to assess the performance of 28 states and 8 union territories (UTs) of India against COVID-19 during the years 2020 and 2021. The study is done in three phases;first of all, the analysis has been done through Exploratory data Analysis (EDA), to get general information about the data;in the second phase, the super-efficiency-data envelopment analysis (SE-DEA) model has been applied to evaluate the performance of states and UTs, and to segregate them into two classes: efficient and inefficient. Finally, in the third phase, an attempt is made to establish a link between the DEA and 5 supervised machine learning techniques viz. Logistic regression (LR), K-nearest neighbours (KNN), Support vector machine (SVM), Naïve Bayes (NB) classifier, and Decision tree (DT) for analysis of the results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Achilles’ Heels: Vulnerable Record Identification in Synthetic data Publishing

arXiv

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arXiv 2023年

作者： Meeus, Matthieu Guépin, Florent Creţu, Ana-Maria de Montjoye, Yves-Alexandre Department of Computing and Data Science Institute Imperial College London London United Kingdom

Synthetic data is seen as the most promising solution to share individual-level data while preserving privacy. Shadow modeling-based Membership Inference Attacks (MIAs) have become the standard approach to evaluate the privacy risk of synthetic data. While very effective, they require a large number of datasets to be created and models trained to evaluate the risk posed by a single record. The privacy risk of a dataset is thus currently evaluated by running MIAs on a handful of records selected using ad-hoc methods. We here propose what is, to the best of our knowledge, the first principled vulnerable record identification technique for synthetic data publishing, leveraging the distance to a record’s closest neighbors. We show our method to strongly outperform previous ad-hoc methods across datasets and generators. We also show evidence of our method to be robust to the choice of MIA and to specific choice of parameters. Finally, we show it to accurately identify vulnerable records when synthetic data generators are made differentially private. The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective. We here propose a simple yet highly effective method to do so. We hope our method will enable practitioners to better estimate the risk posed by synthetic data publishing and researchers to fairly compare ever improving MIAs on synthetic data. Copyright © 2023, The Authors. All rights reserved.

关键词： Risk perception

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