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arXiv 2023年

作者： Su, Jinyan Zhao, Changhong Wang, Di Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Department of Information Engineering The Chinese University of Hong Kong Hong Kong Provable Responsible AI and Data Analytics Lab India Computational Bioscience Research Center India Division of CEMSE King Abdullah University of Science and Technology Saudi Arabia

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general dp spaces. Specifically, we focus on three settings that are still far from well understood: (1) DP-SCO over a constrained and bounded (convex) set in Euclidean space;(2) unconstrained DP-SCO in dp space;(3) DP-SCO with heavy-tailed data over a constrained and bounded set in dp space. For problem (1), for both convex and strongly convex loss functions, we propose methods whose outputs could achieve (expected) excess population risks that are only dependent on the Gaussian width of the constraint set, rather than the dimension of the space. Moreover, we also show the bound for strongly convex functions is optimal up to a logarithmic factor. For problems (2) and (3), we propose several novel algorithms and provide the first theoretical results for both cases when 1 © 2023, CC BY.

关键词： Convex optimization

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Lightweight Deep-Learning Based Music Genre Classification: A Study

Lightweight Deep-Learning Based Music Genre Classification: ...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Rama, A. Mythili, N. Rajakumar, M.P. Arunmozhi, S. Mohammed, Mazin Abed Rajinikanth, V. Saveetha School of Engineering Simats Department of CSE TN Chennai602105 India St. Joseph's Institute of Technology Department of Computer Science and Engineering TN Chennai600119 India St. Joseph's College of Engineering Department of Artificial Intelligence and Data Science TN Chennai600119 India Manakula Vinayagar Institute of Technology Department of Electronics and Communication Engineering TN Puducherry605 107 India College of Computer Science and Information Technology University of Anbar Department of Artificial Intelligence Anbar31001 Iraq Saveetha School of Engineering Simats Division of Research and Innovation Department of CSE TN Chennai602105 India

ISBN: (纸本)9798350315127

Deep-learning (DL) applications that are used real-time across various industries have gained a lot of traction and have become increasingly popular, especially when it comes to data-driven recommendation systems. This work aims to develop a DL scheme to support the music-recommendation system (MS) based on the music data. The various phases of this scheme includes;(i) data collection and signal-image conversion to get the necessary RGB scale images from the data, (ii) pre-trained DL based feature extraction, and (iii) deep-features based detection to recommend the appropriate music. This research considered the classic- (CL) and pop-music (PO) for the examination and the achieved results are evaluated to substantiate the performance of this arrangement. In this work, the signal-image conversion procedure is implemented to convert 1D signal to 2D image and then it is examined using proposed technique. The experimental outcome is separately presented for (i) spectrogram and (ii) synchro-extracting-transform and obtained results are presented. The experimental investigation is presented with MobileNet variants and this study authorizes that the implemented scheme achieved a better detection MobileNetV2 (>99%) compared to other schemes in this study. © 2023 IEEE.

关键词： Real time systems

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Federated learning for privacy-preserving open innovation future on digital health

arXiv

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arXiv 2021年

作者： Long, Guodong Shen, Tao Tan, Yue Gerrard, Leah Clarke, Allison Jiang, Jing Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology University of Technology Sydney Data and Analytics Branch Health Economics and Research Division Australian Department of Health

Privacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across users or organisations without direct access to the data. It has great potential to be the next-general AI model training framework that offers privacy protection and therefore has broad implications for the future of digital health and healthcare informatics. Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community. In particular, this game-changing collaborative framework offers knowledge sharing from diverse data with a privacy-preserving. This chapter will discuss how federated learning can enable the development of an open health ecosystem with the support of AI. Existing challenges and solutions for federated learning will be discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： E-learning

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An Efficient Ensemble Model for Forecasting Time Series Analysis of Crypto Currency data Using Machine Learning

An Efficient Ensemble Model for Forecasting Time Series Anal...

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2021 IEEE International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2021

作者： Saravanakumar, C. Lakshmi, S. Kanaga Prakash, M. Vijayakumar, M. Arun, C. St. Joseph's Institute of Technology Department of Information Technology Chennai India Sri Muthukumaran Institute of Technology Department of Management Studies Chennai India Data Science and Analytics Center Karpagam College of Engineering Coimbatore India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India R.M.K College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665435215

Time series data are analyzed for predicting the future based on the consistent time period using statistical methods. These analyses are based on the data which are extracted from different sources. Existing method for performing the forecasting process does not fit into the crypto currency data. GARCH and ARIMA models with intelligent mechanisms provide the solution but it is not at the proper level. RNN model takes the data from preexisting intervals and decides the next time series data which is represented as a directed graph. Two types of behavior are considered namely spatial and temporal. The linear model uses the homogeneous data and heterogeneous data analysis is based on the nonlinear model. The nodes and layers are connected based on the input and corresponding activation unit. The memory model is attached to the RNN model for making persistent storage for keeping the processed data for a longer time. The proposed model uses the ensemble method for integrating various RNN models for achieving a high level of accuracy. © 2021 IEEE.

关键词： Time series analysis

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Novel Antenna Designs for High data Rate Body-Centric communications in Wearable Devices

Novel Antenna Designs for High Data Rate Body-Centric Commun...

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Automation and Computation (AUTOCOM), International Conference on

作者： Ishika Soni Manjunatha R V Vijaya Baskar Daljeet Pal Singh Komal Parashar Niranjan Panda Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Data analytics and Mathematical Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

There is an ever-increasing demand for high data rate communication embedded in wearable devices owing to the exponential growth of smart devices and related technology. The human body is always around, which means that traditional antenna designs will not work well with body-centric communications operations, as signal loss and interference from the presence of the human body must be addressed. This project addresses this limitation by designing new body-centric communications antenna designs for incorporation into wearable devices. Antennas proposed in the literature can use novel techniques that reach high data rates while providing reliable communication links and features like compactness, low power consumption, and multi-band functionalities. The designs are optimized to ensure that the human body has a minimal effect on the signal enhancing performance and stability. The antennas are designed and simulated using advanced simulation tools and validated in the lab. These findings demonstrate the remarkable advantages of high-speed data transmission and link quality compared to traditional antennas. It contributes to developing novel antenna designs that wearable device manufacturers can incorporate into high data rate body-centric communications, enabling dramatically improved performance within tight space constraints. This will make it possible to create next-generation wearable devices with communication functions for applications ranging from healthcare and sports to virtual reality.

关键词： Performance evaluation Training Design methodology Transmitting antennas Clothing Virtual reality Stability analysis Reliability Wearable devices Antennas

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Detection of TB from Chest X-ray: A Study with EfficientNet

Detection of TB from Chest X-ray: A Study with EfficientNet

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Rama, A. Rajakumar, M.P. Mythili, N. Arunmozhi, S. Mohammed, Mazin Abed Rajinikanth, V. Saveetha School of Engineering Simats Department of CSE TN Chennai602105 India St.Joseph's College of Engineering Department of Artificial Intelligence and Data Science TN Chennai600119 India St.Joseph's Institute of Technology Department of Computer Science and Engineering TN Chennai600119 India Manakula Vinayagar Institute of Technology Department of Electronics and Communication Engineering TN Puducherry605 107 India College of Computer Science and Information Technology University of Anbar Department of Artificial Intelligence Anbar31001 Iraq Saveetha School of Engineering Simats Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India

ISBN: (纸本)9798350315127

The lung is one of the prime organs, and any disease in the lung causes mild to severe breathing problems;untreated lung disease will lead to several complications. Tuberculosis (TB) is a lung ailment that needs premature recognition and handling. The primary objective is to employ the deep-learning (DL) based TB detection using chest $X$-rays. Various stages of the proposed scheme consist of (i) data collection and resizing, (ii) DL-supported feature extraction, (iii) binary classification and five-fold cross-validation, and (iv) comparison with earlier results and confirming the merit of the scheme. This research implements EfficientNet (EN) variants to classify the chosen $\mathrm{X}$-rays into healthy/TB classes using the SoftMax classifier. The proposed scheme with EN_B2 (ENB2) has been successful in providing an accuracy of $96{\% }$ as far as detection accuracy is considered when compared to other methods. The superiority of the suggested strategy is also confirmed by an analysis using the most recent technology, which confirms the worth of the proposed system on the chosen $\mathrm{X}$-ray imagery. © 2023 IEEE.

关键词： Deep learning

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Leveraging Probe data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Leveraging Probe Data and Machine Learning to Derive and Int...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Jin, Ling Xu, Xiaodan Wang, Yuhan Sadabadi, Kaveh Farokhi Lazar, Alina Don, Duleep Rathgamage Needell, Zachary Spurlock, C Anna Amirgholy, Mahyar Asudegi, Mona Energy Technology Area Berkeley National Laboratory BerkeleyCA94720 United States University of Maryland Center for Advanced Transportation Technology College ParkMD20742 United States Youngstown State University Department of Computer Science and Information YoungstownOH44555 United States Kennesaw State University School of Data Science and Analytics MariettaGA30060 United States Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Kennesaw State University Civil and Environmental Engineering Department MariettaGA30060 United States Office of Transportation Policy Studies Federal Highway Administration DC WashingtonDC20590 United States

ISBN: (纸本)9798350324457

Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level. © 2023 IEEE.

关键词： Interaction Shapley values machine learning models macroscopic fundamental diagram TreeExplainer United States vehicle probe data

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Transforming Shopping with IoT: Exploring the Intelligent Way to Design the Smart Trolley using IoT Principles

Transforming Shopping with IoT: Exploring the Intelligent Wa...

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Big data & Machine Learning (ICBDML), IEEE International Conference on

作者： M. Jayaprakash A. Hency Juliet A. Jaya Mabel Rani N.T. Velusudha S. Lakshmisridevi V. Samuthira Pandi Department of Information Technology R.M.K. Engineering College Kavaraipettai Tamil Nadu India Department of Data Analytics Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering BS Abdur Rahman Crescent Institute of Science and Technology Chennai Tamil Nadu Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374117

The interconnection of commonplace items is revolutionizing people's daily lives, thanks to the Internet of Things (IoT). A smart shopping system, consisting of interconnected products, might one day be a reality in supermarkets. In this Internet of Things system, products may be individually identified using low-cost RFID tags, which, when added to an RFID-enabled smart shopping cart, can be read automatically. This means shoppers won't have to endure the checkout line at all—billing can be handled right in the cart. The integration of RFID-equipped smart shelving into this system allows for the monitoring of inventory and, perhaps, the update of a central server. The suggested shopping system, which is dubbed IoT Assisted Smart Trolley (IoTAST), is cross-validated with the standard Smart Trolley (ST) model to clearly evaluate its performance. Since an RFID reader can automatically read all objects instead of a labourer physically scanning them, inventory management becomes considerably easier with this sort of technology. We construct a working system to test its functioning, determine the design criteria of a sophisticated purchasing system, and then create a reliable communication mechanism that renders the system feasible. This study validates the viability of the system. This is the first smart shopping system that takes security into account that we are aware of.

关键词： Protocols Prototypes Machine learning Reliability engineering Security Internet of Things Servers

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Multi to binary class size based imbalance handling technique in wireless sensor networks

Multi to binary class size based imbalance handling techniqu...

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作者： Singh, Neha Virmani, Deepali Dhiman, Gaurav Vimal, S. University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Delhi Dwarka110086 India Department of Computer Science Engineering VIPS-TC School of Engineering and Technology Delhi110034 India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Computer Science and Engineering University Centre for Research and Development Chandigarh University Mohali Gharuan140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara144402 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140601 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajapalayam626117 India

Wireless sensor network (WSN) has entered various disciplines including healthcare, banking, transportation, ocean and wildlife monitoring, earthquake monitoring, and numerous military applications. Now days, there is escalation in size of data which makes it unfeasible to analyse it with accuracy. There are numerous problems that are faced when detecting a pattern between structured and unstructured data which are unworkable by humans, so to make computation fast, easy and accurate, Machine Learning came in existence. Machine Learning is extensively used in WSNs. To make a machine learn, a training dataset is required and output is predicted by testing the dataset. A dataset in WSN has multi-class in its dependent variable. This multiclass classification causes class imbalance problem. The paper proposes multi to binary class size based imbalance handling technique in wireless sensor networks (MBSCIH) technique to solve class imbalance problem in multi-class classification. The technique MBSCIH converts multi-class classification into binary-class classification. The proposed technique MBSCIH is applied on WSN-DS, NSL-KDD and KDD-cup 99 datasets and is tested with five major machine learning algorithm: Naive Bayes, Random Forest, decision tree, support vector machine (SVM) and k-nearest neighbour (KNN). The test method used for testing is 10-fold cross validation. Results infer that the proposed method increases the existing efficiency by 15.13%, 0.28%, 0.01%, 0.01%, 0.12% for Naïve Bayes, Decision Tree, SVM, Random Forest and KNN Algorithms respectively for KDD Cup99 dataset. The proposed method increases the efficiency by 43.1%, 2.79%, 0.01%, 0.53% for Naïve Bayes, decision tree, SVM and KNN algorithms respectively for NSL-KDD dataset. Also, the proposed method increases the efficiency by 0.13%, 0.08%, 0.29%, 0.29% for Naïve Bayes, decision tree, SVM and KNN algorithms respectively for WSN-DS *** the experimental analysis proves that solving multiclas

关键词： Wireless sensor networks

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Towards Privacy-anomaly Detection: Discovering Correlation between Privacy and Security-anomalies

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Procedia Computer Science 2020年 175卷 331-339页

作者： Muhammad Imran Khan Simon N. Foley Barry O’Sullivan Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Ireland Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik Norway

In this paper a notion of privacy-anomaly detection is presented where normative privacy is modelled using k -anonymity. Based on the model, normative privacy-profiles are constructed, and deviation from normative privacy-profile at runtime is labelled as a privacy-anomaly. Furthermore, the paper investigates whether there is a correlation between security-anomalies and privacy-anomalies, that is, whether the privacy-anomalies labelled by privacy-anomaly detection system are detected by conventional security-anomaly detection system used for detecting malicious accesses to databases by insiders.

关键词： Electronic Privacy Anonymization Anomaly Detection k -anonymity Relational databases

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