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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Raj Gaurang Tiwari Himani Maheshwari Vinay Gautam Ambuj Kumar Agarwal Naresh Kumar Trivedi Vishal Jain Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Dehradun Uttrakhand India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

A new feature fusion network called “MedLeafNet” is presented in this study specifically for the categorization of Indian medicinal leaves. Using a hybrid architecture, MedLeafNet integrates the best features of Convolutional Neural Network (CNN) modules for spatial feature extraction and Transformer modules for sequence modeling. Because of the complexity of classifying Indian medicinal leaves—which are famous for their varied and powerful medicinal properties—the suggested methodology is well suited to the task. For this research, 80 different types of Indian leaves were analyzed, each with its well-known therapeutic properties. To improve its capacity to detect complex patterns in leaves, Me dLeafNet incorporates Transformer and CNN modules to efficiently gather both global and local information. The suggested model was optimized using several different optimizers, such as Adam, Adadelta, Gradient Descent, AdaGrad, RMSProp, Adamax, Momentum, and Adaptive Moment Estimation. When trained with the Adam optimizer, MedLeafNet attained its maximum accuracy of 98.97%, as shown by extensive testing. MedLeafNet is also compared against some of the most well-known deep learning architectures, such as ResNet, Inception, VGG, Xception, and MobileNet. In terms of classification accuracy, the findings show that it is superior to these architectures, proving that it is effective for the difficult job of classifying Indian medicinal leaves.

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LossControl: Defending Membership Inference Attacks by Controlling the Loss

LossControl: Defending Membership Inference Attacks by Contr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bo Yang Hongwei Yang Renhao Lu Hui He Weizhe Zhang Haoyu He Rahul Yadav Faculty of Computing Harbin Institute of Technology Harbin China Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Machine learning models are vulnerable to membership inference attacks (MIAs), where adversaries attempt to predict whether specific samples are part of the model’s training set. Previous studies have demonstrated a strong correlation between the distinguishability of training and testing loss distributions and the model’s susceptibility to MIAs. Motivated by existing results, we propose a novel training framework called LossControl, which focuses on manipulating loss to mitigate privacy leaks. In LossControl, we first utilize Soft-label Training to replace the general learning process, which facilitates model training while improving generalization. Next, we monitor overfitting samples during the training process and prevent further loss reduction by applying our designed Loss Ascent to these samples without sacrificing model performance. Through extensive evaluations across four diverse datasets (including images, medical data, and transaction records), our method consistently outperforms defense mechanisms against state-of-the-art attacks and achieves optimal model performance in most experiments, demonstrating LossControl’s superior resilience against MIAs and its ability to strike an unparalleled balance between privacy and utility.

关键词： Training Privacy Process control Machine learning Predictive models Signal processing Speech processing Testing Resilience Overfitting

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Reducing the Elements in a Uniform Concentric Circular Array Using Variable Projection

Reducing the Elements in a Uniform Concentric Circular Array...

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European Conference on Microwave

作者： Ramonika Sengupta Ferre Knaepkens Annie Cuyt David S. Prinsloo Thomas Schäfer A. Bart Smolders Department of Electrical Engineering Eindhoven University of Technology Netherlands Department of Mathematics University of Antwerp Belgium Department of Computing Science and Mathematics University of Stirling UK ASTRON Netherlands SATCUBE Sweden

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

This paper presents a variable projection based method to reduce the number of elements in a uniform concentric circular array, while simultaneously reducing the maximum sidelobe level in the pattern. With the proposed method, we numerically demonstrate, using an example array, a reduction of 25% in the number of elements, while achieving ≈7 dB reduction in the maximum sidelobe level. The resulting array reasonably approximates the performance of an array with more elements and equivalent physical aperture.

关键词： Europe Apertures Microwave theory and techniques

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The Peridigm Meshfree Peridynamics Code

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Journal of Peridynamics and Nonlocal Modeling 2024年第1期6卷 118-148页

作者： Littlewood, David J. Parks, Michael L. Foster, John T. Mitchell, John A. Diehl, Patrick Center for Computing Research Sandia National Laboratories Albuquerque NM United States Hildebrand Department of Petroleum and Geosystems Engineering University of Texas at Austin Austin TX United States Center of Computation & Technology and Department of Physics and Astronomy Louisiana State University Baton Rouge LA United States Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN United States

Peridigm is a meshfree peridynamics code written in C++ for use on large-scale parallel computers. It was originally developed at Sandia National Laboratories and is currently managed as an open-source, community driven software project. Its primary features include bond-based, state-based, and non-ordinary state-based constitutive models, bond failure laws, contact, and support for explicit and implicit time integration. To date, Peridigm has been used primarily by methods developers focused on solid mechanics and material failure. Peridigm utilizes foundational software components from Sandia’s Trilinos project and was designed for extensibility. This paper provides an overview of the solution methods implemented in Peridigm, a discussion of its software infrastructure, and demonstrates the use of Peridigm for the solution of several example problems. © 2023, The Author(s).

关键词： Meshfree methods Open source software Peridynamics

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Density, Kinematic Viscosity and Cetane Number Prediction of Biofuel Feedstocks from Fatty Acid Compositions 1

Density, Kinematic Viscosity and Cetane Number Prediction of...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Beeravalli, Vijayalaxmi Ashwath, Nanjappa Rasul, Mohammad Khan, Masud Capareda, Sergio CQUniversity School of Health Medical and Applied Sciences Rockhampton Australia CQUniversity School of Engineering and Technology Rockhampton Australia Auckland University of Technology School of Engineering Computing and Mathematical Sciences Auckland New Zealand Texas A&M University Department of Agricultural Engineering College StationTX United States

ISBN: (纸本)9781665450461

Stringent emission laws and the depletion of fossil fuels have made biodiesel one of the promising renewable and environmentally friendly fuel alternatives of the future. To get dependable engine performance and safe emission characteristics, it is imperative to know the Cetane number, kinematics viscosity and density of any fuel. While it is experimentally expensive and requires a continued concerted effort to know the actual properties of a fuel feedstock, recent Machine Learning methods offer wide possibilities to predict with close proximity certain characteristics of biomaterials within well-defined confidence levels. The present paper uses six different machine learning algorithms to predict three basic properties of fifty different biodiesel feedstocks from their fatty acid compositions. The issue of sparsity and anomaly in the data is dealt with through tested methods, following which properties for each feedstock are predicted within good accuracy levels. It is observed that the percentage error is very closely related to the coefficient of variation of a property, which will allow scientists to set variability bounds of predicted values. © 2022 IEEE.

关键词： Feedstocks

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Rabble Based Autonomous Assistance Using Machine Learning Algorithms 3

Rabble Based Autonomous Assistance Using Machine Learning Al...

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3rd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2023

作者： Sutariya, Kamal Vishal Gupta, Mohan Lal, Bechoo Rahim Alatba, Sura Sriramakrishnan, G.V. Tripathi, Vikas Parul University Parul Institute of Engineering and Technology Department of CSE Gujarat Vadodara India Teerthanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Department of CSE AP India Al-Turath Universiy College Computer Science Department Baghdad Iraq Mohan Babu University CSE Tirupati India Graphic Era Deemed To Be University Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

In order to properly control rabbles, we shall investigate computer vision and mechanical aid technologies with lending devices here. Machine intelligence, commonly referred to as AI, or algorithms, enables software to improve prediction performance while requiring a deliberate goal to do so. Machine-learning algorithms anticipate new output using historical data as their input. Since information is so crucial, creating novel approaches for effectively administering the neighbourhood, pervasive data gathering systems of today is a crucial next stage for actors and thus is entirely independent. Therefore, deep learning and its autonomous rabbel control support network will be discussed here. With the availability of information expanding through time, education for application in numerous sectors has become more widespread. Many fields have benefited from the use of machine vision, encompassing research and academia database and process visualisation, biomedical imaging, and more. These methods have been applied to safety, computer components, internet websites, and mobile applications. Thus, we can draw an opinion regarding an autonomous assistance system for effective kernel maintenance that is based upon deep ***, COVID-19 is causing mayhem throughout the world. There have found over than 110 million cases, or the number grows daily. Due to the outbreak, or more 4 million people died. The three most affected nations are the Usa, India, and Brazil, and many agencies are working to develop a vaccine. The world is seeking get back to normal after a string of shutdowns while upholding safety precautions. As a consequence, it is necessary to keep an eye on anyone's hat and temperature within public spaces like malls, hospitals, offices, shrines, and the like. Costs increase as a consequence of the lengthy process and extensive contact with people. Consequently, a system is required that can detect respirators, gauge temperatures, and manage the crowd tha

关键词： Computer vision

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Implementation of Data Mining Techniques in Healthcare 2

Implementation of Data Mining Techniques in Healthcare

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2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

作者： Veeramachaneni, Nirosha Vaishnavee, V. Ranjan, Nikhil Saxena, Rashi Vandana, K. Kumar, Sarvesh B v Raju Institute of Technology Department of Cse Narsapur India Sns College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Galgotias University Department of Computing Science and Engineering Greater Noida India Computer Science and Engineering Hyderabad India G L Bajaj Institute of Technology and Management Department of Cse Greater Noida India Bbd University Department of Cse Lucknow India

ISBN: (纸本)9781665474160

Healthcare sector is having lots of data about various disease and newly discovered viruses and bacteria's and so on. The data has been resided for a long at various data warehouses of each country's healthcare department but due to lack of collaboration and employment of advanced machine learning algorithms the meaningful insights which may conclude from data research and association, remain hidden from world. Hence this paper proposes the application of various machine algorithms like linear regression and multi-layer perceptron on a target dataset which is having the attributes of related to healthcare industry. With implementation of algorithm the detection of diabetes in various areas covered in 80 attributes of selected dataset. © 2022 IEEE.

关键词： Data mining

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The ANN Method for Better Living's Method of using Artificial Neural Networks to Predict Heart Attacks Caused by Anxiety Disorders 3

The ANN Method for Better Living's Method of using Artificia...

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3rd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2023

作者： Reddy, A Rama Prathap Jweeg, Muhsin J. Das, Saptorshi Kumar, M. Sunil Pant, Kumud Gates Institute of Technology Cse Andhra Pradesh India Al-Farahidi University College of Technical Engineering Baghdad Iraq Kiit Deemed to Be University Bhubaneswar India School of Computing Department of Cse AP Tirupathi India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

A subset of mental diseases known as anxiety disorders is characterised by intense sensations of fear and worry. In recent years, the development of machine learning techniques has substantially aided the creation of tools. helping doctors anticipate mental problems and providing patient support care. A comparative literature search was done for this paper. based on studies on the prognosis of particular forms of anxiety disorders, utilizing methods of machine learning. The analysis of sixteen (16) papers showed that machine learning techniques can be used to predict anxiety disorders well. Depending on the type of anxiety disorder, the results' accuracy varies and the methodology used to forecast the problem. We can infer that the prediction has undergone significant research utilizing machine learning approaches, of *** attacks and mental illnesses are now recognized as two major physical problems. This research highlights the significance using deep learning and artificial neural systems in correctly forecasting these major medical issues. This research article discusses the need of using ai - based strategies to lessen diagnosing mistakes. Here is a quick summary of how AI technology has advanced in the field of medical diagnostics. This research article goes into detail on the numerous applications of AI, including computer vision and convolutional neural networks, and how these technologies are used to analyse and comprehend cardiac diseases. In order to understand more about the way individuals see the world, qualitative research has also been conducted. This research article goes into detail just on numerous applications of AI, including computer vision and convolutional neural networks, and describes how these technologies are used to analyse and comprehend cardiac diseases. Primary research has also been done to understand more about the ways humans see things. Heart attacks and anxiety symptoms are now recognised as two major physical issues. Th

关键词： Computer vision

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Multi-photon Fock state generation using a single-pixel ultrafast photon-number-resolving detector

Multi-photon Fock state generation using a single-pixel ultr...

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Quantum 2.0, QUANTUM 2024 in Quantum 2.0 Conference and Exhibition

作者： Sonoyama, Tatsuki Takahashi, Kazuma Nomura, Takefumi Sano, Tomoki China, Fumihiro Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Takase, Kan Asavanant, Warit Endo, Mamoru Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Hyogo Kobe651-2492 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

Single-pixel superconducting nanostrip photon detectors (SNSPDs) have both low timing jitter of 50 ps and photon-number-resolving capability. We generate multi-photon Fock states with Wigner negativities using SNSPDs,... 详细信息

ISBN: (纸本)9781557525185

关键词： Timing jitter

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An Early Warning System for Driver Fatigue Detection Using Viola-Jones Over Hog Algorithm

An Early Warning System for Driver Fatigue Detection Using V...

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Intelligent computing and Control for engineering and Business Systems (ICCEBS)

作者： Banu Priya Prathaban R. Subash D. Ferlin Deva Shahila Jenath. M R. Prasanna Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Sri Sairam Engineering College Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India

Accident prevention encompasses a wide range of strategies, practices, and safety measures aimed at reducing the likelihood of accidents, injuries, and fatalities in various settings, including on the road, at home, in the workplace, and in public spaces. Drowsiness detection refers to the use of technology and methods to monitor and identify signs of drowsiness or fatigue in individuals, particularly drivers, in order to prevent accidents and improve road safety. Drowsiness detection systems typically use a combination of sensors, algorithms, and real-time data analysis to assess a person's level of alertness. For predicting the responsiveness of driver drowsiness behavior using Viola-Jones (VJ) machine learning algorithm, it helps to prevent the driver from falling asleep which causes accidents includes severe injuries and may cause death. Viola-Jones (VJ) machine learning algorithm for blink frequency for analyzing the behavior of driver drowsiness behavior. Viola-Jones (VJ) has significantly better sensitivity Blink frequency percentage is better for Viola Jones (95.14%) compared to HOG (90.84%). There was statistical significance between Voila-Jones (VJ) and HOG t (p=0.000). Viola-Jones (VJ) helps in predicting better sensitivity percentage for driver drowsiness behavior. If the driver is said to be drowsy the algorithm will detect it and make the alarm noise to wake him up.

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