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Deep learning application of Automated Facemask Classification and Physical-Distancing Detection 4

Deep Learning Application of Automated Facemask Classificati...

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4th international conference on Vocational Education and Electrical Engineering, ICVEE 2021

作者： Militante, Sammy V. Marin, Edward Louie M. Canja, Genesis V. Paglingayen, Gerard James B. Saballa-Marin, Mary Jezza M. College of Engineering and Architecture University of Antique SibalomAntique Philippines College of Computer Studies University of Antique SibalomAntique Philippines

ISBN: (纸本)9781665405997

COVID-19 adversity has stopped the global economy and most of the countries in the entire world are still in locked down even though there is now a vaccine available to fight the COVID-19 diseases. Many nations including the Philippines proclaimed national emergencies to combat the further spread and to reduce the infections brought about by the COVID-19 virus. People are now getting vaccinated with the COVID-19 yet everyone is still in doubt when this catastrophe will end because the virus has evolved in many variants which is more infectious and deadly compared with the original virus. To fight this disease, health authorities implements wearing of facemask, avoiding crowds, cleaning and washing of hands often and staying at least 1-meter apart. this study aims to detect and classify facemask according to the types of facemask they are wearing and detects the physical distancing observe by the person in the area. Deep learning is a family of Artificial Intelligence and machine learning that emulates the human brain in processing information and creates patterns to be use for decision-making. Dataset uses 4,000 images of the person wearing a facemask and not wearing a facemask and the type of facemask they are wearing including the surgical mask, N95 mask, and cloth mask. A real-time video is used to analyze the observation of physical distancing. the images were trained using the MobileNet model and recorded an accuracy rate of 94.50% during training. the trained model has effectively classified persons wearing a cloth mask, surgical mask, N95 mask, person not wearing a facemask, and detected the persons observing the physical distancing protocol. the study can be implemented in real-time to prevent the spread of COVID-19 by identifying the persons wearing the mask, classify the type of mask they are wearing, and detecting the physical distancing protocols. © 2021 IEEE.

关键词： COVID-19

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WannaLaugh: A Configurable Ransomware Emulator - learning to Mimic Malicious Storage Traces 24

WannaLaugh: A Configurable Ransomware Emulator - Learning to...

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proceedings of the 17th ACM international Systems and Storage conference

作者： Dionysios Diamantopoulos Roman Pletka Slavisa Sarafijanovic A.L. Narasimha Reddy Haris Pozidis IBM Research Europe - Zürich Lab Säumerstrasse 4 Rüschlikon Switzerland Department of Electrical and Computer Engineering Texas A&M University College Station Texas USA

ISBN: (纸本)9798400711817

Ransomware, a fearsome and an evolving cybersecurity threat, continues to inflict severe consequences on individuals and organizations worldwide. Traditional detection methods, reliant on static signatures and application behavioral patterns, are challenged by the dynamic nature of these threats. this paper introduces two primary contributions to address this challenge. First, we introduce the WannaLaugh ransomware emulator. this tool is designed to safely mimic ransomware attacks without causing actual harm or spreading malware, making it a unique solution for studying ransomware behavior. Second, we show how this emulator can be used to mimic the I/O behavior of existing ransomware. Experimental results show that WannaLaugh can mimic six real ransomware with high accuracy. Both the emulator and its mimicking application aim to represent significant steps forward in ransomware detection in the era of machine-learning-driven cybersecurity.

关键词： Ransomware

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Human Pose Estimation using Deep learning Techniques 22

Human Pose Estimation using Deep Learning Techniques

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Anant Grover Deepak Arora Anuj Grover Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus. India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus India

ISBN: (纸本)9781450399937

Human Pose Estimation is a method of detecting and classifying all the key human body joints. A 2D Human Pose Estimation method predicts the two-dimensional coordinates from an input image. Classical methods used deformable parts models for detecting body joints. this paper discusses the state-of-the-art Deep learning based approaches for estimating human poses. It can be implemented by either using the top-down or bottom-up technique for pose estimation. Here, the authors have studied the Convolutional Pose machines, Hourglass Network and MoveNet architectures for Pose Estimation. these architectures are compared against each other in terms of their efficiency by measuring the time taken by each of them and their accuracy by evaluating them using the Percentage of Detected Joints metric on the MPII dataset. the architectures were implemented by using TensorFlow and Open-Source libraries built using PyTorch. the inference results for each joint were compared individually for all the body joints. the Hourglass Network showed the best accuracy but is really slow. the MoveNet architecture is the second best according to accuracy but is very fast and can be used in real-time systems.

关键词： Deep learning

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A Safe Architecture of 5G Network Intelligence Based on Federated learning and NWDAF 22

A Safe Architecture of 5G Network Intelligence Based on Fede...

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proceedings of the 4th international conference on Advanced Information Science and System

作者： Lu Yu Lun Xin Manxue Guo China Mobile Research Institute China

ISBN: (纸本)9781450397933

Abstract: Intelligent communication network is the technical development trend of 5G and post-5G era. Big data analysis is the foundation of intelligent network while data isolation and privacy protection in network data analysis is a bottleneck problem. Federated learning is an emerging distributed machine learning framework which can make use of all parties' data for joint modeling while protecting the data privacy. In this paper, we propose an intelligent communication network framework which combines 5G Network Data Analysis Function (NWDAF) and federated learning to solve the above problem. Our work demonstrates that federated learning technology can ensure the data usage compliance in the process of 5G network intellectualization while solving the data isolation and data privacy protection problem.

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AI-Driven Alert Systems for an Intuitive Animal Monitoring System

AI-Driven Alert Systems for an Intuitive Animal Monitoring S...

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Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： P. Latha Diana Inba Malar Y Hemavarthini C S Manjuparkavi V Sneha S Department of Computer Science and Engineering V.S.B. Engineering College Karur India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

All animals that can sight, including humans, do so in a way that appears instinctive and natural. Creatures use their vision system early in life often even from birth to traverse their surroundings, recognize and communicate with other creatures, and learn about their surroundings. Because of this, an animal's vision system is continuously being educated and modified to enable it to do a variety of activities. In humans, for instance, this system works by way of the optic nerve, which transmits light data from the eye to the brain for interpretation and processing. this sophisticated vision system enables us to focus on objects and see the world in three dimensions, all while being able to autonomously adjust to changing light conditions. It is possible to think of computer vision, digital image processing, and digital image analysis as a combination of phrases that are frequently used to refer to related operations. the main reason for this misconception is that they are related fields that arose with the advancement of digital picture capture. thus, it's important to comprehend how these fields relate to one another, how a digital image is created, and how the various sensors that are available each best suited for a particular application differ. Since the introduction of charge- coupled devices, which signaled the beginning of digital imaging, the field has developed very quickly. In this project, a camera that records the surroundings all day long will be used to periodically monitor the entire farm. We can identify when an animal is entering with the aid of a deep learning model, and then we play the right sounds to eject the animal.

关键词： Deep learning YOLO computer vision Spirals Animals Reviews Digital images machine vision Search problems Monitoring

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An application of XGBoost algorithm for online transaction fraud detection based on improved sailfish optimizer

An application of XGBoost algorithm for online transaction f...

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international conference on machine learning, Big Data and Business Intelligence (MLBDBI)

作者： Hongwei Chen Lun Chen School of Computer Science Hubei University of Technology Wuhan China

Fraud detection is relevant in this context because of the large scale of online transactions, which conceal a small number of fraudulent *** learning (ML) fraud detection models have numerous hyperparameters of the eXtreme Gradient Boosting (XGBoost) algorithm, which are important for the performance impact of fraud detection models. In this paper, the propose an improved version based on Sailfish optimizer(SFO) applied to online transaction fraud detection to optimize the hyperparameters of the XGBoost algorithm. Firstly, the optimization-seeking and local-optimal-skipping capabilities of Sailfish optimizer are improved, and Improved Sailfish optimizer (ISFO) is compared with other classical optimization algorithms, and the results show that the algorithm has outstanding. the Improved Sailfish optimizer is then used to optimize the parameters of XGBoost (ISFO-XGBoost) and compared with several other machine learning classifiers such as: Decision Tree (DT), Random Forest (RF), XGBoost, and several optimization algorithms optimized for the XGBoost model. To validate the performance, a fraud dataset of online transactions is used for evaluation. the experimental results demonstrate that the proposed improved algorithm is highly promising in solving machine learning hyperparameter optimization challenges, and the ISFO-XGBoost algorithm achieves the best results.

关键词： Radio frequency Measurement machine learning algorithms Hyperparameter optimization Fraud Classification algorithms Detection algorithms

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CNN-based Deep learning Approach for MRI-based Brain Tumor Detection

CNN-based Deep Learning Approach for MRI-based Brain Tumor D...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Kanagaraj Narayanasamy Elangovan Sathish Kumar L M Maragatharajan D. Deepa Department of Computer Science Karpagam Academy of Higher Education Coimbatore TamilNadu India Department of DSBS SRM Institute of Science and Technology Kattankulathur TamilNadu India School of Computing Science and Engineering VIT Bhopal University Kotrikalan MP India Department of CSE Kongu Engineering College Perundurai Tamil Nadu India

Conventional brain tumor prediction method is a time-consuming and inaccurate process that involves manually evaluating a large number of MRI (magnetic resonance imaging) scan pictures in search of signs of brain cancer. this might have an effect on the patient's medical care. It may take a long time to analyse a large volume of scan data sets comprising brain pictures. Normal brain tissue and brain cancer tissue seem quite similar under the microscope, making it difficult to distinguish between the two during segmentation. A very accurate automated method for detecting cancer is crucial. this research study has developed a novel method that uses an algorithm to analyse 2D MRI human brain scans and detect brain Tumors. Combining conventional classification algorithms with deep learning methods, the approach makes use of a convolutional neural network (CNN). the model was successfully trained using a dataset of magnetic resonance imaging (MRI) scan images, including Tumors of varied sizes, locations, and intensities. Here, a SVM classifier (Support Vector machine) and several activation methods are also used to check the results, including Softmax, RMSProp, and sigmoid. Our research showed that a CNN could attain an accuracy of 99.87%, which is higher than the present state-of-the-art findings. the CNN -based approach is meant to aid doctors in the precise identification of brain cancers in MRI scans. this innovation might greatly accelerate the pace at which patients are helped.

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Mental Health Analysis using Deep learning of Social Media Data gathered using Chrome Extension

Mental Health Analysis using Deep Learning of Social Media D...

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Emerging Technology (INCET), international conference for

作者： Manvita Joshi Chetashri Mahajan Traividya Korgaonkar Nataasha Raul Meghana Naik Computer Engineering Department Sardar Patel Institute of Technology Mumbai India Applied Sciences Mathematics and Humanities Department Sardar Patel Institute of Technology Mumbai India

Recently the topic of ’mental health’ has gained high awareness, especially among younger generations. the age group that mostly gets affected by mental disorders is between 15-49 years of age. Research conducted on different social media data has found that a person with mental disorders (such as depression, anxiety, etc) tends to use social media more as compared to a mentally healthy person. Most users convey their emotional states through posting on public platforms, tweeting and accordingly watching related videos on YouTube that best describes their current mental state. It is also found that there is a difference in users’ social media activities (on Facebook, Twitter, and YouTube) with a healthy mind and one with an unhealthy mind. A study conducted in 2018 found that 45% of teenagers worldwide are constantly using the internet. According to research, teenagers use the internet on average for about nine hours each day. the internet can prove to be the easiest medium to monitor a person’s online activities (such as posting on social media, writing emails, watching or liking YouTube videos, commenting on YouTube videos, etc). the aforementioned activities and the internet usage time of a person can help to understand his/her mental state. the user’s social media activity can be analysed for finding potential patterns in his emotions/mood. these patterns can be helpful in uncovering underlying mental health issues such as depression, stress disorders, PTSD, schizophrenia etc. these findings can be reported to the user’s therapist for their monitoring record. To predict the mental state of the user by analysing their social media activities, an initiative was taken to develop an application with the objective to forewarn its users of any negative patterns regarding the state of mind that they exhibit. this application makes use of the Bidirectional LSTM algorithm, a deep learning method to identify a user’s emotions (one among the 6 defined emotions) and stores

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Exploring the application of computer Intelligence Algorithms in Logistics and Supply Chain Management 22

Exploring the Application of Computer Intelligence Algorithm...

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proceedings of the 7th international conference on Intelligent Information Processing

作者： Mei-E Xie Lichen Qiao Wuhan Business University China Northeastern University China

ISBN: (纸本)9781450396714

Digital transformation and innovation are the current trend of the times, and computer intelligence algorithms will be an important supporting technology for a new generation of logistics industry. With the continuous development of artificial intelligence technology, the traditional system that originally relied on human beings to make empirical decisions is facing new challenges, and such a decision-making system is undergoing a transformation. the development of a series of technologies such as big data, cloud computing, machine learning and artificial intelligence has improved the ability of logistics data processing, and also helped logistics enterprises to analyze and predict customer behavior, and system equipment will gradually replace manual decision-making, ultimately achieve better cost reduction and efficiency. this paper firstly introduces the concept of artificial intelligence and supply chain management, related research results at home and abroad, the main impact of artificial intelligence on logistics industry, and analyzes the main types of computer intelligence algorithms, and studies the specific application of computer intelligence algorithms in logistics and supply chain management on the basis of the survey of enterprises.

关键词： Logistics Supply Chain Management computer Intelligent Algorithms

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Research and Design of Active Operation application Platform for Power Supply Stations 23

Research and Design of Active Operation Application Platform...

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proceedings of the 2023 4th international conference on Big Data Economy and Information Management

作者： Yifan Hu Zhihao Shen Xiaojun Shen Jinxia Jiang Jiawen Zhan Information and Communication Branch State Grid Zhejiang Electric Power Co. Ltd. China

ISBN: (纸本)9798400716669

In order to implement the company's strategic deployment, continuously improve the service capacity of the power supply station, implement the national rural revitalization strategy, and solve the problem of incomplete informatization of on-site operations and services, this article proposes an active operation application platform based on the digital intelligent management platform of the power supply station, combined with the actual business needs on site. the paper first studies the overall application of computer applications and mobile operating terminals, and then designs the application architecture. the application functions are mainly aimed at low-voltage and medium voltage customers. Low-voltage includes 8 application functions such as low-voltage inspection, low-voltage defects, and low-voltage leakage, while medium voltage includes 8 application functions such as channel clearing, power supply protection services, and abnormal medium voltage line losses, the Technology roadmap and technical architecture of the platform are studied. the construction and application of the platform will effectively reduce the burden on the grassroots team, integrate and integrate, greatly improve the on-site operation and response capabilities of the station managers, and enhance customer satisfaction with electricity consumption.

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