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Footwear 3D Model Generation from Smartphone 2D Images Utilizing Photogrammetry 3

Footwear 3D Model Generation from Smartphone 2D Images Utili...

引用

3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Abrar, Raiyan Islam, Md. Tariquel Naim, Mahapara Tabassum, Nuzhat Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350345360

3D modeling in the shoe industry is growing as the technology becomes more widely available. 3D modeling lets companies customize shoes for individual customers, making manufacturing more flexible and efficient. In the shoe industry of Bangladesh, using smartphone images for 3D modeling is a fairly new idea. This allows designers and manufacturers to collaborate and quickly share designs with customers for feedback. A photogrammetry pipeline is a cost-effective and efficient way to create 3D models. This study proposes a potential and affordable method for using photogrammetry to create 3D representations of footwear. The major goal was to determine whether photogrammetry, as opposed to RGB dept. camera photos, can more conveniently construct 3D models of footwear from a small number of smartphone images. In order to assure correct reconstruction, the pipeline required taking a collection of photos from 6-9 distinct perspectives and augmenting the dataset. Then, from these images, point clouds are produced, and from these aligned point clouds, the 3D mesh is produced. The mesh is then textured and smoothed to give color. In Bangladesh's shoe industry, this 3D modeling pipeline can be used for AR shoe try-on. © 2023 IEEE.

关键词： 3D modeling

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Air Quality Evaluator using Arduino 6

Air Quality Evaluator using Arduino

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Pavan Ganesh Reedy, Y. Kadali Jagadees, H. Pravin, A. Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350398496

Air pollution has a negative impact on our ability to do daily tasks and on our standard of living. Ecosystems and human well-being are under danger. Recent years have seen a notable increase in heavy industry, leaving it even more important to monitor air quality. When it comes to air quality, individuals must be aware of how much control they have. This study proposes a network of sensors to track changes in the air. The Arduino was employed as the platform's microcontroller. The purpose of the air pollution surveillance systems is to continuously track and record data on the state of the air around a certain location and upload that information to a central server for safekeeping and online access. Parts-per-million measures used to quantify pollution levels, and the results were evaluated in Microsoft Excel. The system's ability to monitor air quality as planned worked as intended. The results were displayed on the bespoke hardware's ui and were also stored in the cloud, where they could be accessed by anybody with a smartphone. © 2023 IEEE.

关键词： Air quality

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Artificial Neural Network Technique for DDOS Attack Prediction in Cyber-IOT System 3

Artificial Neural Network Technique for DDOS Attack Predicti...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Asad, Md Tiwari, Rakesh Kumar Technocrats Institute of Technology & Science Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350342338

An intrusion-detection IoT framework is designed to analyze unauthorized access or security breaches in IoT networks. These systems typically use a combination of hardware and software to analyze network traffic, identify anomalies, and alert security personnel when a potential threat is detected. An intrusion-detection system (IDS) is a one type of security checking model that may be used to monitor a set of connections or devices in order to check for telltale indicators of possible infiltration. During the course of the last several years, there has been a general rise in the number as well as the intensity of cyber attacks. As a consequence of this, there is a need for study of security in cyber world and the prevention of assaults, such as the implementation of intrusion-detection. Businesses that rely on the internet absolutely have to make protecting their data online one of their top responsibilities. Within the scope of this study, this paper proposes a neural network technique as a means of anticipating assaults on IDS. Python Spyder is the software that make use of for this simulation. © 2023 IEEE.

关键词： Attack Cyber- IOT DDos IDS NN

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Recognition of Data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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Adaptive Contour Noise Generation Network for Semi-supervised Medical Data Segmentation 6

Adaptive Contour Noise Generation Network for Semi-supervise...

引用

6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Ye, Shunlong Nanjing University of Science and Technology Dept. of Computer Science and Engineering Nanjing China

ISBN: (纸本)9781665491259

When training convolutional neural networks, noise distribution in data can affect segmentation accuracy. Therefore, many neural networks will strengthen the segmentation ability of the model by artificially adding noise. However, because the noise distribution of the input data is not known, the model training process can only generate random noise, e.g., gaussian noise, but cannot simulate real-world noise. In the medical imaging domain, more attention is paid to the image contours, which can also be used for noising simulation. Meanwhile, considering the existence of rich unlabeled data sets in medical images, this paper proposes a semi-supervised framework for uncertainty estimation by using Monte Carlo Dropout and generates adaptive noise using the uncertainty map. Specifically, the framework consists of three parts, including a noise generation model, a teacher model, and a student model. The noise generation part processes the data input to the other two parts and uses the contour feature identified by the posteriori uncertainty map to simulate priori noise to guide the process of training segmentation model. Meanwhile, the student model updates its parameters based on the teacher model output and the ground truth annotations by minimizing a joint loss. Segmentation experiments on left atrium 3D data show that our method has competitive results with other SOTA semi-supervised methods. © 2023 IEEE.

关键词： Medical imaging

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An Efficient Deep Learning Technique for Intrusion Detection in Wireless Sensor Network 3

An Efficient Deep Learning Technique for Intrusion Detection...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Kumari, Prity Tiwari, Rakesh Kumar Technocrats Institute of Technology and Science Dept. of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350322842

Predicting intrusion detection in a wireless sensor network (WSN) involves using machine learning algorithms to analyze network traffic data and predict whether an intrusion or security breach will occur. The goal of intrusion prediction is to provide advanced warning of potential attacks and allow for proactive measures to be taken to prevent or mitigate the attack's impact. Several machines and deep learning algorithms can be used for intrusion prediction in WSNs. This research describes a practical deep-learning approach for Intrusion Detection in WSN based on artificial neural networks. Spyder IDE of python with version 3.7 is used for the simulation work. The results of the simulations demonstrate that the anticipated model is accurate 99.99% of the time. © 2023 IEEE.

关键词： Python

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Intelligent System for Detection of Intrusion in Imbalanced Network Traffic 14

Intelligent System for Detection of Intrusion in Imbalanced ...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Bagla, Piyush Mishra, Amit Kumar Pandey, Neeraj Kumar Sharma, Ravi Dumka, Ankur National Institute of Technology Department of Computer Science & Engineering Jalandhar India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Department of Computer Science & Engineering Dehradun India Women Institute of Technology Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350344837

Malicious cyberattacks can frequently hide among enormous amounts of regular data in networks with uneven traffic patterns. The identification of imbalance network traffic is difficult to find and making a challenge in terms of Signature building. Despite years of improvement, IDSs still struggle to increase detection accuracy. There are distinct machine learning or deep learning algorithms provides the better results and accuracy for imbalance network traffic. In this study, intrusion detection in unbalanced network traffic is accomplished using both machine learning and deep learning. This process a DSSTE algorithm to avoid imbalance problems. Initially the training set is pre-processed to modify imbalanced data and features are extracted. The proposed model is evaluated with trained data using multiple classification algorithms. © 2024 IEEE.

关键词： Intrusion detection

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GANs for Synthetic Environmental Data Generation (Air Quality) for Low-Resource Regions 2

GANs for Synthetic Environmental Data Generation (Air Qualit...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Verma, Seema Jain, Neha Mahapatra, Madhumita Setia, Sonia Sachdeva, Vrinda Kumar, Manoj Dept. of Computer Science & Engineering Delhi Technical campus Uttar Pradesh Greater Noida India Dept. of Computer Science & Engineering Sharda University Uttar Pradesh Greater Noida India Dept. of Computer Science & Engineering G.L Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331541217

Due to the significant expenses and logistical difficulties involved in deploying monitoring equipment, many areas, especially those in developing nations, lack adequate environmental data. This work presents a novel approach that uses Generative Adversarial Networks (GANs) for creating artificial environmental data. The model of GAN is trained for generating synthetic data for under-monitored or unmonitored areas by utilizing high-resolution environmental data from well-monitored regions. The paper discusses the generation of synthetic data and its comparison with real data based on statistical properties. The analysis reveals that the characteristics of the created data closely align with the actual data. With the help of this synthetic data, researchers and policymakers will be able to make more informed decisions by having precise estimations of important environmental indicators like air quality. © 2024 IEEE.

关键词： Generative adversarial networks

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A Review on Identification of Unreliability and Fakeness in Social Media Posts using Blockchain Technology 9

A Review on Identification of Unreliability and Fakeness in ...

引用

9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Bajpai, Saumya Tripathi Sharma, Dilip Kumar Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9789380544441

With the rapidly growing news industry and usage of social media, the spread of fake news and solution to interrupt this spread has become a big concern. Due to the digitization, anyone can generate a piece of news and publish it on the internet. The increased number of fake news creates havoc and confuses the reader in a bad way. It is created in such a way that the reader finds it more appealing. Due to the incompetent regulatory services, the news on social media cannot be verified. Therefore, it has become a topic of concern to subdue this business of floating fake news on the internet. This paper aims to conduct a systematic analysis and summarizes the distribution of fake news in real life, its propagation in a decentralized system, how rumor block technique works. Therefore, a total number of 18 relevant papers published on this subject between Jan 2021 and Dec 2022 are selected and thus assimilates the results of works carried out all over the world. The analysis shows that most of the paper used Blockchain technique by fact checking and data centric methodology. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain

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Design of Alcohol Detection and Alert System for Vehicles 1

Design of Alcohol Detection and Alert System for Vehicles

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Bhardwaj, Indu Saravanan, D. Bhardwaj, Ajay Yadav, Daman Kumari, Vijeta Kumar, R Ramesh Galgotias University Dept. of Electrical Electronics and Communication Engineering Greater Noida India Madanapalle Institute of Technology and Science Dept. of Electrical and Electronics Engineering India Delhi Technological University Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350356816

Recent days accidents are increasing on an exponential basis due to drinking and drive. Driving after drinking alcohol is a major cause of the majority of the accidents due to which sometimes innocent people lose their lives. As the number of vehicles is increasing day by day, accidents are also occurring due to many reasons but alcohol consumption is a major factor in many of the accidents. To avoid these accidents, an alcohol detection and alert system for vehicles is proposed in this paper that is based on MQ3 sensor. The proposed system gives a coherent solution to control injuries because of consumption of alcohol while driving. The alcohol detected by the MQ-3 sensor is processed via an ATmega328P Microcontroller that collate it with a fixed threshold for compliance. The device will nonstop monitor concentration of alcohol by the alcohol recognition sensor and consequently flip off the working of automobile's engine if the alcohol concentration is exceeding threshold value. The replica can even send the alert of locality of the car via SIM900A (GSM module). The model presents an adequate answer to regulate injuries by the influence of alcohol driving. The LCD module is used to show that alcohol has been identified. The proposed system is cost effective, less bulky and more reliable. © 2024 IEEE.

关键词： Microcontrollers

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