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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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Blockchain-Based Paper Review System 9

Blockchain-Based Paper Review System

引用

9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Peter, Allen Xavier Hassan, Sahil Sudev Krishnan, K. Jose, Tony Rakhee, M. Muthoot Institute of Technology and Science Dept. of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

Paper review and publishing is an important part of a researcher's life. It is where a researcher's time and resources that he has spent bears fruit. And this is enabled by getting his work reviewed by different peers. In our current state of things, researchers face a lot of issues while they try to publish their research manuscript. These issues include quality of peer-reviews, rise of predatory journals, bias between reviewers, and other issues regarding academic integrity. These issues in the traditional system are what make the system inefficient. Therefore, we propose a more efficient blockchain-based system that will try to tackle these issues. Hyperledger Fabric is one of the most popular Blockchain platforms which provides a permissioned blockchain. Our proposed system would leverage the permissioned blockchain. This is to ensure that not everyone from outside the system has access to the data present in the system and only the members of the blockchain are able to access the data. The workflow of the peer-review system would be stored as transactions. The blockchain would be used to keep track of these transactions and order them properly in order to maintain provenance. This system would prove to be a lot more efficient than the traditional existing systems. © 2023 IEEE.

关键词： Blockchain

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A Systematic Study of Challenges in Cervical Cytology Image Segmentation of Intelligence based Cervical Cancer Screening 4

A Systematic Study of Challenges in Cervical Cytology Image ...

引用

4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Anu, P.M. Lakshmi, C. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350305258

Cervical Cancer (CC) is the fourth frequent dreadful cancers that prevails in women across the globe. Cervical cytology is being adopted as the standard technique for the screening of pre-cancerous stage and cancerous lesions detection. For accurate segmentation in cervical cytology, the smear images play a crucial role in any Artificial Intelligence based computer Aided Diagnosis (CAD) method. This relies upon the precise segmentation of nucleus present in the cervical cytology smear images. The motive of this study deals with a systematic analysis of the various challenges that are encountered while segmenting the Cervical Cell Nucleus and the countermeasures that are incorporated to eliminate the misdiagnosis and missed diagnoses in the early stage of screening. With the motive of this, a systematic study of the published articles from the sources - Scopus, PubMed and Web of science are analyzed and reviewed. The importance of this study is due to the intensity of the disease that still positions cervical cancer as a bothering disease that disrupts the life of about five lakh female population, every year across the world. © 2023 IEEE.

关键词： Cervical Cancer Cervical Cytology Cervical Nucleus Challenges in Cervical Cytology Segmentation Intelligence based Cervical screening

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An Overview on Breast Cancer Detection & Classification Methods Using Machine Learning Techniques 3

An Overview on Breast Cancer Detection & Classification Meth...

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

作者： Philip, Rohan Mathew Raj, Rishabh K Shaji, Ashwin Althaf, Fathima Sudarsan, Dhanya Muthoot Institute of Technology and Science Dept. of Computer Science & Engineering Kerala India

ISBN: (纸本)9798350322842

Breast cancer is a prevalent and deadly disease that primarily affects women. Breast cancer has claimed the lives of many people all over the world. Early breast cancer detection, on the other hand, can significantly improve a woman's chances of survival. There are various approaches and procedures for detecting cancer in breast tissue. Breast cancer may be detected using Image Processing, Machine Learning, and Deep Learning methodologies and techniques. People who use trustworthy techniques and make better selections will be able to discover breast cancer in its early stages and save a woman's life. This research aims to contemplate and make inferences from diverse studies on the subject of classification using Machine Learning approaches. The researchers and physicians who devote their time and expertise to enhancing breast cancer screening methods will definitely profit from this research study. © 2023 IEEE.

关键词： Convolutional neural networks

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DigiFarmer: Precision Agriculture Empowered by Machine Learning for Sustainable Crop Management 2

DigiFarmer: Precision Agriculture Empowered by Machine Learn...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Patra, Karan Devanand Adesh Bhosle, Arya Kothari, Deepali Mukesh Nagarhalli, Tatwadarshi P. Vidyavardhini's College of Engineering and Technology Dept. Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350365269

In agriculture, the sector grapples with pressing challenges arising from climate change, water shortages, and pest infestations. Particularly affecting farmers in developing nations, these challenges amplify issues of food insecurity and poverty, necessitating innovative solutions for sustainable global agricultural progress. Addressing the complexities of climate variability, water scarcity, and pest outbreaks requires comprehensive tools and information. The absence of predictive insights intensifies crop losses and hampers overall profitability. In response, this research introduces DigiFarmer, an innovative solution employing advanced machine learning models. DigiFarmer adopts a holistic approach, encompassing crop disease detection, weed classification using Convolutional Neural Networks (CNNs), and crop quality analysis with a Light Gradient Boosting Machine (LGBM) classifier. Yield prediction is facilitated by the Random Forest algorithm, with real-time weather data integration enhancing efficacy. DigiFarmer stands out for empowering farmers with tools for sustainable and resilient agriculture. Leveraging cutting-edge technologies, DigiFarmer not only addresses immediate challenges but equips farmers with foresight to mitigate future risks. The ultimate goal is to contribute to the welfare of global farming communities by enabling informed decisions that enhance productivity, profitability, and sustainability in agriculture. © 2024 IEEE.

关键词： Profitability

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A CNN Deep Learning Technique for Botnet Attack Detection for IoT Application 3

A CNN Deep Learning Technique for Botnet Attack Detection fo...

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

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

ISBN: (纸本)9798350322842

The Internet of Things (IoT) is a significant technology that enables us to develop many helpful online applications. The Internet of Things allows remote operating items using preexisting network infrastructure. Services that are based on information technology, computers, and other technologies are included in the cyber world. The cyber-world sees many protocols and technological advancements that lead to improvements. When it comes to cyber-based services, security is an extremely significant issue. Classification methods based on machine learning (ML) and deep learning (DL) can accurately forecast the most effective model for a dataset. A cyber assault on a wide scale carried out by devices infected with malware and managed remotely is known as a botnet attack. Under the context of IoT applications, an effective method for detecting botnet attacks is presented in this research. © 2023 IEEE.

关键词： Internet of things

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Detecting Suspicious Military Tents from Optical Images using Deep Learning Model 3

Detecting Suspicious Military Tents from Optical Images usin...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Vasavi, S. Praneeth Polu, Venkata Sai Manikya Venkata Saketh Nandula, Bala Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350364569

Locating suspicious military tents or encampments is crucial for national security. Recently, deep learning methods have become increasingly popular for detecting military objects at country borders. Traditional methods, which rely on manual checks and morphological algorithms, are often inaccurate and slow at detecting such unauthorized activities. To overcome this issue, a solution that automatically detects suspicious tents from satellite images using deep learning techniques is crucial. An enhanced YOLOv8x-obb model is proposed and trained with a satellite image dataset that is well-prepared with various preprocessing and augmentation techniques. This model was validated and produced an accuracy of 91.9% and false alarm rate of 6.08%, specifying that the model works well in different terrains and situations, and it has the advantage of better border surveillance. © 2024 IEEE.

关键词： Military satellites

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Detecting Iot Botnet Attacks: A Machine Learning Paradigm 10

Detecting Iot Botnet Attacks: A Machine Learning Paradigm

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Purnachandrarao, M. Sushma, K. Anantha, Manisha Reddy, S.Anvith Nithin, M. Ranjithkumar, V. B V Raju Institute of Technology Dept. of CSE-Data Science Telangana Narsapur India

ISBN: (纸本)9798350353068

The rise of IoT gadgets has provided immense connectivity and ease, yet it has also created opportunities for malicious individuals to exploit weaknesses and launch botnet attacks. This paper introduces a inclusive and extensive strategy for preventing and detecting IoT botnet attacks, leveraging integrated machine learning *** methodology comprises a dual-layered system that combines traditional machine learning and deep learning techniques to establish a robust defense mechanism. Initially, a machine learning model is trained on a dataset which is labelled encompassing normal and botnet traffic patterns from devices of IoT. This adaptive model evolves alongside emerging attack patterns, providing an initial layer of defense against known and evolving *** enhance precision and discern sophisticated attack patterns, we incorporate deep learning using neural network architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). These architectures analyze raw IoT network data, uncovering intricate patterns that may elude traditional security ***-world experiments validate the efficacy of our integrated approach, demonstrating superior accuracy and robustness in preventing and detecting IoT botnet attacks. The results position our strategy as a potent safeguard for IoT ecosystems, offering proactive defense against the dynamic landscape of evolving threats. © 2024 IEEE.

关键词： Internet of things

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Military Aircraft Detection from Optical Satellite Images Using Convolutional Neural Networks 8

Military Aircraft Detection from Optical Satellite Images Us...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Vasavi, S. Kalahasti, Tejaswini Kotturu, Manvitha Sri Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331505462

Aircraft detection using optical images plays a vital role in various military operations contributing to security, safety, efficiency, and environmental sustainability in the aviation sector. Recent works such as CGC-net, YOLOv5, DenseNet have showed remarkable outcomes in military aircraft detection. Although the models demonstrate significant results, they often exhibit low speed and unreliable accuracy in detecting aircrafts from foggy optical images. To address these issues, we plan on developing a deep learning model that detects military aircrafts with high speed and accuracy particularly in challenging weather conditions. This paper proposes an enhanced CNN model that has been well trained with MAR20 dataset, with various pre-processing techniques applied. The trained model has consistently shown reliable results when tested with unseen data. This model was validated and it yielded an accuracy of 92.35 % and over all false positive rate is 0.41 %, indicating that it performs well in a variety of landscapes and circumstances, with an additional advantage of improved border surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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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...

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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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