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Empirical Analysis of sampling Methods on Imbalanced Data

Empirical Analysis of Sampling Methods on Imbalanced Data

引用

2022 IEEE North Karnataka subsection Flagship International Conference, NKCon 2022

作者： Disha, N. Chadaga, Neha b singh, Hemang Kayarvizhy, N. B.M.S College of Engineering Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9781665453424

With imbalanced data being one of the significant drawbacks encountered while evaluating the effectiveness of machine learning models, where the distribution of instances within a dataset is skewed, we aim to provide data-level solutions to manipulate the balance of data before feeding them onto classifiers. The decline in performance in such datasets occurs when the accuracy in minority classes is poor due to very few instances available in them to train the model. In this paper, we assess various existing sampling techniques in order to handle skewed data to enhance the classification performance of the model which includes traditional Oversampling and Undersampling and five variants of smote: smote, borderline smote, svM smote, Adasyn, and smoteTomek. We have experimented with these methods on a variety of imbalanced binary classification datasets ranging from highly imbalanced to moderately imbalanced and evaluated them based on performance metrics such as recall, precision, and F-measure. © 2022 IEEE.

关键词： Classification (of information)

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Implementation of decoders for LTE interface messages

Implementation of decoders for LTE interface messages

引用

2nd International Conference on Networking and Distributed Computing, ICNDC 2011

作者： Manjula, M. varaprasad, G. Department of Computer Science and Engineering B.M.S.College of Engineering Bangalore India

ISBN: (纸本)9780769544274

Long-term evolution is the next-generation network beyond 3G. In order to initiatively imitate and improve network performance, by research and analysis of signaling message which is transmitted in the LTE network architecture, a decoder module is needed. In this paper, the decoding modules for decoding s3, s4, s5/s8, s10 and sll interfaces, which use GTPv2 protocol to transmit the messages between the various entities such as sGsN - MME, sGsN - sGW, sGW - PGW, MME - MME, MME - sGW and MME - EIR, respectively are explained. Further, in LTE architecture, there is another interface s1MME which uses s1AP protocol between the eNb and MME entities;in similar way interfaces s13 (MME to EIR), s6a (MME to Hss), Gx (PCRF to PCEF), and sGi (between PGW & PDN) which uses diameter protocol, need to be decoded. Hence, to incorporate the decoding of message fields of above said interfaces in the proposed system, the existing Alcatel-Lucent framework LTEPA is enhanced as the project work. © 2011 IEEE.

关键词： Long Term Evolution (LTE)

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Image and pattern reconstruction using differentiable plasticity 1

Image and pattern reconstruction using differentiable plasti...

引用

1st International Conference on Artificial Intelligence, Computational Electronics and Communication system, AICECs 2021

作者： Priyanka, Jigalur Prasad, b.G. Department of Computer Science and Engineering B.M.S. College of Engineering Bengaluru India

The brain is a substantial boon to humankind that adapts nature accordingly. The brain can learn and unlearn based on the situation. This singularity of human learning led to the research creating models using Artificial Intelligence (AI) to incorporate the brain's behavior. The investigation opened up many new approaches to study AI with neural networks by adding new techniques to imitate the human brain's functionalities. Many models can learn from experience like Recurrent Neural Network(RNN's), Long short Term Memory (LsTM) with the fixed network size. This paper describes the simple method of creating the model which will behave similar to the biological brain and recreates its differentiable plasticity to adopt the features of neural network connection. It also shows that applying plasticity and the Hebbian plastic connection rule can result in optimization in RNN. This new approach of reconstruction of images based on plastic neural network experiments shows that the above novel approach gives more optimized results than the traditionally used RNN techniques. In this paper, a proposal is made where models can memorize and reconstruct unseen sets of images by solving recurrent networks using plasticity rules. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Long short-term memory

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GAN based Model for virtual Try on of Clothes

GAN Based Model for Virtual Try on of Clothes

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCs 2024

作者： Toliya, Nikita Paras Chadaga, Neha b. Harshitha, R.M. Dhruva, M. Nagarathna, N. B.M.S College of Engineering Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9798350372502

E-commerce platforms focused on fashion, imagebased virtual try-on solutions have shown considerable potential for enhancing the user experience and increasing customer satisfaction. The 2 D solutions that exist fail to provide a complete satisfactory experience as they lack the capability to take into consideration customer-specific postures and modifications. They rely majorly on generative models due to which reconstructionof high-quality images of the clothing is difficult. The clothing in 3 D v T solutions is created as 3 D models, which are thendraped on 3 D human avatars with various body types. A huge catalog of digitized 3 D garment and clothing models is a crucial requirement for a 3 D vT system, but this requirement requires expensive multiview capture/scan equipment and cannot scaleup for big catalog volumes, especially in the current fast-fashion settings. Hence, we propose a video virtual Try-On model which is based on fully learnable characteristics preserving virtual try on network C P vTON [3]. It first forms a TsP for transforming clothes to fit the target body shape via Geometric Matching *** use a GAN based model for pose *** accuracy resulting from such a model could help in decreasing the waiting time in traditional stores, and also help in decreasing sanitizing costs. © 2024 IEEE.

关键词： Customer satisfaction

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Leveraging Online social Content for Early Detection of suicidal Ideation: A Multi-Modal Deep Learning Approach

Leveraging Online Social Content for Early Detection of Suic...

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCs 2024

作者： Toliya, Nikita Paras Nagarathna, N. B.M.S. College of Engineering Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9798350372502

The contemporary society is grappling with the alarming rise in suicide rates, exacerbated by factors such as prolonged exposure to negative emotions and challenging life events. Detecting early signs of suicidal ideation holds significant promise in addressing this crisis. In the digital age, online communication platforms have become outlets for individuals to express distress, presenting an opportunity for timely intervention. This study aims to explore online social content for the early identification of suicidal ideation, with a focus on user-generated text that contains valuable insights into people's mental states. The research begins with a thorough content analysis to extract knowledge from suicide-related text, establishing a benchmark for binary classification of suicidal ideation using various classifiers, including feature-based and deep neural networks. The urgency of early detection for prevention underscores the importance of this research. by integrating content analysis, feature engineering, and advanced deep learning methodologies, such as deep neural networks and attentive relation networks, this study aims to contribute to suicide prevention efforts. This paper advocates for the application of multi-modal deep learning techniques to identify early signs of suicidal ideation from online social content. The integration of machine learning and mental health intervention is crucial in addressing the escalating concern of suicide rates, especially among vulnerable populations like youths. Through our research, we aim to provide a valuable tool for promoting mental well-being and ultimately saving lives. © 2024 IEEE.

关键词： Deep neural networks

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Non-Invasive Devices for Early Diagnosis of Oral Potentially Malignant Disorders: A Comparative Analysis 6

Non-Invasive Devices for Early Diagnosis of Oral Potentially...

引用

6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Hegde, Kratharth shenoy, Kedary Krishna, T. sooda, Kavitha Abhilash B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665435598

Oral potentially malignant disorders (OPMDs) combine an assortment of sores and conditions which show an increased danger for change to oral cancer. Early discovery and determination of such disorders are important to avoid malignant change. Globally, it was decreed to be the fifteenth most common cancer in 2012. Many advanced determination methods are used to detect, anticipate their progression, and to assess the risk of transformation. As a part of this study a detailed analysis of four non-invasive devices used to detect OPMDs has been conducted by analysing the studies performed by researchers and comparing their sensitivity and specificity. Despite the fact that the devices involved in this study have good sensitivity and specificity (as high as 80%) and are non-invasive in nature, they cannot replace the golden standard of biopsy. © 2021 IEEE.

关键词： Diseases

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Neural network based bER prediction for 802.16e channel

Neural network based BER prediction for 802.16e channel

引用

2007 15th International Conference on software, Telecommunications and computer Networks, softCOM 2007

作者： Gowrishankar Ramesh babu, H.s. satyanarayana, P.s. Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9789536114931

The prediction of bit Error Rate (bER) in IEEE 802.16e Mobile WirlessMAN network is investigated here. The state of the channel is estimated on symbol by symbol basis on a realistic fading environment. The state of a channel is modeled as nonlinear and temporal system. Neural network method is the best system to predict and analyze the behaviors of such nonlinear and temporal system. In this context, bER prediction by k symbol ahead is investigated by two different recurrent neural network architectures such as Recurrent Radial basis Function (RRbF) Network and Echo state Network (EsN). The Predicted bER will match very well with the simulation results.

关键词： bit error rate

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COvID-19 Prediction Using Time series Models 4

COVID-19 Prediction Using Time Series Models

引用

4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Deepthi, A.R. Indiramma, M. B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

Real-time data has evolved to become an integral part of understanding events across different timelines. Machine Learning uses different varieties of algorithms to determine the relationship between sets of data spread across timelines, visualize the current situation, and forecast the future, which is the most important aspect. Due to the breakout of COvID-19, a novel coronavirus, the entire planet is currently experiencing a disastrous crisis. At this time, the sARs-Cov-2 virus has proven to be a possible hazard to human life. The ARIMA Model i.e., Autoregressive Integrated Moving Average is compared with Facebook's Prophet and vARMAX model to foretell the future. The dataset is divided into the training and testing set. The size of the COvID-19 dataset is relatively small as it is a pandemic that occurred recently, due to which much of the data is used for training purposes and the last twelve days have been used for testing and validating the model. The model is trained and fits on the training data set. The algorithms are now ready to anticipate future forecasts after it has been tested and trained. The models also record the predicted and actual values, allowing them to improve their accuracy in the future. In this paper, the results of the ARIMA model are compared against Prophet and vARMAX which are other popular machine learning time series models. For the ease of visualization of covid trends, a dashboard is built using Python's Plotly and Dash and has been deployed using voila. © 2022 IEEE.

关键词： COvID-19

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Implementation of Land Registry system Using Ethereum blockchain and IPFs 8

Implementation of Land Registry System Using Ethereum Blockc...

引用

8th IEEE International Conference on Computational system and Information Technology for sustainable solutions, CsITss 2024

作者： Chandru, Nithin savanur, Niranjan Asha, G.R. vijayeshjeevan, Matam B.M.S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Frequent changes in property ownership can lead to complex and lengthy records, which may allow fraudulent or incomplete records to go unnoticed. The primary issues stem from incomplete and damaged records caused by different departments storing their own copies, resulting in a lack of comprehensive verification and an increased risk of document forgery. This system proposes a solution using a blockchain-based approach, which creates immutable and transparent records of land ownership by creating an entity to represent land in the blockchain network, making it difficult for unlawful users to forge or modify records. It employs cryptographic methods, consensus protocols, and hashing algorithms to preserve the integrity of the transaction chain. This system proposes an implementation model using Ethereum, and benchmarking shows that the system has a low transaction processing time, making it suitable for practical use. Additionally, the use of the Interplanetary File system (IPFs) for storing all land and user-related documents provides additional security. Cost and performance analysis are carried out based on experiments conducted, demonstrating the system's efficiency and effectiveness. This could streamline the process of land ownership transfer, promote environmental sustainability by minimizing the need for manual paperwork, and reduce the likelihood of disputes and fraud. © 2024 IEEE.

关键词： Ethereum

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DeepFake Detection Through Key video Frame Extraction using GAN 1

DeepFake Detection Through Key Video Frame Extraction using ...

引用

1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRs 2022

作者： Lalitha, s. sooda, Kavitha Department of Computer Science & Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9781665460842

The emergence of deepfake videos in recent years has made image falsification a real threat. A deepfake video uses deep learning technology to substitute a person's face, emotion, or speech with the face, emotion, or speech of another person. Finding such deceptive deepfake videos on social media is the first step in preventing them. A robust neural network-based technique to identify false videos is presented in this paper. An important video frame extraction approach is used to speed up the process of finding deep fake videos. A model made up of a convolutional neural network (CNN) and a classifier network consisting of GAN technology is provided. Resnet, Resnext50 and LsTM were passed over in favor of the Confusion Matrix when deciding which structure to pair with the classifier while detecting the fake video. The model is a method for detecting visual artefacts. The subsequent classifier network uses the feature vectors from the CNN module as this is the input to categorize the video whether it is fake or real one. The dataset is considered from DeepFake Detection Challenge to get the best model. The key goal is to get high accuracy without using a lot of data to train the model. In comparison to earlier efforts, the key video frame extraction method dramatically decreases computations by achieving 97.2% accuracy using the Deepfake Detection Challenge dataset. © 2022 IEEE

关键词： Convolutional neural networks

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