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Fine Tuning GANs for Image Colorization: Effects of Training Data and Epochs 7

Fine Tuning GANs for Image Colorization: Effects of Training...

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Patil, Rahul Patil, Pramod Joshi, Rohit Joshi, Atharv Hastak, Ninad Joshi, Akshay Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Pune India Dr. D. Y. Patil Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Ever since the advent of Deep Learning, there has been a lot of experimentation and research being carried out in image processing. This gave rise to the conversion of black and white i.e. grayscale images into colorized images. Image colorization is a popular image-to-image translation problem. It finds its application in various domains like image restoration, movie colorization, colorization of old aerial surveillance shots, medical image colorization, etc. Earlier this was done manually by artists and photographers but recent advancements in deep learning have enabled automatic image colorization using techniques like Convolutional Neural Networks and Generative Adversarial Networks. CNN is a neural network that has layers that are essential for extracting features from images and can learn the complex mapping between grayscale images and their corresponding ground truth images. On the other hand, GANs is a relatively new technique that consists of two components: generator and discriminator. This research paper aims to present the results obtained by using both the above-mentioned methodologies for image colorization. Many examples of generated images and their respective accuracies and structure according to the technique used are discussed in the later part. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Generative Adversarial Network Grayscale Image Colorization

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Career Prediction Using Machine Learning 8

Career Prediction Using Machine Learning

引用

8th International Conference on Inventive Systems and Control, ICISC 202

作者： Jha, Kundan Likhitha, D. Chandana, M.Siri Reddy, M.Ram Prakash Bhargavi, Maridu Vignan's Foundation for Science Technology and Research Dept.Computer Science and Engineering Guntur AP Vadlamudi India

ISBN: (纸本)9798350386578

This study uses advanced machine learning techniques to predict students' career paths more accurately. By analyzing and preprocessing various data, including academic performance, extracurricular activities, internships, and personality traits, the study identifies patterns. Ethical considerations, privacy protection, and bias prevention are part of the model development process. The practical applications include personalized career counseling, improving curricula, and optimizing alumni networks. Despite challenges like model interpret ability and adapting to changing job markets, interdisciplinary collaboration is crucial. Overall, educational institutions can use these techniques to provide customized support, helping students make informed career decisions in today's ever-changing workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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DigiPramaan: Blockchain Based Certificate Management System 8

DigiPramaan: Blockchain Based Certificate Management System

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Swami, Prathamesh Vhatkar, Kapil Ambekar, Yash Singh, Kartikey Kaware, Yashovardhan Dr. D. Y. Patil Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350391770

With more people taking online courses and earning certifications, there are great new opportunities to improve skills. But, this digital trend has also led to a problem with fake certificates. To address this problem, we propose a robust certificate management system utilizing blockchain technology and smart contracts. Our system comprises three key components: certificate creation, authenticity verification, and a user-friendly digital storage solution. Blockchain technology is at the core of our system, ensuring that each certificate is immutable and trustworthy. We utilize cryptographic hashes as unique identifiers for each certificate, which detect any tampering by resulting in a different hash. Additionally, our system offers secure digital storage based on cloud technology, allowing users to efficiently manage and access their certificates. We also emphasize features for certificate revocation and updates, enhancing the system's security and adaptability. In conclusion, our blockchain based system offers a comprehensive and secure solution to the issue of fake certificates in the digital era. © 2024 IEEE.

关键词： Blockchain

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Recognition and Processing of phishing Emails Using NLP: A Survey

Recognition and Processing of phishing Emails Using NLP: A S...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Anilkumar, Chunduru Karrothu, Aravind Mouli, Nandam Sri Tej, Chaduvula Bhanu GMR Institute of Technology Rajam Dept of Information Technology Andhra Pradesh Vizianagaram India GMR Institute of Technology Dept of Computer Science and Engineering India

ISBN: (纸本)9798350348217

Email is preferred by both individuals and businesses as one of the most important forms of communication. Despite the popularity of alternatives like electronic communications, mobile applications, and social networks, email usage and importance have been raising continuously. Phishing is the fraudulent practice of stealing any person's confidential information, such as financial details, login information etc. In which they look completely authentic and genuine and extremely hard for victim to detect whether the mail is spam or not. Moreover, the number of phishing emails is growing daily. So, handling of phishing emails is a crucial research issue. NLP techniques are useful for training data to detect spam content. In this work we studied leading research areas in phishing email detection and features used in spam emails. Feature extraction and selection plays a key role in phishing email detection. This paper also explains how actually the phishing attack happens and obtained results including information about concepts of natural language processing and working procedure. © 2023 IEEE.

关键词： Electronic mail

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Offensive Message Spam Detection in Social Media Using Long Short-Term Memory 3

Offensive Message Spam Detection in Social Media Using Long ...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sivakumar, M. Abishek, S.A. Karthik, N. Vanitha, J. National Institute of Technology Dept. of Electrical and Electronics Engineering Puducherry India National Institute of Technology Dept. of Computer Science Engineering Puducherry India National Institute of Technology Puducherry India E.G.S.Pillay Engineering College Nagapattinam India

ISBN: (纸本)9798331518592

In this world, online communication has become an essential part in our daily lives. Almost everyone uses social media platforms to communicate with the whole world. But this also leads to a major problem: cyberbullying. This paper proposes a system to detect users who spam offensive messages repeatedly and ban them using natural language processing and machine learning. The system would be beneficial to service providers, social media platforms, and individuals, allowing us to strive for a world of harmony. The amalgamation of NLP, Big Data, and ML approaches presents a robust and efficient solution for offensive message spam detection. The findings of this project will shed light on the potential of these technologies to curb the proliferation of offensive content, thereby fostering a healthier and more respectful online communication ecosystem. This paper delves on offensive spam detection using the LSTM model, and the obtained MSE of this model is 12.58. © 2024 IEEE.

关键词： Social networking (online)

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High Gain, E-Shaped Microstrip Antenna with Two Identical Slits for 5G Applications in the 60 GHz Band 3

High Gain, E-Shaped Microstrip Antenna with Two Identical Sl...

引用

3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ali, Ala'a N. S. Gaid, AbdulGuddoos S. A. Saeed, Mamoon M. Saeed, Rashid A. Hawash, Burkan Taiz University Faculty of Engineering & Information Technology Dept. of Communications & Computer Taiz Yemen Faculty of Engineering Dept. Communications & Electronics Engineering Sana'a Yemen College of Computers & Information Technology Taif University Dept. of Computer Engineering Taif21944 Saudi Arabia Faculty of Information Science & Technology Selangor Malaysia

ISBN: (纸本)9798350305333

This article presents a simple, high-gain, E-shaped antenna suitable for 5G applications in the unlicensed 60 GHz band, characterized by high absorption and path losses. In order to optimize the reflection coefficient performance, enhance the gain, and adjust the resonance frequency of the proposed antenna, two identical slits are incorporated into the front edge of the radiating patch. The proposed structure is built on a Rogers RT/Duroid 5880 dielectric substrate, characterized by a thickness of 0.118 mm, a dielectric constant of 2.2, and a tangent loss of 0.0009. The optimized design with a compact size of 3.8 x 6 x 0.118 mm3 achieves an impedance bandwidth ( © 2023 IEEE.

关键词： Radiation efficiency

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Analysis on Crop Yield Prediction using various Ensemble Methods 8

Analysis on Crop Yield Prediction using various Ensemble Met...

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Hariyani, Gulshan Singh, Aniket Patil, Prathamesh Kothari, Virag Javale, Deepali Dr. Vishwanath Karad MIT-World Peace University Dept. of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350391770

Agriculture is a crucial facet of human existence as it fulfils the primary and fundamental requirement of sustenance, namely food. Therefore, it is incumbent upon us, as human beings, to cultivate and enhance our agricultural methodologies. Crop Yield Prediction is a method that enables farmers to determine the most suitable crops to produce in specific seasons based on historical data, prior to harvesting. This research examines the prediction of crop productivity in three distinct states of India. The states are Bihar, Jharkhand, and Odisha. We employ many ensemble methods, namely Random Forest, XGB Regression, and Stacking Regression, in which we combine Random Forest and XGB Regression. Next, compute the results by evaluating L1 and L2 using LASSO and Ridge Regression. Ultimately, XGB Regression yields a more precise outcome in comparison to the other two methods. © 2024 IEEE.

关键词： Agricultural economics

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0 3

Machine Learning Classifiers for Predicting the Risk of Card...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumari, Palak Kumar, Ashish Chatterjee, Kakali Master of Computer Applications Patna Women's College Patna India National Institute of Technology Patna Dept. of Computer Science and Engineering Patna India

ISBN: (纸本)9798331528201

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body's, brain's, or heart's blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives. © 2024 IEEE.

关键词： Nearest neighbor search

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BSCIAM: A Blockchain based Secure Cloud Identity and Access Management Framework

BSCIAM: A Blockchain based Secure Cloud Identity and Access ...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Das, Swatisipra Sahil, Mohammad Pandit, Naveen Kumar Priyadarshini, Rojalina Gochhayat, Sarada Prasad Fakir Mohan University Postgraduate Dept. of Computer Science Odisha India C.V. Raman Global University Dept. of Computer Science and Engineering Odisha India Indian Institute of Technology Jammu Dept. of Computer Science and Engineering J&K India

ISBN: (纸本)9798350348460

Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user's details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management. © 2024 IEEE.

关键词： access management blockchain cloud identity management

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Enhancing Power Quality in Distribution Systems through Bi-directional Solid-State Transformers 3

Enhancing Power Quality in Distribution Systems through Bi-d...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Ghambirlou, Khaled Esamil SH Abosnina, Adel Ali Moschopoulos, Gerry Engineering Western University Dept. Electrical and Computer London Canada College of Electrical and Electronics Technology Benghazi Libya

ISBN: (纸本)9798331540319

Power quality issues, including voltage sags, swells, harmonic distortion, oscillatory voltage transients, etc. can degrade equipment performance, leading to inefficiencies and costly disruptions. Studying these issues and introducing solutions are essential for maintaining the reliability and efficiency of modern distribution systems. This paper examines and contrasts the benefits of a bidirectional three-stage solid-state transformer (SST) in mitigating power quality issues to traditional transformers. SST introduces reactive power support to improve the system quality by controlling active and reactive power. This paper investigates key power quality issues-voltage sag, swell, harmonic distortion, and oscillatory transients-arising from capacitor bank energizing, focusing on the novel application of solid-state transformers (SSTs) in mitigating these effects. Additionally, it examines SST-based fault isolation across various fault types (three-phase, double line-to-ground, and line-to-ground). Several simulations have been performed using the MATLAB/Simulink platform to highlight the benefits of the Solid-State Transformer (SST) over conventional transformers and cascade multiple-active-bridge SST (CMABSST) types. The proposed SST employs active and reactive power control to alleviate these effects, allowing the SST controller to manage reactive power effectively and provide the required support that makes the system stable and reliable. © 2024 IEEE.

关键词： Distribution transformers

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