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Enhanced Loan Approval Prediction System using Ensemble Machine Learning Techniques 3

Enhanced Loan Approval Prediction System using Ensemble Mach...

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3rd international conference on automation, Computing and Renewable Systems, ICACRS 2024

作者： Gothai, E. Rajalaxmi, R.R. Thamilselvan, R. Sridhar, K. Department of Computer Technology Kongu Engineering College Tamilnadu Perundurai India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India MSc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331532420

Banks play a pivotal role in generating significant profits through loan operations. However, the challenge lies in accurately identifying genuine loan applicants who are likely to repay their loans. Manual processes are prone to misconceptions, prompting the development of a loan prediction system using deep learning. This system aims to automate the selection of eligible candidates, benefiting both bank staff and applicants by drastically reducing the loan approval timeframe. Two key machine learning algorithms such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed in this work for applicant selection. SVM, a popular supervised learning algorithm, creates a decision boundary or hyperplane to segregate classes in n-dimensional space. Conversely, ANN is inspired by biological neural networks, which mimic the organization of neurons in the input layer, hidden and output layers of the human brain. The integration of ANN and SVM in an ensemble model enhances accuracy compared to traditional methods. This collaborative approach ensures precise predictions of loan repayment likelihood based on applicant data and past loan eligibility records. The model's performance is measured using accuracy. The outcomes of the research were promising and the loan prediction system defines the minimum and maximum loan amounts that can be sanctioned to an applicant. By leveraging the strengths of both ANN and SVM, the ensemble model serves as a robust tool for accurately identifying worthy loan applicants, streamlining the loan approval process, and minimizing the risk of default. The accuracy of the algorithm is improved by this method to 91%. © 2024 IEEE.

关键词： Support vector machines

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Innovations in Robotic Technology: A Smart Robotic Design for Effortless Wall Painting using Artificial Intelligence

Innovations in Robotic Technology: A Smart Robotic Design fo...

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2024 IEEE international conference on automation and Computation, AUTOCOM 2024

作者： Rani, A. Jaya Mabel Sumalatha, A. Preethi, S. Annie Angeline Indhu, R. Jayaprakash, M. Preetha, M. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamil Nadu Chennai India Kristu Jayanti College Department of Computer Science Bengaluru India Sathyabama Institute of Science and Technology Department of Ece Tamil Nadu Chennai India R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350382723

A fully autonomous wall-painting robot is the focus of this paper's development, expansion, and implementation efforts. Research efforts have not communicated much about interior wall painting. Numerous studies have shown that the chemicals used in painting are detrimental to human health, particularly the respiratory and visual systems. In addition, the monotony, time commitment, and physical exertion inherent in the painting process are hard to resist. With the right combination of human and robotic labour, the entire construction process may be optimized, leading to time and labour savings. Furthermore, it would provide an opportunity to lessen or eliminate human exposure to challenging and dangerous conditions, which would address most of the issues, provide protection, and make it happen more often. The study introduces a new wall painting robot dubbed Smart AI based Wall Painting Robot (SAIWPR). To test how well the suggested scheme works, it is cross-validated with the traditional Autonomous Wall Painting Robot (AWPR). The creation of an unsupervised robotic painting system is driven by the wall painting robot. The design goal is to meet the requirements of being simple, lightweight, inexpensive, and having a short painting time. To control the range of motion and navigate the room, ultrasonic sensors are attached to the arm and the mobile base. The arm's movement and the mobile base's trajectory are both managed by a specialized control system. © 2024 IEEE.

关键词： Painting

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Home automation with Webserver Deployment with Raspberry Pi Pico 2

Home Automation with Webserver Deployment with Raspberry Pi ...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Shabariram, C.P. Venkateshwaran, M. Abinesh, R. Manoranjith, R. Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (纸本)9798350340235

Home automation system with web server deployment is developed using Raspberry Pi Pico and micropython, which turns ON the lights based on the count of persons inside that particular room. The system uses Infrared Red (IR) sensor, which detects the heat radiation in the environment. This heat radiation is not constant as it changes due to the movement of people in the sensing environment. This change is used as a measure to detect the changes in the environment which allows us to perform a particular action for the event. The data is sent to node which sends the data to the webserver running in the Raspberry pi pico. Based on the result, the light is turned ON or OFF. © 2023 IEEE.

关键词： Home automation Infrared sensor Micropython Raspberry Pi Pico Webserver

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PromptArt: AI-Powered Image Generation 8

PromptArt: AI-Powered Image Generation

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8th IEEE international conference on Computing, Communication, Control and automation, ICCUBEA 2024

作者： Patel, Jay Tale, Krishna Bidwe, Ranjeet Vasant Mishra, Sashikala Deshmukh, Ganesh Shinde, Sumit University Symbiosis Institute of Technology Pune Campus Pune412115 India Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India Itm Skills University Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350391770

Conventional picture creation requests noteworthy ability and labor, frequently constraining inventive expression to a select gather of capable specialists. This restrictiveness makes obstructions for amateurs and devotees, smothering differing qualities and advancement in visual craftsmanship. Whereas cutting-edge AI frameworks guarantee computerized picture amalgamation, they habitually battle to decipher nuanced printed prompts precisely, driving client disappointment when the coming about pictures don't meet desires. PromptArt addresses these challenges by leveraging Generative Ill-disposed Systems (GANs) to create high-quality pictures from literary depictions. This venture aims to democratize the inventive handle, engaging people of all skill levels to make personalized pictures easily. PromptArt's dual-stage GAN design first translates textual prompts into starting picture drafts and after that refines these drafts to improve quality and coherence, guaranteeing that the visual yields closely adjust with the client eagerly. With a user-friendly interface, PromptArt makes it simple for clients to input printed depictions and get high-quality visual representations. This openness changes the imaginative preparation, permitting more individuals to lock in in visual craftsmanship creation and contributing to a more different and inventive social scene. This paper investigates the engineering plan and execution of PromptArt, specifying its strategies and comes about. We moreover talk about the broader suggestions of democratizing picture creation, highlighting how PromptArt can cultivate more prominent imagination, collaboration, and social engagement, revolutionizing the way we make and connect with visual craftsmanship. © 2024 IEEE.

关键词： Generative adversarial networks

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ZenNet-SA: An Efficient Lightweight Neural Network with Shuffle Attention for Facial Expression Recognition 24

ZenNet-SA: An Efficient Lightweight Neural Network with Shuf...

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24th international conference on Control, automation and Systems, ICCAS 2024

作者： Nguyen, Trang Nabi, Hamza Ghulam Han, Ji-Hyeong Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of

ISBN: (纸本)9788993215380

Facial expression recognition (FER) is an important task in computer vision, having practical applications in areas such as human-computer interaction, education, healthcare, and online monitoring. Although significant progress has been made possible by deep learning, real-world deployment of the FER system still needs to be improved, particularly with limited resources such as embedded or mobile systems. From this challenging FER task, we propose a lightweight network that combines a lightweight high performance backbone by applying ZenNAS [1] and a distillation feature part after backbone to refine features. Extensive experimental results demonstrate that the proposed ZenNet-SA achieves state-of-the-art results on three FER benchmark datasets, RAF-DB (88.53%) as well as AffectNet 7 class (65.23%) and 8 class (61.32%) in the light-weight models group with a model size less than 1M and computation costs approximately 450M Flops. Our results demonstrate a notable improvement in efficiency, with a decrease of approximately 97% in FLOPS and 91% in the number of parameters compared to POSTER V2 in terms of computational and resource costs. © 2024 ICROS.

关键词： Deep learning

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Random-Matrix Based Extended Object Tracking Using Multiple Sensors with Different Measurement Matrices 2

Random-Matrix Based Extended Object Tracking Using Multiple ...

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2nd international conference on automation, Robotics and computer engineering, ICARCE 2023

作者： Zhang, Xiaoxiao Lan, Jian Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China

ISBN: (纸本)9798350308341

Random-matrix based extended object tracking (EOT) has received much attention for the simple yet effective scheme. Multiple sensors can bring more information due to the different perspectives and characteristics especially for EOT. However, in practice, multiple sensors have different measurement matrices, which makes EOT even more challenging. This paper develops a variational Bayesian approach to multi-sensor EOT (VB-MS-EOT) based on random matrix. First, the system model of MS-EOT with different measurement matrices is developed. This model also considers the differently distorted observations and various measurement numbers of multiple sensors. To deal with the different measurement matrices and distortions, VB-MS-EOT is proposed, where the kinematic state and extension are estimated iteratively with a simple form. Compared with the traditional EOT based on random matrix using multiple sensors with identical measurement matrix, the effectiveness of VB-MS-EOT is illustrated by simulated data. © 2023 IEEE.

关键词： Bayesian networks

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Optimization Strategy of Dual Active Full Bridge DC-DC Converter 2

Optimization Strategy of Dual Active Full Bridge DC-DC Conve...

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2nd international conference on automation, Robotics and computer engineering, ICARCE 2023

作者： Zheng, Huaihua Zhou, Min Huang, Hongsheng Wu, Zhi Zu, Canwei Wang, Nan Wang, Yang Power Supply Company State Grid Jiaxing Electric Jiaxing China School of Electrical Engineering Southeast University Jiangsu China College of New Energy China University of Petroleum Shandong China

ISBN: (纸本)9798350308341

The efficiency of a bidirectional full-bridge DCDC converter is determined by the magnitude of the reverse power and inductor current stress. Although the presence of inner current shifting in the DPS control reduces the reverse power and inductor current stress, the reduction effect is not significant. Consequently, this research paper employs the approach of constructing the Lagrange function to segmentally optimize and regulate the inductor current stress and reverse power of single-phase shift (SPS) and double-phase shift (DPS) DC-DC converters. The objective is to minimize the inductor current stress and reverse power, thereby enhancing the converter's efficiency. A comparative analysis is conducted on the reverse power and inductor current stress of different optimization strategies. The feasibility and effectiveness of these strategies are verified through simulation on the MATLAB/Simulink platform, and the efficacy of relevant optimization algorithms is ultimately confirmed through experimentation on a dedicated platform. © 2023 IEEE.

关键词： DC-DC converters

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Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning 3

Robust Phishing URL Classification Using FastText Character ...

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3rd IEEE international conference on Robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Al-Sakib, Abdullah Limon, Zishad Hossain Sakib, Anamul Pranto, Md Nuruzzaman Islam, Md Ariful Sultana, Shayma Ahmed, Md. Redwan Rahman, Shafiur Westcliff University Dept. of Information Technology CA92614 United States Westcliff University Dept. of Computer Science CA92614 United States International American University Dept. of Business Administration CA90010 United States International American University Dept. of Management Information System CA90010 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizations. Being able to identify fraudulent websites is crucial in order to effectively address these potential risks. This study introduces a novel method for detecting phishing URLs by using word and character embeddings to capture complex URL patterns. We used a dataset of 80,000 URLs, including 50,000 legitimate ones and 30,000 phishing instances, and applied thorough preprocessing techniques. We utilized word embeddings in FastText to handle unseen words, with the added advantage of n-gram representations. Additionally, we captured character-level features through dense character embeddings. We trained several machine learning and deep learning models, and one model, the Convolutional Bidirectional LSTM (CBiLSTM), stood out with an accuracy of 99.01% and an F1-score of 99.08%. Furthermore, we made a thorough comparison with the most advanced techniques available, and our findings demonstrated clear superiority over previous research. This study presents an effective approach for classifying phishing URLs, providing a valuable tool to combat fraud and protect against identity theft, thereby helping to minimize the financial and emotional harm experienced by victims. © 2024 IEEE.

关键词： Phishing

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The study on the circuit architecture of reconfigurable physics-based unclonable technology 5

The study on the circuit architecture of reconfigurable phys...

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5th international conference on Signal Processing and computer Science, SPCS 2024

作者： He, Baigen Qiu, Ganggang Jiangsu Automation Research Institute Jiangsu Lianyungang222000 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

With the rapid development of computer technology and internet technology, the issue of information security has become increasingly prominent. Physical Unclonable Technology (PUT), based on circuit implementation, is a high-security technology that differs from traditional cryptography in that it employs logic gates with variable quantities and distinct gate circuits. PUT can be applied in various fields such as authentication, secure encryption, cryptographic computation, and more. Compared to traditional cryptography, PUT offers advantages including high security, strong resistance to attacks, and flexible operation, which has led to its widespread application in the field of information security. Information security refers to the protection and maintenance of information resources from unauthorized or inappropriate access, use, modification, and other potential hazards. It is a product of the information age, encompassing any actions or events that may cause or potentially cause damage to information resources. Traditional cryptography has some drawbacks in ensuring data security, such as the ease of device loss, inconvenient key storage, and difficulties in decryption. Therefore, the development of new cryptographic technologies is necessary to address these issues. © 2025 SPIE.

关键词： Digital storage

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Deploying computer-Based Vision to Enhance Safety in Industrial Environment 32nd

Deploying Computer-Based Vision to Enhance Safety in Industr...

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32nd international conference on Flexible automation and Intelligent Manufacturing

作者： Shahin, Mohammad Chen, F. Frank Hosseinzadeh, Ali Koodiani, Hamid Khodadadi Bouzary, Hamed Rashidifar, Rasoul Univ Texas San Antonio Mech Engn Dept San Antonio TX 78249 USA Univ Texas San Antonio Civil & Environm Engn Dept San Antonio TX 78249 USA

ISBN: (纸本)9783031381645;9783031381652

One of the cornerstones of Lean manufacturing, according to academic research, is the 5S+1, which introduces quality in manufacturing. This study aims to demonstrate how computer-based vision and object detection algorithms, can assist in the implementation of safety as 6th S in 5S+1 by monitoring and identifying employees who disregard accepted safety procedures, like wearing Personal Protective Equipment (PPE). The research evaluated the performance indicators of a detection technique and reviewed and analyzed it. To confirmworkers' PPE compliance, the suggested model used the You-Only-Look-Once (YOLO v7) architecture. A deep learning technique was subsequently applied to confirm the safety helmets and safety vests. This strategy is determined to be the most effective when using the VGG-16 algorithm, achieving an 80% F1 score and processing 11.79 frames per second (FPS), making it ideal for real-time detection.

关键词： Lean Manufacturing Machine Learning Industry 4.0 5S+1 Personal Protective Equipment

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