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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Shailaja, P. Kumar, K. Kranthi Sirnivas, K. Simha Reddy, V. Bharath Mohana Lakshmi, Kesana Rajani, D. Department of Computer Science Engineering Warangal India Kommuri Pratap Reddy Institute of Technology Dept. of Eee Hyderabad India Secunderabad India Malla Reddy Engineering College and Management Sciences Department of Mechanical Engineering India Cmr Technical Campus Dept. of Ece Hyderabad India Sree Venkateswara College of Engineering Dept. of Ece Nellore India

ISBN: (纸本)9798350375442

Integrated circuits (ICs) are fundamental components in a wide range of applications, including consumer electronics, automotive systems, telecommunications, and medical devices, where their reliable performance is crucial. However, identifying failures in ICs presents several challenges due to the complex nature of the circuits, variability in manufacturing processes, and the subtleties of faults that may not be easily detectable with traditional methods. To address these issues, a robust methodology incorporating data preprocessing, Synthetic Minority Over-sampling Technique (SMOTE) for data balancing, Principal Component Analysis (PCA) for feature extraction, and Random Forest classification is proposed. Data preprocessing involves cleaning and normalizing the dataset to ensure quality and consistency. SMOTE is then applied to address the imbalance in the dataset, enhancing the detection of minority class failures. PCA is used to reduce the dimensionality of the dataset, retaining essential features that capture the most variance, which improves the efficiency and accuracy of the model. Finally, Random Forest classification is employed due to its robustness and ability to handle large datasets with high dimensionality, providing a reliable prediction of IC failures. © 2024 IEEE.

关键词： Image retrieval

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High Heels Are No More an Accessory of Fashion for Women- A Study Unrevealing the Health Effects of Wearing High Heels 10

High Heels Are No More an Accessory of Fashion for Women- A ...

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

作者： Banu, Shaik Balkhis Akhtar, Saman Waseem Arshad, Sana Banu, Shaik Rehana Chandini, Shaik Ghantasala, G.S. Pradeep Fatima College of Health Sciences Dept of Physiotherapy Al Ain United Arab Emirates University of Bolton Dept. Management Studies Ras al-Khaima United Arab Emirates De Mont Fort University Dept. Business Management Dubai United Arab Emirates Lincoln University College Dept. Business Management Selangor Malaysia Gulf American University Dept. Civil Engineering Sharjah United Arab Emirates Alliance University Dept of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350353068

The high heels are one of the most loved accessories of women. High heels have marked their entry not only into the fashion industry but also into the corporate sector as a symbol of must-wear accessories with corporate dressing. High heels are surely the most loved accessories of women but it is also a fact that high heels are the cause of foot pain and many other health conditions of foot health. The study is conducted with the intent to know whether wearing high heels is affecting the foot health of women or not. The study is conducted between the two controlled groups. each group consists of 125 women in each. One group represents women wearing high heels on a regular basis and the other group represents women not wearing high heels on a regular basis. The study reveals that the women who are not wearing high heels foot health is much better compared to the women who are wearing high heels. © 2024 IEEE.

关键词： Wear of materials

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Advancements in Jute Leaf Disease Detection: A Comprehensive Study Utilizing Machine Learning and Deep Learning Techniques 2

Advancements in Jute Leaf Disease Detection: A Comprehensive...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Haque, Rezaul Miah, Md Miraz Sultana, Shayma Fardin, Hasib Noman, Abdullah Al Al-Sakib, Abdullah Hasan, Md Kamrul Rafy, Al Shihabur, Rahman Md Rahman, Shafiur East West University Dept. of Cse Dhaka1212 Bangladesh Brac University Dept. of Cse Dhaka1212 Bangladesh International American University Dept. of Management Information System CA90010 United States Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University Dept. of Information Technology CA92614 United States Touro University Dept. of Cyber Security CA94592 United States Kyungdong University Dept. of Computer Engineering Gangwon-do Korea Republic of Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331517984

Detecting diseases in jute leaves is difficult due to the variability in how the diseases appear. Manually identifying these diseases is challenging because it requires expert knowledge and visual inspections take a lot of time. Machine Learning (ML) offers a promising solution to these challenges by automating the detection process. However, research in this area is limited due to the lack of specific datasets. This study aims to address this by creating a comprehensive dataset of 10,800 high-quality images of jute leaf diseases. The main goal is to develop a robust system for classifying jute leaves into three categories: Yellow Mosaic, Powdery Mildew, and Healthy. Our methodology involved extensive image preprocessing, including resizing and various augmentation techniques, to enhance the dataset's diversity and ensure model robustness. We trained various ML and Deep Learning (DL) models and conducted a comparative analysis of their performance. Additionally, we compared our approach with the state-of-the-art methods. The results showed that DL models, particularly Inception V3, achieved an outstanding accuracy of 99.98%, compared to 89.75% for Random Forest (RF). This highlights the potential of DL techniques in improving the accuracy of jute leaf disease detection. Our findings contribute to better disease management strategies and increased productivity in jute cultivation. © 2024 IEEE.

关键词： Adversarial machine learning

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Play Everywhere: A Temporal Logic based Game Environment Independent Approach for Playing Soccer with Robots

arXiv

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arXiv 2024年

作者： Suriani, Vincenzo Musumeci, E. Nardi, D. Bloisi, D.D. Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Dept. of Mathematics Computer Science and Economics University of Basilicata Potenza Italy

Robots playing soccer often rely on hard-coded behaviors that struggle to generalize when the game environment change. In this paper, we propose a temporal logic based approach that allows robots’ behaviors and goals to adapt to the semantics of the environment. In particular, we present a hierarchical representation of soccer in which the robot selects the level of operation based on the perceived semantic characteristics of the environment, thus modifying dynamically the set of rules and goals to apply. The proposed approach enables the robot to operate in unstructured environments, just as it happens when humans go from soccer played on an official field to soccer played on a street. Three different use cases set in different scenarios are presented to demonstrate the effectiveness of the proposed approach. © 2024, CC BY.

关键词： Temporal logic

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Leveraging Artificial Intelligence for Enhanced Data Generation in Addressing Imbalance in Binary Classification System 2

Leveraging Artificial Intelligence for Enhanced Data Generat...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and management for Social Innovation, IATMSI 2024

作者： Bandopadhyay, Srijita Dutta, Srimonti Haider, Imran Anuraag, Bhavaraju Zhu, Jerry Bazaz, Saad Ahmed University of Engg. & Management Dept. of Electronics and Communication Engineering Kolkata India Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India Vellore Institute of Technology Dept. of Electronics and Communication Engineering Vellore India University of Waterloo Faculty of Mathematics ON Canada National University of Computer & Emerging Sciences Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350360523

This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the ß- VAE filtered approach employed in previous literature. © 2024 IEEE.

关键词： Generative adversarial networks

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Implementation of Robotic Process Automation (RPA) in Digital Marketing 3

Implementation of Robotic Process Automation (RPA) in Digita...

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3rd International Conference for Emerging Technology, INCET 2022

作者： Mehta, Reshmi Chaher, Riya Mukesh Patel School of Technology Management and Engineering Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9781665494991

Robotic Process Automation (RPA) has flourished since the recent wave of digital transformation. To some RPA may sound intimidating and ominous at first, but in retrospect, it is an extremely practical solution for traditionally time-consuming repetitive tasks. RPA tools aim to automate processes that have always required a human to physically perform the task but do not necessarily require strategic thinking. In this paper we talk about RPA and one of the RPA tools- UiPath, then go on to discuss the prospect and scope of automation in the field of Digital Marketing and how using RPA will largely benefit this section of business by superseding the manually done repetitive tasks of digital marketing. This technology has been a boon to many sectors of business. In the second half of the paper, we describe a use case in digital marketing and how using software bots we can increase efficiency and decrease time and workforce consumed while maintaining the quality of work. We implement our solution using the UiPath tool to build a workflow for the same and deploy the bot. © 2022 IEEE.

关键词： Process control

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Mathematical Models of Heterogeneous Machine Learning Techniques for Ransomware Protection in Cyber-Physical Systems

Mathematical Models of Heterogeneous Machine Learning Techni...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Gupta, Subir Hazra, Sudipta Hazra, Simanta Gayen, Sourav Mukherjee, Sayanti Naskar, Anwesa WB Haldia India Asansol Engineering College Dept. of C.S.E. WB Asansol India Nshm Knowledge Campus Dept. of C.S.E. WB Durgapur India Asansol Engineering College Dept. of Computer Application WB Asansol India College of Engineering and Management Kolaghat Dept. of I.T. WB Kolaghat India

ISBN: (纸本)9798350390759

Ransomware attacks are a growing threat to cyber-physical systems (CPS), capable of causing financial losses, downtime, and even physical harm. Using machine learning to detect and prevent these attacks is a promising approach. A study compared four machine learning techniques - support vector machines (SVM), decision trees, random forests, and neural networks - to detect ransomware attacks on CPS. The models were trained and evaluated using a dataset of real ransomware incidents. Results showed that all four techniques effectively detected ransomware attacks. Neural networks achieved the highest F1 score, while SVMs excelled in precision, recall, and accuracy. However, the differences in performance among the methods were not significant. The models detected ransomware outbreaks within an average of 12 hours, allowing businesses to mitigate the attack's effects promptly. The study concluded that machine learning could effectively detect and prevent ransomware attacks on CPS. However, it is important to note that the study used a limited dataset, highlighting the need for further research with larger datasets to confirm these findings. © 2024 IEEE.

关键词： Cyber attacks

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Optimizing Thermo-Electric Generation with Quasi-Z-Source Converter in Energy Harvesting for Wearable Devices 3

Optimizing Thermo-Electric Generation with Quasi-Z-Source Co...

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3rd International Conference on Mechatronics and Automation Technology, ICMAT 2024

作者： Lotfy, Mostafa Wageh Hussein, Aziza I. Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (纸本)9781643685649

Recent advances in semiconductors industry and microelectronics have created new opportunities for integrating various technologies in energy harvesting projects. These advancements have simplified the process of capturing energy from different sources, such as thermal energy, and converting it into electrical power. Thermo-Electric Generators (TEGs) are commonly used in many applications such as wearable devices. However Thermo-Electric Generators produce very low output voltages, which is insufficient for most low-power electronic circuits to operate. Therefore, a boost converter is essential to step up the voltage output from the TEG to a usable level. In this paper, a DC-DC converter with a high performance in terms of voltage gain is implemented. The proposed quasi-Z-source (q-ZSC) DC-DC converter not only offers high voltage gain but also several significant advantages. The proposed q-ZSC is capable of both boosting and regulating the voltage output efficiently, even under variable input conditions, making it highly suitable for low-power energy harvesting systems such as Thermo-Electric Generators. The design is simulated using MATLAB Simulink software, focusing on integrated circuits, with a strong emphasis on optimizing the circuit's efficiency. This work underscores critical factors in improving energy harvesting circuits, enhancing their performance and effectiveness in low-power applications for wearable technology. © 2024 The Authors.

关键词： DC generator motors

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MoodSync: Personalized Video Recommendation Based on User Face Emotion 2

MoodSync: Personalized Video Recommendation Based on User Fa...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Anurag Gupta, Shaishav Satyawali, Harshit Sharma, Vikrant Awasthi, Shashank Vats, Satvik Graphic Era Hill University Dehradun Dept. of Computer Science Uttarakhand Dehradun India G.L. Bajaj Institute of Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Navigating the vast expanse of video content presents a challenge in discovering personalized, user-specific material. One way to tackle this issue is through a video search feature. However, while using keywords helps, it doesn't always give the best user experience. This abstract explores the use of facial emotion in contrast to watch history for better recommendation of videos to the user. YouTube is a widespread online video-sharing platform where users can upload, share, and view videos. Its recommendation system utilizes algorithms that analyses user behaviors, such as watch history, search patterns, and engagement metrics. Machine learning models, like collective filtering and deep neural networks, are employed to predict what content a user may enjoy. This personalized method enhances user experience, encourages longer engagement, and contributes to the platform's success in delivering applicable and engaging video content. We wanted to know what people liked, so we looked at how they felt when watching videos. With this knowledge, a new method of recommending videos that correspond with their feelings was developed. In real-time, our application reads users' facial expressions and recommends YouTube videos accordingly. © 2024 IEEE.

关键词： Emotion Detection Machine Learning Neural Networks YouTube Data API v3

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System 'Stationary 3-Axis Fire-Emergency Response Base' 8

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kundu, Shyantan Dey, Swarajit Abdullha, Md. Mustafa Sen, Gobinda Gangopadhyay, Malay Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India Institute of Engineering & Management Dept. of Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named 'Stationary 3-Axis Fire-Emergency Response Base' (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management. © 2025 IEEE.

关键词： Fire hazards

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