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5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Kaifas, Theodoros N. F. Democritus University of Thrace Dept. of Electrical and Computer Engineering Xanthi Greece

ISBN: (纸本)9798350352863

The work at hand provides a comprehensive study of Electromagnetic Power Harvesting via Metamaterial type antennas on the context of the Characteristic Mode Theory. Super-directive, parasitic arrays of electrically small but resonant element radiators - particles are studied, designed and evaluated. The presented designs provide more antenna-gain than conventional ones by invoicing and practically matching the theoretical maximum bounds of super-directivity boosting the rectenna system to its absolute limits, as far as the antenna part is concerned. Furthermore, those arrays apart from their own merit they exhibit the characteristic that can be arranged in large host surfaces to scale for even more increased performance. In the current work we will show that the analysis of harvesting particles via Characteristic Mode Theory can provide in-dept. understanding of the physics involved and assist on performance improvement strategies during relative design circles. © 2024 IEEE.

关键词： characteristic mode theory electromagnetic power harvesting super-directivity

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A Study on Deep Fake Face Detection Techniques 3

A Study on Deep Fake Face Detection Techniques

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Arya, Mudit Priyanshu Upwan Akash Goyal, Umang Chawla, Simran Chandigarh University Dept. of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350375190

Advanced technology and the widespread use of deep fake technology has rendered the digital landscape vulnerable to deceptive manipulations, particularly through the creation of synthetic face images. This comprehensive review precisely traces the historical trajectory of deep fake faces, encompassing their evolution and its impact on various domains. It also investigates into the growing concerns regarding misinformation and privacy breaches. The literature review analyzes important works, pivotal milestones, and notable case studies, providing a complete understanding of the dynamic landscape of deep fake faces. This study analyzes existing detection techniques, ranging from neural networks to machine learning approaches, offering insights into the complexities of current methodologies. It sheds light on both the strengths and limitations of these techniques, emphasizing the need for robust solutions to counter adversarial attacks and address data scarcity. Building on this analysis, this study explores breakthroughs within the field of deep fake detection, highlighting instances where identification has been successful. It also underscores the ethical considerations that come with the evolution of these technologies. In proposing innovative solutions and discussing potential optimizations for existing techniques, the review explores interdisciplinary approaches and emerging technologies like blockchain and explainable AI. The findings provide a summary of key insights collected from the literature, identify research gaps, and project the future of deep fake face detection. This study concludes by emphasizing the ongoing societal impact of these developments in the digital era. © 2024 IEEE.

关键词： Face recognition

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A Study on Simplification of LCC Estimation Through Correlation Estimation Between Cost Elements for Railway Catenary System

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Transactions of the Korean Institute of electrical Engineers 2024年第5期73卷 900-905页

作者： Jeng, Chang-Mo Lee, Jong-Woo Dept. of railway Electrical Signaling Engineering Seoul National University of Science and Technology Korea Republic of Dept. of railway Electrical Signaling Engineering Seoul National University of Science Korea and Technology Korea Republic of

Life cycle cost (LCC) analysis is crucial for estimating total cost of system constructions across various industries, especially defense, aerospace, railways where products are complex and long-lasting. A significant challenge in LCC modeling lies in the limited availability of reliable life-cycle data, leading to uncertainties in system specifications, estimation methods, and scenario analysis. Despite the existence of historical construction data, inherent uncertainties often restrict their direct use. In projects like catenary system construction for railways, LCC is heavily influenced by project length, with material costs being one of the most deterministic elements compared to other cost factors. This paper proposes a novel cost element coefficients method to facilitate the estimation of total construction costs. Our method leverages historical data and addresses uncertainties, providing a practical tool for LCC analysis in railway projects. Copyright © The Korean Institute of electrical Engineers.

关键词： Life cycle

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Delay Minimization in UAV-Enabled MEC Networks Using Q-Learning 10

Delay Minimization in UAV-Enabled MEC Networks Using Q-Learn...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Moiz, Syed Khwaja, Ahmed S. Alnoman, Ali Anpalagan, Alagan Jaseemuddin, Muhammad Toronto Metropolitan University Dept. of Electrical and Computer Engineering Toronto Canada American University of Ras Al Khaimah Dept. of Computer Science and Engineering Ras al Khaimah United Arab Emirates

ISBN: (纸本)9798350373011

Mobile edge computing (MEC) is a promising solution for the ever-increasing computational demands of the ubiquitous Internet of things (IoT) applications. However, efficient resource management algorithms are needed to satisfy the quality-of-service (QoS) requirements of delay-sensitive IoT systems. In this paper, we propose a new Q-learning-based load-balancing resource management (QLLB) algorithm to reduce the computational delay in a MEC system. Unlike existing algorithms, the proposed algorithm optimally assigns tasks to unmanned aerial vehicles (UAVs) considering load balancing, and employs the Q-learning algorithm to achieve efficient CPU usage management at the MEC server level. Results show that the QLLB algorithm exhibits a considerable improvement in the overall performance compared to the baseline and round-robin algorithms. Specifically, under heavy load conditions, we observe a 68% reduction in the total delay and a 75% reduction in the overall algorithm execution time with respect to the baseline algorithm. Furthermore, we observe a 59% improvement in the combined UAV selection and task data transmission delay. © 2024 IEEE.

关键词： Quality of service

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Genetic Data for Early Detection and Treatment of Cystic Fibrosis through Machine Learning 8

Genetic Data for Early Detection and Treatment of Cystic Fib...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Deepthi, P. Bhuvaneshwari, M. Chithra, K. Dhanasree, L.P. Jananee, V. Layola, J.A. Adlin Rajalakshmi Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Cystic Fibrosis (CF) is a genetic disorder that significantly impacts the respiratory and digestive systems, requiring early detection and precise management to improve patient outcomes. This research study presents a machine learning model designed to predict the presence, severity, and potential treatments for Cystic Fibrosis using genetic data. By leveraging collaborative filtering techniques, our model not only identifies individuals at risk but also enables personalized treatment plans based on genetic predispositions. The proposed model aims to revolutionize Cystic Fibrosis diagnosis and management by automating the screening process on a mass scale. This automation facilitates the identification of demographic areas and genetic populations with a higher susceptibility to Cystic Fibrosis, enabling targeted interventions. Through large-scale genetic data analysis, our approach ensures that at-risk populations receive timely and appropriate care, ultimately improving the quality of life for Cystic Fibrosis patients. The findings suggest that the implementation of this predictive model in clinical settings can lead to earlier and accurate diagnoses, personalized treatment plans, and significant advancements in Cystic Fibrosis care. The implications of this research extend beyond Cystic Fibrosis, offering a framework for applying machine learning in the diagnosis and treatment of other genetic disorders. © 2024 IEEE.

关键词： Personalized medicine

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Generating Random Petri Nets for GPenSIM environment 9

Generating Random Petri Nets for GPenSIM environment

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Davidrajuh, Reggie University of Stavanger Dept. Electrical Engineering and Computer Science Stavanger Norway

ISBN: (纸本)9798350383706

During the development stages of the modeling processes, there is always a need to create random Petri Nets so that these Petri Nets can be used as test cases. This paper presents new functions for the automated generation of random Petri Nets. The new functions are implemented in the General-purpose Petri Net Simulator (GPenSIM), a tool that runs on the MATLAB platform. Some researchers use GPenSIM for modeling, simulation, and performance analysis of discrete systems. Newer applications of GPenSIM also include modeling and control of cyber-physical systems using distributed modules known as 'Petri Modules.' Firstly, this paper introduces GPenSIM and a few Petri Net types, such as event graphs and Petri modules. Then, the newer functions of GPenSIM for creating random Petri Nets are presented, which is the goal of this paper. © 2023 IEEE.

关键词： Petri nets

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Sentiment Analysis of Customer Reviews on Food Ordering Portals of Bangladesh using Natural Language Processing 6

Sentiment Analysis of Customer Reviews on Food Ordering Port...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Deb, Priom Bhuiyan, Asibur Rahman Ahmed, Farhan Hossain, Md. Rakib Mahrin, Habiba Ahmed, Md Faisal Karim, Dewan Ziaul Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331505790

In recent years, the popularity of online food ordering services has surged, offering consumers a convenient way to order food from restaurants and have it delivered to their doorstep. During this period, HungryNaki and Foodpanda Bangladesh have been identified as key contributors to the growth and advancement of the online food delivery market. This study aims to anticipate the sentiments of Bangladeshi customers towards online meal ordering services, with a specific focus on Foodpanda Bangladesh and HungryNaki. We created a new dataset for this research, subjected it to preprocessing and employed six machine learning models and three deep neural network models. In the machine learning approach, the Random Forest Classifier demonstrated excellence in accuracy, precision, and recall, achieving an accuracy rate of 80.31%. On the other hand, the BERT Classifier performed effectively in the deep learning approach, reaching a peak accuracy of 89%. Despite challenges such as data ambiguity and imbalances in the dataset, our findings underscore the potential of the BERT model in sentiment analysis, offering valuable insights for future research in this field. © 2024 IEEE.

关键词： Data accuracy

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Species Density Analysis Using Deep Learning

Species Density Analysis Using Deep Learning

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Rekha, K.S. Shamlin, A Harini Saveetha Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309140

The primary objective of this project is to develop a machine learning model, specifically Convolutional Neural Network (CNN), to count the density of several particular species in an audio clip. The model is trained on a dataset containing calls of the specified species and other species. This innovation has significant implications for wildlife monitoring and ecological research, as it can help researchers track and understand the behavior of organisms in forest regions more efficiently. Manual effort in audio analysis can be reduced by machine learning, thereby saving time. By improving and broadening techniques, algorithms, and training methodologies, researchers can uncover more about animal behavior and contribute to our collective understanding of ecosystems. The application of this technology can be useful for analyzing the total number of species in a certain rainforest region, eventually reducing the burden on researchers and streamlining the entire process. Overall, this project has immense potential to pave the way for more efficient and accurate wildlife monitoring and ecological research that can ultimately benefit our planet's biodiversity. © 2024 IEEE.

关键词： Forest ecology

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Demystifying PDFs: Emerging AI for text generation and operation on PDF 4

Demystifying PDFs: Emerging AI for text generation and opera...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Sharma, Ashish Baveja, Mannu Kanikicherla, Suhaj Kaushik, Devansh Sharma, Yashuman Jessica, Jessica Chandigarh University Dept of Electrical Engineering Mohali India Chandigarh University Dept. of Computer Science Mohali India

ISBN: (纸本)9798350354218

AI is considered as most disruptive language and revolutionized various sectors with its ability analyze data with its large language model. incapability of other AI to read PDF and accept prompt and do text generation using their large language *** work is based on proposing a new technique to integrate it into current AI. It will eliminate the need manual work of copy pasting Portable Document Format data to AI for prompt. In this can rely on Technologies Like Optical character recognition for reading images to text but challenging part is Portable Document Format to image and then image to text. this work is advancement of Optical character recognition which can eliminate the drawback of already existing technology. © 2024 IEEE.

关键词： Optical character recognition

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A Hybrid Method for the Detection and Plucking of Mature Coconut Using an Advanced Object Detection Technique 1

A Hybrid Method for the Detection and Plucking of Mature Coc...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Muhammed Ilyas, H. Elayidom, Sudheep School of Engineering Cusat Dept. of Computer Science Cochin India

ISBN: (纸本)9798350307795

Vision is a crucial aspect for both artificial intelligence and automated robots. In the case of an automated coconut harvesting machine, a computerized system linked to the machine plays a key role in real-time identification of ripe coconuts within the coconut tree crown. The project employs AI theory to craft an intelligent system. This system utilizes prior knowledge to predict the maturity of coconuts in the current image of the coconut tree crown. A computational board is then employed to compare the captured image with a dataset, enabling the identification of different coconut growth stages and pinpointing the mature coconuts in the current image capture. © 2024 IEEE.

关键词： Object detection

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