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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Mian, Reiaan Mazhar Khan, Sohaib Ali, Raja Hashim Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

The swift progression of technology has resulted in a surge in digital transactions, necessitating robust authentication mechanisms to avert fraudulent activities. Because genuine and counterfeit banknotes seem so identical, it is difficult, time-consuming, and error-prone for humans to determine which one is authentic. Counterfeit notes are manufactured at an alarmingly high rate, which means that systems for authenticating bank notes are essential to ensure counterfeit notes are detected and verified quickly and in due time. Such measures guarantee multiple benefits, including safe transactions, uphold the integrity of financial institutions, and guardianship against fake currency notes in the market. In this work, we show that automated solutions may be deployed to improve the accuracy and efficiency of procedures for banknote authentication. Currently, there is a lack of effective and precise models, which are especially trained for verifying or detecting bank note authentication, despite presence of an extensive research in the fields of artificial intelligence and machine learning. Our study seeks to close this gap by creating and implementing a deep learning model that can accurately identify and differentiate between real and fake banknotes. In the study, we noted that the suggested model outperformed conventional techniques with a notable degree of accuracy with the use of an extensive dataset. The model’s potential for practical implementation in banking and financial establishments is shown by its exceptional accuracy and efficiency in detecting fake banknotes. The high accuracy of the model demonstrates that deep learning may be used successfully to improve security protocols, opening the door for more developments in financial technology and otehr allied fields. © 2024 IEEE.

关键词： Deep learning

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BEESO:Multi-strategy Boosted Snake-Inspired Optimizer for engineering Applications

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Journal of Bionic engineering 2023年第4期20卷 1791-1827页

作者： Gang Hu Rui Yang Muhammad Abbas Guo Wei Department of Applied Mathematics Xi’an University of TechnologyXi’an710054China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Department of Mathematics University of SargodhaSargodha40100Pakistan University of North Carolina at Pembroke PembrokeNC28372USA

This paper presents an efficient enhanced snake optimizer termed BEESO for global optimization and engineering *** a newly mooted meta-heuristic algorithm,snake optimizer(SO)mathematically models the mating characteristics of snakes to find the optimal *** has a simple structure and offers a delicate balance between exploitation and ***,it also has some shortcomings to be *** proposed BEESO consequently aims to lighten the issues of lack of population diversity,convergence slowness,and the tendency to be stuck in local optima in *** presentation of Bi-Directional Search(BDS)is to approach the global optimal value along the direction guided by the best and the worst individuals,which makes the convergence speed *** increase in population diversity in BEESO benefits from Modified Evolutionary Population Dynamics(MEPD),and the replacement of poorer quality individuals improves population *** Elite Opposition-Based Learning(EOBL)provides improved local exploitation ability of BEESO by utilizing solid solutions with good *** performance of BEESO is illustrated by comparing its experimental results with several algorithms on benchmark functions and engineering ***,the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank sum *** findings show that these introduced strategies provide some improvements in the performance of SO,and the accuracy and stability of the optimization results provided by the proposed BEESO are competitive among all *** conclude,the proposed BEESO offers a good alternative to solving optimization issues.

关键词： Snake optimizer Bi-Directional Search Evolutionary Population Dynamics Elite Opposition-Based Learning Strategy Mechanical optimization design

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Determination of Parameters Affecting the Estimation of Iceberg Draft

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China Ocean engineering 2023年第1期37卷 62-72页

作者： Hamed AZIMI Masoud MAHDIANPARI Hodjat SHIRI Civil Engineering Department Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandA1B 3X5St.John’sNLCanada Department of Electrical and Computer Engineering Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandA1B 3X5St.John’sNLCanada C-CORE A1B 3X5St.John’sNLCanada

Recent offshore oil and gas loading facilities developed in the Arctic area have led to a considerable awareness of the iceberg draft approximation, where deep keel icebergs may gouge the ocean floor, and these submarine infrastructures would be damaged in the shallower waters. Developing reliable solutions to estimate the iceberg draft requires a profound understanding of the problem’s dominant parameters. As such, the dimensionless groups of the parameters affecting the iceberg draft estimation were determined for the first time in the present study. Using the dimensionless groups recognized and the linear regression(LR) analysis, nine LR models(i.e., LR 1 to LR 9) were developed and then validated using a comprehensive dataset, which has been constructed in this study. A sensitivity analysis distinguished the premium LR models and important dimensionless groups. The best LR model, as a function of all dimensionless parameters, was able to estimate the iceberg draft with the highest level of precision and correlation along with the lowest degree of complexity. The ratio of iceberg length to iceberg height as the “iceberg length ratio” and the ratio of iceberg width to iceberg height as the “iceberg width ratio” was detected as the important dimensionless groups in the estimation of the iceberg draft. An uncertainty analysis demonstrated that the best LR model was biased towards underestimating the iceberg drafts. The premium LR model outperformed the previous empirical ***, a set of LR-based relationships were derived for estimating the iceberg drafts for practical engineering applications, e.g., the early stages of the iceberg management projects.

关键词： dimensional analysis iceberg draft subsea assets linear regression(LR) sensitivity analysis

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Diabetic Foot Ulcer Detection and Classification using ConvMixer Model 5

Diabetic Foot Ulcer Detection and Classification using ConvM...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Anusha, N. Vidhya, R. Jani Anbarasi, L. Dhanya, D. Pushparani, S. Mohan, Geetha Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Vellore Campus Vellore India Agni College of Technology Department of Information Technology Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Institute of Applied Technology Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331504403

Diabetic foot ulcer (DFUs) are a common and significant consequence of diabetes, frequently resulting in severe illness and amputations if not promptly identified and treated. The growing occurrence of DFUs is a substantial obstacle to healthcare globally. Precise and prompt categorization of DFUs is essential for efficient treatment and avoidance of consequences. This research analyzed ConvMixer model for comprehensive classification designed to accurately evaluate DFU. The DFU images are preprocessed using the watershed algorithm that helps in separating different regions of an image, such as the ulcer and surrounding tissues, to improve the neural network's ability to extract high-quality information. The framework utilizes depthwise and pointwise convolutions to mix spatial and channel-wise information in an efficient manner to effectively extract pertinent information from various clinical DFU dataset. The proposed work features a multi-layered depthwise and pointwise convolutional architecture, created especially to manage the complex patterns and variances found in various diabetic foot ulcer stages and variants (DFUs). By integrating these convolutions and skip associations, the network facilitates hierarchical extraction of features and robust representation of features. Experimental results on a substantial and varied DFU dataset demonstrate that the ConvMixer model achieves an impressive accuracy rate of 97.16%, with a precision, F1-score and a sensitivity of 97%, 97% and 99.08% respectively. © 2024 IEEE.

关键词： Diseases

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Using Convolutional Neural Networks for Enhanced Pneumonia Detection via Chest X-Rays

Using Convolutional Neural Networks for Enhanced Pneumonia D...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Babar, Waqar Ali, Raja Hashim Faheem, Alishba Mansoor, Syed Ahmed Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany

ISBN: (纸本)9798331504465

As a deadly lung disease, pneumonia remains a leading cause of child mortality under five years old. Machine learning, especially deep learning have played a significant role in the improved performance for detection and identification of various diseases in the field of healthcare. Neural networks, especially the recent development of newer architectures, have revolutionized the object identification, and classification applications in clinical diagnosis of various diseases. This study presents the application of Convolutional Neural Networks (CNN) for the timely and accurate detection of pneumonia using chest X-rays, a development with considerable potential in aiding clinical diagnosis. The research deployed dropout regularization in model design to mitigate overfitting and relied on recall and F1 scores for thorough model evaluation. Although comparable studies achieved higher overall accuracy, our models registered a recall rate of 98%, crucial in reducing false negatives and enhancing patient safety. This suggests the potential of our CNN model as a vital tool for healthcare professionals in early pneumonia detection in children and adults, with the capacity to process a high volume of X-ray images rapidly and accurately. The successful model construction was enabled through various parameter tuning techniques, thus enhancing patient care efficiency and the potential to decrease mortality rates. © 2024 IEEE.

关键词： Convolutional neural networks

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Towards Underwater Sustainability with AIoT: Underwater Trash Management System Comprised of YOLOv8 with IoT-applied Segmentation 2

Towards Underwater Sustainability with AIoT: Underwater Tras...

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

作者： Paneru, Biplov Poudel, Buddhi Sagar Shah, Krishna Bikram Paneru, Bishwash Chhetri, Sanjog Poudel, Yam Krishna Nepal Engineering College Pokhara University Department of Electronics and Communication Bhaktapur Nepal Nepal Engineering College Pokhara University Department of Computer Science and Engineering Bhaktapur Nepal Institute of Engineering Tribhuvan University Department of Applied Science & Chemical Engineering Lalitpur Nepal Nepal Research and Collaboration Center Department of Research and Development Kathmandu Nepal

ISBN: (纸本)9798331517984

Effective techniques for managing rubbish underwater are essential due to growing environmental concerns about contamination under the sea. In this research, we offer a novel method for efficient underwater trash detection and management that makes use of the Internet of Things (IoT) and the YOLOV8 object detection model. Using YOLOV8, the suggested system seeks to identify and categorize underwater garbage in real-time. It then uses IoT devices for segmentation and management. We outline the suggested system's design, talk about how YOLOV8 integrates with Internet of Things gadgets, and provide experimental results to validate the system's efficacy. Our findings suggest that the suggested strategy has great potential to advance efforts to manage undersea waste and manage pollution as well as segment using 6-DOF Robotic Arm. The IoT based system allows remote monitoring of trash density and location along with the raspberry-pi server-based video feed allows the users to suitably monitor aquatic conditions remotely. The fusion of IoT and deep learning on YOLO v8 model opens gateway for water sustainability and enhances further opportunities in employing robotic arm-based trash collection system. © 2024 IEEE.

关键词： Robotic arms

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Sign to Speak: Real-time Recognition for Enhance Communication 3

Sign to Speak: Real-time Recognition for Enhance Communicati...

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

作者： Baghavathi Priya, S. Rao, P V R Subba Madeswaran, TamilSelvi Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India University of Technology and Applied Sciences Department of Information Technology Nizwa Oman

ISBN: (纸本)9798350375190

Recent developments in the area of research on sign language recognition have led to making use of deep learning techniques with the goal to assist the self-learning transition of sign language from one language to another. In order to detect and classify characters from photographs in multiple sign languages, a number of deep-learning models, have been deployed. SLR is a prominent area of study and technology that aims to mitigate speaking barriers for individuals who unable to talk and listen. This involves the development of tools capable of understanding and deciphering sign language movements, hence facilitating a wider range of interactions for those who are difficult of listening and speaking. This paper demonstrates a novel model called CNN Inception Net, which is a neural network based on convolution. The use of an Indian sign dataset enables the achievement of a notable performance rate of 98.9%, enabling precise recognition of actions in the language of signs. © 2024 IEEE.

关键词： Deep learning

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Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches

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Computing 2025年第6期107卷 1-64页

作者： Javed, Haseeb Eid, Fatma El-Sappagh, Shaker Abuhmed, Tamer Department of Computer Science and Engineering College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Technology Management Stony Brook University NY11794 United States Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation. © The Author(s) 2025.

关键词： Power management (telecommunication)

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A Few-shot custom CNN Model for Retinal Nerve Fibre Layer Thickness Measurement in OCT Images of Epilepsy

A Few-shot custom CNN Model for Retinal Nerve Fibre Layer Th...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Muhammad, Ruqayya Boukar, Moussa Mahamat Adeshina, Steve Dane, Senol Nile University of Nigeria Faculty of Natural and Applied Sciences Department of Computer Science Abuja Nigeria Nile University of Nigeria Faculty of Natural and Applied Sciences Department of Computer Engineering Abuja Nigeria Nile University of Nigeria College of Health Sciences Department of Physiology Abuja Nigeria

ISBN: (纸本)9798350394528

This study aims to assess the effectiveness of employing deep learning models for measuring retinal nerve fiber layer (RNFL) thickness in optical coherence tomography (OCT) scans of epilepsy patients. Conventional OCT scan segmentation methods typically rely on supervised learning, demanding substantial data for training and assuming fixed network weights post-training. To mitigate these challenges, we explore the applicability of few-shot learning (FSL) in CNN architectures, allowing dynamic fine-tuning of network weights with minimal additional data. Experimental results demonstrate enhanced segmentation accuracy, with the proposed Few-shot Custom CNN achieving a notable 91% accuracy, surpassing both the Custom CNN (86%) and the OCT machine data. This suggests the superiority of the few-shot Custom CNN model in segmentation performance compared to OCT scans. © 2024 IEEE.

关键词： Optical tomography

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CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources 41

CaPS: Collaborative and Private Synthetic Data Generation fr...

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41st International Conference on Machine Learning, ICML 2024

作者： Pentyala, Sikha Pereira, Mayana De Cock, Martine School of Engineering and Technology University of Washington Tacoma United States Department of Electrical Engineering Universidade de Brasilia Brazil Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Data is the lifeblood of the modern world, forming a fundamental part of AI, decision-making, and research advances. With increase in interest in data, governments have taken important steps towards a regulated data world, drastically impacting data sharing and data usability and resulting in massive amounts of data confined within the walls of organizations. While synthetic data generation (SDG) is an appealing solution to break down these walls and enable data sharing, the main drawback of existing solutions is the assumption of a trusted aggregator for generative model training. Given that many data holders may not want to, or be legally allowed to, entrust a central entity with their raw data, we propose a framework for collaborative and private generation of synthetic tabular data from distributed data holders. Our solution is general, applicable to any marginal-based SDG, and provides input privacy by replacing the trusted aggregator with secure multi-party computation (MPC) protocols and output privacy via differential privacy (DP). We demonstrate the applicability and scalability of our approach for the state-of-the-art select-measure-generate SDG algorithms MWEM+PGM and AIM. Copyright 2024 by the author(s)

关键词： Data assimilation

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