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Ensemble Learning with Mixup Style for Ultrasound Image Classification 4th

Ensemble Learning with Mixup Style for Ultrasound Image Clas...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Alblwi, Abdalrahman Barner, Kenneth E. Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9783031821493;9783031821509

Early detection of breast cancer has become an essential medical procedure to reduce mortality rates in patients. Although various deep learning methods enhance cancer analysis and detection, including feature extraction techniques, transfer learning, image fusion, and data augmentation, challenges such as limited data and class imbalance impede progress in early cancer detection. This study introduces a new approach that employs a dual ensemble learning and MixUp data techniques to simultaneously (1) tackle unbalanced classes and (2) improve diversity in ultrasound images. The proposed training methodology involves various strategies, integrating original and augmented datasets to maintain model training and increase performance. Through comprehensive evaluations using benchmark breast ultrasound images (BUSI), the proposed approach demonstrates a substantial improvement and feasibility in the multi-classification of breast cancer.

关键词： Ensemble learning Breast cancer Class imbalance, Multi-Classification MixUp

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Architecting Resilience: Digital Innovations in Humanitarian Logistics for Disaster-Ready Networks 5th

Architecting Resilience: Digital Innovations in Humanitarian...

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5th 5th Olympus - International Conference on Supply Chain

作者： Aretoulaki, Eleni Ponis, Stavros T. Plakas, George Tzanetou, Dimitra Kitsantas, Antonios Natl Tech Univ Athens Sch Mech Engn Heroon Polytech 9 Athens 15780 Greece

ISBN: (纸本)9783031693502;9783031693519

Modern logistics networks, even under normal operating conditions intrinsically face a multitude of issues, which become even more daunting in the event of disasters and emergencies, which either disrupt existing networks by exerting extra strain or necessitate the formation of ad-hoc humanitarian logistics networks for disaster management, to alleviate the impacted areas and deliver aid to the victims. Although it is not feasible to entirely avert all disasters, it is well recognized that utilizing advanced digital technologies, including drones, big data analytics, the Internet of Things, machine learning and more, can significantly revolutionize humanitarian logistics operations implemented prior to, amidst, and following a disaster's occurrence. This paper delves into the realms of user interface design within the humanitarian logistics domain, emphasizing the pivotal role of drone and other digital technologies in refining disaster response mechanisms. It introduces an innovative humanitarian logistics platform, advocating for a design philosophy that merges scalability and modularity with user-centric navigation for diverse disaster scenarios. Through a user interface journey that evolves from conceptual designs to user-validated interfaces, this study highlights the balance between technological infrastructure and user engagement. The contribution of this research lies in its holistic design approach, blending offering a new perspective on developing humanitarian logistics platforms that are technologically advanced and closely attuned to humanitarian goals.

关键词： humanitarian logistics drones unmanned aerial vehicles design science research user interface design

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Lunar Crater Detection Using Customized YOLOv8 Model 4th

Lunar Crater Detection Using Customized YOLOv8 Model

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Chaini, Chinmayee Jha, Vijay Kumar Birla Inst Technol Dept Comp Sci & Engn Ranchi 835215 Bihar India

ISBN: (纸本)9783031837920;9783031837937

Object detection is crucial in analyzing imagery from satellite sensing, with developments in geospatial monitoring technology and deep convolutional networks driving significant progress. Nevertheless, difficulties remain, especially in recognizing objects across different sizes and precisely identifying minor features. This paper presents a novel network, Customized YOLOv8, to tackle these issues and enhance object detection capabilities. Many researchers use digital elevation models (DEMs) for crater classification, but their low resolution often limits the detection of smaller craters. In response, this paper leverages lunar CCD images from NASA's 100-m global mosaic, captured by the Lunar Reconnaissance Orbiter Camera (LROC) WAC. The Customized Yolov8 model is built with DenseNet121 as the backbone for efficient feature reuse, improving gradient flow and minimizing vanishing gradients. This paper used Bi-FPN as the neck for fusing multi-scale features, which is essential for recognizing objects of various dimensions. Additionally, Soft-NMS enhances accuracy by reducing missed detections in closely overlapping objects. This architecture achieves 76.0% precision, 68.6% recall, and a minimum average precision of 78.7% on test data without any prior preprocessing applied to the raw image.

关键词： YOLOv8 Dense-Net121 Bi-FPN CCD image Soft-NMS

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Leveraging Large Language Models for Supply Chain Management Optimization: A Case Study 5th

Leveraging Large Language Models for Supply Chain Management...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Rahman, Sumaya Abdul Chawla, Sanjay Yaqot, Mohammed Menezes, Brenno Hamad Bin Khalifa Univ Div Engn Management & Decis Sci Coll Sci & Engn Doha Qatar Hamad Bin Khalifa Univ Qatar Comp Res Inst Doha Qatar

ISBN: (纸本)9783031807749;9783031807756

This research examines the transformative potential of Large Language Models (LLMs) and Generative AI (GAI) in supply chain management (SCM) and operations research (OR). By leveraging advanced Natural Language Capabilities (NLP) capabilities in models such as OpenAI's GPT-4o, we explore how these technologies can support modeling tasks and streamline complex supply chain problems for optimization steps. Our study specifically focuses on translating mathematical formulations into executable code and interpreting solver outputs. Our work shows that LLMs complement and augment traditional solvers by speeding up the end-to-end process of problem formulation, solution, and interpretation, while also enhancing overall efficiency and reducing the need for manual adjustments. This work systematically identifies the strengths and limitations of LLMs in SCM applications, highlighting their ability to enhance efficiency, accuracy, and decision-making. We conducted a proof-of-concept demonstration using GPT-4o to prepare the model for solving and interpretation of three increasingly complex transportation problems. The results demonstrate that LLMs not only excel at providing error-free code but also exhibit enhanced capabilities in reasoning and interpreting complex outputs from optimization solvers. The proposed framework provides a practical guide for practitioners and researchers in SCM and OR, demonstrating how LLMs can automate and improve optimization tasks.

关键词： Optimization Transportation Agentic AI Human-computer Interaction Supply chain Management

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Blockchain Application in Smart Tourism

Blockchain Application in Smart Tourism

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Guarda, Teresa Augusto, Maria Fernanda Univ Estatal Peninsula Santa Elena La Libertad Ecuador Univ Minho Algoritmi Ctr Guimaraes Portugal BiTrum Res Grp Leon Spain

ISBN: (纸本)9783031834349;9783031834356

The rapid advancement of technology has significantly enhanced various sectors, offering considerable improvements in their operations. One such transformative technology is Blockchain, which has gained increasing importance in the business world and has expanded its reach into numerous areas. Blockchain technology has driven the progress of digital transformation across all business facets, providing robust digital solutions that have bolstered the substantial growth of organizations, steering them towards achieving desired outcomes with heightened efficiency and productivity. This technology ensures secure and transparent transactions and data management, which is particularly valuable in industries with complex and decentralized operations. The application of Blockchain technology brings benefits to the companies that adopt it. In this scenario, despite it being a sector that is still leading in adopting this technology, the aim of this study is to show how Blockchain is being utilized in the smart tourism sector.

关键词： Smart Tourism Blockchain Digital Transformation Security

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XAI-Based Assessment of Software Vulnerability Contributing Factors in Transformer Models 8th

XAI-Based Assessment of Software Vulnerability Contributing ...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Lian, Ding Liu, Yan Concordia Univ Montreal H3G 1M8 PQ Canada

ISBN: (纸本)9783031856273;9783031856280

This paper studies the feature contribution values of software code tokens in the learning task of vulnerability multi-classification in terms of Common Weakness Enumeration (CWE) types. The nuance of sibling CWEs under the same parent CWE category has the challenges of learning the correct types. Such an issue of the semantic meanings in relation to feature attention values and feature contribution values requires a systematic assessment. We devise an assessment framework that integrates the eXplainable AI (XAI) techniques and measurements to examine the importance of factors, including token length, separators, token attention values, abstract syntax tree meta constructs and their effects to learning performance. We apply three open source datasets in both Java and C++ languages, three transformer learning models, two XAI algorithms. The results highlight three clues that (1) higher attention values have more feature contribution values as the impact;(2) the attention values alone may not distinguish the subtle difference among close CWE types;and (3) increasing input token length has more impact on tokens with higher contribution values.

关键词： Software vulnerability Machine learning XAI (eXplainable AI) Natural language processing

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Detecting AI-Generated Text Using Fine-Tuned Transformers: A Study on Academic Integrity 4th

Detecting AI-Generated Text Using Fine-Tuned Transformers: A...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Prajapati, Manish Baliarsingh, Santos Kumar Dev, Prabhu Prasad Saleh, Bashir Maina Hota, Jhalak Biswal, Manas Ranjan KIIT Deemed Univ Bhubaneswar India

ISBN: (纸本)9783031837951;9783031837968

The rise of ChatGPT, a highly advanced generative artificial intelligence (AI) tool, has posed significant challenges for educators in maintaining academic integrity across educational environments. This paper explores methods and strategies essential for addressing this emerging issue. Specifically, we introduce an efficient transformer-based language model that leverages fine-tuning with Bidirectional Encoder Representations from Transformers (BERT) algorithms to detect AI-generated text. During preprocessing, we extracted features and standardized the text through several steps, including lowercase conversion, tokenization, stop-word removal, token embeddings, segment embeddings, position embeddings, and space elimination to ensure high data quality. Our investigation into distinguishing essays generated by large language models (LLMs) from those written by students yielded significant findings. The BERT-uncased model delivered promising results and achieves state-of-the-art results on a diverse set of domains, where it matches or exceeds the performance of strong Transformer models, with a testing accuracy of 98% and an overall accuracy of 98.89%. Moreover, incorporating external data from various AI models, such as GPT, further enhanced performance, underscoring the benefits of diverse training data. Our study highlights the importance of careful model selection, parameter tuning, and leveraging external data in detecting AI-generated text. These findings contribute to the ongoing discourse on academic integrity, providing educators with effective tools to verify essay authenticity and uphold educational standards.

关键词： AI-Generated Text Deep Learning LLMs NLP Tasks Transformer Models Text Detection

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Early Warning of Harmful Algal Blooms (HAB): A Low-Cost Integrated IoT Device with Spectrofluorometry and Automated Plankton Imaging 1st

Early Warning of Harmful Algal Blooms (HAB): A Low-Cost Inte...

引用

1st Global Internet of Things and Edge Computing Summit

作者： Orazi, Gilles Marot, Marianne Khelifi, Iheb Robert, Lea Le Gall, Franck EGM AREP Ctr Traverse Brucs F-06560 Valbonne France

ISBN: (纸本)9783031785719;9783031785726

This paper presents the development of a prototype automated device designed to monitor sea water quality and provide early warnings of potential harmful algal bloom (HAB) outbreaks. HABs pose a significant threat to aquaculture operations and marine ecosystems due to their ability to cause mass mortalities of fish and shellfish. The device integrates a low-cost custom spectrofluorometer capable of measuring absorption and fluorescence spectra of liquid samples, and an automated plankton imager adapted from an open-source design. Key aimed sensing parameters include nutrients, chlorophyll, water temperature, and phytoplankton presence. The affordability of the device is targeted by using low-cost components and integrating the spectrofluorometer and plankton imager into a single unit driven by an embedded computer. Machine learning algorithms are employed for real-time anomaly detection from the multivariate sensor data streams to provide early alerts of potential HAB events. Initial results demonstrate the device's ability to detect low concentrations of fluorescent dyes and phytoplankton, and the effectiveness of an adaptive anomaly detection approach on real aquarium data.

关键词： Harmful algal bloom Water quality monitoring Spectrofluorometer Automated plankton imaging Adaptive anomaly detection

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Empowering Healthcare with Deep Learning: An Application for Early Detection of Skin Cancer 4th

Empowering Healthcare with Deep Learning: An Application for...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Oluwatobi, Komolafe Joseph Asghar, Ikram Diyan, Muhammad Nawaz, Rab Saleem, Sidra Ullah, Rahmat Ahmad, Jawad Univ Teesside Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Univ Agr Faisalabad Faisalabad Pakistan Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9783031821523;9783031821530

Skin cancer is a hazardous ailment and a leading contributor to mortality. Early diagnosis of skin cancer can significantly minimize or prevent these fatalities. The diagnostic process can be both lengthy and costly. This study aims to establish a basis for creating an exact and effective convolutional neural network (CNN) model for detecting skin cancer. This model aims to enhance the early and accurate diagnosis of skin cancers, potentially leading to a decrease in the number of associated fatalities. The model utilizes a convolutional neural network with Keras Tensorflow as the backend to categorize seven distinct categories of skin cancer. Subsequently, the results are analyzed to determine the practical applications of the model. The MobileNet optimizer is used for classification. A web application has been developed that offers dermatologists the three most likely diagnostics for a specific blister. It will aid in swiftly recognizing patients with high priorities and accelerating their workflow. The application generates a result within a time frame of 5-10 seconds.

关键词： Skin Cancer CNN machine learning image classification medical data security

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Conservation of Wildlife in Hunting Tourism Using Artificial Intelligence and Image Processing in Smart Firearms 2nd

Conservation of Wildlife in Hunting Tourism Using Artificial...

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2nd International Conference on information Technologies and Their Applications

作者： Asil, Ufuk Nasibov, Efendi Dokuz Eylul Univ Izmir Turkiye

ISBN: (纸本)9783031734199;9783031734205

This paper presents an innovative technological approach to ensure the sustainability of state-controlled nature park hunting tourism and to protect wildlife. The developed smart fire control systems utilize artificial intelligence (AI) and advanced image processing techniques to prevent firing by activating the safety pin in various situations. Through an integrated camera module and a minicomputer from the Jetson family, animals can be detected in real-time, and various data analysis techniques, along with object recognition algorithms, allow for ethical intervention in many situations that are invisible to the human eye and could be exploited by hunters. Particularly in various national parks, the frequent reports of accidental or uninformed hunting of rare and endangered species, especially during their breeding seasons, underline the importance of this technology. Furthermore, hunters' preference for the most dominant animal in a herd as a trophy poses a significant threat in evolutionary terms. This technology contributes significantly to the conservation of wildlife while promoting ethical and sustainable practices in hunting tourism. The study addresses the development, testing, and implementation of this technology and adds a technological dimension to wildlife conservation strategies.

关键词： Artificial Intelligence Image Processing Smart Firearms Wildlife Conservation Hunting Tourism

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