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Evaluating Histopathological Cancer Detection: A Comparative Analysis of CNN Architectures for Tumor Detection in Lymph Node Pathology 12th

Evaluating Histopathological Cancer Detection: A Comparative...

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12th Ecuadorian Conference on information and Communication Technologies

作者： Marcillo-Vera, Ana Caceres-Benitez, Karen Almeida-Galarraga, Diego Tirado-Espin, Andres Univ Yachay Tech Sch Math & Computat Sci Urcuqui 100119 Ecuador Univ Yachay Tech Sch Biol Sci & Engn Urcuqui 100119 Ecuador

ISBN: (纸本)9783031754302;9783031754319

This study evaluates four well-known convolutional neural networks: VGG19, EfficientNetB0, ResNet50, and InceptionV3, for tumor detection in lymph node pathology images. Using a significant subset of the PCam dataset, models were trained on binary classification tasks focused on identifying tumor tissue, with data augmentation applied to enhance generalization. The methodology involved a rigorous process of data preparation, model selection, training, and evaluation under limited hardware resources, using a standard laptop. The dataset was split into training and validation sets with an 80/20 ratio, and models were trained using the Adam optimizer with a learning rate of 0.001 over multiple epochs. VGG19 achieved the highest validation accuracy at 77.38% and AUC of 85.35% but required substantial computational time and exhibited overfitting. EfficientNetB0, though faster to train (20m 40 s), showed lower validation accuracy (58.91%) and AUC (60.14%). ResNet50 performed well during training but faced generalization challenges. InceptionV3 demonstrated a balanced performance with a validation accuracy of 70.74% and AUC of 76.41%, making it a promising option across varied datasets. These findings highlight the strengths and limitations of different CNN architectures in enhancing cancer diagnosis in lymph node pathology, providing insights for future research and clinical applications.

关键词： Metastatic cancer EfficientNetB0 VGG19 ResNet50 InceptionV3 Image segmentation Tumor detection

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Beyond Spatial: A Wavelet Fusion-Based Deep Learning CAD for Skin Cancer Diagnosis 4th

Beyond Spatial: A Wavelet Fusion-Based Deep Learning CAD for...

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

作者： Attallah, Omneya Arab Acad Sci Technol & Maritime Transport Dept Elect & Commun Engn Coll Engn & Technol Alexandria 21937 Egypt Arab Acad Sci Technol & Maritime Transport Wearables Biosensing & Biosignal Proc Lab Alexandria 21937 Egypt

ISBN: (纸本)9783031821554;9783031821561

Skin cancer (SC) comprises approximately 33% of all reported cancer cases globally. Thus, rapid and accurate diagnosis is crucial for effective treatment and better patient outcomes. computer-aided diagnostic (CAD) systems are necessary for assisting and improving traditional diagnostic approaches, however, most of them count on only spatial information and/or individual deep-learning models to realize a diagnosis. Furthermore, current CADs retrieve deep features with substantial sizes, resulting in increased training complexity and speed. This article presents a CAD framework to classify multiple subcategories of SC based on multiple Convolutional Neural Networks (CNNs) with distinct configurations. The study explores various combinations of CNNs to select the best mixture of CNNs that impact performance. Additionally, it incorporates spatial and time-frequency demonstrations to successfully diagnose SC through the utilisation of wavelet analysis. In addition, wavelet analysis is employed to reduce the large dimensionality of deep features, which is further minimised using principal component analysis. The results indicate that the suggested CAD accomplished a rate of accuracy of 0.956 using only 159 features. It surpasses the CADs currently available. Therefore, it can serve as a powerful tool for identifying SC and could be employed to enhance the precision of medical diagnostics.

关键词： Skin Cancer Classification Feature Fusion Convolutional Neural Networks Discrete Wavelet Transform

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Digital Literacy: Challenges and Opportunities 53rd

Digital Literacy: Challenges and Opportunities

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53rd Southern African computer Lecturers Association Meeting

作者： Zepe, Xolile Petratos, Sue Nelson Mandela Univ Port Elizabeth South Africa

ISBN: (纸本)9783031824777;9783031824784

When the #FeesMustFall student movement, successfully swayed the South African Government to introduce free Higher Education tuition in 2017, the biggest obstacle to further education for the majority of matriculants, was removed. Due to the fast-paced world of technology, Higher Education Institutions have significantly changed how learning and teaching are conducted. This was further advanced with the onset of the COVID-19 pandemic when classes were forced to go online. Students and lecturers battled to find a balance between the computer skills needed, and how to use them in a digital classroom. The Institute of Race Relations reported in 2018 that the South African Education system is in crisis and that the learners leaving school are poorly prepared for the world after school. computer skills are among the essential skills required to be successful in any environment, yet many students find themselves encountering a computer for the first time at university. The objective of this study is to reflect on the experience of Nelson Mandela University lecturers, in computer Skills classes over the last seven years. An overview of how the offering of the module has changed will be presented. The lecturers' perceptions of the successes and failures over the same period will be captured;including the observed challenges that students experienced. A possible solution, to the challenges identified, such as lack of digital skills for classroom readiness, time management, and student numbers, which would be suitable at all HEIs, to provide a better learning and teaching experience in computer Skills classes will be presented.

关键词： computer Skills Digital Skills Classroom Readiness End User Skills Online Learning Blended Learning

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Approximating Temporal Katz Centrality with Monte Carlo Methods 7th

Approximating Temporal Katz Centrality with Monte Carlo Meth...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Yan, Haonan Yang, Zhengyi Zhang, Tianming Wen, Dong Luo, Qi Ukey, Nimish Univ New South Wales Sydney NSW Australia Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Graphs have long served as fundamental data models across various disciplines such as data mining, social media analysis, and knowledge management. In real-world applications, interactions between nodes often evolve over time, necessitating the use of temporal graphs. Centrality measures are pivotal in graph analysis for identifying key nodes. Specifically, Temporal Katz Centrality (TKC) has garnered significant attention in recent years for its ability tomeasure node influence in temporal graphs. TKC evaluates the influence of nodes by considering all walks originating from a node, where contributions are weighted based on a user-specified time decay factor. This approach captures temporal dynamics by incorporating both the timing of interactions and the intervals between them, offering a comprehensive assessment of node importance over time. However, computing TKC in large temporal graphs is computationally intensive due to the requirement to traverse all edges and update centrality values along temporal paths. To address this challenge, this paper proposes an efficient Monte Carlo approximation method for TKC. This approach employs random sampling via Simple Random Walk and Alpha Walk to estimate TKC values. The paper rigorously proves the asymptotic consistency of the method using the Law of Large Numbers and Central Limit Theorem, ensuring that estimated TKC values converge to true TKC values as the sample size increases. Experiments conducted on six real-world temporal graph datasets demonstrate the effectiveness of the proposed approximation methods. Under the same running time, the Alpha walk outperforms other methods on large temporal graphs, exhibiting the lowest mean relative error and highest precision.

关键词： Temproal Graphs Katz Centrality Monte Carlo Sampling

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Skeleton-Based Point Cloud Sampling and Its Facilitation to Classification 5th

Skeleton-Based Point Cloud Sampling and Its Facilitation to ...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Guo, Yugang Tang, Xue-song Hao, Kuangrong Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Advances in 3D computer vision have increased the amount of point cloud data, highlighting the growing importance of point cloud sampling. Commonly used sampling methods like random sampling and farthest point sampling often fail to capture the geometric topology information of an object. We propose a skeleton-based point cloud sampling method to preserve the geometric topology information of the input point cloud. Skeletons have properties such as the extraction of global topology and the preservation of morphological features. Our method fully leverages these features to enhance the preservation of geometric topological information during the sampling stage. We utilize the variability of the local neighborhood normals as a prior information for sampling. Analyzing changes in skeleton normals facilitates the capture of salient points, enhancing the accuracy of point cloud sampling. To evaluate the effectiveness of our method, we conducted extensive comparative experiments using various sampling methods and scales, and found that it outperforms existing techniques.

关键词： Skeleton Extraction Point Cloud Sampling Shape Classification

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The Role of AI in Modern Language Translation and Its Societal Applications: A Systematic Literature Review 5th

The Role of AI in Modern Language Translation and Its Societ...

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5th Southern African Conference for Artificial Intelligence Research

作者： Ssemugabi, Samuel Univ South Africa Pretoria South Africa

ISBN: (纸本)9783031782541;9783031782558

Artificial intelligence (AI) has greatly transformed language translation, especially over the past five years. The main aim of this research was to explore the role AI has played in enhancing language translation technologies and its societal implications to provide an understanding of its current state, usage and future prospects. A systematic review was performed using the PRISMA protocol and the Scopus database, resulting in the identification of 351 publications. After applying strict exclusion and inclusion criteria, 52 articles were closely examined, and 16 were included in the final review. The findings demonstrate that AI, mainly through the adoption of neural machine translation (NMT) and transformer-based models, has greatly improved the accuracy and efficiency of translations. These advancements have led to innovations in fields such as education, healthcare and business that facilitate communication in different languages. However, the review also identified important societal implications, including ethical concerns, possible job displacement and the risk of cultural homogenisation. The study identified the need for future AI language translation technologies to focus on culturally sensitive models, universal language frameworks and ethical AI systems that prioritise transparency and fairness. This research provides valuable information for researchers, industry professionals and policymakers on how to harness AI to maximise social benefits while addressing the challenges associated with language translation.

关键词： Artificial intelligence Language translation Systematic literature review Society Applications Language Translation AI SLR

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Starting Logistics Centers: An In-depth Analysis of the Greek Legal and Regulatory Framework 5th

Starting Logistics Centers: An In-depth Analysis of the Gree...

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5th 5th Olympus - International Conference on Supply Chain

作者： Sofia, Mavridou Afroditi, Fragkaki Dimitris, Folinas Int Hellen Univ Dept Supply Chain Management Katerini 60100 Greece

ISBN: (纸本)9783031693434;9783031693441

A logistics center, or hub, comprises a range of organized logistics services and infrastructure related to various modes of transportation. It is established and operated in an area where activities facilitating intermodal transport and storage services are permitted. A key component of such a center is its connection to or inclusion of a railway station, port, or airport. Alternatively, it can be described as a geographical zone where companies engaged in the transport, storage, management, and distribution of goods are situated, providing logistics services that involve interconnecting and transitioning between different modes of transport, assembling, distributing, and consolidating loads, as well as packaging, unpacking, and transferring goods. Additionally, these centers offer value-added services aimed at streamlining load flow, fostering the adoption of information and communication technologies, and implementing telematics systems. The establishment and operation of a logistics center help reduce the cost of transporting goods by shifting the transport flow from road to rail, thus ensuring more environmental, economic, and safety benefits. Our research seeks to examine the organizational and legal dimensions of logistics centers with the objective of fostering sustainable economic development through public and private partnerships. Employing contemporary legal science and advanced research methodologies, we aim to refine the Greek legislative framework governing the provision of transport and logistics services. Our investigation encompassed a thorough review and analysis of current Greek legislation, scrutinizing its implementation by economic entities (both public and private). Through a comprehensive examination of Greek legislation and a doctrinal exploration of logistics services, we undertook a theoretical and practical investigation into existing legal regulations within this domain. In light of our findings, we presented some concluding remarks abo

关键词： Logistics centers Legal and regulatory framework Greece

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Web System for Recognizing Actions of Physical Violence in Urban Spaces Using CNN with Transfer Learning 11th

Web System for Recognizing Actions of Physical Violence in U...

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11th Latin American Conference on High Performance Computing

作者： Diaz, Jose Edgar Garcia Romero, Wilder Suarez Rodriguez, Ciro de la Cruz, Jorge Puga Yalan, Victor Manuel Cabrejos Temoche, Isis Ayme Moran Carranza, Jhon Charlie Martinez Univ Nacl Federico Villarreal UNFV Escuela Univ Posgrado Lima 15001 Peru Univ Nacl Amazonia Peruana UNAP Facultad Ingn Sistemas & Informat Iquitos 16001 Peru Univ Nacl Mayor San Marcos UNMSM Dept Software Engn Lima 15081 Peru Inst Invest Amazonia Peruana IIAP Iquitos 16001 Peru

ISBN: (纸本)9783031800832;9783031800849

Violence is a problem of great impact that affects the countries of the world, violence is a soft system that to date cannot be controlled, because it can manifest itself in different ways, represented by high levels of crime and delinquency. But now, there is Artificial Intelligence (AI), which uses innovative resources and will help close gaps in the Sustainable Development Goals (SDG), such as in health, traffic management, climate change and also violence. One way to apply AI is through image processing with Convolutional Neural Networks (CNN). This research evaluates the effectiveness of classifying actions of interpersonal violence such as: strangulation, grappling, kicking or punching, using CNN with Transfer Learning. First, a personalized dataset was developed using images with simulated violent actions, obtaining a total of 2000 images, which were distributed into 60% for training, 30% for validation and 10% for testing. Second, the pre-trained CNN models were trained: VGG16, MobileNetV2 and YOLOv8, subjected to 150 epochs, where the performance results were compared between them and it was determined that the best performance was for YOLOv8, obtaining an accuracy rate of 94.25% and an accuracy of 89%. In the end, a prototype web system was developed using Python with OpenCV and YOLOv8 to detect actions of interpersonal violence in real time and alert the authorities involved in a timely manner. This research will serve as a reference for future real-time video surveillance applications.

关键词： Physical violence Convolutional Neural Networks Web System Deep Learning

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Implementing and Evaluating Trustworthy Conversational Agents for Children 1

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Escobar-Planas, Marina Ruiz-Sanchez, Roberto Frau-Amar, Pedro Charisi, Vicky Martinez-Hinarejos, Carlos-D. Gomez, Emilia Merino, Luis Univ Politecn Valencia Cami VeraS-N Valencia 46022 Spain Joint Res Ctr Calle Inca Garcilaso 3 Seville 41092 Spain Univ Pablo Olavide Ctra Utrera km 1 Seville 41013 Spain Lambda Dev SL Calle granon 12-4 1 Madrid 28050 Spain

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

Conversational Agents (CAs) have become increasingly popular in many settings, including households. However, despite the increasing frequency of children's interactions with these systems, there is still little research on the ethical design of CAs, particularly for this special population. To address this gap, in this study we design, develop and evaluate a Child-Friendly CA for collaborative storytelling, implementing specific guidelines to ensure a trustworthy design for children based on key principles such as human agency, data privacy or transparency as outlined by the High-Level Expert Group on artificial intelligence (HLEG). To evaluate the trustworthiness of the Child-Friendly CA, designers and developers conduct a collaborative assessment by applying the Assessment List for Trustworthy Artificial Intelligence (ALTAI) using the Delphi methodology. Our results demonstrate that our Child-Friendly CA design improves the trustworthiness of the system and highlights the importance of designing CAs that consider the particularities of children's interactions. Our findings contribute to the still scarce literature on trustworthy CAs and provide insights for developers striving to ensure a trustworthy experience for children.

关键词： Conversational agents Children Trustworthy AI Human-computer interaction AI Evaluation Ethical AI

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Maximum Independent Set Using Hummingbird Optimization 4th

Maximum Independent Set Using Hummingbird Optimization

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

作者： Verma, Ritika Mahato, Dharamendra Prasad Natl Inst Technol Hamirpur Dept Comp Sci & Engn Hamirpur 177005 HP India

ISBN: (纸本)9783031837821;9783031837838

The Maximum Independent Set problem in distributed systems is an NP-hard problem. This paper uses a soft computing approach to solve this problem. In this paper, we use Hummingbird Optimization to solve the problem. We also compare the proposed algorithm with deterministic algorithm, non-deterministic algorithms and other soft computing algorithms. We tested the algorithms on DIMACS benchmark datasets.

关键词： Maximum Independent Set Maximal Independent Set Hummingbird Optimization

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