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Deep Learning-Based Leukemia Diagnosis from Bone Marrow Images 12th

Deep Learning-Based Leukemia Diagnosis from Bone Marrow Imag...

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12th Ecuadorian Conference on information and Communication Technologies

作者： Zhinin-Vera, Luis Moya, Alejandro Pretel, Elena Astudillo, Jaime Jimenez-Ruescas, Javier Univ Castilla La Mancha LoUISE Res Grp Albacete 02071 Spain Yachay Tech Univ Sch Math & Computat Sci Urcuqui 100650 Ecuador Model Intelligent Networks Dev MIND Res Group Urcuqui Ecuador

ISBN: (纸本)9783031754302;9783031754319

Identifying and classifying features in Bone Marrow Aspirate Smear (BMAS) images is essential for diagnosing various leukemias, such as Acute Myeloid Leukemia. The complexity of microscopy image analysis necessitates a computational tool to automate this process, reducing the workload on hematologists. Our study introduces a Deep Learning-based method designed to efficiently detect and classify cell characteristics in BMAS images. Current systems struggle with cell and nucleus segmentation due to variations in cell size, appearance, texture, and overlapping cells, often influenced by different microscopy conditions. We addressed these challenges by experimenting with the Munich AML Morphology Dataset and a custom dataset from Hospital 12 de Octubre in Madrid. The proposed system achieved over 90% accuracy and 92% precision in identifying and classifying leukemia cells, marking a substantial advancement in supporting clinical specialists in their decision-making processes when traditional analysis methods are insufficient.

关键词： deep learning image classification leukemia cells bone marrow aspirate smear

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Entropy Based Embedded Unsupervised Feature Selection 4th

Entropy Based Embedded Unsupervised Feature Selection

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

作者： Rastogi, Reshma Aich, Era South Asian Univ Machine Learning & Stat Inference MLSI Lab Delhi India

ISBN: (纸本)9783031837920;9783031837937

Machine-learning techniques often encounter significant challenges when dealing with high-dimensional data due to the substantial memory requirements and extended processing times involved. In this paper, we introduce a novel unsupervised feature selection method called Entropy based Embedded Unsupervised Feature Selection (EEUFS). This method aims to select a suitable and concise subset of features while minimizing redundancy by assessing the entropy of each feature. Computing entropy for each feature, evaluating correlation between features and eliminating redundant ones, our approach reduces the feature set size while retaining essential information from the original dataset. Experimental evaluations conducted across six datasets using six different entropy measures demonstrate that our proposed Entropy based Embedded Unsupervised Feature Selection(EEUFS) method is effective and often outperforms state-of-the-art unsupervised feature selection methods.

关键词： Unsupervised feature selection Entropy information theory Feature dependency Feature redundancy

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Identifying the Root Cause of a Cyber Attack Through Log Data Analysis: An Overview of the Challenges Faced by Novice Learners 17th

Identifying the Root Cause of a Cyber Attack Through Log Dat...

引用

17th India Compute Conference-COMPUTE-Annual

作者： Nagvekar, Priya V. Das, Syaamantak Iyer, Sridhar Indian Inst Technol Ctr Educ Technol Mumbai Maharashtra India

ISBN: (纸本)9783031843907;9783031843914

Cyber security has become a critical concern in todays digital age, and log data analysis plays a pivotal role in investigating the root causes of cyber attacks. However, the teaching and learning of the log data analysis process is intricate, especially in root cause analysis (RCA), necessitating diverse domains of expertise and background knowledge. This study addresses a significant research gap in the existing literature by a comparative analysis of cyber attack RCA performed by novices and experts through log data analysis. The study design process initially involved novices with limited cyber security experience (senior undergraduate computer science students) performing a log data analysis of a cyber attack, followed by industry professionals (subject matter experts) performing the same log data analysis. In the research, the participants identified the root causes of a cyber attack through log data analysis, culminating in generating an attack tree as an outcome. The objective was to assess novices ability to identify and deduce the root causes of cyber attacks from log data and generate a comprehensive attack tree through causal reasoning. The follow-up observation and reflections were supplemented by artefact analysis and simulated recall interviews. The studys findings shed light on the disparities in log data analysis skills between novices and subject matter experts. It reveals novices key challenges compared to experts and the nature of novices difficulties. The results of this research also offer valuable insights into the industry practices of log data analysis by experts and their advice contributing to developing more effective teaching-learning methodologies for cyber security education.

关键词： cyber security log data analysis novice vs expert root cause analysis

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Application of Industry 4.0 for Enhancing Climate Compliant Irrigation System in Agriculture 2nd

Application of Industry 4.0 for Enhancing Climate Compliant ...

引用

2nd International Conference on Agriculture-Centric Computation

作者： Nayak, Ramesh Chandra Roul, Manmatha K. Mishra, Bishnu P. Khedikar, Amey R. Kulkarni, Mahesh Vasantrao Vijaybhai, Suryavanshi Bhagyeshkumar IHub Anubhuti IIITD Fdn New Delhi India Synergy Inst Technol Bhubaneswar Odisha India GITA Autonomous Coll Dept Mech Engn Bhubaneswar Odisha India Tulsiramji Gaikwad Patil Coll Engn & Technol Dept Civil Engn Nagpur Maharashtra India Dr Vishwanath Karad World Peace Univ Dept Mech Engn Pune Maharashtra India Larsen & Toubro L&T Heavy Engn Surat Gujarat India

ISBN: (纸本)9783031744396;9783031744402

Irrigation is the most important part of agriculture. Technology-based irrigation systems will help farmers with their active involvement and enhancement of productivity. In this work, two separate technology-based irrigation methods have been presented. The first one describes an IoT-enabled solar-powered irrigation system for small farms and the second one describes an IoT-enabled epicyclic gear train-operated irrigation method that can be used during less sunshine periods. The rapid evolution of the Internet of Things (IoT) has ushered in a new era of digitized agricultural practices, offering unprecedented opportunities to enhance crop productivity and resource efficiency. In this study, we explore the integration of IoT with climate-responsive technologies to revolutionize agricultural irrigation practices using green energy. Focusing on the versatile ESP8266 microcontroller, renowned for its user-friendly interface and Wi-Fi connectivity, we present a comprehensive framework for IoT-enabled irrigation systems for small farms. The primary objective of this research encompasses the design and implementation of a sophisticated circuit utilizing the ESP8266 microcontroller to monitor critical environmental parameters including Relative Humidity (RH), Dry Bulb Temperature of air, and Soil Moisture content. Our system offers programmable adjustments tailored to varying soil moisture requirements across diverse agricultural contexts, emphasizing the maintenance of optimal soil moisture levels at 80% saturation. Furthermore, we propose the integration of prevailing solar water pumping and storage models with our IoT-based irrigation framework to devise a climate-compliant irrigation system suitable for fruits and vegetables, leveraging drip and sprinkler irrigation techniques. During less sunshine periods, the epicyclic gear train-operated irrigation system will work. Ultimately, this research contributes to the growing body of knowledge in IoT-enabled agriculture, of

关键词： Internet of Things (IoT) ESP8266 agricultural irrigation climate-responsive technologies precision agriculture sustainable farming resource efficiency

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Securing RC Based P2P Networks: A Blockchain-Based Access Control Framework Utilizing Ethereum Smart Contracts for IoT and Web 3.0 37th

Securing RC Based P2P Networks: A Blockchain-Based Access Co...

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37th International Conference on computer Applications in Industry and Engineering

作者： Ghosh, Saurav Mitra, Reshmi Roy, Indranil Gupta, Bidyut Southeast Missouri State Univ Cape Girardeau MO 63701 USA Southern Illinois Univ Carbondale IL USA

ISBN: (纸本)9783031762727;9783031762734

Ensuring security for highly dynamic peer-to-peer (P2P) networks has always been a challenge, especially for services like online transactions and smart devices. These networks experience high churn rates, making it difficult to maintain appropriate access control. Traditional systems, particularly Role-Based Access Control (RBAC), often fail to meet the needs of a P2P environment. This paper presents a blockchain-based access control framework that uses Ethereum smart contracts to address these challenges. Our framework aims to close the gaps in existing access control systems by providing flexible, transparent, and decentralized security solutions. The proposed framework includes access control contracts (ACC) that manage access based on static and dynamic policies, a Judge Contract (JC) to handle misbehavior, and a Register Contract (RC) to record and manage the interactions between ACCs and JC. The security model combines impact and severity-based threat assessments using the CIA (Confidentiality, Integrity, Availability) and STRIDE principles, ensuring responses are tailored to different threat levels. This system not only stabilizes the fundamental issues of peer membership but also offers a scalable solution, particularly valuable in areas such as the Internet of Things (IoT) and Web 3.0 technologies.

关键词： Ethereum

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Hybrid Wiener Filter and Sharpening Filter for Image De-blurring 4th

Hybrid Wiener Filter and Sharpening Filter for Image De-blur...

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

作者： Habeeb, Nada Jasim Middle Tech Univ Baghdad Iraq

ISBN: (纸本)9783031821554;9783031821561

The Wiener filter is used efficiently in removing noise from images, as it is used to remove Gaussian noise. However, its use causes the loss of edge details and blurring of images. The proposed image de-blurring using a combination of the Wiener filter and a sharpening filter is presented to solve the problem of blurry images. It retains or preserves edge content while reducing noise in an image. This enhancement process is performed by multiplying the frequency coefficients of the image by the weights of the sharpening filter. The inverse transform is then applied to obtain the de-blurred image. Different focus operators were used for testing the proposed algorithm. The experimental results showed that the proposed deblurring method produced good results compared with the traditional de-blurring methods.

关键词： De-blurring process Wiener filter Total variation (TV) regularization Richardson-Lucy Deconvolution sharpening filter

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Towards a Taxonomy of Infrastructure as Code Misconfigurations: An Ansible Study 18th

Towards a Taxonomy of Infrastructure as Code Misconfiguratio...

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18th Symposium and Summer School on Service Oriented Computing

作者： Nasiri, Roya Kumara, Indika Tamburri, Damian Andrew van den Heuvel, Willem-Jan Tilburg Univ Jheronimus Acad Data Sci Shertogenbosch Netherlands Eindhoven Univ Technol Jheronimus Acad Data Sci Eindhoven Netherlands

ISBN: (纸本)9783031725777;9783031725784

Infrastructure as Code (IaC) enables the management and provisioning of infrastructure using code instead of manual processes. While the IaC approach can simplify and automate infrastructure management, the configuration errors (i.e., misconfigurations) in the IaC scripts can significantly hinder achieving the benefits of the automation, incur undue costs, and leave the system faulty and insecure. In this paper, we present a taxonomy of IaC misconfigurations to assist practitioners and researchers in building tools for detecting misconfigurations. The user manuals of the IaC frameworks describe configuration parameters and their usage constraints, and provide recommendations on configuring certain parameters correctly. Hence, we systemically collected and analyzed 100 user manuals of the Ansible IaC language to compile a catalog of 25 configuration errors. We also developed a proofo-f-concept tool for using the information in the user manuals to generate misconfiguration detection rules.

关键词： Infrastructure as Code IaC Misconfiguration Misuse Ansible

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Third Molar Angle Detection in Dental X-Ray Panoramic Radiographs Using YOLO and GoogleNet Convolutional Neural Networks 4th

Third Molar Angle Detection in Dental X-Ray Panoramic Radiog...

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

作者： Vilcapoma, Piero Parra Melendez, Diana Nicole Vasconez, Ingrid Gatica, Gustavo Pablo Vasconez, Juan Univ Andres Bello Fac Engn Santiago 7500735 Chile Univ Amer Fac Dent Quito 170513 Ecuador Univ Tecn Federico Santa Maria Ctr Biotecnol Dr Daniel Alkalay Lowitt Valparaiso 2390136 Chile Univ Andres Bello Energy Transformat Ctr Fac Engn Santiago 7500971 Chile

ISBN: (纸本)9783031832093;9783031832109

Artificial intelligence (AI) has proven very useful in dental applications in recent years. The automatic extraction of information from X-ray panoramic dental radiographs represents an opportunity to streamline dental diagnosis and improve the precision of detecting oral pathologies. In particular, deep learning (DL) algorithms based on convolutional neural networks (CNNs) have demonstrated high performance in dental image classification, detection, and segmentation applications. However, several challenges remain due to the high variability of categories in dental applications. To characterize four different third molars angles employing X-ray panoramic radiographs, we focused on using the CNN-based object detection technique You Only Look Once (YOLO), which works with the GoogleNet feature extractor CNN. For this, we applied Winter's classification criterion, which describes the position of the third molars with respect to the longitudinal axis of the second molar. Third molar angles were divided into different categories: distoangular, vertical, mesioangular, and horizontal. A total of 644 panoramic X-ray images were used to train the YOLO algorithm. The proposed model reached an average accuracy performance of up to 97% on the test dataset. These findings exhibit the potential and reassuring results of employing CNNs for dental applications, specifically for object detection in panoramic X-rays.

关键词： Artificial Intelligence Convolutional Neural Networks Dentistry Third Molars Angle Detection

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Self-attention Siamese Network for Unsupervised Few-Shot Learning Tasks 4th

Self-attention Siamese Network for Unsupervised Few-Shot Lea...

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

作者： Bhattarai, Binit Luitel, Pratik Kartheek, Mukku Nisanth Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

ISBN: (纸本)9783031821523;9783031821530

In the real world, it is hard to extract large data with the characteristics that are labeled. This becomes a problem when trying to implement artificial intelligence to real-life problems. In this work, we propose a self-attention siamese network guided by the principles of unsupervised learning and few-shot learning which can learn robust representation without requiring the large number of labeled data. Our approach introduces a self-attention module to capture rich contextual information and a projection head that embeds features into a discriminative space. We have performed a comprehensive experiment evaluation to show the effectiveness of our approach compared to other supervised training methods and other few-shot learning techniques. Our results highlight the potential of adding self-attention to the siamese network for enabling unsupervised few-shot learning tasks, paving the way for more efficient machine learning systems capable of learning from limited unlabeled data.

关键词： Self Attention Unsupervised Learning Few-shot Learning Siamese Network SASN

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Privacy Preserving Enablers for Data Space Ecosystems 1st

Privacy Preserving Enablers for Data Space Ecosystems

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1st Global Internet of Things and Edge Computing Summit

作者： Borgonos Garcia, Natalia Hernandez Padilla, Maria Skarmeta Gomez, Antonio Fernando Univ Murcia Murcia Spain

ISBN: (纸本)9783031785719;9783031785726

Data Spaces are ecosystems designed to allow multiple organizations or companies to share data in a secure manner. Despite the potential of these technologies, they encounter a number of challenges and privacy issues that limit their use. Privacy Preserving Enablers are mechanisms developed to tackle these difficulties, ensuring data integrity and access control. This paper aims to analyze the role of some Privacy Preserving Enablers and its integration with Connectors in the context of Data Spaces. The research will focus on key enablers, including a Self-Sovereign Identity with Zero-Knowledge Proof, which is a privacy preserving approach that allows users to verify their identity and attributes without the need to disclose underlying data, ensuring their privacy. Additionally, the usage of Sticky Policies instantiated through Attribute-Based Encryption attaches control policies into the encrypted data in order to have an attribute-based access control, enhancing its security. The application of Policies Enforcement assure the consistent application of policies and the maintenance of the security within the Data Space.

关键词： Data Spaces Privacy Preserving Enablers Self-Sovereign Identity Sticky Policies Policy Enforcement

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