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Checklist for Effective Knowledge Visualization 4th

Checklist for Effective Knowledge Visualization

引用

4th International Conference on Society 5.0

作者： Scholtz, Iddo-Imri Smuts, Hanlie Univ Pretoria Dept Informat Pretoria South Africa

ISBN: (纸本)9783031714115;9783031714122

Rapid increase of data in the current digital age has led to an unprecedented rate of knowledge acquisition while the half-life of knowledge decreases. Communication and transfer of knowledge is a challenging and time-consuming task, especially when using the written word. Visual representation of knowledge is superior to verbal and written communication but has a high level of difficulty and cost regarding the maintenance of the visualizations, the reification of invalid views and the possibility of misinterpretation that could mislead or manipulate users. Therefore, the aim of this study is to provide a checklist of knowledge visualization success factors to guide designers in creating effective visualizations with maximum communicative power. The checklist of knowledge visualization success factors was developed through a document analysis where 25 success factors were identified. The 25 success factors were grouped according to the why, what, for whom, and how of knowledge before a questionnaire was distributed to software engineering professionals to validate and triangulate the findings. The use of the checklist provides designers with a list of knowledge visualization success factors to be considered to improve the effectiveness of the visualization.

关键词： Knowledge visualization knowledge visualization success factors knowledge management knowledge transfer knowledge communication

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Designing a Smart Healthcare IoT System for Precise Fall Detection in Older Adults Using Multi Sensor Data Fusion and Machine Learning 4th

Designing a Smart Healthcare IoT System for Precise Fall Det...

引用

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

作者： Krishna, Awantika Yadav, Brijkant Prakash, Devansh Dwivedi, Rajendra Kumar Madan Mohan Malaviya Univ Technol Dept ITCA Gorakhpur Uttar Pradesh India

ISBN: (纸本)9783031837821;9783031837838

This study presents a novel approach to deal with prototyping a smart healthcare IoT framework for precise fall detection in older adults using multi-sensor data fusion. The proposed framework utilizes advanced filtering and context-aware analysis to enhance the accuracy of fall detection, addressing key challenges in current systems such as high false positives and slow reaction times. A MPU6050 accelerometer and gyrator, incorporated with an ESP32 microcontroller,is used to distinguish falls while limiting false-alarms precisely. The framework utilizes Blynk, an IoT platform, to work with continuous observing and warnings. The methodology includes gathering information from the MPU6050 sensor, handling it with the ESP32, and utilizing AI calculations to separate falls from different exercises of everyday living. The system includes coordinating wearable sensors with lightweight AI calculations to guarantee continuous location and similarity with low-power gadgets. The discoveries exhibit improved reliability, sensibility, and real-world applicability of the system in elderly care settings. This research has critical ramifications for improving the wellbeing and freedom of older adults by giving convenient mediation in case of a fall.

关键词： Internet of Things (IoT) ESP32 MPU6050 ESP32

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Anomaly Detection with Machine Learning Models Using API Calls 2nd

Anomaly Detection with Machine Learning Models Using API Cal...

引用

2nd International Conference on information Technologies and Their Applications

作者： Sahin, Varol Satilmis, Hami Yazar, Bilge Kagan Akleylek, Sedat Ondokuz Mayis Univ Samsun Turkiye Istinye Univ Dept Comp Engn Istanbul Turkiye Univ Tartu Tartu Estonia

ISBN: (纸本)9783031734199;9783031734205

Malware is malicious code developed to damage telecommunications and computer systems. Many malware causes anomaly events, such as occupying the systems' resources, such as CPU and memory, or preventing their use. Malware causing these events can hide their destructive activities. Therefore, monitoring their behavior to detect and block such malicious software is necessary. In other words, the anomalies they cause are detected and intervened by monitoring the behaviors exhibited by malware. Various features such as application programming interface (API) calls or system calls, registry modification, and network activities constitute malware behavior. API calls and various statistical information of these calls, extracted by dynamic analysis, are considered one of the most representative features of behavior-based detection systems. Each API call in the sequences is associated with previous or subsequent API calls. Such relationships may contain patterns of destructive functions of malware. Many intrusion/anomaly detection systems are proposed, including machine and deep learning models, in which various information about API/system calls are used as features. This paper aims to evaluate the effect of various statistical information of API calls on the models in detecting anomaly events and classification performances. The anomaly detection performances of various machine learning (ML) models with known effects in the literature are examined using a dataset containing API calls. As a result of the experiments, it is seen that the models using statistical features of API calls have reached high performance in terms of precision, recall, f1-score, and accuracy metrics.

关键词： Anomaly Detection API Call Machine Learning Deep Learning Comparative Analysis

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The Delicate Balance of Ethics and Control for Smart Cities: A Network-Oriented Analysis Approach 8th

The Delicate Balance of Ethics and Control for Smart Cities:...

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

作者： Daza, Moaz Duivesteijn, Doris Jouma, Maria Reichardt, Florian Barelds, Nick Bouma, Debby Treur, Jan Roelofsma, Peter H. M. P. Hague Univ Appl Sci Ctr Expertise Cybersecur Risk Management & Cybersecur Grp The Hague Netherlands Vrije Univ Amsterdam Dept Comp Sci Social Grp Amsterdam Netherlands

ISBN: (纸本)9783031838446;9783031838453

This paper focuses on the complex interplay between personal privacy, technological monitoring, and government response in a healthcare scenario. A patient's journey illustrates the dynamic relationship between these elements and reveals significant insights into how privacy concerns can shape public satisfaction and governmental actions. This is analysed computationally using a designed adaptive network model.

关键词： Ethics Control Smart city Network Analysis

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Investigating Consumers and Growers Views on Edible Olive Quality Assurance: A Case Study 5th

Investigating Consumers and Growers Views on Edible Olive Qu...

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5th 5th Olympus - International Conference on Supply Chain

作者： Tsarouhas, Panagiotis Anthopoulou, Kyriaki Univ Thessaly Dept Comp Sci & Biomed Informat Papasiopoulou 2-4 Galaneika 35131 Lamia Greece Int Hellen Univ Dept Supply Chain Management Logist Kanellopoyloy 2 Katerini 60100 Greece

ISBN: (纸本)9783031693434;9783031693441

The development and implementation of quality systems contributes to the production of quality products. The quality and safety of food starts with the quality of the raw material produced on the farm. The aim of this study is to investigate the views of consumers and growers regarding quality assurance of edible olives. The study analyses olive cultivation and consumption, focusing on the importance of quality certification (such as ISO 9001) for producers. It examines how demographic variables such as geographical location, gender, education level and income affect cultivation and consumer behavior. The study finds that consumers prefer products with respect for the environment and certified quality. It also points out its methodological limitations, such as the use of convenience sampling and social media for data collection, which may not adequately reflect the wider population of producers and consumers. Finally, future research should use more diverse and representative samplingmethods, possibly including qualitative research techniques such as interviews or focus groups, to gain deeper insights into the motivations, challenges and perceptions in the olive growing sector.

关键词： edible olive management quality customer satisfaction

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Pancreas Segmentation Using SRGAN Combined with U-Net Neural Network 12th

Pancreas Segmentation Using SRGAN Combined with U-Net Neural...

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12th Ecuadorian Conference on information and Communication Technologies

作者： Elizabeth Tualombo, Mayra Reyes, Ivan Vizcaino-Imacana, Paulina Morocho-Cayamcela, Manuel Eugenio Yachay Tech Univ Sch Math & Computat Sci Hda San Jose S-N & Proyecto Yachay New York 10019 NY 10019 USA Univ Internac Ecuador Fac Tech Sci Sch Comp Sci Quito 170411 Ecuador

ISBN: (纸本)9783031754302;9783031754319

Pancreatic cancer, a serious disease, is challenging to detect early, hindering timely treatments. Medical imaging, especially computer tomography, is vital for early diagnosis. This study focused on accurate pancreatic segmentation in medical images using subtle changes in 2D U-Net architecture to aid in diagnosis and treatment. Additionally, we proposed using a super-resolution generative adversarial network (SRGAN) for better-resolution images. The proposed methodology considering the Dice-Sorensen coefficient (DSC), showed superior DSC values: 0.921163 +/- 0.012 with subtle changes in U-Net architecture and 0.5354 +/- 0.0002 DSC with SRGAN and the proposed U-Net considering low training time. Furthermore, the recall 0.6351 +/- 0.0159 and precision 0.6422 +/- 0.0056 were obtained. These results are important to the progression of medical imaging analysis for complex decision-making.

关键词： Pancreas segmentation Pancreatic tumour segmentation Artificial intelligence U-Net SRGAN

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Introducing Novel Parallel Computing Using Orbital Data 37th

Introducing Novel Parallel Computing Using Orbital Data

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37th International Conference on computer Applications in Industry and Engineering

作者： Mekhiel, Nagi Toronto Metropolitan Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9783031762727;9783031762734

We propose a novel parallel computing that allows processors to access data in predictable time without the need to access it from different locations in memory using addresses. It uses orbital data that is mapped to time and is made available to multiple processors at the same time in multiple different orbits and at a specific predictable time in each orbit. This allows processors in different orbits to share the same data, eliminating the problem of sharing data at the same time among multiple processors. It provides processors with the ability to hide the waiting time when accessing shared data by overlapping it with useful work on another data while allowing other processors to work on the shared data in another orbit. The performance of this novel method shows significant improvements in scalability compared to that of conventional parallel computing.

关键词： Data accesses computer models Parallel computers Memory systems Scalability of parallel Computing

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Deep Neural Network Compression for Lightweight and Accurate Fish Classification 5th

Deep Neural Network Compression for Lightweight and Accurate...

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5th Southern African Conference for Artificial Intelligence Research

作者： Salie, Daanyaal Brown, Dane Chieza, Kenneth Rhodes Univ Dept Comp Sci Makhanda South Africa

ISBN: (纸本)9783031782541;9783031782558

This research demonstrates the effectiveness of compression of deep learning models in a fish species classification system. In a bid to enhance model efficiency, response-based knowledge distillation was employed, balancing model compression and accuracy retention. The underwater footage collected in the Seychelles was annotated in collaboration with the South African Institute for Aquatic Biodiversity (SAIAB), and named the Aldabra data set. The accuracies of the MobileNet and DenseNet models were 89.26% and 86.78%, respectively, in the Aldabra dataset. Although the student model (MobileNet) achieved a lower accuracy, it enabled a three-fold reduction in parameters. In particular, in the much larger Fish4Knowledge dataset, MobileNet trained via knowledge distillation exceeded ResNet's accuracy, reaching 96.64% with an 18.7-fold size reduction. This performance improvement was attributed to efficient learning facilitated by knowledge distillation. The edge computing-friendly MobileNet model assimilates knowledge from the much larger ResNet output with information gain.

关键词： Classification Deep learning Knowledge distillation Model compression computer vision Marine biodiversity

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Grapevine SDLC Model for Real-Time Fake News Classification 4th

Grapevine SDLC Model for Real-Time Fake News Classification

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

作者： Aishwarya, C. Shekokar, Tanay Praveen Mukesh, Konatham Naga Venkatesan, M. Prabhavathy, P. NIT CSE Dept Pondicherry India NITK CSE Dept Mangaluru India VIT Vellore Tamil Nadu India

ISBN: (纸本)9783031837951;9783031837968

In an era of rapid information distribution, the presence of fake news presents enormous difficulties to society, influencing public opinion and decision-making on a global scale. To address this issue, a reliable and efficient system capable of detecting and classifying fake news in real time must be developed. This project proposes the design and implementation of a specialized Software Development Life Cycle (SDLC) model, called the Grapevine SDLC, specifically designed for developing a real-time fake news classifier using Large Language Models (LLMs) and Apache Kafka. The Grapevine SDLC takes a methodical, iterative approach, starting with a thorough requirements analysis that identifies both system capabilities and limitations. During the design and development phase, the system architecture is crafted with a focus on scalability and real-time processing, integrating LLMs for highly accurate content analysis and categorization. Kafka's distributed messaging platform ensures seamless and efficient data streaming, enabling the system to handle large volumes of data in real time. Further, the model includes continuous monitoring and feedback loops to improve detection accuracy and adapt to evolving fake news patterns.

关键词： Real-Time Fake news Software Development Life Cycle Large Language models Scrum Agile

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Exploring the Use of Q-Learning in Causal Inference for Adaptive Interventions 6th

Exploring the Use of Q-Learning in Causal Inference for Adap...

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6th Pacific Causal Inference Conference

作者： Zhou, Sha Jiang, YanHua Jin, ZhiWei Qian, ZhenZhen Ji, MengMeng Liu, Chi Li, HongYi Xuan, GuoWei Shuai, YuXing Chen, XinLin Guangzhou Univ Chinese Med Affiliated Clin Sch 2 Guangzhou 510403 Guangdong Peoples R China China Acad Chinese Med Sci Inst Clin Basic Med Beijing 100700 Peoples R China Chinese Med Sci Postdoctoral Stn China Acad Beijing 100700 Peoples R China Ningxia Med Univ Yinchuan 750004 Ningxia Peoples R China Guangdong Prov Hosp Tradit Chinese Med Guangzhou 510120 Guangdong Peoples R China Guangzhou Univ Basic Med Coll Chinese Med Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9789819778119;9789819778126

Causal inference aids in estimating treatment effects for informed decision-making. Over time, there has been an increasing focus on customizing and tailoring intervention services, leading to the emergence of adaptive interventions. These interventions implement personalized sequences of treatment options by employing decision rules that integrate participant data to formulate customized recommendations for intervention progression. However, causal inference in adaptive interventions faces several challenges, a key one being the distinction between correlation and causation. This requires robust study designs, examples include the Sequential Multiple Assignment Randomized Trial (SMART), which is resource-intensive and complex. In addition, SMART data often contain confounding variables that mask the true interplay between interventions and their outcomes. Another obstacle is the diversity of populations;adaptive interventions that are effective in one subgroup may be ineffective in another. Response-adaptive designs potentially reducing the likelihood of selecting the most appropriate treatment for each individual. In this context, we present Q-learning, an extension of regression analysis designed for situations where decisions are made sequentially regarding choices or intervention options available. Utilizing Q-learning involves applying it to assess the effectiveness of intervention options. Specifically, we utilize Q-learning alongside linear regression to approximate the optimal sequence of decision rules. This integration demonstrates how Q-learning, when combined with SMART data, enhances the refinement of decision rules, surpassing those originally embedded within the SMART framework. Eventually, we illustrate this methodology using the adaptive interventions for BPPV SMART (Reg. No. ChiCTR2100048603).

关键词： Adaptive Intervention Sequential Multiple Assignment Randomized Trials (SMART) Q-Learning Individualized Treatment Causal Inference

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