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The Role of Consumers in the Honey Supply Chain 5th

The Role of Consumers in the Honey Supply Chain

引用

5th 5th Olympus - International Conference on Supply Chain

作者： Papachristos, Ioannis Kontopanou, Maria Anastasiadis, Foivos Tsoulfas, Giannis T. Agr Univ Athens Dept Agribusiness & Supply Chain Management Thiva 32200 Greece

ISBN: (纸本)9783031693502;9783031693519

In recent years, the consumption of honey has attracted increasing interest among consumers due to its beneficial health properties. Greek honey is prized in the Mediterranean diet and has gained global recognition for its unique quality. The significance of consumers within the honey supply chain is vital since their preferences play a pivotal role in the entire industry. The choices made by consumers for high-quality, safe, and sustainable honey significantly influence producers' decisions regarding transparency in the supply chain. Consumer centricity in the honey supply chain is indispensable, as it establishes a strong connection between supply chain stakeholders and end-users. Consumer demand for high-quality, safe, and sustainable honey drives producers to adhere to these standards, resulting in effective supply chains that meet customer needs. The current study examines the drivers influencing consumers to choose honey and analyses the impact of those drivers end-to-end in the supply chain. Producers and traders must understand consumer drivers to align their offerings with expectations to increase satisfaction and loyalty.

关键词： honey supply chain consumer stakeholders sustainability

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UAV-Borne Hyperspectral Imaging Dataset of Pearl Millet Canopy Water Stress 2nd

UAV-Borne Hyperspectral Imaging Dataset of Pearl Millet Cano...

引用

2nd International Conference on Agriculture-Centric Computation

作者： Sankararao, Adduru U. G. Kiran, Sai K. Rajalakshmi, P. Choudhary, Sunita Indian Inst Technol Hyderabad Hyderabad Telangana India Int Crops Res Inst Semi Arid Trop Hyderabad Telangana India

ISBN: (纸本)9783031744396;9783031744402

Developing sustainable crop varieties for climate change, drought, and heat waves is an immediate need for the agricultural community. Breeding scientists are actively developing better crop varieties;however, assessing the performance of thousands of genotypes, called plant phenotyping, presents a significant logistical challenge. Unmanned Aerial Vehicle (UAV) -based Hyperspectral imaging (HSI) is a potential technology to address this challenge, which acquires rich spectral information of objects in hundreds of spectral channels with large fields of coverage. However, very few crop HSI datasets are available for public use in the literature. In this study, we leveraged UAV-based HSI covering the spectrum range of 400-1000nm to create a comprehensive dataset of pearl millet canopy water stress. Utilizing state-of-the-art machine learning (ML) benchmarks, we conducted classification experiments to showcase the dataset's effectiveness in characterizing progressive water stress in pearl millet canopy. Moreover, we emphasize our commitment to making this dataset publicly available, recognizing its potential to advance research activities in the agriculture domain using UAV-based sensing.

关键词： Dataset Hyperspectral imaging Pearl millet canopy Unmanned aerial vehicle Water stress

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Enhancing Customer Segmentation and Behavior Prediction Through Machine Learning and Medium Gaussian SVM 4th

Enhancing Customer Segmentation and Behavior Prediction Thro...

引用

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

作者： Pandyan, Uma Maheswari Usha, Gandhimathi Alias S. Manoj, S., V Chandru, M. Abinandan, S. Velammal Coll Engn & Technol Dept ECE Madurai 625009 Tamil Nadu India

ISBN: (纸本)9783031837951;9783031837968

Customer segmentation and prediction, driven by machine learning algorithms, plays a vital role in today's fast-paced and data-driven market. This paper focuses on applying machine learning techniques, specifically the k-means clustering algorithm and Medium Gaussian Support Vector Machine(SVM) regression learning, for customer segmentation and prediction. The process starts with data col-lection and cleaning to ensure the reliability of the data clarity gained. The k-means clustering algorithm plays a central role in identifying patterns within customer data, revealing natural groupings that may not be apparent through traditional analysis methods. Using machine learning algorithms, with a focus on k-means clustering, the segmentation process is refined, allowing for the discovery of complex patterns in customer behavior. This, in turn, enables the creation of personalized interactions through predefined customer segments. Furthermore, the segmented customer information is analyzed using different regression techniques to predict segmented customers better. Experimental results show that the proposed approach achieves the lowest mean average error of 15.723 with a model size of 12KB. The advantages of this approach are substantial, including the development of tailored marketing strategies, personalized recommendations, and the optimization of resource allocation. This understanding allows companies to create customized approaches for each group, leading to happier customers, more people recommending their services or products, and a better overall image for the brand.

关键词： Customer segmentation Machine Learning Medium Gaussian SVM

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A Scoping Review of the Use of Blockchain to Support Medical Imaging Data Sharing Across Multiple Healthcare Institutions 4th

A Scoping Review of the Use of Blockchain to Support Medical...

引用

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

作者： Pavao, Joao Bastardo, Rute Rocha, Nelson Pacheco Univ Tras Os Montes & Alto Douro Sch Sci & Technol INESC TEC Vila Real Portugal Univ Tras Os Montes & Alto Douro Sch Sci & Technol UNIDCOM Vila Real Portugal Univ Aveiro Dept Med Sci IEETA Aveiro Portugal

ISBN: (纸本)9783031840777;9783031840784

The goal of the present review was to analyse the application of blockchain to support medical imaging data sharing across multiple healthcare institutions. The review included thirty-three studies that aimed to use blockchain to provide (i) authentication mechanisms, (ii) trusted communication channels, (iii) distributed mechanisms for securing the privacy for both storage and sharing of medical images, and (iv) federated machine learning algorithms. Despite these potential applications, the current research does not present effective use of blockchain in clinical settings to support medical imaging data sharing across multiple healthcare institutions.

关键词： Blockchain Medical imaging Data security Data sharing

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Monitoring Software Tool to Prevent Data Leaks in a RESTful API 10th

Monitoring Software Tool to Prevent Data Leaks in a RESTful ...

引用

10th International Conference on Technologies and Innovation

作者： Cuevas Valencia, Rene Edmundo Catalan Villegas, Arnulfo Feliciano Morales, Angelino Alvarez Carmona, Carlos Univ Autonoma Guerrero Chilpancingo De Bravo 39000 Mexico

ISBN: (纸本)9783031757013;9783031757020

This research work proposes a monitoring tool to detect data leaks in a RESTful API. Data leaks present an increasing risk to organizations, compromising confidential and sensitive information. Various entities define data leaks as events that compromise the confidentiality, availability or integrity of information. The European Commission and the Agencia Espanola de Proteccion de Datos emphasize the protection of personal data in this context. The proposed monitoring software tool relies on a "data-driven" approach to analyze API traffic and detect anomalies that could indicate data leaks. The software tool proposed for monitoring was developed with the prototype model, which is characterized by the construction of incremental prototypes with minimal functionalities, which are evaluated and modified based on user comments. This iterative process is repeated until a final prototype is obtained that serves as the basis for the final product. The proposed monitoring tool provides API administrators with a configurable solution to prevent data leaks in RESTful API. Its detailed logging, risk detection, flexible configuration options, and design make it a valuable tool for improving API security. Finally, in this work, it is proposed that the monitoring software tool be part of the official repository of Composer as a process that collaborates with the development of software under the standards and good practices of API programming.

关键词： Data leaks Data-Driven API monitoring RESTful API

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How Different Blink Patterns of Pet Robots Evoke Feelings of Affection in People 1

引用

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

作者： Ichino, Junko Takahashi, Daiki Tokyo City Univ Yokohama Kanagawa Japan

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

An alternative to animals, which bring various benefits to people but are not always easy to own, is pet robots, whose central role is to communicate with people. However, there is insufficient research on how pet robots should behave to evoke feelings of affection in people. We focused on blinking as one of the behaviors of the eye and investigated what type of pet robot blinking pattern evokes more feelings of affection in people. First, we designed seven different blink patterns combining short and long blinks through a pilot study. We then conducted a study with 15 participants to compare the patterns using a paired comparison method. The results showed that the blink patterns that were more likely to evoke affection were those with consecutive short eye closures (three times rather than two), whereas the blink patterns that were less likely to evoke affection were those with several long eye closures.

关键词： Human-Robot interaction Pet robot Companion robot Social robot Blink patterns Feelings of affection Paired comparison

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Machine Learning-Based Projections of Long-Term Electricity Consumption: The Case Study of Ecuador

Machine Learning-Based Projections of Long-Term Electricity ...

引用

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

作者： Guaman, Wilian Benalcazar, Pablo Cordova-Garcia, Jose Torres, Miguel Escuela Super Politecn Litoral Fac Ingn Elect & Computac Campus Gustavo Galindo Km 30-5 Via Perimetral Guayaquil Ecuador Univ Tecn Cotopaxi Fac Ciencias Ingn & Aplicadas Campus La Matriz Ave Simon Rodriguez Latacunga Ecuador Polish Acad Sci Mineral & Energy Econ Res Inst Div Energy Econ Ul Wybickiego 7A PL-31261 Krakow Poland

ISBN: (纸本)9783031834318;9783031834325

Traditional econometric models often fail to accurately predict long-term electricity demand due to their inability to adapt to the dynamic nature of energy consumption. This study employs a Multi-Layer Perceptron (MLP) model, integrated with Non-linear Autoregressive Neural Network predictions of population, gross domestic product, electricity access, and urban population, to forecast Ecuador's electricity demand from 2023 to 2050. Comparative analysis shows that the MLP model significantly outperforms other machine learning models, such as Support Vector Machines and Robust Linear Regression, demonstrating lower prediction errors and higher accuracy. Specifically, the MLP model achieves notably better precision, making it a valuable tool for policymakers and planners aiming to meet future energy needs effectively. The study reveals that electricity consumption in Ecuador could range from 38.74 TWh to 62.1 TWh from 2023 to 2050, underscoring the potential of advanced neural networks to enhance the reliability of electricity demand forecasting in developing countries.

关键词： Electricity consumption Projections Multi-Layer Perceptron Machine Learning Ecuador

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An Improved Stroke Type Detection Approach: Combining Chatbot Voice, Ontology, and LSTM-GRU Methods 8th

An Improved Stroke Type Detection Approach: Combining Chatbo...

引用

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

作者： Ben Kahla, Mayssa Kanzari, Dalel Ben Amor, Sana Ghannouchi, Sonia Ayachi Univ Sousse Higher Inst Comp Sci & Commun Techn H Sousse Sousse 4011 Tunisia Univ Manouba Natl Sch Comp Sci RIADI Lab Manouba 2010 Tunisia Univ Sousse Higher Inst Appl Sci & Technol Taffela 4003 Sousse Tunisia Univ Tunis LARODEC Liberte Bardo 41 Tunis Tunisia Univ Sousse Fac Med Ibn El jazzar Sousse 4002 Tunisia Dept Neurol Sahloul Sousse 4054 Tunisia Univ Sousse Higher Inst Management Sousse Sousse 4000 Tunisia

ISBN: (纸本)9783031838446;9783031838453

Detecting stroke type is important as it ranks second globally in causing dementia after Alzheimer disease. Late recognition often leads to high mortality rates due to misinterpretation. Early identification enables prompt treatment, minimizing neurological damage and reducing long-term disabilities like paralysis or cognitive impairment. The study presents a comprehensive method for the detection of stroke types, considering both unstructured non-clinical data and structured clinical data. It is founded on three key modules: data collection, ontology-based knowledge database modeling, and stroke type detection. In our approach, we seamlessly integrated heterogeneous data, encompassing structured and unstructured formats. Based on ontology and advanced deep learning techniques, we aimed to significantly improve the accuracy and efficiency of stroke-type detection, increasing both accuracy and efficiency. Combining various intelligent algorithms for stroke-type detection leverages their complementary strengths, enhancing diagnostic and prognostic accuracy. This integration reduces misdiagnosis risks, leading to more precise and effective patient care. In the experimental study, we used structured clinical stroke data from Sahloul Hospital, supplemented by patient information from the voice chatbot technique. The use of a combination of voice chatbot, ontology, and deep learning methods for stroke type detection resulted in higher levels of accuracy compared to other existing approaches. These findings highlight the effectiveness of our approach for the detection of stroke types.

关键词： Stroke type detection Ontology-based knowledge database modelling Chatbot voice DL-based-support system

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Decentralized Management of IoT Platform Federations and Data Marketplaces 1st

Decentralized Management of IoT Platform Federations and Dat...

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1st Global Internet of Things and Edge Computing Summit

作者： Pietri, Ilia Darzanos, George Spanos, Georgios Karadimou, Dimitra Papaioannou, Thanasis G. Stamoulis, George D. Votis, Konstantinos Tzovaras, Dimitrios Intracom Single Member SA Telecom Paiania Greece Athens Univ Econ & Business Athens Greece Ctr Res & Technol Hellas Informat Technol Inst Thessaloniki Greece

ISBN: (纸本)9783031785719;9783031785726

The Internet of Things (IoT) paradigm has been rapidly adopted in a plethora of application domains, promoting the development of a variety of smart services relying on data from different sources. As a consequence, Sensing-as-a-Service is also gaining popularity, enabling IoT data providers and consumers to share, exchange and trade IoT data. In such a data-driven ecosystem, data marketplaces play a pivotal role, which besides the need for interoperable solutions, necessitates also sophisticated market mechanisms that ensure trusted and secure data exchange. In this paper, we present the IoTFeds solution which offers a complete open source interoperability framework combined with blockchain technologies, and thus provides a decentralized federation management and marketplace platform. The IoTFeds platform enables trustworthy and secure transactions allowing the creation and monetization of composite IoT data services offered by multiple federated data providers.

关键词： federated marketplaces IoT data market blockchain

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Advanced Multi-view Structural MRI Analysis with Self-attention for Alzheimer's Disease Detection 8th

Advanced Multi-view Structural MRI Analysis with Self-attent...

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

作者： Hlawa, Safa Ben Romdhane, Nadra Fendri, Emna Sousse Univ MIRACLHigher Inst Comp Sci & Commun Techn Sousse Sousse 4011 Tunisia Sfax Univ MIRACL FS Fac Sci Sfax Rte Sokra Km 3BP 802 Sfax 3018 Tunisia

ISBN: (纸本)9783031838446;9783031838453

Accurate diagnosis of Alzheimer's Disease (AD) remains a critical challenge, despite its growing prevalence. Recent advancements in neuroimaging have opened new avenues for enhancing diagnostic precision through deep learning techniques. In this study, we introduce a new method that combines diverse pretrained CNN models and attention mechanisms. We select the most suitable model for each view (axial, coronal and sagittal) after a series of experiments. This method aims to capture a broader range of features from the multi-view input sMRI images, leading to improved classification performance. The method presented here was tested on ADNI dataset. The obtained results prove the efficiency of our proposed method (https://***/).

关键词： Alzheimer's disease Cognitive normal Magnetic Resonance Imaging (MRI) Deep Learning Attention mechanism Classification Neuroimaging

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