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BHealth: A Blockchain-Based Platform for Healthcare Data 3rd

BHealth: A Blockchain-Based Platform for Healthcare Data

引用

3rd International Conference on Network Security and Blockchain Technology

作者： Patel, Parva Maheshbhai, Patel Yash Kamble, Sumeet Rana, Bhagya Patil, Shriniwas Parmar, Keyur S V Natl Inst Technol Surat India

ISBN: (纸本)9789819780501;9789819780518

There is a well-known trilemma in the blockchain: "A block-chain system can only at most have two of the following three features: decentralization, scalability, and security." In this article, we propose a healthcare blockchain system for Electronic Health Records (EHRs) with all three features, namely decentralization, security, and scalability. We present a novel model for EHRs sharing that prioritizes all three features using InterPlanetary File System (IPFS) to store patient EHRs. The proposed system protects the privacy of content addresses and enables efficient access revocation by creating a pool on InterPlanetary Naming System (IPNS). In addition, the proposed system modifies the mapping whenever the patient revokes access of any doctor's address. The proposed system allows healthcare providers to add patients' EHRs after the patient's approval. The patient's approval ensures the authenticity and integrity of the patient's EHRs while sharing with other healthcare providers. As a result, the proposed system effectively addresses privacy concerns, enables access revocation, and ensures the authenticity and integrity of patient EHRs, thus offering a robust solution for secure and efficient EHRs sharing in the healthcare domain achieving all three features: decentralization, security, and scalability.

关键词： Blockchain Decentralization Scalability Security Access revocation Health care

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Decision System in a Building to Coordinate the Energy Forecasting Tasks 21st

Decision System in a Building to Coordinate the Energy Forec...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Ramos, Daniel Faria, Pedro Vale, Zita Polytech Porto LASI Intelligent Syst Associate Lab GECAD Res Grp Intelligent Engn & Comp Adv Innovat Porto Portugal

ISBN: (纸本)9783031820724;9783031820731

Nowadays, energy buildings have a huge impact in society regarding the active role in the management of energy consumption. Hence, building owners are required to avoid energy losses and improve energy efficiency as high as possible. Therefore, it is required to plan an optimization strategy to buy and sell energy in the market ahead of time. To formulate this optimization plan, building owners require the work of specialists responsible for processing, training, forecasting, and evaluation tasks regarding the prediction of energy consumption data from a building for a specific target of time. Therefore, a multiagent-system is needed to allow the cooperation of various agents including the building owner, forecast provider, data structurer and error analysis. Moreover, forecasting algorithms such as artificial neural networks should be taken into consideration in order to process large quantities of energy consumption data during the training and forecasting phases.

关键词： agents artificial neural networks building owner energy building

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On Improvement of the DE-MCZ Algorithm with Modes Identification 20th

On Improvement of the DE-MCZ Algorithm with Modes Identifica...

引用

20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based systems

作者： Skarzynska, Aleksandra Romaniuk, Maciej Warsaw Univ Technol Fac Math & Informat Sci Koszykowa 75 PL-00662 Warsaw Poland PAS Syst Res Inst Newelska 6 PL-01447 Warsaw Poland WIT Acad Newelska 6 PL-01447 Warsaw Poland

ISBN: (纸本)9783031739965;9783031739972

The DE-MCZ algorithm is an improvement of the DE-MC method, which joins the differential evolution with the theory of the Markov chains. It aims to ensure the numerical effectiveness and the convergence speed of the special variant of the Metropolis-Hastings algorithm with the help of an additional, self-adapting initial matrix. In this paper, we add the modes detection procedures to the DE-MCZ algorithm to increase its abilities in sampling from multimodal target densities. As our numerical experiments suggest, the obtained DE-MCmodes algorithm provides results that give a better fit to the desired target density than the classical approaches.

关键词： Statistical simulations Markov chain Mode detection Unsupervised learning Non-parametric model

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Dynamic Hybrid Model for Biopesticides Production Using Bacillus Thuringiensis Strains 21st

Dynamic Hybrid Model for Biopesticides Production Using Baci...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Figueroa-Cardona, Sergio Robles-Rodriguez, Carlos E. El-Jeni, Rim Fillaudeau, Luc Aceves-Lara, Cesar A. Univ Toulouse CNRS TBI INRAEINSA Toulouse France

ISBN: (纸本)9783031820724;9783031820731

Commercial biopesticides are primarily based on the bacterium Bacillus Thuringiensis which produces insecticidal proteins. Dynamic models have been proposed to describe the production of these proteins, but their approximation can still be improved. Dynamic hybrid modelling combines kinetic models with data-driven algorithms which could be promising to improve the existing dynamic models. This work presents a dynamic hybrid model using Support Vector Machine (SVM) to predict the specific production of protein and spore by different strains of B. thuringiensis. The dynamic hybrid model was trained and validated with independent datasets of batch fermentations using the production rates of biomass, protein and spores computed with a kinetic model and the strain type as predictors. Additionally, Shapley values were calculated to determine the pertinence of each predictor. The Normalized root mean squared errors (NRMSE) revealed an improvement of the dynamic hybrid model of 12% for proteins and 7% for spores over the dynamic model. Although good, the model could be further improved by training the model with a larger quantity of data.

关键词： B.thuringiensis Support Vector Machine hybrid modelling optimization fermentation

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Liver Cancer Classification Approach Using Yolov8 20th

Liver Cancer Classification Approach Using Yolov8

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20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based systems

作者： Abdulsahib, Fatimah I. Al-Khateeb, Belal Koczy, Laszlo T. Nagy, Szilvia Szechenyi Istvan Univ Egyet ter 1 H-9026 Gyor Hungary Univ Anbar Anbar Iraq Al Ayen Iraqi Univ Engn Tech Coll Thi Qar Iraq

ISBN: (纸本)9783031739965;9783031739972

Liver cancer is a common and often fatal disorder that is becoming more commonplace worldwide. An accurate and timely diagnosis is necessary for both effective treatment and patient survival. In machine learning techniques, particularly deep learning, obtaining a large and diverse dataset is still a challenge for deep neural network training, particularly in the medical industry. This paper presents a classification of circulating tumor cells based on the YOLOv8 algorithm. Tumor cell identification and classification can be achieved by utilizing the algorithm's multi-layer high-level stacking, weight sharing, local connection, and pooling characteristics. The goal is to design a liver cancer classification system that makes it easier and increases the efficiency of doctors in analyzing the results of liver cancer. The models show the absolute the accuracy is 100%, 100%, 98%, 96% to Yolov8n, Yolov8s, Yolov8m, and Yolov8l respectively.

关键词： Liver cancer classification Deep Learning YOLO Yolov8 Liver CT

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Insights into Quantitative Evaluation Techniques for AI Explainability 21st

Insights into Quantitative Evaluation Techniques for AI Expl...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Gomes, Hugo Ferreira, Joao Rodrigues, Manuel Univ Minho LASI ALGORITMI Ctr Braga Portugal

ISBN: (纸本)9783031820724;9783031820731

The field of Artificial Intelligence (AI) has seen an exponential growth in recent years, with new models being created that achieve almost human-level accuracy in various tasks. However, this increase in accuracy sacrifices a key aspect for critical fields like medicine and finance, which is interpretability. This led to the rise of Explainability in Artificial Intelligence (XAI), which works towards giving more trustworthiness and transparency to these black-box models by generating explanations that translate the decision-making process of these models. One of the posing challenges in this field is to properly quantitatively evaluate these XAI methods due to limited research. This systematic review gives an overview of the current trends in the state-of-the-art for quantitative evaluation methods for evaluating XAI methods, giving more insight on what a good explanation should be and what properties it should meet. Then, we propose an evaluation benchmark based on the reviewed literature, comprising the properties we deemed most important to be met by an explanation. Finally, we present the limitations and challenges this field still poses and directions for future research.

关键词： Explainability in Artificial Intelligence XAI Quantitative evaluation

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Blockchain-Based Secured Ration Distribution System 3rd

Blockchain-Based Secured Ration Distribution System

引用

3rd International Conference on Network Security and Blockchain Technology

作者： Malasane, Ayush Parida, Milan Kumar Kotkar, Harshal Vaijapurkar, V. B. SCTRs Pune Inst Comp Technol Pune Maharashtra India

ISBN: (纸本)9789819780501;9789819780518

This survey paper explores the integration of blockchain technology into Ration Distribution systems (RDS) and its potential to address long-standing challenges. It begins by explaining blockchain technology and its various types. The paper then meticulously examines the persistent issues in traditional ration distribution systems, emphasizing the advantages and limitations of adopting blockchain solutions. Real- world examples, such as India's blockchain-based PDS and the World Food Program's "Building Blocks" initiative, demonstrate how blockchain can enhance transparency, traceability, security, and operational efficiency in ration distribution. The paper highlights the benefits of integrating blockchain, including improved transparency, traceability, security, and operational efficiency. It also discusses architectural aspects and practical applications of smart contracts within blockchain integration. The importance of permissioned blockchains and userfriendly interfaces is emphasized. Challenges in adopting blockchain technology, such as scalability, privacy, and adoption barriers, are identified. The authors suggest future research directions, including incorporating IoT and AI, pursuing interoperability, sustainability initiatives, and designing user-centric interfaces. This comprehensive analysis serves as a roadmap for creating more equitable, secure, and efficient ration distribution systems worldwide.

关键词： Ration distribution system (RDS) Public distribution system (PDS) World food programme (WFP)

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TRUNC: A Transfer Learning Unsupervised Network for Data Clustering 21st

TRUNC: A Transfer Learning Unsupervised Network for Data Clu...

引用

21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Xavier, Rita Peller, John de Castro, Leandro N. Univ Estadual Campinas Grad Program Technol Sao Paulo Brazil Florida Gulf Coast Univ UA Whitaker Coll Engn Ft Myers FL 33965 USA

ISBN: (纸本)9783031820724;9783031820731

Clustering aims to group similar data objects together while keeping dissimilar objects apart. Various bioinspired algorithms have been developed to address different challenges in clustering tasks. One promising approach is the use of self-organizing neural networks, which can adapt and learn the underlying patterns in the data. Transfer Learning (TL) has also gained attention for its ability to leverage knowledge from one domain to improve learning in another. In this context, a Transfer Learning Unsupervised Network (TRUNC) is proposed, integrating a self-organizing network with TL to enhance clustering performance. This paper introduces TRUNC, presents a sensitivity analysis of the algorithm to the transfer learning term, and an evaluation of its effectiveness when applied to synthetic data.

关键词： Unsupervised Network Self-Organizing Neural Network Transfer Learning Data Clustering Bioinspired Computing

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Chat GPT in Supporting Education Instruction Sector: An Empirical Literature Review 9th

Chat GPT in Supporting Education Instruction Sector: An Empi...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Tan, Azlini Anuar Huda, Miftachul Rohim, Moh Abdul Hassan, Tuan Rusmawati Raja Ismail, Azman Siregar, Maragustam Univ Pendidikan Sultan Idris Fac Commun & Language Tanjong Malim 35900 Malaysia Univ Pendidikan Sultan Idris Fac Human Sci Tanjong Malim 35900 Malaysia Univ Malaysia Kelantan Malaysia Fac Language Studies & Human Dev Kota Baharu 16100 Kelantan Malaysia Natl Acad Arts Culture & Heritage Kuala Lumpur 50480 Malaysia Univ Islam Negeri Sunan Kalijaga Yogyakarta 55281 Indonesia

ISBN: (纸本)9789819750344;9789819750351

ChatGPT technology has the potential to provide several benefits for an educational institution, including increasing engagement, collaboration, and student accessibility. The further innovations emerged in the development of education, such as strengthening students' writing abilities in schools or universities as a new learning approach. Not many studies are trying to elaborate more on looking into detail about supporting or disturbing. In order to maximize the benefits of adopting ChatGPT while addressing existing constraints, it is imperative to carry out ongoing research and development. GPT Chat technology can support and enhance the learning process, both individually and collectively. As such, this paper aims to examine the chat GPT in education instruction sector, supporting or disturbing pathway. The finding revealed that this concept has the potential to be a valuable tool both in the classroom and in self-study situations outside of the classroom.

关键词： Chat GPT Education instruction sector Supporting Disturbing

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Bayesian Optimization-Based Hyper-parameter-Tuned Neural Network Regression for Smart Home Energy Consumption Modelling Using Weather Information 9th

Bayesian Optimization-Based Hyper-parameter-Tuned Neural Net...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Alzimami, Ahmed Das, Saptarshi Mueller, Markus Univ Exeter Fac Environm Sci & Econ Ctr Environm Math Penryn Campus Exeter TR10 9FE Cornwall England Univ Exeter Inst Data Sci & Artificial Intelligence North Pk Rd Exeter EX4 4QE Devon England Univ Exeter Environm & Sustainabil Inst Penryn Campus Exeter TR10 9FE Cornwall England

ISBN: (纸本)9789819754403;9789819754410

This paper presents an illustrative study on the predictive analysis of household energy consumption patterns in the context of hybrid energy-powered smart homes having wide variety of appliances. Leveraging the proliferation of smart sensors and the integration of the Internet of Things (IoTs), our research focuses on the integration of weather information in predicting the user behaviour for different household appliances and the total energy consumption. By employing nonlinear regression modelling using artificial neural networks (ANNs), we develop black-box predictive models aimed at predicting total energy usage given the user behaviour patterns and weather information in order seamlessly design an optimized smart home energy system. We use the adjusted R-2 or the coefficient of determination as the performance metric in deciding the best fitted model. In such data-driven modelling, the regression methods need to be further fine-tuned over a wide choice of hyper-parameters which significantly affect the accuracy, speed, and consistency of the machine learning (ML) models. We here benchmark the hyper-parameter optimized model with a simpler hyper-parameter optimized linear model to show the efficacy of the nonlinear model over the linear counterpart. The reported research endeavours to advance the paradigm of sustainable urban living by leveraging datadrivenMLmodels for hybrid energy-powered smart homes, with the goal of fostering energy-efficient and ecologically responsible urban environments.

关键词： Hyper-parameter optimization Bayesian optimization Artificial neural network (ANN) regression Hybrid energy systems Smart homes

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