Acta Informatica Pragensia

Insaf Boumezbeur

Abdelhalim Benoughidene

Imane Harkat

Farah Boutouatou

Dounia Keddari

Karim Zarour

BACP-LRS: Blockchain and IPFS-based Land Record System

BACP-LRS：基于区块链和IPFS的土地记录系统

Kemal Elciyar

Neşe Satılmış

Development and Validation of a Blockchain Literacy Scale

区块链素养量表的开发与验证

Over the past few decades, the enormous expansion of medical data has led to searching for ways of data analysis in smart healthcare systems.Acquisition of data from pictures, archives, communication systems, electronic health records, online documents, radiology reports and clinical records of different styles with specific numerical information has given rise to the concept of multimodality and the need for machine learning and deep learning techniques in the analysis of the healthcare system.Medical data play a vital role in medical education and diagnosis; determining dependency between distinct modalities is essential.This paper gives a gist of current radiology medical data analysis techniques and their various approaches and frameworks for representation and classification.A brief outline of the existing medical multimodal data processing work is presented.The main objective of this study is to spot gaps in the surveyed area and list future tasks and challenges in radiology.The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (or PRISMA) guidelines were incorporated in this study for effective article search and to investigate several relevant scientific publications.The systematic review was carried out on multimodal medical data analysis and highlighted advantages, limitations and strategies.The inherent benefit of multimodality in the medical domain powered with artificial intelligence has a significant impact on the performance of the disease diagnosis frameworks.

Shashank Shetty

Ananthanarayana V S

Ajit Mahale

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

多模态医学数据分析综合评述：开放问题与未来研究方向

In recent years, community detection is important because members of the same community share the same concepts.For efficient community detection in a social network, the influence node plays a vital role.A node in the social network or a user that has great influence and power would have a close relationship with a core of the group, termed a community.Therefore, the status of a person is determined by the user's influence strength.That is, a user who has greater influence and strength plays a vital role in the social media community and also acts as a core in the community of the social network.Therefore, a community is a group of nodes in the complex network structure which are interlinked with one another.Effective community detection in a complex structure is a challenging task.Many studies have been done based on topological networks.The approaches are ineffective, inefficient and require more time to process.To overcome these issues, this paper proposes improving the influence nodes in complex networks by using the InfoMap with sigmoid fish swarm optimization algorithm (I-SFSO).Our proposed I-SFSO gives better accuracy rates for the data sets: 92% for Dolphin, 95% for the Facebook dataset, 96% for the Twitter data set, 94% for the YouTube data set, 93% for a karate club and 94% for football.

Devi Selvaraj

Rajalakshmi Murugasamy

Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

基于Sigmoid鱼群优化算法的InfoMap改进复杂网络中影响力节点社区检测评估

The COVID-19 coronavirus illness is caused by a newly discovered species of coronavirus known as SARS-CoV-2.Since COVID-19 has now expanded across many nations, the World Health Organization (WHO) has designated it a pandemic.Reverse transcription-polymerase chain reaction (RT-PCR) is often used to screen samples of patients showing signs of COVID-19; however, this method is more expensive and takes at least 24 hours to get a positive or negative response.Thus, an immediate and precise method of diagnosis is needed.In this paper, chest X-rays will be utilized through a deep neural network (DNN), based on a convolutional neural network (CNN), to detect COVID-19 infection.Based on their X-rays, those with COVID-19 indications may be categorized as clean, infected with COVID-19 or suffering from pneumonia, according to the suggested CNN network.Sample pieces from every group are used in experiments, and categorization is performed by a CNN.While experimenting, the CNN-derived features were able to generate the maximum training accuracy of 94.82% and validation accuracy of 94.87%.The F1-scores were 97%, 90% and 96%, in clearly categorizing patients afflicted by COVID-19, normal and having pneumonia, respectively.Meanwhile, the recalls are 95%, 91% and 96% for COVID-19, normal and pneumonia, respectively.

Ali Mohammed Saleh Ahmed

Inteasar Yaseen Khudhair

Salam Abdulkhaleq Noaman

Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images

使用胸部X光图像进行SARS-CoV-2检测的深度学习卷积神经网络

Rachid Beghdad

Faiza Benmenzer

Alaa Eddine Khalfoune

Cloud Survivability Scenarios Under Attacks With and Without Countermeasures

云环境下攻击有/无对抗措施的生存性场景

The integration of generative artificial intelligence (AI) into adaptive and personalized learning represents a transformative shift in the educational landscape.This research paper investigates the impact of incorporating generative AI into adaptive and personalized learning environments, with a focus on tracing the evolution from conventional artificial intelligence methods to generative AI and identifying its diverse applications in education.The study begins with a comprehensive review of the evolution of generative AI models and frameworks.A framework of selection criteria is established to curate case studies showcasing the applications of generative AI in education.These case studies are analysed to elucidate the benefits and challenges associated with integrating generative AI into adaptive learning frameworks.Through an in-depth analysis of selected case studies, the study reveals tangible benefits derived from generative AI integration, including increased student engagement, improved test scores and accelerated skill development.Ethical, technical and pedagogical challenges related to generative AI integration are identified, emphasizing the need for careful consideration and collaborative efforts between educators and technologists.The findings underscore the transformative potential of generative AI in revolutionizing education.By addressing ethical concerns, navigating technical challenges and embracing human-centric approaches, educators and technologists can collaboratively harness the power of generative AI to create innovative and inclusive learning environments.Additionally, the study highlights the transition from Education 4.0 to Education 5.0, emphasizing the importance of social-emotional learning and human connection alongside personalization in shaping the future of education.

Manel Guettala

Samir Bourekkache

Okba Kazar

Saad Harous

Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning

生成式人工智能在教育中的应用：推进自适应与个性化学习

Integration of the internet of things (IoT) and robotics into the internet of robotic things (IoRT) presents inherent security and trust challenges.This article introduces an innovative blockchain-centred framework designed to address these challenges.By harnessing the features of oneM2M and blockchain technology, the architecture enhances accessibility, security and data management within IoRT.Encompassing key aspects such as data flow, user authentication, health data management and security, the proposed framework ensures compliance with the oneM2M standard.A notable contribution is the integration of a private blockchain network on ZedBoard development boards, focusing on access control and data integrity verification.External IoT data are securely transmitted to a cloud platform hosted on AWS, ensuring streamlined data management with minimal impact on the resources of ZedBoards.In a stride towards bolstering IoRT security, the study implements elliptic curve digital signature algorithm (ECDSA) with Radix-2 w optimizations on ZedBoards, demonstrating a significant 58.63% reduction in addition operation costs compared to standard ECDSA.The practical implementation on ZedBoards validates the effectiveness of this approach, offering a comprehensive solution for securing IoRT networks.This research caters to industries relying on the seamless integration of IoT and robotics, concluding with noteworthy findings and suggesting potential avenues for future research, including optimizations in hardware/software co-design for ECDSA on Zynq boards.

Abdessamed Echikr

Ali Yachir

Chaker Abdelaziz Kerrache

Abdelkrim Kamel Oudjida

Zakaria Sahraoui

Interoperable IoRT for Healthcare: Securing Intelligent Systems with Decentralized Blockchain

医疗保健中的互操作性物联网：利用去中心化区块链保障智能系统安全

短名
Journal Impact	0.79
国际分区	SOCIAL SCIENCES, INTERDISCIPLINARY(Q3)

涉及主题	计算机科学哲学物理政治学操作系统法学工程类数学经济业务艺术程序设计语言万维网人文学科生物
出版信息	出版商: Prague University of Economics and Business，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2019，原创研究文献占比: 0.00%，自引率:0.00%， Gold OA占比: 98.59%
平均审稿周期	网友分享经验：12 Weeks

Acta Informatica Pragensia

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BACP-LRS: Blockchain and IPFS-based Land Record System

Development and Validation of a Blockchain Literacy Scale

Comprehensive Review of Multimodal Medical Data Analysis: Open Issues and Future Research Directions

Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images

BACP-LRS: Blockchain and IPFS-based Land Record System

Development and Validation of a Blockchain Literacy Scale

Cloud Survivability Scenarios Under Attacks With and Without Countermeasures

Generative Artificial Intelligence in Education: Advancing Adaptive and Personalized Learning

Interoperable IoRT for Healthcare: Securing Intelligent Systems with Decentralized Blockchain

ISSN	1805-4951
h-index	6