In recent years, COVID-19 has impacted millions of individuals worldwide, resulting in numerous fatalities across several countries. While RT-PCR technology remains the most reliable method for detecting COVID-19, it is both expensive and time-consuming. As a result, researchers have explored various machine learning and deep learning-based approaches to rapidly identify COVID-19 cases using X-ray images, with reduced costs and shorter processing times. However, preserving patient confidentiality poses challenges within third-party-controlled systems, potentially failing to safeguard patients from potential disgrace and discomfort. Nonetheless, blockchain technology offers the potential to securely store sensitive medical data anonymously, without requiring third-party intervention. Consequently, the combination of deep learning and blockchain could offer a viable solution to mitigate the spread of COVID-19 while ensuring patient privacy protection. In this paper, we propose a hybrid model of blockchain and deep learning model for automatically detecting COVID-19 using chest X-rays (CXR). The deep learning model includes a stacking ensemble of three modified pre-trained Deep Learning (DL) models: VGG16, Xception, and DenseNet169. The model obtained an accuracy of 99.10% and 98.60% for binary and multi-class respectively. Further, To ensure COVID-19 patients’ privacy and security, the Ethereum blockchain has been adopted to store information related to COVID-19 cases. In addition, a smart contract on the blockchain has been designed for handling X-ray images in the Interplanetary File System (IPFS).
Open access
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Blockchain, one of these new digital technologies, has special qualities like immutability, decentralization, and transparency that can be helpful in many different areas including managing electronic medical data and access rights, as well as mobile health. We reviewed all COVID-19-related and unrelated blockchain applications in the healthcare industry. MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar were searched for pertinent reports up to July 29, 2021. There were articles with both technical and clinical designs, with or without prototype development. A total of 85 375 articles were assessed, and 415 full-length reports—37 of which were connected to COVID-19 and 378 of which were unrelated—were ultimately incorporated into the study. The three primary COVID-19-related applications that were reported were contact tracing, monitoring of immunity or vaccination passports, and pandemic control and surveillance. Management of electronic medical records, internet of things (such as remote monitoring or mobile health), and supply chain monitoring were the top three non-COVID-19-related applications. The majority of publications (277 [667%] of 415] focused on the technical performance of blockchain prototype systems, whereas nine (2%) research indicated actual clinical use and uptake. Only technical studies (129 [311%] of 415) made up the remaining investigations. The most popular platforms were Hyperledger and Ethereum. Numerous COVID-19-related and unrelated health care applications of blockchain technology are possible. The necessity to adapt fundamental blockchain technology for use in healthcare settings is highlighted by the fact that the majority of current research is still in the technical stage and only a small number offers practical clinical applications.
Fahad F. Alruwaili, Bayan Alabduallah, Hamed Alqahtani, Ahmed S. Salama · 6 authors
Blockchain (BC) and Artificial intelligence (AI) based technologies have earned a better reputation amongst the research community, especially in the medical field. BC technology has emerged as a promising solution to revolutionize the medical field by addressing challenges related to efficiency, data security, and interoperability. A BC-aided smart healthcare system leverages the immutable and decentralized nature of BC to construct a secured and transparent ecosystem to manage processes and healthcare data. It leverages the secure and decentralized nature of BC to optimize the processes, security, interoperability, and efficiency of medical data. The existing system is exposed to security attacks on healthcare data. It can be necessary to construct a real-time detection device utilizing a cyber-physical system (CPS) with BC technology in a significant way. This article designs a novel Blockchain-Enabled Smart Healthcare System using Jellyfish Search Optimization with Dual-Pathway Deep Convolutional Neural Network (JSO-DPCNN) technique. The presented JSO-DPDCNN technique exploits the concept of BC-enabled secure data transmission and DL-based diagnosis model for moneypox disease on smart healthcare monitoring. To accomplish this, the JSO-DPCNN technique uses Ethereum-based public BC to secure the privacy of healthcare images. In addition, the JSO-DPCNN technique applies a feature extraction module using DPCNN, which extracts the suitable set of features in the input images. Moreover, the multiplicative long short-term memory (MLSTM) approach was used for the disease detection process. Lastly, the JSO system can be employed for the parameter tuning of the MLSTM model. The simulation result of the JSO-DPCNN system was executed on a benchmark medical dataset. The comprehensive outcomes highlighted the significant outcome of the JSO-DPCNN approach in terms of different measures.
On February 24, 2022, Russian forces began their invasion of Ukraine. As of May 2023, approximately 20% of Ukraine has been occupied by Russia, and the war is still ongoing. Conflicts and wars devastate many buildings, infrastructure, regional transportation networks, and telecommunications networks. The outbreak of war threatens the very existence of not only the occupied territories but also the nation itself. Obviously, this has a major impact on the continuity of social life itself.On January 30, 2020, the World Health Organization declared COVID-19 a Public Health Emergency of International Concern. This declaration remained in effect until its termination on May 5, 2023. During this period, the pandemic caused global logistical outages and disrupted human interaction. The outbreak of infection caused by the pandemic restricted the ability of people to meet or talk directly with each other.Extreme weather events caused by climate change are becoming more frequent and more damaging every year. In July 2022, temperatures exceeding 40°C were observed in eastern England for the first time in recorded history. Abnormally high temperatures caused by heat waves lead to major fires in the region. The largest wildfire in southwestern France burned more than 19,000 hectares of land. It is reported that more than 34,000 residents were evacuated.Whatever the cause, natural disasters or conflicts, they generally have a significant impact on the lives of citizens and social activities. The impacts are long-lasting. Depending on the type of disaster, the disaster recovery frameworks that have been effective in the past may not work in some situations.In the field of higher education, such as university education, the use of learning analysis, which aims to clarify learners' learning behavior based on their learning history, is being actively pursued. Learning histories are stored in public clouds such as Amazon Web Services and Google Cloud Platform, and are protected by the large-scale disaster recovery mechanism of cloud storage. However, the outbreak of war or regional conflict, or the occurrence of a disaster that threatens the survival of a country itself, makes it difficult to provide public cloud services, which are merely private commercial services. We must ensure that the learning history of learners, which cannot be recovered once it is lost, is stored and maintained even in multi-hazard situations.In this study, we construct a learning history storing framework that applies blockchain technology in order to store and maintain learners' learning history even in multi-hazard situations. By applying the decentralized and autonomous nature of blockchain technology, the learning history can be maintained and restored even in the event of a functional failure or data loss of information communication networks or data centers due to a disaster. In this presentation, we describe the design of a blockchain mechanism for learning history retention and describe a learning history retention mechanism linked to an existing Learning Management System. The design and effectiveness of the prototype system implemented for validation are also described.
COVID-19 diagnosis using AI
Advanced Data Processing Techniques
Environmental and Biological Research in Conflict Zones
RICARDO CARREÑO AGUILERA, ADAN ACOSTA BANDA, Miguel Patiño-Ortiz, Julián Patiño-Ortiz
This paper proposes an innovative method to take advantage of Blockchain Convolutional Neural Networks (BCNNs) in Emotion Recognition (ER). Based on Artificial Intelligence, this proposal uses audio-visual emotion patterns to determine psychiatric profiles to attend to the most urgent as a priority. BCNN architectures were used to identify emergency patterns. The results indicate that the proposed method is adequate for classifying and identifying audio-visual patterns using Deep Learning (DL) with Boltzmann’s restricted machines. It is concluded that it is sufficient to consider the audio-visible critical features from the patient’s face and voice for the proposed model to recognize a psychiatric services emergency for immediate action: the emergency with no control and the Emergency under control. User personal dynamic profiles are stored in the blockchain ecosystem since they are deemed sensitive data. System security is provided by blockchain and authentication uses non-fungible tokens (NFT) technology.
The integration of blockchain technology in biomedical diagnostics offers a promising solution to the challenges of data security and privacy in infectious disease surveillance. As the digitalization of healthcare systems accelerates, the need to protect sensitive health information becomes increasingly critical. Blockchain, with its decentralized and immutable nature, provides a robust framework for ensuring the integrity and confidentiality of biomedical data. This abstract explores how blockchain technology can be leveraged to enhance data security and privacy in the context of infectious disease surveillance, where rapid and accurate data sharing is essential for effective public health responses. Infectious disease surveillance relies on the collection, analysis, and dissemination of large volumes of data, often shared across multiple institutions and geographical regions. Traditional systems for managing this data are vulnerable to breaches, unauthorized access, and data tampering, which can compromise public health efforts and patient privacy. Blockchain technology addresses these vulnerabilities by enabling secure, transparent, and tamper-proof data exchanges. Each transaction or data entry is recorded in a distributed ledger, accessible only to authorized participants, thus ensuring that the data remains secure and unaltered. Moreover, blockchain’s inherent transparency allows for real-time monitoring and auditing of data flows, which is crucial in the timely detection and response to infectious disease outbreaks. The use of smart contracts within blockchain networks further enhances the automation and efficiency of data management, ensuring that data is only accessed and shared according to predefined rules and conditions. This not only safeguards patient privacy but also builds trust among stakeholders, including patients, healthcare providers, and public health authorities. In conclusion, the integration of blockchain technology in biomedical diagnostics presents a transformative approach to addressing the critical issues of data security and privacy in infectious disease surveillance. By leveraging blockchain's unique features, healthcare systems can ensure that sensitive diagnostic data is protected, thus supporting more effective and secure public health interventions in the fight against infectious diseases. Keywords: One Health Approach, Zoonotic Disease, Early Detection, Development, Portable Diagnostic Device.
Blockchain (BC) and artificial intelligence (AI) technologies have independent applications in multiple industries, including banking, finance, healthcare, construction, transportation, hospitality, manufacturing, and insurance, to name a few. Moreover, these two technologies can be integrated seamlessly, thanks to their complementary and mutually supportive features. AI algorithms can make the medical BC storage efficient by their processing algorithms, also playing the role of knowledgeable gatekeepers. BC can support AI models by providing secure, sizeable, traceable, diverse, and immutable healthcare data for the training purpose. The integration of BC and AI has multiple use cases in the healthcare industry ranging from disease prediction to pandemic management. Previously, researchers have reviewed the applications of each of these technologies in healthcare independently. Although the integration of BC and AI has been fruitful, to the best of our knowledge, there has been no work in the past reviewing the confluence of these two technologies in the healthcare sector. We have classified the works based on two different classification schemes: 1) application-based and 2) AI-training paradigm-based classification. We have also provided a compilation of tools used in the integrated systems of BC and AI for healthcare. We identified that the integration of BC and AI technologies had been applied in quite different areas of healthcare ranging from biomedical research to pandemic management. It is also noted that the supervised learning algorithms and federated learning paradigm for secure decentralized AI model training are often used in the integration. Our findings reveal that majority of the reviewed works use BC as a secure database for AI models. Furthermore, we also have pointed out the potential applications of these two technologies in healthcare.
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.
Rayed AlGhamdi, Madini O. Alassafi, Abdulrahman A. Alshdadi, Mohamed M. Dessouky · 6 authors
The Internet of Things (IoT) has grown more pervasive in recent years. It makes it possible to describe the physical world in detail and interact with it in several different ways. Consequently, IoT has the potential to be involved in many different applications, including healthcare, supply chain, logistics, and the automotive sector. IoT-based smart healthcare systems have significantly increased the value of organizations that rely heavily on IoT infrastructures and solutions. In fact, with the recent COVID-19 pandemic, IoT played an important role in combating diseases. However, IoT devices are tiny, with limited capabilities. Therefore, IoT systems lack encryption, insufficient privacy protection, and subject to many attacks. Accordingly, IoT healthcare systems are extremely vulnerable to several security flaws that might result in more accurate, quick, and precise diagnoses. On the other hand, blockchain technology has been proven to be effective in many critical applications. Blockchain technology combined with IoT can greatly improve the healthcare industry’s efficiency, security, and transparency while opening new commercial choices. This paper is an extension of the current effort in the IoT smart healthcare systems. It has three main contributions, as follows: (1) it proposes a smart unsupervised medical clinic without medical staff interventions. It tries to provide safe and fast services confronting the pandemic without exposing medical staff to danger. (2) It proposes a deep learning algorithm for COVID-19 detection-based X-ray images; it utilizes the transfer learning (ResNet152) model. (3) The paper also presents a novel blockchain-based pharmaceutical system. The proposed algorithms and systems have proven to be effective and secure enough to be used in the healthcare environment.
Bocheng Ren, Laurence T. Yang, Qingchen Zhang, Jun Feng · 5 authors
The rapid development and gradual integration of artificial intelligence and the Internet of Things have brought unprecedented opportunities for radically changing healthcare and treatments. However, the burgeoning in intelligent healthcare systems is severely bounded by data privacy and the security of AI models. Meanwhile, the limited local data forces conventional AI models to face the predicament in achieving personalized healthcare. Hence, we propose a blockchain-powered tensor meta-learning-driven intelligent healthcare system with IoT assistance. IoT devices as light nodes upload the local shareable data to the edge server(full node) for model training and perform the local private data by non-tampered model downloaded via smart contract. The system can not only use blockchain technology to ensure the strong consistency of the healthcare model but also protect private data from being leaked. Especially, we develop a tensor meta-learning model named tensor-prototype graph network to achieve efficient modeling of heterogeneous healthcare data. Building on the tensors and graph network, the model is conducive to capturing the data distribution when there are few labeled data. To evaluate our proposed approach, we have conducted experiments on three classic databases. The results demonstrate that our approach is capable of effectively promoting the performance of intelligent healthcare.
Abdul Razzaq, Syed Agha Hassnain Mohsan, Shahbaz Ahmed Khan Ghayyur, Nouf Al-Kahtani · 6 authors
COVID-19 has become a very transmissible disease that has had a worldwide impact, resulting in a huge number of infections and fatalities. Testing is critical to the pandemic's successful response because it helps detect illnesses and so attenuate (isolate/cure) them and now vaccination is a life-safer innovation against the pandemic which helps to make the immunity system stronger and fight against this infection. Patient-sensitive information, on the other hand, is now held in a centralized or third-party storage paradigm, according to COVID-19. One of the most difficult aspects of using a centralized storage strategy is maintaining patient privacy and system transparency. The application of blockchain technology to support health initiatives that can minimize the spread of COVID-19 infections in the context of accessibility of the system and for verification of digital passports. Only by combining blockchain technology with advanced cryptographic algorithms can a secure and privacy-preserving solution to COVID-19 be provided. In this article, we investigate the issue and propose a blockchain-based solution incorporating conscience identity, encryption, and decentralized storage via interplanetary file systems (IPFS). For COVID-19 test takers and vaccination takers, our solution includes digital health passports (DHP) as a certification of test or vaccination. We explain smart contracts constructed and tested with Ethereum to preserve a DHP for test and vaccine takers, allowing for a prompt and trustworthy response from the necessary medical authorities. We use an immutable trustworthy blockchain to minimize medical facility response times, relieve the transmission of incorrect information, and stop the illness from spreading via DHP. We give a detailed explanation of the proposed solution's system model, development, and assessment in terms of cost and security. Finally, we put the suggested framework to the test by deploying a smart contract prototype on the Ethereum TESTNET network in a Windows environment. The study's findings revealed that the suggested method is effective and feasible.
Çalışmada, 2016-2022 yılları arasında blok zincir ve akıllı şehir konuları ile ilgili uluslararası alan yazınında yayınlanan çalışmaların bibliyometrik özelliklerinin belirlenmesi amaçlanmıştır. Scopus veri tabanı üzerinden “blockchain and smart city” anahtar kelime araması yapılmıştır. Arama yapılan tarihte toplam 1143 makaleye ulaşılmıştır. Araştırma kısıtlarında anahtar kelime bazında sadece “blockchain ve smart city” kelimelerinin birlikte geçtiği çalışmalar dikkate alınmış ve 329 çalışma ile analiz gerçekleştirilmiştir. Bibliyometrik analiz kapsamında öncelikle yayınların dağılımlarına ait genel bilgiler verilmiş, daha sonra birlikte bulunma (co-occurance) ve ortak atıf (co-citation) haritalamaları VOSviewer programı aracılığıyla görselleştirilmiştir. Çalışmanın sonuçları incelendiğinde; konuyla ilgili çalışmalara olan ilginin yıllar geçtikçe arttığı, en çok makale türünde yayın yapıldığı, konuyla ilgili literatüre en çok yayın yapılan ülke olarak Hindistan’ın katkıda bulunduğu, haritalama sonuçlarına göre ise ilk yıllarda bitcoin, ethereum gibi kavramlara öncelik verilirken zamanla bu kavramların yerini nesnelerin interneti (IoT), güvenlik (security) ve blok zincir teknolojileri gibi kavramların aldığı görülmüştür.
Asthma is a deadly disease that affects the lungs and air supply of the human body. Coronavirus and its variants also affect the airways of the lungs. Asthma patients approach hospitals mostly in a critical condition and require emergency treatment, which creates a burden on health institutions during pandemics. The similar symptoms of asthma and coronavirus create confusion for health workers during patient handling and treatment of disease. The unavailability of patient history to physicians causes complications in proper diagnostics and treatments. Many asthma patient deaths have been reported especially during pandemics, which necessitates an efficient framework for asthma patients. In this article, we have proposed a blockchain consortium healthcare framework for asthma patients. The proposed framework helps in managing asthma healthcare units, coronavirus patient records and vaccination centers, insurance companies, and government agencies, which are connected through the secure blockchain network. The proposed framework increases data security and scalability as it stores encrypted patient data on the Interplanetary File System (IPFS) and keeps data hash values on the blockchain. The patient data are traceable and accessible to physicians and stakeholders, which helps in accurate diagnostics, timely treatment, and the management of patients. The smart contract ensures the execution of all business rules. The patient profile generation mechanism is also discussed. The experiment results revealed that the proposed framework has better transaction throughput, query delay, and security than existing solutions.
Rajesh Kumar, Jay Kumar, Abdullah Aman Khan, Zakria Zakria · 8 authors
Medical healthcare centers are envisioned as a promising paradigm to handle the massive volume of data for COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and training models within a single organization. This practice can be considered a weakness as it leads to several privacy and security concerns related to raw data communication. To overcome this weakness and secure raw data communication, we propose a blockchain-based federated learning framework that provides a solution for collaborative data training. The proposed framework enables the coordination of multiple hospitals to train and share encrypted federated models while preserving data privacy. Blockchain ledger technology provides decentralization of federated learning models without relying on a central server. Moreover, the proposed homomorphic encryption scheme encrypts and decrypts the gradients of the model to preserve privacy. More precisely, the proposed framework: (i) train the local model by a novel capsule network for segmentation and classification of COVID-19 images, (ii) furthermore, we use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, (iii) finally, the model is shared over a decentralized platform through the proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing over the decentralized network. To validate our proposed model, we conducted comprehensive experiments and the results demonstrate the superior performance of the proposed scheme.
P. R. Agbedanu, Faiza Umar Bawah, V. Akoto-Adjepong, N. S. Awarayi · 8 authors
The global impact of the COVID-19 pandemic has been felt in diverse ways. Although the death rate in Africa has not been as devastating as predicted by the World Health Organization (WHO), its economic and social impact has been fully felt by the African continent. As the world goes through the vaccination process to achieve herd immunity, Africa has not only faced problems like the inability to produce and procure vaccines, but some countries in the west are doubting the authenticity of the vaccination process and even vaccine certificates coming from various countries on the continent. The approach of using centralized systems to validate COVID-19 vaccine certificates makes these systems susceptible to Denial of Service (DoS), modification, and Man-in-the-Middle (MiTM) attacks. To curb this problem, we proposed a blockchain-based digital COVID-19 vaccination certificate verification system called BLOCOVID. The proposed system uses the decentralized approach of distributed ledgers to ensure that vaccine certificates are secured, immutable, and verifiable. Our proposed system stores vaccine serial numbers and their corresponding certificates as hash values. These hash values are stored on the blockchain network as transaction values. The authenticity of a vaccine certificate is determined by the availability of the hash values of the certificate and its corresponding vaccine serial number on the blockchain network. The proposed system was simulated using the BlockSim simulator. To begin with, the simulation results show that the proposed system can ensure system availability, thereby minimizing DoS attacks. Secondly, the proposed system can ensure the integrity of vaccine certificates by allowing third parties to verify the authenticity of these certificates. The simulation results show that even with 10240 nodes, the average transaction time was 137.2ms, with a total transaction rate of 9911.034 transactions per second.
A. B. Pawar, M.A. Jawale, P. William, Gurpreet Singh Chhabra · 7 authors
Early identification of lung cancer is essential since the disease progresses quickly. Early-stage lung cancer diagnosis will be the first usage of the Internet of Things (IoT). With a worldwide network of IoT devices and a high degree of trust in the model's accuracy, on-the-fly training for IoT devices is very essential. As many as a million lives are saved each year because to early detection of illness, which seals the airways and prevents infection. Image processing and machine learning techniques provided the first evidence of malignant growth. Symptoms of lung cancer generally don't show up until the disease has advanced very far. At this stage, getting medical help becomes quite difficult. A whistling sound, hoarseness, weight gain in the face and/or an increase in the size of the upper chest may appear first, followed by the curling or rising of your fingers or the experience of pain when swallowing. Sputum with a red or rust-colored hue is a sign of malignancy, as is shortness of breath and chronic chest pain. In addition to identifying and arranging lung knobs, a lung computed tomography image may also be utilised to estimate their risk level. Preparation does not have as much of an impact on ECNN's accuracy and temporal complexity as it did on previous frameworks. They are made up of abnormal cells that form a tumour. An uncontrolled development and destruction of the lungs. Various kinds of lung cancer begin to develop as a result of this process, which continues until a tumour forms. Lung cells are damaged when they come into contact with airborne contaminants. + The new approach offered is ECNN+.
Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun
As ML models have increased in capabilities and accuracy, so has the complexity of their deployments. Increasingly, ML model consumers are turning to service providers to serve the ML models in the ML-as-a-service (MLaaS) paradigm. As MLaaS proliferates, a critical requirement emerges: how can model consumers verify that the correct predictions were served, in the face of malicious, lazy, or buggy service providers? In this work, we present the first practical ImageNet-scale method to verify ML model inference non-interactively, i.e., after the inference has been done. To do so, we leverage recent developments in ZK-SNARKs (zero-knowledge succinct non-interactive argument of knowledge), a form of zero-knowledge proofs. ZK-SNARKs allows us to verify ML model execution non-interactively and with only standard cryptographic hardness assumptions. In particular, we provide the first ZK-SNARK proof of valid inference for a full resolution ImageNet model, achieving 79\% top-5 accuracy. We further use these ZK-SNARKs to design protocols to verify ML model execution in a variety of scenarios, including for verifying MLaaS predictions, verifying MLaaS model accuracy, and using ML models for trustless retrieval. Together, our results show that ZK-SNARKs have the promise to make verified ML model inference practical.
Much has been written about the fourth industrial revolution’s (4IR) contributions to and its impact on higher education (HE). In addition, review studies have been conducted on the contributions of 4IR technologies to and on their impact on HE. Most of these studies have reviewed single 4IR technologies in isolation as attested to by the review studies cited in the current study. Against this backdrop, the current study reviewed, discussed, and synthesized the applications, prospects, and challenges of artificial intelligence (AI), robotics, and blockchain at given higher education institutions (HEIs) between 2013 and 2019 as reported by 26 selected journal articles. Employing a slightly modified version of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines for searching and screening, three of the findings of this study are worth mentioning. Firstly, the dominant AI technologies for learning are chatbots, and AI holds the prospect of personalized, scalable, and affordable learning. Secondly, the applications of robotics are exploratory in nature, and have a meta-teaching and a meta-learning orientation. Thirdly, some of the applications of blockchain relate to digital grading, digital credentialing and digital certification, and to real-time contracting and time stamping of learning. The implications of this review are that the three sets of technologies reviewed, have a lot applications for HE, barring the challenges that have been outlined.
Open access
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Due to the high transmission rate and high pathogenicity of the novel coronavirus (COVID-19), there is an urgent need for the diagnosis and treatment of outbreaks around the world. In order to diagnose quickly and accurately, an auxiliary diagnosis method is proposed for COVID-19 based on federated learning and blockchain, which can quickly and effectively enable collaborative model training among multiple medical institutions. It is beneficial to address data sharing difficulties and issues of privacy and security. This research mainly includes the following sectors: in order to address insufficient medical data and the data silos, this paper applies federated learning to COVID-19's medical diagnosis to achieve the transformation and refinement of big data values. With regard to third-party dependence, blockchain technology is introduced to protect sensitive information and safeguard the data rights of medical institutions. To ensure the model's validity and applicability, this paper simulates realistic situations based on a real COVID-19 dataset and analyses problems such as model iteration delays. Experimental results demonstrate that this method achieves a multiparty participation in training and a better data protection and would help medical personnel diagnose coronavirus disease more effectively.
Open access
Privacy-Preserving Technologies in Data
COVID-19 diagnosis using AI
Artificial Intelligence in Healthcare and Education
Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People's mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human's various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area with data concentrated where many details are accessed, generated, and circulated periodically. Healthcare systems with many existing approaches like tracing and tracking continuously disclose the system's constraints in controlling patient data privacy and security. In the healthcare system, majority of the work involves swapping or using decisively confidential and personal data. A key issue is the modeling of approaches that guarantee the value of health-related data while protecting privacy and observing high behavioral standards. This will encourage large-scale perception, especially as healthcare information collection is expected to continue far off this current ongoing pandemic. So, the research section is looking for a privacy-preserving, secure, and sustainable system by using a technology called Blockchain. Data related to healthcare and distribution among institutions is a very challenging task. Storage of facts in the centralized form is a targeted choice for cyber hackers and initiates an accordant sight of patients' facts which will cause a problem in sharing information over a network. So, this research paper's approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.
Muhammad Umar Nasir, Safiullah Khan, Shahid Mehmood, Muhammad Adnan Khan · 6 authors
Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.