Enhancing Rehabilitation Assessment with Artificial Intelligence: A Comprehensive Investigation of Posture Quality Prediction Using Machine Learning by Zhang Wenxi

“…This paper concludes by highlighting the need for more adaptable and interpretable AI systems that can be seamlessly integrated into different rehabilitation scenarios while maintaining patient data privacy and ethical standards.…”
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Exploring real estate blockchain adoption: An empirical study based on an integrated task-technology fit and technology acceptance model. by Hailan Yang, Zixian Zhang, Chen Jian, Nisar Ahmad

“…The study's findings indicate that attitude, perceived usefulness (PU) and data privacy and security (DPS) exerts highest influence in the proposed theoretical model. …”
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A comprehensive review of large language models: issues and solutions in learning environments by Tariq Shahzad, Tehseen Mazhar, Muhammad Usman Tariq, Wasim Ahmad, Khmaies Ouahada, Habib Hamam

“…Furthermore, the study presents practical case studies and solutions to barriers, such as data privacy and bias, offering insights into their role in enhancing the teaching–learning process. …”
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Modeling Nonusers’ Behavioral Intention towards Mobile Chatbot Adoption: An Extension of the UTAUT2 Model with Mobile Service Quality Determinants by Gatzioufa Paraskevi, Vaggelis Saprikis, Giorgos Avlogiaris

“…In addition, equipment, interface, and trust have a significant impact on users’ trust in the context of mobile chatbots. Personal data privacy issues also have a negative effect on trust, in contrast to effort expectancy, which positively affects performance expectancy. …”
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Hierarchical Learning: A Hybrid of Federated Learning and Personalization Fine-Tuning by Li Shuyi, Zhang Bairong

“…This hybrid approach not only enhances model accuracy but also preserves data privacy and increases scalability, making it a promising solution for decentralized recommendation systems.…”
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A Bibliometric Analysis on Federated Learning by Ersin Namlı, Yusuf Sait Türkan, Mesut Ulu, Ömer Algorabi

“…With the rapid advancement of technology and growing concerns about data privacy, federated learning (FL) has attracted considerable attention from the scientific community. …”
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Access control scheme for medical data based on PBAC and IBE by Yi-ting ZHANG, Yu-chuan FU, Ming YANG, Jun-zhou LUO

“…Due to the large amount of personal privacy information contained,the medical big data formed in the health care industry was faced with potential threats of both external attacks and internal data leakages.However,traditional access control technology didn’t take into account the important role of user access purpose in the access control schemes that emphasized data privacy,and existing symmetric and asymmetric encryption technologies both face problems such as the complexity of key and certificate management.To address these problems,a novel access control scheme based on PBAC model and IBE encryption technology was proposed,which could provide flexible access control of encrypted medical data.By introducing the concept of conditioned purpose,the PBAC model was extended to achieve full coverage of purpose trees.Furthermore,the scheme used patient ID,conditioned bit and intended purpose as the IBE public key,with which patients’ data were encrypted.Only users who pass the authentication and whose access purposes conform to the intended purposes can obtain the corresponding private keys and the encrypted data,thereby achieving access to patients’ information.Experimental results prove that the scheme can achieve the goals of fine-grained access control and privacy protection with high performance.…”
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The Role of Artificial Intelligence in the Future of Language Teaching and Learning Practices in Higher Education by Job W. Mwakapina

“…However, challenges such as data privacy, lack of proficiency in AI, lack of suitable equipment, plagiarism issues, and high dependency on AI need to be addressed. …”
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Privacy-preserving security of IoT networks: A comparative analysis of methods and applications by Abubakar Wakili, Sara Bakkali

“…However, deploying IoT networks introduces critical privacy and security challenges, including resource constraints, scalability issues, interoperability gaps, and risks to data privacy. Addressing these challenges is vital to ensure the reliability and trustworthiness of IoT applications. …”
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Regulation and protection of personal health data in the AI era: international experience by N. M. Galkina, D. V. Kuznetsova

“…Each country is endeavoring to strike a balance between the protection of personal data privacy and the advancement of technological innovations. …”
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Privacy-enhanced federated learning scheme based on generative adversarial networks by Feng YU, Qingxin LIN, Hui LIN, Xiaoding WANG

“…Federated learning, a distributed machine learning paradigm, has gained a lot of attention due to its inherent privacy protection capability and heterogeneous collaboration.However, recent studies have revealed a potential privacy risk known as “gradient leakage”, where the gradients can be used to determine whether a data record with a specific property is included in another participant’s batch, thereby exposing the participant’s training data.Current privacy-enhanced federated learning methods may have drawbacks such as reduced accuracy, computational overhead, or new insecurity factors.To address this issue, a differential privacy-enhanced generative adversarial network model was proposed, which introduced an identifier into vanilla GAN, thus enabling the input data to be approached while satisfying differential privacy constraints.Then this model was applied to the federated learning framework, to improve the privacy protection capability without compromising model accuracy.The proposed method was verified through simulations under the client/server (C/S) federated learning architecture and was found to balance data privacy and practicality effectively compared with the DP-SGD method.Besides, the usability of the proposed model was theoretically analyzed under a peer-to-peer (P2P) architecture, and future research work was discussed.…”
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Integrating AI and statistical methods for enhancing civil structures: current trends, practical issues and future direction by Asraar Anjum, Meftah Hrairi, Abdul Aabid, Maisarah Ali

“…The investigation also highlights the need for substantial computational resources, data privacy, security, and software interoperability. …”
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Why IoT Enablement of Agrifood Transportation Disappoints Its Stakeholders: Unravelling Barriers for Enhanced Logistics by Deepika Joshi, Sumit Gupta, Amit Vishwakarma, Sandeep Jagtap

“…They are responsible for creating issues like data processing, vehicle tracking, and data privacy. This study offers a contextual phenomenon of barriers that may assist AgriTech stakeholders in developing appropriate strategies to embrace IoT transformation. …”
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Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments by Zheyi Chen, Xu Zhao, Bing Lin

“…The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. …”
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Artificial intelligence and machine learning in production efficiency enhancement and sustainable development: a comprehensive bibliometric review by Aristidis Bitzenis, Nikos Koutsoupias, Marios Nosios

“…Despite the promising advancements, the review identifies gaps in ethical considerations, especially in data privacy and labor market implications, and suggests avenues for future research, including the implementation of AI and ML in developing economies and Small and Medium Enterprises (SMEs).…”
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Leveraging AI to optimize vaccines supply chain and logistics in Africa: opportunities and challenges by Sulaiman Muhammad Musa, Usman Abubakar Haruna, Lukman Jibril Aliyu, Mubarak Zubairu, Don Eliseo Lucero-Prisno, Don Eliseo Lucero-Prisno, Don Eliseo Lucero-Prisno

“…AI has the potential to increase productivity by streamlining logistics and inventory management, but it is hampered by issues with data privacy and technology infrastructure. This perspectiveoffers ways for utilizing AI to enhance vaccine supply chains in Africa, citing successful experiences in Nigeria, Malawi, Rwanda, and Ghana as examples of AI’s advantages. …”
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Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective by Fahimeh Mirakhori, Sarfaraz K. Niazi

“…The term AI itself has become commonplace to argue that greater “human oversight” for “machine intelligence” is needed to harness the power of this revolutionary technology for both potential and risk management, and hence to call for more practical regulatory guidelines, harmonized frameworks, and effective policies to ensure safety, scalability, data privacy, and governance, transparency, and equitable treatment. …”
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Clients selection method based on knapsack model in federated learning by Jiahui GUO, Zhuoyue CHEN, Wei GAO, Xijun WANG, Xinghua SUN, Lin GAO

“…In recent years, to break down data barriers, federated learning (FL) has received extensive attention.In FL, clientscan complete the model training without uploading the raw data, which protects the user’s data privacy.For the issue of clients’ heterogeneity, the contribution of each client to accelerating convergence of the global model as well as the communication cost in the system was considered, aiming at maximizing the weight change of the client's local training model, a client selection optimization problem in FL under theconstraint ofthe delay foreach training round was solved.Subsequently, two federated learning protocols based on the knapsack model were proposed, namely OfflineKP-FL protocol and OnlineKP-FL protocol.OfflineKP-FL protocol was based on the offline knapsack model to select appropriate clients to participate in the aggregation and update of the global model.In order to reduce the complexity of the OfflineKP-FL protocol, OnlineKP-FL protocol based on the online knapsack model to select clients was proposed.Through simulations, it is found that OfflineKP-FL protocol converges faster than the previously proposed methods in certain cases.Furthermore, compared with OfflineKP-FL protocol and FedCS protocol, underthe proposed OnlineKP-FL protocol, not only does the system select fewer clients per round, but also it can complete the model training in 64.1% of the time required by FedCS protocol to achieve the same accuracy for the global model.…”
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A Trusted Federated Learning Method Based on Consortium Blockchain by Xiaojun Yin, Xijun Wu, Xinming Zhang

“…Federated learning (FL) has gained significant attention in distributed machine learning due to its ability to protect data privacy while enabling model training across decentralized data sources. …”
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