Human Behavior Analysis Using Radar Data: A Survey by Patrycja Miazek, Alicja Zmudzinska, Pawel karczmarek, Adam Kiersztyn

“…Transfer learning addresses issues related to data scarcity, while data fusion models integrate information from multiple sources. …”
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Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities by Mahmoud Ragab, Ehab Bahaudien Ashary, Bandar M. Alghamdi, Rania Aboalela, Naif Alsaadi, Louai A. Maghrabi, Khalid H. Allehaibi

“…Federated Learning (FL) offers an encouraging solution to address these challenges by providing a privacy-preserving solution for investigating and detecting cyberattacks in IoT systems without negotiating data privacy. …”
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Ethical and privacy challenges of integrating generative AI into EHR systems in Tanzania: A scoping review with a policy perspective by Augustino Mwogosi

“…Results The review identified six key ethical and privacy challenges associated with generative AI in EHR systems: data privacy and security risks, algorithmic bias and fairness concerns, transparency and accountability issues, consent and autonomy challenges, human oversight gaps and risks of data re-identification. …”
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Enhancing accuracy through ensemble based machine learning for intrusion detection and privacy preservation over the network of smart cities by Mudita Uppal, Yonis Gulzar, Deepali Gupta, Jayant Uppal, Mukesh Kumar, Shilpa Saini

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Aerial pathways to resilience: the acceptance of drones in logistics transformation by Sofia Gomes, João M. Lopes, Tiago Trancoso

“…This study examines the determinants of consumer acceptance of drone delivery services, focusing on the roles of perceived usefulness, ease of use, and privacy concerns. We collected data from 1,108 Portuguese consumers through an online survey to assess how these factors influence consumer attitudes and intentions toward adopting drone delivery services. …”
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Smart Grid Intrusion Detection for IEC 60870-5-104 With Feature Optimization, Privacy Protection, and Honeypot-Firewall Integration by Pedamallu Sai Mrudula, Rayappa David Amar Raj, Archana Pallakonda, Yanamala Rama Muni Reddy, K. Krishna Prakasha, V. Anandkumar

“…Furthermore, the proposed framework includes a federated learning-based scheme that utilizes differential privacy and homomorphic encryption to ensure the privacy and integrity of the data to enhance model interpretability and efficiency with feature ranking to provide insights into attack patterns and anomaly characteristics. …”
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Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework by Fatemeh Sarani Rad, Juan Li

“…Furthermore, these approaches typically rely on centralized data processing, which raises privacy concerns due to the sensitive nature of health care data. …”
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NN-QuPiD Attack: Neural Network-Based Privacy Quantification Model for Private Information Retrieval Protocols by Rafiullah Khan, Mohib Ullah, Atif Khan, Muhammad Irfan Uddin, Maha Al-Yahya

“…Numerous techniques are available to address privacy infringement, including Private Information Retrieval (PIR) protocols that use peer nodes to preserve privacy. …”
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A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans by Abdullah Al-Saleh, Ghanshyam G. Tejani, Shailendra Mishra, Sunil Kumar Sharma, Seyed Jalaleddin Mousavirad

“…Because traditional deep learning models store all their data together, they raise questions about privacy, complying with regulations and the different types of data used by various institutions. …”
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A novel end-to-end privacy preserving deep Aquila feed forward networks on healthcare 4.0 environment by Ponugoti Kalpana, Sunitha Tappari, L. Smitha, Dasari Madhavi, K. Naresh, Maddala Vijayalakshmi

“…Though sensor-driven devices have largely eased everyday lives, these healthcare systems have been suffering from various security breaches and data privacy problems. This evokes a need for designing intelligent systems to eradicate data breaches and privacy problems. …”
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Effect of Workshop Training on Nursing Students’ Knowledge and Attitude Regarding Children’s Privacy Protection: A Quasi-experimental Study by Fatemeh Azimi, Fariba Asgari, Yasaman Yaghobi, Saman Maroufizadeh, Morteza Rahbar Taramsari

“…Data analysis was performed by using descriptive (Mean±SD, frequency, percentage) and inferential statistics (Pearson correlation test and analysis of covariance). …”
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A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems by Sarah Qahtan, Khaironi Yatim, Mohd Hafeez Osman, Hazura Zulzalil, Muhammad Luqman Mahamad Zakaria

“…Second, the weights of each privacy and security property are calculated using the SFS-FWZIC method. …”
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How Social Scene Characteristics Affect Customers’ Purchase Intention: The Role of Trust and Privacy Concerns in Live Streaming Commerce by Wenjian Li, Steiner Cujilema, Lisong Hu, Gang Xie

“…The research employs Structural Equation Modeling (SEM) to test the hypothesis using data from 504 valid responses. (3) The results demonstrate that the characteristics of social scenes proposed in this study enhance consumer trust and positively impact purchase intention. …”
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Enhancing Cybersecurity and Privacy Protection for Cloud Computing-Assisted Vehicular Network of Autonomous Electric Vehicles: Applications of Machine Learning by Tiansheng Yang, Ruikai Sun, Rajkumar Singh Rathore, Imran Baig

“…However, more connected and engaged nodes also increase the system’s vulnerability to cybersecurity and privacy breaches. Various security and privacy challenges in vehicular cloud computing and its variants (VEC, VFC) can be efficiently tackled using machine learning (ML). …”
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Community views on the secondary use of general practice data: Findings from a mixed‐methods study by Annette J. Braunack‐Mayer, Carolyn Adams, Alberto Nettel‐Aguirre, Belinda Fabrianesi, Lucy Carolan, Justin Beilby, Felicity Flack

“…Abstract Introduction General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance, evaluation, health service planning and research. …”
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Network Attack Detection for Business Safety by Fadia Abduljabbar Saeed, Ghalia Nassreddine, Joumana Younis

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Tabular Data Augmentation Using Artificial Intelligence: A Systematic Review and Taxonomic Framework by Mauro Henrique Lima de Boni, Iwens Gervasio Sene Junior, Ronaldo Martins da Costa

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Psychiatrists’ and trainees’ knowledge, perception, and readiness for integration of artificial intelligence in mental health care in Nigeria by Olatunji Alao Abiodun, Peter Omoniyi Ajiboye, Mumeen Olaitan Salihu, Dauda Sulyman, Adesanmi Akinsulore, Okwudili Obayi, Hassan Bala Salihu

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The Impact of Artificial Intelligence on the Future Business World by Hebah Y. AlQato

“…In order to assist businesses better understand their consumers' requirements and preferences, AI is being used to examine customer data and generate insights. …”
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AdaBoost algorithm based on target perturbation by Shufen ZHANG, Yanling DONG, Jingcheng XU, Haoshi WANG

“…Aiming at the problem that the multi-round iteration process in the AdaBoost algorithm will amplify the noise added to achieve differential privacy protection, which leads to slow model convergence and greatly reduced data availability, an AdaBoost algorithm based on target perturbation—DPAda was proposed.Target perturbation was used to add noise to sample weights, accurately calculated their sensitivity, and a dynamic privacy budget was given.In order to solve the problem of excessive noise superposition, three noise injection algorithms based on swing sequence, random response and improved random response were proposed.The experimental results show that compared with DPAda_Random and DPAda_Swing, DPAda_Improved achieves the privacy protection of data, has higher classification accuracy, as well as better than other differential privacy AdaBoost algorithm, and can also solve the problem of excessive noise caused by continuous noise addition.…”
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