No abstract

No abstract

Artificial intelligence (AI) applications are an integral and emerging component of digital agriculture. AI can help ensure sustainable production in agriculture by enhancing agricultural operations and decision-making. Recommendations about soil condition and pesticides or automatic devices for milking and apple picking are examples of AI applications in digital agriculture. Although AI offers many benefits in farming, AI systems may raise ethical issues and risks that should be assessed and proactively managed. Poor design and configuration of intelligent systems may impose harm and unintended consequences on digital agriculture. Invasion of farmers' privacy, damaging animal welfare due to robotic technologies, and lack of accountability for issues resulting from the use of AI tools are only some examples of ethical challenges in digital agriculture. This paper examines the ethical challenges of the use of AI in agriculture in six categories including fairness, transparency, accountability, sustainability, privacy, and robustness. This study further provides recommendations for agriculture technology providers (ATPs) and policymakers on how to proactively mitigate ethical issues that may arise from the use of AI in farming. These recommendations cover a wide range of ethical considerations, such as addressing farmers' privacy concerns, ensuring reliable AI performance, enhancing sustainability in AI systems, and reducing AI bias.

No abstract

Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applications to propose robust authentication protocols. Since machine learning methods are trained based on hidden concepts in biometric and physical layer data, these dynamic authentication models can be more reliable than traditional methods. The main advantage of these methods is that the behavioral traits of humans and devices are tough to counterfeit. Furthermore, machine learning facilitates continuous and context-aware authentication.

With the increasing use of precision agriculture and technological development, the agricultural sector has been majorly transformed. Precision agriculture uses technological innovations such as sensors, drones, and data analysis tools to improve the productivity of resources and management decisions on the farm. Since these technologies collect a large amount of data related to the farm, the farmers are concerned about the privacy of their data. The farmers are worried about unauthorized access, collection, and sharing of their data with third parties by the agricultural technology providers (ATPs). Furthermore, the ambiguity of agreements and legal frameworks around data collection, processing, and sharing may result in uncertainty in data privacy practices. Furthermore, this situation is aggravated by a lack of adoption of best practices and standards for farm data protection. Violation of privacy can cause reluctance among farmers to adopt new technologies which can negatively impact various stakeholders, government, and public. Protecting farmers' privacy and respecting their rights related to the collected data should be addressed collectively by the actors in the farming ecosystem, including farmers, agricultural technology providers, governments, and supply chain stakeholders. This paper aims at providing recommendations on how to minimize privacy risks and concerns for farmers and reviews some of the data governance best practices for data protection.

No abstract

In digital healthcare systems, with digitalization, data can be easily accessed. Considering the sensitivity of confidential information, the need for security is accelerated during this time. One of the most important security aspects is authentication which should be utilized. The available authentication models that rely on Machine Learning (ML) have some shortcomings, such as difficulties in appending new users to the system or model training sensitivity to imbalanced data. To address these problems, we propose an application of the Siamese networks using ECG signals which are easily reachable in digital healthcare systems. Adding some preprocessing for feature extraction in such a model could lead us to prominent results. This model is performed on ECD-ID and PTB datasets and approaches 92% and 95% of accuracy, respectively. A combination of simplicity and high performance made it an exclusive choice for smart healthcare and telehealth.