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Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely used, they are also increasingly exposed to various types of vulnerabilities and medical errors. In healthcare information systems, about 90% of vulnerabilities emerge from medical error and human error. As a result, there is a need for additional research and development of security tools to prevent such attacks. This article proposes a zero-trust-based context-aware framework for managing access to the main components of the cloud ecosystem, including users, devices, and output data. The main goal and benefit of the proposed framework is to build a scoring system to prevent or alleviate medical errors while using distributed medical devices in cloud-based healthcare information systems. The framework has two main scoring criteria to maintain the chain of trust. First, it proposes a critical trust score based on cloud-native microservices for authentication, encryption, logging, and authorizations. Second, a bond trust scoring system is created to assess the real-time semantic and syntactic analysis of attributes stored in a healthcare information system. The analysis is based on a pre-trained machine learning model that generates the semantic and syntactic scores. The framework also takes into account regulatory compliance and user consent in the creation of the scoring system. The advantage of this method is that it applies to any language and adapts to all attributes, as it relies on a language model, not just a set of predefined and limited attributes. The results show a high F 1 score of 93.5%, which proves that it is valid for detecting medical errors. [ABSTRACT FROM AUTHOR]

The advancement in technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of finite impulse response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of a comprehensive analysis show that the genetic algorithm-based filter is more effective than conventional filter designs in terms of noise removal efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a systematic student-level approach to assess the impact of problem-based learning on enhancing students' performance in an undergraduate Digital Logic Design course. The problem was assigned as a mini-project and chosen to strengthen the student's understanding of an advanced part of the course, namely the sequential logic design. The study included data from semesters where students were assigned projects and other semesters without projects. The proposed approach relies on dividing students into groups based on their academic performance level to study how the project impacts these groups' performance. A baseline performance metric was created at the beginning of the semester to classify the students into three groups. At the end of the semester, the students' groups were reassessed to capture performance changes. The results consistently showed improvement across all students' levels in the semesters where the project was conducted. While it is natural for students' performance to change between the beginning and the end of the semester, the results show that the percentage of students who improved their performance level has increased in semesters with a project compared to the semesters with no projects. Also, the percentage of students whose performance degraded by the end of the semester has decreased in the semesters with a project. These results were also supported by an independent student survey that confirmed the positive project impact on the students' grasp of sequential circuit design. [ABSTRACT FROM AUTHOR]

The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that plays an important role in digital health applications. Medical images account for 90% of the data in digital medicine applications. This article discusses the core foundations of the ViT architecture and its digital health applications. These applications include image segmentation, classification, detection, prediction, reconstruction, synthesis, and telehealth such as report generation and security. This article also presents a roadmap for implementing the ViT in digital health systems and discusses its limitations and challenges. [ABSTRACT FROM AUTHOR]

In remote sensing data processing, cover classification on decimeter-level data is a well-studied but tough subject that has been well-documented. The majority of currently existent works make use of orthographic photographs or orthophotos and digital surface models that go with them (DSMs). Urban land cover classification plays a significant role in the field of remote sensing to enhance the quality of different applications including environment protection, sustainable development, and resource management and planning. Novelty of the research done in this area is focused on extracting features from high-resolution satellite images to be used in the classification process. However, it is well known in machine learning literature that some of the extracted features are irrelevant to the classification process with a negative or no effect on its accuracy. In this work, a genetic algorithm-based feature selection approach is used to enhance the performance of urban land cover classification. Neural networks (NNs) and random forest (RF) classifiers were used to evaluate the proposed approach on a recent urban land cover dataset of nine different classes. Experimental results show that the proposed approach achieved better performance with RF classifier using only 27% of the features. The random forest tree has achieved highest accuracy 84.27%; it is concluded that the RF algorithm is an appropriate algorithm for classifying cover land. [ABSTRACT FROM AUTHOR]

We present a systematic methodology for exploring the design space of similarity distance computation in machine learning algorithms. Previous architectures proposed in the literature have been obtained using ad hoc techniques that do not allow for design space exploration. The size and dimensionality of the input datasets have not been taken into consideration in previous works. This may result in impractical designs that are not amenable for hardware implementation. The methodology presented in this work is used to obtain the 3-D computation domain of the similarity distance computation algorithm. A scheduling function determines whether an algorithm variable is pipelined or broadcast. Four linear scheduling functions are presented, and six possible 2-D processor array architectures are obtained and classified based on the size and dimensionality of the input datasets. The obtained designs are analyzed in terms of speed and area, and compared with previously obtained designs. The proposed designs achieve better time and area complexities. [ABSTRACT FROM AUTHOR]

This paper proposes a hardware realization of the crossover module in the genetic algorithm for the travelling salesman problem (TSP). In order to enhance performance, we employ a combination of pipelining and parallelization with a genetic algorithm (GA) processor to improve processing speed, as compared to software implementation. Simulation results showed that the proposed architecture is six times faster than the similar existing architecture. The presented field-programmable gate array (FPGA) implementation of PMX crossover operator is more than 400 times faster than in software. [ABSTRACT FROM AUTHOR]