Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the edge allowing the remote healthcare centers, lacking advanced diagnostic facilities, to benefit from the multi-modal data securely. To this aim, we utilize the emerging concept of clustered federated learning (CFL) for an automatic diagnosis of COVID-19. Such an automated system can help reduce the burden on healthcare systems across the world that has been under a lot of stress since the COVID-19 pandemic emerged in late 2019. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific type of COVID-19 imagery) are trained with central data, and improvements of 16\% and 11\% in overall F1-Scores have been achieved over the multi-modal model trained in the conventional Federated Learning setup on X-ray and Ultrasound datasets, respectively. We also discuss in detail the associated challenges, technologies, tools, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.Comment: preprint versio

ENHANCEMENT STUDY OF POLYMER RAPID TOOLING FOR METAL INJECTION MOLDING

Machined metal mold are used for high-volume manufacturing of metal injection molding (MIM) parts. The main limitations associated with the machined metal mold are that its manufacturing is pricy and time consumin

ENHANCEMENT STUDY OF POLYMER RAPID TOOLING FOR METAL INJECTION MOLDING

Machined metal mold are used for high-volume manufacturing of metal injection molding (MIM) parts. The main limitations associated with the machined metal mold are that its manufacturing is pricy and time consumin

Assessment of Convolutional Neural Network Pre-Trained Models for Detection and Orientation of Cracks

Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a key component of the process. Images can automatically be classified using convolutional neural networks (CNNs), a subtype of deep learning (DL). For image categorization, a variety of pre-trained CNN architectures are available. This study assesses seven pre-trained neural networks, including GoogLeNet, MobileNet-V2, Inception-V3, ResNet18, ResNet50, ResNet101, and ShuffleNet, for crack detection and categorization. Images are classified as diagonal crack (DC), horizontal crack (HC), uncracked (UC), and vertical crack (VC). Each architecture is trained with 32,000 images equally divided among each class. A total of 100 images from each category are used to test the trained models, and the results are compared. Inception-V3 outperforms all the other models with accuracies of 96%, 94%, 92%, and 96% for DC, HC, UC, and VC classifications, respectively. ResNet101 has the longest training time at 171 min, while ResNet18 has the lowest at 32 min. This research allows the best CNN architecture for automatic detection and orientation of cracks to be selected, based on the accuracy and time taken for the training of the model

Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML

Internet of Medical Things (IoMT) supports traditional healthcare systems by providing enhanced scalability, efficiency, reliability, and accuracy of healthcare services. It enables the development of smart hardware as well as software platforms that operate on the basis of communication systems and the algorithms that process the data collected by the sensors to support decision-making. Although IoMT is involved in large-scale services provisioning in the medical paradigm; however, the resource-constrained nature of these devices makes them vulnerable to immense security and privacy issues. These vulnerabilities are not only disastrous for IoMT but threaten the whole healthcare ecosystem, which can in turn bring human lives in danger. During the past few years, threat vectors against IoMT have been evolved in terms of scalability, complexity, and diversity, which makes it challenging to detect and provide stringent defense solutions against these attacks. In this paper, we classify security and privacy challenges against different IoMT variants based on their actual usage in the healthcare domain. We provide a comprehensive attack taxonomy on the overall IoMT infrastructure comprising different device variants as well as elaborate taxonomies of security protocols to mitigate attacks against different devices, algorithms and describe their strengths and weaknesses. We also outline the security and privacy requirements for the development of novel security solutions for all the attack types against IoMT. Finally, we provide a comprehensive list of current challenges and future research directions that must be considered while developing sustainable security solutions for the IoMT infrastructure. 2022 The Author(s)Junaid Qadir is a Professor at the Qatar University in Doha, Qatar, and the Information Technology University (ITU) of Punjab in Lahore, Pakistan. He directs the IHSAN Research Lab. His primary research interests are in the areas of computer systems and networking, applied machine learning, using ICT for development (ICT4D); human-beneficial artificial intelligence; ethics of technology, artificial intelligence, and data science; and engineering education. He has published more than 150 peer-reviewed articles at various high-quality research venues including publications at top international research journals including IEEE Communication Magazine, IEEE Journal on Selected Areas in Communication (JSAC), IEEE Communications Surveys and Tutorials (CST), and IEEE Transactions on Mobile Computing (TMC). He was awarded the highest national teaching award in Pakistan-the higher education commission's (HEC) best university teacher award-for the year 2012-2013. He has obtained research grants from Facebook Research, Qatar National Research Fund, and the HEC, Pakistan. He has been appointed as ACM Distinguished Speaker for a three-year term starting from 2020. He is a senior member of IEEE and ACM.Scopu

Grain refinement of ASTM A356 aluminum alloy using sloping plate process through gravity die casting

Sloping plate flow is used for enhancement of material properties through grain refinement in gravity die casting of Aluminum alloy ASTM A356. The castings are prepared with different slope angles of an 800 mm long, naturally cooled stainless steel plate. The specimens obtained are then tested for tensile strength and elongation. Microstructure of the cast specimens is observed and conclusions drawn on the grain size and precipitate morphology as a function of angle of sloping plate. Analysis is presented for the boundary layer created while the material flows over the plate. An indication of the boundary layer thickness is determined by measuring the thickness of the residual metal layer on the plate after casting. An analytical solution of the boundary layer thickness is also presented. It is shown that the calculated boundary layer thickness and the thickness of the layer of material left in the channel after casting are in good agreement. Moreover, microstructure examination and tensile tests show that best properties are achieved with a 60° sloping plate

Quantum Computing for Healthcare: A Review

In recent years, the interdisciplinary field of quantum computing has rapidly developed and garnered substantial interest from both academia and industry due to its ability to process information in fundamentally different ways, leading to hitherto unattainable computational capabilities. However, despite its potential, the full extent of quantum computing's impact on healthcare remains largely unexplored. This survey paper presents the first systematic analysis of the various capabilities of quantum computing in enhancing healthcare systems, with a focus on its potential to revolutionize compute-intensive healthcare tasks such as drug discovery, personalized medicine, DNA sequencing, medical imaging, and operational optimization. Through a comprehensive analysis of existing literature, we have developed taxonomies across different dimensions, including background and enabling technologies, applications, requirements, architectures, security, open issues, and future research directions, providing a panoramic view of the quantum computing paradigm for healthcare. Our survey aims to aid both new and experienced researchers in quantum computing and healthcare by helping them understand the current research landscape, identifying potential opportunities and challenges, and making informed decisions when designing new architectures and applications for quantum computing in healthcare. 2023 by the authors.Scopu

Metal Injection Molding Process Parameters as A Function of Filling Performance of 3D Printed Polymer Mold

Metal injection molding (MIM) is a swift manufacturing process, which can produce complex and intricate parts with good repeatability and accuracy. However, to quickly address low-volume demands of customized MIM parts, manufacturing of mold could be a potential challenge. Typically, machined metal molds are used for MIM, but they are expensive and need more lead time. The machined metal mold becomes useless once the design is changed or requirement of MIM parts is met. Therefore, for MIM production of a low volume of highly customized parts, machined metal mold could be substituted by 3D printed polymer molds. However, knowledge of filling behavior of MIM feedstock in polymer mold is a grey area, which demands study to investigate the effects of injection parameters on mold filling. The present study investigates the effects of machine injection parameters on feedstock filling behavior in 3D printed polymer molds. An attempt has been made to determine the trend of feedstock filling in the polymer mold as a function of injection parameters. Further, the design of experiment (DOE) has been used to estimate the weight of injection parameters

Metal Injection Molding Process Parameters as A Function of Filling Performance of 3D Printed Polymer Mold

Metal injection molding (MIM) is a swift manufacturing process, which can produce complex and intricate parts with good repeatability and accuracy. However, to quickly address low-volume demands of customized MIM parts, manufacturing of mold could be a potential challenge. Typically, machined metal molds are used for MIM, but they are expensive and need more lead time. The machined metal mold becomes useless once the design is changed or requirement of MIM parts is met. Therefore, for MIM production of a low volume of highly customized parts, machined metal mold could be substituted by 3D printed polymer molds. However, knowledge of filling behavior of MIM feedstock in polymer mold is a grey area, which demands study to investigate the effects of injection parameters on mold filling. The present study investigates the effects of machine injection parameters on feedstock filling behavior in 3D printed polymer molds. An attempt has been made to determine the trend of feedstock filling in the polymer mold as a function of injection parameters. Further, the design of experiment (DOE) has been used to estimate the weight of injection parameters

Predicting characteristics of cracks in concrete structure using convolutional neural network and image processing

The degradation of infrastructures such as bridges, highways, buildings, and dams has been accelerated due to environmental and loading consequences. The most popular method for inspecting existing concrete structures has been visual inspection. Inspectors assess defects visually based on their engineering expertise, competence, and experience. This method, however, is subjective, tiresome, inefficient, and constrained by the requirement for access to multiple components of complex structures. The angle, width, and length of the crack allow us to figure out the cause of the propagation and extent of the damage, and rehabilitation can be suggested based on them. This research proposes an algorithm based on a pre-trained convolutional neural network (CNN) and image processing (IP) to obtain the crack angle, width, endpoint length, and actual path length in a concrete structure. The results show low relative errors of 2.19%, 14.88%, and 1.11%, respectively for the crack angle, width, and endpoint length from the CNN and IP methods developed in this research. The actual path length is found to be 14.69% greater than the crack endpoint length. When calculating the crack length, it is crucial to consider its irregular shape and the likelihood that its actual path length will be greater than the direct distance between the endpoints. This study suggests measurement methods that precisely consider the crack shape to estimate its actual path length