Cloud-based Artificial Intelligence Research Articles

Delocalized, asynchronous, closed-loop discovery of organic laser emitters.

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.

Ensuring Compliance Integrity in AI ML Cloud Environments: The Role of Data Guardianship

Artificial intelligence (AI) has become ubiquitous across various industries, including security, healthcare, finance, and national defense. However, alongside its transformative potential, there has been a concerning rise in malicious exploitation of AI capabilities. Simultaneously, the rapid advancement of cloud computing technology has led to the emergence of cloud-based AI systems. Unfortunately, vulnerabilities inherent in cloud infrastructure also pose security risks to AI services. We recognize the critical role of maintaining the integrity of training data, as any compromise therein directly impacts the effectiveness of AI systems. In response to this challenge, we emphasize the paramount importance of preserving data integrity within AI systems. To address this need, we propose a data integrity architecture guided by the National Institute of Standards and Technology (NIST) cybersecurity framework. Leveraging blockchain technology and smart contracts presents a suitable solution for addressing integrity challenges, given their features of shared and decentralized ledgers. Smart contracts enable automated policy enforcement, facilitate continuous monitoring of data integrity, and help mitigate the risk of data tampering.

Ensuring Compliance Integrity in AI ML Cloud Environments: The Role of Data Guardianship

Artificial intelligence (AI) has become ubiquitous across various industries, including security, healthcare, finance, and national defense. However, alongside its transformative potential, there has been a concerning rise in malicious exploitation of AI capabilities. Simultaneously, the rapid advancement of cloud computing technology has led to the emergence of cloud-based AI systems. Unfortunately, vulnerabilities inherent in cloud infrastructure also pose security risks to AI services. We recognize the critical role of maintaining the integrity of training data, as any compromise therein directly impacts the effectiveness of AI systems. In response to this challenge, we emphasize the paramount importance of preserving data integrity within AI systems. To address this need, we propose a data integrity architecture guided by the National Institute of Standards and Technology (NIST) cybersecurity framework. Leveraging blockchain technology and smart contracts presents a suitable solution for addressing integrity challenges, given their features of shared and decentralized ledgers. Smart contracts enable automated policy enforcement, facilitate continuous monitoring of data integrity, and help mitigate the risk of data tampering.

AI-based fully automatic image analysis: Optimal abdominal and thoracic segmentation volumes for estimating total muscle volume on computed tomography scans

ObjectivesEvaluation of sarcopenia from computed tomography (CT) is often based on measuring skeletal muscle area on a single transverse slice. Automatic segmentation of muscle volume has a lower variance and may be a better proxy for the total muscle volume than single-slice areas. The aim of the study was to determine which abdominal and thoracic anatomical volumes were best at predicting the total muscle volume. MethodsA cloud-based artificial intelligence tool (recomia.org) was used to segment all skeletal muscle of the torso of 994 patients who had performed whole-torso CT 2008–2020 for various clinical indications. Linear regression models for several anatomical volumes and single-slice areas were compared with regard to predicting the total torso muscle volume. ResultsThe muscle volume from the tip of the coccyx and 25 cm cranially was the best of the abdominal volumes and was significantly better than the L3 slice muscle area (R2 0.935 vs 0.830, P < 0.0001). For thoracic volumes, the muscle volume between the top of the sternum to the lower bound of the Th12 vertebra showed the best correlation with the total volume, significantly better than the Th12 slice muscle area (R2 0.892 vs 0.775, P < 0.0001). Adjusting for body height improved the correlation slightly for all measurements but did not significantly change the ordering. ConclusionsWe identified muscle volumes that can be reliably segmented by automated image analysis which is superior to single slice areas in predicting total muscle volume.

Empirircal investigation into y-the integration of cloud-based artificial intelligence in auditing

Purpose- The increasing prevalence of cloud computing and the rapid proliferation of artificial intelligence technologies have opened up novel prospects for enhancing auditing methodologies. This research aims to scrutinize the consequences of incorporating cloud-based artificial intelligence (CBAI) in auditing, focusing on its implications for audit clients, auditors, and the overall audit process. Methodology- A method of quantitative research was utilized in this study, where 322 questionnaires were distributed to external auditors in Jordan. The objective was to collect information on the potential enhancements brought about by cloud-based artificial intelligence in the auditing field. The study employed convenience random sampling, a technique involving the collection of data from readily available members of the population, which, in this context, refers to external audit offices in Jordan. Jordan has a total of 454 audit offices with a diverse range of auditors, including partner-owner auditors, assistant auditors, and certified auditors. The analysis of the gathered data was conducted using SmartPLS software, which employs structural equation modeling (SEM). Findings- The study's outcomes reveal the potential for cost savings associated with the adoption of CBAI, as well as the streamlining of audit procedures and the enhancement of overall efficiency. Moreover, the research observes the transformation of auditors' roles, with a shift towards a greater focus on analytical and advisory responsibilities, departing from traditional manual tasks. These findings underscore the potential advantages for audit clients, auditors, and the audit process, underscoring the significance of embracing these technologies to advance the auditing profession in the digital age. Conclusion- The study investigates how CBAI influences audit quality and efficiency through a thorough examination of data sourced from a sample of external auditors operating within the context of Jordan. Additionally, it delves into the significant advancements that CBAI can introduce to the auditing field, encompassing its potential for data storage and analysis, the improvement of communication between auditors and clients, the adoption of innovative audit techniques, and the shift from manual to digital auditing methodologies. Keywords: Cloud-based artificial intelligence, Auditing, Auditors, Cloud computing, Artificial Intelligence, Audit quality. JEL Codes: O30, O33, O35

Fog-based Edge AI for Robotics: Cutting-edge Research and Future Directions

The fusion of Fog-based Edge Artificial Intelligence (AI) is an emerging and transformative research area within robotics. This research examines the significant potential of augmenting robotic systems by integrating Edge AI and Fog Computing, aiming to enhance their cognitive abilities, independence, and operational effectiveness. The feasibility of real-time data analysis and decision-making is enhanced by deploying AI algorithms at the network edge, near the robots, and by leveraging fog computing capabilities. This study investigates the diverse implementations of Fog-based artificial intelligence (AI) in robotics. These applications encompass autonomous navigation, object detection, and human-robot interaction. By showcasing these examples, the research demonstrates the potential for a transformative impact on the capabilities of robotic systems through the integration of Fog-based AI. Additionally, this study explores the obstacles and potential advantages within this interdisciplinary field, providing valuable perspectives on the promising avenues that can facilitate advancements in robotics by leveraging the combined power of Fog-based Edge Artificial Intelligence. This study elucidates how the amalgamation of Fog Computing and Edge AI confers enhanced capabilities upon intelligent robotic systems, enabling them to operate autonomously in real time. This integration effectively addresses the obstacles commonly encountered in conventional cloud-based AI systems, such as latency, internet connectivity, and data security concerns. The study highlights the importance of architecture, security, and ethical factors in utilizing robotic intelligence. It emphasizes the need for data protection standards and transparency to ensure responsible and reliable utilization of this technology in a rapidly changing environment.

An automated approach for human-animal conflict minimisation in Assam and protection of wildlife around the Kaziranga National Park using YOLO and SENet Attention Framework

Human-animal conflict in Assam, India's north-eastern state, is rising continuously. Because it occurs year-round, it damages agricultural productivity and kills people and animals, including elephants. When a herd of wild elephants emerges from a deep forest and enters human-inhabited territory around the Kaziranga National Park (KNP) in Assam, an alert must be sounded for the neighbourhood residents and forest workers to prevent conflicts. Another concern is that many wild animals die near the KNP while crossing the national highway NH-37 which traverses the area. During floods, animals flee to the highlands for food and shelter. An automated animal identification and warning system near the KNP may reduce human-animal confrontations. This paper reports the design of a system that attempts to address the above concerns. Artificial Intelligence (AI)-based strategies are utilized to recognize wild animals from live video sequences, provide warnings to avoid encounters, and protect humans and animals. Deep learning models and YoloV5 with the SENet attention layer are used to recognize wild animals in real-time. This model is trained using a public and customized dataset of animal species. Cameras attached to the cloud-based AI system take photographs from several KNP locations to confirm the model. The model's 96% accuracy in animal photographs and videos taken day and night and in feed from contemporaneous location has shown its utility. The model also improves reliability by 1–13% over previous methods.

Development of LuxIA, a Cloud-Based AI Diabetic Retinopathy Screening Tool Using a Single Color Fundus Image.

Diabetic retinopathy (DR) is the leading cause of vision impairment in working-age adults. Automated screening can increase DR detection at early stages at relatively low costs. We developed and evaluated a cloud-based screening tool that uses artificial intelligence (AI), the LuxIA algorithm, to detect DR from a single fundus image. Color fundus images that were previously graded by expert readers were collected from the Canarian Health Service (Retisalud) and used to train LuxIA, a deep-learning-based algorithm for the detection of more than mild DR. The algorithm was deployed in the Discovery cloud platform to evaluate each test set. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were computed using a bootstrapping method to evaluate the algorithm performance and compared through different publicly available datasets. A usability test was performed to assess the integration into a clinical tool. Three separate datasets, Messidor-2, APTOS, and a holdout set from Retisalud were evaluated. Mean sensitivity and specificity with 95% confidence intervals (CIs) reached for these three datasets were 0.901 (0.901-0.902) and 0.955 (0.955-0.956), 0.995 (0.995-0.995) and 0.821 (0.821-0.823), and 0.911 (0.907-0.912) and 0.880 (0.879-0.880), respectively. The usability test confirmed the successful integration of LuxIA into Discovery. Clinical data were used to train the deep-learning-based algorithm LuxIA to an expert-level performance. The whole process (image uploading and analysis) was integrated into the cloud-based platform Discovery, allowing more patients to have access to expert-level screening tools. Using the cloud-based LuxIA tool as part of a screening program may give diabetic patients greater access to specialist-level decisions, without the need for consultation.

Survey on Cloud Computing Integrated with Artificial Intelligence

The combination of cloud computing with Artificial Intelligence (AI) has become a viable approach in recent years to handle the increasing processing needs of AI applications. This article offers a thorough analysis of distributed and scalable artificial intelligence frameworks that use cloud computing to improve the effectiveness and performance of Deep Learning (DL). First, we provide a summary of common cloud services and AI frameworks, emphasizing the advantages and disadvantages of each. The important topics of data management and storage in cloud-based AI systems are then covered in detail, including feature engineering, data pretreatment, privacy, and security. Next, we investigate the distributed and parallel training methods for AI models, with particular emphasis on cloud-based training topologies, communication mechanisms, and model splitting. On the next chapters, we examine auto-scaling, load balancing, resource allocation, and performance benchmarking as optimization methodologies for AI workloads on the cloud. We also cover serverless deployment alternatives, containerization, best practices for monitoring, and the deployment and servicing of AI models on the cloud. We give a comprehensive study of costs, optimization methodologies, and case studies exhibiting successful installations to guarantee the cost-effectiveness of cloud-based AI systems. In conclusion, we provide an overview of the study's main conclusions, go through the difficulties and restrictions associated with cloud-based artificial intelligence, and point out new directions for future research in this area. For academics and practitioners trying to use cloud computing to create scalable, effective, and affordable artificial intelligence systems, this article will be a great resource.

EyeArt artificial intelligence analysis of diabetic retinopathy in retinal screening events.

Early detection and treatment of diabetic retinopathy (DR) are critical for decreasing the risk of vision loss and preventing blindness. Community vision screenings may play an important role, especially in communities at higher risk for diabetes. To address the need for increased DR detection and referrals, we evaluated the use of artificial intelligence (AI) for screening DR. Patient images of 124 eyes were obtained using a 45° Canon Non-Mydriatic CR-2 Plus AF retinal camera in the Department of Endocrinology Clinic (Newark, NJ) and in a community screening event (Newark, NJ). Images were initially classified by an onsite grader and uploaded for analysis by EyeArt, a cloud-based AI software developed by Eyenuk (California, USA). The images were also graded by an off-site retina specialist. Using Fleiss kappa analysis, a correlation was investigated between the three grading systems, the AI, onsite grader, and a US board-certified retina specialist, for a diagnosis of DR and referral pattern. The EyeArt results, onsite grader, and the retina specialist had a 79% overall agreement on the diagnosis of DR: 86 eyes with full agreement, 37 eyes with agreement between two graders, 1 eye with full disagreement. The kappa value for concordance on a diagnosis was 0.69 (95% CI 0.61-0.77), indicating substantial agreement. Referral patterns by EyeArt, the onsite grader, and the ophthalmologist had an 85% overall agreement: 96 eyes with full agreement, 28 eyes with disagreement. The kappa value for concordance on "whether to refer" was 0.70 (95% CI 0.60-0.80), indicating substantial agreement. Using the board-certified retina specialist as the gold standard, EyeArt had an 81% accuracy (101/124 eyes) for diagnosis and 83% accuracy (103/124 eyes) in referrals. For referrals, the sensitivity of EyeArt was 74%, specificity was 87%, positive predictive value was 72%, and negative predictive value was 88%. This retrospective cross-sectional analysis offers insights into use of AI in diabetic screenings and the significant role it will play in automated detection of DR. The EyeArt readings were beneficial with some limitations in a community screening environment. These limitations included a decreased accuracy in the presence of cataracts and the functional cost of EyeArt uploads in a community setting.

Cloud-Based Artificial Intelligence Analytics to Assess Combined Sewer Overflow Performance

Cloud-Based Artificial Intelligence Analytics to Assess Combined Sewer Overflow Performance

Secure and Privacy-Preserving Human Interaction Recognition of Pervasive Healthcare Monitoring

A cloud-based Artificial Intelligence (AI) service has recently empowered the Internet of Medical Things (IoMT) in many applications on the remote Human Interaction Recognition of Pervasive Healthcare Monitoring (HIR-PHM). In this work, a framework of the skeleton-based HIR-PHM under secure Edge-Fog-Cloud computing (EFCC) was proposed to manage the computation and storage resources, latency, cyber-attack, and privacy-preserving simultaneously. At the Edge, IoMT with a camera, record the human interaction as videos and sends them to the Fog, which installed a human pose estimation model, PoseNet, to convert the videos into human skeleton data. At the Cloud, a skeleton-based Spatial-Temporal Graph Convolution Network with Pairwise Adjacency Matrix (STGCN-PAM) was employed to recognize the human interaction. A Hybrid of One Time Password (OTP), Hashed Messages Authentication Code (HMAC), and symmetric cipher were employed to secure the skeleton data. The UT-Interaction dataset was used to evaluate the proposed framework. Besides, the computation performance and latency under EFCC were compared with Edge-Fog and Edge-Cloud deployments. Experimental results confirmed two major contributions: 1) the proposed framework has promising performance compared with other methods on the dataset. 2) The skeleton-based HIR-PHM with secure EFCC shows the advantages of better computation performance in terms of frame per second.

Cloud-Based Internet of Things in Healthcare Applications: A Systematic Literature Review

Introduction: The Internet of Things (IoT) and Cloud computing are two recent technological advances whose potential have not been realized in a range of industries. These technologies have been used in healthcare systems to improve their performance. In this regard, the IoT generates a large amount of data; also, cloud computing is a viable option for data storage and complex computing. The purpose of this study is to identify and categorize various aspects of CIoT-based healthcare in terms of main application domains, sensors, wireless communication technologies, messaging protocols, cloud platforms, and artificial intelligence (AI) algorithms.Material and Methods: We conducted a literature systematic review and reported according to the PRISMA guideline. PubMed, IEEE, Scopus, and Web of Science were searched using the related keywords and their synonyms. Two independent authors reviewed the papers' eligibility according to the defined inclusion and exclusion criteria.Results: Of the 2,118 papers retrieved, 61 were eventually selected in the study. Results of the present study revealed that the majority of CIoT research works were applied to patient monitoring systems and cardiovascular patient monitoring systems using Amazon and IBM dominating Cloud platforms. In addition, the most widely used communication technologies for CIoT in healthcare are cellular networks (3G and 4G), Wi-Fi, and Bluetooth. Cardiovascular, environmental, and position sensors are also the most common types of sensors used in healthcare CIoT applications. Among the Cloud platform providers, Amazon and IBM have the highest utility in healthcare systems. The majority of the included studies used Cloud-based AI algorithms to diagnose, classify, and predict diseases.Conclusion: The integration of the Cloud into the IoT can support healthcare systems in terms of processing power, storage capacity, security, privacy, performance, reliability, and scalability. We suggest researchers conduct experimental studies to evaluate the effectiveness of the CIoT approach in healthcare applications.

Proof-of-Concept: Smartphone- and Cloud-Based Artificial Intelligence Quantitative Analysis System (SCAISY) for SARS-CoV-2-Specific IgG Antibody Lateral Flow Assays.

Smartphone-based point-of-care testing (POCT) is rapidly emerging as an alternative to traditional screening and laboratory testing, particularly in resource-limited settings. In this proof-of-concept study, we present a smartphone- and cloud-based artificial intelligence quantitative analysis system (SCAISY) for relative quantification of SARS-CoV-2-specific IgG antibody lateral flow assays that enables rapid evaluation (<60 s) of test strips. By capturing an image with a smartphone camera, SCAISY quantitatively analyzes antibody levels and provides results to the user. We analyzed changes in antibody levels over time in more than 248 individuals, including vaccine type, number of doses, and infection status, with a standard deviation of less than 10%. We also tracked antibody levels in six participants before and after SARS-CoV-2 infection. Finally, we examined the effects of lighting conditions, camera angle, and smartphone type to ensure consistency and reproducibility. We found that images acquired between 45° and 90° provided accurate results with a small standard deviation and that all illumination conditions provided essentially identical results within the standard deviation. A statistically significant correlation was observed (Spearman correlation coefficient: 0.59, p = 0.008; Pearson correlation coefficient: 0.56, p = 0.012) between the OD450 values of the enzyme-linked immunosorbent assay and the antibody levels obtained by SCAISY. This study suggests that SCAISY is a simple and powerful tool for real-time public health surveillance, enabling the acceleration of quantifying SARS-CoV-2-specific antibodies generated by either vaccination or infection and tracking of personal immunity levels.

Cloud-Based Artificial Intelligence Framework for Battery Management System

As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques.

Designing and Evaluating an Emotionally Responsive Virtual Patient Simulation.

Virtual patient (VP) simulations have been widely used for healthcare training, education, and assessment. However, few VP systems have integrated emotion sensing and analyzed how a user's emotions may influence the overall training experience. This article presents a VP that can recognize and respond to 5 human emotions (anger, disgust, fear, joy, and sadness), as well as 2 facial expressions (smiling and eye contact). The VP was developed by combining the capabilities of a facial recognition system, a tone analyzer, a cloud-based artificial intelligence chatbot, and interactive 3-dimensional avatars created in a high-fidelity game engine (Unity). The system was tested with healthcare professionals at Changzhou Traditional Chinese Medicine Hospital. A total of 65 participants (38 females and 27 males) aged between 23 and 57 years (mean = 38.35, SD = 11.48) completed the survey, and 19 participants were interviewed. Most participants perceived that the VP was useful in improving their communication skills, particularly their nonverbal communication skills. They also reported that adding users' affective states as an additional interaction increased engagement of the VP and helped them build connections with the VP. The emotionally responsive VP seemed to be functionally complete and usable. However, some technical limitations need to be addressed before the system's official implementation in real-world clinical practice. Future development will include improving the accuracy of the speech recognition system, using more sophisticated emotion sensing software, and developing a natural user interface.

PO-05-171 DOES LEFT ATRIAL DILATION PREDICT ATRIAL FIBRILLATION SEVERITY AND ABLATION EFFECTIVENESS?

Left atrium (LA) geometric changes with advancing chronicity and severity of atrial fibrillation (AF). During catheter ablation for AF, electroanatomical mapping (e.g. with CARTO) of the LA can be used to measure the LA volume and atrial width to heigh ratio to guide ablation procedures. To examine whether LA dilation measured by height-to-width ratio of posterior wall predicts paroxysmal AF vs persistent AF or one-year AF recurrence post ablation. CARTO procedural case files were loaded into CARTONET (a cloud-based artificial intelligence powered system) and linked to deidentified Mercy Health electronic health record (EHR) data among AF patients who underwent the first ablation. Machine learning was used for automatic segmentation of atria. Atrial dilation was calculated by height-to-width ratio, which was used to rank patients into thirds of high (>0.69), medium and low (<0.55) dilation. Patient demographic and clinical characteristics (AF type, BMI, age, Elixhauser Comorbidity Index score, and CHA2DS2-VASC score) at ablation were included in logistic regression examining the relationship of LA dilation with AF diagnosis at ablation. AF recurrence was measured by repeat ablation, electrical cardioversion, and AF rehospitalization within one-year post-ablation. Among 274 ablated AF patients, 146 (53.2%) patients had persistent AF and 58 (21.2%) experienced AF recurrence within one-year post-ablation. The high LA dilation group was 2.8 times more likely to have persistent AF than the low group (odds ratio (OR): 2.81, 95% confidence interval (CI): 1.42-5.67). The high LA dilation group was not associated with AF recurrence (OR: 0.64 95% CI: 0.30-1.35) compared with the low dilation group. Patients with high LA atrial dilation were more likely to have the persistent AF diagnosis. AF dilation may be useful to determine progression/chronicity of AF disease and assist physician to tailor the ablation procedures for AF treatment.

PO-04-034 NOVEL HIGH DENSITY OCTA-SPLINE MAPPING CATHETERS IMPROVED LEFT ATRIAL MAPPING EFFICIENCIES IN ATRIAL FIBRILLATION

The standard of care for atrial fibrillation (AF) ablation involves a high-density mapping of the left atrium and has until recently been performed with a penta-spline multipolar mapping catheter capable of acquiring 10 points simultaneously. A novel high density, multi-spline (octa-splines/24 points simultaneously) mapping catheter with a close internal unipolar reference electrode became available, which may improve mapping efficiencies and image resolutions. Compare mapping efficiency the penta-spline and the novel octa-spline high density mapping catheters in patients who underwent left atrial mapping and ablation for atrial fibrillation. Procedure related complications were evaluated as well. The REAL AF registry is a prospective multi-center observational study of real-world clinical experience of AF ablation. Consecutive first-time AF ablation patients between August 2020-September 2022 from 3 sites with complete procedure data and corresponding electro-anatomical maps were included in this analysis. Patients with paroxysmal and persistent atrial fibrillation were included. Mapping data were analyzed via a cloud-based artificial intelligence analytic platform. A total of 706 eligible patients were included in this analysis (Penta-spline: 636; Octa-spline: 70). Patients in the octa-spline cohort were older, but otherwise comparable to those in the penta-spline cohort. Procedures using the octa-spline mapping catheter had a significantly lower mean mapping time (11.74 vs. 16.57 minutes, p=0.018), procedure time (52.86 vs. 70.32 minutes, p=0.006) as well as >4x higher mapping density (13,201 vs 3,142 points, p=0.001) relative to procedure performed with the penta-spline catheter (Figure). There were no statistically significant differences in the total RF time between the two groups. The novel octa-spline high density mapping catheter improved left atrial mapping efficiencies with significantly shorter mapping and procedures times and higher point density per map. As expected, the radiofrequency ablation duration is not different, while the percentage of scar trends towards a larger area with the octa-spline catheter likely as a result of a higher density, more uniform point collection.

LuoJiaAI: A cloud-based artificial intelligence platform for remote sensing image interpretation

ABSTRACT The rapid processing, analysis, and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms (RS-CCPs) have recently become a new trend. The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation, which ignores remote sensing data characteristics such as large image size, large-scale change, multiple data channels, and geographic knowledge embedding, thus impairing computational efficiency and accuracy. We construct a LuoJiaAI platform composed of a standard large-scale sample database (LuoJiaSET) and a dedicated deep learning framework (LuoJiaNET) to achieve state-of-the-art performance on five typical remote sensing interpretation tasks, including scene classification, object detection, land-use classification, change detection, and multi-view 3D reconstruction. The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application. In addition, LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium (OGC) standards for better developing and sharing Earth Artificial Intelligence (AI) algorithms and products on benchmark datasets. LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism, showing great potential in high-precision remote sensing mapping applications.

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology.

Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of sensing to monitor freely swimming fish even in turbid or low-ambient-light waters. This paper developed a two-mode underwater surveillance camera system consisting of a sonar imaging device and a stereo camera. The sonar imaging device has two cloud-based Artificial Intelligence (AI) functions that estimate the quantity and the distribution of the length and weight of fish in a crowded fish school. Because sonar images can be noisy and fish instances of an overcrowded fish school are often overlapped, machine learning technologies, such as Mask R-CNN, Gaussian mixture models, convolutional neural networks, and semantic segmentation networks were employed to address the difficulty in the analysis of fish in sonar images. Furthermore, the sonar and stereo RGB images were aligned in the 3D space, offering an additional AI function for fish annotation based on RGB images. The proposed two-mode surveillance camera was tested to collect data from aquaculture tanks and off-shore net cages using a cloud-based AIoT system. The accuracy of the proposed AI functions based on human-annotated fish metric data sets were tested to verify the feasibility and suitability of the smart camera for the estimation of remote underwater fish metrics.