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Statement of problem Artificial intelligence applications are increasing in prosthodontics. Still, the current development and performance of artificial intelligence in prosthodontic applications has not yet been systematically documented and analyzed. Purpose The purpose of this systematic review was to assess the performance of the artificial intelligence models in prosthodontics for tooth shade selection, automation of restoration design, mapping the tooth preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. Material and methods An electronic systematic review was performed in MEDLINE/PubMed, EMBASE, Web of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with artificial intelligence models were selected based on 6 criteria: tooth shade selection, automated fabrication of dental restorations, mapping the finishing line of tooth preparations, optimizing the manufacturing casting process, predicting facial changes in patients with removable prostheses, and designing removable partial dentures. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. Results A total of 36 articles were reviewed and classified into 6 groups based on the application of the artificial intelligence model. One article reported on the development of an artificial intelligence model for tooth shade selection, reporting better shade matching than with conventional visual selection; 14 articles reported on the feasibility of automated design of dental restorations using different artificial intelligence models; 1 artificial intelligence model was able to mark the margin line without manual interaction with an average accuracy ranging from 90.6% to 97.4%; 2 investigations developed artificial intelligence algorithms for optimizing the manufacturing casting process, reporting an improvement of the design process, minimizing the porosity on the cast metal, and reducing the overall manufacturing time; 1 study proposed an artificial intelligence model that was able to predict facial changes in patients using removable prostheses; and 17 investigations that developed clinical decision support, expert systems for designing removable partial dentures for clinicians and educational purposes, computer-aided learning with video interactive programs for student learning, and automated removable partial denture design. Conclusions Artificial intelligence models have shown the potential for providing a reliable diagnostic tool for tooth shade selection, automated restoration design, mapping the preparation finishing line, optimizing the manufacturing casting, predicting facial changes in patients with removable prostheses, and designing removable partial dentures, but they are still in development. Additional studies are needed to further develop and assess their clinical performance.

The Secured exams are one of the foremost troublesome challenges in impersonation Judgment. For any Educational Institution, conducting an online authenticated examination is a major task due to its diverse security components. The main objective of our paper is to provide maximum security and to avoid the malpractice due to impersonation which are happening during the conduct of the examinations. The main objective of our paper is to provide maximum security and to avoid the impersonation and it is related to malpractices which are happening during the conduct of the examinations. The methodology we have adapted deals with the automation process of all the manual verification processes which are done. This is done by the combination of IoT and Image Processing technology. The verification of image and fingerprint confirms the authenticity of the person. The final result is displayed in the IoT database which reduces man power and time consumption. This paper has proposed a technique which eliminates all the errors and illegal activities which happen at the initial stage of the examination process. This technique has proved to provide a better security rather than the manual checkup process.

The way Data Science and Machine Learning have set modern trends for automation. It is thoughtful to simplify our day to day activities which are related to these domains. One such task is the repetitive submission of the same personal details while submitting any online form regarding academic or other purposes. The repetitiveness seems a bit dilatory. Plus, the involvement of machine learning further espouses the concept to automate re-iteration of personal details each time we fill a form, instead of manual entry. To obviate the above redundancy, we have proposed to implement “Exam Form Automation Using Facial Recognition” which includes real time face recognition with the help of webcam, pre-storing data on web servers with Panda and NumPy library of Python and then automating the entries on form using selenium library of python. The system captures a real time image of a user and collects the particular details from data hosted on a web server. Thus, precluding the monotonous procedure of manual entry.

In industrial processes, data-driven soft sensors have played an important role for effective process control, optimization and monitoring. However, the shortcomings for deep learning technology seriously hinder the application of deep learning in industrial processes. For example, the knowledge cannot be added into the model and the model prediction could not be well explained. To solve those problems, the graph mining, convolution, and explanation (GMCE framework is proposed for knowledge automation in this work. Based on the equivalence analysis of the self-attention mechanism (SAM and graph convolution (GC operation, the spital SAM is adopted for knowledge discovery from data directly. After that, the GC layer considering the relationship between process variables can utilize the knowledge for constructing soft sensor models. Besides, to explain which knowledge contributes to the final model prediction, the graph neural network explainer (GNNExplainer is designed for explaining the model output. Finally, the effectiveness and feasibility of the framework are evaluated on an industrial process, in which the knowledge discovered from the data is of great consistence with the prior knowledge and the final explanation indicated that most of the knowledge consists with the prior knowledge contributed to the prediction.

As the general development trend of the automotive industry, connected and autonomous vehicles (CAVs) can be used to increase transportation safety, promote mobility choices, reduce user costs, and create new job opportunities. However, with the increasing level of connectivity and automation, malicious users are able to easily implement different kinds of attacks, which threaten the security of CAVs. Hence, this paper provides a comprehensive survey on the cyber-security in the environment of CAVs with the aim of highlighting security problems and challenges. Firstly, based on the types of communication networks and attack objects, it classifies various cyber-security risks and vulnerabilities in the environment of CAVs into in-vehicle network attacks, vehicle to everything network attacks, and other attacks. Next, it regards cyber-risk as another type of attacks in the environment of CAVs. Then, it describes and analyzes up-to-date corresponding defense strategies for securing CAVs. In addition, it concludes several available cyber-security and safety standards of CAVs, which is helpful for the practical application of CAVs. Finally, several challenges and open problems are discussed for the future research.

Passwords are pervasively used to authenticate users' identities in mobile apps. To secure passwords against attacks, protection is applied to the password authentication protocol (PAP). The implementation of the protection scheme becomes an important factor in protecting PAP against attacks. We focus on two basic protection in Android, i.e., SSL/TLS-based PAP and timestamp-based PAP. Previously, we proposed an automated tool, GLACIATE, to detect authentication flaws. We were curious whether orchestration (i.e., involving manual-effort) works better than automation. To answer this question, we propose an orchestrated approach, AUTH-EYE in this paper and compare its effectiveness GLACIATE. We study requirements for correct implementation of PAP and then apply GLACIATE to identify protection enhancements automatically. Through dependency analysis, GLACIATE matches the implementations against the abstracted flaws to recognise defective apps. To evaluate AUTH-EYE, we collected 1,200 Android apps from Google Play. We compared AUTH-EYE with the automation tool, GLACIATE, and two other orchestration tools, MalloDroid and SMV-Hunter. The results demonstrated that orchestration tools detect flaws more precisely although F1 of GLACIATE is higher than AUTH-EYE. Further analysis of the results reveals that highly popular apps and e-commerce apps are not more secure than other apps.

A key step in remanufacturing is disassembly of the ``core'' or the returned product to be remanufactured. Disassembly sequence planning is challenging due to uncertainties in the conditions of the cores. Rust, corrosion, deformation, and missing parts may require disassembly plans to be changed and adapted frequently. Conventional industrial automation that usually serves in repetitive and structured activities may fail when it is applied to disassembly. This research investigates the flexible sequencing of robotic disassembly in the presence of failed automation operations and develops online recovery by incorporating backup actions. It starts with modeling the time and success rate of a backup action. The expected disassembly time and completion rate of a disassembly plan are deduced according to the failure probability of both the operations and their backup actions. A biobjective optimization model for robotic disassembly sequence planning is established using a dual-selection multiobjective evolutionary algorithm. Two solution selection criteria are combined to produce potential offspring candidates in each evolutionary generation. Experimental results show that the backup actions allow efficient recovery from automation and can potentially improve the robustness of robotic disassembly.

Currently, with the growing applicability of nonlinear electrical devices, power quality disturbances (PQDs) often occur in power systems. Previous works usually extracted statistical features from electrical signals manually and constructed classifiers with traditional machine learning methods for PQD monitoring. Furthermore, noisy tags or unlabeled data (different from noisy signals) are usually ignored in the traditional training stage, and these methods fail to meet the high accuracy and automation demands of real-world scenarios. To overcome the shortcomings of existing methods, this paper proposes a practical method called PowerCog for accurately recognizing PQDs in noisy environments. First, an input voltage waveform signal is divided into several intrinsic mode functions by an empirical wavelet transform (EWT), and these functions are then aligned into columns to form a matrix. Second, tri-training is utilized for label refactoring to improve the generalization ability of the model in a noisy environment. Then, an optimized convolutional neural network (CNN) structure combined with principal component analysis (PCA) is deployed to extract and select the universal features automatically. Finally, a support vector machine (SVM) classifier is constructed to recognize PQD patterns. Several comparative experiments are performed to verify the effectiveness and accuracy of PowerCog in complex environments.

Women worldwide are afflicted by breast cancer, a widely occuring health issue that causes a large number of deaths. This paper aims to review and present several approaches to identify breast cancers using ML algorithms and Biosensors. The objective is to investigate the application of multiple algorithms based on Machine Learning approach and biosensors for early breast cancer detection. Biosensors and machine learning are needed to identify cancers based on microscopic images, that is why automation is needed. ML aims to make computers capable of self-learning. Rather than relying on explicit pre-programmed rules and models, it is based on identifying patterns in observed data and building models to predict outcomes. We have compared and analyzed various types of algorithms like fuzzy ELM-RBF, SVM, SVR, RVM, Naive Bayes, K-NN, DT, ANN, BPNN and Random forest over varieties of databases like images digitized from fine needle aspirations (FNAs) of breast masses, Scanned film mammography, Breast infrared images, MR Images, Data collected by using the blood analyses, and Histopathology image samples. The result is compared on performance metric elements like accuracy, precision and recall. Further, we have used Biosensors to determine the presence of a specific biological analyte by transforming the cellular constituents of proteins, DNA, or RNA into electrical signals that can be detected and analyzed. Here, we have compared detection of different types of analyte like HER2, miRNA 21, miRNA 155, MCF-7 cells, DNA, BRCA1, BRCA2, human tears and saliva using different types of biosensors like FET, Electrochemical, Sandwich electrochemical, etc. Several biosensors use a different type of specification which is also discussed. The result of which is analysed on the basis of detection limit, linear ranges and response time. Different studies and related articles were ultimately reviewed and analyzed systematically and the ones published from 2010 to 2021 were considered. Biosensors and machine learning both have the potential to detect breast cancer in a fast and effective way.

Running applications on a rooting device makes the application vulnerable to data leakage. Therefore, many applications that require a high level of security are not allowed to run on rooted device. Common technique of detecting rooted device is by using Android API to discover rooting trace. However, the detection can be bypassed using Java function hooking script by the people who want to run the app on rooted device. This research will give illustration that the bypassing process becomes more easy with automation tool and hybrid analysis. In order to create the script, we use combination of static and dynamic analysis with three phases with specific function. Phase 1 aims to detect the estimated Java method that detect rooting, phase 2 will analyze that method on an unrooted device, then phase 3 will create the bypassing script based on the previous result. We also use automation tool to speed up the static analysis. We create two types of script: one that can be used on general application, and the other one that only can be used on specific app. Those types implement different scope: one with the certain Java method, and the other one with specific parameter or return value. In the end, we find that bypassing rooting detection is not complicated if the app use Java function to detect the rooted device. To complicate bypassing process, we encourage the developers to implement more advanced detection rooting technique.

Objective To automate surgeon skills evaluation using robotic instrument kinematic data. Additionally, to implement an unsupervised mislabeling detection algorithm to identify potentially mislabeled samples that can be removed to improve model performance. Methods Video recordings and instrument kinematic data were derived from suturing exercises completed on the Mimic FlexVR robotic simulator. A structured human consensus-building process was developed to determine Robotic Anastomosis Competency Evaluation technical scores across 3 human graders. A 2-layer long short-term memory–based classification model used instrument kinematic data to automate suturing skills assessment. An unsupervised label analyzer (NoiseRank) was used to identify potential mislabeling of skills data. Performance of the long short-term memory model’s technical skill score prediction was measured by best area under the curve over the training runs. NoiseRank outputted a ranked list of rated skills assessments based on likelihood of mislabeling. Results 22 surgeons performed 226 suturing attempts, which were broken down into 1,404 individual skill assessment points. Automation of needle entry angle, needle driving, and needle withdrawal technical skill scores performed better (area under the curve 0.698–0.705) than needle positioning (0.532) at baseline using all available data. Potential mislabels were subsequently identified by NoiseRank and removed, improving model performance across all domains (area under the curve 0.551–0.766). Conclusion Using ground truth labels from human graders and robotic instrument kinematic data, machine learning models have automated assessment of detailed suturing technical skills with good performance. Further, an unsupervised mislabeling detection algorithm projected mislabeled data, allowing for their removal and subsequent improvement of model performance.

Different from the existing train delay studies that had strived to explore sophisticated algorithms, this paper focuses on finding the bound of improvements on predicting multi-scenario train delays with different machine learning methods. Motivated by the observation of deep learning methods failing to improve the prediction performance if the delay occurs rarely, we present a novel augmented machine learning approach to improve the overall prediction accuracy further. Our solution proposes a rule-driven automation (RDA) method, including a delay status labeling (DSL) algorithm, and the resilience of section (RSE) and resilience of station (RST) indicators to generate the forecast for train delays. The experiment results demonstrate that the Random Forest based implementation of our RDA method (RF-RDA) can significantly improve the generalization ability of multivariate multi-step forecast models for multi-scenario train delay prediction. The proposed solution surpasses state-of-art baselines based on real-world traffic datasets, which treat various real-time delays differently. Even when the predictability of conventional deep learning methods decreases, the performance of our method is still acceptable for practical use to provide accurate forecasts.

In edge computing, heterogeneous automation tasks like networked motion control and reconfiguration management have diverse weights on determinism and flexibility. Such heterogeneities escalate the demands on the runtime environments (RTEs) of programmable logic controllers (PLCs) to provide the best-suited rather than one-size-fits-all execution policies for reconfigurable programs. This paper presents the proposal and implementation of a novel IEC 61499-based RTE capable of offering hybrid synchronous and asynchronous execution models for function block (FB) based programs. We also construct a runtime reconfigurable FB type repository by realizing a just-in-time FB type definitions compiler in the RTE. Finally, we evaluate and compare the proposed RTE and state-of-the-art works by running a set of synthetic benchmark programs and a realistic networked motion control kernel and application. Experiment results from the benchmark programs have shown a maximal 40% faster execution speed in our RTE. Furthermore, our RTE can ensure fewer jitters during distributed motion control with a 1ms cycle time on a realistic motion control testbed.

OBJECTIVE Robotic-assisted minimally invasive surgery (RAMIS) became a common practice in modern medicine and is widely studied. Surgical procedures require prolonged and complex movements; therefore, classifying surgical gestures could be helpful to characterize surgeon performance. The public release of the JIGSAWS dataset facilitates the development of classification algorithms; however, it is not known how algorithms trained on dry-lab data generalize to real surgical situations. METHODS We trained a Long Short-Term Memory (LSTM) network for the classification of dry lab and clinical-like data into gestures. RESULTS We show that a network that was trained on the JIGSAWS data does not generalize well to other dry-lab data and to clinical-like data. Using rotation augmentation improves performance on dry-lab tasks, but fails to improve the performance on clinical-like data. However, using the same network architecture, adding the six joint angles of the patient-side manipulators (PSMs) features, and training the network on the clinical-like data together lead to notable improvement in the classification of the clinical-like data. DISCUSSION Using the JIGSAWS dataset alone is insufficient for training a gesture classification network for clinical data. However, it can be very informative for determining the architecture of the network, and with training on a small sample of clinical data, can lead to acceptable classification performance. SIGNIFICANCE Developing efficient algorithms for gesture classification in clinical surgical data is expected to advance understanding of surgeon sensorimotor control in RAMIS, the automation of surgical skill evaluation, and the automation of surgery.

The Internet of Things (IoT) is made up of billions of physical devices connected to the Internet via networks that perform tasks independently with less human intervention. Such brilliant automation of mundane tasks requires a considerable amount of user data in digital format, which in turn makes IoT networks an open-source of Personally Identifiable Information data for malicious attackers to steal, manipulate and perform nefarious activities. Huge interest has developed over the past years in applying machine learning (ML)-assisted approaches in the IoT security space. However, the assumption in many current works is that big training data is widely available and transferable to the main server because data is born at the edge and is generated continuously by IoT devices. This is to say that classic ML works on the legacy set of entire data located on a central server, which makes it the least preferred option for domains with privacy concerns on user data. To address this issue, we propose federated learning (FL)-based anomaly detection approach to proactively recognize intrusion in IoT networks using decentralized on-device data. Our approach uses federated training rounds on Gated Recurrent Units (GRUs) models and keeps the data intact on local IoT devices by sharing only the learned weights with the central server of the FL. Also, the approach’s ensembler part aggregates the updates from multiple sources to optimize the global ML model’s accuracy. Our experimental results demonstrate that our approach outperforms the classic/centralized machine learning (non-FL) versions in securing the privacy of user data and provides an optimal accuracy rate in attack detection.

Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study.

Software-Defined Networking (SDN) is a modern paradigm that provides a platform for implementing reliable, centrally managed, and automated security solutions for conventional and new generation networks, such as IoT, cloud computing, 5G/6G mobile communication networks, and vehicular communications. In these complex systems, manual security operations can delay or obstruct the identification, mitigation, and prevention of ever-increasing sophisticated threats. Thus, the idea of security automation for networks using the SDN paradigm has become fundamental, given that SDN was created to facilitate the operation and management of complex networks with minimal human intervention, which is considered error-prone. This survey studies the state-of-the-art research efforts concerned with security automation in SDN environments. We identified and ranked various classes of security solutions with different levels of automation and complexity. The level of automation is measured using four well-defined qualitative parameters: self-healing, self-adaptation, self-configuration, and self-optimization. The complexity is characterized by the amount of processing and storage resources and implementation requirements. This work represents the first endeavor to analyze the level of automation and complexity of security solutions in SDN environments. Our findings reveal important advances in the area of security automation in SDN. However, there are still several open problems and challenges, which we detail in this work.

Background This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 research studies. Methods A ML classifier for retrieving COVID-19 research studies (the ‘Cochrane COVID-19 Study Classifier’) was developed using a data set of title-abstract records ‘included’ in, or ‘excluded’ from, the CCSR up to 18th October 2020, manually labelled by information and data curation specialists or the Cochrane Crowd. The classifier was then calibrated using a second data set of similar records ‘included’ in, or ‘excluded’ from, the CCSR between October 19 and December 2, 2020, aiming for 99% recall. Finally, the calibrated classifier was evaluated using a third data set of similar records ‘included’ in, or ‘excluded’ from, the CCSR between the 4th and 19th of January 2021. Results The Cochrane COVID-19 Study Classifier was trained using 59,513 records (20,878 of which were ‘included’ in the CCSR). A classification threshold was set using 16,123 calibration records (6005 of which were ‘included’ in the CCSR) and the classifier had a precision of 0.52 in this data set at the target threshold recall >0.99. The final, calibrated COVID-19 classifier correctly retrieved 2285 (98.9%) of 2310 eligible records but missed 25 (1%), with a precision of 0.638 and a net screening workload reduction of 24.1% (1113 records correctly excluded). Conclusions The Cochrane COVID-19 Study Classifier reduces manual screening workload for identifying COVID-19 research studies, with a very low and acceptable risk of missing eligible studies. It is now deployed in the live study identification workflow for the Cochrane COVID-19 Study Register.

Feature engineering is one of the major challenges of machine learning . While multiple automation solutions have been proposed in recent years, the vast majority focuses on extracting features from the analyzed dataset itself and not from other (external) sources. In this study we present FGSES, a general framework for automatic feature engineering and its application to DBpedia. Our framework automatically matches the entities in the analyzed dataset to those of the external data source, and then proceeds to generate a large and diverse set of candidate features, both from structured and unstructured content. To efficiently process the large number of generated features, FGSES uses a meta learning-based ranking approach. Our evaluation, conducted on 18 tabular datasets with diverse characteristics, shows that FGSES achieves an average error reduction of 16.5%, significantly outperforming the evaluated baselines.

Prior studies reported that the time needed to manually convert a point cloud to an as-is geometric model using cutting edge modelling software is ten times greater than the time needed to obtain the point cloud. The laborious nature of manually modelling infrastructure such as bridges is the reason behind the significant cost of modelling which impedes the proliferation of the usage of Bridge Information Models (BrIM) in Bridge Management Systems. Existing commercial solutions can automatically recognize geometric shapes embedded in segmented point cloud data (PCD) and generate the corresponding IFC objects. Researchers have taken further studies and have additionally automated surface reconstruction through generating parametric surface-based primitives in order to automate the segmentation process. However, surface-based segmentation for bridge modelling is an unsolved problem, which is neither straightforward nor consistent, and thus hinders the automation of BrIM. This paper presents a top-down PCD detection solution that follows a knowledge-based heuristic approach for BrIM generation that can semi-automatically segment a bridge point cloud recursively. We leverage bridge domain knowledge as strong input priors through a histogram-based algorithm to conduct the segmentation and classification. We implemented the solution and tested on one highway bridge. The experimental results indicated that the detection precision of this solution is 92%.

Made available in DSpace on 2022-05-01T07:59:06Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-12-01 The fourth industrial revolution aims to achieve greater productivity, also seeking to improve the quality and efficiency of production processes. For this, the use of information and automation technologies together becomes indispensable. Collaborative automation through the sharing and use of services has been a recent paradigm in the quest to obtain a distributed, flexible and integrated on network architecture. In this article, the development of control and automation applications as a service using a Microservices Oriented Architecture is investigated. The architecture is elaborated using the Moleculer framework, which simplifies the development of microservices and applications that make up the Microservice Oriented Architecture used in this work. The development and evaluation of several services are presented, such as Data Acquisition (DAQ), Programmable Logic Controller (PLC) and PID Controller (PIDPlus). The experimental results in different scenarios demonstrated the feasibility of implementing control and automation applications as a service. A relevant advantage that this type of architecture provides is interoperability through automatic network communication between applications and microservices, which is required for Industry 4.0. Sao Paulo State University (UNESP) National Service for Industrial Learning (SENAI) Federal Institute of Sao Paulo (IFSP) Sao Paulo State University (UNESP)

Although a “Self-Driving” photonic network is still a long way to go, many time-consuming complex tasks and decision making in photonic networks can be automated using machine learning, and other data-driven solutions. This study explores recent contributions towards photonic network automation, such as alarm prediction, fault localization, resource auto-scaling, quality of transmission prediction, dynamic controller placement, automated service restoration, resource allocation and optimization, minimization of electricity and power supply cost, user data analysis, etc. The studies explored in this letter, provide solution approaches to these problems using wide range of data-driven methods, machine learning, AI, and deep learning based methods. This study discusses different challenges involving these interesting research areas and provides directions for future research opportunities as well.

The automation of inspection methods for concrete structures is a pressing issue worldwide. Weakly supervised approaches, i.e., approaches based on supervision in other forms than traditional class labels, offer a unique mix of automation and human involvement that is highly effective for critical tasks such as inspection work. Generating weak supervision is less tedious than generating training data for supervised learning approaches. However, since it is less informative, high amounts of weak supervision are often needed. In practice, it is often the case that only scarce amounts of weak supervision are available. In this paper, we propose a novel approach for weakly supervised acoustic defect detection in concrete structures that augments human-provided weak supervision. Experiments in both laboratory and field conditions showed that the proposed method allows for considerable performance gains for low amounts of weak supervision.

With the accessibility of Synthetic Aperture Radar (SAR) data and evolution of deep learning, several experiments have been performed in the recent years to actively monitor human activities at sea. Due to the flexible network configuration and complex nature, models based on Convolutional Neural Networks have been able to boost the performance of maritime target classification systems. The novel idea behind these techniques is to enhance the efficiency and automation in the process. However, the limited availability of labeled data and lower resolution of Sentinel-1 (SAR) images have constrained the accuracy of classification models based on the deep learning methods. Besides, it is observed that image processing techniques have resulted in the loss of information present in the images. To overcome these issues, a new method has been developed in the present work that integrates transfer learning techniques (referred to as ResNet50) with additional scale-variant features extracted before image processing. To test our model, SAR and Automatic Identification System (AIS) data were collected from 3 locations: Jawaharlal Nehru (JN) Port (India), Singapore Port (Singapore), and Shanghai-Zhoushan Port (China). A labeling process was done by using the SAR-AIS Neighbourhood integration. Experimental work showed that the proposed architecture of ResNet50 model synergized with extracted scale variant features provides promising results with the classification accuracy of 82.35% and F score of 0.82 on the test dataset, when no models have been developed and trained on Sentinel-1 20 m resolution data for given ship classes.

Medical systems and assistive technologies, human-computer interaction, human-robot interaction, industrial automation, virtual environment control, sign language translation, crisis and disaster management, entertainment and computer games, and so on all use RGB cameras for hand gesture recognition. However, their performance is limited especially in low-light conditions. In this paper, we propose a robust hand gesture recognition system based on high-resolution thermal imaging that is light-independent. A dataset of 14,400 thermal hand gestures is constructed, separated into two color tones. We also propose using a deep CNN to classify high-resolution hand gestures accurately. The proposed models were also tested on Raspberry Pi 4 and Nvidia AGX edge computing devices, and the results were compared to the benchmark models. The model also achieves an accuracy of 98.81% and an inference time of 75.138 ms on Nvidia Jetson AGX. In contrast to hand gesture recognition systems based on RGB cameras, which have limited performance in the dark-light conditions, the proposed system based on reliable high resolution thermal images is well-suited to a wide range of applications.

In the growth of the Pharmacy industry, the automation of smart traceability system is needed to prevent the fraud attempt. The proposed Blockchain integrated Internet of Things (BIoT) is construct...

Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.