Context-aware Decision Research Articles

Comparing Confidence Intervals for the Mean of Symmetric and Skewed Distributions

In context-aware decision analysis, mean can be an important measure, even when the distribution is skewed. Previous comparative studies showed that it is a real challenge to construct a confidence interval that performs well for highly skewed data. In this study, we propose new confidence intervals for the population mean based on Edgeworth expansion that include both skewness and kurtosis corrections. We compared existing and newly proposed confidence intervals for a range of samples from symmetric and skewed distributions of varying levels of kurtosis. Using Monte Carlo simulations, we evaluated the performance of these intervals based on the coverage probability, mean length, and standard deviation of the length. The proposed bootstrap Edgeworth-based confidence interval outperformed other confidence intervals in terms of coverage probability for both symmetric and skewed distributions and can be recommended for general use in practice.

Sensor-fused simulation-based decision support system for earthmoving projects

Discrete Event Simulation (DES) is an effective method to study cyclic and uncertain projects at the operation level. However, qualified DES results highly depend on the input data quality. This paper describes a simulation-enabled context-aware Decision Support System (DSS) based on sensor fusion to model earthwork operations realistically. The DSS backbone is a DES continually updated using real-time data. The information on earthmoving operations is exchanged between reality and DES using GPS and equipment-mounted cameras. The equipment spatiotemporal data and shovel-mounted cameras enhance the DES quality (i.e., locational and contextual awareness) by providing timely information about task durations, load types, and bucket teeth status. Sensor data is fused and fed into the DES model to replicate the earthmoving project. The proposed method is successfully implemented and validated in a large-scale earthmoving case study. The developed DES is proven promising as a reliable DSS for enhancing resource allocation and time-cost reduction.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of ‹instrument, verb, target› triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition.

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹instrument, verb, target› combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

Forest Graph Convolutional Network for Surgical Action Triplet Recognition in Endoscopic Videos

Recognizing surgical activities in endoscopic videos is of vital importance for developing context-aware decision support in the operating room. In this work, we model each surgical activity as an action triplet, consisting of the surgical instrument, the action, and the target organ that the instrument is interacting with. The goal is to recognize these action triplets from endoscopic videos. However, correctly recognizing fine-grained activity triplets is challenging because of the long-tail distribution of the triplet classes and the complex associations between triplets as well as within each triplet. In addition, multiple triplets may appear in a given video frame. To address these challenges, we propose a new model for surgical action triplet recognition based on a classification forest and Graph Convolutional Network (GCN), which we call Forest GCN. The classification forest is employed to calibrate fine-grained triplet classifiers by the upstream parent classifiers to suppress noisy logits of the triplet classes in the long tail. And stacked GCNs are designed to model the dependencies between triplet classes while leveraging the language embedding. Experiments on the endoscopic video dataset, CholecT50, demonstrate that our proposed method outperforms current state-of-the-art methods on surgical action triplet recognition.

Intelligent slime mold algorithm for proficient jamming attack detection in wireless sensor network

Wireless Sensor Networks (WSNs) depicts to the group of spatially distributed sensor nodes, each node is having individual battery for power consumption which senses the factors like Temperature, Pressure, Soil makeup, Vehicular movement, and checks the physical presence of objects, which are communicate through internet and passing messages between nearest neighbor nodes. WSN are extensively used in several applications like Environmental Monitoring, Area Monitoring, Engineering Monitoring, Health Care Monitoring, and Military Applications. Since WSN are highly susceptible for many attacks especially Jamming Attack. This causes high energy consumption, reduces the life time of nodes, affects the entire sensor networks and most significantly also the confidentiality and integrity of the data packets which are transmitted through dissimilar nodes in the network. In order to overcome these issues, a new bio-inspired algorithm called Intelligent Slime Mold (ISM) has been introduced for detection jamming node and to setting up of alternate optimal path form source node to destination node in WSN to avoid jammer affected nodes by applying Context Aware Decision Rule (CADR). This work ensures the confidentiality and integrity of both the network and data packets.

Cloud service recommendation for small and medium-sized enterprises: A context-aware group decision making approach

Recently, the enormous advantages of cloud services make them increasingly appealing to the small and medium-sized enterprises. The growing number of available services makes it challenging to select trustworthy services. Existing approaches focus on user preferences to guide personalized services recommendation for individual users, but lack of the research on trustworthy service recommendation for the small and medium-sized enterprises that represents a group user consisting of multiple individual users. For this type of enterprise, the cloud services recommendation must address the challenges from the diverse client context of individual users, the imprecise quality of experience in an uncertain cloud environment and the invalid or unsatisfactory recommendations. A client context-aware approach is proposed to recommend trustworthy cloud services for the small and medium-sized enterprises based on non-compensatory multi-criteria decision-making. In it, a type of client context is viewed as an independent evaluation criterion, and the interval neutrosophic numbers are employed to measure the fuzzy trustworthiness of cloud services. Based on the investigated outranking relations of interval neutrosophic numbers, a non-compensatory multi-criteria decision-making procedure via an improved ELECTRE III method is developed to rank candidate services. Experimental results demonstrate that this approach could efficiently produces the accurate ranking results of cloud services and effectively recommend the trustworthy service for small and medium-sized enterprises.

A Model For risk-Based adaptive security controls

Security controls and countermeasures have shifted from static desktop-based and corporate network environments to heterogeneous, distributed and dynamic environments (e.g., cloud and mobile computing or Internet of Things). Due to this paradigm shift, adaptive and risk-based approaches have gained significant importance. These approaches allow security managers to perform context-aware decision making, adapting controls’ deployment, configuration or use to every specific situation, depending on the current value of risk indicators or scores and on the level of risk tolerated by the organisation at any given time. This paper proposes a model to automatically adapt security controls to different risk scenarios in almost real-time (if required). This model is based on a three-layer architecture and a three-step flow (measurement-decision-adaptation), relying on a scalable policies&rules framework capable of integrating with different kinds of controls. Furthermore, the proposed model is validated and evaluated with an actual use case.

Using perception cues for context-aware navigation in dynamic outdoor environments

Continued advancements in robot autonomy have allowed the research community to shift from using robots as tools in the field to deploying robot teammates capable of learning, reasoning, and executing tasks. Autonomous navigation is one necessary capability of a robot teammate that must operate in large field environments. In relatively static environments a simple navigation solution such as obstacle avoidance along the shortest path may suffice; however, as robot teammates are deployed to highly dynamic environments with changing mission requirements, additional environment context may be necessary to ensure safe and reliable navigation. Although recent works in urban autonomous driving have advanced the state-of-the-art in context-aware decision making, the spectrum of behaviors deployed for context-switching is more narrowly focused (by defining constraints specific to operation in structured environments) than what might be required for human-agent teaming field missions. As such, establishing a context-aware intelligent system for dynamic, unstructured environments is still an open problem. We discuss our approach to the integration of several context-aware navigation behaviors on a small unmanned ground vehicle (UGV) and a perception stack that provides cues used to transition between these different learned behaviors. Specifically, we integrate socially compliant, terrain-aware, and covert behaviors in an outdoor navigation scenario where the UGV encounters moving pedestrians, different terrains, and weapon threats. We provide a detailed account of the overall system integration, experiment design, component- and system-level analysis, and lessons learned.

Context Reasoning for Predicting Gestational Diabetes Mellitus Using CA-RETE Algorithm

Today, IoT-related applications play an important role in scientific world development. Context reasoning emphasizes the perception of various contexts by means of collection of IoT data which includes context-aware decision making. Context-aware computing is used to improve the abilities of smart devices and is increased by smart applications. In this paper, context-aware for the internet of things middleware (CAIM) architecture is used for developing a rule-based system using CA-RETE algorithm. The objective of context-aware systems are concentrated on 1) context reasoning methodologies and analyzing how the technologies will involve enhancing the high-level context data, 2) framework of context reasoning system, 3) implementation of CA-RETE algorithm for predicting gestational diabetes mellitus in healthcare applications.

Context-aware system for cardiac condition monitoring and management: a survey

ABSTRACT Health monitoring assists physicians in the decision-making process, which in turn, improves quality of life. As technology advances, the usage and applications of context-aware systems continue to spread across different areas in patient monitoring and disease management. It provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters. In this survey, we consider context-aware systems proposed by researchers for health monitoring and management. More specifically, we investigate different technologies and techniques used for cardiac condition monitoring and management. This paper also proposes ‘mCardiac’, an enhanced context-aware decision support system for cardiac condition monitoring and management during rehabilitation.

ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services

This paper formulates the problem of a rule-based machine learning method to discover the behavioral rules of individual smartphone users to provide context-aware intelligent services. Smartphones nowadays are considered as one of the most important Internet-of-Things (IoT) devices for providing various context-aware personalized services. These devices can record individuals' contextual data - for example, temporal, spatial, or social contexts, and their daily behavioral activity records. Association rule mining (ARM) is the most popular rule-based machine learning method for discovering rules for a particular constraint preference utilizing a given dataset. However, it generates numerous uninteresting contextual associations which lead to generate huge number of redundant rules that become useless in making context-aware decisions. This redundant generation makes not only the rule-set unnecessarily large but also makes the context-aware decision making process more complex and ineffective. To minimize these issues, in this paper, we propose a rule-based machine learning method “ABC-RuleMiner” that effectively identifies the redundancy in associations, and discovers a set of non-redundant behavioral rules(IF-THEN) for individual users by taking into account the precedence of relevant contexts. Our experiments on individuals’ contextual smartphone datasets show that this rule discovery approach is more effective while comparing with traditional rule-based methods.

Adaptive Learning in Electricity Market Negotiations Based on Determinism Theory

This research proposes a novel methodology for adaptive learning in electricity markets negotiations, based on the principles of the determinism theory. The determinism theory states that all events are predetermined due to the cause-effect rule. At the same time, it is unmanageable to consider all causes to a certain effect, making it impossible to predict future events. However, in a controlled simulation environment, it is possible to access and analyze all involved variables; thus, making the application of this theory promising in such environments. This research applies the principles of the determinism theory to a new learning methodology, which optimizes players’ actions, considering the predicted behavior of all involved players, with the objective of maximizing market gains. A case-based reasoning approach is used, providing adaptive context-aware decision support. Results show that the proposed approach is able to achieve better market results than all reference market strategies.

Methodologies for calculating decision-related event occurrence times, with applications to effective routing in diverse MANET environments

To capture the dynamics of context-aware decision making for network functions in Mobile Ad Hoc Networks (MANETs), the paper introduces the concept of Decision-Related Event Occurrence Times (DREOTs). By considering a generic scenario, the paper develops a broadly applicable analysis methodology, which produces upper and lower bounds for the tail probabilities and moments of a DREOT. The nature and effect of the decision is abstracted away to quantities called ‘decision policy impact descriptors’. These are calculated on a case-by-case basis, by considering properties of the specific decision policy and mobility pattern in hand. The power of the approach is demonstrated through a detailed application targeting the analysis of a preexisting routing protocol that bases its next hop forwarding decisions on the location and motion of the neighboring nodes. In the considered application the DREOT represents the time a node retains a message before forwarding it to the next hop and the network nodes move according to the Random Direction mobility model. The proposed methodology is seen to bring more rigorous understanding of the relationship between the retaining time and the relevant context and to yield refined expressions, whose use improves the protocol’s performance for all network conditions in a very diverse set. Furthermore, the broad applicability of the methodology is illustrated through a brief discussion of another application, not related to routing.

Collaborative cross-layer framework for handover decision in overlay networks

Up to date the vertical handover (VHO) management across heterogeneous wireless networks has received large attention in recent researches. Independent decisions on different protocol layers responding to the same event may lead to un-optimal operation during VHO over heterogeneous wireless networks. There is demand for solutions capable of managing handovers while ensuring sufficient Quality of Experience for running applications with low signaling overhead. However, user perception has not been handled intensively in context-aware decision algorithms when the Mobile Node undergoes handover. The proposed cross-layer decision framework integrates a set of Quality of Experience based decision-making modules to provide the ability for upper layers to adapt dynamically to fluctuating environmental conditions and optimize the access selection process using a unified signaling messages. This middleware solution manage the signaling coordination actions based on feedback triggers and various policies to reduce false handover indications, compensate for link-layer fluctuations and minimize the impact of mobility. Numerical results highlight enhancements brought by the proposed framework over reference frameworks in terms of perceived quality, handover initiation time and delay constraints.

Multi-View Incremental Segmentation of 3-D Point Clouds for Mobile Robots

Mobile robots need to create high-definition 3D maps of the environment for applications such as remote surveillance and infrastructure mapping. Accurate semantic processing of the acquired 3D point cloud is critical for allowing the robot to obtain a high-level understanding of the surrounding objects and perform context-aware decision making. Existing techniques for point cloud semantic segmentation are mostly applied on a single-frame or offline basis, with no way to integrate the segmentation results over time. This paper proposes an online method for mobile robots to incrementally build a semantically-rich 3D point cloud of the environment. The proposed deep neural network, MCPNet, is trained to predict class labels and object instance labels for each point in the scanned point cloud in an incremental fashion. A multi-view context pooling (MCP) operator is used to combine point features obtained from multiple viewpoints to improve the classification accuracy. The proposed architecture was trained and evaluated on ray-traced scans derived from the Stanford 3D Indoor Spaces dataset. Results show that the proposed approach led to 15% improvement in point-wise accuracy and 7% improvement in NMI compared to the next best online method, with only a 6% drop in accuracy compared to the PointNet-based offline approach.

Pervasive health monitoring through video-based activity information integrated with sensor-cloud oriented context-aware decision support system

Medical informatics comprises of huge amount of medical resources to enhance storage, retrieval, and employ these resources in healthcare. The advancement has been done to monitor the health of the patients and provide the details to the caretakers, who are near by the remote areas. This could be done in a real-time with the help of the internet access. Due to the condition of monitoring the patient at a real-time, the caretaker can provide the suggestions regarding their essential signs of their body situation through a video conference. In this paper, we have proposed a system to report the progress of the elderly in an appropriate manner with the help of the technology used in the healthcare system and integrate the report of progress to the remote caretakers employing smartphones and videos. Through this advanced method we could able to identify the abnormalities at early stages so that the doctors could cure it without any difficulty. This could increase the physical and mental health of the patients. The system incorporated in this method requires certain sensors which are of very low in cost, certain electronic devices and smart phone for the communication purpose and WSN.

Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data

PurposeProbe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changes in the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residual cancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation of endomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology. Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissue characterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted intervention procedures.MethodsThe aim of this work is to propose a deep learning-based framework for brain tissue characterisation for context aware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE data is presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimate the probability of each tumour class. The video classification accuracy is compared for different network architectures and data representation and video segmentation methods.ResultsWe demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningioma brain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classification framework over state-of-the-art feature-based methods. The use of video data further improves the classification performance, achieving accuracy equal to 99.49%.ConclusionsThis work demonstrates that deep learning can provide an efficient representation of pCLE data and accurately classify Glioblastoma and Meningioma tumours. The performance evaluation analysis shows the potential clinical value of the technique.

Consistent context aware behaviour in smart home environment

The home automation controls the electronic devices remotely and automatically with the help of context aware behaviour. Context awareness is the process where data from various sensors and input from user or user action must be interpreted and corresponding commands to actuators must be generated. This context aware automation and decision support process is done with the help of adaptive rules which is defined for the automatic behaviour of devices in the smart home environment. The consistent behaviour of the devices in the smart environment will capture the customer satisfaction. This work describes the process of checking the consistent behaviour of the context aware system in smart home environment using formal modelling and verification method. We have discussed a mechanism to check the consistent behaviour of the adaptive mechanism of smart home environment. The result ensures the consistent behaviour of the context aware system in the smart environment.

Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis

Heart attack, a complex health problem in which the electrical activity of the heart becomes chaotic due to extreme heart failure conditions, has been ranked the deadliest human diseases. Recent studies have reported that remote monitoring of patients with heart failure disease could help quantify their level of risks and provide useful information for efficient therapy. Additionally, such platforms could help increase accessibility to health care delivery at a relatively lower cost. Therefore this study proposed a context aware clinical decision support model using support vector machine (SVM) for heart failure risk prediction. The proposed model’s performance was evaluated using dataset of potential heart failure patients with metrics including prediction accuracy, sensitive, specificity, and receiving operating characteristic (ROC). An average prediction accuracy of 87.9% and 82.0%, were respectively achieved for the training and testing sessions of the built Radial basis function (RBF) based SVM classifier with a sensitivity value of 76.9%. The results obtained from this study might aid the development of an efficient context aware clinical decision support systems in smart cities and the society at large.