Cloud-based Framework Research Articles

Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure

BackgroundInadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ultrafiltration rate which in turn has a positive effect on intradialytic symptoms. It has been clinically challenging to identify changes RBV in real time to proactively intervene and reduce potential negative consequences of volume depletion. Leveraging advanced technologies to process large volumes of dialysis and machine data in real time and developing prediction models using machine learning (ML) is critical in identifying these signals.MethodWe conducted a proof-of-concept analysis to retrospectively assess near real-time dialysis treatment data from in-center patients in six clinics using Optical Sensing Device (OSD), during December 2018 to August 2019. The goal of this analysis was to use real-time OSD data to predict if a patient’s relative blood volume (RBV) decreases at a rate of at least − 6.5 % per hour within the next 15 min during a dialysis treatment, based on 10-second windows of data in the previous 15 min. A dashboard application was constructed to demonstrate how reporting structures may be developed to alert clinicians in real time of at-risk cases. Data was derived from three sources: (1) OSDs, (2) hemodialysis machines, and (3) patient electronic health records.ResultsTreatment data from 616 in-center dialysis patients in the six clinics was curated into a big data store and fed into a Machine Learning (ML) model developed and deployed within the cloud. The threshold for classifying observations as positive or negative was set at 0.08. Precision for the model at this threshold was 0.33 and recall was 0.94. The area under the receiver operating curve (AUROC) for the ML model was 0.89 using test data.ConclusionsThe findings from our proof-of concept analysis demonstrate the design of a cloud-based framework that can be used for making real-time predictions of events during dialysis treatments. Making real-time predictions has the potential to assist clinicians at the point of care during hemodialysis.

Paas EUD Tool for Developing Expert Context-Aware Mobile Applications

Context-awareness enables systems to be tailored to the needs of users and their real circumstances at certain times. A noteworthy trend in software development is that an increasing number of software systems are being developed by individuals with expert knowledge in other sectors. Because most of the current context-aware development toolkits are intended for software developers, these types of systems cannot be easily developed by non-technical consumers. The development of tools for designing context-aware frameworks by consumers who are not programming experts but are specialists in the area of implementation would result in faster adoption of such services by businesses. This paper provides a cloud-based framework for people without programming experience to create context-aware mobile applications. The platform can provide a lightweight distribution of packaged applications that allows experts to send specified information to mobile users based on their context data without overlapping between the rules of the application. An energy-efficient mobile application was developed to acquire contextual information from the user device and to create quality data accordingly. The framework adopts Platform as a Service (PaaS) and containerization to facilitate development of context-aware mobile applications by experts in various domains rather than developing a tool for each domain in isolation, while considering multitenancy.

CUBIC-Cloud provides an integrative computational framework toward community-driven whole-mouse-brain mapping

SummaryRecent advancements in tissue clearing technologies have offered unparalleled opportunities for researchers to explore the whole mouse brain at cellular resolution. With the expansion of this experimental technique, however, a scalable and easy-to-use computational tool is in demand to effectively analyze and integrate whole-brain mapping datasets. To that end, here we present CUBIC-Cloud, a cloud-based framework to quantify, visualize, and integrate mouse brain data. CUBIC-Cloud is a fully automated system where users can upload their whole-brain data, run analyses, and publish the results. We demonstrate the generality of CUBIC-Cloud by a variety of applications. First, we investigated the brain-wide distribution of five cell types. Second, we quantified Aβ plaque deposition in Alzheimer’s disease model mouse brains. Third, we reconstructed a neuronal activity profile under LPS-induced inflammation by c-Fos immunostaining. Last, we show brain-wide connectivity mapping by pseudotyped rabies virus. Together, CUBIC-Cloud provides an integrative platform to advance scalable and collaborative whole-brain mapping.

Cloud Based Extensible Business Management Suite and Data Predictions

Introduction: The present research work was carried out using an enhanced market prediction algorithm in the cloud-based business, handing out the CRM system. The approach that is being predicted will handle and forecast the business with a higher degree of precision and constancy, the outcome solution of which is found to be compatible with any organization. Methods: The CRM framework was proposed in order to overcome the lack of multi-module integration and functionalities. The developed application is a complete tool for the purpose of managing the business over cloud. The business owner can host this application on any user interface PHP- and MySQL-enabled server. An optimized regression model was developed to predict the future of business and relationship with the customer. For the experimental test, a sample dataset was collected from the UCI machine learning repository. Results: The proposed prediction algorithm is an extension of the time series–based forecasting model. The projected system is tested with the income and expense cluster data of one year that were projected for analysis and the result shows better accuracy and constancy than the traditional opportunity creation driven model. Conclusion: It has been observed that the proposed Cloud-Based CRM Framework supports the organizations to achieve business predictions and maintain healthy relations with their clients and has worked best for the companies that have to maintain customer end relations. In addition, the customized CRM tool helps business managers and entrepreneurs to define innovative business strategies. The CRM application influences customer satisfaction on a large scale and is dominant for the supply chain system. Overall, the business model indicates that corporate organizations could grab business insights in a cost-efficient way with upcoming forecasting of sales. Discussion: Customer Relationship Management (CRM) is a technology used by the corporate sector to manage potential customers and their experiences in the view of the seller to consumer through purchase and post-purchase. In this era of advanced information technology, the sales departments of multi-product and service-end businesses are struggling hard in order to attain the signal view of the business. The key criterion for business-boosting decisions includes a single dashboard interface with a description of relevant information. The biggest challenge for businesses with umbrella views is to combine the data from various places and obtain market forecasts from the description. The deficiency in data collection and group management causes market loss or a lack of consumer interest in the product.

A Review of Cloud-Based Malware Detection System: Opportunities, Advances and Challenges

Cloud computing has an important role in all aspects of storing information and providing services online. It brings several advantages over traditional storing and sharing schema such as an easy access, on-request storage, scalability and decreasing cost. Using its rapidly developing technologies can bring many advantages to the protection of Internet of Things (IoT), Cyber-Physical Systems (CPS) from a variety of cyber-attacks, where IoT, CPS provides facilities to humans in their daily lives. Since malicious software (malware) is increasing exponentially and there is no well-known approach to detecting malware, the usage of cloud environments to detect malware can be a promising method. A new generation of malware is using advanced obfuscation and packing techniques to escape from detection systems. This situation makes almost impossible to detect complex malware by using a traditional detection approach. The paper presents an extensive review of cloud-based malware detection approach and provides a vision to understand the benefit of cloud for protection of IoT, CPS from cyber-attack. This research explains advantages and disadvantages of cloud environments in detecting malware and also proposes a cloud-based malware detection framework, which uses a hybrid approach to detect malware.

Watermarking of Electronic Patient Record in Parkinson Disease Affected Speech: A Robust and Secure Audio Hiding Technique for Smart e-healthcare Application

A cloud-based framework for security of electronic patient record, in telediagnosis of Parkinson's disease (PD) speech signal has been proposed in this paper. The security and authenticity of patient's personal information is achieved using proposed Discrete Cosine Transform-Singular Value Decomposition (DCT-SVD) based audio watermarking technique. The automatic classification of PD affected speech signal from healthy person's speech signal requires high computational accuracy and in this work, it is addressed by extracting various time frequency features for the Support Vector Machine (SVM) classifier. In the proposed framework the speech signal of PD suspected person is recorded by a mobile application. The personal information is securely embedded in this speech signal using proposed DCT-SVD based audio watermarking technique, before transmitting to cloud for automatic classification. To ensure the security of electronic patient record (EPR), proposed watermarking technique is tested against various signal processing attacks and the performance of classifier has been evaluated by computing classification sensitivity, specificity, accuracy and area under the curve of receiver operating characteristics (ROC). The theoretical proofs and experimental results, show that proposed framework provides telediagnosis of PD affect patients without compromising the security and privacy of patient's records. The classification accuracy of $89\%$ with high data payload of $6kbps$ is proposed leading to a smart and secure e-healthcare application.

Binary Integer Programming Approach to Optimal Content Placement in Cloud-based Content Delivery Networks

Over the last decades, Content Delivery Networks (CDNs) have been developed to overcome the limitation of user perceived latency by replicating contents from origin server to its content servers around the globe close to clients. As some contents occupy most of the storage capacity and processing power in traditional private content servers, cloud computing can provide a pool of storage and processing power resources for caching contents. By adopting cloud computing to CDN, the content provider can use the cloud infrastructure by distributing the contents to cloud servers which will then deliver to near clients. In this paper, we propose a cloud-based CDN framework designed by two schemes 1) UDP/TCP-based content distribution from origin server to cloud servers and 2) SDN-based cloud server coordination. In addition, we also formulate the optimal content placement problem using binary integer programming to minimize the total cost of renting resources including storage, processing power, and network bandwidth in cloud providers for hosting contents from origin server. Then, the optimal solution obtained from binary integer programming is evaluated by greedy algorithm and simulations. The proposed framework helps content provider to offer high quality of services to clients while minimizing the cost of rented cloud resources.

Collaborative manufacturing design: a mixed reality and cloud-based framework for part design

Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) can be considered as the cornerstones of the lifecycle of a manufacturing asset. Since the above-mentioned processes often involve multiple engineers, from different departments even from different companies, it is crucial to ensure the flawless communication between the different individuals as well as to make the design process more intuitive. In the era of digitalization and Internet of Things, digital technologies such as Mixed Reality (MR) are utilized from engineers in order to leverage the capabilities of existing computer aided tools (CAx). Therefore, in this research work, the design and development of a Cloud-based and Mixed Reality-based framework is presented. The purpose of the framework is to facilitate engineers during the design and manufacturing phase of new components by providing a mobile application and common design tools via MR graphical user interfaces (GUI). Further to that, taking into consideration the advanced capabilities of MR devices in terms of user interaction, special emphasis is given on the interaction of engineers with the holograms of the components via simple hand gestures. The resulting 3D geometries can be imported in CAD suites either for further development or for manufacturing the designed component. The applicability of the proposed framework is tested and validated in a laboratory-based machine shop.

EFFICIENT AND RELIABLE PUBLIC HEALTHCARE SERVICES WITH G-CLOUD INFRASTRUCTURE

An improvised social insurance system is required in light of the limitations of the outdated medical care framework and the progress of technology. In the human services industry, cloud-based software development is becoming more and more popular as a means of managing and fulfilling current and future demands in social insurance administrations. We suggest a flexible, secure, effective, useful, and protected cloud-based framework for medical services. We've provided a secure and efficient framework for the administration EHR, where access control arrangements are made possible by many tiers of information structure and multi-authority ciphertext property based encryption (CP-ABE), which governs fine-grained access control. Top Saudi officials stand to gain greatly from egovernment distributed computing, since the planned framework will enable them to expand their social insurance industry.

Designing A Cloud-Based Framework using Data Mining for Healthcare Services in Remote Areas

Advancement is recently made inthe medicinal field producesnew innovative technologies to the healthcare sector and medical services. Access to quality healthcare is a major problem in remote areas, with a doctor-to-patient ratio as high as 1:20,000 which is far above the recommended ratio of the World Health Organization (WHO). This has been antagonized by a lack of access to critical infrastructures such as the health care facilities, roads, electricity, and many others. To watch basic medical parameters for identifying the abnormalities within the first stage of chronic diseases need regular interval hospital visits, which can be a comparatively costly and time-consuming process. Rare availabilities of doctors or medical centers, ignorance of the people, and proper care at the right time are the prime causes of great medical concern, which leads to unexpected death. This work is an attempt to solve basic health problems and take advice from registered medical experts for the betterment of the targeted community.Rapid development in the cloud environment, health care services are reasonable to the people in remote areas. It is necessary to predict the disease and connect with the doctor to get an early diagnosis of disease. The imperative goal of the paper is to develop a cloud-based framework using data mining to enhance healthcare in remote areas. The cloud-based framework is designed and simulated by using Matlab R2018b. Fast Search-Growing Self Classifier (FS-GS) data mining classifier is developed to separate the data from the cluster to correlate the symptoms of patients with specialists. The classifier parameters like accuracy, precision, recall, sensitivity, and specificity are analyzed to compare the efficiency of the proposed algorithm with the other data mining algorithms like Naive Bayes, Random Forest, K Nearest Neighbor, and Support Vector Machine Linear. The proposed FS-GS data mining Classifier obtains an accuracy of 92%, the precision of 90.01%, recall of 90.06%, the sensitivity of 94.91%, and specificity of 92.6%. For the effectiveness, the proposed algorithm is compared with the various mining data classification algorithms to showmance of the proposed algorithm. Ultimately, the result shows the proposed algorithm scores higher outputs than all other algorithms in real-time scenarios respectively.

Robust Data-Driven Framework for Driver Behavior Profiling Using Supervised Machine Learning

Driver behavior profiling has been gaining increased attention due to its relevance in many applications. For instance, car insurance telematics and fleet management entities have been recently using smartphones’ embedded sensors, On-Board Diagnostics II (OBDII) units and other on-board IoT devices to collect data on vehicles’ behavior and evaluate the risk profile of drivers. In this context, this paper presents a robust data-driven framework for calculating drivers’ risk profile measured in terms of the additive inverse of the predicted risk probability. The Strategic Highway Research Program 2 (SHRP2) naturalistic driving study (NDS) dataset, which is the largest dataset of its kind to date, is utilized to build the risk prediction models. Crash and near-crash events are used to quantify riskiness whereas balanced baseline driving events (i.e., events captured during normal day to day driving episodes) are used to reflect total exposure or driving time per driver. Thirteen mutually exclusive behavioral risk predictors are identified, and the feature matrix is formulated. A sensitivity analysis is then performed to find the best number of balanced baseline events below which drivers are filtered out. Different machine learning models are selected, customized, and compared to achieve best risk prediction performance. Finally, the utilization of the proposed prediction model within an envisioned driver profiling cloud-based framework is briefly discussed.

Optimal supply chain design with product family: A cloud-based framework with real-time data consideration

When the product family (PF) and the supply chain designs (SCD) are aligned and integrated, original equipment manufacturers (OEM) are more likely to improve their operational performance.In this paper,we propose a novel approach, which demonstrates how both the product and the supply chain can simultaneously be designed based on real-time data. At the heart of the proposed model is the utilisation of a cloud-based management system comprising of three steps. In the first step, a generic bill of materials is modelled to design a set of product families using “AND” and “OR” nodes. In the second step,a cloud-based framework is designed to manage real-time costs viz. echelons. In the third step, a mixed integer linear programming model is then applied, which optimizes the SCD based on real-time costs.We use a metaheuristic method based on Genetic Algorithm (GA) to solve the optimization problem. We further illustrate the model using power transformer numerical example. Then the critical parameters of GA are examined to determine the best settings.We believe that the proposed SCD is an intelligent and expert management system, which can facilitate effective decision-making support by taking into account real-time cost data. This is particularly important when there are uncertain and volatile market conditions.

Engineering resilient systems cloud computing architecture (ECCA): a collaborative and secure analysis framework

Design and tradespace analysis of increasingly complex systems often require proprietary models from multiple industrial sources. These multi-partner collaborations require assurances that each company’s intellectual property will remain protected from unauthorized access or disclosure to competitors. In this paper, we present the security risks and considerations for these collaborations, and propose a cloud-based framework to address these concerns. The Engineering Resilient Systems (ERS) Cloud Computing Architecture (ECCA) system enables secure collaboration in the Department of Defense community, providing an infrastructure restricting access to connectable containerized assets: models and simulations, each of which remain isolated and maintained on the owner’s own cloud. ECCA further prevents unauthorized access to sensitive assets and infection by malicious media with an access control system supported by secure gRPC messaging and access agreements. We demonstrate the capability to connect disparate models for a simplified combat simulation and then how the new multi-model scenario enables additional capabilities in tradespace analysis.

Cement strength prediction using cloud-based machine learning techniques

ABSTRACT This paper describes a cloud-based software framework to predict cement strength for 2 days, 7 days and 28 days. Levenbarg-Marquardt back-propagation-artificial neural network (LMBP-ANN) is used to build a prediction model. This ANN model uses 70% of data for training (70%, 212 data records), testing (15%, 46 data records) and for validation (15%, 46 data records). A total of 16 significant input parameters are considered for the cement strength prediction. The user interface and software framework are built using the Python programming language. Multiple Python packages are used for the implementation of the ANN model. The cloud server having Ubuntu operating system has been used to host the web application for prediction of cement strength. The software application is tested using real-time data from various cement industries. The prediction of the cement strength of the proposed ANN-based software application appears to be very similar to those currently generated in experimental data in the cement manufacturing industry. The adequacy of the developed model based on the back-propagation ANN algorithm is confirmed as the Pearson correlation of experimental value and predicted value. The calculated value of R for experimentations on the data is 0.82539 and is 0.6813.

EEG-Based Pathology Detection for Home Health Monitoring

An electroencephalogram (EEG)-based remote pathology detection system is proposed in this study. The system uses a deep convolutional network consisting of 1D and 2D convolutions. Features from different convolutional layers are fused using a fusion network. Various types of networks are investigated; the types include a multilayer perceptron (MLP) with a varying number of hidden layers, and an autoencoder. Experiments are done using a publicly available EEG signal database that contains two classes: normal and abnormal. The experimental results demonstrate that the proposed system achieves greater than 89% accuracy using the convolutional network followed by the MLP with two hidden layers. The proposed system is also evaluated in a cloud-based framework, and its performance is found to be comparable with the performance obtained using only a local server.

A cloud-based framework for sensitivity analysis of natural hazard models

Computational models for natural hazards usually require a large number of input parameters that affect the model outcome in a complex manner. The sensitivity of the input parameters to the output variables can be quantified using sensitivity analysis, which provides insight into the key factors driving the model and can guide modeling optimization. However, performing a sensitivity analysis typically requires a large number of simulations, which can be prohibitively time-consuming on workstations or local servers. To address this issue, this study proposes a Cloud-based framework that takes advantage of scalable Cloud resources. The efficacy of the framework is demonstrated by the scalability achieved while running large-scale wildfire simulations. Moreover, a comprehensive sensitivity analysis of the input parameters used in these models is presented. The ability to efficiently perform sensitivity analysis using the framework could allow such analysis to be performed as an on-demand service for operational disaster management.

Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq.

Massively parallel single-cell and single-nucleus RNA-seq (sc/snRNA-seq) have opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so does the need for computational pipelines for scaled analysis. Here, we developed Cumulus, a cloud-based framework for analyzing large scale sc/snRNA-seq datasets. Cumulus combines the power of cloud computing with improvements in algorithm implementations to achieve high scalability, low cost, user-friendliness, and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.

A Secure Privacy Preserving Cloud-based Framework for Sharing Electronic Health Data.

There exists a need for sharing user health data, especially with institutes for research purposes, in a secure fashion. This is especially true in the case of a system that includes a third party storage service, such as cloud computing, which limits the control of the data owner. The use of encryption for secure data storage continues to evolve to meet the need for flexible and fine-grained access control. This evolution has led to the development of Attribute Based Encryption (ABE). The use of ABE to ensure the security and privacy of health data has been explored. This paper presents an ABE based framework which allows for the secure outsourcing of the more computationally intensive processes for data decryption to the cloud servers. This reduces the time needed for decryption to occur at the user end and reduces the amount of computational power needed by users to access data.

A Cloud Based Electronic Office Framework for Ethiopian Public Universities (CBEOFEPU)

An effective cloud-based electronic office management system plays a crucial role in delivering services in a better way. The main goal of this research is to create a smart environment, a digital workplace solution, good communication, and collaboration between employers and Ethiopian higher education institutions and to add some cloud computing capabilities to the electronic office. The proposed system is intended to better drive the effective development of Smart society and to advance a step into an era of the paperless, replace the previous manual handling of documents with an efficient electronic system. The study uses questionnaire, Interview, and observation data collection techniques and the developed prototype and proposed framework evaluate by different users, experts in software engineering and related fields. This study proposes a private cloud platform architecture for electronic office in Ethiopian public universities Software as a Service. The Cloud-Based Electronic Office Framework (CBEOF) adapts the IBM Cloud Reference Architecture (IBMCRA), the NIST Cloud Reference Architecture, and the Microsoft Web Application Architecture to take advantage of all the considerations and capabilities of a cloud-based software architecture.

Cloud-Based Outsourcing Framework for Efficient IT Project Management Practices

The optimum utilization of human resources is one of the crucial exercises in IT organizations. To provide a well-organized and cohesive working environment, organizations need to review their work culture in reference to newly evolved tools and techniques. To reduce the development cost of the IT projects and the optimum utilization of human resources, organizations need to review and redesign the project development processes. The significant challenges faced by IT organizations are the rapid switch-over (attrition) of IT professionals, physical migration or deployment, and redeployment of the human resources. This research paper is an effort towards the multilateral exploration of the techniques to adapt and improve the ICT enabled project management practices in an outsourced environment. This research is an effort with special reference to developing countries such as Ethiopia, where an acute shortage of high skilled IT human resources and their physical migration from one project location to another project location is a costly and challenging task. Ethiopia as a developing country and its IT industry is challenged by several issues like the capacity of ICT infrastructure and the skilled human resources. In such situations, IT projects are either challenged, impaired, or completed failed due to lack of IT human resources with desired skills and ultramodern up to date IT infrastructure. In this research paper, cloud computing technology is assumed as a key to the solution. For this, a systematic and careful investigation using mixed data analysis approach was used to adopt cloud-based outsourcing in IT project management practices i.e. design, development, and testing over outsourced systems by outsourced IT human resources. The major findings of this paper are to investigate and analyze how these cloud-based resources can be explored without physical movement or migration. For the novel improvement in the existing IT project management practices, the salient stakeholders’ views were collected and analyzed for designing cloud-based outsourcing IT project management framework for the Ethiopian IT industry. The framework was functionally tested over the cloud-based Bitrix24 platform.