Cloud-based System Research Articles

Application of deep learning technique in next generation sequence experiments

In recent years, the widespread utilization of biological data processing technology has been driven by its cost-effectiveness. Consequently, next-generation sequencing (NGS) has become an integral component of biological research. NGS technologies enable the sequencing of billions of nucleotides in the entire genome, transcriptome, or specific target regions. This sequencing generates vast data matrices. Consequently, there is a growing demand for deep learning (DL) approaches, which employ multilayer artificial neural networks and systems capable of extracting meaningful information from these extensive data structures. In this study, the aim was to obtain optimized parameters and assess the prediction performance of deep learning and machine learning (ML) algorithms for binary classification in real and simulated whole genome data using a cloud-based system. The ART-simulated data and paired-end NGS (whole genome) data of Ch22, which includes ethnicity information, were evaluated using XGBoost, LightGBM, and DL algorithms. When the learning rate was set to 0.01 and 0.001, and the epoch values were updated to 500, 1000, and 2000 in the deep learning model for the ART simulated dataset, the median accuracy values of the ART models were as follows: 0.6320, 0.6800, and 0.7340 for epoch 0.01; and 0.6920, 0.7220, and 0.8020 for epoch 0.001, respectively. In comparison, the median accuracy values of the XGBoost and LightGBM models were 0.6990 and 0.6250 respectively. When the same process is repeated for Chr 22, the results are as follows: the median accuracy values of the DL models were 0.5290, 0.5420 and 0.5820 for epoch 0.01; and 0.5510, 0.5830 and 0.6040 for epoch 0.001, respectively. Additionally, the median accuracy values of the XGBoost and LightGBM models were 0.5760 and 0.5250, respectively. While the best classification estimates were obtained at 2000 epochs and a learning rate (LR) value of 0.001 for both real and simulated data, the XGBoost algorithm showed higher performance when the epoch value was 500 and the LR was 0.01. When dealing with class imbalance, the DL algorithm yielded similar and high Recall and Precision values. Conclusively, this study serves as a timely resource for genomic scientists, providing guidance on why, when, and how to effectively utilize deep learning/machine learning methods for the analysis of human genomic data.

Novel framework based on ensemble classification and secure feature extraction for COVID-19 critical health prediction

The Covid outbreak necessitated the use of an automated method for treating patients with critical symptoms. Increasing use of the Internet of Things (IoT) and smart devices requires access to real-time data over robust and secure network infrastructures. During the lifetime of healthcare applications, the data must be transferred in a controlled manner. In this context, this study proposes, an IoT-based decision support system for the analysis of Covid-related data in healthcare systems. Through in-vehicle monitoring, it is intended to safely send important data and patient updates to attending physicians. The suggested approach uses feature extraction methods to identify patients' crucial states while protecting their private information. To improve feature extraction, two techniques are combined: ensemble classification and cipher substitution for secure Json Web Tokens (JWTs). By incorporating the Secure Hashing Algorithm-2 (SHA-2) methods into the Feature Extraction based Logistic Regression (FELR), security protections are substantially reinforced. The system uses the Message Queuing Telemetry Transport (MQTT) protocol to provide secure communication between devices and access to the extracted characteristics from rescue vehicles. The system analyzes passengers, traffic data, and medical issues, which are continually updated with cloud-based security, by using vehicle-based communication networks. The secure movement of patient data inside the hospital network is ensured by this cloud-based system. By using sophisticated regression analysis and ensemble learning approaches, our suggested methodology has shown significant advantages compared to traditional models such as Logistic Regression (LR) and Mixed Regression (MR). Our technique demonstrates exceptional performance with an accuracy rate of 96%, a recall rate of 91%, and an F-measure of 91%. We also precisely compared the performance of our proposed FELR algorithm with hybrid prediction model using deep learning, the extra tree convolutional neural network-based ensemble model, ensemble-based random forest, ensemble transformation learning, and machine learning with regression. Significantly, FELR algorithm has demonstrated exceptional superiority by consistently surpassing the predictive capabilities of all these algorithms. The system has exceptional performance and exhibits improved efficiency and secure transmission capabilities. The unique model that we propose offers a feasible and effective approach for detecting crucial states in patients within healthcare facilities. The success of our framework is shown via a well-planned approach that includes important processes such as data preprocessing, feature extraction, ensemble model construction, and evaluation. The proposed framework offers a promising avenue for aiding healthcare professionals in making informed decisions regarding the critical health trajectories of COVID-19 patients.

Cloud-based non-invasive cognitive breath monitoring system for patients in health-care system

Cloud-based non-invasive cognitive breath monitoring system for patients in health-care system

Big data cloud-based recommendation system using NLP techniques with machine and deep learning

Big data cloud-based recommendation system using NLP techniques with machine and deep learning

Pilot Study of Peer Review in Low Middle-Income Country (LMIC) through Cloud-Based Platform.

Peer review is an essential step in clinical quality assurance and can impact patient safety and treatment outcomes. Most published data on peer review is from developed nations, with little data on peer review from low middle income countries (LMIC). Major challenges to peer review can include a lack of time, expertise and commitment from team members. With increasing access to advanced technology in LMIC, peer review is becoming more important to maintain quality and standard of care. We evaluated cloud-based e- peer review (Varian) in our network of hospitals in India with an aim to see feasibility and impact on care. Four of 15 centers across India were selected for this pilot study. All team members were trained on the platform prior to implementation. New cases for the week treated with definitive intent were selected by the dosimetrist. The link to the cases were sent through email to reviewing physicians. Various aspects which were reviewed for each case were.1) Work up & staging (Documents were scanned and loaded).2) Treatment intent & prescription.3) Target contours.4) Normal Organ at risk contours.5) Dose- Volume -Histogram (DVH) with clinical goals attached. Cases were marked as "Not Appropriate", "Appropriate", "Appropriate with minor finding", "Represent with major revisions" as per volume and plan review. Over a period of 2 months, a total of 80 cases underwent e-Peer Review at our network of hospitals prior to the start of treatment. Median turnover time (like from link sent to time to completion of review) was 48 (6-360) hours. Mean time taken by physician for review was 9 minutes (range 3 to15). 31.2% of cases were accepted without any changes, 51.9 % had a minor change and 16.9 % cases had major changes. Most frequent reason of major changes was contouring corrections 16.9%. 31% of major changes underwent recontouring and replanning before initiation of treatment. Peer review was feasible in our network through this e-peer review system, with average turnover time and mean time taken for review of 48 hours & 9 min respectively. Peer review led to significant changes which could impact patient care delivery and outcome. The ability to review cases asynchronously via this cloud-based e-peer review system, helped to ease the burden of scheduling between treating and reviewing physician. We plan to implement this across the remaining centers in our network.

Enhancing Data Security in Higher Education Administration: Strategies for Protecting Student Information and Institutional Integrity in Cloud-Based Systems

The widespread adoption of cloud-based systems in higher education has brought significant benefits and heightened concerns over data security. This research paper explores practical strategies for enhancing data security in cloud-based systems used by higher education institutions. The study employed a literature review, case studies, and surveys to gather data on current practices, challenges, and technological solutions in data security. The findings reveal that while cloud computing offers convenience and cost savings, it also introduces new vulnerabilities that must be addressed through a comprehensive security program. The paper discusses the importance of risk assessment, transparent governance, encryption, access controls, and the integration of emerging technologies like blockchain and AI. The implications of this research suggest the need for policymakers, administrators, and IT professionals to prioritize data security as a critical component of cloud-based system adoption in higher education.

Machine Learning Based Diabetes Decision Support System by using Decision Level Fusion

Abstract: It is fundamental in medication to have the option to expect illnesses early with the goal that they can be restored. Diabetes is considered one of the most perilous infections worldwide. The prevalence of this disease has reached nearly 100% due to the inclusion of sugar and fat in our high-calorie diets. To know when the contamination will strike, it is indispensable to comprehend what its signs are. Machine-learning (ML) procedures are useful for tracking down disorders right now. In this article, a "melded ML"- based diabetes forecast model is examined. The central strategy comprises of the Supports vector machine (SVM) and artificial neural network (ANN) models. These models examine the data to determine whether the diabetes conclusion is complete or negative. 70% of the information used in this review is preparatory information and 30% is test information. The fluffy model's feedback participation capability is the consequence of these models. In the final analysis, the outcome of diabetes is determined by logical and rational deliberation. The integrated models are reserved for future utilization in a cloud-based storage system. The merged model can sort out regardless of whether a patient has diabetes considering the patient's consistent clinical information. The suggested entwined ML model has a higher accuracy (94.87) than the procedures that have proactively been circulated

Development of LoRaWAN-Based IoT Monitoring Device for Pressure Rate Profiling in Water Pipelines

Water Pipeline systems are critical infrastructure for the delivery of safe and reliable water to communities. However, these systems are vulnerable to leaks, bursts, and other failures that can lead to water loss, property damage, and disruptions in service. One key factor in the health and longevity of water pipeline systems is the maintenance of proper pressure levels. Monitoring pressure in water pipelines is essential for ensuring that the system is functioning properly, and for identifying potential issues before they become major problems. The development of IoT technology has opened new possibilities for monitoring water pipeline pressure. IoT devices can be designed to collect and transmit pressure data wirelessly, eliminating the need for manual data collection and allowing for real-time monitoring and early warning systems. This study aims to develop an IoT monitoring device for pressure rate profiling in water pipeline systems. The device utilized pressure sensor and GSM module to collect and transmit pressure data in real-time to a cloud-based data management system. Through laboratory experimentation, the developed monitoring system in this study was able to both detect and localize leakages with the possibility of monitoring the leakage over a mobile application. The device also exhibits an average pressure variation of 7.7 KPA which indicates a high consistency.

Weather-aware object detection method for maritime surveillance systems

The development of machine learning-based maritime object detection technology aims to assist ship operators in maritime surveillance. However, as maritime environments can be quite complex, developing object detection models that can handle these situations is a challenging research problem, particularly when dealing with adverse weather conditions like rain and haze. While prior research has attempted to remove weather noise and improve object detection models under various weather conditions, they are limited by computing resources and hard to adapt to the constantly changing weather conditions of maritime environments. Preventing performance degradation as weather conditions shift is a significant challenge in maritime surveillance systems. To overcome these challenges, this paper proposes a weather-aware object detection method, Weather-OD, that employs an on-board edge and on-shore cloud-based system for maritime surveillance. It employs specialized machine learning models for object detection, which can be dynamically selected based on the weather conditions to ensure highly accurate object detection with low latency at sea. Weather-OD continuously improves accuracy by periodically training the models with newly collected datasets during voyages, and efficiently manages the life cycle of multiple object detection models, taking into account the constraints of limited edge computing resources. In addition, Weather-OD uses synthetic image data with weather noise to supplement the training data under different weather conditions. We conducted an evaluation of our weather-aware object detection models using a maritime benchmark dataset, the Singapore Maritime Dataset. Our experimental results demonstrated the feasibility of our mechanism with weather classification and a significant improvement in the mean Average Precision (mAP) of maritime object detection in rainy and hazy conditions. Additionally, our approach enables the continuous improvement of object detection accuracy through model retraining with small datasets.

Optimization of Deep Generative Intrusion Detection System for Cloud Computing: Challenges and Scope for Improvements

The large amount of data and its exponential increase result in security problems which subsequently cause damage to cloud computing and its environments. The Intrusion detection system (IDS) is among the systems that monitor and analyse data for malicious attacks in the cloud environment. High volume, high redundancy, and high dimensionality of network traffic in cloud computing make it difficult to detect attacks by contemporary techniques. To improve the performance of IDS features selection and data imbalance issues need to be resolved. This paper includes techniques and surveys of cloud-based IDS with ML techniques and IDS performance on the different types of cloud-based datasets. It also analyses the gaps and scope for enhancement of evaluation parameters of IDS. It provides a cloud-based IDS system which will produce a good performance result as compared to the other contemporary system. Moreover, this paper offers a current overview of cloud-based IDS, Data imbalance technique, Dataset and proposed cloud IDS system architecture.

Designing a Cloud-Based MES-SaaS Platform Model in Precision Machining

Smart manufacturing environments are spreading at home and abroad. As a result, SMEs are also recognizing the need for digital transformation. However, they are facing difficulties in introducing MES/ERP software due to the initial cost burden and lack of resources. Under these circumstances, this paper aims to design a cloud-based subscription MES platform that enables SMEs specializing in the precision machining industry to operate efficient production processes and improve productivity and competitiveness. To build a cloud-based subscription MES system, we collect and store manufacturing interlocking data based on the AAS standard and utilize data link sockets such as the EDC standard, which is an important factor in securing compatibility and integration between various systems. Therefore, we aim to design an MES-SaaS platform that enables SMEs to utilize MES and ERP software in line with the growth of the smart manufacturing environment.

OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis.

The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.

Cloud-based system for monitoring loads generated on the quay wall by ship propeller jets

The paper presents a cloud-based system for monitoring loads generated on the quay wall by ship propeller jets during berthing and unberthing maneuvers. The system provides online measurements of the dynamic pressure generated loads by propellers and warns of exceeding the permissible jet velocities over the seabed protection along the berth. The system has a modular structure consisting of a network of pressure sensors, information mapping server, communication module and web server. The suitability of the system was confirmed during two years of operation in Port of Gdynia. The open architecture of the system allows for its further extension with additional berths and integration with the smart port system.

Factors Influencing the Adoption of Cloud-based Village Information System: A Technology-Organization-Environment Framework and AHP–TOPSIS Integrated Model

Factors Influencing the Adoption of Cloud-based Village Information System: A Technology-Organization-Environment Framework and AHP–TOPSIS Integrated Model

The lived experience of depression: a bottom-up review co-written by experts by experience and academics.

We provide here the first bottom-up review of the lived experience of depression, co-written by experts by experience and academics. First-person accounts withinand outside the medical field were screened and discussed in collaborative workshops involving numerous individuals with lived experience of depression, family members and carers, representing a global network of organizations. The material was enriched by phenomenologically informedperspectives and shared with all collaborators in a cloud-based system. The subjective world of depression was characterized by an altered experience of emotions and body (feeling overwhelmed by negative emotions, unable to experience positive emotions, stuck in a heavy aching body drained of energy, detached from the mind, the body and the world); an altered experience of the self (losing sense of purpose and existential hope, mismatch between the past and the depressed self, feeling painfully incarcerated, losing control over one's thoughts, losing the capacity to act on the world; feeling numb, empty, non-existent, dead, and dreaming of death as a possible escape route); and an altered experience of time (experiencing an alteration of vital biorhythms, an overwhelming past, a stagnation of the present, and the impossibility of the future). The experience of depression in the social and cultural context was characterized by altered interpersonal experiences (struggling with communication, feeling loneliness and estrangement, perceiving stigma and stereotypes), and varied acrossdifferentcultures, ethnic or racial minorities, and genders. The subjective perception of recovery varied (feeling contrasting attitudes towards recovery, recognizing recovery as a journey, recognizing one's vulnerability and the need for professional help), as did the experience of receiving pharmacotherapy, psychotherapy, and social as well as physical health interventions. These findings can inform clinical practice, research and education. This journey in the lived experience of depression can also help us to understand the nature of our own emotions and feelings, what is to believe in something, what is to hope, and what is to be a living human being.

Moodle as online platform for cardiac nurses’ competency: nursing staff experiences

Abstract Background Aswan Heart Centre competency project were started in 2017, nurse’s competency were tillered and implemented within the nursing units. Self-assessment and preceptor grading ideationally to constrictive comment should be documented in the competency tool for every staff, was no unified way to keep tracking these documentations and very hard to secure the hard copy. Aim Is to establish an online platform where the competencies are accessible and high secured that also controls and facilitates the use of nursing competencies. Process Moodle as a cloud-based learning management system (LMS) selected to be the new Online competency platform. Moodle expert were contacted by the nursing education department to assist and support the process. Online could server to host and upload the platform were established by the information technology department. After competencies and users uploaded pilot phase were started to identify the gap in the Moodle platform. After 4 months of use survey were conducted to get a clear view from the staff. Data analysis Most of the staff were satisfied about Moodle use, but the form still need farther support from the information technology department to host the Moodle cloud in stable server to avoid the browser render. Recommendation The use of cloud-based learning management system like Moodle will maintain the easy accessibility and the high security of the data also will facilitate the data audit for further plans.

Investigation of indicators for personal exposure and occupancy in offices by using smart sensors

Despite the advent of smart building sensors, real-time methods for capturing inhalation exposure and occupancy are limited. This study utilized stationary and wearable environmental sensors, along with Decision Tree and correlation analyses, to determine the optimal set of indicators for approximating dynamic inhalation exposures and occupancy in office environments. In a 2-week field campaign in two Swiss office buildings, real-time measures of air temperature, relative humidity, CO2, and size-resolved particle concentrations were taken at two scales: personal – via personalized vests with sensors for personal exposure detection; and room – via stationary sensors at sidewalls and office desks. Occupancy and activity profiles were collected at three scales: personal – via smartwatches; room – via visual inspections; and building – via a cloud-based location service system. A desk-mounted CO2 sensor in the center of an office was an effective indicator of personal exposures to CO2, PM2.5, and PM10. Sidewall CO2 measurements accurately captured room occupancy in open-plan offices, while desk-mounted PM10 sensors were effective in areas with transient occupancy (e.g., cafeteria). Findings from this study aid improved understanding of the complex dynamics of air pollutants in offices, and could support the development of refined methods for smart building monitoring and ventilation control.

Cloud-based virtual flow metering system powered by a hybrid physics-data approach for water production monitoring in an offshore gas field

This work presents a cloud-based Virtual Flow Metering (VFM) system powered by a hybrid physics-data approach to estimate the water production per well in a gas field. This hybrid approach, which allows accurate calculations near real-time conditions, is based on the description of the flow through the wellbore using physics-based models pertaining to gas-liquid flows with high gas volume fraction. A data-driven approach is implemented to tune the flow model using well test data. This implementation accounts for changes in the well performance and increase in water production, resulting in a self-calibrating solution. This means that the model will remain accurate and relevant as production and well conditions change. Results from the VFM show good agreement with the well test data for steady-state conditions. The VFM calculations are performed remotely using a cloud-based DataOps platform where results are also stored. This allows continuous access to live sensor data to be used as input to other applications or visualized through a web interface. The VFM system uses a set of readily available sensors installed in the wells. Thus, it represents cost reduction in both capital and operating expenditures when compared to the installation of multiphase flow meters or separators.

Development of a Cloud-Based Image Processing Health Checkup System for Multi-Item Urine Analysis.

With the busy pace of modern life, an increasing number of people are afflicted by lifestyle diseases. Going directly to the hospital for medical checks is not only time-consuming but also costly. Fortunately, the emergence of rapid tests has alleviated this burden. Accurately interpreting test results is extremely important; misinterpreting the results of rapid tests could lead to delayed medical treatment. Given that URS-10 serve as a rapid test capable of detecting 10 distinct parameters in urine samples, the results of assessing these parameters can offer insights into the subject's physiological condition. These parameters encompass aspects such as metabolism, renal function, diabetes, urinary tract disorders, hemolytic diseases, and acid-base balance, among others. Although the operational procedure is straightforward, the variegated color changes exhibited in the outcomes of individual parameters render it challenging for lay users to deduce causal factors solely from color variations. Moreover, potential misinterpretations could arise due to visual discrepancies. In this study, we successfully developed a cloud-based health checkup system that can be used in an indoor environment. The system is used by placing a URS-10 test strip on a colorimetric board developed for this study, then using a smartphone application to take images which are uploaded to a server for cloud computing. Finally, the interpretation results are stored in the cloud and sent back to the smartphone to be checked by the user. Furthermore, to confirm whether the color calibration technology can eliminate color differences between different cameras, and also whether the colorimetric board and the urine test strips can perform color comparisons correctly in different light intensity environments, indoor environments that could simulate a specific light intensity were established for testing purposes. When comparing the experimental results to real test strips, only two groups failed to reach an identification success rate of 100%, and in both of these cases the success rate reached 95%. The experimental results confirmed that the system developed in this study was able to eliminate color differences between camera devices and could be used without special technical requirements or training.

Cloud-based digital healthcare development for precision medical hospital information system.

Aim: This study aims to develop a cloud-based digital healthcare system for precision medical hospital information systems (P-HIS). Methods: In 2020, international standardization of P-HIS clinical terms and codes was performed. In 2021, South Korea's first tertiary hospital cloud was established and implemented successfully. Results: P-HIS was applied at Korea's first tertiary general hospital. Common data model-compatible precision medicine/medical service solutions were developed for medical support. Ultrahigh-quality medical data for precision medicine were acquired and built using big data. Joint global commercialization and dissemination/spreading were achieved using the P-HIS consortium and global common data model-based observational medical outcome partnership network. Conclusion: To provide personalized precision medical services in the future, establishing and using big medical data is essential.