A comparative recognition research on excretory organism in medical applications using artificial neural networks

Shitharth, S.; Manoharan, Hariprasath; Iwendi, Celestine; Alsowail, Rakan A.; Pandiaraj, Saravanan

doi:10.3389/fbioe.2023.1211143

Cited by 15 publications

(8 citation statements)

References 24 publications

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“…Furthermore, a study of 143 participants measured the adoption of shopping online via both websites and mobile shopping. The results show that mobile apps are more adopted in terms of accessibility [7,8].…”

Section: Overview Of Research In the Area Of Mobile Commercementioning

confidence: 96%

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Blockchain-based cyber-security proposal in commerce mobile platforms for social and sustainability businesses

Riskhan,

Almassri,

Hussain

et al. 2024

Metaverse

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<p>This study aims to propose the integration of cyber security measures with sustainable mobile commerce platforms and evaluate their efficacy in promoting social entrepreneurship and sustainability in the field of mobile commerce. A platform was developed for Android and iOS platforms using the Flutter Framework and Dart Programming Language, and it included features designed to help social entrepreneurs and non-profit organizations connect with potential donors, volunteers, and partners. The study includes participants from social entrepreneurs, non-profit organizations, and potential donors who are recruited to use the platform to create and promote their social business projects and to provide feedback on their experience using the platform. The results of the integration are expected to show that the mobile platform for social business is effective in promoting social entrepreneurship and sustainable development and in connecting social entrepreneurs and non-profit organizations with potential donors and partners due to the security measures that have been taken in the application and data protection, ensuring privacy with a comparatively lower risk than other platforms. In conclusion, this paper presents a blockchain-based secure mechanism for mobile platforms for m-commerce and social business, which is a valuable tool for promoting social entrepreneurship and sustainable development. It can be used to create a positive social impact in local and, conceivably, international communities. The proposal explicitly explains the methodology and implementation of the platform and highlights the significance and impact of this research in advancing the fields of mobile commerce, social entrepreneurship, and sustainability.</p>

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Section: Overview Of Research In the Area Of Mobile Commercementioning

confidence: 96%

“…Secure communication: Along with cloud storage of data, there is a secure communication protocol that helps prevent eavesdropping and man-in-themiddle attacks. (8). Code obfuscation: Obfuscating the code can make it harder for attackers to reverse engineer the app and find vulnerabilities.…”

Section: Cyber Security Considerationsmentioning

confidence: 99%

Blockchain-based cyber-security proposal in commerce mobile platforms for social and sustainability businesses

Riskhan,

Almassri,

Hussain

et al. 2024

Metaverse

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“…While using BLE access points for location tracking within the rehabilitation wards, we found that only 284 out of the 43 measurements (82.8%) achieved a data-acquisition rate exceeding 90%. This indicates that data loss remains a challenge that needs addressing, which is similar to other medical-sensor-network systems (Selvarajan et al, 2023). This data loss could be due to equipment malfunctions, such as battery depletion, interference from electromagnetic shields in the environment, and human errors.…”

Section: Data Loss Of Location Trackingmentioning

confidence: 98%

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Ogasawara,

Mukaino,

Matsunaga

et al. 2024

Front. Bioeng. Biotechnol.

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Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman’s rank correlation coefficient (R) of 0.56 and −0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics.Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements.

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“…Other communication units are not required in the representation scenarios due to the flexibility of data points being expanded above the stated ranges. In addition, fuzzy offers a hard clustering of data points with precise membership functions in comparison to other clustering methods ( Al-ani et al, 2023 ; Selvarajan & Manoharan, 2023 ). As a result, Eq.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation

Manoharan,

Selvarajan,

Aluvalu

et al. 2023

PeerJ Computer Science

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The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using robotic automation. In the predicted model, an analytical framework is created to operate the robot within predetermined areas while maximizing communication ranges. Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. A cluster node is connected in each clustered region and provides the central control center with the necessary data. The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation.

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A comparative recognition research on excretory organism in medical applications using artificial neural networks

Cited by 15 publications

References 24 publications

Blockchain-based cyber-security proposal in commerce mobile platforms for social and sustainability businesses

Blockchain-based cyber-security proposal in commerce mobile platforms for social and sustainability businesses

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation

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