2692-4048ISSN (Online)
2770-0070ISSN (Print)
Wireless Body Sensor Network (BSN) comprises wearables with different sensing, processing, storing, and broadcast abilities. Once several devices acquire the data, multi-sensor fusion was needed for transforming erroneous sensor information into maximum quality fused data. Deep learning (DL) approaches are utilized in different application domains comprising e-health for applications like activity detection, and disease forecast. In recent times, it can be demonstrated that the accuracy of classification techniques is enhanced by the combination of feature selection (FS) approaches. This article develops a Multi-sensor Data Fusion based Medical Data Classification Model using Gorilla Troops Optimization with Deep Learning (MDFMDC-GTODL) algorithm. The proposed MDFMDC-GTODL method enables collection of various daily activity data using different sensors, which are then fused to produce high-quality activity data. In addition, the MDFMDC-GTODL technique applies optimal attention based bidirectional long short term memory (ABLSTM) for heart disease prediction. In this study, Gorilla Troops Optimization Algorithm based FS (GTOA-FS) technique is involved to improve the classification performance. The simulation outcome of the MDFMDC-GTODL technique are validated and the results are investigated in different prospects. A wide-ranging simulation analysis stated the better performance of the MDFMDC-GTODL method over other compared approaches.
Read MoreDoi: https://doi.org/10.54216/FPA.150101
Vol. 15 Issue. 1 PP. 08-18, (2024)
Electrical loading prediction is a key aspect of the power system governing, operating, and scheduling. Energy suppliers can control the running system cost by using a lot of information it provides thereby optimizing the power system operation performance. The demand for the electricity well forcasted means more than half of their energy efficiency. Implementation of this work traces out an in-depth detail of integrated quality time series forecasting models on the prediction of electrical consumption. The primary goal of the study is to assess the performance of two state-of-the-art forecasting models: Deep LSTM version and long short-term memory (LSTM) neural networks, Seasonal autoregressive integrated ma. The main task is to evaluate the models’ precision in predicting daily energy consumption based on the historical demand data, holiday data and other time-related lines of evidence. The performance of the models is assessed based on the Mean Absolute Percentage Error (MAPE). The method covers feature engineering, the data preparation, model selection, and assessment. The generated MAPE values illuminated the performance of the models— SARIMA performed relatively inaccurately, and LSTM and deep LSTM significantly improved, obtaining a very good MAPEs of 7.5% and 7.45%, respectively. Notably, the deep LSTM version shows a superiority in prediction compared to other models, with particular emphasis on capturing the temporal relationships. This study makes a great contribution to the field of energy forecasting as it shows applicability of LSTM- and SARIMA- based models for the very good forecast of the consumption power. It captures the attention on how the LSTM networks at 20% of depth; may help in improving prediction accuracy when there are complex patterns and long-distance dependence is a concern. To utility companies, the grid operators and lawmakers who are out to harness every energy resource, to cut the costs, and ensure a continuous flow of electricity; such results are so very helpful.
Read MoreDoi: https://doi.org/10.54216/FPA.150102
Vol. 15 Issue. 1 PP. 19-31, (2024)
Cryptography is a well-known technology for providing confidential data transfer via asymmetric or symmetric algorithms with public or private keys. Secure data transmission over networks using unreliable, untrusted channels is made achievable by cryptography. As a result of the quick digital transition, network traffic is rapidly rising, and consumers remain constantly connected and accessible online. Extortions, including transforming, spoofing, and tracking data through unauthorised access, are quite widespread over the internet. Many more cryptographic algorithms already exist, but they need to be consistently improved and optimized for better performance within the constraints imposed by new technology and a wide variety of application domains. To overcome these limitations, we suggest a novel FishyCurve Cipher technique by combining an elliptic curve-based algorithm (ECA) with a Threefish cipher algorithm (TCA) to improve cipher security and performance, the data will be encrypted using TFCA, and the key will be secured by the EC technique. To verify data integrity, a digital signature algorithm (DSA) is employed. To evaluate the effectiveness of the proposed FishyCurve Cipher technique, comprehensive experimental tests have been conducted. The results clearly demonstrate its superiority in terms of cipher security when compared to traditional encryption algorithms. Its outstanding resilience against a wide range of attacks makes it a strong method of securing resilience infrastructure from malicious actors who seek to compromise data confidentiality and integrity.
Read MoreDoi: https://doi.org/10.54216/FPA.150103
Vol. 15 Issue. 1 PP. 32-44, (2024)
Nowadays, intelligent information technology can implement high-level information processing and decision-making activities that can support risk assessment of autonomous. Risk assessment is a critical process for deploying autonomous ships, ensuring these innovative vessels' safe and efficient operation. There is a need to identify, analyze, and mitigate potential risks associated with system reliability, collision avoidance, cybersecurity, environmental conditions, human interaction, regulatory compliance, sensor performance, data integrity, emergency response, and testing and validation. This work provides an overview of the essential considerations and objectives of risk assessment in autonomous boats. We used the multi-criteria decision-making model to deal with various criteria. The Ranking of Alternatives through Functional Mapping Criterion Sub-Intervals into Single Interval (RAFSI) method is applied to rank the alternatives. We used the ten criteria and twenty options in this study. The results show that the proposed framework can provide a comprehensive risk assessment framework that can enable stakeholders to gain insights into potential hazards and vulnerabilities unique to autonomous ships.
Read MoreDoi: https://doi.org/10.54216/FPA.150104
Vol. 15 Issue. 1 PP. 45-58, (2024)
The advent of the gig economy has triggered an unprecedented transformation in labor markets worldwide. Leveraging an intricate network analysis, this paper aims to delve into the multi-layered complexities of labor market metamorphosis within the context of a digital gig economy. We construct a bipartite labor-market network model that allows us to explore the nexus between gig workers and employment platforms using a robust set of parameters – connectivity, centrality, and clustering coefficient. Consequently, our empirical investigation elucidates how traditional labor market paradigms are being disrupted, engendering the emergence of new socio-economic stratifications. The results unveil a counterintuitive network structure where high centrality does not necessarily correlate with enhanced economic benefits for gig workers. Moreover, the findings underscore the potential pitfalls of a skewed clustering coefficient, manifesting as increased vulnerability to systemic shocks. The ubiquity of digital technology has engendered a seismic shift in economic frameworks, predominantly by initiating the concept of the gig economy. Although a plethora of research has been conducted on the gig economy from various disciplinary vantage points, limited endeavors have been undertaken to explore the intricacies of labor market changes via a network analysis paradigm. As a result, this study provides vital insights for policymakers, platform operators, and labor market participants, promoting a nuanced understanding of the gig economy’s implications for labor market architecture.
Read MoreDoi: https://doi.org/10.54216/FPA.150105
Vol. 15 Issue. 1 PP. 59-65, (2024)