2690-6791ISSN (Online)
2769-786XISSN (Print)
Bankruptcy detection becomes one of the major subjects in finance. Indeed, for apparent reasons, several actors like shareholders or managers show more attention to the possibility of a firm’s bankruptcy. Subsequently, various researches are being conducted on the matter of bankruptcy prediction. Recently numerous research works have explored the application of machine learning (ML) techniques to bankruptcy prediction by having financial ratios as predictors. This article devises an Enhanced Sparrow Search Optimization with Deep Learning Enabled Bankruptcy Prediction (ESSODL-BP) model. The proposed ESSODL-BP technique involves the forecasting of the bankruptcy of a financial firm. To accomplish this, the ESSODL-BP technique primarily follows the Z-score normalization approach. Followed by, the bidirectional long short-term memory (BLSTM) model is designed to predict the bankruptcy status of a financial firm. Then, the ESSO algorithm is utilized for optimally tuning the hyperparameters related to the BLSTM model and also boosts the prediction performance to a maximum extent. The performance validation of the ESSODL-BP technique is tested using a benchmark dataset. The experimental outcomes reported better performance of the ESSODL-BP technique over other approaches.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060101
Vol. 6 Issue. 1 PP. 09-19, (2022)
Energy has a critical role in human survival and societal progress. Hydrogen is a possible energy carrier for long-term power generation. Known as both an environmental nuisance and an essential hydrogen source, Hydrogen Sulfide (H2S) may be found in large quantities in the waters of the Black Sea. The primary goal of this research is to determine which breakdown processes, such as thermal, thermochemical, electrochemical, plasma, photochemical and thermal suit sustainability requirements better than others. The most acceptable hydrogen generation technique is chosen based on characteristics such as financial viability, environmental viability, effectiveness, simplicity of the process, energy consumption, safety and dependability, application and operational adaptability, and technological maturity. This paper proposes innovative additions to the CoCoSo approach. The COCOSO method is used to compute the weights of criteria and rank the alternatives. This paper proposed 8 criteria and 5 alternatives.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060102
Vol. 6 Issue. 1 PP. 20-29, (2022)
Accurate and prompt detection of security attacks in the Industrial Internet of Things (IIoT) is important to reduce security risks. Since a massive number of IoT devices are placed over the globe and the quantity gets increased, an effective security solution is necessary. A botnet is a computer network comprising numerous hosts executing on standalone software. In this view, this article develops a novel Glowworm Swarm Optimization Driven Gated Recurrent Unit Enabled Botnet Detection (GSOGRU-BD) model in IIoT Environment. The presented GSOGRU-BD model intends to effectually identify the presence of botnet attacks in the IIoT environment. To do so, the GSOGRU-BD model initially pre-processed the input data to get rid of missing values. In addition, the GSOGRU-BD model involves the GRU model for the effective recognition and classification of botnets. Besides, the GSO algorithm is used for optimal hyperparameter tuning of the GRU model. Comparative experimental validation of the GSOGRU-BD model is tested using a benchmark dataset and the results reported the better outcomes for the GSOGRU-BD model.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060103
Vol. 6 Issue. 1 PP. 30-40, (2022)
The success of sustainable management of the heritage building in an intelligent city is a difficult multi-criteria decision-making (MCDM) issue including the coexistence of conflicting elements. There is an issue with incomplete decision information utilization and information loss throughout the decision-making process, and the interaction difficulty in a fuzzy environment is easy to miss. This paper provides a hybrid MCDM framework that combines the spherical fuzzy analytical hierarchy process (SF-AHP). The SF-AHP is used to assess the significance levels of building heritage. To use the stage MCDM model, a thorough set of assessment criteria based on the notion of sustainable development has been identified via literature research and expert interviews. To assess the efficacy of the suggested strategy, an application is done in this paper. Using the decision framework, the building heritage in intelligent cities has been identified. The suggested technique may be utilized to achieve management of the building heritage in intelligent cities.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060104
Vol. 6 Issue. 1 PP. 41-58, (2022)
Traditional and sustainable forms of energy are examined from a variety of angles, including economics, technology, society, ecology, politics, and flexibility, to ensure that Vietnam’s energy industry continues to expand sustainably. These sources have been evaluated and assessed using an extended spherical fuzzy MCDM method. Thermal, gas, nuclear, solar, wind, biomass, and hydro energy alternatives are employed in the decision-making model. The weights of evaluation criteria are determined using the spherical fuzzy AHP model, and renewable power choices are prioritized using the WPM model. We used six main criteria, twenty-six sub-criteria, and seven alternatives. In Vietnam, solar power was found to be the most suitable, followed by wind and hydropower, followed by hydro, followed by Biomass. The worst alternative is thermal. After that, fourteen situations were built, taking into consideration the first five renewable technologies (solar, wind, hydro, biomass, and gas power), in assessing the ideal energy mix scenario for the slow gestation of Vietnam's energy sector. Solar, wind, and hydro energy growth in a pass shipping scenario.
Read MoreDoi: https://doi.org/10.54216/JISIoT.060105
Vol. 6 Issue. 1 PP. 59-74, (2022)