Scopus Research — Mustafa Fahim Ibrahim

Big data and cloud computing are becoming more critical in transportation systems as these technologies develop. Transportation companies can recognize and forecast potential traffic problems and offer appropriate responses. To avoid hindering mobility, one might use predictive analytics to assess the effect of various development initiatives and suggest a viable alternative. Due to automobiles’ flexibility and rapid changes in their environment, creating an effective communication system for vehicular networks is tough. An intelligent transportation system with big data analytics and cloud computing (STS-BCC) is the goal of this research work. Data mining is used to anticipate traffic conditions using a machine learning method. The cloud platform provides a secure storage service and processing unit to aid traffic forecasting. The experimental analysis finds the prediction accuracy of 97.45% and proves the efficient integration of big data analytics and cloud computing technologies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

This research used a method called DFT to study how CH3Br, CH3Cl, and CH3F halomethanes stick to boron carbide (BC3) nanosheets, with and without gallium and aluminum doping. Geometric optimisation was conducted for all structures using the B3LYP/6-311 + G (d) method. Furthermore, three different methods were employed to calculate the energy: M06-2X, ωB97X-D3, and CAM-B3LYP/6-311 + G(d). NBO and the QTAIM analysis was conducted to evaluate the WBI, partial natural charge, and donor–acceptor interactions within the molecules. The adsorption energy results showed that adding gallium to BC3 made it best at adsorbing stuff, while BC3 without any additives absorbed the least. However, CH3Cl stuck best to the aluminum-doped BC3 and least to the plain BC3 because it had less energy holding onto it. Also, CH3Br stuck to the nanosheet surfaces the most, while CH3Cl stuck the least. BC3(Ga)NS was discovered to be better at detecting halomethanes than BC3(Al)NS and BC3NS. Also, when the halomethanes were absorbed, the doped nanosheets experienced big changes in their electronic behaviour. The energy gaps for BC3NS, BC3(Al)NS, and BC3(Ga)NS were found to be 3.5%, 15.2%, and 13.6%, respectively. Gallium and aluminum mixed with BC3 seem good for making new sensors to detect halomethanes. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

For analyzing hydrogenation process for treatment of petroleum-based fuels, solubility of hydrogen in the feed should be well correlated. The main correlation factors are temperature and pressure which have a great effect on hydrogen solubility. This research paper presents the development of three models for predicting the solubility of hydrogen gas (H2) in diesel. Pressure and Temperature are the input parameters and solubility is single output. The models were fine-tuned using the Bat Algorithm (BA). The three models include Orthogonal Matching Pursuit Regression (OMP), K Nearest Neighbors Regression (KNN), and Tweedie Regression (TDR). The results of the study revealed that the OMP model achieved the highest level of accuracy, with an R2 score of 0.98, and the least RMSE and MAE error rates of 0.24 and 0.19, respectively. The KNN model also performed well with an R2 score of 0.92, an RMSE of 0.42, and an MAE of 0.37. The TDR model had the lowest accuracy compared to the other two models. These results imply that the OMP model is the most suitable one for predicting H2 solubility. The models can be used to enhance the efficiency of fuel production by providing accurate predictions of H2 solubility. © 2024 The Authors

The advent of Network Function Virtualization (NFV) technology has brought flexible provisioning to Fog–Cloud Computing-based Networks (FCCNs) for enterprises to outsource their network functions to data center networks. Service Function Chaining (SFC) is a networking concept in NFV by which traffic is steered through an ordered set of Virtual Network Functions (VNFs) composing an end-to-end service. When hundreds of users outsource their network functions to FCCN, the optimal placement of VNFs in the network becomes important for assembling SFCs with the aim of resource utilization efficiency. Motivated by the scalability shortcomings of existing schemes, we propose Deep Reinforcement Learning (DRL)-based approaches by simultaneously considering parallelized SFC and reuse of VNFs to solve this problem, i.e., Asynchronous Advantage Actor–Critic (A3C). A parallelized SFC consists of several sub-SFCs, which can reduce delay and guarantee availability. Also, reuse of preliminary VNFs in SFC placement can improve computation acceleration. The proposed scheme pursues the maximization of the long-term cumulative reward for the trade-off between Quality of Service (QoS) and service cost. The results of the experiments show that the proposed scheme performs better than the state-of-the-art methods. © 2022 The Author(s)

Optimization of SO2 emission and the process cost in catalytic hydrodesulfurization (HDS) is of great importance for the petroleum industry. Given that the process of HDS is complicated, machine learning-based models are suitable for the purpose of process optimization in which the cost and separation efficiency can be optimized efficiently. In this investigation, we are working with a data collection on the HDS process to model via machine learning models. Pressure, temperature, initial sulfur content, and catalyst dose constitute the inputs for the models. Outputs include sulfur concentration (ppm), emission of gas (%), and HDS process cost ($). To model the process, for the first time, four tree-based ensemble methods are developed including Gradient Boosting, Extreme gradient boosting, Random Forest, and Extra Trees to optimize the HDS process. The models tuned on the available dataset and then the best ones selected for each output For sulfur concentration the extra tree model is the most accurate and for other outputs extreme gradient boosting has the best performance. For the models, the R2 scores for outputs are 0.983, 0.982, and 0.995, respectively. © 2023 Elsevier Ltd

Purpose: Microarray information is crucial for the identification and categorisation of malignant tissues. The very limited sample size in the microarray has always been a challenge for classification design in cancer research. As a result, by pre-processing gene selection approaches and genes lacking their information, the microarray data are deleted prior to categorisation. In essence, an appropriate gene selection technique can significantly increase the accuracy of illness (cancer) classification. Methods: For the classification of high-dimensional microarray data, a novel approach based on the hybrid model of multi-objective particle swarm optimisation (MOPSO) is proposed in this research. First, a binary vector representing each particle’s position is presented at random. A gene is represented by each bit. Bit 0 denotes the absence of selection of the characteristic (gene) corresponding to it, while bit 1 denotes the selection of the gene. Therefore, the position of each particle represents a set of genes, and the linear Bayesian discriminant analysis classification algorithm calculates each particle’s degree of fitness to assess the quality of the gene set that particle has chosen. The suggested methodology is applied to four different cancer database sets, and the results are contrasted with those of other approaches currently in use. Results: The proposed algorithm has been applied on four sets of cancer database and its results have been compared with other existing methods. The results of the implementation show that the improvement of classification accuracy in the proposed algorithm compared to other methods for four sets of databases is 25.84% on average. So that it has improved by 18.63% in the blood cancer database, 24.25% in the lung cancer database, 27.73% in the breast cancer database, and 32.80% in the prostate cancer database. Therefore, the proposed algorithm is able to identify a small set of genes containing information in a way choose to increase the classification accuracy. Conclusion: Our proposed solution is used for data classification, which also improves classification accuracy. This is possible because the MOPSO model removes redundancy and reduces the number of redundant and redundant genes by considering how genes are correlated with each other. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Over the last decades, significant drawbacks of organic solvents such as high toxicity have motivated the scientists to find more eco-friendly solvents. Supercritical fluids (SCFs), especially SCCO2, are known as a promising class of solvent, which have shown their indisputable potential of application in industrial-based pharmaceutical activities due to possessing various advantages such as high abundancy, low cost, and insignificant toxicity. Machine Learning (ML) is considered as a numerical approach to estimate drug solubility in pharmaceutical industry. The purpose of this manuscript is to estimate the solubility of salicylsalicylic acid in SCCO2 and compare it with experimental data using machine learning (ML) approach. A regression problem with 32 input vectors is the subject of this study, which is being conducted. This dataset contains two input features (P and T) and one output feature. We utilized Decision Tree (DT), K-nearest neighbor (KNN), and Multilayer perceptron (MLP) regression models as the first time for salicylsalicylic acid, which had error rates of 1.10E-01, 1.07E-01, and 7.13E-01, respectively, when using the MAPE measure. In addition, the R-squared scores for the DT, KNN, and MLP models are 0.974, 0.996, and 0.809, respectively. The third statistic is MAE, in which the error rates of models are 5.27E-05 for DT, 5.53E-05 for KNN, and 2.61E-04 for MLP. The error rates of DT, KNN, and MLP are all 5.27E-05. Finally, KNN was the most general model, with optimal values of P = 400, T = 338.0, and Y = 0.00388 being obtained. © 2022 Elsevier B.V.