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Machine learning is the most commonly used technique to address larger and more complex tasks by analyzing the most relevant information already present in databases. In order to better predict the future trend of the index, this paper proposes a two-dimensional numerical model for machine learning to simulate major U.S. stock market index and uses...
Citations
... Qingzhen Xu [38] showed that financial prediction can be done using machine learning. In [40] the saliency map of a complex image is proposed. ...
The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and don’t know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.
... Another restriction is the absence of tool support at the moment for using cloud datacenters. Although computer forensics is still a young subject of study, it has advanced to the point where tools are suitable for handling typical localised investigations [70][71][72]. The forensic investigator may utilise instruments like EnCase, Helix, and FTK to help with tasks like the initial data gathering all the way through the process of producing written evidence that is acceptable in a court environment. ...
Cloud computing has become increasingly popular in recent years, evolving into a computing paradigm that is both cost-effective and efficient. It has the potential to be one of the technologies that has had the most significant impact on computing throughout its history. Regrettably, cloud service providers and their customers have not yet developed major forensic tools that can assist with the investigation of criminal conduct that occurs in the cloud. Because it is difficult to prevent cloud vulnerabilities and criminal targeting, it is necessary to be aware of how digital forensic investigations of the cloud may be carried out. This is because cloud vulnerabilities and criminal targeting are difficult to avoid. In this context, the current study examines current and future trends in cloud forensics, methodology for cloud forensics, and cloud forensic tools. In addition, the study also looks at cloud forensic approaches.
... Another restriction is the absence of tool support at the moment for using cloud datacenters. Although computer forensics is still a young subject of study, it has advanced to the point where tools are suitable for handling typical localised investigations [70][71][72]. The forensic investigator may utilise instruments like EnCase, Helix, and FTK to help with tasks like the initial data gathering all the way through the process of producing written evidence that is acceptable in a court environment. ...
... Optimization through meta-heuristics has emerged as a prominent trend for problem-solving and systematic resource management in multi-disciplinary research and real-world scenarios with exchange to provide investors with reliable practical investment guidance and receive more returns. Machine learning strategies based on two-dimensional numerical models in financial engineering [66] to reduce the prediction error and improve forecasting precision for major U.S. stock market index fall under the same category. ...
This article presents a competitive learning-based Grey Wolf Optimizer (Clb-GWO) formulated through the introduction of competitive learning strategies to achieve a better trade-off between exploration and exploitation while promoting population diversity through the design of difference vectors. The proposed method integrates population sub-division into majority groups and minority groups with a dual search system arranged in a selective complementary manner. The proposed Clb-GWO is tested and validated through the recent CEC2020 and CEC2019 benchmarking suites followed by the optimal training of multi-layer perceptron’s (MLPs) with five classification datasets and three function approximation datasets. Clb-GWO is compared against the standard version of GWO, five of its latest variants and two modern meta-heuristics. The benchmarking results and the MLP training results demonstrate the robustness of Clb-GWO. The proposed method performed competitively compared to all its competitors with statistically significant performance for the benchmarking tests. The performance of Clb-GWO the classification datasets and the function approximation datasets was excellent with lower error rates and least standard deviation rates.
... The design of the PID controller is shown in Fig. 1. Moreover, some recent trends in the digital application is fall prediction [30], Drosophila interest detection [28], Saliency Detection [29], Virtual Face prediction [12], Mobile recommendation system [27], Two-Dimensional Numerical Models [26]. ...
Nowadays, high speed and high power density Brushless Direct Current (BLDC) motors have been widely utilized in the industrial area. Moreover, the design of motor simulation strategies is used in the drive system, which controls the complicated problems in the BLDC motors. However, speed regulation is a vital challenge since it affects the controller performance; the Proportional-Integral-Derivative (PID) controller is used in mechanical concerns. Therefore, this study introduces the novel Wavelet-based Fuzzy Adaptive Hybrid Bat-Vulture PID (WFA-HBVPID) controller to control the BLDC motor acceleration. Also, the developed WFA-HBVPID controller organizes the loads in the BLDC motor while verifying the gain scheduling conditions. Furthermore, this proposed PID controller is implemented using MATLAB/Simulink. Here, the performance of the motor is assessed in two ways, i.e., with hybrid optimization and without hybrid optimization. In addition, the efficiency of the developed controller has been checked over the time domain specifications like settling time, rise time, peak overshoot, and gain. To calculate the presented controller efficiency, the performances of the controller were compared with existing techniques. From the comparison of the outcomes, it is found that the proposed controller has less computation time and error rate.
... Qingzhen Xu [39] showed that financial prediction can be done using machine learning. In [40] the saliency map of a complex image is proposed. ...
The present world is badly affected by the novel coronavirus (COVID-19). Using medical kits to identify coronavirus-affected persons is very slow. What happens in the next, nobody knows. The world is facing an erratic problem and doesn't know what will happen in near future. This paper is trying to make a prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data from 258 regions, their latitude and longitude, and the number of death in 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect in extracting highly essential features for time series data (TSD) analysis. There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.
... To achieve this target, the Artificial Neural Network (ANN) is proposed to be integrated with the fuzzy logic system. ANN is a low-level computational structure that performs well when dealing with raw data [39,51]. It can learn to produce output even with incomplete information, after being trained. ...
The risk-based access control model is one of the dynamic models that use the security risk as a criterion to decide the access decision for each access request. This model permits or denies access requests dynamically based on the estimated risk value. The essential stage of implementing this model is the risk estimation process. This process is based on estimating the possibility of information leakage and the value of that information. Several researchers utilized different methods for risk estimation but most of these methods were based on qualitative measures, which cannot suit the access control context that needs numeric and precise risk values to decide either granting or denying access. Therefore, this paper presents a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) model for risk estimation in the risk-based access control model for the Internet of Things (IoT). The proposed ANFIS model was implemented and evaluated against access control scenarios of smart homes. The results demonstrated that the proposed ANFIS model provides an efficient and accurate risk estimation technique that can adapt to the changing conditions of the IoT environment. To validate the applicability and effectiveness of the proposed ANFIS model in smart homes, ten IoT security experts were interviewed. The results of the interviews illustrated that all experts confirmed that the proposed ANFIS model provides accurate and realistic results with a 0.713 in Cronbach’s alpha coefficient which indicates that the results are consistent and reliable. Compared to existing work, the proposed ANFIS model provides an efficient processing time as it reduces the processing time from 57.385 to 10.875 Sec per 1000 access requests, which demonstrates that the proposed model provides effective and accurate risk evaluation in a timely manner.
... Some novel-based approaches proposed by Qingzhen et al. in [30,31,[41][42][43], respectively. In [42], the authors designed a data set for tracking older people. ...
... They applied M/G/1 queue framework with multiple vacations and server close-down time to measure practical money flow. The author of [41] proposed a novel two-dimensional numerical heuristic for machine learning to simulate the primary U.S. stock market index. It uses a nonlinear implicit finite-difference procedure to find numerical solutions to the two-dimensional simulation model. ...
The fast evolution of the Internet of Things (IoT) marketplace demands real-time interactive services. Cloud computing systems aim to harness remote data center-based computing resources to perform these services instantly. However, these cloud systems fall short due to the distances from users to the data source, affecting response time and scheduling reliability. The newest drift is to integrate fog resources and cloud resources to perform data analytics in proximity to the edge end-users. However, the makespan and reliability are two prime concerns in such integration that requires attention. Most application placement policies in the literature do not consider makespan and reliability simultaneously. In this paper, we propose a hybrid multi-criteria decision-making (Hybrid-MCD) model to optimize the scheduling reliability and workflow makespan simultaneously. It formulates the problem as a bi-objective task scheduling problem that enhances the scheduling reliability and improves the service delivery time ratio of workflow tasks placed on computing resources. Furthermore, we formed a Deadline-aware stepwise Reliability Optimization (DARO) algorithm that maximizes the application’s execution time and reliability by adapting the reliability-recursive maximization algorithm and remapping workflow applications that are not on the critical path. The proposed algorithm’s performance is evaluated in a simulated cloud-fog environment using iFogSim. The results demonstrate that the algorithm is more efficient in optimizing makespan and system reliability jointly than other comparable algorithms.
... Qingzhen Xu [42] showed that financial prediction can be done using machine learning. In [43] the saliency map of a complex image is proposed. ...
The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and do not know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM (Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.
... Qingzhen Xu [42] showed that financial prediction can be done using machine learning. In [43] the saliency map of a complex image is proposed. ...
The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and don't know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.
