In-Firm Planning and Business Processes Management Using Deep Neural Networks

Objective - The objective of this paper is to consider using machine learning approaches for in-firm processes prediction and to give an estimation of such values as effective production quantities. Methodology - The research methodology used is a synthesis of a deep-learning model, which is used to predict half of real business data for comparison with the remaining half. The structure of the convolutional neural network (CNN) model is provided, as well as the results of experiments with real orders, procurements, and income data. The key findings in this paper are that convolutional with a long-short-memory approach is better than a single convolutional method of prediction. Findings - This research also considers useof such technologies on business digital platforms. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Novelty - This paper describes the practical usage of 1-dimensional(1D) convolutional neural networks and a mixed approach with convolutional and long-short memory networks for in-firm planning tasks such as income prediction, procurements, and order demand analysis. Type of Paper - Empirical. Keywords: Business; Neural, Networks; CNN; Platform JEL Classification: C45

Solutions and memory effect of fractional-order chaotic system: A review (Interdisciplinary Physics)

Fractional calculus is a 300 years topic, which has been introduced to real physics systems modeling and engineering applications. In the last few decades, fractional-order nonlinear chaotic systems have been widely investigated. Firstly, the most used methods to solve fractional-order chaotic systems are reviewed. Characteristics and memory effect in those method are summarized. Then we discussed the memory effect in the fractional-order chaotic systems through the fractional-order calculus and numerical solution algorithms. It shows that the integer-order derivative has full memory effect, while the fractional-order derivative has nonideal memory effect due to the kernel function. Memory lose and short memory are discussed. Finally, applications of the fractional-order chaotic systems regarding the memory effects are investigated. The work summarized in this manuscript provides reference value for the applied scientists and engineering community of fractional-order nonlinear chaotic systems.

Mobility-aware personalized service recommendation in mobile edge computing

With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation (MPSR) approach based on user trajectory and quality of service (QoS) predictions. In the proposed method, users trajectory is firstly discovered by a hybrid long-short memory network. Then, given users trajectories, service QoS is predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy through MPSR. Experimental results on a real dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of accuracy in mobile edge computing.

Examination of the Non-Orthogonal Multiple Access System Using Long Short Memory Based Deep Neural Network

This paper investigates deep learning (DL) non-orthogonal multiple access (NOMA) receivers based on long short-term memory (LSTM) under Rayleigh fading channel circumstances. The performance comparison between the DL NOMA detector and the traditional NOMA method is established, and results have shown that the DL-based NOMA detector performance is far better in comparison with conventional NOMA detectors. Simulation curves are compared with the performance of the DL detector in terms of minimum mean square estimate (MMSE) and least square error (LSE) estimate, taking all realistic circumstances, except the cyclic prefix (CP), and clipping distortion into account. The simulation curves demonstrate that the performance of the DL-based detector is exceptionally good when it equals 1 when the noise signal ratio (SNR) is more than 15 dB, assuming that the DL method is more resilient to clipping distortion.

Ghost Attractor in Fractional Order Blinking System and its Application

In this paper, the occurrence of ghost attractor is verified in three cases of a proposed fractional order Rössler blinking system. Firstly, the dynamical behaviors of the short memory fractional order prototype-4 Rössler system with Chua’s diode are explored via bifurcation diagrams and Lyapunov exponents. It is depicted that this system exhibits a variety of dynamics including limit cycles, period doubling and chaos. Then, a proposed non-autonomous fractional order Rössler blinking system is introduced. Numerical simulations are employed to confirm the existence of ghost attractors at specific cases which involve very fast switching time between two composing autonomous fractional subsystems. It is found that the presented fractional order blinking system is very sensitive to system parameters, initial conditions and stochastic process parameters. Thus, the induced chaotic ghost attractor is utilized in a suggested ghost attractor-based chaotic image encryption scheme for first time. Finally, a detailed security analysis is carried out and reveals that the proposed image cryptosystem is immune against different types of attacks such as differential attacks, brute force attacks, cropping and statistical attacks.

The Diagnostic Value of a Short Memory Test: The TNI-93

Background: The TNI-93 is a quick memory test designed for all patients regardless of their education level. A significant proportion of patients with Alzheimer’s disease (AD) are illiterate or poorly educated, and only a few memory tests are adapted for these patients. Objective: In this study we aimed at assessing the diagnostic value of the TNI-93 for diagnosis of patients with biologically confirmed amyloid status. Methods: We included all patients who had an analysis of AD cerebrospinal fluid biomarkers, a neuropsychological assessment including a TNI-93 and an anatomical brain imaging at Avicenne Hospital between January 2009 and November 2019. We compared the TNI-93 scores in patients with amyloid abnormalities (A+) and patients without amyloid abnormalities (A-) according to the AT(N) diagnostic criteria. Results: 108 patients were included (mean age: 66.9±8.5 years old, mean education level: 8.9±5.2 years). Patients from the A + group (N= 80) were significantly more impaired than patients from the A- group (N= 28) on immediate recall (A+: 5.9±2.8; A-: 7.4±2.6; p = 0.001), free recall (A+: 3.5±2.7; A-: 5.9±2.8; p ≤ 0.001), total recall (A+: 5.7±3.5; A-:7.8±2.8; p ≤ 0.001), and on number of intrusions during the recall phase (A+: 1±1.8; A-: 0.1±0.3; p = 0.002). ROC curves revealed that the best scores to discriminate A + from A- patients were immediate recall (Area under curve (AUC): 0.70), number of encoding trials (AUC: 0.73), free recall (AUC: 0.74), and total recall (AUC: 0.74). Conclusion: The TNI-93’s immediate, free, and total recalls are valuable tools for the 39 diagnosis of AD.