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As mobile data traffic increases, and the number of services provided by the mobile network increases, service load flows as well, which requires changing in the principles, models, and strategies for media transmission streams serving to guarantee the given nature of giving a wide scope of services in Flexible and cost-effective. Right now, the fundamental question remains what number of network slices will be cost effective for slice managing and giving the required functionality. So, the aim is to improve the efficiency of mobile network by forming an ideal slice in a multi-service communication network. In this paper, we propose a model to demonstrate network resource allocation system that forms devoted network slices to serve particular types of services independently on shared infrastructure. This model solves the problem of creating a strategy to form multi-service core mobile communication network slices, which allow the providing of a wide scope of services with certain quality indicators according to the effective dynamic configuration of the system. A resource management system model is created, to provide a method that considers costs related with excessive resource allocation, and also reduces the number of network recalculations, allowing for a reasonable proportion of management costs and Qualities of Service.

Electrocardiogram (ECG) is a tool used for the electrical analysis of the status of human heart activity. When the ECG signal is recorded, it gets contaminated with different types of noises. So, for accurate analysis, noises must be eliminated from the ECG signal. There are different types of noises that contaminate the characteristics of ECG signal i.e Power line interference, baseline wander, Electromyogram (EMG). In this paper, different techniques have implemented for the removal of noises. A median filter is used for removal of DC component and Savitzky-Golay filter (SG) is used for smoothing noised waveform and then wavelet transform (db4) is used to decompose the ECG signal for removal of various artifacts. Wavelet transform provides the information in frequency and time domain and then thresholding has been applied for the implementation of algorithms in MATLAB. The measured results i.e. SNR(Signal to Noise ratio) and MSE(Mean square error) have been calculated using different databases like MIT-BIH, Long-term ST database, European ST-T database. The results are examined with proposed methods that are better than those reported in the literature.

The continuing outbreak of COVID-19 pneumonia is globally concerning. Timely detection of infection ensures prompt quarantine of patient which is crucial for preventing the rapid spread of this contagious disease and also supports the patient with necessary medication. Due to the high infection rate of COVID-19, our health management system needs an automatic diagnosis tool that equips the health workers to pay immediate attention to the needy person. Chest CT is an essential imaging technique for diagnosis and staging of 2019 novel coronavirus disease (COVID-19). The identification of COVID-19 CT findings assists health workers on further clinical evaluation, especially when the findings on CT scans are trivial, the person may be recommended for Reverse-transcription polymerase chain reaction (RT-PCR) tests. Literature reported that the ground-glass opacity (GGO) with or without consolidation are dominant CT findings in COVID-19 patients. In this paper, the U-Net based segmentation approach is proposed to automatically segment and analyze the GGO and consolidation findings in the chest CT scan. The performance of this system is evaluated by comparing the auto-segmented infection regions with the manually-outlines ones on 100 axial chests CT scans of around 40 COVID-19 patients from SIRM dataset. The proposed U-Net with pre-process approach yields specificity of 0.91 ± 0.09 and sensitivity of 0.87 ± 0.07 on segmenting GGO region and specificity of 0.81 ± 0.13 and sensitivity of 0.44 ± 0.17 on segmenting consolidation region. Also the experimental results confirmed that the automatic detection method identifies the CT finding with a precise opacification percentage from the chest CT image.

In this paper a novel digital watermarking algorithm is proposed. The proposed method comprises of a watermarking embedding and extraction algorithm using bio inspired optimization technique. Dragonfly algorithm (DA) is based on the static and dynamic swarming behaviors of dragonflies in nature. The dragonfly algorithm is used to optimize the scaling factor of the watermarking so that an optimal watermark is embedded. Watermarking algorithms take as input a cover image and the message. The cover image in the proposed method is decomposed into sub bands using discrete wavelet transform (DWT). Thereafter, it is converted to discrete cosine blocks (DCT). An optimal scaling factor is required for performing the watermarking. In this paper, DA is used for computing the scaling factor. The DA generated scaling factor is optimal and improves the performance of the watermarking. The inverse DWT and DCT are computed to extract the watermarked image from the cover image. The proposed method is applied on different images to evaluate the performance. The results obtained are compared with other state of the art methods.

The conjecture of stock exchange is the demonstration of attempting to decide the forecast estimation of a particular sector or the market, or the market as a whole. Every stock every investor needs to foresee the future evaluation of stocks, so a predicted forecast of a stock’s future cost could return enormous benefit. To increase the accuracy of the Conjecture of stock Exchange with daily changes in the market value is a bottleneck task. The existing stock market prediction focused on forecasting the regular stock market by using various machine learning algorithms and in-depth methodologies. The proposed work we have implemented describes the new NN model with the help of different learning techniques like hyperparameter tuning which includes batch normalization and fitting it with the help of random-search-cv. The prediction of the Stock exchange is an active area for research and completion in Numerai. The Numerai is the most robust data science competition for stock market prediction. Numerai provides weekly new datasets to mold the most exceptional prediction model. The dataset has 310 features, and the entries are more than 100000 per week. Our proposed new neural network model gives accuracy is closely 86%. The critical point, it isn’t easy with our proposed model with existing models because we are training and testing the proposed model with a new unlabeled dataset every week. Our ultimate aim for participating in Numerai competition is to suggest a neural network methodology to forecast the stock exchange independent of datasets with reasonable accuracy.

In the digital age, e - learning systems have been employed as new equipment in the higher education system in different universities. Consideringthe importance of optimization of this system, this research is aimed at providing a modelfor the effectiveness of e - learning at in higher education systems . The study is a descriptive survey study in terms of its data collection method . The population includes all the students of electronic courses at the University of Tehran . This population includes 1481 students of the University of Tehran in the academic year 2019 - 2020 . Regarding the population size, 300 students were selectedbased onstratified sampling, using Cochran ’ s formula.Lisrel and Amos softwarewere used for data analysis . In the first step, byliterature review, and based on the collected information, 87 components were identified to be related toe - learning effectiveness . Then, based on the highest frequency of the identified components in one hand, and their significance from the experts ’ viewpoints, on the other hand, 14 components were finally selected and classified in three major classes including;pedagogical, individual and technicalrelated factors.

Organizations nowadays focus on, not implementing ERP systems, but also leveraging ERP systems as part of their digital strategy. They holistically address people, processes, and technology for a digital transformation. Meanwhile, higher education institutions (HEIs) are also facing an imperative need for the implementation of modern technologies to stay competitive and differentiate them as an innovation leader. Higher education management is challenged with maintaining high-level information systems. These systems can generate complex real-time reports for effective resource allocation and better decision making. Enterprise Resource Planning (ERP) systems can help HEIs manage their resources and operations effectively. A study of ERP among 112 HEIs in Egypt was conducted. This study originally investigates the Egyptian HEIs’ perception of the ERP system as a new integrating tool for its value. The results showed that Egyptian HEIs are still at the embryonic level as the majority have not adopted these systems yet. However, ERP value has been undoubtedly perceived by HEIs’ managers. Therefore, the present study fruitfully reflected HEIs’ understanding of the imperative need of ERP systems as strategic systems for their competitiveness. Consequently, the study suggests that Egyptian HEIs and ERP vendors take steps to remove any barriers and accelerate the ERP adoption process. Also, this research contributes to the advancement of ERP concepts and characteristics from HEIs' standpoint and a grants practical verification to the higher education context. Overall, the study advances existing knowledge and research on ERP, strategic management systems, and HEIs.

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated where it considers eventuality. So, it is necessary to consider the highly data-driven technologies and to use new methods of analysis, like machine learning and visualization tools, with the ability of interaction and connection to different data resources with varieties of data regarding the type of big data aimed at reducing the risks of policy-making institution’s investment in the field of IT. The main scientific contribution of this article is presenting a new approach of policy-making for the now-casting of economic indicators in order to improve the performance of forecasting through the combination of deep nets and deep learning methods in the data and features representation. In this regard, a net under the title of P-V-L Deep: Predictive Variational Auto Encoders - Long Short-term Memory Deep Neural Network was designed in which the architecture of variational auto-encoder was used for unsupervised learning, data representation, and data reconstruction; moreover, long short-term memory was adopted in order to evaluate now-casting performance of deep nets in time-series of macro-econometric variations. Represented and reconstructed data in the generative network of variational auto-encoder to determine the performance of long-short-term memory in the forecasting of the economic indicators were compared to principal data of the net. The findings of the research argue that reconstructed data which are derived from variational auto-encoder embody shorter training time and outperform of prediction in long short-term memory compared to principal data.