درخت حوزه‌های تخصصی

The spread of COVID-19 pandemic starting in late 2019 has changed the way we conduct our teaching and learning activities especially in Higher Education Institutions (HEIs). Since March 2020, classes have been conducted via online platforms. As a consequence, students missed the campus life, teamwork has been given less emphasis, fieldwork, industry visits and community service have been put aside, and most importantly the achievement of the learning outcomes towards a certain extent has been compromised. The implications of these changes need to be highly considered as they might affect the quality of graduates. This paper intends to discuss the impact of COVID-19 pandemic on the education system and highlight some potential solutions that can be considered by the academics and the top management of HEIs to address the negative repercussions of the current practices. Some research implications are also highlighted in the paper.

Managers should pay more attention to flexible production systems in order to cope with the challenges posed by factors such as global competition, changing customer demands, increasing market penetration, and rapid technological growth. On the other hand, the development of information and communication technology (ICT) provides significant opportunities for a more effective supply chain management including mobile supply chains. This research is aimed at identifying and prioritizing the factors affecting the diffusion of mobile supply chain management systems in dairy industries. To do this, the effective factors and key performance variables were identified through the reviewing associated literature followed by utilizing the Interpretative Ranking Process (IRP). The Prioritization of the factors was conducted with respect to the performance variables. Applying this method provides the ability to prioritize indicators with respect to variables that are very influential in business based on experts' opinions. The results of this study indicate that having appropriate knowledge, compatibility with existing infrastructure, Privacy of data stored and company revenue have the highest rank in this prioritization.

Fog computing is considered a promising solution to minimize processing and networking demands of the Internet of things (IoT) devices. In this work, a model based on the energy consumption evaluation criteria is provided to address the deployment issue in fog computing. Numerous factors, including processing loads, communication protocols, the distance between each connection of fog nodes, and the amount of traffic that is exchanged, all have an impact on the re-search system's overall energy consumption. The power consumption for implementing each com-ponent on the fog node as well as the power consumption for information exchange between the fog nodes are taken into account when calculating each fog node's energy use. Each fog node's energy consumption is closely correlated to how its resources are used, and as a result, to the average normalized resource utilization of a fog node. When the dependent components are spread across two distinct fog nodes, the transfer energy is taken into account in the computations. The sum of the energy used for transmission and the energy used for computational resources is the entire amount of energy consumed by a fog node. The goal is to reduce the energy consumption of the fog network while deploying components using a novel metaheuristic method.  Therefore, this work presents an enhanced water strider algorithm (EWSA) to address the problem of deploying application components with minimum energy consumption. Simulation experiments with two scenarios have been conducted based on the proposed EWSA algorithm. The results show that the EWSA algorithm achieved better performance with 0.01364 and 0.01004 optimal energy consumption rates.

The article defines that the composition and structure of creative industries, their branch specialization and cooperation, the scale and directions of development of industrial and other relations are determined by means of solving spatial problems and are conditioned by the level of digital transformation as imperative system-reflex management of information security of society at parity-legal principles. The process of formation of digital transformation of creative industries as an imperative of system-reflexive management of information security of society on a parity-legal basis in modern conditions today must meet globalization challenges that dictate the development of the country's economy as a whole. This should be manifested in the application of modern integration models for the formation and development of creative industries. It is substantiated that the main tasks of the strategy of financial capacity for digital transformation of creative industries as an imperative of system-reflexive management of information security of society on a parity-legal basis are to achieve balance of financial opportunities and needs of industries, efficiency of their financial relations, efficiency of processes of formation, movement, allocation and use of financial resources, rational structure of sources of financial resources, under which is possible stable financial support of digital transformation.

Net Asset Value (NAV) has long been a key performance metric for mutual fund investors. Due to the considerable fluctuation in the NAV value, it is risky for investors to make investment decisions. As a result, accurate and reliable NAV forecasts can help investors make better decisions and profit. In this research, we have analysed and compared the NAV prediction performance of our proposed deep learning models, such as N-BEATS and NBSL, with the FLANN model in both univariate and multivariate settings for five Indian mutual funds for forecast periods of 15, 20, 45, 63, 126, and 252 days using RMSE, MAPE, and R2 as evaluation metrics. A large forecast horizon was chosen to assess the model's consistency, reliability, and accuracy. The result reveals that the N-BEATS model outperforms the FLANN and NBSL models in the univariate setting for all datasets and all prediction horizons. In a multivariate setting, the outcome demonstrates that the N-BEATS model outperforms the FLANN model across all datasets and prediction horizons. The result also shows that, as the number of forecast days grew, our suggested models, notably N-BEATS, maintained consistency and attained the highest R2 value throughout the longest forecast duration.

As electronic commerce has gained widespread popularity, payments made for users' transactions through credit cards also gained an equal amount of reputation. Whenever shopping through the web is made, the chance for the occurrence of fraudulent activities are escalating. In this paper, we have proposed a three-phase scheme to detect fraudulent activities. A profile for the card users based on their behavior is created by employing a machine learning technique in the second phase extraction of a precise communicative pattern for the card users depending upon the accumulated transactions and the user's earlier transactions. A collection of classifiers are then trained based on all behavioral pattern. The trained collection of classifiers are then used to detect the fraudulent online activities that occurred. If an emerging transaction is fraudulent, feedback is taken, which resolves the drift's difficulty in the notion. Experiments performed indicated that the proposed scheme works better than other schemes.

This work serves to propose a national electronic health system based on the Big Data approach. First of all, we assessed the practice of health information systems (HIS) in Morocco and their obstacles. We performed a survey that was founded on 24 questions to specify the necessary details on this topic. This study shows that there is a primary need for the establishment of an HIS that facilitates the control, analysis, and management of health data in Morocco. For this reason, we have proposed the implementation of the Moroccan Health Data Bank (MHDB). This system will be based on powerful big data technologies that save, manage, and process health data with greater efficiency. The information present in this proposed system can provide the necessary resources for several actors to exploit this wealth, which is embodied in this massive data. We have developed a general description of the MHDB, its components, its conceptual architecture, and an example of a use case.

Predicting the development of cancer has always been a serious challenge for scientists and medical professionals. The prompt identification and prognosis of a disease is greatly aided by early-stage detection. Researchers have proposed a number of different strategies for early cancer detection. The purpose of this research is to use meta-learning techniques and several different kinds of convolutional-neural-networks(CNN) to create a model that can accurately and quickly categorize breast cancer(BC). There are many different kinds of breast lesions represented in the Breast Ultrasound Images (BUSI) dataset. It is essential for the early diagnosis and treatment of BC to determine if these tumors are benign or malignant. Several cutting-edge methods were included in this study to create the proposed model. These methods included meta-learning ensemble methodology, transfer-learning, and data-augmentation. With the help of meta-learning, the model will be able to swiftly learn from novel data sets. The feature extraction capability of the model can be improved with the help of pre-trained models through a process called transfer learning. In order to have a larger and more varied dataset, we will use data augmentation techniques to produce new training images. The classification accuracy of the model can be enhanced by using meta-ensemble learning techniques to aggregate the results of several CNNs. Ensemble-learning(EL) will be utilized to aggregate the results of various CNN, and a meta-learning strategy will be applied to optimize the learning process. The evaluation results further demonstrate the model's efficacy and precision. Finally, the suggested model's accuracy, precision, recall, and F1-score will be contrasted to those of conventional methods and other current systems.