درخت حوزه‌های تخصصی

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.

Dynamic Balanced Scorecard (DBSC) is an effective business performance management control tool for dealing with business uncertainty, performance monitoring, evaluation and forecasting. DBSC has been proposed and utilized extensively over the years as an effective tool to manage and control the dynamics of business processes (BP) and their performance. However, there is little evidence of its application in knowledge-intensive (KI) organizations and how they can develop and enhance key aspects of their business processes, such as product-service systems innovation, and sustainability, for example. Moreover, the literature does not mention nor does it provide a vision or a DBSC model in cases where business process management (BPM), linked to knowledge creation and organizational transformation initiatives, are factored in the DBSC model. Hence this article explores this vein and aims to demonstrate the advantages of DBSC in this type of scenarios, with stark contrast of failed organizations of the past, particularly in small and medium-size enterprises (SME). Most of the private sector in developing countries like Chile is comprised of SMEs, which thrive and seek to grow sustainably adhering to a global economic trend. The DBSC model being shown here illustrates SMEs strategy, which reveals how intrinsic characteristics of knowledge-intensive organizations can foster sustainability and innovation in BPM.

The main aim of this thesis is to investigate the factors that could affect the students to adopt e-learning system in Libyan universities. This study is quantitative approach, a questionnaire was adopted from previous studies and distributed among the students to collect the data. The sample of the study consists 365 students from Libya. AMOS software was used to analysis the data. The results indicated that Performance Expectancy, Efforts Expectancy, Facilitating Conditions, Habit and Trust have significant impact on behavioural intention. Moreover, the relationship between Behavioural intention and use behaviour is also significantly positive. However, the relationship of social influence and behavioural intention was not found significant. Finally, the moderation effect was significant and supported between social influence, trust, and Habit with behavioural intention.

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.

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.

Developed an integrated intellectual computerized system of ecological-economic monitoring, modeling, and managing the life cycle of the products of technogenic enterprises of transport engineering, which is presented in the form of a 3-equation structure, functioning in conditions of instability. The proposed paradigm system life cycle management applicable to any other control system of large and complex systems, such as techno-genic type. This new paradigm of complex systems and process management, including technical systems. The system is based on the following findings: concepts, principles, a set of non-linear models, decision-making methods, and the environmental and economic governance, integral criteria. As an example, in this paper offer solution to the problem of estimating the cost of the product life cycle of railway transport.

Flash floods are considered to be one the worst kind of hazard. They are characterized by their suddenness, rarity, small scale, heavy rain and peak discharge, unpredictable, fast and violent movement. It has severe effects on human society in the form life losses, damages to property, roads, communication and on natural settings. Advances in hydrology, meteorology, engineering, using of GIS and remote sensing still not able to increase real time forecast. Researchers from developed countries have stressed to more focus to improve very short time an effective early warning system with collaboration of local communities for flash flood risk supervision. The data were combined with the Geographical Information System to analyze the temporal and spatial distribution of flood events in Salah Al-Din Governorate and Ramadan Valley. The analysis of the spatial distribution of the floods proves that most of the occurrences are recorded in the southern part of the study area. Most of the flooded areas in the study area are mainly pre-classified areas within the areas threatened by the flood due to the low level of its surface and its proximity to the course of the main valley (Wadi Jarnav), which flows into the Tigris River. The proposed method estimates the localization of sites prone to flood, and it may be used for flood hazard assessment mapping and for flood risk management. It was therefore, suggested that government agencies and policy makers should adopt this powerful technique for reliable and well synthesized information which is a vital component of flood risk assessment and planning.

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.

Purpose: The current research aims to develop a model for digital transformation within the virtual space of the Broadcasting Organization in accordance with increasing the functionality of the virtual space among the audiences. Due to the lack of a model in this field in order to benefit from it, this research aims to extract the components of the model using the thematic qualitative method or theme analysis.Method: The statistical population includes 15 experts in media management and virtual space. To measure reliability, intra-subject agreement method (reliability between two coders/evaluators) was used to determine the reliability of the texts, and the reliability coefficient obtained for all three interviews and the total reliability coefficient was (0.87), surpassing the minimum acceptable threshold of 0.7, confirming the reliability of the codings and the interviews.Findings: The findings indicate that the model requirements consist of 6 main categories, 9 sub-categories, and 38 sub-categories essential for creating digital transformation and enhancing the interaction of the Islamic Republic of Iran's radio and television with the virtual space. The main categories of the model encompass content production, comprising six main components: content production (with six indicators), opportunities and threats (with 11 indicators), strengths and weaknesses (with 13 indicators), digital transformation components (with five indicators), platforms of introduction (with seven indicators), and consequences of digital transformation (with four indicators), totaling 46 indicators.Conclusion: The results show that establishing a favorable interaction in the virtual space and new media by creating a digital transformation in the national media is effective in attracting the audience and improving the performance of the national media and ensuring the satisfaction of the stakeholders.

In today’s era when a substitute for almost every product is readily available, acceptance and adoption of a new product in a market requires substantial amount of promotion. Here we formulate and analyze policies for promoting sales of a product in a market through optimal control theory problems. The market is partitioned into various segments depending upon multifarious demands of customers and promotion of the product is done segment-wise. The aim is to maximize the profits keeping in mind the demand requirements and the available budget for promotion. In order to provide a realistic model, the total available budget is taken to be imprecise. The optimal control model with fuzzy parameter is converted into crisp form using necessity and possibility constraints, and thereafter solved by using Pontryagin Maximum principle. To illustrate this technique, a numerical example is also considered by discretizing the model. The analysis also gives a deep insight of how the promotional effort should be planned by the decision makers keeping in mind the financial constrains without hindering the promotional effort at the end of the planning period. This paper mirrors the real time situation that could be faced by any industry, including that of software development, where budgets may have variable components and promotion of products may vary according to different regions and markets. The experimental data reveals that profitability can still be maximized if real-life constraints are applied in promotional planning by any industry.