فیلتر های جستجو: فیلتری انتخاب نشده است.
نمایش ۲٬۷۴۱ تا ۲٬۷۶۰ مورد از کل ۲٬۷۹۴ مورد.
۲۷۴۱.

The Digital Assessment of Learning: Current Situation and Perspectives: Case of Teachers of Life and Earth Sciences(مقاله علمی وزارت علوم)

کلید واژه ها: Digitizing Practice Assessment Technology News

حوزه های تخصصی:
تعداد بازدید : ۹۸ تعداد دانلود : ۷۶
The constant evolution of technological tools and the benefit of the introduction of new technologies in the teaching and learning process in schools, suggests that the possibilities of using digital technology in the practices of the evaluation of learning inside and outside the classroom. Faced with the diversity of technical tools and methods, the teacher is called upon, before addressing his learners, to make a certain number of technical, didactic and pedagogical choices, to set his pedagogical scenario, this is all the more so. Necessary for the conduct of learning activities or to design and implement its assessment tool. In this perspective, we are entitled to ask the question relating to the attitudes and feedbacks of teachers and learners regarding the integration of ICT in assessment practices. This question is broken down into several sub-questions: "At what points, and how do teachers of Life and Earth Sciences integrate digital technology into their assessment practices?" is this integration determined by the educational pathways? What interest and what limit of the use of digital technology according to teachers and learners? "  To answer our problem, we opted for export research, the recommended methodology of which is based on a multidimensional survey, in which we first questioned the teachers around the use of new technologies in the evaluation process, and secondly, to identify the degree of motivation and commitment of learners in instrumented and innovative assessment situations. Our research context is represented by 34 schools, 22 from qualifying secondary education and 12 from college education (from the provincial delegation of Taza), with a varied population of 431 students of all levels and sectors, as well as than a number of 132 teachers of life and earth sciences. Finally, confirming that despite the satisfaction of the majority of teachers and students with respect to the functional qualities and the contribution of technological tools in evaluation practices, some disparities were noted whether at the technical, spatiotemporal level or even organizational.
۲۷۴۲.

Factors Effecting the Adoption of E-Learning: An Empirical Study of Libyan Universities(مقاله علمی وزارت علوم)

کلید واژه ها: E-Learning UTAUT2 Trust Structural Equation Modeling Libya

حوزه های تخصصی:
تعداد بازدید : ۹۷ تعداد دانلود : ۸۴
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.
۲۷۴۳.

بررسی تاثیر رفتارهای پنهان کننده دانش بر سکوت کارکنان و رفتارهای منحرف سازمانی با نقش میانجی نقض قرارداد روانشناختی (نمونه پژوهش: اداره کل امور مالیاتی مودیان بزرگ)(مقاله علمی وزارت علوم)

کلید واژه ها: رفتارهای پنهان کننده دانش سکوت کارکنان رفتارهای منحرف سازمانی

حوزه های تخصصی:
تعداد بازدید : ۹۶ تعداد دانلود : ۱۰۷
هدف از پژوهش حاضر، بررسی تاثیر رفتارهای پنهان کننده دانش بر سکوت کارکنان و رفتارهای منحرف سازمانی با نقش میانجی نقض قرارداد روانشناختی می باشد. این پژوهش از نظر نوع هدف، کاربردی و از نظر نوع ماهیت، توصیفی- پیمایشی است. جامعه آماری پژوهش حاضر، شامل کارکنان اداره امور مالیاتی مودیان بزرگ که مشتمل بر 400 نفر می باشند که تعداد 227 نفر به روش تصادفی ساده و به روش تحلیل توان به عنوان نمونه آماری انتخاب گردیدند. جهت گردآوری اطلاعات از پرسشنامه استاندارد استفاده شده است و داده ها بوسیله تحلیل چندمتغیره مبتنی بر مدل سازی معادلات ساختاری با رویکرد کواریانس محور در بستر نرم افزار Amos ورژن 24 مورد تجزیه و تحلیل قرار گرفت. نتایج تحقیق حاکی از تایید تاثیر پنهان کاری منطقی بر سکوت تدافعی، پنهان کاری گریزان بر سکوت رابطه ای، پنهان کاری منطقی بر سکوت رابطه ای، پنهان کاری گریزان بر سکوت بی اثر، پنهان کاری منطقی بر سکوت بی اثر و سکوت تدافعی بر رفتار منحرف سازمانی، سکوت رابطه ای بر رفتار منحرف سازمانی و سکوت بی اثر بر رفتار منحرف سازمانی می باشد و همچنین نتایج حاصل از تحلیل میانجی نشان می دهد که سازه "نقض قرارداد روانشناختی" برای تمامی روابط میان ابعاد پنهان کاری و ابعاد سکوت دارای نقش میانجی است، به طوری که فرآیند میانجی گری مذکور برای روابط علی میان "پنهان کاری خاموش" و "سکوت تدافعی/ سکوت بی اثر" به صورت کامل و برای مابقی روابط به صورت جزئی است. در نهایت، از میان بیست و یک فرضیه مطروحه، هفده فرضیه مورد تائید قرار گرفت که از این بین تاثیر سکوت رابطه ای بر رفتار منحرف سازمانی از بالاترین ضریب مسیر (0.33) برخوردار است.
۲۷۴۴.

Chronic Kidney Disease Risk Prediction Using Machine Learning Techniques(مقاله علمی وزارت علوم)

کلید واژه ها: Machine Learning CKD Prediction SVM RF Data Analysis

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تعداد بازدید : ۹۶ تعداد دانلود : ۵۰
In healthcare, a diagnosis is reached after a thorough physical assessment and analysis of the patient's medicinal history, as well as the utilization of appropriate diagnostic tests and procedures. 1.7 million People worldwide lose their lives every year due to complications from chronic kidney disease (CKD). Despite the availability of other diagnostic approaches, this investigation relies on machine learning because of its superior accuracy. Patients with chronic kidney disease (CKD) who experience health complications like high blood pressure, anemia, mineral-bone disorder, poor nutrition, acid abnormalities, and neurological-complications may benefit from timely and exact recognition of the disease's levels so that they can begin treatment with the most effective medications as soon as possible. Several works have been investigated on the early recognition of CKD utilizing machine-learning (ML) strategies. The accuracy of stage anticipations was not their primary concern. Both binary and multiclass classification methods have been used for stage anticipation in this investigation. Random-Forest (RF), Support-Vector-Machine (SVM), and Decision-Tree (DT) are the prediction models employed. Feature-selection has been carried out through scrutiny of variation and recursive feature elimination utilizing cross-validation (CV). 10-flod CV was utilized to assess the models. Experiments showed that RF utilizing recursive feature removal with CV outperformed SVM and DT.
۲۷۴۵.

Social Media Value Creation Practices and Interactivity of Electronic Word of Mouth Systems(مقاله علمی وزارت علوم)

کلید واژه ها: social media Value Creation Social media marketing Consumer engagement Online shopping

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تعداد بازدید : ۹۴ تعداد دانلود : ۸۱
The main drivers of value creation in a ‘brand community’ are social networking, community engagement, impression management, and brand use. Marketers are therefore interested in determining which factors affect the value creation practices. This study examines the impact of the Interactivity of Electronic Word of Mouth (EWOM) systems on value creation practices in a brand community, which in turn influences the loyalty of the customers. In this regard, a conceptual model was developed and tested by the researchers of the current study. The results indicate that perceptions of the users regarding the interactivity of EWOM systems, highly impact only three of the four value creation practices including community engagement practices, impression management practices, and brand use practices. Furthermore, the researchers found that collective value creation practices could significantly and directly enhance brand loyalty. Several theoretical contributions and managerial implications were also discussed
۲۷۴۶.

AI-WSN: Direction of Arrival Estimation Based on Bee Swarm Optimization for Wireless Sensor Networks(مقاله علمی وزارت علوم)

نویسنده:

کلید واژه ها: Wireless Sensor Network Direction of arrival Bees Swarm Optimization Energy estimation

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تعداد بازدید : ۹۲ تعداد دانلود : ۸۹
An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantly
۲۷۴۷.

Information Systems in Fiscal Administration and Modeling of Excise Tax(مقاله علمی وزارت علوم)

کلید واژه ها: information systems Fiscal Efficiency Tax Administration Excise Tax Functions of the Excise Tax Modeling Methods Brewing Industry

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تعداد بازدید : ۹۶ تعداد دانلود : ۵۹
The purpose of the article is to substantiate the fiscal role of the excise tax by studying its information and functional potential and to model the dynamics of its payment by the brewing industry. Excise tax occupies a special place in a tax system of each state because, in addition to significant fiscal importance, it has a considerable regulatory impact on the production and consumption of certain categories of goods. Based on information systems in the article analyses and monitors the indicators of the excise tax payments on goods produced in Ukraine on the example of a particular enterprise in the brewing industry. By means of the initial data analysis of autocorrelation functions of volumes’ indicators of the accrued excise taxes on beer the expediency of modelling realization of such indicator dynamics on the basis of ARIMA model is proved. The analytical and statistical approaches to the formation of models for the implementation of forecast for the calculation of excise tax on beer of brewing industry enterprises are improved. The proposed approach is based on the values of autocorrelation of balances and partial autocorrelation, as well as methods of analysis of time series with gaps, which allows to use it in the economic activity of enterprises to make forecasts for the calculation and payment of the excise tax. This will produce financial effects for the brewing industry in terms of cost optimization and minimization of the excise tax risks.
۲۷۴۸.

Perspectives of Big Data Quality in Smart Service Ecosystems (Quality of Design and Quality of Conformance)(مقاله علمی وزارت علوم)

نویسنده:

کلید واژه ها: Big data quality Information Quality smart cities Service design Smart services Data quality model Smart service ecosystem

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تعداد بازدید : ۹۱ تعداد دانلود : ۸۲
Despite the increasing importance of data and information quality, current research related to Big Data quality is still limited. It is particularly unknown how to apply previous data quality models to Big Data. In this paper we review Big Data quality research from several perspectives and apply a known quality model with its elements of conformance to specification and design in the context of Big Data. Furthermore, we extend this model and demonstrate it utility by analyzing the impact of three Big Data characteristics such as volume, velocity and variety in the context of smart cities. This paper intends to build a foundation for further empirical research to understand Big Data quality and its implications in the design and execution of smart service ecosystems.
۲۷۴۹.

Managing Customer Trust and Satisfaction on Chatbots in the Retail Industry(مقاله علمی وزارت علوم)

کلید واژه ها: Chatbots User Interface Problem-Solving Intention

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تعداد بازدید : ۹۰ تعداد دانلود : ۶۴
This study investigates the relationship between the user interface and problem-solving towards the continuous intention to use the services. New products or services will always face tough challenges for the customer, especially when the new procedures require them to learn and change some behaviors. Chatbots are also facing the same situation in Malaysia, where customers refuse to accept using chatbots to represent their physical presence. To understand customer behaviors, a quantitative survey was designed. Four hundred twenty-two data were collected from the online survey method. As per the results, the predictors of chatbot continuous intention are user interface and problem-solving. Apart from that, this study also measures the role of mediator, namely trust and customer satisfaction. This study contributes to unique academic and practical insights that can be used to explore the effectiveness of chatbots. The results revealed that both predictors were significant towards the continuous intentions. Besides, the role of the mediator was found to be significant and relevant in the relationship between trust and customer satisfaction and customer satisfaction and trust towards continuous attention.
۲۷۵۰.

Digitalization of Biocluster Management on Basis of Balanced Scorecard(مقاله علمی وزارت علوم)

کلید واژه ها: Bioeconomy digitalization Biocluster Strategic Management balanced scorecard Forecasting

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تعداد بازدید : ۹۰ تعداد دانلود : ۶۰
The article is devoted to the digitalization of biocluster management on the basis of a balanced scorecard. It is proved that a biocluster, as a local model of business concentration that integrates environmentally oriented enterprises, through a combination of traditional and new technologies, resource saving and diversification of the range of environmental products, is able to satisfy various customer requests in one place and time, to ensure competitive advantages and integration into the world economic space. The concept of applying a balanced scorecard in the strategic biocluster management was formed. The technology of formation and mechanism of implementation of the balanced scorecard and digital data processing technologies into the management information system of strategic biocluster management was proposed. The digital outline of the strategic program for transferring the mission and strategy of the biocluster to the mode of effective use, capacity building and development was formed. The scorecard for strategic management of the biocluster was developed, the study of the dynamics of which allows to determine the strengths and weaknesses of the biocluster, to identify tolerance and resilience to changes in the business environment, to identify ways to achieve the set development goals.
۲۷۵۱.

The Influence of Social Media Marketing Activities on Purchase Intention: A Study of the E-Commerce Industry(مقاله علمی وزارت علوم)

کلید واژه ها: Social media marketing purchase intention brand awareness

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تعداد بازدید : ۸۸ تعداد دانلود : ۶۴
This paper sought to examine the impact of perceived Social Media Marketing Activities (SMMAs) on customer purchase intention via brand awareness in an online context. An online questionnaire was used to collect data from 188 samples. The data were analyzed using the structural equation modeling approach, and the research hypotheses were examined using SEM. The study measured SMMAs through personalization, customer community, and live video. The results revealed that SMMAs were insignificant towards brand awareness and purchase intention. The result also stated that brand awareness does not mediate the relationship between SMMA and purchase intention. However, brand awareness was found to affect purchase intention positively. The current study introduces the stimulus–organism–response model as a theoretical support to examine SMMAs of e-commerce to customers' purchase intention via brand awareness.
۲۷۵۲.

Effectiveness of AI-Driven Knowledge Management System in Improving the Performance of Banking Sector in Jordan(مقاله علمی وزارت علوم)

کلید واژه ها: artificial intelligence (AI) Banking sector Customer Satisfaction Jordan Knowledge Management service quality

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تعداد بازدید : ۸۸ تعداد دانلود : ۵۲
The present research examines the benefits of implementing knowledge management (KM) principles in the Jordanian banking sector to enhance performance. The study emphasizes the significance of Artificial Intelligence (AI) and how Jordanian banks utilize it to improve the quality of customer service they provide. This study targets managers at all levels and focuses on the Jordanian banking sector as its research environment. A questionnaire is created to gather information from a random sample to achieve the research's objectives. The study involves a sample of 250 managers. Additionally, the research adopts a descriptive methodology, and SPSS is used to analyze the data. The statistical findings provide robust evidence for the importance of performance expectations, social influence, and perceived risk in influencing consumer intentions. Marketers and decision-makers within the banking industry can leverage these insights to shape their long-term strategies for effectively utilizing and maximizing AI technology in the banking sector. Furthermore, by providing policymakers and practitioners of Jordanian commercial banks with insight into the variables influencing user satisfaction, the findings will help these complex institutions operate more effectively.
۲۷۵۳.

Towards Supporting Exploratory Search over the Arabic Web Content: The Case of ArabXplore(مقاله علمی وزارت علوم)

نویسنده:

کلید واژه ها: Exploratory Search Arabic Wikipedia PageRank Entity Ranking

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تعداد بازدید : ۸۶ تعداد دانلود : ۶۰
Due to the huge amount of data published on the Web, the Web search process has become more difficult, and it is sometimes hard to get the expected results, especially when the users are less certain about their information needs. Several efforts have been proposed to support exploratory search on the web by using query expansion, faceted search, or supplementary information extracted from external knowledge resources. However, these solutions are not well explored for the general web search in an open-domain setting. In addition, they mostly focus on supporting search in content expressed in English and Latin based languages. In this research, we propose a fully automated approach that aims to support exploratory search over the Arabic web content. It exploits the Arabic version of Wikipedia to extract complementary information that supports visual representation and deeper exploration of the search engine's results. Key Wikipedia entities are extracted from the text snippets produced by the search engine in response to the user's query. Entities are then filtered and ranked by using a novel ranking algorithm that extends the conventional PageRank algorithm. Finally, a graph is built and presented to the user to visually represent highly ranked topics and their relationships. The proposed approach was realized by developing ArabXplore, a system that integrates with the web browser to support the web search process by executing our approach in query time. It was assessed over a dataset of 100 Arabic search queries covering different domains, and results were assessed and rated by human subjects. The underlying ranking algorithm was also compared with the conventional PageRank.
۲۷۵۴.

In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study(مقاله علمی وزارت علوم)

کلید واژه ها: Software Engineering Defects Prediction Artificial Intelligence ML ANN DNN CNN

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تعداد بازدید : ۸۶ تعداد دانلود : ۵۹
In this paper, we have extended our literature survey with experimental implementation. Analyzing numerous Artificial Intelligence (AI) techniques in software engineering (SE) can help understand the field better; the outcomes will be more effective when used with it. Our manuscript shows various AI-based algorithms that include Machine learning techniques (ML), Artificial Neural Networks (ANN), Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Genetic Algorithms (GA) applications. Software testing using Ant Colony Optimization (ACO) approach, predicting software maintainability with Group Method of Data Handling (GMDH), Probabilistic Neural Network (PNN), and Software production with time series analysis technique. Furthermore, data is the fuel for AI-based model testing and validation techniques. We have also used NASA dataset promise repository in our script. There are various applications of AI in SE, and we have experimentally demonstrated one among them, i.e., software defect prediction using AI-based techniques. Moreover, the expected future trends have also been mentioned; these are some significant contributions to the research
۲۷۵۵.

The Moroccan Health Data Bank: A Proposal for a National Electronic Health System Based on Big Data(مقاله علمی وزارت علوم)

کلید واژه ها: National Electronic Health System Big Data Health Information System survey Moroccan Health Data Bank MHDB

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تعداد بازدید : ۸۶ تعداد دانلود : ۵۳
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.
۲۷۵۶.

Brain Tumor Image Prediction from MR Images Using CNN Based Deep Learning Networks(مقاله علمی وزارت علوم)

کلید واژه ها: Brain tumour Magnetic Resonance Images (MRI) deep learning CNN SVM Image reorganization

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تعداد بازدید : ۸۴ تعداد دانلود : ۶۱
Finding a brain tumor yourself by a human in this day and age by looking through a large quantity of magnetic-resonance-imaging (MRI) images is a procedure that is both exceedingly time consuming and prone to error. It may prevent the patient from receiving the appropriate medical therapy. Again, due to the large number of image datasets involved, completing this work may take a significant amount of time. Because of the striking visual similarity that exists between normal tissue and the cells that comprise brain tumors, the process of segmenting tumour regions can be a challenging endeavor. Therefore, it is absolutely necessary to have a system of automatic tumor detection that is extremely accurate. In this paper, we implement a system for automatically detecting and segmenting brain tumors in 2D MRI scans using a convolutional-neural-network (CNN), classical classifiers, and deep-learning (DL). In order to adequately train the algorithm, we have gathered a broad range of MRI pictures featuring a variety of tumour sizes, locations, forms, and image intensities. This research has been double-checked using the support-vector-machine (SVM) classifier and several different activation approaches (softmax, RMSProp, sigmoid). Since "Python" is a quick and efficient programming language, we use "TensorFlow" and "Keras" to develop our proposed solution. In the course of our work, CNN was able to achieve an accuracy of 99.83%, which is superior to the result that has been attained up until this point. Our CNN-based model will assist medical professionals in accurately detecting brain tumors in MRI scans, which will result in a significant rise in the rate at which patients are treated.
۲۷۵۷.

Energy-Efficient and Reliable Deployment of IoT Applications in a Fog Infrastructure Based on Enhanced Water Strider Algorithm(مقاله علمی وزارت علوم)

کلید واژه ها: Internet of Things Fog Computing energy-efficient applications deployment courtship learning-based water strider algorithm Metaheuristic

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تعداد بازدید : ۸۲ تعداد دانلود : ۸۲
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.
۲۷۵۸.

Clinical Healthcare Applications: Efficient Techniques for Heart Failure Prediction Using Novel Ensemble Model(مقاله علمی وزارت علوم)

کلید واژه ها: Machine Learning Heart failure Cardiovascular Diseases Ensemble learning Healthcare

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تعداد بازدید : ۸۲ تعداد دانلود : ۴۸
Heart failure is a severe medical ailment that significantly impacts patients’ well-being and the healthcare system. For improved results, early detection and immediate treatment are essential. This work aims to develop and evaluate predictive models by applying sophisticated ensemble learning techniques. In order to forecast heart failure, we used a clinical dataset from Kaggle. We used the well-known ensemble techniques of bagging and random forest (RF) to create our models. With a predicted accuracy of 82.74%, the RF technique, renowned for its versatility and capacity to handle complex data linkages, fared well. The bagging technique, which employs several models and bootstrapped samples, also demonstrated a noteworthy accuracy of 83.98%. The proposed model achieved an accuracy of 90.54%. These results emphasize the value of group learning in predicting cardiac failure. The area under the ROC curve (AUC) was another metric to assess the model’s discriminative ability, and our model achieved 94% AUC. This study dramatically improves the prognostic modeling for heart failure. The findings have extensive implications for clinical practice and healthcare systems and offer a valuable tool for early detection and intervention in cases of heart failure.
۲۷۵۹.

Prediction Financial Distress: The Pro-Technology Technique of Altman Z-Score Model(مقاله علمی وزارت علوم)

کلید واژه ها: Investment Pro-Technology Altman Z-Score Model Prediction Tool Sustainability

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تعداد بازدید : ۸۱ تعداد دانلود : ۵۹
The Covid-19 outbreak has had a severe effect on the world economy. The company's business operations and profitability are damaged during the covid 19 outbreak. This deterioration is not only threatening the company’s survival position but also destroy the investor’s investment return. Therefore, it is vital to establish an effective early prediction technical method to foresee a corporate distress by a Pro-technical measurement to enhance the corporate sustainability. This study applies Altman Z-Score Model to as a Pro-Technology technique to the financial distress prediction of Malaysia’s Government Linked Plantation Companies (GLC-P) over a period of 10 years starting from 2012 to 2021. The significant contribution of the study is that the Z-Score Model provides an advanced indication tool regarding the financial stability of the respective GLC-P companies. The findings indicate that Financial Distress Prediction was dependent via in-time application of leverage, liquidity, activity, and profitability to the Altman Z-Score Model. Profitability and leverage were found to be superior prediction tool to financial distress.
۲۷۶۰.

An Accurate Prediction Framework for Cardiovascular Disease Using Convolutional Neural Networks(مقاله علمی وزارت علوم)

کلید واژه ها: Deep-Learning CNN Heart-Disease Prediction Cardiovascular disease Accuracy

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Cardiovascular-Diseases (CVD) are a principal cause of death worldwide. According to the World-Health-Organization (WHO), cardiovascular illnesses kill 20 million people annually. Predictions of heart-disease can save lives or take them, depending on how precise they are. The virus has rendered conventional methods of disease anticipation ineffective. Therefore, a unified system for accurate illness prediction is required. The study of disease diagnosis and identification has reached new heights thanks to artificial intelligence. With the right kind of training and testing, deep learning has quickly become one of the most cutting-edge, reliable, and sustaining technologies in the field of medicine. Using the University of California Irvine (UCI) machine-learning (ML) heart disease dataset, we propose a Convolutional-Neural-Network (CNN) for early disease prediction. There are 14 primary characteristics of the dataset that are being analyzed here. Accuracy and confusion matrix are utilized to verify several encouraging outcomes. Irrelevant features in the dataset are eliminated utilizing Isolation Forest, and the data is also standardized to enhance accuracy. Accuracy of 98% was achieved by employing a deep learning technique.

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