درخت حوزه‌های تخصصی

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.

Nowadays, many companies cannot see the digital investment that plays a main role in the IR 4.0. Therefore, this study is investigating the study of investment as plays a critical role in an analytical activity to assess the benefits and costs of an investment and can be used as an investment justification. Traditional investment appraisal uses a financial approach where the benefits and costs are quantified in a certain amount of value for money and then compared in value. Moreover, this study is revealed the fruitful outcomes because revealed the investment valuation method with NPV (Net Present Value) and ROV (Real Option Valuation). ROV is an alternative to financial valuation. Seeding from the same philosophy as Financial Option, ROV has advantages in handling the flexibility, risk, and volatility that may occur from an investment. Thus, ROV is considered more able to appreciate an investment that has these characteristics. Investment appraisal with ROV is better able to appreciate investment than traditional financial methods, as shown by ROV's NPV results in the case of marketing with Loyalty points through email communication as a digital investment that are greater than ordinary NPV. This is because ROV can appreciate flexibility in investments that have choices of investment plans in the future

This paper is aimed at evaluating critical success factors in Business Intelligence (BI) implementation in an Enterprise Resource Planning (ERP) environment. The data analysis method used in this paper is the Decision Making Trial and Evaluation Laboratory Model (DEMATEL). The study has been conducted on a cement manufacturing strategic holding company that has implemented ERP since 2010. This research is done through literature review and interviews with the head of the BI development team as the expert for this research, before distributing questionnaires to Information and Communication Technologies (ICT) Team and BI stakeholders. The questionnaire has been addressed to 18 respondents consisting of the BI development team and stakeholders, which include the Strategic Planning Division, Business Development Division, Transformation Management Division, and Accounting Division. There are 13 factors evaluated, consisting of 4 factors related to the organization, four factors related to the development process, three factors related to the technology, and two factors associated with the external environment. The most critical factor for organizational criteria is Top management support. The most important factor for process criteria is Effective project management. The most important factor for technology criteria is System reliability, flexibility, and scalability. The most important factor for environment criteria is the selection of a vendor.

Recently, Massive Open Online Courses have become sensational in the field of distance learning. There is a plethora of advantages being listed in learning through MOOCs but this pedagogy lacks in few areas when compared with traditional classes. One of those inabilities of MOOC is its support to prepare the students for laboratory-based courses. The authors of this study chose a MOOC course that teaches Digital Photography and created an Augmented Reality (AR) experience for a module that explains the different parts of a digital camera. The 2 nd year Multimedia students of Vellore Institute of Technology have been asked to experience the MOOC video followed by the AR experience. Their feedbacks before and after the AR experience has been statistically tested and reported. The results revealed that the students feel more confident and concentrate more when the instructional video was given as an AR experience. This study suggests that AR integrated MOOC modules might help in training students better for practice-based courses.

On a global scale, one of the prevalent causes of dementia is Alzheimer’s disease (AD). It will cause a steady deterioration in the individual from the mild stage to the severe stage, and thus impair their capacity to finish any tasks with no aid. The diagnosis is done with the utilization of existing methods which include medical history; neuropsychological testing as well as MRI (Magnetic Resonance Imaging), a lack of sensitivity as well as precision does affect the consistency of efficient procedures. With the deep learning network’s utilization, it is possible to create a framework for detecting specific AD characteristics from the MRI images. While automatic diagnosis is done with the application of diverse machine learning techniques, the existing ones do suffer from certain constraints with regards to accuracy. Thus, this work’s key goal is to increase the classification’s accuracy through the inclusion of a pre-processing approach prior to the deep learning model. The Alzheimer's disease Neuroimaging Initiative (ADNI) database of AD patients was used to develop a deep learning approach for AD identification. In addition, this study will present ideas for Haralick features, feature extraction from Local Binary Pattern (LBP), Artificial Neural Network (ANN), and Visual Geometry Group (VGG)-19 network techniques. The results of the experiments show that the deep learners offered are more effective than other systems already in use.

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.

What is clear is that judges usually judge cases based on their knowledge, experience, personality, and sentiment. Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. In this research, we are looking at predicting legal sentences in drug cases involving the purchase, possession, concealment, or transportation of illicit drugs, using machine learning methods, and the effect of sentiment and emotions in case texts on predicting the severity of whipping, fines, and imprisonment. So, the text documents of 6000 Persian drug-related cases were pre-processed and then the translation of the NRC Glossary of Emotions and sentiment was used to give each item a score for positive or negative sentiment and a score for emotion. Then machine learning methods were used for modeling. BERT, TFIDF+Adaboost, and Skipgram+LSTM+CNN methods had the highest accuracy, respectively. Also, evaluation criteria were analyzed in situations where sentiment scores, emotional scores, or both were used in the prediction process along with judicial texts. Finally, it was found that the use of sentiment and emotion scores improves the accuracy of legal judgment predictions for all three types of sentences and that sentiments have a greater impact on the accuracy of legal judgment predictions than emotions

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.