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Topic Review
Cloud Computing Failure Prediction
To date, despite the significant improvement in the performance of the hardware elements of the cloud infrastructure, the failure rate remains substantial. Moreover, the cloud is not as reliable as the cloud service providers, such as Amazon AWS and Ali Cloud, claimed, which is more than 99.9%. For example, multiple instances of failure have been reported, such as the failure of Amazon’s cloud data servers in early October 2012, which resulted in the collapse of Reddit, Airbnb, and Flipboard, the loss of Amazon AWS S3 on 28 February 2017, and the crash of Microsoft cloud services on 22 March 2017. Such failures show that cloud service providers are not as reliable as they claim.
  • 857
  • 06 Apr 2022
Topic Review
Machine Learning Methods
Machine learning (ML) has a well-established reputation for successfully enabling automation through its scalable predictive power.
  • 857
  • 15 Nov 2022
Topic Review
Deep Learning Techniques in Fault Diagnosis and Prognosis
The fault diagnosis and prognosis (FDP) technique based on data-driven machine learning (ML) methods recognizes or learns the health features of the system from historical data, and tries to discover and mine the information hidden in the data, so that it can accurately analyze and predict future system behavior without precisely knowing the forward physical model.
  • 855
  • 06 Feb 2023
Topic Review
Using Colored Petri Net for Accounting System
Many learners who are not familiar with the accounting terms find blended learning very complex to understand with respect to the computerized accounting system, the journal entries process, and tracing the accounting transaction flows of accounting system. A simulation-based model is a viable option to help instructors and learners make understanding the accounting system components and monitoring the accounting transactions easier. This entry briefly introduce a colored Petri net (CPN)-based model.
  • 852
  • 28 Mar 2022
Topic Review
Bi-Directional Text
Bi-directional text is text containing text in both text directionalities, both right-to-left (RTL or dextrosinistral) and left-to-right (LTR or sinistrodextral). It generally involves text containing different types of alphabets, but may also refer to boustrophedon, which is changing text directionality in each row. Some writing systems of the world, including the Arabic and Hebrew scripts or derived systems such as the Persian, Urdu, and Yiddish scripts, are written in a form known as right-to-left (RTL), in which writing begins at the right-hand side of a page and concludes at the left-hand side. This is different from the left-to-right (LTR) direction used by the dominant Latin script. When LTR text is mixed with RTL in the same paragraph, each type of text is written in its own direction, which is known as bi-directional text. This can get rather complex when multiple levels of quotation are used. Many computer programs fail to display bi-directional text correctly. For example, the Hebrew name Sarah (שרה) is spelled: sin (ש) (which appears rightmost), then resh (ר), and finally heh (ה) (which should appear leftmost). Note: Some web browsers may display the Hebrew text in this article in the opposite direction.
  • 851
  • 11 Nov 2022
Topic Review
Time-Series Forecasting Models
The time-series forecasting method is a suitable pricing solution for Digital Signage Advertising (DSA), as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting.
  • 849
  • 03 Nov 2021
Topic Review
Transformers in Natural Language Processing Applications
The field of Natural Language Processing (NLP) has undergone a significant transformation with the introduction of Transformers. From the first introduction of this technology in 2017, the use of transformers has become widespread and has had a profound impact on the field of NLP.
  • 848
  • 24 May 2023
Topic Review
Energy Consumption Patterns in Urban Buildings
Energy has been one of the most important topics of political and social discussion in recent decades. A significant proportion of the country’s revenues is derived from energy resources, making it one of the most important and strategic macro policy and sustainable development areas. Energy demand modeling is one of the essential strategies for better managing the energy sector and developing appropriate policies to increase productivity. With the increasing global demand for energy, it is necessary to develop intelligent forecasting methods and algorithms. Different economic and non-economic indicators can be used to estimate the energy demand, including linear and non-linear statistical methods, mathematics, and simulation models.
  • 847
  • 28 Apr 2022
Topic Review
Brain Immunoinformatics
Breakthrough advances in informatics of the last decade have thoroughly influenced the field of immunology. In particular, the immunoinformatics of the central neural system is referred to as neuroimmunoinformatics (NII). This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using a bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and protein-protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII opts to individualize treatment strategies, personalize disease prognosis and treatment response.   
  • 845
  • 28 Mar 2022
Topic Review
COVID-19 Fake News in Brazilian Portuguese Language
Public health interventions to counter the COVID-19 pandemic have accelerated and increased digital adoption and use of the Internet for sourcing health information. Unfortunately, there is evidence to suggest that it has also accelerated and increased the spread of false information relating to COVID-19. The consequences of misinformation, disinformation and misinterpretation of health information can interfere with attempts to curb the virus, delay or result in failure to seek or continue legitimate medical treatment and adherence to vaccination, as well as interfere with sound public health policy and attempts to disseminate public health messages. While there is a significant body of literature, datasets and tools to support countermeasures against the spread of false information online in resource-rich languages such as English and Chinese, there are few such resources to support Portuguese, and Brazilian Portuguese specifically.
  • 845
  • 29 Apr 2022
Topic Review
Deep Learning in COVID-19
Various deep-learning (DL) methods that utilize a combination of omics data and imaging data have been applied to the diagnosis, prognosis, and treatment options of clinical COVID-19 patients. Even with the emerging deep-learning methods, human intervention is still essential in the clinical diagnosis and treatment of COVID-19 patients.
  • 845
  • 06 Apr 2023
Topic Review
Impact of AI on the Future of Work
Artificial Intelligence (AI) is transforming the way we work, creating new opportunities for efficiency, innovation, and growth. However, it also poses several challenges, including job displacement, skills gaps, and ethical concerns. This research explores the potential impact of AI on the future of work and discusses strategies for addressing these challenges. By embracing AI technology and investing in the development of new skills, we can create a future of work that is more productive, equitable, and sustainable.
  • 843
  • 22 May 2023
Topic Review
Fashion Recommendation System Using Deep Learning
Recommender systems are one of the great improvements in Internet technology and e-commerce, and the origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Later, recommender systems went through numerous improvements to facilitate users’ navigation through fashion, videos, books, papers, and especially e-commerce.
  • 842
  • 22 Aug 2023
Topic Review
Self-Supervised Learning (SSL) in Deep Learning Contexts
Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. 
  • 840
  • 18 Mar 2024
Topic Review
Machine Learning for Precision Agriculture with UAV
Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. 
  • 840
  • 19 Jun 2023
Topic Review
Corpus Statistics Empowered Document Classification
In natural language processing (NLP), document classification is an important task that relies on the proper thematic representation of the documents. Gaussian mixture-based clustering is widespread for capturing rich thematic semantics but ignores emphasizing potential terms in the corpus. Moreover, the soft clustering approach causes long-tail noise by putting every word into every cluster, which affects the natural thematic representation of documents and their proper classification. It is more challenging to capture semantic insights when dealing with short-length documents where word co-occurrence information is limited.
  • 837
  • 05 Aug 2022
Topic Review
Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these ratings alone may not be sufficient to offer personalized and accurate suggestions. To overcome this limitation, additional sources of information, such as reviews, can be utilized. However, analyzing and understanding the information contained within reviews, which are often unstructured data, is a challenging task. To address this issue, sentiment analysis (SA) has attracted considerable attention as a tool to better comprehend a user’s opinions, emotions, and attitudes.
  • 832
  • 21 Aug 2023
Topic Review
Stereo Matching Algorithm
With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot’s ability for autonomous navigation during self-driving.
  • 831
  • 18 Dec 2023
Topic Review
Segmentation of Liver Tumor in Computed Tomography Scan
Segmentation of images is a common task within medical image analysis and a necessary component of medical image segmentation. The segmentation of the liver and liver tumors is an important but challenging stage in screening and diagnosing liver diseases. Many automated techniques have been developed for liver and tumor segmentation; however, segmentation of the liver is still challenging due to the fuzzy & complex background of the liver position with other organs. As a result, creating a considerable automated liver and tumour division from computed tomography (CT) scans is critical for identifying liver cancer.
  • 827
  • 15 Sep 2022
Topic Review
Detecting Dementia from Face-Related Features
Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features.
  • 827
  • 08 Aug 2023
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