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Topic Review
Linked Data Interfaces
In the era of big data, linked data interfaces play a critical role in enabling access to and management of large-scale, heterogeneous datasets. This research investigates forty-seven interfaces developed by the semantic web community in the context of the Web of Linked Data, displaying information about general topics and digital library contents. The interfaces are classified based on their interaction paradigm, the type of information they display, and the complexity reduction strategies they employ. The main purpose to be addressed is the possibility of categorizing a great number of available tools so that comparison among them becomes feasible and valuable. The analysis reveals that most interfaces use a hybrid interaction paradigm combining browsing, searching, and displaying information in lists or tables. Complexity reduction strategies, such as faceted search and summary visualization, are also identified. Emerging trends in linked data interface focus on user-centric design and advancements in semantic annotation methods, leveraging machine learning techniques for data enrichment and retrieval. Additionally, an interactive platform is provided to explore and compare data on the analyzed tools. Overall, there is no one-size-fits-all solution for developing linked data interfaces and tailoring the interaction paradigm and complexity reduction strategies to specific user needs is essential.
  • 199
  • 08 Sep 2023
Topic Review
Digital Twin Applications in Manufacturing Industry
The existing literature notes a range of potential benefits that digital twin (DT) projects can deliver, including efficiency gains, waste reduction, and improved decision-making, and DTs are also considered integral to smart manufacturing environments and Industry 4.0. Research on three DT projects in a German multi-national concludes that digital twin projects are likely to involve incremental rather than disruptive change, and that successful implementation is usually underpinned by ensuring technology, people and process change factors are progressed in a balanced and integrated fashion. Building upon existing frameworks, three “properties” are identified as being of particular value in digital twin projects - workforce adaptability, technology manageability and process agility–and a related set of steps and actions is put forward as a template and point of reference for future digital twin implementations.
  • 181
  • 08 Sep 2023
Topic Review
Executable Digital Process Twins
An Executable Digital Process Twin (xDPT) is a digital twin specifically designed for process-driven systems. It enriches the monitoring and analysis functionalities of a digital process twin with the possibility of actively driving the execution of the entire system. An xDPT receives real-time event logs from the running organization and allows direct monitoring by marking the status of the system in the running process. Moreover, it can exploit existing process mining techniques to check the conformance of the executed behavior, to provide an enriched view over multiple system perspectives, and to store historical data to predict process evolution. Finally, the process driving the system behavior can be refined and deployed into the organization. The main objective of an xDPT is to enable the monitoring and analysis of a system but also to execute it. In this regard, the process should serve both for performing monitoring and analysis stages and for enacting and controlling the whole system execution.
  • 294
  • 08 Sep 2023
Topic Review
Brain Pathology Classification of MR Images
A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. 
  • 125
  • 08 Sep 2023
Topic Review
Deep Learning Networks and YOHO English Speech Dataset
The rapid momentum of deep neural networks (DNNs) has yielded state-of-the-art performance in various machine-learning tasks using speaker identification systems. Speaker identification is based on the speech signals and the features that can be extracted from them.
  • 153
  • 07 Sep 2023
Topic Review
Multimodal Sentiment Analysis in Realistic Environments
In the real world, multimodal sentiment analysis (MSA) enables the capture and analysis of sentiments by fusing multimodal information, thereby enhancing the understanding of real-world environments. The key challenges lie in handling the noise in the acquired data and achieving effective multimodal fusion. When processing the noise in data, existing methods utilize the combination of multimodal features to mitigate errors in sentiment word recognition caused by the performance limitations of automatic speech recognition (ASR) models.
  • 300
  • 07 Sep 2023
Topic Review
Development of Discrete Global Grid Systems
The rapid growth in Earth’s global geospatial data necessitates an efficient system for organizing the data, facilitating data fusion from diverse sources, and promoting interoperability. Mapping the spheroidal surface of the planet presents significant challenges as it involves balancing distortion and splitting the surface into multiple partitions. The distortion decreases as the number of partitions increases, but, at the same time, the complexity of data processing increases since each partition represents a separate dataset and is defined in its own local coordinate system.
  • 249
  • 07 Sep 2023
Topic Review
Eye Tracking Technology
Eye tracking is a technique for detecting and measuring eye movements and characteristics. An eye tracker can sense a person’s gaze locations and features at a certain frequency.
  • 307
  • 06 Sep 2023
Topic Review
Practical AI Cases for Solving ESG Challenges
Artificial intelligence (AI) is a rapidly advancing area of research that encompasses numerical methods to solve various prediction, optimization, and classification/clustering problems. Recently, AI tools were proposed to address the environmental, social, and governance (ESG) challenges associated with sustainable business development. While many publications discuss the potential of AI, few focus on practical cases in the three ESG domains altogether, and even fewer highlight the challenges that AI may pose in terms of ESG.
  • 215
  • 06 Sep 2023
Topic Review
Detecting Deceptive Behaviours
Interest in detecting deceptive behaviours by various application fields, such as security systems, political debates, advanced intelligent user interfaces, etc., makes automatic deception detection an active research topic. This interest has stimulated the development of many deception-detection methods in the literature in recent years.
  • 253
  • 06 Sep 2023
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