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Topic Review
Concept Prerequisite Learning with PTM and GNN
Prerequisite chains are crucial to acquiring new knowledge efficiently. Many studies have been devoted to automatically identifying the prerequisite relationships between concepts from educational data. Though effective to some extent, these methods have neglected two key factors: most works have failed to utilize domain-related knowledge to enhance pre-trained language models, thus making the textual representation of concepts less effective; they also ignore the fusion of semantic information and structural information formed by existing prerequisites.
  • 294
  • 04 Sep 2023
Topic Review
ML Techniques for Intrusion Detection in Cyber-Physical Systems
Cyber-physical systems (CPS) are vital to key infrastructures such as Smart Grids and water treatment, and are increasingly vulnerable to a broad spectrum of evolving attacks. Whereas traditional security mechanisms, such as encryption and firewalls, are often inadequate for CPS architectures, the implementation of Intrusion Detection Systems (IDS) tailored for CPS has become an essential strategy for securing them. In this context, it is worth noting the difference between traditional offline Machine Learning (ML) techniques and understanding how they perform under different IDS applications. 
  • 293
  • 06 Sep 2023
Topic Review
Comprehensive Background on Tiny Machine Learning
Internet of Things (IoT) systems frequently generate vast quantities of data, posing substantial management and analysis challenges. Researchers have introduced several frameworks and architectures to address these challenges in IoT Big Data management and knowledge extraction. One such proposal is the Cognitive-Oriented IoT Big Data Framework (COIB Framework), as outlined in Mishra’s works. This framework encompasses an implementation architecture, layers for IoT Big Data, and a structure for organizing data. An alternative method involves employing a Big-Data-enhanced system, adhering to a data lake architecture. Key features of this system include a multi-threaded parallel approach for data ingestion, strategies for storing both raw and processed IoT data, a distributed cache layer, and a unified SQL-based interface for exploring IoT data. Furthermore, blockchain technologies have been investigated for their potential to maintain continuous integrity in IoT Big Data management. This involves five integrity protocols implemented across three stages of IoT operations.
  • 292
  • 01 Feb 2024
Topic Review
AutoML with Bayesian Optimizations for Big Data Management
The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models.
  • 292
  • 03 Nov 2023
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.
  • 291
  • 08 Sep 2023
Topic Review
Sign Language Recognition Techniques
Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. Building a sign language recognition system using deep learning technology plays a vital role in interpreting sign language to ordinary individuals and the reverse. This system would ease the process of communication between deaf and normal people.
  • 291
  • 10 Oct 2023
Topic Review
Hybrid LSTM Model and Air Pollution Prediction
Air pollution is a critical environmental concern that poses significant health risks and affects multiple aspects of human life. ML algorithms provide promising results for air pollution prediction. In the existing scientific literature, Long Short-Term Memory (LSTM) predictive models, as well as their combination with other statistical and machine learning approaches, have been utilized for air pollution prediction. However, these combined algorithms may not always provide suitable results due to the stochastic nature of the factors that influence air pollution, improper hyperparameter configurations, or inadequate datasets and data characterized by great variability and extreme dispersion. To identify optimal hyperparameters for the LSTM model, a hybridization with the Genetic Algorithm is proposed. To mitigate the risk of overfitting, the bagging technique is employed on the best LSTM model. The proposed predicitive model aims to determine the Common Air Quality Index level for the next hour in Niksic, Montenegro. With the hybridization of the LSTM algorithm and by applying the bagging technique, the approach aims to significantly enhance the accuracy and reliability of hourly air pollution prediction. The major contribution is in the application of advanced machine learning analysis and the combination of the LSTM, Genetic Algorithm, and bagging techniques, which have not been previously employed in the analysis of air pollution in Montenegro. The proposed model will be made available to interested management structures, local governments, national entities, or other relevant institutions, empowering them to make effective pollution level predictions and take appropriate measures.
  • 290
  • 22 Sep 2023
Topic Review
Audio–Visual Emotion Recognition
Emotion recognition can be formulated as a problem where some source produces several streams of data (features) of various modalities (e.g., audio and video), each with its own distribution, and the goal is to estimate the distributions and map them onto the source.
  • 289
  • 21 Nov 2023
Topic Review
Convolutional Neural Networks Applied to Face Mask Classification
The preventive measures taken to curb the spread of COVID-19 have emphasized the importance of wearing face masks to prevent potential infection with serious diseases during daily activities or for medical professionals working in hospitals. Due to the mandatory use of face masks, various methods employing artificial intelligence and deep learning have emerged to detect whether individuals are wearing masks.
  • 289
  • 24 Jan 2024
Topic Review
GAN-Based Tabular Data Generator for Constructing Synopsis
In data-driven systems, data exploration is imperative for making real-time decisions. However, big data are stored in massive databases that are difficult to retrieve. Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis) that closely replicates the behavior of the actual data. The use of Generative Adversarial Networks (GANs) for generating tabular data has emerged as a pivotal method in AQP for constructing accurate synopses. Moreover, the advancement of tabular GAN architectures addresses the specific challenges encountered in synopsis construction. These advanced GAN variations exhibit a promising capacity to generate high-fidelity synopses, potentially transforming the efficiency and effectiveness of AQP in data-driven systems. 
  • 289
  • 25 Jan 2024
Topic Review
Predictive Maintenance of Ball Bearing Systems
In the era of Industry 4.0 and beyond, ball bearings remain an important part of industrial systems. The failure of ball bearings can lead to plant downtime, inefficient operations, and significant maintenance expenses.
  • 289
  • 01 Feb 2024
Topic Review
Deep Learning for Alzheimer’s Disease Detection
Deep learning has become a prominent approach in Alzheimer’s disease (AD) detection using medical image data, incorporating modalities like positron emission tomography (PET) and magnetic resonance imaging (MRI). These advances in deep learning and multimodal imaging have improved AD detection accuracy and effectiveness, leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative modelling techniques. 
  • 289
  • 05 Mar 2024
Topic Review
Gastrointestinal Disease Classification
Gastrointestinal (GI) tract diseases are on the rise in the world. These diseases can have fatal consequences if not diagnosed in the initial stages. WCE (wireless capsule endoscopy) is the advanced technology used to inspect gastrointestinal diseases such as ulcerative-colitis, polyps, esophagitis, and ulcers. WCE produces thousands of frames for a single patient’s procedure for which manual examination is tiresome, time-consuming, and prone to error; therefore, an automated procedure is needed.
  • 288
  • 26 Jan 2024
Topic Review
Methods for Building Ensembles of Convolutional Neural Networks
In computer vision and image analysis, Convolutional Neural Networks (CNNs) and other deep-learning models are at the forefront of research and development. These advanced models have proven to be highly effective in tasks related to computer vision. One technique that has gained prominence in recent years is the construction of ensembles using deep CNNs. These ensembles typically involve combining multiple pretrained CNNs to create a more powerful and robust network.
  • 288
  • 08 Dec 2023
Topic Review
Speech Emotion Recognition
Speech is the most natural way of human communication. Affective computing systems based on speech play an important role in promoting human–computer interaction, and emotion recognition is the first step. Due to the lack of a precise definition of emotion and the inclusive and complex influence of emotion generation and expression, accurately recognizing speech emotions is still difficult. Speech emotion recognition (SER) is an important problem that is receiving increasing interest from researchers due to its numerous applications, such as e-learning, clinical trials, audio monitoring/surveillance, lie detection, entertainment, video games, and call centers.
  • 288
  • 25 Jan 2024
Topic Review
Industrial Drying Hopper Operations
The advancement of Industry 4.0 and smart manufacturing has made a large amount of industrial process data attainable with the use of sensors installed on machines. This stands true for Industrial Dryer Hoppers, which are used for most polymer manufacturing processes. Insights derived via AI from the collected data allow for improved processes and operations.
  • 287
  • 15 Sep 2023
Topic Review
Segmentation and Path Planning of Unmanned Ariel Vehicle
Unmanned aerial vehicles (UAVs), sometimes known as “drones”, are unmanned aircraft that can be flown without a pilot on board. Aircraft, ground control stations, and communications systems all fall under the umbrella term unmanned aircraft systems (UAS), which describes the infrastructure necessary for sophisticated drone operations. An autonomous drone is a UAV that can fly missions independently of a human pilot. It can take off, execute its task, and return to base without human assistance. Rather than relying on a human pilot, communications management software handles mission planning and flight control for autonomous drones.
  • 287
  • 15 Dec 2023
Topic Review
Federated Learning for Clinical Event Classification
A novel approach to clinical event classification in healthcare using federated learning (FL) and cross-device ensemble models based on vital signs data from the MIMIC-IV dataset was presented. The FL structure allows training directly on each client's device, ensuring patient privacy. The proposed method achieves an impressive accuracy of 98.9%, highlighting the significant potential of FL and ensemble technology in handling sensitive patient data for healthcare applications.
  • 287
  • 14 Nov 2023
Topic Review
Semantically Interoperable Social Media Platforms
Competitive intelligence in social media analytics has significantly influenced behavioral finance worldwide in recent years; it is continuously emerging with a high growth rate of unpredicted variables per week. Several surveys in this large field have proved how social media involvement has made a trackless network using machine learning techniques through web applications and Android modes using interoperability.
  • 287
  • 19 Sep 2023
Topic Review
Quantifying Digital Biomarkers for Well-Being
Wearable devices have become ubiquitous, collecting rich temporal data that offers valuable insights into human activities, health monitoring, and behavior analysis. Leveraging these data, researchers have developed innovative approaches to classify and predict time-based patterns and events in human life. Time-based techniques allow the capture of intricate temporal dependencies, which is the nature of the data coming from wearable devices.
  • 287
  • 27 Nov 2023
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