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Topic Review
Multispectral Facial Recognition in the Wild
Face recognition systems in uncontrolled environments have shown impressive performance improvements. However, most are limited to the use of a single spectral band in the visible spectrum. The use of multi-spectral images makes it possible to collect information that is not obtainable in the visible spectrum when certain occlusions exist (e.g., fog or plastic materials) and in low- or no-light environments. The state of the art regarding face recognition systems in an uncontrolled environment has led to the conclusion that image synthesis methods, mainly with GANs, have been used to combat intrapersonal variations, such as the difference in pose and facial expression. On the other hand, in the area of multispectral face recognition, with a plurality of solutions presented by the use of multispectral images, fusion methods are those that make the most use of images captured in different spectral bands in order to make a decision. The main problem encountered is the limited number of images (and people) in multispectral databases in an uncontrolled environment, which makes it challenging to train convolutional neural networks, which are the most used method for feature extraction.
  • 653
  • 25 Aug 2022
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.
  • 652
  • 22 Sep 2023
Topic Review
Security Threats for Medical Wearables
In the past few years, “smart” objects and products have given rise to significant progress in industry production and its security. Advances in digitization that have occurred in the industry, combined with internet technologies and future-oriented technologies in the field of so-called “smart” objects (machines and products), have led to a new and fundamental paradigm shift in industrial production and in their security.
  • 650
  • 14 Mar 2022
Topic Review
Requirements for Trustworthy AI
Artificial Intelligence (AI) can be very beneficial in the criminal justice system for predicting the risk of recidivism. AI provides unrivalled high computing power, speed, and accuracy; all harnessed to strengthen the efficiency in predicting convicted individuals who may be on the verge of recommitting a crime. The application of AI models for predicting recidivism has brought positive effects by minimizing the possible re-occurrence of crime. However, the question remains of whether criminal justice system stakeholders can trust AI systems regarding fairness, transparency, privacy and data protection, consistency, societal well-being, and accountability when predicting convicted individuals’ possible risk of recidivism. These are all requirements for a trustworthy AI. 
  • 650
  • 02 Aug 2023
Topic Review
Enhanced Genetic Method for Optimizing Multiple Sequence Alignment
In the realm of bioinformatics, Multiple Sequence Alignment (MSA) is a pivotal technique used to optimize the alignment of multiple biological sequences, guided by specific scoring criteria. Existing approaches addressing the MSA challenge tend to specialize in distinct biological features, leading to variability in alignment outcomes for the same set of sequences.
  • 649
  • 26 Dec 2023
Topic Review
Multilingual Evidence for Fake News Detection
The rapid spread of deceptive information on the internet can have severe and irreparable consequences. As a result, it is important to develop technology that can detect fake news. Although significant progress has been made in this area, current methods are limited because they focus only on one language and do not incorporate multilingual information. Multiverse—a new feature based on multilingual evidence that can be used for fake news detection and improve existing approaches.
  • 649
  • 02 Feb 2024
Topic Review
Serious Games for Learning Artificial Intelligence Algorithms
Artificial Intelligence (AI) is the technology of the future, as its applications are constantly expanding in every aspect of human life. The spread of the internet has given a great impetus to technologies that apply AI algorithms and make their presence more and more intense in everyday life. However, despite the ubiquitous presence of AI, few people can comprehend its true meaning and reason for its existence, especially the way it is applied. Serious games, that is games with a "serious purpose" other than entertaninment, can play an important role in comprehending and applying AI algorithms.
  • 648
  • 05 Jun 2023
Topic Review
Connecting the Elderly Using VR
An innovative approach for creating a social virtual reality (VR) platform that seamlessly blends art, technology, artificial intelligence (AI), and VR. Developed as part of a European project, the methodology is designed to safeguard and improve neurological, cognitive, and emotional functions, with a particular emphasis on promoting mental health.
  • 648
  • 19 Mar 2024
Topic Review
Computer-Aided Diagnosis Approach for Breast Cancer
Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world’s leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. A computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist’s mission A new transfer learning approach is introduced for breast cancer classification using a set of pre-trained Convolutional Neural Network (CNN) models with the help of data augmentation techniques. Multiple experiments are performed to analyze the performance of these pre-trained CNN models through carrying out magnification dependent and magnification independent binary and eight-class classifications. Xception model has shown a promising performance through achieving the highest classification accuracy for all experiments.
  • 647
  • 17 Oct 2022
Topic Review
Image-Based Fault Monitoring in Additive Manufacturing
Fault monitoring in additive manufacturing (AM) refers to the systematic process of monitoring and detecting deviations, anomalies, or faults during printing to ensure the printed parts’ quality, integrity, and reliability. It involves continuously monitoring the AM process’s critical parameters, variables, or characteristics and comparing them against predetermined thresholds or expected values. The goal is to identify and address any faults or anomalies that may compromise the final part’s quality or performance. It involves using various techniques, such as in-process monitoring, real-time data analysis, and automated systems, to identify faults or deviations from desired specifications. By monitoring parameters such as temperature, pressure, laser power, material flow, layer deposition, or surface quality, fault monitoring allows for the early detection of defects, material inconsistencies, structural irregularities, or printing errors.
  • 647
  • 14 Aug 2023
Topic Review
Video Super-Resolution
Super-resolution (SR) refers to yielding high-resolution (HR) images from corresponding low-resolution (LR) images. As a branch of this field, video super-resolution (VSR) mainly utilizes the spatial information of the frame and the temporal information between neighboring frames to reconstruct the HR frame. 
  • 647
  • 27 Oct 2023
Topic Review
Decentralized Federated Learning and Knowledge Graph Embedding
Anomaly detection plays a crucial role in data security and risk management across various domains, such as financial insurance security, medical image recognition, and Internet of Things (IoT) device management. Researchers rely on machine learning to address potential threats in order to enhance data security.
  • 647
  • 15 Dec 2023
Topic Review
Blockchain and Machine Learning
Blockchain is the foundation of all cryptocurrencies, while machine learning (ML) is one of the most popular technologies with a wide range of possibilities. Blockchain may be improved and made more effective by using ML.
  • 646
  • 25 May 2023
Topic Review
World-Wide Federated Content-Based Medical Image Retrieval
Content-based medical image retrieval (CBMIR) is a recent DL-based methodology that allows pathologists a quick and precise search in previously diagnosed and treated cases. In CBMIR, image features, such as texture, shape, color, and intensity, are extracted from the query and data set; then, a similarity measure is applied to compare the query features with the features of the database.
  • 646
  • 17 Oct 2023
Topic Review
Compression of Bio-Signals for IoMT Systems
Bio-signals are records of biological events inside the human body, such as a heartbeat or muscle contraction. These signals are used to detect whether there is a problem or disorder in a human organ.
  • 645
  • 17 Nov 2023
Topic Review
Artificial Intelligence and Photovoltaic Fault
The global transition to sustainable energy has positioned photovoltaic (PV) systems at the top of renewable energy solutions. Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. 
  • 645
  • 20 Nov 2023
Topic Review
Hierarchical Multi-Objective Optimization for Dedicated Bus Punctuality
Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. 
  • 642
  • 05 Jun 2023
Topic Review
Emotion Recognition Systems
Emotion recognition systems (ERS) are an emerging technology with immense potential, exemplifying the innovative utilization of artificial intelligence (AI) within the context of the fourth industrial revolution (IR 4.0). Given that personalization is a key feature of the fifth industrial revolution (IR 5.0), ERS has the potential to serve as an enabler for IR 5.0. Furthermore, the COVID-19 pandemic has increased the relevance of this technology as work processes were adapted for social distancing and the use of face masks. Even in the post-pandemic era, many individuals continue to wear face masks. Therefore, ERS offers a technological solution to address communication challenges in a masked world. The existing body of knowledge on ERS primarily focuses on exploring modalities or modes for emotion recognition, system development, and the creation of applications utilizing emotion recognition functions.
  • 642
  • 14 Nov 2023
Topic Review
The Detection of Lanes and Lane Markings
Vision-based identification of lane area and lane marking on the road is an indispensable function for intelligent driving vehicles, especially for localization, mapping and planning tasks. However, due to the increasing complexity of traffic scenes, such as occlusion and discontinuity, detecting lanes and lane markings from an image captured by a monocular camera becomes persistently challenging. The lanes and lane markings have a strong position correlation and are constrained by a spatial geometry prior to the driving scene. Most existing studies only explore a single task, i.e., either lane marking or lane detection, and do not consider the inherent connection or exploit the modeling of this kind of relationship between both elements to improve the detection performance of both tasks.
  • 641
  • 08 Aug 2023
Topic Review
Applications of Blockchain-Based Federated Learning
Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. Blockchain-based federated learning (BFL) has gained the capability and prospects for applications in highly privacy-sensitive industries. 
  • 640
  • 08 Mar 2024
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