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Topic Review
NeRF-Based SLAM
Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gains, especially when Neural Radiance Fields (NeRFs) are implemented. NeRF-based SLAM in mapping aims to implicitly understand irregular environmental information using large-scale parameters of deep learning networks in a data-driven manner so that specific environmental information can be predicted from a given perspective. NeRF-based SLAM in tracking jointly optimizes camera pose and implicit scene network parameters through inverse rendering or combines VO and NeRF mapping to achieve real-time positioning and mapping. 
  • 495
  • 05 Mar 2024
Topic Review
Convolution Neural Network  and Transformer-Based Human Pose Estimation
Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images.
  • 494
  • 03 Aug 2023
Topic Review
Cancer Metastasis Detection via Effective Contrastive Learning
The metastasis detection in lymph nodes via microscopic examination of H&E stained histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathological images. Deep learning has been utilized in automatic cancer metastasis detection in recent years. The success of supervised deep learning is credited to a large labeled dataset, which is hard to obtain in medical image analysis. Contrastive learning, a branch of self-supervised learning, can help in this aspect through introducing an advanced strategy to learn discriminative feature representations from unlabeled images.
  • 493
  • 13 Sep 2022
Topic Review
A Robust Vehicle Detection Model for LiDAR Sensor
Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. 
  • 492
  • 15 Jun 2023
Topic Review
Domain Name System-based Blackhole List
A Domain Name System-based blackhole list, Domain Name System blacklist (DNSBL) or real-time blackhole list (RBL) is a service for operation of mail servers to perform a check via a Domain Name System (DNS) query whether a sending host's IP address is blacklisted for email spam. Most mail server software can be configured to check such lists, typically rejecting or flagging messages from such sites. A DNSBL is a software mechanism, rather than a specific list or policy. Dozens of DNSBLs exist. They use a wide array of criteria for listing and delisting addresses. These may include listing the addresses of zombie computers or other machines being used to send spam, Internet service providers (ISPs) who willingly host spammers, or those which have sent spam to a honeypot system. Since the creation of the first DNSBL in 1998, the operation and policies of these lists have frequently been controversial, both in Internet advocacy circles and occasionally in lawsuits. Many email systems operators and users consider DNSBLs a valuable tool to share information about sources of spam, but others including some prominent Internet activists have objected to them as a form of censorship. In addition, a small number of DNSBL operators have been the target of lawsuits filed by spammers seeking to have the lists shut down.
  • 491
  • 14 Nov 2022
Topic Review
Intelligent Deep Learning in IoT Smart Home Networks
The Internet of Things (IoT) is the interconnection of sensors, machines, objects, or other computing devices over the internet to communicate with the least human interference. Specific types of sensors are involved in obtaining information from physical entities, and after analysis, it is stored in local storage, which is then sent to cloud storage, where appropriate action is taken according to the information.
  • 491
  • 03 Jan 2023
Topic Review
Deep Learning Models in Video Deepfake Detection
The increasing use of deep learning techniques to manipulate images and videos, commonly referred to as “deepfakes”, is making it more challenging to differentiate between real and fake content, while various deepfake detection systems have been developed, they often struggle to detect deepfakes in real-world situations. In particular, these methods are often unable to effectively distinguish images or videos when these are modified using novel techniques which have not been used in the training set.
  • 491
  • 22 May 2023
Topic Review
Low-Dimensional Layered Light-Sensitive Memristive Structures for Machine Vision
Layered two-dimensional (2D) and quasi-zero-dimensional (0D) materials effectively absorb radiation in the wide ultraviolet, visible, infrared, and terahertz ranges. Photomemristive structures made of such low-dimensional materials are of great interest for creating optoelectronic platforms for energy-efficient storage and processing of data and optical signals in real time.
  • 490
  • 15 Mar 2022
Topic Review
Global Trends in Cancer Nanotechnology
This study presents a new way to investigate comprehensive trends in cancer nanotechnology research in different countries, institutions, and journals providing critical insights to prevention, diagnosis, and therapy. This paper applies the qualitative method of bibliometric analysis on cancer nanotechnology using the PubMed database during the years 2000-2021. Inspired by hybrid medical models and content-based and bibliometric features for machine learning models, our results show cancer nanotechnology studies have expanded exponentially since 2010. The highest production of articles in cancer nanotechnology is mainly from US institutions, with several countries notably the USA, China, UK, India, and Iran as concentrated focal points as centers of cancer nanotechnology research, especially in the last five years. The analysis shows the greatest overlap between nanotechnology and DNA, RNA, iron oxide or mesoporous silica, breast cancer, and cancer diagnosis and cancer treatment. Moreover, more than 50% of information related to the keywords, authors, institutions, journals, and countries are considerably investigated in the form of publications from the top 100 journals. This study has the potentials to provide past and current lines of research that can unmask comprehensive trends in cancer nanotechnology, key research topics, or pmost productive countries and authors in the field.
  • 489
  • 10 Sep 2021
Topic Review
Spatial and Temporal Hierarchy for Autonomous Navigation
Robust evidence suggests that humans explore their environment using a combination of topological landmarks and coarse-grained path integration. This approach relies on identifiable environmental features (topological landmarks) in tandem with estimations of distance and direction (coarse-grained path integration) to construct cognitive maps of the surroundings. This cognitive map is believed to exhibit a hierarchical structure, allowing efficient planning when solving complex navigation tasks.
  • 489
  • 29 Jan 2024
Topic Review
Application Profiling System Architecture
Along with the rise of cloud and edge computing has come a plethora of solutions that regard the deployment and operation of different types of applications in such environments. Infrastructure as a service (IaaS) providers offer a number of different hardware solutions to facilitate the needs of the growing number of distributed applications. It is critical in this landscape to be able to navigate and discover the best-suited infrastructure solution for the applications, taking into account not only the cost of operation but also the quality of service (QoS) required for any given application. The proposed solution has two main research developments: (a) the creation and optimisation of multidimensional vectors that represent the hardware usage profiles of an application, and (b) the assimilation of a machine learning classification algorithm, in order to create a system that can create hardware-agnostic profiles of a vast variety of containerised applications in terms of nature and computational needs and classify them to known benchmarks. Given that benchmarks are widely used to evaluate a system’s hardware capabilities, having a system that can help select which benchmarks best correlate to a given application can help an IaaS provider make a more informed decision or recommendation on the hardware solution, not in a broad sense, but based on the needs of a specific application.
  • 488
  • 20 Dec 2022
Topic Review
Application of Artificial Intelligence in a Cephalometric Analysis
The application of artificial intelligence (AI) has become more and more widespread in medicine and dentistry. It may contribute to improved quality of health care as diagnostic methods are getting more accurate and diagnostic errors are rarer in daily medical practice. The accuracy of determining cephalometric landmarks using widely available commercial AI-based software and advanced AI algorithms was presented. Most AI algorithms used for the automated positioning of landmarks on cephalometric radiographs had relatively high accuracy. At the same time, the effectiveness of using AI in cephalometry varies depending on the algorithm or the application type, which has to be accounted for during the interpretation of the results.
  • 488
  • 15 Aug 2023
Topic Review
Deepfake Detection Datasets
Deepfakes are notorious for their unethical and malicious applications to achieve economic, political, and social reputation goals. Although deepfakes were initially associated with entertainment such as movie visual effects, camera filters, and digital avatars, they are defined as “believable generated media by Deep Neural Network” and have evolved into a mainstream tool for facial forgery. With the development of multiple forgery methods, deepfake data are increasing at a annual rate of ~300%. However, the data published online have different forgery qualities. 
  • 488
  • 28 Feb 2024
Topic Review
Full-Reference Image Quality Assessment
To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). Having a metric to quantify the image quality to the HVS is always a tough task. Image quality assessment (IQA) can be subjective or objective.
  • 487
  • 09 Jan 2024
Topic Review
Optimizing Straw Bale Retrieval Process
During a baling operation, the operator of the baler should decide when and where to drop the bales in the field to facilitate later retrieval of the bales for transport out of the field. Manually determining the time and place to drop a bale creates extra workload on the operator and may not result in the optimum drop location for the subsequent front loader and transport unit. Therefore, there is a need for a tool that can support operators during this decision process. The key objective of this study is to find the optimal traversal sequence of fieldwork tracks to be followed by the baler and bale retriever to minimize the non-working driving distance in the field. Two optimization processes are considered for this problem. Firstly, finding the optimal sequence of fieldwork tracks considering the constraints of the problem such as the capacity of the baler and the straw yield map of the field. Secondly, finding the optimal location and number of bales to drop in the field. A simulation model is developed to calculate all the non-productive traversal distances by baler and bale retrieval in the field. In a case study, the collected positional and temporal data from the baling process related to a sample field were considered. The output of the simulation model was compared with the conventional method applied by the operators. The results show that application of the proposed method can increase efficiency by 12.9% in comparison with the conventional method with edited data where the random movements (due to re-baling, turns in the middle of the swath, reversing, etc.) were removed from the data set. 
  • 486
  • 22 Jul 2021
Topic Review
Machine Learning Methods for Stock Market Prediction
Stock market prediction models are developed with different goals. The primary focus of stock market prediction has been on forecasting the price of a share for a specific future period. The price of a share is a numerical value, and its variation over time is often treated as a time series in various studies. 
  • 485
  • 21 Jul 2023
Topic Review
Deep Learning Methods in Plant Taxonomy
Plant taxonomy is the scientific study of the classification and naming of various plant species. It is a branch of biology that aims to categorize and organize the diverse variety of plant life on earth. Traditionally, plant taxonomy has been performed using morphological and anatomical characteristics, such as leaf shape, flower structure, and seed and fruit characters. Artificial intelligence (AI), machine learning, and especially deep learning can also play an instrumental role in plant taxonomy by automating the process of categorizing plant species based on the available features.
  • 483
  • 26 Jul 2023
Topic Review
Generative Attentional Networks for Image-to-Image Translation: Progressive U-GAT-IT
Unsupervised image-to-image translation has received considerable attention due to the recent remarkable advancements in generative adversarial networks (GANs). In image-to-image translation, state-of-the-art methods use unpaired image data to learn mappings between the source and target domains. However, despite their promising results, existing approaches often fail in challenging conditions, particularly when images have various target instances and a translation task involves significant transitions in shape and visual artifacts when translating low-level information rather than high-level semantics. To tackle the problem, a novel framework called Progressive Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization (PRO-U-GAT-IT) for the unsupervised image-to-image translation task was proposed. In contrast to existing attention-based models that fail to handle geometric transitions between the source and target domains, the model can translate images requiring extensive and holistic changes in shape. Experimental results show the superiority of the proposed approach compared to the existing state-of-the-art models on different datasets.
  • 483
  • 12 Sep 2023
Topic Review
A Symbol Recognition System for Single-Line Diagrams Developed
In numerous electrical power distribution systems and other engineering contexts, single-line diagrams (SLDs) are frequently used. The importance of digitizing these images is growing. This is primarily because better engineering practices are required in areas such as equipment maintenance, asset management, safety, and others. Processing and analyzing these drawings, however, is a difficult job. With enough annotated training data, deep neural networks perform better in many object detection applications. Based on deep-learning techniques, a dataset can be used to assess the overall quality of a visual system
  • 483
  • 01 Nov 2023
Topic Review
Taxonomy of Machine Learning Methods for Urban Applications
Machine Learning (ML) as an intersection of informatics and statistics is a promising challenge for more evidence-based decisions to fill in the gap of existing technological tools and instruments for spatiotemporal requirements. As ML transcended the conventional techniques of modeling, a huge potential of big data management to address complex city problems is presented at the crossroads of modern urban planning challenges to make up their dynamics. Generally speaking, the ML methods are categorized based on the type of ‘learning’.
  • 482
  • 10 Jan 2023
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