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Topic Review
Knowledge Reasoning
A knowledge graph (KG) organizes knowledge as a set of interlinked triples, and a triple ((head entity, relation, tail entity), simply represented as (h, r, t)) indicates the fact that two entities have a certain relation. The availability of formalized and rich structured knowledge has emerged as a valuable resource in facilitating various downstream tasks, such as question answering and recommender systems. Although KGs such as DBpedia, Freebase, and NELL contain large amounts of entities, relations, and triples, they are far from complete, which is an urgent issue for their broad application. To address this, researchers have introduced the concept of knowledge graph completion (KGC), which has garnered increasing interest. It utilizes knowledge reasoning techniques to automatically discover new facts based on existing ones in a KG.
  • 820
  • 12 Oct 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.
  • 819
  • 22 May 2023
Topic Review
Strawberry Ripeness Classification
Image analysis-based artificial intelligence (AI) models leveraging convolutional neural networks (CNN) take a significant role in evaluating the ripeness of strawberry, contributing to the maximization of productivity. However, the convolution, which constitutes the majority of the CNN models, imposes significant computational burdens. Additionally, the dense operations in the fully connected (FC) layer necessitate a vast number of parameters and entail extensive external memory access. Therefore, reducing the computational burden of convolution operations and alleviating memory overhead is essential in embedded environment.
  • 819
  • 07 Feb 2024
Topic Review
Healthcare Sustainability: Hospitalization Rate Forecasting
Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. Herein, researchers demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. A transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) is adapted to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. Researchers combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. Extensive experiments are conducted to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. The results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability.
  • 818
  • 24 Nov 2023
Topic Review
Intrusion Detection System
The increased adoption of cloud computing resources produces major loopholes in cloud computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital defenses against threats and attacks to cloud computing. IDSs encounter two challenges, namely, low accuracy and a high false alarm rate. Due to these challenges, additional efforts are required by network experts to respond to abnormal traffic alerts. To improve IDS efficiency in detecting abnormal network traffic, an IDS using a recurrent neural network based on gated recurrent units (GRUs) was developed and long short-term memory (LSTM) through a computing unit to form Cu-LSTMGRU was improved. 
  • 817
  • 30 Sep 2022
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.
  • 812
  • 13 Sep 2022
Topic Review
Routing Services in Smart Cities
The vehicle routing problem (VRP) is a complex optimization problem, in which there exists a set of clients at various locations, each one with a shipment need, and a fleet of vehicles, departing from the central depot that shall optimally satisfy the needs of the clients. The aim of a typical VRP is to find out the optimal route to minimize the total costs. Furthermore, various factors affecting route planning, such as vehicle capacity, fuel consumption, traffic congestion, etc., have to be considered to accomplish the minimization of the total route costs.
  • 812
  • 31 Jan 2023
Topic Review
Deep Learning-Based Depression Detection from Social Media
Depression is a prevalent mental health condition that affects a substantial number of individuals worldwide [1]. It is characterized by persistent feelings of sadness, loss of interest, and impaired functioning, leading to a significant decline in overall well-being and quality of life.
  • 808
  • 17 Nov 2023
Topic Review
Gallbladder Diseases
The gallbladder (GB) is a tiny pouch and a hollow organ located beneath the liver. Its primary role is to temporarily store bile. Bile is a fluid formed by the liver, which is used to aid digestion.  
  • 807
  • 25 May 2023
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. 
  • 805
  • 22 Jul 2021
Topic Review
AI and Neural Network Algorithms
Al increases the potential of Micro-Electro-Mechanical System biosensors and opens up new opportunities for automation, consumer electronics, industrial manufacturing, defense, medical equipment, etc. Micro-Electro-Mechanical System microcantilever biosensors are currently making their way into the daily lives and playing a significant role in the advancement of social technology. Micro-Electro-Mechanical System biosensors with microcantilever structures have a num- ber of benefits over conventional biosensors, including small size, high sensitivity, mass production, simple arraying, integration, etc. These advantages have made them one of the development avenues for high-sensitivity sensors. The next generation of sensors will exhibit an intelligent development trajectory and aid people in interacting with other objects in a variety of scenario applications as a result of the active development of artificial intelligence (AI) and neural networks. A neural algorithm application in Micro-Electro-Mechanical System microcantilever biosensors is anticipated through the associated application of the principal com-ponent analysis approach. Researchers investigation has more scientific study value, because there are currently no favorable reports on the market regarding the use of AI with Micro-Electro-Mechanical System microcantilever sensors.
  • 805
  • 13 Sep 2022
Topic Review
Small Object Detection and Traffic Signs Detection
The detection of traffic signs is easily affected by changes in the weather, partial occlusion, and light intensity, which increases the number of potential safety hazards in practical applications of autonomous driving.
  • 805
  • 14 Jun 2023
Topic Review
Intelligence Edge Computing
Intelligence Edge Computing (IEC) is the key enabler of emerging 5G technologies networks and beyond. IEC is considered to be a promising backbone of future services and wireless communication systems in 5G integration. In addition, IEC enables various use cases and applications, including autonomous vehicles, augmented and virtual reality, big data analytic, and other customer-oriented services. 
  • 803
  • 23 Jun 2021
Topic Review
DetectFormer
Object detection plays a vital role in autonomous driving systems, and the accurate detection of surrounding objects can ensure the safe driving of vehicles. A category-assisted transformer object detector called DetectFormer for autonomous driving. The proposed object detector can achieve better accuracy compared with the baseline. Specifically, ClassDecoder is assisted by proposal categories and global information from the Global Extract Encoder (GEE) to improve the category sensitivity and detection performance. This fits the distribution of object categories in specific scene backgrounds and the connection between objects and the image context. Data augmentation is used to improve robustness and attention mechanism added in backbone network to extract channel-wise spatial features and direction information.
  • 803
  • 15 Jul 2022
Topic Review
Multilevel Distribution Propagation Network
A two-stage framework based on the distribution propagation graph neural network (DPGN) called the multilevel distribution propagation network (MDPN). An instance-segmentation-based object localization (ISOL) module and a graph-based multilevel distribution propagation (GMDP) module are both included in the MDPN.
  • 803
  • 05 Jul 2023
Topic Review
Anomaly Detection in Video Surveillance
The escalating use of security cameras has resulted in a surge in images requiring analysis, a task hindered by the inefficiency and error-prone nature of manual monitoring. Research on anomaly detection in CCTV videos is being actively conducted using various techniques.
  • 803
  • 22 Jan 2024
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.
  • 800
  • 14 Nov 2022
Topic Review
FCAN–XGBoost
Emotion recognition has broad application prospects in fields such as artificial intelligence (AI), intelligent healthcare, remote education, and virtual reality (VR) games. Accurately recognizing human emotions is one of the most urgent issues in the brain–computer interface. FCAN XGBoost is a electroencephalogram (EEG) based emotion recognition model that can quickly and accurately recognize four types of emotions in EEG.
  • 800
  • 05 Sep 2023
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.
  • 799
  • 10 Sep 2021
Topic Review
Wind–Solar Hybrid System Modeling
Wind–solar hybrid systems combine solar photovoltaic cells and wind turbines to produce power from both solar and wind energy.
  • 799
  • 31 May 2023
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