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Topic Review
Abstractive vs. Extractive Summarization
Due to the huge and continuously growing size of the textual corpora existing on the Internet, important information may go unnoticed or become lost. At the same time, the task of summarizing these resources by human experts is tedious and time consuming. This necessitates the automation of the task. Natural language processing (NLP) is a multidisciplinary research field, merging aspects and approaches from computer science, artificial intelligence and linguistics; it deals with the development of processes that semantically and efficiently analyze vast amounts of textual data. Text summarization (TS) is a fundamental NLP subtask, which has been defined as the process of the automatic creation of a concise and fluent summary that captures the main ideas and topics of one or multiple documents.
  • 1.5K
  • 07 Jul 2023
Topic Review
Intrusion Detection and Datasets
With the significant increase in cyber-attacks and attempts to gain unauthorised access to systems and information, Network Intrusion-Detection Systems (NIDSs) have become essential detection tools. Anomaly-based systems use machine learning techniques to distinguish between normal and anomalous traffic. They do this by using training datasets that have been previously gathered and labelled, allowing them to learn to detect anomalies in future data. However, such datasets can be accidentally or deliberately contaminated, compromising the performance of NIDS.
  • 1.5K
  • 30 Jan 2024
Topic Review
Data Fusion in Agriculture
The term “data fusion” can be defined as “the process of combining data from multiple sources to produce more accurate, consistent, and concise information than that provided by any individual data source”. Other stricter definitions do exist to better fit narrower contexts. This type of approach has been applied to agricultural problems since the first half of the 1990s, and there has been an increase in the use of this approach. Arguably, the main challenge involved in the use of data fusion techniques involves finding the best approach to fully explore the synergy and complementarities that potentially exist between different types of data and data sources.
  • 1.5K
  • 07 Apr 2022
Topic Review
Application of Triboelectric Nanogenerator in Fluid Dynamics Sensing
The triboelectric nanogenerator (TENG) developed by Z. L. Wang’s team to harvest random mechanical energy is a promising new energy source for distributed sensing systems in the new era of the internet of things (IoT) and artificial intelligence (AI) for a smart world. In industry and academia, fluid dynamics sensing for liquid and air is urgently needed but lacking. In particular, local fluid sensing is difficult and limited to traditional sensors. Fortunately, with advantages for ordinary TENGs and TENGs as fluid dynamics sensors, fluid dynamics sensing can be better realized.
  • 1.5K
  • 30 Sep 2022
Topic Review
Hidden Surface Determination
In 3D computer graphics, shown surface determination (also known as hidden surface removal (HSR), occlusion culling (OC) or visible surface determination (VSD)) is the process used to determine which surfaces and parts of surfaces are not visible from a certain viewpoint. A hidden surface determination algorithm is a solution to the visibility problem, which was one of the first major problems in the field of 3D computer graphics. The process of hidden surface determination is sometimes called hiding, and such an algorithm is sometimes called a hider. The analogue for line rendering is hidden line removal. Hidden surface determination is necessary to render an image correctly, so that one may not view features hidden behind the model itself, allowing only the naturally viewable portion of the graphic to be visible.
  • 1.5K
  • 26 Oct 2022
Topic Review
Stereo Matching Algorithm
With the advancement of artificial intelligence technology and computer hardware, the stereo matching algorithm has been widely researched and applied in the field of image processing. In scenarios such as robot navigation and autonomous driving, stereo matching algorithms are used to assist robots in acquiring depth information about the surrounding environment, thereby improving the robot’s ability for autonomous navigation during self-driving.
  • 1.5K
  • 18 Dec 2023
Topic Review
Efficient Structural Design with ANNs
Artificial Neural Networks (ANNs) are showing their potential as structural design tools. ANNs are applied to design a dry precast concrete connection. They can be easily and effectively adapted to different connection parameters, being possible to use them in both precast or cast in situ concrete connection design.
  • 1.5K
  • 27 Oct 2020
Topic Review
Control-Based 4D Printing
Building on the recent progress of four-dimensional (4D) printing to produce dynamic structures, this study aimed to bring this technology to the next level by introducing control-based 4D printing to develop adaptive 4D-printed systems with highly versatile multi-disciplinary applications, including medicine, in the form of assisted soft robots, smart textiles as wearable electronics and other industries such as agriculture and microfluidics. This study introduced and analyzed adaptive 4D-printed systems with an advanced manufacturing approach for developing stimuli-responsive constructs that organically adapted to environmental dynamic situations and uncertainties as nature does. The adaptive 4D-printed systems incorporated synergic integration of three-dimensional (3D)-printed sensors into 4D-printing and control units, which could be assembled and programmed to transform their shapes based on the assigned tasks and environmental stimuli. This paper demonstrates the adaptivity of these systems via a combination of proprioceptive sensory feedback, modeling and controllers, as well as the challenges and future opportunities they present.
  • 1.5K
  • 28 Oct 2020
Topic Review
Artificial Intelligence in CORONA Virus
AI is the most demanding field of the world. It is playing a vital role in many aspects like prediction of any pandemic or making any vaccine faster.
  • 1.5K
  • 11 Feb 2021
Topic Review
Artificial Intelligence to Solve the IoT Security Challenges
The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need artificial intelligence (AI)-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system.
  • 1.5K
  • 16 Jun 2023
Topic Review
Image Segmentation Techniques
Image segmentation, which has become a research hotspot in the field of image processing and computer vision, refers to the process of dividing an image into meaningful and non-overlapping regions, and it is an essential step in natural scene understanding. 
  • 1.5K
  • 06 May 2023
Topic Review
Feature Extraction Methods in Autonomous Driving
The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Herein, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. Representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. 
  • 1.5K
  • 28 Feb 2024
Topic Review
Deep Learning Algorithms in Agriculture
The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. 
  • 1.5K
  • 18 Mar 2022
Topic Review
Walking Recognition in Mobile Devices
Presently, smartphones are used more and more for purposes that have nothing to do with phone calls or simple data transfers. One example is the recognition of human activity, which is relevant information for many applications in the domains of medical diagnosis, elderly assistance, indoor localization, and navigation. The information captured by the inertial sensors of the phone (accelerometer, gyroscope, and magnetometer) can be analyzed to determine the activity performed by the person who is carrying the device, in particular in the activity of walking. Nevertheless, the development of a standalone application able to detect the walking activity starting only from the data provided by these inertial sensors is a complex task. This complexity lies in the hardware disparity, noise on data, and mostly the many movements that the smartphone can experience and which have nothing to do with the physical displacement of the owner. In this work, we explore and compare several approaches for identifying the walking activity. We categorize them into two main groups: the first one uses features extracted from the inertial data, whereas the second one analyzes the characteristic shape of the time series made up of the sensors readings. Due to the lack of public datasets of inertial data from smartphones for the recognition of human activity under no constraints, we collected data from 77 different people who were not connected to this research. Using this dataset, which we published online, we performed an extensive experimental validation and comparison of our proposals.
  • 1.5K
  • 01 Nov 2020
Topic Review
Network Traffic Analysis
Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different needs and different scenarios. Many approaches have been followed, both before and after the introduction of machine and deep learning algorithms as intelligence computation. The network traffic analysis is the process of incarcerating traffic of a network and observing it deeply to predict what the manifestation in traffic of the network is. To enhance the quality of service (QoS) of a network, it is important to estimate the network traffic and analyze its accuracy and precision, as well as the false positive and negative rates, with suitable algorithms. 
  • 1.5K
  • 14 Jul 2022
Topic Review
MobDet3
MobDet3, a novel object detection network based on the YOLOv5 framework. By utilizing Attentive Feature Aggregation, MobDet3 provides an improved lightweight solution for object detection in autonomous driving applications. The network is designed to be efficient and effective, even on resource-limited embedded systems such as the NXP BlueBox 2.0.
  • 1.5K
  • 30 Aug 2023
Topic Review
Self-Supervised Learning (SSL) in Deep Learning Contexts
Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. 
  • 1.5K
  • 18 Mar 2024
Topic Review
Hashtag Recommendation
Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking.
  • 1.5K
  • 25 May 2021
Topic Review
Voting-Based Leader-Election Scheme in Lead-Follow UAV Swarm
The recent advances in unmanned aerial vehicles (UAVs) enormously improve their utility and expand their application scope. The UAV and swarm implementation further prevail in Smart City practices with the aid of edge computing and urban Internet of Things. The lead–follow formation in UAV swarm is an important organization means and has been adopted in diverse exercises, for its efficiency and ease of control. The reliability of centralization makes the entire swarm system in risk of collapse and instability, if a fatal fault incident happens in the leader. Researchers propose a voting-based leader election scheme inspired by the Raft method in distributed computation consensus to build a mechanism helping the distributed swarm recover from possible failures.
  • 1.5K
  • 15 Aug 2022
Topic Review Peer Reviewed
Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities
Environment changes, water scarcity, soil depletion, and urbanisation are making it harder to produce food using traditional methods in various regions and countries. Aquaponics is emerging as a sustainable food production system that produces fish and plants in a closed-loop system. Aquaponics is not dependent on soil or external environmental factors. It uses fish waste to fertilise plants and can save up to 90–95% water. Aquaponics is an innovative system for growing food and is expected to be very promising, but it has its challenges. It is a complex ecosystem that requires multidisciplinary knowledge, proper monitoring of all crucial parameters, and high maintenance and initial investment costs to build the system. Artificial intelligence (AI) and the Internet of Things (IoT) are key technologies that can overcome these challenges. Numerous recent studies focus on the use of AI and the IoT to automate the process, improve efficiency and reliability, provide better management, and reduce operating costs. However, these studies often focus on limited aspects of the system, each considering different domains and parameters of the aquaponics system. This paper aims to consolidate the existing work, identify the state-of-the-art use of the IoT and AI, explore the key parameters affecting growth, analyse the sensing and communication technologies employed, highlight the research gaps in this field, and suggest future research directions. Based on the reviewed research, energy efficiency and economic viability were found to be a major bottleneck of current systems. Moreover, inconsistencies in sensor selection, lack of publicly available data, and the reproducibility of existing work were common issues among the studies.
  • 1.5K
  • 19 Feb 2024
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