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Topic Review
Distributed Deep Learning: From Single-Node to Multi-Node Architecture
During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). Local parallelism is considered quite important in the design of a time-performing multi-node architecture because DDL depends on the time required by all the nodes. 
  • 1.5K
  • 08 Jun 2022
Topic Review
ATOM Program System
The ATOM computer system is designed to study the structure of atoms and the physical processes occurring with their participation. 
  • 643
  • 07 Jun 2022
Topic Review
Radar Object Detection
With the improvement of automotive radar resolution, radar target classification has become a hot research topic. Deep radar detection can be classified into point-cloud-based and pre-CFAR-based. Radar point cloud and pre-CFAR data are similar to the LiDAR point cloud and visual image, respectively. Accordingly, the architectures for LiDAR and vision tasks can be adapted for radar detection. 
  • 1.9K
  • 07 Jun 2022
Topic Review
Self-Sovereign Identity (SSI)
Self-sovereign identity (SSI), a new concept, is becoming more popular as a secure and reliable identity solution for users based on identity principles. SSI provides users with a way to control their personal information and consent for it to be used in various ways. In addition, the user’s identity details are stored in a decentralized manner, which helps to overcome the problems with digital identity solutions.
  • 1.8K
  • 07 Jun 2022
Topic Review
Energy Efficient Clustering Protocol for FANETS
FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. Researchers employ a moth flame optimization algorithm for network building and node deployment. Then, researchers employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster’s lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against the proposed EECP-MFO. Several metrics are used to gauge the efficiency of the proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption.
  • 602
  • 06 Jun 2022
Topic Review
Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
The domain of information-centric networking (ICN) is expanding as more devices are becoming a part of connected technologies. New methods for serving content from a producer to a consumer are being explored, and Named Data Networking (NDN) is one of them. The NDN protocol routes the content from a producer to a consumer in a network using content names, instead of IP addresses. This facility, combined with content caching, efficiently serves content for very large networks consisting of a hybrid and ad hoc topology with both wired and wireless media.
  • 516
  • 06 Jun 2022
Topic Review
Text Mining and Software Metrics in Vulnerability Prediction
Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques and software metrics for predicting the existence of vulnerabilities in software components. However, limited attention has been given on the comparison and the combination of text mining- based and software metrics- based vulnerability prediction models.
  • 728
  • 02 Jun 2022
Topic Review
Swarm Robotics
Swarm robotics is a dynamic research field that integrates two important concepts: Swarm Intelligence (SI) and Multi-Robotics System (MRS).
  • 731
  • 02 Jun 2022
Topic Review
Sign Language Recognition Models
A hybrid system for sign language is a combination of both vision-sensor-based and combination of different sensors based models. 
  • 2.4K
  • 01 Jun 2022
Topic Review
Deep Learning-Based Diagnosis of Alzheimer’s Disease
Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. 
  • 606
  • 01 Jun 2022
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