Nadia Nedjah

Dr. Nadia Nedjah

Rio de Janeiro State University, Rio de Janeiro, Brazil
orcid

Research Focus & Metrics

Academic Career Length

30Years (1997-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 November 2019 in Swarm and Evolutionary Computation
Verified
Journal Article
Closed access
Nadia NedjahNadia Nedjah
Nadia Nedjah
LJLuneque Silva Junior
Luneque Silva Junior

4 January 2022 in IEEE Transactions on Intelligent Transportation Systems
Verified
Journal Article
Open access
He XuHe Xu
He Xu
MGMingtao Guo
Mingtao Guo
Nadia NedjahNadia Nedjah
Nadia Nedjah
JZJindan Zhang
Jindan Zhang
PLPeng Li
Peng Li
Aiming at the shortcomings of the current YOLOv3 model, such as large size, slow response speed, and difficulty in deploying to real devices, this paper reconstructs the target detection model YOLOv3, and proposes a new lightweight target detection network YOLOv3-promote: Firstly, the G-Module combined with the Depth-Wise convolution is used to construct the backbone network of the entire model, and the attention mechanism is introduced and added to perform weighting operations on each channel to get more key features and remove redundant features, thereby strengthening the identification ability of feature network model’s to distinguish target objects among background; Secondly, in order to delete some less important channels to achieve the effect of compressing the model size and improving the calculation speed, the size of the scaling factor gamma in the batch normalization layer is used; Finally, based on NVIDIA’s TensorRT framework model conversion and half-precision acceleration were carried out, and the accelerated model was successfully deployed on the embedded platform Jetson Nano. The performed KITTI experimental results show that the inference speed of our proposed method is about 5 times that of the original model, the parameter volume is reduced to one tenth, the mAP is increased from 86.1% of the original model to 93.1%, and the FPS reaches 25.5fps, realizing the requirements of real-time detection with high precision.

1 January 2015 in International Journal of Bio-Inspired Computation
Verified
Journal Article
Closed access
Nadia NedjahNadia Nedjah
Nadia Nedjah
Luiza de Macedo MourelleLuiza de Macedo Mourelle
Luiza de Macedo Mourelle
Real-world engineering problems often require concurrent optimisation of several design objectives. These objectives are conflicting in most of the cases. Such an optimisation is generally called multi-objective or multi-criterion optimisation. Unlike single-objective optimisations, which target a single best solution, multi-objective optimisations aim at a set of equally good solutions. Classical multi-objective methods have serious drawbacks. The most limiting one is that one needs to apply the optimisation method, as many times as different solution are required. Evolutionary computations are now well-known. They are based on the Darwinian natural selection theory. They have been proven to be much more efficient than the classical methods as they can provide a whole set of good solutions after a single optimisation process. This paper introduces multi-criterion optimisation and states the classical multi-criterion optimisation problem. We review the most successful evolutionary algorithms for multi-objective optimisation. For each of the described methods, we sketch the underlying advantages vs. disadvantages. We give some statistics about applied work, and we survey the works about the most recent applications of the reviewed algorithms to solve real-world problems in science and technological fields.

1 January 2016 in Applied Soft Computing
Verified
Journal Article
Closed access
CWChao Wang
Chao Wang
XLXi Li
Xi Li
XZXuehai Zhou
Xuehai Zhou
AWAili Wang
Aili Wang
Nadia NedjahNadia Nedjah
Nadia Nedjah

1 January 2010 in Integration
Verified
Journal Article
Closed access
LJLech Jóźwiak
Lech Jóźwiak
Nadia NedjahNadia Nedjah
Nadia Nedjah
MFMiguel Figueroa
Miguel Figueroa

8 July 2023 in Information
Verified
Journal Article
Open access
PDF file
Kwok Tai ChuiKwok Tai Chui
Kwok Tai Chui
Brij GuptaBrij Gupta
Brij Gupta
JLJIAQI LIU
JIAQI LIU
VAVarsha Arya
Varsha Arya
Nadia NedjahNadia Nedjah
Nadia Nedjah
PCPriyanka Chaurasia
Priyanka Chaurasia
The smart city vision has driven the rapid development and advancement of interconnected technologies using the Internet of Things (IoT) and cyber-physical systems (CPS). In this paper, various aspects of IoT and CPS in recent years (from 2013 to May 2023) are surveyed. It first begins with industry standards which ensure cost-effective solutions and interoperability. With ever-growing big data, tremendous undiscovered knowledge can be mined to be transformed into useful applications. Machine learning algorithms are taking the lead to achieve various target applications with formulations such as classification, clustering, regression, prediction, and anomaly detection. Notably, attention has shifted from traditional machine learning algorithms to advanced algorithms, including deep learning, transfer learning, and data generation algorithms, to provide more accurate models. In recent years, there has been an increasing need for advanced security techniques and defense strategies to detect and prevent the IoT and CPS from being attacked. Research challenges and future directions are summarized. We hope that more researchers can conduct more studies on the IoT and on CPS.