A modular CNN-based building detector for remote sensing images

Konstantinidis, Dimitrios; Argyriou, Vasileios; Stathaki, Tania; Grammalidis, N.

doi:10.1016/j.comnet.2019.107034

Cited by 30 publications

(12 citation statements)

References 34 publications

(38 reference statements)

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“…Primarily, the CNN model is considered a useful algorithm in various applications such as object detection, computer vision and pattern recognition due to its ability to extract features from the input data very efficiently [61][62][63][64]. Fig.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Akbar

Sung

2021

IEEE Access

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Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms. We for the first time report an efficient and accurate DL approach with device simulation for gate-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors (MOSFETs) to predict electrical characteristics of device induced by work function fluctuation. By using three different DL models: artificial neural network (ANN), convolutional neural network (CNN), and long short term memory (LSTM), the variability of threshold voltage, on-current and off-current is predicted with respect to different metal-grain number and location of the low and high values of work function. The comparison is established among the ANN, CNN and the LSTM models and results depict that the CNN model outperforms in terms of the root mean squared error and the percentage error rate. The integration of device simulation with DL models exhibits the characteristic estimation of the explored device efficiently; and, the accurate prediction from the DL models can accelerate the process of device simulation. Notably, the DL approach is able to extract crucial electrical characteristics of a complicated device accurately with 2% error in a cost-effective manner computationally.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Akbar

Sung

2021

IEEE Access

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“…Konstantinidis et al 23 proposed a modular convolution neural network (CNN) architecture to detect the three‐dimensional and two‐dimensional performances as well as to enhance the ability of certain constraints such as robustness and discriminations. Vashistha et al 24 classified the social media post into three main classes' namely positive, negative, and neutral posts.…”

Section: Review Of Related Workmentioning

confidence: 99%

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Alqahtani

Pandiaraj²,

Murali³

et al. 2021

Concurrency and Computation

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Presently, large recordings like audio and video are uploaded each day on the public networks particularly Facebook, YouTube, etc. Such postings generate unlimited data via the Internet. Over the next few decades adapting and dealing with data mining to obtain relevant details from social media is considered as a difficult task. To tackle these challenges, a novel text-audio-video consistency-driven multimodal sentiment analysis method is proposed in this paper. This proposed system examines the correlation among the text, audio and video followed by multimodal sentiment analysis.A different set of features are extracted and then the extracted features are chosen optimally by employing a new hybrid grass bee optimization algorithm (HGBEE) thus obtaining a feature set containing an optimal value for better precision and low computational time. Then an utterance level multimodular fusion regarding text, audio, and video features is developed. Finally, the proposed multikernal extreme learning classifier (MKELM) is employed for sentiment classification. Then the proposed system is evaluated by testing with three multimodal datasets in terms of precision, accuracy, recall, and F-measure, respectively. From the simulation, it is clear that the proposed system accomplishes the maximum classification accuracy value of 98.3% with minimum computation time. The implementation of our proposed approach is done under the MATLAB platform. K E Y W O R D Saccuracy and computation time, feature level fusion, grass bee, kernal extreme learning classifier, multimodal sentiment analysis Recently, the public network facilitates gathering information for posting and sharing various types of multimodal features in social media without knowing much detail regarding the network topology and architecture. 1 Individuals are now widely utilizing the internet-based life condition, for example, YouTube, Facebook, Blog, and Microblog so as to express their opinions. 2 Here, sentiment analysis acts as a key for unlocking huge data since

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“…There is a specific function for each neuron of the subsequent layer like it is only responsible for only a part of the input. CNN is now widely used for remote sensing, computer vision, audio, and text processing [10].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

Saadat¹,

Shuaib²

2020

Advances and Applications in Deep Learning

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The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced. In Introduction, there is a brief overview of the research achievements of deep learning. After Introduction, a brief history of deep learning has been also discussed. The history started from a famous scientist called Allen Turing (1951) to 2020. In the start of a chapter after Introduction, there are some commonly used terminologies, which are used in deep learning. The main focus is on the most recent applications, the most commonly used algorithms, modern platforms, and relevant open-source databases or datasets available online. While discussing the most recent applications and platforms of deep learning, their scope in future is also discussed. Future research directions are discussed in applications and platforms. The natural language processing and auto-pilot vehicles were considered the state-of-the-art application, and these applications still need a good portion of further research. Any reader from undergraduate and postgraduate students, data scientist, and researchers would be benefitted from this.

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A modular CNN-based building detector for remote sensing images

Cited by 30 publications

References 34 publications

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

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