Smart Object Detection and Home Appliances Control System in Smart Cities

Khan, Sulaiman; Nazir, Shah; Khan, Habib Ullah

doi:10.32604/cmc.2021.013878

Cited by 16 publications

(10 citation statements)

References 17 publications

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“…Neural network and machine learning (ML) techniques play a significant role in many industrial applications such as; internet security [27,28], text recognition [29,30], security purposes [31], wireless localization for indoor navigation purposes [32], and many others. Due to its automatic feature extraction capabilities and high recognition rates the neural networks will be applied to the IoT devices, as these devices generates a massive data over the 5G network.…”

Section: Methodsmentioning

confidence: 99%

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

Khan

Ali

et al. 2022

J Netw Syst Manage

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In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.

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Section: Methodsmentioning

confidence: 99%

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

Khan

Ali

et al. 2022

J Netw Syst Manage

Self Cite

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“…Marie-Sainte and Alyani used the Firefly algorithm for the classification of Arabic text [25]. After analysing the existing research work reported (2008 -2020 (a section of 2020 is included)) in the cursive text recognition domain, it was concluded that after 2010 the deep learning-based recognition models gained significant attention of the research community in many research problems like traffic prediction [26], object detection [27], and characters recognition [28], network security [29] as depicted in (Figure 3). This significant attention is due to automatic feature extraction capabilities and achieving high recognition rates for deep learning architectures in many pattern recognition problems especially in cursive text recognition problems.…”

Section: Classification Techniquesmentioning

confidence: 99%

Deep Learning Based Pashto Characters Recognition

Khan

Nazir

2022

PPASA

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In artificial intelligence, text identification and analysis that are based on images play a vital role in the text retrieving process. Automatic text recognition system development is a difficult task in machine learning, but in the case of cursive languages, it poses a big challenge to the research community due to slight changes in character’s shapes and the unavailability of a standard dataset. While this recognition task becomes more challenging in the case of Pashto language due to a large number of characters in its dataset than other similar cursive languages (Persian, Urdu, Arabic) and a slight change in character’s shape. This paper aims to address accept these challenges by developing an optimal optical character recognition (OCR) system to recognise isolated handwritten Pashto characters. The proposed OCR system is developed using multiple long short-term memory (LSTM) based deep learning model. The applicability of the proposed model is validated by using the decision trees (DT) classification tool based on the zoning feature extraction technique and the invariant moment approaches. An overall accuracy rate of 89.03% is calculated for the multiple LSTM-based OCR system while DT-based recognition rate of 72.9% is achieved using zoning feature vector and 74.56% is achieved for invariant moments-based feature map. Applicability of the system is evaluated using different performance metrics of accuracy, f-score, specificity, and varying training and test sets.

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“…An MQTT based object detection and home appliance control integrated AWS cloud, and GSM modem for application control in smart cities applications [16]. The model used deep neural networks for recognition and classification under different environmental conditions [16]. A smart home system using Raspberry Pi for remote monitoring and surveillance is described in [17].…”

Section: Related Workmentioning

confidence: 99%