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  • Deep Learning Based License Plate Number Recognition for Smart Cities
  • Abstract Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.…
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  • Secure Rotation Invariant Face Detection System for Authentication
  • Abstract Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid…
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  • Data Traffic Reduction with Compressed Sensing in an AIoT System
  • Abstract To provide Artificial Intelligence (AI) services such as object detection, Internet of Things (IoT) sensor devices should be able to send a large amount of data such as images and videos. However, this inevitably causes IoT networks to be severely overloaded. In this paper, therefore, we propose a novel oneM2M-compliant Artificial Intelligence of Things (AIoT) system for reducing overall data traffic and offering object detection. It consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing (CS) rate, an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing, and a…
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  • A Novel Database Watermarking Technique Using Blockchain as Trusted Third Party
  • Abstract With widespread use of relational database in various real-life applications, maintaining integrity and providing copyright protection is gaining keen interest of the researchers. For this purpose, watermarking has been used for quite a long time. Watermarking requires the role of trusted third party and a mechanism to extract digital signatures (watermark) to prove the ownership of the data under dispute. This is often inefficient as lots of processing is required. Moreover, certain malicious attacks, like additive attacks, can give rise to a situation when more than one parties can claim the ownership of the same data by inserting and detecting…
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  • An Efficient Energy Aware Routing Mechanism for Wireless Body Area Networks
  • Abstract The accelerated development of wireless network technology has resulted in the emergence of Wireless Body Area Network (WBAN), which is a technology commonly used in the medical field. WBAN consists of tiny sensor nodes that interconnect with each other and set in the human body to collect and transmit the patient data to the physician, to monitor the patients remotely. These nodes typically have limited battery energy that led to a shortage of network lifetime. Therefore, energy efficiency is considered one of the most demanding challenges in routing design for WBAN. Many proposed routing mechanisms in WBAN did not cover…
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  • Two-Stage Production Planning Under Stochastic Demand: Case Study of Fertilizer Manufacturing
  • Abstract Agriculture is a key facilitator of economic prosperity and nourishes the huge global population. To achieve sustainable agriculture, several factors should be considered, such as increasing nutrient and water efficiency and/or improving soil health and quality. Using fertilizer is one of the fastest and easiest ways to improve the quality of nutrients inland and increase the effectiveness of crop yields. Fertilizer supplies most of the necessary nutrients for plants, and it is estimated that at least 30%–50% of crop yields is attributable to commercial fertilizer nutrient inputs. Fertilizer is always a major concern in achieving sustainable and efficient agriculture. Applying…
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  • Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease
  • Abstract Early diagnosis and detection are important tasks in controlling the spread of COVID-19. A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays. However, these methods suffer from biased results and inaccurate detection of the disease. So, the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network (OCOA-DDCNN) for COVID-19 prediction using CT images in IoT environment. The proposed methodology works on the basis of two stages such as pre-processing and prediction. Initially, CT scan images generated from prospective COVID-19 are collected…
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  • Improving Supply Chain Performance Through Supplier Selection and Order Allocation Problem
  • Abstract Suppliers play the vital role of ensuring the continuous supply of goods to the market for businesses. If businesses do not maintain a strong bond with their suppliers, they may not be able to secure a steady supply of goods and products for their customers. As a result of failure to deliver products, the production and business activities of the business can be delayed which leads to the loss of customers. Normally, each trading enterprise will have a variety of commodity supply chains with multiple suppliers. Suppliers play an important role and contribute to the value of the entire supply…
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  • A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19
  • Abstract Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal…
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  • Improved RC6 Block Cipher Based on Data Dependent Rotations
  • Abstract This paper introduces an Improved RC6 (IRC6) cipher for data encryption based on data-dependent rotations. The proposed scheme is designed with the potential of meeting the needs of the Advanced Encryption Standard (AES). Four parameters are used to characterize the proposed scheme. These parameters are the size of the word (w) in bits, the number of rounds (r), the length of the secret key (b) in bytes, and the size of the block (L) in bits. The main feature of IRC6 is the variable number of working registers instead of just four registers as in RC6, resulting in a variable…
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  •   Views:103       Downloads:54        Download PDF
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