Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition include in postal mail sorting, bank check ...processing, form data entry, etc. The main problem lies within the ability on developing an efficient algorithm that can recognize hand written digits, which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning techniques. The main objective of this paper is to ensure the effectiveness and reliability of the approached recognition of handwritten digits. Several machines learning algorithms (i.e. Multilayer Perceptron, Support Vector Machine, Naïve Bayes, Bayes Net, Random Forest, J48, and Random Tree) have been used for the recognition of digits using WEKA. The experimental results showed that the highest accuracy was obtained by Multilayer Perceptron with the value of 90.37%.
ZigBee is a wireless standard based on IEEE 802.15.4 specification for a set of communication protocols. It is intended for remote control and sensor applications. ZigBee is designed to use low power ...digital radio signals for personal area networks which require secure networking. The use of the ZigBee protocol accommodates and facilitates for carrying out secure communications, ciphering frames and controlling devices, protecting establishment and transport of cryptographic keys. This paper presents a security enhancement technique for ZigBee by modifying the MAC layer. This work also serves enhanced security processing in Network and Application layer of ZigBee protocol. The improved security mechanism of Network and Application layer has been developed using Radio Frequency Identification (RFID) and proxy firewall techniques respectively. Riverbed v17.5 is used to simulate and scrutinize different ZigBee layers to enrich security. The result of this study provides reliable and stable ZigBee protocol scheme to end users. Furthermore, the paper shows security adoption in MAC, Network, and Application layers of ineligible nodes, packets, and data blocks. The benefits of the work include end-to-end security with confidentiality, data packet integrity, message and device authentication.
The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images ...provide vital information for clinical diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and nuclear imaging in detecting severe illnesses. However, manual evaluation and storage of these images can be a challenging and time-consuming process. To address this issue, artificial intelligence (AI)-based techniques, particularly deep learning (DL), have become increasingly popular for systematic feature extraction and classification from imaging modalities, thereby aiding doctors in making rapid and accurate diagnoses. In this review study, we will focus on how AI-based approaches, particularly the use of Convolutional Neural Networks (CNN), can assist in disease detection through medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images, and as such, will be the primary area of discussion in this study. Therefore, we have considered CNN as our discussion area in this study to diagnose ailments using medical imaging technology.
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