Transfer Learning using CNN for Handwritten Devanagari Character Recognition

This paper presents an analysis of pre-trained models to recognize handwritten Devanagari alphabets using transfer learning for Deep Convolution Neural Network (DCNN). This research implements AlexNet, DenseNet, Vgg, and Inception ConvNet as a fixed feature extractor. We implemented 15 epochs for each of AlexNet, DenseNet 121, DenseNet 201, Vgg 11, Vgg 16, Vgg 19, and Inception V3. Results show that Inception V3 performs better in terms of accuracy achieving 99% accuracy with average epoch time 16.3 minutes while AlexNet performs fastest with 2.2 minutes per epoch and achieving 98\% accuracy

A clinical audit of Pap smear test for screening of premalignant and malignant cervical lesions

Background: Cervical cancer, the second most common type of cancer in females, can be easily screened and prevented by Papanicolaou smear test which is a very simple, effective and inexpensive testing modality. The clinical audit aimed to estimate the rate of routine testing as well as the prevalence of various findings of Pap smear tests done at a tertiary level hospital.Methods: It was an analytical cross-sectional study that utilised results of 100 pap smear tests chosen against a set of exclusion and inclusion criteria out of the total 719 pap smears done at Adesh Medical College and Hospital between the duration of 1 January 2022 to 31 May 2022.Results: Only 4.29% of total patients that attended gynaecology OPD got a Pap smear done. A mere 22% patients of those under study came for routine testing. 93% of these 100 patients were negative for intraepithelial lesions, of which 42% were normal, 35% showed non-neoplastic changes and infection was seen in 16% of patients. 7% showed epithelial cell abnormality and 0% had malignant changes.Conclusions: The acceptance of Pap smear for routine screening continues to be low in the Indian setting. There is a massive need to spread awareness among the general public about the importance of Pap smear Test

Packet-level and IEEE 802.11 MAC frame-level network traffic traces data of the D-Link IoT devices

With the growth of wireless network technology-based devices, identifying the communication behaviour of wireless connectivity enabled devices, e.g. Internet of Things (IoT) devices, is one of the vital aspects, in managing and securing IoT networks. Initially, devices use frames to connect to the access point on the local area network and then, use packets of typical communication protocols through the access point to communicate over the Internet. Toward this goal, network packet and IEEE 802.11 media access control (MAC) frame analysis may assist in managing IoT networks efficiently, and allow investigation of inclusive behaviour of IoT devices. This paper presents network traffic traces data of D-Link IoT devices from packet and frame levels. Data collection experiment has been conducted in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD). All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (D-Link) with different types, such as camera, home-hub, door-window sensor, and smart-plug