No abstract

No abstract

No abstract

The Internet of Things (IoT) connects physical objects such as baby monitors, cars, tablet computers, fridges through the internet and they are equipped with capabilities to communicate with each other. They exchange information about themselves and their surroundings and provide improved efficiencies for the benefit of users. The future Internet is an emerging world of highly networked smart items that will be able to independently communicate with each other with little or no human intervention as the world moves into the era of smart phones, smart homes, smart offices, smart vehicles, smart classrooms, smart factories to smart everything. As the Internet of Things (IoT) continues to grow security including new attack vectors, new vulnerabilities, and perhaps most concerning of all, a vastly increased ability to use remote access to cause physical destruction becomes a major concern. In this paper we seek to explain what the Internet of Things is, its future impact, challenges and how Digital Forensics Technology can be used to get evidence to prosecute offenders in the law court.

Data are crucial to the growth of e-commerce in today's world of highly demanding hyper-personalized consumer experiences, which are collected using advanced web scraping technologies. However, core data extraction engines fail because they cannot adapt to the dynamic changes in website content. This study investigates an intelligent and adaptive web data extraction system with convolutional and Long Short-Term Memory (LSTM) networks to enable automated web page detection using the You only look once (Yolo) algorithm and Tesseract LSTM to extract product details, which are detected as images from web pages. This state-of-the-art system does not need a core data extraction engine, and thus can adapt to dynamic changes in website layout. Experiments conducted on real-world retail cases demonstrate an image detection (precision) and character extraction accuracy (precision) of 97% and 99%, respectively. In addition, a mean average precision of 74%, with an input dataset of 45 objects or images, is obtained.