In the recent digital age, information and communication technologies are rapidly contributing to remodel the media and journalism. Numerous technologies can be utilized by the media industry to capture news or events, taking footage and pictures of a breaking news. Technology and the media are interwoven, and neither can be detached from contemporary society in most nations. Unsurprisingly, technology has affected how and where information is shared. Nowadays, it is impractical to discuss media and the methods in which societies communicate without addressing the rapidity of technology change. Thus, the aerial journalism term has emerged, which refers to the ability of creating and conveying media content in a timely and efficient fashion. This work aims to integrate a drone with AI to empower aerial journalism via training a neural network to obtain an accurate channel using the NN-RBFN approach. The proposed work can enhance aerial media missions including investigative reporting (e.g., humanitarian crises), footage of news events (e.g., man-made and/or natural disasters), and livestreams for short-term, large-scale events (e.g., Olympic Games). In our digital media era, such a smart journalism approach would help to become far more sustainable and an eco-efficient process. Both MATLAB and 3D Remcom Wireless Insite tools have been used to carry out the simulation work. Simulated results indicate that the proposed NN-RBFN managed to obtain an accurate channel propagation model in a 3D scenario with a high accuracy rate reaching 99%. The proposed framework also could offer various media and journalism services (e.g., high data rate, wider coverage footprint) in timely and cost-effective manners in both normal scenarios or even in hard-to-reach zones and/or short-term, large-scale events.

The days of storing data manually are behind us. We are opting for the online form of data storage and transfer. The new era of data digitization comes with its own perks and detriments. Cybersecurity is still a crucial concern today. As more data transfer occurs through an online medium, the risks of a breach and cyberattacks are inevitable. The whole foundation of e-commerce is based on the online transfer of goods and transactions without the need to travel. Transferring transactional data and transactions in e-commerce are prone to cyber threats. Our research’s major objective is to develop a system that protects against such mishaps, especially during the transfer of transactional data, and also implement an automated system that ensures these transactions occur without any errors. To implement this, we are taking advantage of new emerging technologies called blockchain and smart contract. Blockchain allows a decentralized, immutable digital ledger to safely store and transfer data across the network. Blockchain technology is used in e-commerce to transfer transactions in a safe, secure, and faster way. Blockchain enables a peer-to-peer transaction system and data encryption that enables the safe transfer of transactional data. Blockchain is used to transfer transactional data. A smart contract is a special program that enables, verifies, and enforces the terms of a contract digitally. It provides transactional security as the contact is in place. The blockchain, coupled with smart contracts, will revolutionize the future of e-commerce. We have combined blockchain technology to ensure data security and user privacy with smart contracts to ensure that the protocol for the transaction is maintained. The results are presented by building and implementing the proposed system that provides the solution for transactional data privacy.

As the most prevalent and deadly malignancy, brain tumors have a dismal survival rate when they are at their most hazardous. Using mostly traditional medical image processing methods, segmenting and classifying brain malignant tumors is a challenging and time-consuming task. Indeed, medical research reveals that categorization performed manually with the help of a person might result in inaccurate prediction and diagnosis. This is mostly due to the fact that malignancies and normal tissues are so dissimilar and comparable. The brain, lung, liver, breast, and prostate are all studied using imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. This research makes significant use of CT and X-ray imaging to identify brain malignant tumors. The purpose of this article is to examine the use of convolutional neural networks (CNNs) in image-based diagnosis of brain cancers. It expedites and improves the treatment’s reliability. As a result of the abundance of research on this issue, the provided model focuses on increasing accuracy via the use of a transfer learning method. This experiment was conducted using Python and Google Colab. Deep features were extracted using VGG19 and MobileNetV2, two pretrained deep CNN models. The classification accuracy is used to evaluate this work’s performance. This research achieved a 97 percent accuracy rate by MobileNetV2 and a 91 percent accuracy rate by the VGG19 algorithm. This allows us to find malignancies before they have a negative effect on our bodies, like paralysis.

This paper describes the results of an in-depth interview study targeting three different stakeholders - privacy professionals, doctor's office administrators, and information technology designers - in an aim to understand the current practices, challenges and knowledge regarding compliance with privacy legislation in the management of patients' Personal Health Information (PHI). We apply the grounded theory as an analytical approach to form privacy-preserving guidelines. Further, we derive themes related to PHI access, breach conditions, Electronic Medical Records (EMRs), and privacy legislation Compliance. Based on our results, we propose privacy-preserving design guidelines to assist IT privacy designers in showing compliance with privacy legislation in the design process of online patient portals in Canada.