Iraqi Journal of Intelligent Computing and Informatics

It is deemed that human brain is highly flexible which provides the ground for learning so many new things, while it is also the case that when it comes to reading from different mediums, that flexibility can be a problem. That is, the brain mechanism during paper reading and e-reading is significantly different. There are numerous studies from cognitive sciences, neuroscience, education, etc. which have studied the topic from various perspectives, while there is a lack of literature which systematically reviewed the primary studies to gain insightinto comprehension change across media platforms, a comparison between e-reading and paper reading in terms of comprehensionand whether e-reading substitutes paper reading.In the present paper, the main objective is the comparison between e-reading and paper reading in terms of comprehension. For this purpose, systematic literature review method was adopted and three major indexes, namely, Scopus, WoS and IEEE Xplore were selected as the source of corpus. Totally,27 papers were found, after applying inclusion and exclusion criteria the number of the papers was decreased. The results showed that there are several factors effective on reading comprehension,such as Story elements, Characters analysis, Main idea and details, Problems and solution Eyestrain, Headaches, Distraction, Mentally mapping, Availability, Portability, Eco-friendly, Font size, Tools, Reading program, Convenience, Compatibility, Media richness, Licensing issues, Graphic display capabilities, Reading task and Reading techniques Eye strain, Time constraint Gender, Age, Pleasure, Motivation, Challenge, Involvement, Curiosity, Competition, Recognition, Social aspects, Compliance, Grades, Personal relationship, Readership, Reading experience, Context area of school, Context constraints, Reader comfort and Mental values. Moreover, while some studies point out that there is no significant difference between comprehension of reading across media, other studies showed that there are differences among study subjects in terms of comprehension, if age, gender, experience, etc. are taken into account.

Enabling mobile robots to navigate unpredictable and ever-changing environments while avoiding static and moving obstacles is a critical challenge for dynamic path planning. Advanced sensors have simplified the robot’s work by enabling it to navigate autonomously without human intervention. Optimal path planning in dynamic environments requires sophisticated algorithms consideringessentialfactors such as time, energy, and distance. These problems can be solved using deep neural networks (DNNs) and reinforcement learning (RL). An artificial intelligence (AI) agent learns from reward signals usingtrial and error to identify humans' optimal behavioral strategies. This review paper explores how deep reinforcement learning (DRL) techniques can be combined with other path-planningtechniques to enhance the efficiency of these methods and solutions to address the problem of efficient navigation in unfamiliar environments with obstacles, with a focus on processessuch as policy gradient, model-free and model-basedlearning, and the actor-criticapproach. We comprehensively examinethe key concepts, challenges, and recent developments in DRL, focusingon its application to revolutionize robotic navigation in complex scenarios.

Over recent decades, image data violations have created great difficulties, and image encryption has been an appealing field of research. Most people acknowledge this as a practical method of safe transmission. However, many studies have been conducted in various ways, and new and helpful algorithms have been proposed to improve the encryption systems of safe images. To increase this security, blockchain technology used by this technology eliminates or reduces the role of the third party by enabling two parties to transact with each other means that the Blockchain system can function in a peer-to-peer manner without a reliable third party required to ensure trust. This technology was first adopted to create digital cryptocurrencies, like Bitcoin and Ethereum. This technology has a range of characteristics, including transparency, anonymity, decentralization, immutability, and minting. With the advancement of Internet and communications, the exchange of sensitive data across multiple parties has become very easy. However, the problems associated with such communications are lack ofintegrity, confidentiality, and authenticity etc, caused by insecure channels. This paper aims to review recent key studies on secure image encryption based on blockchain in addition, explore how these two technologies can be combined to solve the problem of transport between unsafe channels where the blockchain network ensures that any tampering in the image will lead to the detection as any transaction in the blockchain tampers theassociated hash. In addition, we comprehensively examine the key concepts and the most important metrics used to evaluate the model's performance and discussion of future work.