Bilad Alrafidain Journal for Engineering Science and Technology (BAJEST)

Nowadays, lightweight cryptography attracts academicians, scientists and researchers to concentrate on its requisite with the increasing usage of low resource devices. In this paper, a new lightweight encryption scheme is proposed using the chaotic map. This encryption scheme is an addition–rotation–XOR block cipher designed for its supremacy, efficacy and speed execution. In this addition–rotation–XOR cipher, the equation for chaotic map is iteratively solved to generate unique random numbers in a speedy manner using the logistic and Chirikov map. Chaotic maps, encryption algorithms, and cryptography are three approaches that are frequently used to safeguard digital data from unauthorized access and use. Chacha20 is a lightweight encryption algorithm, fast and secure and provides a balance between high security and little complexity and execution time the addition, in this work the development of the Chacha20 algorithm is used to provide the required security for data transmission.

Advancements in wireless communication systems have positioned digital communication as a fundamental technology. Among the array of digital modulation methods, Amplitude Shift Keying (ASK) holds a significant position. This study centrally involves the simulation of the ASK modulation scheme using MATLAB software, accentuating practicality in design and execution. Notably, the correlation between simulated results and practical implications stands as a noteworthy achievement. The investigation demonstrates the conversion of a Unipolar return-to-zero square pulse message signal into a sinusoidal form, facilitating seamless transmission to the endpoint.


Three key elements structure this study: the generation of the message signal, the creation of the carrier signal, and their amalgamation to generate the ASK signal. Diverse integrated circuits are employed, encompassing the utilization of a 555 timer to generate the message signal, an LM324 quad operational amplifier chip for carrier signal generation, and a CD4016 multiplexer chip for ASK signal generation. The simulated ASK system closely aligned with the practically designed setup, validating the accuracy of the theoretical model. This convergence can be attributed to meticulous considerations in circuit design and the careful selection of standard values, resulting in a compelling correlation between simulation predictions and real-world implementation.


The graphical representation of the ASK signal, plotted via MATLAB, exhibited a remarkable match between theoretical and practical outputs. Furthermore, the outcomes suggest promising avenues for future exploration. One such direction involves enhancing the system's capacity by implementing an M-ray ASK system, a potential means to augment data transmission rates beyond the binary framework. Additionally, introducing randomness to the binary signal through the integration of a binary number generator emerges as a prospective area for future enhancements

The image-to-text description can be indicated by creating captions for images that comply with human language perception. Nowadays, with the speedy progress of deep learning models, image-to-text description (or image captioning) has an expanding consideration by numerous researchers in diverse artificial intelligence relevant applications. In general, accurately getting the semantic information of the principal objects in the images and captioning the association among them represents a crucial issue in this field. In this paper, an image-to-text description approach based on Inception-ResNetV2-LSTM with an attention technique is proposed for effective textual descriptions of images.


In this proposed approach, Inception-ResNetV2 is exploited to extract essential features, and the integration of LSTM with the attention technique is implemented as a sentence-creation model in such a way that the learning could be concentrated on specific portions within the images, hence enhancing the performance of image-to-text description approach. In terms of the Meteor and BLEU (1-4) measurements, the proposed approach outperformed other state-of-the-art approaches with 0.787 and (0.977, 0.964, 0.886, and 0.759), respectively

In the era of information abundance, the demand for personalized content recommendations has become paramount. Recommendation engines, particularly those employing collaborative filtering, play a pivotal role in delivering tailored suggestions based on user preferences. As technology evolves, the need to enhance the effectiveness of prediction algorithms within these engines becomes increasingly crucial. This research endeavors to contribute to this evolving landscape by delving into collaborative filtering methodologies, identifying challenges, and proposing novel strategies to elevate the accuracy and relevance of predictions in recommendation systems. Through this exploration, we aim to not only refine existing models but also pave the way for more sophisticated and reliable personalized content recommendations.


This research aims to enhance prediction accuracy in recommendation engines utilizing collaborative filtering. Through an in-depth exploration of collaborative filtering techniques, we propose innovative approaches to improve the effectiveness of predictions. Our study addresses key challenges in collaborative filtering models, offering insights into refined algorithms and methodologies. By fine-tuning the collaborative filtering process, we anticipate a substantial boost in the overall performance of recommendation engines, ultimately advancing the field of personalized content suggestion. The simulation is performed using Java language and using two datasets Movie Lens 1M and Movie Lens 100K.The proposed model was evaluated using the Mean Absolute Error, Precision, and Recall.


The proposed model achieved a mean absolute error value ranging between 0.78 and 0.84 using the Movie Lens 100K dataset, and a mean absolute error value ranging between 0.72 and 0.74 using the Movie Lens 1M dataset for different values of the number of user groups. As for precision and recall, the precision of the proposed model ranged between 0.97 and 0.985 using the Movie Lens 100K data set, and a precision value ranging between 0.944 and 0.954 using the Movie Lens 1M data set, also for different values of the number of user groups.


As for the recall results, the proposed model achieved a recall value ranging between 0.755 and 0.85 using the Movie Lens 100K dataset, and a recall value ranging between 0.72 and 0.75 using the Movie Lens 100K dataset, also for different values of the number of user groups. These results were compared with the PMF, HPF, and NMF algorithms, where the proposed model proved its clear superiority over these algorithms. Using this analysis of the matrix allows us to obtain a good prediction accuracy of users' preferences and to find common groups of people with similar preferences

Urban sprawl is one of the main threats in the city, and the present study focuses on the discussion and investigation of urban sprawl. Urban sprawl impacts the environment and agricultural fields in Erbil. The city has witnessed a remarkable rapid increase in population and urbanization at the expense of agricultural and pastoral lands, political, economic, and social factors. All these factors led to prevent the city from becoming green, which resulting in some issues like economic, social, and environmental. The most essential application of (GIS) technology is urban sprawl monitoring. It helps to determine the direction of expansion of urban areas, its pattern, and finding the most suitable sites for further urban development.


(GIS) spatial analysis covered the geo-processing application, which is the classification of maps of Erbil to detect the change detection (LULC) through two periods, 2010 to 2018. It is to discover how much the city has changed and improved over the selected years. The changes and developments that happened over the passing years illustrated in the figures and tables in the results and discussion sections. The purpose of this study is to recommend organized solutions for the unlimited growth of urbanization.


The results showed the Landsat and Satellite Images of Erbil city due years 2010 and 2018 using ArcGIS Pro. Geo-referencing and digitizing the maps of Erbil gave the spatial data surveying as a result for analyzing the changes and comparing them due to the specified years to see the differences that happened to the city. Furthermore, as a result, the vacant land area decreased by 33.20%, whereas the urban area increased by 15.86% from 2010 to 2018