Iraqi Journal of Data Science

The selection of the initial centers of the communities is also significant in iteration-based methods for finding the communities in the networks. This is the reason why, if the initial centers of the communities are not chosen correctly, the errors and the time required for the application of the algorithm in the detection of the communities will be higher. Hence, selecting more significant nodes as starting points of communities can be the appropriate solution. Various techniques can be employed to achieve the selection of more significant nodes. In this thesis, the algorithm under discussion employs density and modularity criteria in the identification of communities in complex networks. This algorithm initially defines the number of nodes or the distinctive members of the community, in which these nodes have higher density levels and all the other nodes in their neighborhood have lower density levels. Next, the local communities are defined as the nodes that are in some way connected to the core nodes. Finally, the final communities are defined with the assistance of the merging algorithm, which is based on increasing modularity. In this algorithm, increasing modularity is used as a criterion for joining local communities together. Modularity is a criterion that indicates how the graph is like a modular or an organized community. When modularity becomes higher, local communities merge to form the final community. This means that it is possible to apply the presented algorithm and to use both density and modularity criteria to detect communities in complex networks. When the core nodes and local communities are first detected and then merged based on the increasing value of modularity, the resultant communities are more accurate. The results of the conducted experiments prove that the method applied in the Karate Club network clustering is equal to 0. 6913 for the NMI criterion and a value of 0. 733 for the accuracy criterion.

Advances in artificial intelligence (AI) are transforming the landscape of mathematical modelling in areas including physics, biology, and chemistry. Research suggests that ChatGPT, Gemini, and other AI tools can change the way researchers use simulation and modeling for complex phenomena by helping to produce models faster with less computational complexity and real-time insights. Here, we introduce a novel framework for building mathematical models of life sciences using AI tools for applications in disease dynamics and ecological systems. The approach integrates AI tools into the process for a hybrid model that combines initial model formulations based on AI-assisted discussions and refinements based on expert validation of AI-generated output. To give an example, if we are interested in modelling disease outbreaks, AI platforms such as ChatGPT or Gemini can instantly build a simple susceptible-infectious-recovered (SIR) model. This also helps with high dataset processing and making parameter suggestions based on real-time data, which in turn helps in the dynamic adaptation of models to changing data (e.g. transmission rates or intervention strategies). Likewise, in ecological modelling, AI tools can aid in the generation of predator-prey models that consider these complex interactions, such as habitat fragmentation or reserved zones and then suggest parameter sensitivities based on observed trends. These abilities make the future of AI-based mathematical modelling especially exciting, as they will further decrease the time that is traditionally spent by researchers on manually defining models and allow them to focus on result interpretation and strategic decision-making. With the rapidly changing advances in AI tools, incorporating some new capabilities and developments in the mathematical modelling procedure may allow for unprecedented improvements in predictive performance, model flexibility and interdisciplinary investigations. Further research and real-world efforts with this approach are needed to determine if AI tools can improve the cost-effectiveness and affordability of mathematical modelling in many fields of science.

Different medical devices for imaging used by centers and clinics produce an increasing number of sequential medical images. ‎Different imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Fluoroscopy ‎produce a set of series for the same patient. Within these images, most of the image parts are fixed against noticeable changes ‎in the remaining part. This consumes non-ignorable storage space. This paper proposes a near-lossless compression ‎technique that considers the fixed image parts to focus on changing parts for a higher compression ratio. In some applications, lossless compression techniques are highly preferable against preferring lossy techniques in some applications. In other applications, near-lossless compression techniques are preferable to lossless and lossy compression techniques, where lossy ones may ‎lose significant details, and the lossless ones produce less compression ratios than near-lossless ones. Previous works dealt with Fluoroscopy images as individual images or using ‎video compression techniques. This work tends to handle the whole series of ‎images as an integrated object. This paper considers subtracting successive ‎images to detect ROI areas producing zero overall values over similar ‎areas and non-zero ones within ROI ones. The double coding technique and near-lossless concept of compression increase the compression ratio. ‎Conducted experiments showed encouraging results benchmarking the other published ‎works in medical image compression.‎

Detecting smoke that precedes fire is a vital matter since it will detect fire incidents in a very early stage since these incidents have very high catastrophic effects on people's lives as well as industrial matters. In order to produce a more reliable detection system, in this article, we dove deeper to examine the effect of colour conversion of the captured footage to enhance the detection percentage using a pre-trained CNN model (ResNet50) that was altered to do a binary classification and was trained on a dataset that consists of smoke and non-smoke scenario images. We examined the system using the footage's original status (RGB) and also tested four colour spaces (HSV, YCbCr, LAB, and grayscale). The testing results showed that HSV had the highest accuracy of 92.1% and the lowest errors during training and testing. Regarding accuracy, the order after HSV was RGB, YCbCr, LAB, and finally, grayscale. Grayscale was the lowest in the testing results, with 85.4%. These results indicate that colour spaces do affect the detection quality and using them would improve the quality of smoke detection systems.