Today, cybersecurity is considered one of the most noteworthy topics that are circulated frequently among companies in order to protect their data from hacking operations. The emergence of cyberspace ...contributed to the growth of electronic systems. It is a virtual digital space through which interconnection is established between computers and smartphones connected within the Internet of Things environment. This space is critical in building a safe digital environment free of threats and cybercrime. It is only possible to make a digital environment with the presence of cyberspace, which contains modern technologies that make this environment safe and far from unauthorized individuals. Cybersecurity has a wide range of challenges and obstacles in performance, and it is difficult for companies to face them. In this report, the most significant practices, sound, and good strategies will be studied to stop cybercrime and make a digital environment that guarantees data transfers between electronic devices safely and without the presence of malicious software. This report concluded that the procedures provided by cybersecurity are required and must be taken care of and developed.
Metaverse: open possibilities Mohammad Aljanabi; Sahar Yousif Mohammed
Iraqi Journal for Computer Science and Mathematics,
8/2023, Volume:
4, Issue:
3
Journal Article
Peer reviewed
Open access
To create a completely immersive, highly spatiotemporal, and self-sustaining virtual shared place for humans to play, work, and interact, the Metaverse is an emerging paradigm of the next-generation ...Internet. Metaverse is transitioning from science fiction to a near-future reality thanks to recent developments in new technologies like extended reality, artificial intelligence, and blockchain.In this study, we investigate the Metaverse, a shared, fully immersive virtual environment that could serve as a model for the Internet’s future generation. Safety, education, games, entertainment, social effects, the economy, ethics, and future issues are just few of the many areas covered. The Metaverse’s innovative tools for communication and collaboration may have a profound impact on people’s daily lives and the way society operates as a whole. However, there are obstacles that must be addressed before it can be implemented. If the Metaverse is accepted, a far different digital future is possible.
Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. This condition is attributed to the increasing number of audit data ...features and the decreasing performance of human-based smart intrusion detection systems regarding classification accuracy, false alarm rate, and classification time. Feature selection and classifier parameter tuning are important factors that affect the performance of any intrusion detection system. In this paper, an improved intrusion detection algorithm for multiclass classification was presented and discussed in detail. The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). The selection of the least number of features without causing an effect on the result accuracy in FSS is a multiobjective optimisation problem. This work proposes ITLBO as an FSS mechanism, and its algorithm-specific, parameterless concept (no parameter tuning is required during optimisation) was explored. IPJAYA in this study was used to update the C and gamma parameters of the support vector machine (SVM). Several experiments were performed on the prominent intrusion ML dataset, where significant enhancements were observed with the suggested ITLBO-IPJAYA-SVM algorithm compared with the classical TLBO and JAYA algorithms.
This work presents a novel method for securing federated learning in healthcare applications, focusing on skin cancer classification. The suggested solution detects and mitigates data poisoning ...attacks using deep learning and CNN architecture, specifically VGG16. In a federated learning architecture with ten healthcare institutions, the approach ensures collaborative model training while protecting sensitive medical data. Data is meticulously prepared and preprocessed using the Skin Cancer MNIST: HAM10000 dataset. The federated learning approach uses VGG16's powerful feature extraction to classify skin cancer. A robust strategy for spotting data poisoning threats in federated learning is presented in the study. Outlier detection techniques and strict criteria flag andevaluate problematic model modifications. Performance evaluation proves the model's accuracy, privacy, and datapoisoning resilience. This research presents federated learning-based skin cancer categorization for healthcareapplications that is secure and accurate. The suggested approach improves healthcare diagnostics and emphasizesdata security and privacy in federated learning settings by tackling data poisoning attacks.
The present article focuses on the analytical approach using fractional orders and its applicationin the dynamics of physical processes. Fractional order models align better with experimental data ...compared tonon-fractional ones. This study primarily focuses on employing the new approximate analytical method to solveshallow water models with fractional orders. Numerical examples within the Caputo fractional derivative showcasethe method’s application. Results for both integer and fractional orders are graphically depicted, demonstrating thefractional solutions’ closeness to actual data. Analysis of 3D and 2D fractional order graphs reveals convergencetoward integer order graphs as fractional derivatives approach non-fractional ones. This method shows promise fordirect application in solving targeted problems and can be easily adapted for other fractional nature problems.
Heart disease is the leading cause of death in developed countries, as it causes many deaths annually. Despite the availability of effective treatments, heart disease remains a significant challenge ...to public health, so early detection is essential in enhancing patient outcomes and reducing mortality. Artificial intelligence seeks to help physicians make the right decisions about a patient's health condition. In this regard, the authors decided to utilize machine learning techniques (k-nearest neighbor, decision tree, linear regression, support vector machine, naïve bayes, multilayer perceptron, random forest) to contribute to the classification of the heart disease dataset, where it is determined whether a person is suffering or not. After that, the execution of all techniques will be measured, and the accuracy of each technique will be compared to determine the most suitable performer. The public dataset is organized from the UC Irvine machine learning repository and have significantly different characteristics. The dataset will be divided such that 80% of the data is designated for training and 20% is designated for testing. This article concluded that the adequate performance is for the multilayer perceptron technique, as it gained an accuracy of more than 88%, while the poor performance is for the decision tree technique, as it gained an accuracy of more than 79%.
Machine learning (ML) and deep learning (DL) techniques have demonstrated significant potential in the development of effective intrusion detection systems. This study presents a systematic review of ...the utilization of ML, DL, optimization algorithms, and datasets in intrusion detection research from 2018 to 2023. We devised a comprehensive search strategy to identify relevant studies from scientific databases. After screening 393 papers meeting the inclusion criteria, we extracted and analyzed key information using bibliometric analysis techniques. The findings reveal increasing publication trends in this research domain and identify frequently used algorithms, with convolutional neural networks, support vector machines, decision trees, and genetic algorithms emerging as the top methods. The review also discusses the challenges and limitations of current techniques, providing a structured synthesis of the state-of-the-art to guide future intrusion detection research.
Large language models (LLMs) have become prominent tools in various domains, such as natural language processing, machine translation, and the development of creative text. Nevertheless, in order to ...fully exploit the capabilities of Language Models, it is imperative to establish efficient communication channels between humans and machines. The discipline of engineering involves the creation of well-constructed and informative prompts, which act as a crucial link between human intention and the execution of tasks by machines. The present study examines the concept of rapid engineering, elucidating its underlying concepts, methodologies, and diverse range of practical applications.
Alfa Company is a medium-sized Iraqi distributor of laboratory equipment and supplies; in this study, we analyze their experience with implementing an automated inventory management system. This ...research explains the potential benefits and implementation challenges of such a system for small and medium-sized businesses. Improved inventory tracking, less storage fees, and happier consumers resulted from adopting this system. However, implementation was complex and time-consuming, requiring the expertise of outside IT consultants as well as the training of internal staff. This case study shows how beneficial it can be for small and medium-sized enterprises to work with external IT consultants and vendors to help them evaluate the costs and benefits of installing an automated inventory management system. In sum, the case study is instructive for other SMEs thinking about investing in automated inventory management systems to boost efficiency and cut costs.
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