Revolutionizing Systematic Reviews and Meta-analyses: The Role of Artificial Intelligence in Evidence Synthesis
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Publication Date
Mon Jan 01 2024
Journal Name
Explainable Artificial Intelligence In The Digital Sustainability Administration
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Publication Date
Fri Aug 30 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
Artificial Intelligence and Cybersecurity in Face Sale Contracts: Legal Issues and Frameworks
The sale of facial features is a new modern contractual development that resulted from the fast transformations in technology, leading to legal, and ethical obligations. As the need rises for human faces to be used in robots, especially in relation to industries that necessitate direct human interaction, like hospitality and retail, the potential of Artificial Intelligence (AI) generated hyper realistic facial images poses legal and cybersecurity challenges. This paper examines the legal terrain that has developed in the sale of real and AI generated human facial features, and specifically the risks of identity fraud, data misuse and privacy violations. Deep learning (DL) algorithms are analyzed for their ability to detect AI genera
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Publication Date
Mon Dec 30 2024
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Reservoir permeability prediction based artificial intelligence techniques
Quanti-Elan
Artificial Neural Networks
Data-Driven Approach
Permeability
Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be
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Publication Date
Sat May 31 2025
Journal Name
3rd International Scientific Conference For Human And Social Studies And Epistemological Challenges
Frankenstein Complex in Daniel H. Wilson's Robopocalypse ( ): Artificial Intelligence Conspiracies
With the advent of technology
the fear of losing control has consistently
accompanied humanity. While humans have discovered
invented
and
created many remarkable things
they have also been haunted by the
possibility of their creations turning against them. This fear
often referred to
as the Frankenstein Complex
derives its name from Mary Shelley's
Frankenstein ()
in which Dr. Frankenstein's creation ultimately destroys
its maker. The Frankenstein Complex embodies the subconscious fear of
artificial intelligence (AI) surpassing and potentially dominating humankind.
This apprehension has inspired numerous literary works
including Daniel H.
Wilson’s novels. The present study examines the widespread fear of AI
which has fueled conspiracy theories in literature. These theories have not
only amplified public anxiety but also hindered understanding among those
with limited knowledge of AI and robotics. Dr. Wilson
a robotics expert
uses
his imagination as a driving force for both his writing and his inventions. The
research focuses on incidents leading to the rise of robots
the concept of the
New War
how it was fought
and how it concluded. By comparing
contrasting
and analyzing literary elements and devices with prominent
conspiracy theories
the study highlights the interplay between fiction and
scientific development. The researchers conclude that while many conspiracy
theories contain elements of truth
they often inspire scientists to explore them
further. Additionally
some conspiracy theories warrant serious
consideration. The study recommends acknowledging the Frankenstein
Complex while maintaining faith in the potential benefits of artificial
intelligence.
Publication Date
Thu Mar 30 2023
Journal Name
Journal Of Economics And Administrative Sciences
An Artificial Intelligence Algorithm to Optimize the Classification of the Hepatitis Type
Regression Tree Classification (CART)
Radial Basis Function (RBF)
Genetic Algorithm (GA)
شجرة الانحدار التصنيفية
شبكة دالة الاساس الشعاعي
الخوارزمية الجينية
Hepatitis is one of the diseases that has become more developed in recent years in terms of the high number of infections. Hepatitis causes inflammation that destroys liver cells, and it occurs as a result of viruses, bacteria, blood transfusions, and others. There are five types of hepatitis viruses, which are (A, B, C, D, E) according to their severity. The disease varies by type. Accurate and early diagnosis is the best way to prevent disease, as it allows infected people to take preventive steps so that they do not transmit the difference to other people, and diagnosis using artificial intelligence gives an accurate and rapid diagnostic result. Where the analytical method of the data relied on the radial basis network to diagnose the
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Publication Date
Sun Jan 01 2023
Journal Name
Lecture Notes In Networks And Systems
Using Artificial Intelligence and Metaverse Techniques to Reduce Earning Management
This study aims to demonstrate the role of artificial intelligence and metaverse techniques, mainly logistical Regression, in reducing earnings management in Iraqi private banks. Synthetic intelligence approaches have shown the capability to detect irregularities in financial statements and mitigate the practice of earnings management. In contrast, many privately owned banks in Iraq historically relied on manual processes involving pen and paper for recording and posting financial information in their accounting records. However, the banking sector in Iraq has undergone technological advancements, leading to the Automation of most banking operations. Conventional audit techniques have become outdated due to factors such as the accuracy of d
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Publication Date
Wed Jun 28 2023
Journal Name
Al–bahith Al–a'alami
The Future of Television Work in the Light of Artificial Intelligence Challenges an Exploratory Study
Artificial Intelligence
Automation
Television
Automated Journalism
This research examines the future of television work in light of the challenges posed by artificial intelligence (AI). The study aims to explore the impact of AI on the form and content of television messages and identify areas where AI can be employed in television production. This study adopts a future-oriented exploratory approach, utilizing survey methodology. As the research focuses on foresight, the researcher gathers the opinions of AI experts and media specialists through in-depth interviews to obtain data and insights. The researcher selected 30 experts, with 15 experts in AI and 15 experts in media. The study reveals several findings, including the potential use of machine learning, deep learning, and na
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Publication Date
Sun Jan 01 2023
Journal Name
4th International Conference On Distributed Sensing And Intelligent Systems (icdsis 2023)
Publication Date
Sat Dec 17 2022
Journal Name
Applied Sciences
A Hybrid Artificial Intelligence Model for Detecting Keratoconus
Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
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Publication Date
Sat Apr 12 2025
Journal Name
Mustansiriyah Journal Of Sports Science
A Review of the Use of Artificial Intelligence Algorithms for Predicting Injuries and Performance in Football Players
The purpose of this study is to investigate the research on artificial intelligence algorithms in football, specifically in relation to player performance prediction and injury prevention. To accomplish this goal, scholarly resources including Google Scholar, ResearchGate, Springer, and Scopus were used to provide a systematic examination of research done during the last ten years (2015–2025). Through a systematic procedure that included data collection, study selection based on predetermined criteria, categorisation based on AI applications in football, and assessment of major research problems, trends, and prospects, almost fifty papers were found and analysed. Summarising AI applications in football for performance and injury p
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