Babylonian Journal of Artificial Intelligence
The Babylonian Journal of Artificial Intelligence (EISSN: 3006-5437) is a leading publication dedicated to the convergence of modern AI with ancient Babylonian insights. It showcases peer-reviewed research, articles, and reviews exploring the interface between AI technologies and historical Babylonian methodologies. The journal serves as a hub for interdisciplinary discussions, fostering collaboration among researchers and practitioners pushing the boundaries of AI while drawing inspiration from Babylonian knowledge systems.
Resolution of Expression of Concern: Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency
Resolution of Expression of Concern regarding for: Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency (R. A. . Hasan & T. M. . Hameed , Trans.). (2025). Babylonian Journal of Artificial Intelligence, 2025, 64-71. https://doi.org/10.58496/BJAI/2025/006 DOI of this Notice: https://doi.org/10.58496/BJAI/2026/009 Original Article: https://doi.org/10.58496/BJAI/2025/006 Expression of Concern: https://doi.org/10.58496/BJAI/2026/001 Following the publication of the article, Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency Published 2025-04-15 an Expression of Concern was issued by the Babylonian Journal of Artificial Intelligence on 2026-03-03 to alert our readers to concerns regarding methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility The editorial team of the Babylonian Journal of Artificial Intelligence has completed a thorough post-publication review of the article, the supporting data, and the authors' responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The findings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research. The Babylonian Journal of Artificial Intelligence thanks the readers who brought these matters to our attention, as well as the authors for their cooperation during the review process.
Copyright (c) 2026 Babylonian Journal of Artificial Intelligence
Resolution of Expression of Concern: A Systematic Survey on Artificial Intelligence in 6G Wireless Networks: Security, Opportunities, Applications, Advantages, Future Research Directions and Challenges
Resolution of Expression of Concern regarding for: Karthik Kumar Vaigandla. (2025). A Systematic Survey on Artificial Intelligence in 6G Wireless Networks: Security, Opportunities, Applications, Advantages, Future Research Directions and Challenges. Babylonian Journal of Artificial Intelligence, 2025, 99-106. https://doi.org/10.58496/BJAI/2025/009 DOI of this Notice: https://doi.org/10.58496/BJAI/2026/008 Original Article: https://doi.org/10.58496/BJAI/2025/009 Expression of Concern: https://doi.org/10.58496/BJAI/2026/003 Following the publication of the article, A Systematic Survey on Artificial Intelligence in 6G Wireless Networks: Security, Opportunities, Applications, Advantages, Future Research Directions and Challenges Published 2025-07-21 an Expression of Concern was issued by the Babylonian Journal of Artificial Intelligence on 2026-03-03 to alert our readers to concerns regarding methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility The editorial team of the Babylonian Journal of Artificial Intelligence has completed a thorough post-publication review of the article, the supporting data, and the authors' responses to the queries raised. Upon careful assessment, we have determined that the concerns have been addressed and clarified to the satisfaction of the editors. We have found no evidence of misconduct or invalidity in the research. The findings presented in the original article are considered robust and accurate. This notice formally resolves the previously published Expression of Concern. The original article stands as published, and we affirm the integrity of the research. The Babylonian Journal of Artificial Intelligence thanks the readers who brought these matters to our attention, as well as the authors for their cooperation during the review process.
Hybrid AI-Driven IDS for IoT Using Cooja and Explainable Deep Learning
The explosive growth in the adoption of the Internet of Things (IoT) has unveiled important security concerns, especially considering energy and computational constraints for low-power devices, increased levels of advanced routing attacks for RPL-based networks. Current IDSs are concentrated mainly on detection of anomaly with no clear explanation of the reason behind, which limits their application in safety systems. In addition, the lack of IoT-related benchmark datasets also prevents the construction of generalizable IDS systems. To narrow the gap, we design a hybrid AI-based IDS with combining Convolutional Neural Networks (CNNs), Bidirectional LSTM (BiLSTM) and Random Forest (RF) together for spatial, temporal and decision-level feature extraction. When modelling sinkhole, version number manipulation, or flooding attacks in the Contiki-NG Cooja simulator, three IoT-specific datasets were derived that allow a detailed packet-level investigation of realistic network behaviour. The proposed model outperforms traditional deep learning methods (CNN, LSTM and CNN-AO), showing better accuracy, precision, recall, F1-score AUC and MCC results. We use explainable AI (XAI) techniques, namely SHAP and LIME, to provide intuitive explanation on the feature contribu- tions and attack signatures. Experimental results show that the model has strong detection ability, low false alarm rate and is applicable for realtime deployment in a resource limited IoT environment. This work proposes a fully traceable IoT specific ready-to-use powerful IDS framework intensively validated through comprehensive simulation and evaluation with novel IoT-specific datasets.
Hybrid Deep Learning; Explainable AI (XAI),
Expression of Concern: A Metacognitive and Modular Approach to Self-Organizer AI in Open-Ended, Dynamic Environments
EXPRESSION OF CONCERN FOR: A Metacognitive and Modular Approach to Self-Organizer AI in Open-Ended, Dynamic Environments (S. . Yousef , Trans.). (2025). Babylonian Journal of Artificial Intelligence, 2025, 128-139. https://doi.org/10.58496/BJAI/2025/012 https://mesopotamian.press/journals/index.php/BJAI/article/view/907 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “A Metacognitive and Modular Approach to Self-Organizer AI in Open-Ended, Dynamic Environments”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Artificial Intelligence Approaches to Mitigating Network Congestion in IoT Systems
EXPRESSION OF CONCERN FOR: Artificial Intelligence Approaches to Mitigating Network Congestion in IoT Systems (A. H. . Oleiwi , Trans.). (2025). Babylonian Journal of Artificial Intelligence, 2025, 117-127. https://doi.org/10.58496/BJAI/2025/011 https://mesopotamian.press/journals/index.php/BJAI/article/view/906 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Artificial Intelligence Approaches to Mitigating Network Congestion in IoT Systems”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: A Systematic Survey on Artificial Intelligence in 6G Wireless Networks: Security, Opportunities, Applications, Advantages, Future Research Directions and Challenges
EXPRESSION OF CONCERN FOR: A Systematic Survey on Artificial Intelligence in 6G Wireless Networks: Security, Opportunities, Applications, Advantages, Future Research Directions and Challenges (K. K. . Vaigandla , Trans.). (2025). Babylonian Journal of Artificial Intelligence, 2025, 99-106. https://doi.org/10.58496/BJAI/2025/009 https://mesopotamian.press/journals/index.php/BJAI/article/view/852 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “A Systematic Survey on Artificial Intelligence in 6G Wireless Networks: Security, Opportunities, Applications, Advantages, Future Research Directions and Challenges”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Elderly People Health Care Monitoring System Using Internet of Things (IOT) For Exploratory Data Analysis
EXPRESSION OF CONCERN FOR: Elderly People Health Care Monitoring System Using Internet of Things (IOT) For Exploratory Data Analysis (R. S. Gopi, R. Suganthi, J. J. Hephzipah, G. Amirthayogam, P. Sundararajan, & T. Pushparaj , Trans.). (2024). Babylonian Journal of Artificial Intelligence, 2024, 54-63. https://doi.org/10.58496/BJAI/2024/008 https://mesopotamian.press/journals/index.php/BJAI/article/view/420 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Elderly People Health Care Monitoring System Using Internet of Things (IOT) For Exploratory Data Analysis”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
Expression of Concern: Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency
EXPRESSION OF CONCERN FOR: Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency (R. A. . Hasan & T. M. . Hameed , Trans.). (2025). Babylonian Journal of Artificial Intelligence, 2025, 64-71. https://doi.org/10.58496/BJAI/2025/006 https://mesopotamian.press/journals/index.php/BJAI/article/view/774 Reason for Expression of Concern: The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.
A Metacognitive and Modular Approach to Self-Organizer AI in Open-Ended, Dynamic Environments
This paper introduces a practical and flexible self-organizing artificial intelligence (AI) architecture that can be effectively employed in dynamic, non-contextual environments (lacking clear labels, fixed goals, or stable features). Supervised learning, rule-based systems, and classical reinforcement learning are the traditional models that typically require predesigned rewards and a fixed environment structure, which reduce the diversity of these models. On the contrary, the proposed framework stresses on meta-cognitive regulation and cognitive metonymy, allowing agents to self-organize their internal behaviors and strategies under variable inputs. The architecture is component-based multiagent with perception–feedback loops, decentralized communication protocols and dynamic heuristics. Together, these components enable emergent adaptability, where agents can build goal hierarchies on the fly, monitor their learning, and collaborate in the absence of central control. Unlike static models, this approach supports dynamic goal selection and rapid re-planning through internal monitoring and feedback. The framework was evaluated in simulation experiments on two complex tasks: autonomous navigation in unknown terrains and unsupervised anomaly detection in non-stationary data streams. Results demonstrate superior performance compared to conventional models, achieving higher average goal completion rates (87.4% vs. 65–78%), faster reaction times (43 ms vs. 62–94 ms), and greater resilience to disturbances. These observations serve to illustrate the promise of the self-organizing AI paradigm for open-ended, uncertain domains, like robotics, IoT and autonomous systems. In summary, our work questions conventional wisdoms and beliefs in AI design arguing in favor of naturally adaptive on cognition and continuous self-evolution in realistic worlds.
Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
Artificial Intelligence Approaches to Mitigating Network Congestion in IoT Systems
The unprecedented explosion of Internet of Things (IOT) devices has elevated the requirements of the network infrastructures to unprecedented levels, causing severe congestion problems, especially in applications which demand low latency, high throughput, and real-time feedback. Static routing protocols, AQM, and TCP variants are some of the traditional mechanisms for congestion control that are unable to perform efficiently in dynamic and diverse IoT environments as they are reactive-based and inflexible. To this end, in this paper, we explore the promising ability of Artificial Intelligence (AI) methods such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and their combination in natura for proactive and intelligent traffic management for IoT. A comparative review of strengths (e.g., adaptivity in RL, pattern recognition in DL) and weaknesses (in terms of its scalability, interpretability, resources) of each method is also discussed. Moreover, the paper indicates some crucial research challenges on model generalization, evaluation criterion and platform integration. Future possible research directions to bridge these gaps include the development of lightweight AI architectures, Explainable AI (XAI) frameworks, cross-platform model deployment, scalable FL, and standardized benchmarking datasets. This work also leads to a hybrid AI model for traffic congestion prediction and control with an application of simulation tool and real data. Simulation results show significant improvements in latency, packet loss, and energy consumption. Finally, the study presents a ground work for incorporating the scalable, secure and intelligent AI enabled congestion control systems in a wide area of IoT applications.
Explainable Artificial Intelligence (XAI)
The Impact of AI-Generated Content on Customer and Patient Service Optimization with Clinical Decision Support
Clinical Decision Support Systems (CDSS) are revolutionizing healthcare by providing real-time, evidence-based insights that support accurate and timely clinical decisions. This paper investigates the role of AI-generated content (AIGC) in enhancing patient care and customer service through its integration with CDSS. Leveraging AI technologies such as machine learning (ML), natural language processing (NLP), and deep learning (DL), CDSS can significantly improve clinical outcomes. However, challenges like alert fatigue, inefficient notifications, and the need for ongoing system optimization persist. This study explores the use of ChatGPT-based solutions to refine clinical alerts, offering a scalable, cost-effective method to improve their relevance, timing, and clarity. Rather than replacing clinicians, the goal is to support and enhance human decision-making. The paper also examines the broader role of AI in shaping clinical guidelines and improving patient communication, emphasizing its growing influence in modern healthcare.
Clinical Decision Support Systems (CDSS) Alert Optimization
A Systematic Survey on Artificial Intelligence in 6G Wireless Networks: Security, Opportunities, Applications, Advantages, Future Research Directions and Challenges
Artificial Intelligence (AI) has emerged as a transformative force in wireless communication, paving the way for unprecedented capabilities in the forthcoming 6G networks. AI is set to revolutionize 6G wireless networks by enabling intelligent, adaptive, and efficient communication systems. This article presents a comprehensive survey of AI's role in 6G, covering its security implications, opportunities, applications, advantages, challenges, and future research directions. It explores how AI enhances network optimization, real-time decision-making, and personalized services while addressing critical security and privacy concerns. The survey highlights the advantages of AI, such as energy efficiency, low latency, and robust security, alongside challenges like high computational demands, scalability, and ethical considerations. Future research directions focus on energy-efficient algorithms, adaptive models, privacy-preserving techniques, and AI's integration with emerging technologies. This survey provides valuable insights into the transformative potential of AI in shaping 6G networks, offering a roadmap for researchers and industry stakeholders to realize intelligent, secure, and sustainable wireless communication
Artificial Intelligence in Malware and Network Intrusion Detection: A Comprehensive Survey of Techniques, Datasets, Challenges, and Future Directions
As cyber threats evolve in complexity and scale, traditional detection mechanisms are increasingly inadequate. This paper presents a comprehensive survey of artificial intelligence (AI) applications in malware and network intrusion detection, emphasizing the integration of intelligent techniques for real-time threat mitigation. We categorize state-of-the-art methods across supervised, unsupervised, and reinforcement learning, including deep learning architectures such as CNNs, RNNs, LSTMs, and ensemble models. Static, dynamic, and hybrid analysis techniques are compared, with a focus on feature engineering, behavioral modeling, and real-world deployment constraints. A novel AI-based Malware Detection and Prevention Framework is proposed, combining machine learning classifiers with Network Intrusion Prevention Systems (NIPS) to enhance proactive defense capabilities. The study evaluates publicly available and synthetic datasets, addressing challenges such as class imbalance, adversarial evasion, and data scarcity. We also highlight ethical considerations including bias, privacy, and accountability in AI-enabled cybersecurity systems. Case studies from mobile and IoT ecosystems demonstrate the practicality and limitations of AI-based defenses in dynamic threat landscapes. Finally, the paper outlines future research directions in explainable AI, automated model generation (AutoML), and adaptive, context-aware intrusion prevention systems. This work serves as a critical resource for developing resilient, scalable, and intelligent cybersecurity infrastructures aligned with modern digital ecosystems
Network Intrusion Prevention Systems (NIPS)
The Rise of Transformers – Redefining the Landscape of Artificial Intelligence
The 2017 paper 'Attention Is All You Need' by Vaswani et al. marked a major paradigm shift in AI. Rather than clinging to tired methods like recurrence or convolutions, its Transformer design boldly flipped the script melding self-attention into a fresh take that not only remade natural language processing but also trickled over into computer vision, robotics, quirky multi-modal setups, and more [1]. At its very core lies a refreshingly simple idea self-attention, which lets models dynamically figure out which bits of the input deserve extra focus instead of being bound by strict, linear routines. This clever tweak kicked the old, rigid rules to the curb and opened up levels of parallel processing that we hadn’t seen before. In many cases, this shift not only ramped up the training speed dramatically but also proved essential in our data-swamped world where speed and nimbleness really do make all the difference.
Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency
Cloud computing is such a revolution concerning the IT world offering computing as services capable of diminishing operational costs and complications. Recently, these service models, ranging from IaaS, PaaS, and SaaS, and deployment models in private, public, and hybrid clouds, offer users almost unlimited computing and storage capabilities on a pay-per-use basis. This elasticity of cloud systems makes it very easy to dynamically provision and de-provision resources to cater to very different needs. This facility has led to its widespread use within domains such as social networking, defense, scientific computing, financial services, and medical. IDG Communications has now announced that 73% of corporations are currently utilizing clouds, with a further 17% in the process of implementing. Service abstraction to increase usability raises yet a fresh set of issues in terms of operational costs, reliability, energy efficiency, and security. Especially in cases where the framework is applicable to critical ventures, as exhibited just a while back by Knight Capital in 2013, system failures may have serious financial and credibility repercussions. Fault tolerance strategies through resource redundancy increase the cost of downtime risk but lower energy consumption, hence less cost and less environmentally unfriendly; they affect profit. The bulk of the operational expense in data centers is associated with the use of energy, whereby the use of energy is environmentally unfriendly and poses environmental concerns; clouds are forecasted to contribute to 5.5% of carbon emissions globally by 2025. Balancing energy efficiency and reliability will require novel optimization approaches for today's and future cloud computing systems with robust fault tolerance
Improving Diagnostic Accuracy of Brain Tumor MRI Classification Using Generative AI and Deep Learning Techniques
Brain Tumors are one of the most aggressive and deadly medical diseases with living individuals requiring a quick and highly sensitive diagnostic approaches to improve patient outcomes. Classification of brain tumors from magnetic resonance imaging (MRI) is still a difficult problem for medical imaging because the problem is highly complex, varies and the differences between the tumor types are subtle. Diagnostic accuracy and consistency are recently improved through the advances in Generative Artificial Intelligence (AI) and Deep Learning (DL) techniques. However, this paper presents a robust and efficient model based on the combination of the Generative AI approaches i.e. Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) for the multi-classification of brain tumors from MRI images. With medical image dataset being limited, the GAN model is trained to augment the dataset which solves the problem of limited dataset and also that of the class imbalance. It trains and evaluates the CNN architecture on both original and artificially augmented datasets and shows that increased accuracy and generalization. The experimental results demonstrate that proposed combined approach significantly performs better than the traditional methods, thus allowing radiologists to reach higher precision, sensitivity and specificity in tumor identification and classification. The proposed methodology is a significant improvement in speeding, reliability, and precision in clinical diagnostics for Brain Tumors.
Analyzing Trustworthy in AI: A Comprehensive Bibliometric Review of Artificial Intelligence Research
This bibliometric analysis delves into the landscape of Trustworthy Artificial Intelligence (AI) research, revealing intriguing patterns and key insights. Lotka's Law unveils a skewed distribution in author productivity, underscoring the concentration of scholarly output among a select few. Bradford's Law identifies core journals significantly contributing to the field's scientific productivity. Author affiliations shed light on influential institutions, with Tsinghua University and Beijing University of Posts and Telecommunications emerging as major contributors. Examining corresponding authors' countries emphasizes China's dominance in both citations and hosting corresponding authors. Authors with an h-index of 9 and above showcase local impact, with researchers like LI J and ZHANG J standing out. The Collaboration Network visualizes the interconnectedness of researchers, revealing collaborative clusters. Analyzing countries' scientific production underscores China's leadership, with a substantial global contribution. Noteworthy documents and their impact, such as LIU R's work garnering high total citations, illustrate the significance of specific publications. These findings underscore the global and multifaceted nature of Trustworthy AI research, providing valuable insights for future investigations, policy considerations, and international collaborations in this rapidly evolving field.
Internet of Things (IoT) is known as a network of various physical devices that are implanted with different sensors, software along with Internet connectivity to allow connected devices to communicate without human interference. Today, IoT has spread in almost all fields, such as agriculture, industries, supply chain management, sports, smart cities, traffic management, driverless vehicle technology, healthcare, etc. Devices can communicate any time at anywhere in all places, which makes the IoT called the Internet of Everything. Medical care is known as the progression of human health through the prevention of ailments, the diagnosis and treatment of illnesses, accidents and mental disabilities. With technological advancements, machine learning (ML) and deep learning (DL) are gaining popularity in many applications, such as natural language processing (NLP), disease prediction, bioinformatics, speech recognition, etc. The main data sources for the DL system are medical IoT, electronic health records, digitized images, and healthcare records. There are certain issues that can be encountered in DL, such as privacy, implementation, and optimization. This paper introduces ML applications for IoT in health systems and medical sciences by reviewing related work. Finally, this paper will discuss the challenges, opportunities, advantages, and disadvantages of each study.
Image Generation Using Generative AI: Comparison Between OpenAI Art and Stable Diffusion
Generative AI has made significant strides in image generation, with OpenAI Art and Stable Diffusion emerging as two prominent tools in the field. This study aims to compare the capabilities of these two models in terms of performance, quality, and creativity in generating images based on text prompts. We evaluate both tools using a range of image categories, assessing their output for accuracy, creativity, and consistency with provided instructions. The findings suggest that while OpenAI Art offers faster responses and simpler outputs, Stable Diffusion excels in producing more realistic and diverse images. This paper delves into the methodologies of both tools, offering insights into their strengths and limitations, and provides a comprehensive comparison based on experimental results.
Chinese Generative AI Models Challenge Western AI in Clinical Chemistry MCQs: A Benchmarking Follow-up Study on AI Use in Health Education
Background: The emergence of Chinese generative AI (genAI) models, such as DeepSeek and Qwen, has introduced strong competition to Western genAI models. These advancements hold significant potential in healthcare education. However, benchmarking the performance of genAI models in specialized medical disciplines is crucial to assess their strengths and limitations. This study builds on prior research evaluating ChatGPT (GPT-3.5 and GPT-4), Bing, and Bard against human postgraduate students in Medical Laboratory Sciences, now incorporating DeepSeek and Qwen to assess their effectiveness in Clinical Chemistry Multiple-Choice Questions (MCQs). Methods: This study followed the METRICS framework for genAI-based healthcare evaluations, assessing six models using 60 Clinical Chemistry MCQs previously administered to 20 MSc students. The facility index and Bloom’s taxonomy classification were used to benchmark performance. GenAI models included DeepSeek-V3, Qwen 2.5-Max, ChatGPT-4, ChatGPT-3.5, Microsoft Bing, and Google Bard, evaluated in a controlled, non-interactive environment using standardized prompts. Results: The evaluated genAI models showed varying accuracy across Bloom’s taxonomy levels. DeepSeek-V3 (0.92) and ChatGPT-4 (1.00) outperformed humans (0.74) in the Remember category, while Qwen 2.5-Max (0.94) and ChatGPT-4 (0.94) surpassed human performance (0.61) in the Understand category. ChatGPT-4 (+23.25%, p < 0.001), DeepSeek-V3 (+18.25%, p = 0.001), and Qwen 2.5-Max (+18.25%, p = 0.001) significantly outperformed human students. Decision tree analysis identified cognitive category as the strongest predictor of genAI accuracy (p < 0.001), with Chinese AI models performing comparably to ChatGPT-4 in lower-order tasks but exhibiting lower accuracy in higher-order domains. Conclusions: The findings highlighted the growing capabilities of Chinese genAI models in healthcare education, proving that DeepSeek and Qwen can compete with, and in some areas outperform, Western genAI models. However, their relative weakness in higher-order reasoning raises concerns about their ability to fully replace human cognitive processes in clinical decision-making. As genAI becomes increasingly integrated into health education, concerns regarding academic integrity, genAI dependence, and the validity of MCQ-based assessments must be addressed. The study underscores the need for a re-evaluation of medical assessment strategies, ensuring that students develop critical thinking skills rather than relying on genAI for knowledge retrieval.
Role of Artificial Intelligence (AI) in Improving Educational Quality for Students and Faculty
AI in education is the new groundbreaking innovation that seeks to improve efficiency of addressing students, faculty and university administration needs, in delivering high quality of educational services. In order to reveal the impact of application of AI on the educational outcomes of Al-Mustansiriyah University, Baghdad, Iraq has to be examined. Quantitative case study research was conducted collecting data from students, faculty, and university administration officials across the study programs and added information from the faculty members. The study objectives will seek to establish the effectiveness of AI in improving the student performance, faculty productivity and quality of education. AI has made learning more personalized and interactive, timely feedback, real time teaching and learning adjustments enabling all makes learning enjoyable, fun and inclusive. Further, the study assesses the generally recommended approaches for integrating AI technology, existing concerns and risks, and associated ethical issues. In this study, the authors fully explain the features and benefits that AI provides for learners, educators, and administrative staff by showing how the technology can modify and improve the educational process to increase its efficiency and usefulness to students, teachers, and those who manage universities.
Copyright (c) 2023 Babylonian Journal of Artificial Intelligence
Improving Financial Forecasting Accuracy with Artificial Intelligence (AI) Models
In this paper, the author examines the role of the AI models in promoting a radical shift in financial forecasting, planning, and analysis (FP&A). Compared to its human counterparts, AI is uniquely suited to the process of interpreting large data sets so that it can greatly refine the estimates upon which business finances depend. Most of the classical AI approaches, which are mainly developed for making prediction, need to be adjusted for dealing with planning processes in organization, which often require more causal analysis. This paper describes the difficulties arising from the non-critical implementation of the AI in this context, and assesses the potential of the double machine learning framework in responding to causal questions. Also, we investigate how AI transforms the approaches to assessing and optimising the financial risks. Harnessing state of the art algorithms including neural networks and big data analytics, AI models allow for bringing up real time big data analysis of various financial data, where patterns could be obscure even to standard techniques. All these capabilities enable the right prognosis of market trends, estimation of credit risk and then provide the best means of investment. Another issue discussed in the paper is the challenges related to AI integrated financial forecasting, including the issues of data protection, model explainability, as well as the challenges that arise in terms of AI based decision making. Using a simulation analysis, we explain that as the data grows, both accuracy in forecasting and effectiveness in planning are enhanced, stressing that AI is indispensable for creating a wiser and stronger financial industry.
Enhancing Privacy in Artificial Intelligence Services Using Hybrid Homomorphic Encryption
The increasing occurrence of cyberattacks specifically aimed at critical infrastructure has led to the adoption of network intrusion detection techniques for the Internet of Things (IoT). AI is transforming multiple sectors today, the growth of adversarial attacks on AI models and models present imperative privacy issues which hinder its larger implementation. Some of the Privacy-Preserving Artificial Intelligence (PPAI) methods including HE make it possible to secure data during the calculation process. Yet conventional HE techniques experience certain disadvantages at present with applicability to highly scalable and resource-limited applications. Moreover, this paper presents an HHE technique that is designed by integrating symmetric cryptography with HE to overcome the above-mentioned challenges successfully. To this end, we propose the GuardAI framework for end devices with limited resources such that encrypted data can be classified while preserving the privacy of input data and AI models. To show the effectiveness of the HHE, we apply it to the actual problem of heart disease classification based on the easily contaminated ECG signals. In this way, the proposed method maintains the privacy of the data with little computational and communication cost for analysts and devices and has a fairly reasonable level of accuracy in comparison with unencrypted inference. This work therefore provides a foundation for secure and private approach in AI especially for those developed to suit devices and systems with limited resources by incorporating HHE into the PPAI systems.
Privacy-Preserving Artificial Intelligence (PPAI)
Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
Enhancing IoT Security with AI-Driven Hybrid Machine Learning and Neural Network-Based Intrusion Detection System
The increasing occurrence of cyberattacks specifically aimed at critical infrastructure has led to the adoption of network intrusion detection techniques for the Internet of Things (IoT). Securing IoT networks is difficult because of the growing number of connected devices and the advanced methods used by attackers. This study investigates the application of machine learning and neural networks in the prevention of prevalent online fraud and assesses their efficacy. The text discusses important ideas related to email filtering, machine learning, artificial neural networks, and network intrusion techniques. The study discusses the difficulties related to e-fraud detection and suggests methods to improve detection systems. Furthermore, it offers a thorough examination of IoT intrusion detection, emphasizing the risks, weaknesses, assaults, and methods of detection. Securing the billions of autonomous nodes in the Internet of Things (IoT), each with distinct characteristics, poses a significant challenge. Conventional techniques like as encryption, access control, and authentication are inadequate when used individually. Thus, this work utilizes deep learning techniques to detect widespread IoT vulnerabilities, such as Distributed Denial of Service (DDoS) assaults. The models are evaluated using different datasets, including NSL-KDD, DS2OS, and IoT Botnet. The evaluation is based on measures such as accuracy, precision, recall, and F1-score. The deep machine learning intrusion detection system has a high accuracy rate of 96.38%, which shows its efficiency in recognizing risks related to the Internet of Things (IoT) Where the data was trained by 80% and the data was tested by 20.
Advancing Arabic Handwritten Digit Recognition with AI-Enhanced Neural Network Architectures
Neural network model developed in this paper aims at classification of the hand written digits using the data set from Arabic Handwritten Digits Dataset (AHDD). It also includes data preprocessing, model design, training, validating, hyperparameter optimisation, and comparison methodologies of the project. Some preprocessing included scaling of pixel intensity and data augmentation to improve variation, as well as data separation between training and validation. proposed architecture of the model were updated through adding of dropout layers as a form of regularization, tuning of the quantity of hidden layers and neurons in them, and providing dynamic form of learning rates in attempt to diminish overfitting and to improve the model’s predictive ability. The improvements obtained in classification accuracy and in sparsity of the weights of the neural net allows to underline its accuracy in recognizing the patterns of a large data set when compared to the traditional approach. However, in this study, to better assess the performances of the developed model on the AHDD, it is compared to a model built by Tariq Rashid using a raw MNIST database and various tests are conducted to point out the peculiarities of Arabic handwritten digit recognition. The study also finds avenues to improve the model beyond what is presented in this paper: 1) incorporating Convolutional Neural Network (CNN) to learn spatial hierarchies; 2) using Transfer Learning and fine tuning from pretrained models; 3) having a larger dataset which cover other patterns that may not have been included in this study. The results of this research call attention to hyperparameter optimization and architectural improvements for AI approaches to accurate digit recognition of handwritten numbers. Apart from enriching the Arabic handwriting recognition research area, this study also opens avenues for further work that seek to take these methodologies to other complex script recognitional problems in the future.
Generative Models in Natural Language Processing: A Comparative Study of ChatGPT and Gemini
Generative models in natural language processing (NLP) have transformed machine interactions with human language. This research offers a comparative examination of ChatGPT and Gemini, two cutting-edge NLP models. This research elucidates the distinct strengths and shortcomings of each model by analyzing conversational fluency, diversity, and contextual accuracy. ChatGPT has exceptional originality in word production, but Gemini excels in contextual variety and sophisticated learning methodologies. The results highlight the revolutionary capability of generative models in fields such as education, customer service, and content production. Anticipated future improvements in this field are projected to enhance these capabilities, promoting wider societal and industrial advantages.
Smart Wearables Powered by AI Transforming Human Activity Recognition
Smart clothing has changed the ways that human behaviour is observed and analyzed, finding its uses in health and fitness, and assisting in daily living. Nevertheless, conventional techniques used in HAR are mostly based on feature extraction by designers and the use of fixed algorithms that cannot address the dynamic aspects of human activities. HAR can be advanced through devices supported by artificial intelligence, and this research seeks to investigate how wearable technologies can improve this field of study. Hence, using CNN and RNN deep learning architectures this study constructs a comprehensive model with the potential of detecting various human activities instantaneously and accurately. The framework includes the use of sensor fusion approaches to process data collected from accelerometers, gyroscopes and heart rate sensors, to fully capture physical movements. Specifically, to high performance and efficiency in the computations of the model, several preprocessing and feature extraction techniques are employed. Outcome analysis shows that the proposed AI-based framework recognizes a subject’s identity with more than 95% accuracy across the different datasets comping basic machine learning techniques. The versatility of the system demonstrated in this work regarding wearable platforms and activities suggests that the proposed solution could be useful for practical application in fitness, health care, and rehabilitation. The findings of this work point to critical opportunities that are driven by AI for future wearable technology; a more adept and integrated system that can preserve the health and efficiency of humans. The results pave the way for further advancements in using AI to support HAR with a focus on scalability, real-time applicability, and inclusiveness.
Integrated Learning Paradigm for Ecological Predictive Modeling
This study introduces the Integrated Learning Paradigm (ILP), a novel construct designed to improve the predictive capability of the biodiversity impact assessment model. The ILP brings together Support Vector Regression (SVR), Linear Regression, and Random Forest in a single model so that each of these algorithms can be used to its fullest. The intent behind this integration is to enhance the battle against overfitting within the model improving mainly R² and Root Mean Square Error (RMSE). Our study systematically evaluates the ILP relative to standalone models using ecological data datasets. The findings are rather surprising: the ILP specializes in R² which correlates with the amount of prediction error and fluctuations with standard deviation which create an RMSE of 688.2 which greatly surpasses that of SVR, Linear Regression and Random Forest. It implies a figure that explains the data more appropriately while making prediction errors in magnitude that is lower than anticipated. Supporting evidence is supplied through detailed visual analyses on the ILP, including residual and overlapped histogram plots, which showed differences in ILP’s performance on consistency and reliability of prediction. As these analyses indicate, the ILP is highly promising for the analysis of ecological data. In the end, the exceptional level of effective incidence of the ILP on the other hand, dimension, focuses more on the needs of the conservation methods and sustainable development processes in relation to policy provision and management strategies for the ecological system.
Authentication System Based on Fingerprint Using a New Technique for ROI selection
Through the analysis of biological, behavioral, or a combination of both traits, biometrics entails the identification of specific persons. Finger veins, iris patterns, fingerprints, and DNA are examples of common biometric qualities. Of these, fingerprints are the most commonly utilized since they are highly distinctive, simple to obtain, and can be obtained from a variety of sources (because each person has ten fingers). In addition to introducing a method for identifying the region of interest (ROI), which is a specific area selected from the fingerprint image to improve feature extraction, this research focuses on several models for extracting fingerprint features. The study suggests a novel technique known as the flexible region of interest (FROI) for extracting a wider region of interest. Using the GLCM algorithm and invariant moments, features were retrieved from four fingerprint models (gray, binary, ridges thinning, and valleys thinning) using this FROI. The highest performance was obtained with invariant moments recovered from the valleys thinning model, according to experimental results from a verification system. This led to a false rejection rate (FRR) of 7.5% and a false acceptance rate (FAR) of 0.3846%
Enhancement of The Performance of Machine Learning Algorithms to Rival Deep Learning Algorithms in Predicting Stock Prices.
This paper objectives to improve stock market prediction accuracy by training data on sentiment analysis of tweets, overcoming volatility and complexity. Utilizing the Use of natural language processing (NLP) algorithms, the tweet's sentiments were classified into (negative - neutral - and positive). The stock value price was predicted by implementing Machine learning algorithms (KNN, SVM, GBM, LR, DT, RF, EL). Among the techniques of ML, (GBM) achieved the greatest results in terms of accuracy (96%). Its results were compared with the results of a deep learning algorithm that uses the same data where GBM got better results, and other algorithms showed results (KNN = 55%, SVM=90%, LR=82%, DT=90%, RF=90%, EL = 88%). The results obtained were superior to previous studies.