Babylonian Journal of Artificial Intelligence
type: channel
description: 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.
publisher: Mesopotamian Academic Press
language: en
publication name: Babylonian Journal of Artificial Intelligence
issn: 3006-5437
items: A Metacognitive and Modular Approach to Self-Organizer AI in Open-Ended, Dynamic Environments
items: Artificial Intelligence Approaches to Mitigating Network Congestion in IoT Systems
items: The Impact of AI-Generated Content on Customer and Patient Service Optimization with Clinical Decision Support
items: A Systematic Survey on Artificial Intelligence in 6G Wireless Networks: Security, Opportunities, Applications, Advantages, Future Research Directions and Challenges
items: Artificial Intelligence in Malware and Network Intrusion Detection: A Comprehensive Survey of Techniques, Datasets, Challenges, and Future Directions
items: The Rise of Transformers – Redefining the Landscape of Artificial Intelligence
items: Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency
items: Improving Diagnostic Accuracy of Brain Tumor MRI Classification Using Generative AI and Deep Learning Techniques
items: Analyzing Trustworthy in AI: A Comprehensive Bibliometric Review of Artificial Intelligence Research
items: Role of IoT and ML in Healthcare
items: Image Generation Using Generative AI: Comparison Between OpenAI Art and Stable Diffusion
items: Chinese Generative AI Models Challenge Western AI in Clinical Chemistry MCQs: A Benchmarking Follow-up Study on AI Use in Health Education
items: Role of Artificial Intelligence (AI) in Improving Educational Quality for Students and Faculty
items: Improving Financial Forecasting Accuracy with Artificial Intelligence (AI) Models
items: Enhancing Privacy in Artificial Intelligence Services Using Hybrid Homomorphic Encryption
items: Enhancing IoT Security with AI-Driven Hybrid Machine Learning and Neural Network-Based Intrusion Detection System
items: Advancing Arabic Handwritten Digit Recognition with AI-Enhanced Neural Network Architectures
items: Generative Models in Natural Language Processing: A Comparative Study of ChatGPT and Gemini
items: Smart Wearables Powered by AI Transforming Human Activity Recognition
items: Integrated Learning Paradigm for Ecological Predictive Modeling
items: Authentication System Based on Fingerprint Using a New Technique for ROI selection
items: Enhancement of The Performance of Machine Learning Algorithms to Rival Deep Learning Algorithms in Predicting Stock Prices.
items: Predictive analytics model for students' grade prediction using machine learning
items: Revolutionizing Language Learning: How AI Bots Enhance Language Acquisition
items: Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance
items: AI-Driven Marketplaces and Price Prediction Tools for Rag Pickers: Enhancing Economic Opportunities in Africa's Circular Economy
items: Improving Bus Passenger Flow Prediction Using Bi-LSTM Fusion Model and SMO Algorithm
items: Healthcare Analysis Based on Diabetes Prediction Using a Cuckoo-Based Deep Convolutional Long-Term Memory Algorithm
items: Elderly People Health Care Monitoring System Using Internet of Things (IOT) For Exploratory Data Analysis
items: Using Deep Learning Technology Based Energy-Saving For Software Defined Wireless Sensor Networks (SDWSN) Framework

A Metacognitive and Modular Approach to Self-Organizer AI in Open-Ended, Dynamic Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Self-Organizer AI
  topic: keywords
subject: Adaptive Framework
  topic: keywords
subject: Non-Contextual Environments
  topic: keywords
subject: Meta-Cognition
  topic: keywords
subject: Dynamic Systems
  topic: keywords
subject: Reinforcement Learning (RL)
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
creator: Sufian Yousef
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-09-16
publication date: 2025-09-16
starting page: 128
ending page: 139
doi: 10.58496/BJAI/2025/012

Artificial Intelligence Approaches to Mitigating Network Congestion in IoT Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Machine Learning (ML)
  topic: keywords
subject: Deep Learning (DL)
  topic: keywords
subject: Reinforcement Learning (RL)
  topic: keywords
subject: Federated Learning (FL)
  topic: keywords
subject: Explainable Artificial Intelligence (XAI)
  topic: keywords
creator: Aysar Hadi Oleiwi
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-09-14
publication date: 2025-09-14
starting page: 117
ending page: 127
doi: 10.58496/BJAI/2025/011

The Impact of AI-Generated Content on Customer and Patient Service Optimization with Clinical Decision Support
type: item
description: 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.
subject: Articles
  topic: section
subject: AI-Generated Content (AIGC)
  topic: keywords
subject: Clinical Decision Support Systems (CDSS) Alert Optimization
  topic: keywords
subject: Personalized Recommendations
  topic: keywords
subject: AI
  topic: keywords
creator: Aitizaz Ali
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-07-20
publication date: 2025-07-20
starting page: 107
ending page: 116
doi: 10.58496/BJAI/2025/010

A Systematic Survey on Artificial Intelligence in 6G Wireless Networks: Security, Opportunities, Applications, Advantages, Future Research Directions and Challenges
type: item
description: 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
subject: Articles
  topic: section
subject: 6G
  topic: keywords
subject: AI
  topic: keywords
subject: security
  topic: keywords
subject: IoT
  topic: keywords
subject: ML
  topic: keywords
creator: Karthik Kumar Vaigandla
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-07-12
publication date: 2025-07-12
starting page: 99
ending page: 106
doi: 10.58496/BJAI/2025/009

Artificial Intelligence in Malware and Network Intrusion Detection: A Comprehensive Survey of Techniques, Datasets, Challenges, and Future Directions
type: item
description: 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
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Malware Detection
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Network Intrusion Prevention Systems (NIPS)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Intelligent Threat Detection
  topic: keywords
subject: AI-Driven Cybersecurity Framework
  topic: keywords
creator: Saif A. H. Moamin
creator: Muhannad Kaml Abdulhameed
creator: Rusul Mansoor Al-Amri
creator: Ahmed Dheyaa Radhi
creator: Rusul Kadhim Naser
creator: Liaw Geok Pheng
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-06-13
publication date: 2025-06-13
starting page: 77
ending page: 98
doi: 10.58496/BJAI/2025/008

The Rise of Transformers – Redefining the Landscape of Artificial Intelligence
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Fault Tolerance
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Service Provisioning
  topic: keywords
creator: Nyagong Santino David Ladu
creator: Benson Turyasingura
creator: Byamukama Willbroad
creator: Abraham Atuhaire
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-04-22
publication date: 2025-04-22
starting page: 72
ending page: 76
doi: 10.58496/BJAI/2025/007

Optimizing Cloud Computing: Balancing Cost, Reliability, and Energy Efficiency
type: item
description: 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
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Fault Tolerance
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Service Provisioning
  topic: keywords
creator: Raed A. Hasan
creator: Teba Majed Hameed
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-04-15
publication date: 2025-04-15
starting page: 64
ending page: 71
doi: 10.58496/BJAI/2025/006

Improving Diagnostic Accuracy of Brain Tumor MRI Classification Using Generative AI and Deep Learning Techniques
type: item
description: 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.
subject: Articles
  topic: section
subject: Brain Tumor
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Generative Adversarial Networks
  topic: keywords
subject: Medical Imaging
  topic: keywords
creator: Chem Sokea
creator: Soun Marina
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-04-09
publication date: 2025-04-09
starting page: 55
ending page: 63
doi: 10.58496/BJAI/2025/005

Analyzing Trustworthy in AI: A Comprehensive Bibliometric Review of Artificial Intelligence Research
type: item
description: 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.
subject: Articles
  topic: section
subject: Trustworthy AI
  topic: keywords
subject: bibliometric analysis
  topic: keywords
subject: Artificial intelligence
  topic: keywords
creator: Klodian Dhoska
creator: Elena Bebi
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-03-31
publication date: 2025-03-31
starting page: 37
ending page: 54
doi: 10.58496/BJAI/2025/004

Role of IoT and ML in Healthcare
type: item
description: 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.
subject: Articles
  topic: section
subject: Machine Learning (ML)
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
creator: Karthik Kumar Vaigandla
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-03-10
publication date: 2025-03-10
starting page: 23
ending page: 36
doi: 10.58496/BJAI/2025/003

Image Generation Using Generative AI: Comparison Between OpenAI Art and Stable Diffusion
type: item
description: 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.
subject: Articles
  topic: section
subject: Generative AI
  topic: keywords
subject: Image Generation
  topic: keywords
subject: OpenAI Art
  topic: keywords
subject: Stable Diffusion
  topic: keywords
subject: AI Art Comparison
  topic: keywords
creator: Ismael Khaleel Khlewee
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-03-05
publication date: 2025-03-05
starting page: 15
ending page: 22
doi: 10.58496/BJAI/2025/002

Chinese Generative AI Models Challenge Western AI in Clinical Chemistry MCQs: A Benchmarking Follow-up Study on AI Use in Health Education
type: item
description: 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, Weste…
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Benchmarking
  topic: keywords
subject: LLM
  topic: keywords
subject: DeepSeek
  topic: keywords
subject: Qwen
  topic: keywords
creator: Malik Sallam
creator: Kholoud Al-Mahzoum
creator: Huda Eid
creator: Khaled Al-Salahat
creator: Mohammed Sallam
creator: Guma Ali
creator: Maad M. Mijwil
rights: Copyright (c) 2025 Babylonian Journal of Artificial Intelligence
date: 2025-02-08
publication date: 2025-02-08
starting page: 1
ending page: 14
doi: 10.58496/BJAI/2025/001

Role of Artificial Intelligence (AI) in Improving Educational Quality for Students and Faculty
type: item
description: 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.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Personalized Learning
  topic: keywords
subject: Educational Quality
  topic: keywords
subject: Student Performance
  topic: keywords
subject: Faculty Efficiency
  topic: keywords
subject: Real-time Feedback
  topic: keywords
subject: Adaptive Learning Environment
  topic: keywords
creator: Vincent Omollo Nyangaresi
rights: Copyright (c) 2023 Babylonian Journal of Artificial Intelligence
date: 2023-12-08
publication date: 2023-12-08
starting page: 83
ending page: 90
doi: 10.58496/BJAI/2023/012

Improving Financial Forecasting Accuracy with Artificial Intelligence (AI) Models
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Financial Forecasting
  topic: keywords
subject: Causal Inference
  topic: keywords
subject: Big Data Analytics
  topic: keywords
subject: AI Models
  topic: keywords
creator: Janan Farag Yonan
rights: Copyright (c) 2023 Babylonian Journal of Artificial Intelligence
date: 2023-11-15
publication date: 2023-11-15
starting page: 74
ending page: 82
doi: 10.58496/BJAI/2023/011

Enhancing Privacy in Artificial Intelligence Services Using Hybrid Homomorphic Encryption
type: item
description: 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.
subject: Articles
  topic: section
subject: Hybrid Homomorphic Encryption (HHE)
  topic: keywords
subject: Privacy-Preserving Artificial Intelligence (PPAI)
  topic: keywords
subject: Encrypted Data Classification
  topic: keywords
subject: Secure AI Framework
  topic: keywords
subject: Resource-Constrained Devices
  topic: keywords
creator: Mustafa A Jalil
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-12-08
publication date: 2024-12-08
starting page: 168
ending page: 174
doi: 10.58496/BJAI/2024/018

Enhancing IoT Security with AI-Driven Hybrid Machine Learning and Neural Network-Based Intrusion Detection System
type: item
description: 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.
subject: Articles
  topic: section
subject: KDD
  topic: keywords
subject: DS2OS
  topic: keywords
subject: IoT Botnet datasets
  topic: keywords
subject: SVM
  topic: keywords
creator: Thaker Nay
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-12-05
publication date: 2024-12-05
starting page: 158
ending page: 167
doi: 10.58496/BJAI/2024/017

Advancing Arabic Handwritten Digit Recognition with AI-Enhanced Neural Network Architectures
type: item
description: 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.
subject: Articles
  topic: section
subject: Neural Networks
  topic: keywords
subject: AHD Dataset
  topic: keywords
subject: Handwritten Digit Recognition
  topic: keywords
subject: Hyperparameter Tuning
  topic: keywords
subject: Dropout Regularization
  topic: keywords
subject: Model Optimization
  topic: keywords
creator: Sarah Salman Qasim
creator: Safa Hussein Oleiwi
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-11-28
publication date: 2024-11-28
starting page: 146
ending page: 157
doi: 10.58496/BJAI/2024/016

Generative Models in Natural Language Processing: A Comparative Study of ChatGPT and Gemini
type: item
description: 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.
subject: Articles
  topic: section
subject: Natural Language Processing
  topic: keywords
subject: Generative Models
  topic: keywords
subject: Computer Sciences
  topic: keywords
subject: ChatGPT
  topic: keywords
subject: Gemini
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Conversational AI
  topic: keywords
creator: Abdulazeez Alsajri
creator: Hasan Ahmed Salman
creator: Amani Steiti
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-11-15
publication date: 2024-11-15
starting page: 134
ending page: 145
doi: 10.58496/BJAI/2024/015

Smart Wearables Powered by AI Transforming Human Activity Recognition
type: item
description: 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.
subject: Articles
  topic: section
subject: Computer Vision
  topic: keywords
subject: IoT
  topic: keywords
subject: Machine learning
  topic: keywords
subject: CNNs and RNNs
  topic: keywords
subject: Classification
  topic: keywords
subject: Wearable-based activity recognition
  topic: keywords
creator: Vincent Omollo Nyangaresi
creator: Abeer Mohammed Shanshool
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-10-17
publication date: 2024-10-17
starting page: 128
ending page: 133
doi: 10.58496/BJAI/2024/014

Integrated Learning Paradigm for Ecological Predictive Modeling
type: item
description: 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.
subject: Articles
  topic: section
subject: Integrated Learning Paradigm (ILP)
  topic: keywords
subject: Biodiversity impact assessments
  topic: keywords
subject: Support Vector Regression (SVR)
  topic: keywords
subject: Linear Regression
  topic: keywords
subject: Random Forest
  topic: keywords
creator: Subir Gupta
creator: Soumojit Roy
creator: Kalyan Maji
rights: Copyright (c) 2023 Babylonian Journal of Artificial Intelligence
date: 2023-10-12
publication date: 2023-10-12
starting page: 64
ending page: 73
doi: 10.58496/BJAI/2023/010

Authentication System Based on Fingerprint Using a New Technique for ROI selection
type: item
description: 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%
subject: Articles
  topic: section
subject: Data analytics
  topic: keywords
subject: Bi-LSTM
  topic: keywords
subject: Traffic management
  topic: keywords
subject: Bus passenger
  topic: keywords
subject: Urban mobility
  topic: keywords
creator: Wisam K. Jummar
creator: Ali M. Sagheer
creator: Hadeel M Saleh
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-08-25
publication date: 2024-08-25
starting page: 102
ending page: 117
doi: 10.58496/BJAI/2024/013

Enhancement of The Performance of Machine Learning Algorithms to Rival Deep Learning Algorithms in Predicting Stock Prices.
type: item
description: 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.
subject: Articles
  topic: section
subject: Gradient Boosting machine (GBM)
  topic: keywords
subject: Natural Language Processing (NLP)
  topic: keywords
subject: Stock Market
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
creator: Rusul Mansoor Al-Amri
creator: Ahmed Adnan Hadi
creator: Mayameen S. Kadhim
creator: Ayad Hameed Mousa
creator: Ali Z.K Matloob
creator: Hasanain Flayyih Hasan
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-09-25
publication date: 2024-09-25
starting page: 118
ending page: 127
doi: 10.58496/BJAI/2024/012

Predictive analytics model for students' grade prediction using machine learning
type: item
description: This project aims to develop a predictive analytics model using machine learning to forecast student grades, helping educational institutions identify struggling students early for targeted support. By leveraging machine learning, the model can analyze large datasets to detect complex patterns, enhancing prediction accuracy in education. The project employs neural networks due to their ability to capture non-linear relationships in data. Two models were created: one trained with data from low-rated schools and tested on both low- and high-rated schools, achieving 85.7% and 83.3% accuracy, respectively. The second model, trained with high-rated school data, yielded 88.9% accuracy for high-rated schools but only 35.7% for low-rated ones. Results indicate that separate models for different school levels are more effective due to discrepancies in grade reporting accuracy among Iraqi schools.
subject: Articles
  topic: section
subject: Predictive analytics model
  topic: keywords
subject: Machine Learning Model
  topic: keywords
subject: students' grade
  topic: keywords
subject: AI
  topic: keywords
creator: Muhammed Fareed Flayyih
creator: Hassan TOUT
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-08-05
publication date: 2024-08-05
starting page: 83
ending page: 101
doi: 10.58496/BJAI/2024/011

Revolutionizing Language Learning: How AI Bots Enhance Language Acquisition
type: item
description: Language learning has changed in recent years with the inclusion of Artificial Intelligence (AI) bots. This paper discusses how AI bots have changed language acquisition, paying more attention on how they improve language learning experience. The Article looks at what prominent AI bots such as Gemini, ChatGPT and Cloud can do to make personalized feedback a reality as well as enhance interactivity and convenience in language learning.This article examines the functionality of adaptive learning algorithms which correct errors in real time, along with immersive environments that demonstrate the importance of artificial intelligence bots in achieving effective language acquisition. AI Bots are enabling better tutoring, increased cultural awareness and expanded learning choices.The field of foreign language education has been revolutionized thanks to tailored support provided by AI bots that promote cultural understanding and also offer flexible ways for studying.
subject: Articles
  topic: section
subject: AI bots
  topic: keywords
subject: Language learning
  topic: keywords
subject: Cloud-based platforms
  topic: keywords
subject: Gemini
  topic: keywords
subject: Real-time corrections
  topic: keywords
creator: Sahar Yousif Mohammed
creator: Mohammed Aljanabi
creator: Alaa Mohammed Mahmood
creator: Isa AVCI
rights: Copyright (c) 2023 Babylonian Journal of Artificial Intelligence
date: 2023-09-11
publication date: 2023-09-11
starting page: 55
ending page: 63
doi: 10.58496/BJAI/2023/009

Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance
type: item
description: The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. Special emphasis was placed on the use of algorithms such as decision trees. The study includes an analysis of factors that affect students' academic performance such as previous academic achievement in educational activities, as well as social and psychological factors. Classification models were applied using the KNIME platform and the WEKA tool to analyse students' performance in three courses: database technology, artificial intelligence, and image processing in the ICT degree program. The results showed that the use of decision trees can effectively classify students' performance and determine the success and failure rate. The cruel outright mistakes, RMS error and relative supreme mistake all showed 0% whereas the kappa esteem got from the analysis extended between 0.991 and 1.00 which significantly concurs with most statistical values.
subject: Articles
  topic: section
subject: Data Mining
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Academic Performance
  topic: keywords
subject: Decision Trees
  topic: keywords
subject: Education Analysis
  topic: keywords
creator: Hussein Alkattan
creator: Alhumaima Ali Subhi
creator: Oluwaseun A. Adelaja
creator: Mostafa Abotaleb
creator: Maad M. Mijwil
creator: Pradeep Mishra
creator: Denis Sekiwu
creator: Wilson Bamwerinde
creator: Benson Turyasingura
rights: Copyright (c) 2023 Babylonian Journal of Artificial Intelligence
date: 2023-08-02
publication date: 2023-08-02
starting page: 43
ending page: 54
doi: 10.58496/BJAI/2023/008

AI-Driven Marketplaces and Price Prediction Tools for Rag Pickers: Enhancing Economic Opportunities in Africa's Circular Economy
type: item
description: This paper aims at identifying and analyzing the use of the AI solutions in enhancing the economic status of rag pickers within Africa’s circular economy sector and their access to markets. In this study, we explore how effectively the employment of AI technologies advances the welfare of the weaver people by identifying new potential methods of income generation and skill development in waste management techniques. Our research adopts a case study approach, examining the potential of two key AI applications: The resources include a marketplace that helps rag pickers find buyers for recyclable materials and price forecasting applications to aid selling. The role of such technologies in enhancing the efficiency of materials exchange, increasing or decreasing income, and improving rag pickers’ understanding of the market is discussed. The study entailed the creation and pilot testing of an AI marketplace that can be accessed through mobile phones; and market and price prediction algorithms generated through machine learning. Focus group discussion was performed with rag pickers and buyers to guide development and a pre-test was executed in three cities, Africa with 100 rag pickers and 20 buyers over a period of three months. Recognising the fact that the usage of independent AI Driven marketplaces along with AI Driven price prediction tools can have an enormous positive impact on the earning potential and position of rag pickers in the market. The study also reveals the African environ-specific factors which include, how to overcome the technological barriers and how everyone in the value chain can be given equal opportunity to access market information.
subject: Articles
  topic: section
subject: Circular economy
  topic: keywords
subject: AI recycling
  topic: keywords
subject: Waste management
  topic: keywords
subject: Sustainable development
  topic: keywords
subject: Construction waste
  topic: keywords
creator: Pushan Kumar Dutta
creator: Sayed M. El-kenawy
creator: Mostafa Abotaleb
creator: Marwa M. Eid
rights: Copyright (c) 2023 Babylonian Journal of Artificial Intelligence
date: 2023-07-24
publication date: 2023-07-24
starting page: 33
ending page: 42
doi: 10.58496/BJAI/2023/007

Improving Bus Passenger Flow Prediction Using Bi-LSTM Fusion Model and SMO Algorithm
type: item
description: Bus passenger flow prediction integrates data analytics and modelling techniques to forecast the number of passengers using bus services, incorporating historical usage patterns, demographics, weather, and events for optimal scheduling and resource allocation. The Bi-LSTM fusion model enhances accuracy by processing past and future features simultaneously, leveraging bidirectional LSTM layers and an attention mechanism to capture temporal dependencies. This approach not only refines insights crucial for urban mobility challenges like traffic management and demand forecasting but also improves route planning and service efficiency. The SMO algorithm initializes with a diverse spider monkey population exploring solution spaces. Through local and global leader phases, it iteratively updates positions based on fitness and probabilistic selections, maintaining a balance between exploration and exploitation. Perturbation-based updates in the local leader phase ensure adaptability, preventing premature convergence, while the global leader phase guides towards better solutions, enhancing efficiency in complex optimization tasks and promoting dynamic adaptation. In Dataset 1, the proposed model achieved a training time of 137 seconds, slightly longer than HA (115s), SARIMA (112s), GRU (123s), and ST-ResNet (113s). It demonstrated superior accuracy at 89%, surpassing HA (66%), SARIMA (68%), GRU (63%), DeepST (78%), and ST-ResNet (84%). In Dataset 2, the model exhibited the lowest RMSE, MAE, and MAPE%, indicating superior predictive accuracy over SVR, CNN, GCN, LSTM, and CONV LSTM models. These findings validate the proposed model's effectiveness in enhancing predictive capabilities for transit forecasting, underscoring its potential to optimize urban mobility and transportation management strategies significantly.
subject: Articles
  topic: section
subject: Data analytics
  topic: keywords
subject: Bi-LSTM
  topic: keywords
subject: Traffic management
  topic: keywords
subject: Bus passenger
  topic: keywords
subject: Urban mobility
  topic: keywords
creator: Karthika Balasubramani
creator: Uma Maheswari Natarajan
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-07-05
publication date: 2024-07-05
starting page: 73
ending page: 82
doi: 10.58496/BJAI/2024/010

Healthcare Analysis Based on Diabetes Prediction Using a Cuckoo-Based Deep Convolutional Long-Term Memory Algorithm
type: item
description: In recent years, the demand for mobile medical applications utilizing the Internet of Things (IoT) for diabetes diagnosis has been progressively increasing. Diabetes is commonly known as a chronic illness that presents a significant danger to individuals, analysing when blood sugar levels surpass the typical range. If diabetes is not promptly treated, hyperosmolar conditions can lead to serious health issues like hyperglycaemia and possibly even death. Since early detection enables lifestyle changes that prevent the disease's progression, it is crucial for diabetes management and health systems. However, diabetes diagnosis has a long computational time and low prediction accuracy. To address this issue, we propose a Cuckoo-Based Deep Convolutional Long-Term Memory (CDC-LSTM) algorithm that increases accuracy by classifying diabetics or non-diabetics. Additionally, we utilize the Standardized Feature Scaler (SFS) method to normalize the variance data by removing the mean of each feature. Moreover, we select the optimal features in the diabetes dataset utilising the Filter-Based Decision Tree (FBDT) technique. Finally, the proposed CDC-LSTM method can be used to distinguish between diabetics and non-diabetics, improving the accuracy of identifying diabetic patients. Additionally, the proposed method can predict diabetes using performance assessments such as precision, recall, and F-measure. Furthermore, the method's accuracy can be improved to 95.18% compared to previous approaches.
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: Diabetes prediction healthcare system
  topic: keywords
subject: CDC-LSTM
  topic: keywords
subject: Feature selection
  topic: keywords
subject: FBDT
  topic: keywords
creator: T. Kavitha
creator: G. Amirthayogam
creator: J. Jasmine Hephzipah
creator: R. Suganthi
creator: Venkata Anjani Kumar G
creator: T. Chelladurai
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-06-11
publication date: 2024-06-11
starting page: 64
ending page: 72
doi: 10.58496/BJAI/2024/009

Elderly People Health Care Monitoring System Using Internet of Things (IOT) For Exploratory Data Analysis
type: item
description: The majority of senior persons are suffering a loss of physical condition their health is vital, the prevalence of illnesses has increased in the current age compared to previous generations, making health management in hospital sector. One of the concepts that can harness the advantages of the Internet of Things to better the older lifestyle is health monitoring for active and aided functioning. This approach aims to create a wireless communication system designed to remotely monitor patients. Each patient is wirelessly monitored based on Body temperature is measured via a temperature sensor, which gives an accuracy of around some certain value. The sensor carries a heart rate monitor with a pulse oximetry sensor. There are two light-emitting in it; one produces red light and the other infrared light. For tracking the amount of oxygen in the blood, two infrared LEDs are needed, but only one is needed to calculate the heart rate. The ESP 8266 (Node MCU) controller gathers the data and sends it to the specific application and Communication will be sent again to the consumers based on the data examination. The outcome results manage each patient's data separately and are used in hospitals, old age homes, and physically challenged people. Every sensor of data, such as temperature, heart rate, and abnormal heartbeat, will be managed in the database processed with GPS coordinates and data analyses using Think Speak Web Application is for informing about personal health data.
subject: Articles
  topic: section
subject: ESP 8266
  topic: keywords
subject: Temperature Sensor
  topic: keywords
subject: Pulse Rate (BPM)
  topic: keywords
subject: GPS (Global positioning system)
  topic: keywords
subject: Buzzer
  topic: keywords
subject: Think Speak (IoT).
  topic: keywords
creator: Rahul Sanmugam Gopi
creator: R. Suganthi
creator: J. Jasmine Hephzipah
creator: G. Amirthayogam
creator: P.N. Sundararajan
creator: T. Pushparaj
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-06-02
publication date: 2024-06-02
starting page: 54
ending page: 63
doi: 10.58496/BJAI/2024/008

Using Deep Learning Technology Based Energy-Saving For Software Defined Wireless Sensor Networks (SDWSN) Framework
type: item
description: This paper discusses the significance of Wireless Sensor Networks (WSNs) in collecting critical data from various environments, highlighting the challenges presented by the limited resources of small, highly mobile sensors. The integration of WSNs into the Internet of Things (IoT) enables the collection and transmission of data to centralized locations. Especially in complex network topologies, efficient routing of packets is crucial for optimizing resource utilization in WSN nodes. Software-Defined Networks (SDNs), in which a centralized controller makes routing decisions based on network and packet data, are replacing traditional static routing. Nevertheless, due to the complexity of WSN topologies and cost-effectiveness concerns, Machine Learning (ML) techniques are currently being used to improve SDNWSN decision-making. This paper presents a technique that employs a neural network trained via Deep Reinforcement Learning (DRL) to extend the lifespan of WSNs by optimizing energy utilization via efficient routing. 2DCNN and 3DCNN neural networks are evaluated, with 3DCNN showing superior performance, resulting in an 18% increase in network lifespan. Additionally, the study emphasizes the significance of avoiding resource depletion in high-traffic nodes by considering alternative routing paths to guarantee the lifespan of the network.
subject: Articles
  topic: section
subject: SDN
  topic: keywords
subject: IoT
  topic: keywords
subject: WSN
  topic: keywords
subject: DRL
  topic: keywords
subject: ANN
  topic: keywords
subject: 2DCNN
  topic: keywords
subject: 3DCNN
  topic: keywords
creator: Sarah Mohammed Alqaraghuli
creator: Oguz Karan
rights: Copyright (c) 2024 Babylonian Journal of Artificial Intelligence
date: 2024-04-30
publication date: 2024-04-30
starting page: 34
ending page: 45
doi: 10.58496/BJAI/2024/006