The Babylonian Journal of Machine Learning (BJML) (EISSN: 3006-5429) is a specialized publication dedicated to the exploration and integration of modern machine learning methodologies. As a platform for researchers and scholars, the journal focuses on the intersection of cutting-edge advancements in machine learning. Through high-quality articles, it fosters interdisciplinary discussions aimed at propelling forward the field of machine learning research.
A Preemptive Ensemble Learning Framework for KnowledgeBased Network Intrusion Detection
The rapid growth of Internet-connected systems has increased the complexity, diversity, and volume of network traffic, making intrusion detection a significant cybersecurity challenge. Traditional machine learning-based intrusion detection systems often suffer from high-dimensional data, severe class imbalance, and poor detection of low-frequency attacks. To address these limitations, this paper proposes a hybrid framework, FD-ANN, which integrates Density Peak Clustering (DPC), Fractal Fuzzy Membership (FFM), and Artificial Neural Networks (ANNs). The framework employs wrapper-based feature selection to eliminate irrelevant features and reduce dimensionality. DPC partitions the training data into homogeneous clusters, preserving minority attack structures while mitigating class imbalance. Separate ANN classifiers are trained for each cluster, and their outputs are combined through an adaptive FFM weighting mechanism that dynamically assigns weights according to local data distributions. Experiments on the NSL-KDD and UNSW-NB15 benchmark datasets demonstrate the effectiveness of the proposed approach. FD-ANN achieved accuracies of 82.47%, 92.24%, and 95.59% on NSL-KDDTest-21, NSL-KDDTest+, and UNSW-NB15, respectively. The framework also significantly improved the detection of minority attack classes while reducing false alarm rates. Comparative results against KNN, RF, SVM, DT, and conventional ANN models confirm that FD-ANN provides an effective, scalable, and robust solution for modern network intrusion detection.
Copyright (c) 2026 Maad Kamal Al-anni, Ammar A Al-Hamadani, Rafah M. Almuttairi, Alharith A Abdullah https://creativecommons.org/licenses/by/4.0
An AI-Driven Intrusion Detection and Real-Time Autonomous Response Framework Using Network Traffic Logs: AMulti-Algorithm Approach with LightGBM Optimization
Considering the recent technological advancement of cyber threats, the conventional intrusion detection systems (IDS), cannot support dynamic and large scale network conditions. In this paper, a hybrid intrusion detection model that integrates offline supervised learning with online adaptive learning will be described to improve the accuracy of intrusion detection and prompt response to attacks. Upon choosing the dataset by CICIDS2017, a series of machine learning models were trained and tested on them, such as the Logistic Regression, the Random Forest, the Light Gradient Boosting Machine (LightGBM) using such key performance indicators as precision, recall, and F1 score.. Also, SMOTE technology was applied to address data imbalance, resulting in significant improvements in detecting rare attack classes. Therefore, Experimental results appear to show that all models achieved a recall rate of ≥97%. The SMOTE + RF model achieved 100% accuracy with no false positives, and the LightGBM model achieved 100% full recall for all attacks. This study demonstrates the effectiveness of the proposed approach in combining high performance with self-adaptation, making it a powerful solution for modern intrusion detection systems in cybersecurity infrastructures.
Copyright (c) 2026 Ahmed Sileh Gifal, Hussein Mohammed Ali Makassees, Mayameen S. Kadhim, Ahmed Dheyaa Radhi, Rusul Mansoor Al-Amri, Liaw Geok Pheng https://creativecommons.org/licenses/by/4.0
HGS-RF: A Heuristic-Guided Selective Random Forest for Multi-Label Vulnerability Detection in Smart Contracts
Smart contracts execute the transactions in the blockchain network. Smart contracts are prone to various types of security vulnerabilities, which may lead to huge financial losses. The detection of smart contract vulnerabilities is mainly focused on binary classification, where smart contracts are classified as either vulnerable or non-vulnerable. The detection of smart contract vulnerabilities is also focused on multi-class classification, where each smart contract is classified into different types of vulnerabilities. Smart contracts in the real world have various types of vulnerabilities; hence, multi-label detection of smart contract vulnerabilities is required. There are a limited number of studies that have addressed the topic of multi-label classification. In order to overcome the above-mentioned limitation, a new hybrid framework, namely Heuristic Guided Selective Random Forest (HGS-RF), for the detection of multi-label smart contract vulnerabilities has been proposed in the present research work. To achieve the above-mentioned objective, the present research proposes a new framework that integrates the transformer-based feature extraction method with the RoBERTA architecture and the multi-stage-based method for the detection of smart contract vulnerabilities. The framework that is being proposed utilizes a few-shot learning module based on 'Prototypical Networks' for binary vulnerability detection. This utilizes a Multilayer Perceptron (MLP) to map the contract embeddings into a metric space, where the 'vulnerable' and 'healthy' class prototypes are computed based on the support samples, and the contracts are classified based on their Euclidean distance from these computed prototypes, with final predictions refined using k-nearest neighbors (KNN). Second, the proposed HGS-RF architecture utilizes an inferential filtering mechanism that mimics the principle of passive selection of the human immune system. This inferential filtering mechanism can be considered a layer that filters out weak decision trees generated during the initial detection phase. Thus, the inferential filtering mechanism filters out weak decision trees and retains high-performing trees capable of accurately identifying malicious samples from smart contracts. This selective filtering process enables the model to focus on informative vulnerability patterns, thereby improving classification reliability and reducing false positives in multi-label vulnerability detection. Experimental evaluation of the proposed framework, with up to 37 types of vulnerability included, proves its efficiency. F1 scores of up to 0.99 were obtained for the binary classification stage, and the HGS-RF model achieved an accuracy of up to 0.97 in detecting multi-label vulnerability . These results demonstrate that the proposed approach improves vulnerability detection accuracy while enabling more comprehensive security analysis of smart contracts.
Copyright (c) 2026 Saad Nasser AlAzzam , Ghassan ALDharhani, Raenu ALKolandaisamy https://creativecommons.org/licenses/by/4.0
MOGNN-EC: A Multi-Objective Optimization Framework for Efficient Energy Management and Charging Coordination in Electric Vehicle Networks
The fast growth of electric vehicles (EVs) raises imminent challenges in energy management, charging scheduling, and grid resilience. Traditional rule-based scheduling, reinforcement learning, and metaheuristic techniques suffer from scalability, flexibility, and real-time decision-making issues. This paper introduces MOGNN-EC, a graph neural network (GNN) approach to multi-objective optimization for intelligent EV energy management and scheduling. MOGNN-EC applies EVs, charging points, and power supplies in the form of a dynamic graph to exploit spatiotemporal interdependences and achieve real-time optimization of energy distribution, load balancing, and renewable usage. Experimental evaluations indicate 92% energy efficiency, 75% use of renewables, and 0.4 s computation time for each round of optimization, along with a minimized mean absolute error (MAE) of 2.8 for demand forecasts. MOGNN-EC also optimizes battery thermal management via waste heat recovery in low-temperature climates. Scalar issues in scaling up to voluminous EV networks, e.g., computational intensity and availability of data, could be addressed using federated learning, uncertainty-aware multi-objective optimization, and vehicle-to-grid (V2G) operations. Further work will expand MOGNN-EC in multi-agent reinforcement learning (MARL) for cooperative sharing of energies and fault-tolerant real-time forecast operations. The evaluations prove MOGNN-EC to form an extensible, adjustable, and sustainable remedy for future EV energy infrastructures and intelligent grids.
Copyright (c) 2026 Yaseen Ali Sahood, Najlaa Nsrulaah Faris, Ahmed Rashid Alkhuwaylidee, Ahmed Saihood https://creativecommons.org/licenses/by/4.0
An Explainable Hybrid GWO-LightGBM Model for Breast Cancer Diagnosis Using SHAP Interpretation
The rapid growth of machine learning and data mining has changed the discipline of medical diagnostics. They developed automated systems that can identify complicated disease patterns with high accuracy. Most current models, on the other hand, still include redundant and correlated features that make computations more expensive to understand and sometimes cause overfitting. This research proposes a hybrid diagnostic system that combines Grey Wolf Optimization (GWO) for feature selection alongside the Light Gradient Boosting Machine (LightGBM) classifier to improve both accuracy and interpretability. It examines the suggested GWO-LightGBM model on the Breast Cancer Wisconsin dataset. The framework successfully reduced the number of input features from 30 to 12. In addition, the test accuracy of 97.37%, and the cross-validation accuracy of 98.02% ± 0.02. This was better than the baseline LightGBM that was trained on all features. Furthermore, the model shortened the training time by 25% and showed statistically significant improvement (p < 0.05). Furthermore, the SHAP analysis exposed that the selected features were biologically important, which contributed to the model's transparency and trustworthiness. The proposed model shows that using feature selection with LightGBM and explainable artificial intelligence may make diagnostic models that are fast, easy to understand in healthcare applications.
Copyright (c) 2026 Ahmed Aljuboori, Hamsa M Ahmed, M. M. A. Abdulrazzq https://creativecommons.org/licenses/by/4.0
Effective Android Malware Detection Using CNN and LSTM Model with GWO-Based Feature Selection
Malware or malicious software represents one of the most significant cybersecurity threats, compromising the integrity, confidentiality, and availability of computer systems and networks. Traditional malware detection methods seek to locate specific malware samples and families to recognize harmful code, and can be located using traditional signature- and rule-based detection methods. The study aims at the creation of malware detectors based on deep learning and optimization algorithms. The present study will propose a more advanced malware detection system that employs deep learning models and the Grey Wolf Optimization (GWO) algorithm to select essential features. This model used data balancing Synthetic Minority Oversampling Technique (SMOTE), and the models were tested on the CICMalDroid2020 dataset. Using two different models were tested: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with Grey Wolf Optimization. The experimental findings indicate that the CNN-GWO model has an model achieves 93% accuracy, 92% precision, and 92% recall, and the LSTM-GWO model has an 90% accuracy, 90% precision, and 90% recall, respectively. These findings show that both models can be effective in detecting and classifying malware, but CNN has a few higher performance rates. The novelty of this method is the independent application of GWO-based feature selection to CNN and LSTM architectures, enhancing the detection accuracy and efficiency of these architectures in comparison to the latest research.
Copyright (c) 2026 Hawraa.O. Musa, Muhanad.T. Younis https://creativecommons.org/licenses/by/4.0
DPON: A Novel Deep Learning Framework for Efficient Image Processing
The Deep Processing Optimization Network (DPON) is a novel deep learning framework designed to enhance image processing and classification tasks. By incorporating advanced techniques such as convolutional layers, residual connections, attention mechanisms, and global average pooling, DPON optimizes both performance and computational efficiency. The architecture is carefully structured to address key limitations in traditional convolutional neural networks (CNNs), particularly in terms of feature extraction and gradient flow, while maintaining a balance between accuracy and processing speed. Through comprehensive evaluations on benchmark datasets, including CIFAR-10, DPON demonstrates superior performance across multiple metrics, including accuracy, precision, recall, and F1-score, consistently outperforming established models such as ResNet-50, EfficientNet-B0, and DenseNet-121. The integration of attention mechanisms allows DPON to focus on the most relevant features of the input, improving classification precision while reducing misclassification rates. Additionally, the use of global average pooling significantly reduces the number of parameters, enhancing computational efficiency without sacrificing accuracy. DPON's robust design makes it highly adaptable for a wide range of applications, from image classification to real-time systems requiring fast inference times. This paper presents the architectural details of DPON, its mathematical foundation, and extensive performance evaluations, demonstrating its potential as a state-of-the-art solution for modern image processing challenges.
Copyright (c) 2025 Wafaa M. Salih Abed, Ghada Al-Kateb , Hussein Alkattan https://creativecommons.org/licenses/by/4.0
Automated Parkinson's disease Detection from Images Using Deep Transfer Learning and Optimization
The diagnosis and treatment of Parkinson's disease (PD) is critical to effectively managing this progressive neurological disorder, which significantly affects motor and non-motor functions. This study presents a deep transfer learning-based algorithm for PD detection. The features are extracted from handwritten image datasets using pre-trained convolutional neural networks such as ResNet50, VGG19, and Inception-V3. To achieve precise classification, a hybrid classification framework that combines a genetic algorithm-optimized k-nearest neighbour (KNN) classifier with a support vector machine (SVM) is implemented. The proposed model offers a reliable, scalable, and efficient solution for diagnosing Parkinson's disease. The experimental results demonstrate the model's state-of-the-art accuracy. AI-driven methodologies are being used in this research to advance automated medical diagnostics, reduce diagnostic delays, and improve patient outcomes.
Copyright (c) 2025 Thanaa Alsalem , Mohammed Amin https://creativecommons.org/licenses/by/4.0
An Analytical Comparison of Transfer Learning Techniques for Brain Tumor Detection and Classification
Because brain tumors are complex, they must be diagnosed accurately and early. Brain tumor diagnosis requires expert radiologists and large datasets of annotated images, which is resource-intensive and time-consuming. This study investigates the use of transfer learning techniques to detect and classify brain tumors using MRI images. Using transfer learning, pre-trained models can be tuned to perform specific medical tasks while reducing the impact of large datasets. AlexNet, GoogleNet, ResNet-50, and VGG-16 were compared for classifying gliomas, meningioma’s, and pituitary tumors based on transfer learning models. Based on the findings, the proposed hybrid GN-AlexNet model showed superior accuracy, sensitivity, specificity, and F1 score when compared with all other models, demonstrating its potential for improving brain tumor detection efficiency. The findings of this research pave the way for the adoption of transfer learning in clinical settings, providing medical professionals with more efficient and accessible solutions.
Copyright (c) 2025 Mohammed Amin Almaiah, Fuad Ali El-Qirem https://creativecommons.org/licenses/by/4.0
The Impact of Generative AI on Advancing Graduate Medical Education
Graduate Medical Education (GME) is integral to the development of healthcare professionals, but traditional educational methods are often limited in resources, limited in time, and do not meet the needs of students from all backgrounds. As generative artificial intelligence (GAI) advances, it is possible to enhance and personalize GME to address some of these challenges. With GAI, you can create content, create personalized learning pathways, train using simulations, and get feedback in real-time, such as ChatGPT, one of the large language models. Medical training can be revolutionized by these technologies, which allow educators to create dynamic, tailored learning experiences and provide students with realistic, interactive clinical scenarios. While GAI has great potential, its integration in medical education raises ethical concerns, privacy concerns, bias concerns, and mentorship concerns. Having generative AI integrated into GME can have significant effects on the quality of curriculum delivery, clinical decision-making skills, and patient outcomes. This paper explores the benefits, challenges, and future prospects of this integration.
Copyright (c) 2025 Vugar Abdullayev , Alex Khang , Nazila Ragimova https://creativecommons.org/licenses/by/4.0
Enhancing Governmental Decision-Making through Predictive Analytics with Machine Learning-Based Data-Driven Framework
Government bodies around the world are going digital and slowly starting to make use of data driven technologies to make better, faster and more transparent decisions. From these technologies, machine learning (ML) has become one of the most significantly employed tools, especially via its ability to predict. Predictive analytics allows governments to identify obscure trends that previously were hidden, predict potential future scenarios with an acceptable level of certainty and better inform decision-making in important areas, such as public finance, healthcare planning, emergency management, and resource allocation. In this work we explore the use of predictive modeling (implemented as our own Linear Regression, Decision Trees, Random Forests and Artificial Neural Networks) in the context of governmental decision models. The models were tested on real-world cases such as quarterly budget planning or estimation of healthcare service demand or emergency resource allocation using publicly available data from open government data platforms. Performance was evaluated based on the well-known RMSE, MAE and R² score. Results show that Artificial Neural Network always leads the highest in predictive accuracy, especially in dense or complex data setting, and there is no significant difference between Random Forest and Neural Network (the Random Forest has more generalization between interpretability and predictive power. On the other hand, Linear Regression and Decision Trees are more interpretable but have restrictions in using non-linear or high-dimensional datasets. In addition, the paper covers practical challenges including algorithmic bias, data quality considerations, and infrastructure capabilities, and ethical implications of automated decision making. This study has implications for the growing smart governance by proposing an integrated machine learning framework suitable for evidence-based policymaking. Future work involves improving the accuracy of prediction by incorporating explainable AI methodologies and customizing the model locally to enhance transparency, accountability, and generalization across different regional offices.
Copyright (c) 2025 Mahdi Salah Mahdi AL-Inizi https://creativecommons.org/licenses/by/4.0
Privacy-Preserving Transfer Learning for Community Detection in Multiple Networks: A Review.
In order to identify communities in various networks, this study gives a thorough analysis of privacy-preserving transfer learning methods. In order to better understand the specific difficulties of implementing transfer learning in decentralized and diverse settings, it classifies current solutions according to their learning paradigms, privacy measures, and network topologies. The scalability, privacy, and utility trade-offs are used to assess anonymization, deep learning, and federated learning methods. There is a critical discussion of the gaps in the present research, including the absence of defined assessment standards and the inadequate incorporation of privacy into transfer systems. Also, this research points the way toward potential future possibilities for developing privacy-first models that can generalize across different types of networks. Researchers and practitioners in the field of graph-based machine learning may use the results as a guide to create safe and efficient solutions.
Copyright (c) 2025 Afrig Aminuddin , Marshima Mohd Rosli https://creativecommons.org/licenses/by/4.0
The Global Landscape of Technology-assisted English Language Teaching Research: A Bibliometric Analysis
The purpose of this study is to provide a comprehensive overview of the development and notable patterns in the field of Technology-Assisted English Language Teaching (TAELT) research by conducting a bibliometric analysis of the domain. Author productivity, citation rate, annual scientific production, average citations, most-cited papers, and frequently-used words are only few of the bibliometric markers that can be examined for a more in-depth picture. This research sheds light on the dynamic nature of academic research in the (TAELT) subject. An impressive rise in research output and citations over time is shown by the statistics, which may indicate (TAELT's) growing significance in academic research discourse. Computers and Education, the Proceedings of the ACM International Conference Proceeding Series, and the International Journal of Emerging Technologies in Learning are three of the most important and relevant journals in this area. These publications stand out because of the important role they play in disseminating high-impact research throughout the (TAELT) community. To further illustrate the complexity and multidimensional nature of utilizing technology in English language education, this article examines fundamental problems such e-learning, interactive learning environments, and English language teaching to provide a thorough knowledge of (TAELT) research.
Copyright (c) 2025 Sara S. Alnakeeb , Eslam Hossam , Ramy Aldallal , Omega John Unogwu , Gertrude Milat https://creativecommons.org/licenses/by/4.0
Enhancing Semantic Image Retrieval Using Self-Supervised Learning: A Label-Efficient Approach
In this study, we tackle the fundamental problem of cross-modal retrieval and propose a novel self-supervised learning framework to improve the performance of semantic image search systems with limited dependency on labeled data. This research covers the problem of high retrieval accuracy on the image task, when dealing with small datasets rolling close annotation. The study presents a novel approach that mixes high-dimensional unlabeled data (e.g., millions of images) with a carefully created set of labeled ones to significantly improve semantic understanding and retrieval efficacy when evaluated against the proposed model. Indeed, experimental outcomes establish that our deployed self-supervised mechanism not only yields state-of-the-art performance on numerous routine trials, but also possesses being proof against the limited amount of labeled examples, giving rise to the signs of 30% accuracy increase for retrieval missions. The implications of these results are relevant to healthcare multimodal applications, in which fast retrieval of medical images is essential for diagnosis and treatment planning. This research may, therefore, enable better clinical workflows, assist disease recognition, and gradually lead to improved patient outcomes by providing more accurate and robust image retrieval systems. Above all, these significances note that the self-supervised learning paradigm they introduce here may extend beyond healthcare to transform how image data is used in other areas, ultimately leading to more powerful and automated vision systems across numerous domains that depend on visual data analysis.
Copyright (c) 2025 Malik Sallam , Marwan Ali Shnan https://creativecommons.org/licenses/by/4.0
A Bibliometric Analysis of Segment Anything (SA) Research: Global Trends, Key Contributors, and Thematic Insights
This bibliometric analysis provides a comprehensive overview of the research landscape surrounding "Segment Anything (SA)" technology, drawing on 704 documents from the Scopus database between 2020 and 2025. The study reveals a significant surge in scientific production, with publications peaking in 2024, marking it as a critical year for the field's growth. Lecture Notes in Computer Science emerged as the leading publication source, underscoring the foundational role of computer science in SA research, while high publication counts in Remote Sensing and IEEE Transactions on Geoscience and Remote Sensing demonstrate the technology's interdisciplinary applications. Key contributors include a concentrated group of prolific authors, led by Zhang Y and Li Y, who significantly shape the field’s development. Geographically, Chinese institutions dominate research output, particularly Wuhan University, Tsinghua University, and the University of Chinese Academy of Sciences, establishing China as a central research hub. The analysis also highlights the influence of Canada and Brazil, where fewer yet highly impactful publications underscore the field's global relevance. Major thematic focuses, such as "image segmentation," "deep learning," and "medical imaging," indicate the field’s blend of foundational AI advancements and practical applications, especially in healthcare. Overall, this analysis showcases a dynamic and expanding research domain, driven by international collaborations and diverse interdisciplinary applications, setting the stage for further developments in SA technology.
Copyright (c) 2025 Fredrick Kayusi , Rubén González Vallejo , Linety Juma , Michael Keari Omwenga , Petros Chavula https://creativecommons.org/licenses/by/4.0
Analysis of the performance of algorithms (K-Means, Farthest First, Hierarchical) Using the data analysis and modeling tool Weka
This study assesses the performance of three clustering algorithms—K-Means, Farthest First, and Hierarchical—using the Weka data mining tool. These algorithms were applied to five diverse datasets representing healthcare, industrial, and benchmark applications to evaluate their clustering accuracy, execution time, and consistency. The experimental results show that the Farthest First algorithm achieves the highest accuracy and the fastest execution time, making it suitable for real-time applications. K-Means delivers balanced performance but is sensitive to initialization and outliers, while the Hierarchical algorithm effectively captures complex relationships but incurs high computational costs. The findings highlight the importance of selecting appropriate clustering techniques based on dataset characteristics and application requirements. Future work will explore advanced clustering methods such as DBSCAN and Gaussian Mixture Models to improve scalability and performance on large datasets.
Copyright (c) 2025 Mohammed Basil Abdulkareem https://creativecommons.org/licenses/by/4.0
Integrating Machine Learning and Genetic Algorithms to Enhance Gene-Disease Classification: An XBNet-Based Framework
In bioinformatics, the classification of gene-disease associations is crucial. It directly affects whether we can untangle the genetic roots of various disease as well as if we will find some justifiable therapy for these cured diseases.Using XBNet to construct genetic algorithms for higher accuracy and speeds of gene-disease classification--this is the method developed in the book.Consisting of gene expression profiles for six diseases--Alzheimer's, Asthma, Cancer, Diabetes, Fabry and Down syndrome--our research has applied a comprehensive pre-processing technique to this data set from Kaggle. This has included such things as eliminating stop-words and punctuation marks and tokenization. Using the terms of Frequency (TF) and of Term Frequency-Inverse Document Frequency (TF-IDF method) for features extraction, our text data on genes are transformed into numerical axes fit for input to machine learning models.
Copyright (c) 2025 Rana Khalid Hamad https://creativecommons.org/licenses/by/4.0
Leveraging AI and Blockchain in MANETs to enhance Smart City Infrastructure and Autonomous Vehicular Networks
The incorporation or combination of Artificial Intelligence (AI) and blockchain technology into Mobile Ad Hoc Networks (MANETs) shows important factor for modern and advance smart city infrastructure and autonomous vehicular networks. This paper describes the complementary potential of the technologies to help the built-in difficulties of MANETs includes flexibility, protection, and data integrity. AI techniques such as machine learning and reinforcement learning, are emphasized to improve routing protocols to optimize data transmission rates, and decrease latency. Blockchain technology using Practical Byzantine Fault Tolerance (PBFT) and other consensus mechanisms, gives a tight and decentralized architecture for data handling assuring trust and integrity amidst network nodes. The appeal of these incorpoarted technologies is especially related for smart cities which depand on collection of data and evaluation for effective handling of urban operations such as flow of traffic, environmental observing, and consumption of energy. Autonomous vehicular networks needing rigd and strong communication and data transfer between vehicles and infrastructure, also help from the enhanced network functions and security provided by AI and blockchain incorpoaration. Experimental evaluation denotes improvements in crucial performance metrics. Sensor 2 persists the highest data transmission rate of 12 Mbps. Sensor 4 had the decreased at 9 Mbps. Latency measurements observed that Sensor 2 recorded the lowest latency at 45 ms, with Sensor 3 having the highest at 55 ms.
Practical Byzantine Fault Tolerance (PBFT)
Copyright (c) 2024 S. Gopalakrishnan, E.D. Kanmani Ruby, D. Hemanand, R. Anitha, D. Suresh, Shruti Bhargava choubey https://creativecommons.org/licenses/by/4.0
Application of Artificial Intelligence (AI) in Environment and Societal Trends: Challenges and Opportunities
AI is increasingly pivotal in addressing global challenges by integrating technological advancements into environmental conservation and societal development. The aim of the study was understanding the application of AI in environmental and societal trends: challenges and opportunities. The secondary data analytical methodology regarding the application of AI in environmental and social trends was harnessed through a systematic search across various databases such as Web of Science, Scopus, Dimensions, Google Scholar, and JSTOR. Keywords used were "artificial intelligence," "environmental impact," "societal trends," "challenges," and "opportunities." The search strategy will be further refined with Boolean operators, namely AND, or, for instance, using terms like "AI AND environmental sustainability" or "AI AND social impact." Over 1895 documents were researched, but only 26 were used from 2015 to 2024. Exclusion criteria omitted studies outside the specified timeframe, non-English sources, and articles focused solely on AI technology without societal or environmental context. The research highlights AI applications in environmental management, such as optimizing resource use, monitoring ecosystems, and enhancing renewable energy efficiency. In societal contexts, AI's transformative potential extends to healthcare, education, and urban planning, fostering equitable access to essential services. However, significant barriers, including data scarcity, limited technical infrastructure, regulatory gaps, and socio-economic inequalities, hinder widespread AI adoption. By analyzing secondary data from recent literature, this study identifies critical challenges and presents actionable policy recommendations to ensure inclusive, ethical, and impactful AI integration. The findings underscore the urgency of collaborative efforts among governments, industries, and academia to leverage AI for achieving the SDGs and addressing pressing global issues. There is a need to strengthen collaboration between governments, private firms, and research institutions to drive AI innovation and scaling.
Copyright (c) 2024 Benson Turyasingura, Natal Ayiga, Fredrick Kayusi, Mellon Tumuhimbise https://creativecommons.org/licenses/by/4.0
A Comprehensive Review of Deep Learning and Machine Learning Techniques for Real-Time Car Detection and Wrong-Way Vehicle Tracking
This research focuses on the advancements in car detection techniques, particularly targeting wrong-way driving vehicles, using deep learning and machine learning methodologies. In recent years, numerous techniques have been proposed to address vehicle detection in real-time scenarios, leveraging algorithms such as YOLO (You Only Look Once) and centroid tracking to detect vehicles in various traffic situations. Additionally, methods involving UAV imagery, infrared imaging, and frame differencing approaches have enhanced the capabilities of real-time vehicle detection systems. Despite achieving significant milestones in accuracy and efficiency, existing methods still face limitations, such as high false-positive rates, imbalanced datasets, and challenges in complex environments like poor lighting and diverse road conditions. This study provides a comprehensive review of recent car detection approaches, comparing various algorithms including YOLO variants, CTAD, CNN, and DLMTD, and evaluating their strengths and limitations. A critical analysis of these methods reveals areas for improvement, particularly in terms of enhancing robustness, optimizing real-time response, and expanding detection capabilities to accommodate complex traffic patterns. The findings underscore the potential of hybrid approaches that combine object detection, tracking, and feature extraction techniques to achieve higher accuracy and adaptability in real-time applications. The study concludes by proposing a framework that addresses the observed limitations, suggesting pathways for future research in developing efficient, AI-powered car detection systems tailored for real-world applications.
Copyright (c) 2023 Waseem Ghafori Yass , Mohammad Faris https://creativecommons.org/licenses/by/4.0
Diagnosis of HIV (AIDS) by Using deep learning and machine learning
Human Immunodeficiency Virus (HIV) is a global health issue that can progress to Acquired Immunodeficiency Syndrome (HIV) if not diagnosed and treated early. The advent of Artificial Intelligence (AI), particularly in machine learning and deep learning, presents new opportunities for improving the accuracy and efficiency of HIV diagnosis. This research explores the application of AI techniques in diagnosing HIV by reviewing previous studies and proposing a novel AI-based approach. The proposed methodology leverages deep learning algorithms, such as convolutional neural networks (CNNs), along with advanced data preprocessing techniques to enhance diagnostic accuracy, sensitivity, and specificity. The results of the proposed CNN-based model show an accuracy of 96.2%, sensitivity of 95.8%, specificity of 96.8%, and an AUC-ROC score of 0.965. Compared to Random Forest (accuracy: 92.1%), SVM (accuracy: 91.5%), and traditional methods (accuracy: 89.0%), the CNN model outperforms existing techniques significantly in terms of accuracy, sensitivity, and specificity. This demonstrates the effectiveness of the proposed AI approach for enhancing early and accurate HIV detection.
Copyright (c) 2023 Hadeel M Saleh , Abdulrahman Kareem Oleiwi, Ahmed Abed Hwaidi Abed https://creativecommons.org/licenses/by/4.0
Detecting attacks in banks by cyber security: an applied study
In the present era, digital banks are more defenseless to cyber banks due to their banking responses. With the increasing reliance on the maximum in digital business operations, these targets have become attractive targets for people who want to exploit personal and financial information. This aims to explore how cyber security can be used to reveal passwords in banks. By using several choices such as usage detection (IDS), behavior study, and learning algorithms, this study is current in identifying chances before major damages. By studying the CICIDS 2017 dataset, we highlight this study on the real application of Random Forest algorithm to enhance security levels in banks. The outcomes emphasize the need for continuous asset in varied cyber safety and employee training Francisco x Late Cyber.
Object Detection Using Capsule Neural Network: An Overview
Appropriate remedies for tasks like language translation, object identification, object segmentation, picture recognition, and natural language processing are needed for today's computer vision tasks. This paper explores the use of Capsule neural Networks (Caps Nets) in object identification and offers a thorough analysis of their developments and uses. It presents an analysis of the transformative impact of Caps Nets on object identification tasks. This distinctive the architecture of neural network is presented as an alternative to convolutional neural networks (CNNs). The paper highlights that how Caps Nets are unique in capturing spatial correlations and hierarchical patterns in visualization data by analyzing the fundamental ideas of the technology. Additionally, it demonstrates how Caps net out perform CNNs in terms of generalization, interpretability, and last in the resistance to spatial distortions, confirming their goodness at object detection. By integrating the Caps networks with the latest scientific findings and advances, the paper shows the current status and potential future paths for object detection methods that use this leading neural network architecture.
Copyright (c) 2024 Eman Turki Mahdi, Shokhan M. Al-Barzinji , Waleed Kareem Awad https://creativecommons.org/licenses/by/4.0
Advancements in Machine Learning and Deep Learning for Early Diagnosis of Chronic Kidney Diseases: A Comprehensive Review
Chronic kidney disease (CKD) is a prevalent and debilitating condition worldwide, characterized by progressive loss of kidney function over time. Early detection plays a crucial role in mitigating its impact on patient health and healthcare systems. In recent years, there has been a burgeoning interest in leveraging machine learning (ML) and deep learning (DL) techniques to enhance the early diagnosis of CKD. This comprehensive review explores the advancements in ML and DL models applied to CKD diagnosis, focusing on their ability to integrate diverse data sources including clinical biomarkers, imaging modalities, and patient demographics. Key ML algorithms such as Support Vector Machines (SVM), Random Forests (RF), and neural network architectures like Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) are examined in the context of their performance in predicting CKD progression, classifying disease stages, and identifying at-risk populations. Furthermore, the review discusses challenges such as data quality, model interpretability, and integration into clinical practice, alongside emerging trends in explainable AI, transfer learning, federated learning, and integration with electronic health records (EHRs). By synthesizing findings from recent literature, this paper aims to provide insights into current methodologies, identify gaps for future research, and underscore the transformative potential of ML and DL in revolutionizing early CKD diagnosis and management..
Copyright (c) 2024 Akeel Shaker Mahmoud, Olfa Lamouchi , Safya Belghith https://creativecommons.org/licenses/by/4.0
AI-Powered Anomaly Detection for Kubernetes Security: A Systematic Approach to Identifying Threats
This study delves into the intricacies of AI-based threat detection in Kubernetes security, with a specific focus on its role in identifying anomalous behavior. By harnessing the power of AI algorithms, vast amounts of telemetry data generated by Kubernetes clusters can be analyzed in real-time, enabling the identification of patterns and anomalies that may signify potential security threats or system malfunctions. The implementation of AI-based threat detection involves a systematic approach, encompassing data collection, model training, integration with Kubernetes orchestration platforms, alerting mechanisms, and continuous monitoring. AI-powered threat detection offers numerous advantages, including predictive threat detection, increased accuracy and scalability, shorter response times, and the ability to adapt to evolving threats. However, it also presents challenges, such as ensuring data quality, managing model complexity, mitigating false positives, addressing resource requirements, and maintaining security and privacy standards. The proposed AI-powered anomaly detection framework for Kubernetes security demonstrated significant improvements in threat identification and mitigation. Through real-time analysis of telemetry data and leveraging advanced AI algorithms, the system accurately identified over 92% of simulated security threats and anomalies across various Kubernetes clusters. Additionally, the integration of automated alerting mechanisms and response protocols reduced the average response time by 67%, enabling rapid containment of potential breaches.
Copyright (c) 2024 Arvind Kumar Bhardwaj, P.K. Dutta , Pradeep Chintale https://creativecommons.org/licenses/by/4.0
A Novel Method of Using Machine Learning Techniques to Protect Clouds Against Distributed Denial of Service (DDoS) Attacks.
The term "cloud computing" describes a method of providing hardware- and software-based services over the internet. This allows users to access their data and apps from any device. The benefits of cloud computing include scalability, virtualization, access to user assets, lower infrastructure costs, and flexibility. However, one drawback is that it is susceptible to distributed denial of service attacks, which occur when multiple computer systems collaborate to target a particular resource, website, or server. Distributed denial of service (DDoS) attacks present a serious risk to computer networks and constitute a major cyber security challenge. This results in a denial of service for end users, as a result of false connection requests, a flood of messages, and twisted packets causing the system to slow down or even crash. Real people and services cannot access cloud computing. The issue of machine learning algorithms for identifying distributed denial of service (DDoS) attacks is explored in this article. In order to identify and defend cloud systems from harmful assaults, we developed a new machine learning approach in this work that is based on transfer learning. This study offers NSL-KDD datasets and two methodologies. There are two types of filters available: the Learning Vector Quantization (LVQ) Filters and the Principal Component Analysis (PCA) method, which reduces dimensionality. The features selected from each method were pooled using Decision Tree (DT), Naïve Bayes (NB), and Support Vector Machine (SVM) to detect distributed denial of service attacks (DDoS). We contrasted the results of several classifications. In terms of attack detection, LVQ-based DT performed better results as compared to other methods.
Copyright (c) 2024 SANGEETA DEVI, Pranjal Maury , Upendra Nath Tripathi https://creativecommons.org/licenses/by/4.0
Analysis and prediction of evaporation rates using random forest models: a case study of Almaty city
In this study, Climate Research Unit (CRU) data crossing from 1901 to 2022 was utilized to analyze and predict evaporation rates in Almaty, Kazakhstan. The data was prepared utilizing Python, leveraging the Random Forest (RF) machine learning Techniques for its capability to handle complex and non-linear information. The essential measurements for assessing the model's execution were Cruel Supreme Blunder (MAE), Root Cruel Square Mistake (RMSE), and Cruel Squared Mistake (MSE). The Random Forest model was utilized in prediction and data examination. This model is recognized by its capacity to process non-linear and complex data by making a set of choice trees and joining their comes about to get exact and steady predictions. The model is based on arbitrary testing with bootstrap testing, which makes a difference decrease change and increment the precision of the demonstrate.
Copyright (c) 2023 H. K. Al-Mahdawi, Hussein Alkattan, Alhumaima Ali Subhi , Haider Flaiyih Al-hadrawi, Mostafa Abotaleb, Ghassan K. Ali, Maad M. Mijwil, Al-Sayed K. Towfeek, Amr Hosny Helal https://creativecommons.org/licenses/by/4.0
Analysis and prediction of rainfall using support vector machine (SVM) in the city of Najaf
This study depends on the utilize of Support Vector Machine (SVM) method to analyze and predict monthly rainfall in Najaf city, Iraq over the past ten a long time. Support Vector Machine is one of the foremost imperative instruments in classifying and analyzing information in multiple areas, counting climate and climate data investigation. The Climatic Inquire about Unit (CRU) information traversing the a long time 1901 to 2022 were utilized to analyze and foresee monthly rainfall. The comes about appeared shifting precision in predicting between distinctive months, as there were expansive contrasts between the anticipated and real values in a few months such as April, whereas the predict were more exact in other months such as March.
A Fuzzy Wavelet Neural Network (FWNN) and Hybrid Optimization Machine Learning Technique for Traffic Flow Prediction
Traffic go with the flow forecasting is essential in urban planning and management, optimizing transportation structures and resource allocation. However, accurately predicting visitors glide is tough because of its inherent complexity, nonlinearity, and diverse uncertain factors. The trouble declaration underscores the issue in as it should be forecasting site visitors flow, mainly in urban environments characterized through dynamic and complex site visitor’s styles. In the existing paintings there are numerous traditional devices getting to know models used for visitors flow prediction, however those conventional strategies show off barriers in reaching excessive prediction accuracy. Therefore, the proposed work targets to put into effect hybrid optimization techniques for correct prediction in shipping machine. Here fuzzy wavelet neural community (FWNN) is used to address complicated nonlinear structures with uncertain conditions and hybrid optimization method called hybrid firefly and particle swarm optimization (HFO-PSO) which combines the exploration and exploitation talents of firefly and this fusion allows the version to capture intricate visitor’s styles efficiently and optimize the prediction technique, improving accuracy and efficiency. Moreover, the prediction performance of the proposed model is established and compared by means of the usage of distinct measures.
Copyright (c) 2024 Karthika Balasubramani, Uma Maheswari Natarajan https://creativecommons.org/licenses/by/4.0
Data Analysis of An Exploring the Information Systems Success Factors for Early Warning Systems Adoption
Naturally occurring floods are an essential part of life in many parts of the world. Floods, of all the natural dangers, have the greatest effect on society because they cover large geographic regions, happen often, and have a lasting negative socioeconomic impact. Thus, it becomes necessary to design a comprehensive and successful strategy for preventing floods, which will require technical advancements to improve the operational efficacy of government organizations. The Flood Early Warning and Response System (FEWRS), which gives pertinent stakeholders fast information and practical reaction guidelines, emerges as a critical instrument in reducing the loss of lives and property. Unfortunately, current FEWRS frequently fall short of providing enough information on flood disasters, which reduces their ability to mitigate local-level effects and impedes attempts to save lives. Assessing the effectiveness of information systems (IS) within this particular setting is a noteworthy obstacle for scholars, professionals, and administrators. The objective of this research is to tackle this difficulty by exploring the factors that lead to the success of FEWRS. This involves incorporating risk knowledge and response capabilities into the standard IS success model. The present study employs the DeLone and McLean (D&M) models due to their efficaciousness in meeting the designated requirements that are essential for mitigating the impact of flooding disasters.
Copyright (c) 2024 Waleed A Hammood, Omar Abdulmaged Hammood , Salah A. Aliesawi, Ejiro U. Osiobe , Raed Abdulkareem Hasan, Safia Malallah https://creativecommons.org/licenses/by/4.0