Enhancing Cybersecurity with Machine Learning: Development and Evaluation of Intrusion Detection Systems (Published)
The widespread adoption of digital networks and information systems has transformed modern society, but it has also led to a surge in sophisticated cyber threats such as malware, phishing, denial-of-service (DoS) attacks, ransomware, and advanced persistent threats (APTs). Traditional rule-based security systems are increasingly ineffective against these evolving threats, often failing to detect novel attack patterns, leading to false positives, missed detections, and delayed responses. This study aimed to address these challenges by applying machine learning algorithms to improve the accuracy and efficiency of cyber-attack detection. Using the UNSW-NB15 dataset, which contains 175,341 training and 82,332 testing records representing both benign and malicious network traffic with 49 relevant features, the research applied synthetic minority over-sampling technique (SMOTE) to balance the dataset and principal component analysis (PCA) to reduce feature dimensionality by retaining up to 95% of data variance. Five machine learning models Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Decision Tree, and Random Forest were trained and evaluated using metrics such as accuracy, precision, recall, and F1 score.The results demonstrated that KNN achieved the highest accuracy of 94.69%, with balanced precision (95.31%), recall (93.96%), and F1 score (94.63%), showing robust classification of both attack and non-attack instances. Random Forest and ANN also showed strong performances with accuracies of 92.81% and 95%, respectively, highlighting their effectiveness in handling complex cybersecurity data. SVM and Decision Tree had slightly lower accuracies of 90.88% and 92.22%. These findings confirm the value of machine learning, especially KNN and ensemble methods, for real-world intrusion detection. Regular model retraining is essential to address emerging attack patterns and maintain effective cybersecurity defenses.
Keywords: Feature Selection, cyber security, cyber threats, intrusion detection, machine learning
Applying AI/ML to Kubernetes Logging and Monitoring in Enhancing Observability Through Intelligent Systems (Published)
As Kubernetes adoption accelerates in cloud-native architectures, ensuring robust observability across dynamic, large-scale clusters has become a critical operational challenge. Traditional logging and monitoring systems—relying heavily on rule-based alerting and manual log inspection—struggle to scale with the volume, velocity, and complexity of modern workloads. These approaches often lead to alert fatigue, delayed incident response, and incomplete root cause analysis.This paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance observability within Kubernetes environments. By leveraging unsupervised learning for anomaly detection, natural language processing (NLP) for log parsing, and supervised models for event classification, the proposed intelligent observability framework significantly improves signal-to-noise ratios and accelerates troubleshooting processes. Through empirical evaluation on a production-grade Kubernetes testbed, the system demonstrated a 35% improvement in anomaly detection accuracy and reduced mean time to resolution (MTTR) by over 40% compared to baseline tools. These results highlight the transformative potential of AI/ML in enabling proactive, scalable, and context-aware monitoring solutions for complex cloud-native infrastructures.
Keywords: Artificial Intelligence, Logging, Monitoring, anomaly detection, kubernetes, machine learning, observability
AI-Driven Fraud Detection Models in Cloud-Based Banking Ecosystems: A Comprehensive Analysis (Published)
The digital transformation of banking services has fundamentally altered the financial fraud landscape, creating sophisticated threats that traditional rule-based security systems cannot adequately address. Contemporary fraudulent activities leverage advanced technologies, including synthetic identity creation, real-time social engineering attacks, and deepfake-enabled deceptions to exploit vulnerabilities in digital banking infrastructures. Conventional fraud detection mechanisms demonstrate critical limitations through static architectures, inability to adapt to novel fraud patterns, excessive false positive rates, and scalability constraints that compromise effectiveness in high-velocity transaction environments. Cloud-native infrastructures provide essential foundations for advanced fraud detection through elastic scalability mechanisms, real-time data streaming technologies, and seamless integration of external intelligence sources. AI-powered fraud detection models represent a paradigm shift toward adaptive security frameworks, incorporating ensemble learning methodologies, deep neural networks, and real-time inference capabilities that enable instantaneous transaction evaluation. Machine learning algorithms deployed within cloud environments can process vast transactional datasets simultaneously, identifying subtle correlations and behavioral patterns impossible to detect through manual processes or traditional systems. Performance evaluation demonstrates superior detection accuracy through precision, recall, and F1-score metrics while maintaining model interpretability and regulatory compliance requirements. The integration of artificial intelligence with cloud-native infrastructure creates comprehensive fraud detection ecosystems that evolve alongside emerging threat vectors, ensuring continuous protection against sophisticated financial crimes in modern banking environments.
Keywords: Artificial Intelligence, Cloud-Native Infrastructure, Fraud Detection, financial security, machine learning
Demystifying Deep Learning and Neural Networks (Published)
Deep learning and neural networks have revolutionized artificial intelligence, transforming industries and daily life with applications ranging from voice assistants to medical diagnostics. Despite their ubiquity, these technologies remain enigmatic to many enthusiasts and practitioners. This article demystifies the fundamental concepts underlying neural networks, exploring their biological inspiration, architectural components, and learning mechanisms. Various deep learning architectures are examined, including convolutional neural networks, recurrent neural networks, transformers, and generative adversarial networks, elucidating their distinctive features and applications. The discussion extends to practical considerations in training neural networks, highlighting data requirements, optimization challenges, and regularization techniques. By exploring applications across computer vision, natural language processing, speech recognition, and recommendation systems, the transformative impact of these technologies is illustrated. The article concludes by addressing limitations and ethical considerations, emphasizing the importance of interpretability, fairness, resource efficiency, and environmental sustainability as the field continues to advance.
Keywords: Artificial Intelligence, Neural Networks, cognitive computing, deep learning, machine learning
Advancing Energy Efficiency in Bluetooth LE for Android Wearable Ecosystem (Published)
This article presents an innovative approach to optimizing energy efficiency in Bluetooth Low Energy (BLE) implementations for Android wearable devices. The article addresses critical challenges in power management through the development of an adaptive connection manager that utilizes machine learning techniques. The proposed solution integrates an intelligent layer between the application and Bluetooth stack, implementing dynamic power state adjustments and smart reconnection protocols. By analyzing various operational modes and connection parameters, this article demonstrates significant improvements in power consumption while maintaining optimal performance. The article findings validate the effectiveness of AI-driven power management strategies and provide insights into future developments in BLE technology, particularly focusing on enhancing battery life in healthcare monitoring and fitness tracking applications.
Keywords: Bluetooth low energy, energy optimization, machine learning, power management, wearable technology
Cloud-First FinTech: No-Code and ML Use Cases Across Banking and Insurance (Published)
The financial services industry is experiencing a profound technological transformation driven by cloud-native architectures, no-code development platforms, and advanced machine learning applications. This transformation extends beyond infrastructure modernization to fundamentally reshape how financial services are conceived, developed, and delivered across banking, insurance, and wealth management sectors. Cloud-first approaches are enabling dramatic improvements in operational efficiency, market responsiveness, and customer experience while simultaneously reducing costs and expanding service accessibility. No-code platforms are democratizing development capabilities, allowing business experts to directly implement process improvements without traditional technology dependencies. Machine learning implementations are revolutionizing claims processing in insurance, simultaneously enhancing fraud detection and assessment accuracy while accelerating settlement timelines. In wealth management, AI-driven advisory platforms are dramatically lowering barriers to professional financial guidance while maintaining service quality comparable to traditional advisory relationships. Together, these technologies are creating competitive differentiation for early adopters while reshaping market dynamics across the financial services ecosystem. The examination of real-world implementations across these domains provides actionable insights for
Keywords: AI-driven advisory, Cloud-Native Architecture, financial services transformation, machine learning, no-code development
AI-Driven Decision Support Systems in Healthcare Integration: Transforming Clinical Decision-Making Through Intelligent Data Analysis (Published)
Worldwide, Healthcare systems encounter unprecedented challenges in managing complex patient data while ensuring accurate diagnoses and optimal treatment outcomes. The exponential growth of medical data and increasing patient complexity and healthcare demands have created an urgent need for sophisticated decision support mechanisms that transcend traditional clinical decision-making constraints. Artificial Intelligence has emerged as a transformative solution, offering unprecedented capabilities in data analysis, pattern recognition, and predictive modeling that fundamentally reshape healthcare delivery paradigms. AI-driven decision support systems represent a paradigm shift from reactive to proactive healthcare delivery, enabling clinicians to leverage comprehensive data analysis for enhanced decision-making processes by integrating multiple data sources, including electronic health records, medical imaging, laboratory results, and real-time patient monitoring data. Integrating Natural Language Processing for unstructured data analysis, Machine Learning for predictive modeling, and Expert Systems for knowledge-based reasoning creates comprehensive decision support frameworks that augment clinical expertise while maintaining essential human elements in patient care. Deep learning architectures, particularly convolutional neural networks, demonstrate exceptional capability in medical image analysis, achieving performance levels comparable to trained specialists across diverse diagnostic scenarios. Clinical applications span diagnostic decision support, predictive analytics, treatment optimization, patient monitoring, and population health management, illustrating comprehensive impact across the healthcare continuum. Implementation strategies require sophisticated technical integration addressing data infrastructure, interoperability standards, workflow integration, and extensive training programs. However, significant challenges persist, including data quality standardization, algorithmic bias mitigation, regulatory compliance navigation, ethical considerations regarding AI roles in clinical decision-making, and professional acceptance challenges. Addressing these multifaceted challenges demands collaborative efforts among technologists, clinicians, regulators, and ethicists to ensure AI systems enhance healthcare quality and equity.
Keywords: Artificial Intelligence, Decision Support Systems, clinical applications, healthcare integration, machine learning, medical data analysis
Causal Inference in Data Science: A Framework for Attribution Systems (Published)
This article explores the fundamental principles and applications of causal inference in data science, particularly focusing on attribution systems across business domains. It examines how causal inference methods enable organizations to move beyond traditional correlation to establish more robust attribution frameworks. The article discusses key methodological approaches, including directed acyclic graphs, counterfactual analysis, and machine learning integration, while addressing implementation challenges in real-world business settings. Through analysis of recent research and case studies, the article demonstrates how causal inference techniques enhance decision-making accuracy in marketing, customer analytics, and financial strategies. The article highlights both the theoretical foundations and practical applications of causal inference, emphasizing its role in improving attribution accuracy and business outcomes across various organizational contexts.
Keywords: Decision Making, attribution systems, business analytics, causal inference, machine learning
AI in Insurance: Transforming Fraud Detection and Claims Processing through Salesforce Integration (Published)
The insurance industry is experiencing a profound transformation through artificial intelligence integration, particularly in fraud detection and claims processing operations. This article delves into how Salesforce Einstein serves as a pivotal platform for implementing AI solutions that address longstanding challenges in insurance workflows. Insurers face substantial financial losses from fraudulent claims and operational inefficiencies in claims handling, creating opportunities for technological innovation to drive competitive differentiation. Through the synergistic combination of sophisticated AI algorithms and Salesforce’s customer relationship management infrastructure, insurance providers can simultaneously enhance fraud detection accuracy and accelerate legitimate claims processing. The evolution of insurance operations has progressed from basic automation to advanced cognitive technologies, with Einstein’s capabilities spanning predictive analytics, natural language processing, and automated decision support. These technologies enable insurers to detect complex fraud patterns through both supervised and unsupervised machine learning techniques while streamlining claims workflows through intelligent automation. Document processing capabilities extract crucial information from submitted materials with remarkable precision, while comprehensive customer data integration facilitates personalized experiences. The resulting operational improvements include dramatic reductions in claims cycle times, decreased processing costs, enhanced payment accuracy, and significantly higher customer satisfaction scores. This technological paradigm shift ultimately creates more secure, responsive insurance systems that benefit both providers and policyholders, enabling insurers to maintain competitive advantages in an increasingly complex marketplace.
Keywords: Artificial Intelligence, Claims Automation, Fraud Detection, Salesforce Einstein, insurance technology, machine learning
The Transformation of Incident Management Through Artificial Intelligence: A Systematic Review (Published)
This systematic review examines the transformative impact of Artificial Intelligence (AI) on incident management systems across various organizational contexts. The article analyzes the evolution from traditional rule-based approaches to AI-powered solutions, highlighting significant improvements in operational efficiency, response times, and incident prevention capabilities. Through a comprehensive analysis of implementation challenges and success metrics, the article demonstrates how AI-driven systems have revolutionized incident detection, classification, and resolution processes. The article encompasses multiple performance indicators, exploring how machine learning algorithms, natural language processing, and predictive analytics have enhanced incident management frameworks while addressing integration challenges and human factors in system adoption.
Keywords: Artificial Intelligence, enterprise operations, incident management, machine learning, predictive analytics