Daily Archives: January 12, 2026

Authors: Pranesh Mudiraj

Abstract: The integration of advanced machine learning models into SAP ERP systems has revolutionized the traditional landscape of financial planning and analysis by shifting organizational focus from reactive reporting to proactive forecasting. This review article evaluates the transition from manual, spreadsheet-based accounting toward automated predictive frameworks that leverage the in-memory computing power of SAP S/4HANA. We examine a diverse taxonomy of algorithms, ranging from classical time-series analysis and ensemble methods to sophisticated deep learning architectures, and their specific applications in revenue projection, cash flow management, and risk mitigation. The study details the technical synergy between the SAP Business Technology Platform and embedded analytical engines, emphasizing the importance of data preprocessing and feature engineering in a complex enterprise environment. Furthermore, we provide a comparative analysis between traditional and machine-learning-based forecasting, highlighting improvements in accuracy, cycle time, and scalability. The paper concludes by discussing emerging trends such as generative AI and real-time predictive accounting, offering a strategic roadmap for financial leaders aiming to implement data-driven decision-making processes. By synthesizing current methodologies and practical use cases, this study demonstrates how predictive analytics serves as a cornerstone for the modern intelligent enterprise.

DOI: http://doi.org/10.5281/zenodo.18221566

Published by: vikaspatanker

Authors: Nivaan Varma

Abstract: As global business environments become increasingly volatile, traditional enterprise risk management frameworks struggle to keep pace with high-velocity, interconnected disruptions. This review article investigates the integration of artificial intelligence into risk management lifecycles to enhance business resilience. We examine how machine learning, natural language processing, and predictive analytics transform the stages of risk identification, assessment, and mitigation from reactive to proactive processes. The study highlights the role of AI in critical domains such as cybersecurity, supply chain elasticity, and financial stability, while also addressing the theoretical shift toward the anticipate-absorb-recover-adapt cycle of resilience. Furthermore, the article explores the significant challenges associated with AI adoption, including model opacity, data bias, and the urgent need for explainable AI and human-in-the-loop governance. By synthesizing current research with emerging trends like generative AI and quantum-resistant modeling, we provide a strategic roadmap for organizations aiming to build antifragile systems. This study concludes that the synergy between human strategic judgment and machine intelligence is the fundamental requirement for maintaining long-term survivability in the digital age.

DOI: http://doi.org/10.5281/zenodo.18221560

Published by: vikaspatanker

Authors: Omkar Walunj, Pranav Hole, Sarthak Thigale, Sohan SandbhorD

Abstract: With the rapid advancement of Artificial Intelligence (AI), healthcare systems are shifting from reactive to proactive models capable of predicting, diagnosing, and preventing diseases. This paper presents Smarthealth, a cloud-based predictive healthcare system that utilizes machine learning algorithms to analyze patient data, anticipate potential health issues, and generate timely alerts. The system integrates AI models for disease prediction and employs Firebase for real-time synchronization and secure data storage. The objective of this work is to develop an efficient, scalable, and secure AI-driven healthcare prediction platform that assists doctors and patients in early diagnosis and informed medical decision-making.

Published by: vikaspatanker

Authors: Gayathri S, Varshameena. M

Abstract: Cloud computing has emerged as a revolutionary technology that enables on-demand access to shared computing resources such as storage, applications, and processing power through the internet. In recent years, educational institutions have increasingly adopted cloud computing to modernize teaching, learning, and administrative processes. This shift is driven by the growing demand for flexible learning environments, digital collaboration, remote accessibility, and cost-effective infrastructure management. Traditional educational systems rely heavily on physical hardware and locally installed software, which often leads to high maintenance costs, limited scalability, and restricted access to learning resources. Cloud computing overcomes these limitations by offering scalable, reliable, and affordable solutions tailored to academic needs. This paper explores the adoption of cloud computing in educational institutions, focusing on its architecture, service models, and practical applications. Cloud-based platforms such as Learning Management Systems (LMS), virtual classrooms, digital libraries, and online assessment tools have transformed the educational ecosystem by enabling anytime-anywhere learning. The study highlights key benefits of cloud adoption, including reduced operational costs, improved collaboration among students and faculty, enhanced data storage and backup capabilities, and increased institutional efficiency. Additionally, cloud computing supports innovation in education by integrating emerging technologies such as artificial intelligence, big data analytics, and smart learning environments. Despite its advantages, the adoption of cloud computing in education also presents challenges such as data security, privacy concerns, internet dependency, and vendor lock-in. This paper discusses these challenges and emphasizes the importance of implementing strong security policies, data protection mechanisms, and regulatory compliance to ensure safe and effective cloud usage. The study concludes that cloud computing plays a vital role in the digital transformation of educational institutions and has the potential to significantly improve the quality, accessibility, and sustainability of education. With proper planning and governance, cloud computing can serve as a powerful enabler for the future of education.

Published by: vikaspatanker

Authors: K. Sai Teja, M.Surya Teja, S.Bharath Simha Rao, Y.Hemanth Kumar

Abstract: Face morphing attacks represent a critical vulnerability in biometric authentication sys- tems, where two or more facial images are digitally blended to create a forged identity. Such morphed images can successfully deceive automated face verification systems, leading to severe risks in applications like passport issuance, border control, and iden- tity management. Traditional detection techniques, relying on handcrafted features or differential meth- ods, often fail to generalize across diverse morphing techniques and image qualities. To overcome these limitations, we propose MorphDetect, a deep learning-based Single- Image Morphing Attack Detection (S-MAD) system powered by the EfficientNet-B7 model. The system first preprocesses face images for normalization and then extracts high- dimensional features using EfficientNet-B7’s advanced convolutional blocks. These features are passed through a classification layer that determines whether an input is genuine or morphed, producing a reliable confidence score for decision-making. MorphDetect eliminates the need for a trusted reference image and provides a scal- able, real-time solution for morph detection. By leveraging a strong pretrained back- bone, it ensures robustness against unseen morphing techniques and diverse imaging conditions. This makes the system well-suited for deployment in high-security appli- cations such as e-passport verification, financial KYC procedures, and secure access systems.

Published by: vikaspatanker