Daily Archives: November 25, 2025

Authors: Ishaan Rathore

Abstract: Hyperautomation represents a significant evolution in enterprise digital workflows, blending advanced technologies like artificial intelligence, machine learning, robotic process automation, and analytics to automate complex business processes end-to-end. This innovation is not merely about substituting human tasks with machines but driving intelligent automation that enhances decision-making, efficiency, and agility. Hyperautomation enables organizations to streamline operations, reduce costs, improve accuracy, and enhance customer experiences while fostering continuous improvement through data insights. As enterprises encounter rapid technological shifts, market volatility, and customer expectations, hyperautomation offers a strategic lever to maintain competitiveness and scalability. By integrating multiple automation tools, hyperautomation transforms traditional workflows into dynamic, adaptive systems capable of responding quickly to changing demands and operational conditions. This comprehensive article explores the multifaceted impact of hyperautomation on streamlining enterprise digital workflows, detailing how it redefines business processes, technology integration, workforce roles, and organizational culture. Through real-world examples, key technologies, implementation strategies, challenges, and future trends, the narrative aims to provide valuable insights for stakeholders seeking to harness hyperautomation to drive digital transformation initiatives effectively.

DOI: https://doi.org/10.5281/zenodo.17707712

 

Published by: vikaspatanker

Authors: Fashagba, I, Asonibare, R. O, Babatunde, K, Ajadi, B. S

Abstract: Land encroachment has become a major challenge affecting land administration and institutional expansion in Nigeria, particularly in peri-urban areas. This study assesses the pattern, extent, and progression of land encroachment on the permanent site of Kwara State Polytechnic, Ilorin, from 2004 to 2024 using aerial drone imagery and GIS techniques. The objectives were to: (i) identify areas encroached upon by surrounding settlements, (ii) determine the proportion of land currently occupied by the institution, and (iii) visualize encroachment trends through maps and imagery. Primary data were collected using a DJI Phantom 4 Pro drone, and the imagery obtained was processed into digital, detailed, topographic, and perimeter maps. Results show rapid and continuous expansion of settlements such as Ara, Ajia, Magaji, Budo-Oba, Yerima, Dangiwa, Akuo, and others, with several fusing into larger settlement clusters. Encroachment is most severe along the southern and eastern axes of the Polytechnic. Less than one-third of the acquired institutional land remains undeveloped, creating opportunities for illegal occupation. The study recommends the construction of a perimeter fence, government-led relocation of encroaching settlements, and the provision of institutional accommodation through public-private partnerships.

Published by: vikaspatanker

Authors: Kavya Sunder

Abstract: Autonomous incident response systems are rapidly transforming how organizations manage IT operations and cybersecurity events. These systems leverage advanced technologies such as artificial intelligence (AI), machine learning (ML), and automation to detect, analyze, and respond to incidents without requiring manual intervention. By enabling faster and more accurate identification of threats and operational anomalies, autonomous incident response systems substantially reduce downtime and improve overall business continuity. This article explores the mechanisms through which these systems operate, their impact on reducing downtime, and the advantages they provide over traditional, manual incident management approaches. With the increasing complexity of IT infrastructure and the rising frequency of cyber-attacks, traditional incident response methods often fall short in speed and efficiency. Human-led responses are constrained by limited capacity, prone to errors, and unable to keep pace with modern threats. Autonomous systems address these challenges by continuously monitoring environments, correlating data from diverse sources, and executing predefined or adaptive response strategies swiftly. This results in minimized disruption, faster recovery, and better alignment with organizational objectives.This article also discusses various case studies and real-world applications where autonomous incident response systems have significantly decreased downtime and optimized operational resilience. Challenges associated with implementing these systems, such as integration complexity and trust in automated decisions, are analyzed alongside future trends, emphasizing the growing importance of AI-driven incident response in digital transformation strategies. Ultimately, autonomous incident response systems empower organizations to proactively manage incidents, thus preserving service availability and enhancing stakeholder confidence.

DOI: https://doi.org/10.5281/zenodo.17707593

 

Published by: vikaspatanker

Authors: Aarav Menon

Abstract: -driven observability is revolutionizing the landscape of application performance monitoring (APM). Traditional methods reliant on manual analysis and static threshold alerts are increasingly insufficient to cope with the complexity and dynamic nature of modern digital applications. AI-enabled observability leverages advanced machine learning, anomaly detection, and automated root cause analysis to provide real-time, actionable insights into application health, user experience, and infrastructure performance. This paradigm shift enables organizations to swiftly identify and mitigate performance bottlenecks, reduce downtime, and optimize resource utilization. By integrating telemetry data from logs, metrics, and traces, AI-driven solutions synthesize vast amounts of heterogeneous data into meaningful patterns that empower proactive decision-making. This article explores the transformative impact of AI-driven observability on APM, detailing its core mechanisms, benefits, key technologies, practical applications, challenges, and future trends. The integration of AI not only enhances detection accuracy but also enables predictive analytics, thereby preventing issues before they affect end users. Through this comprehensive examination, readers will gain insight into how organizations can harness AI-driven observability to achieve superior application reliability, operational efficiency, and business agility in an increasingly digital economy.

DOI: https://doi.org/10.5281/zenodo.17707529

Published by: vikaspatanker