Category Archives: Uncategorized

Authors: Aditya Bhandari

Abstract: Artificial Intelligence (AI) has emerged as a transformative force in IT infrastructure management, particularly in optimizing workload balancing across multi-cloud environments. Multi-cloud infrastructures, which involve the utilization of multiple cloud services from different providers, present a complex landscape for businesses seeking high availability, scalability, and cost efficiency. The dynamic nature of workloads, variability in service level agreements (SLAs), and diverse cloud resource characteristics necessitate intelligent automation to optimize performance. AI-driven approaches leverage machine learning algorithms, predictive analytics, and autonomous decision-making to manage workload distribution effectively, ensuring optimal utilization of resources while minimizing latency and operational costs. This article delves into the integration of AI in multi-cloud workload balancing, exploring how it addresses challenges such as resource heterogeneity, network latency, and fluctuating demand patterns. We discuss various AI techniques, including reinforcement learning, neural networks, and evolutionary algorithms, that are employed to predict workload behavior and automate deployment decisions. Additionally, the article examines real-world case studies highlighting successful AI implementations and outlines the future trajectory of this synergy. By adopting AI-driven workload optimization, organizations can enhance resilience, improve user experience, and achieve sustainable cloud operations amid the rapidly evolving digital ecosystem.

DOI: https://doi.org/10.5281/zenodo.17708834

Published by: vikaspatanker

Authors: Yash Pratap Singh, Tanya Dwivedi

Abstract: Agriculture plays a major role in the economy and livelihood of many people, especially in developing countries. Farmers often face difficulties in choosing the correct crop because soil nutrients, weather conditions, and rainfall vary from place to place. Choosing the wrong crop can reduce yield and lead to financial loss. To solve this problem, a machine learning based crop recommendation system can be used. This system analyzes soil features such as Nitrogen (N), Phosphorus (P), Potassium (K), pH value, and environmental factors like temperature, rainfall, and humidity. Based on these inputs, the system suggests the most suitable crop for cultivation. In this research, different machine learning algorithms are studied, and Random Forest is selected theoretically because it provides high accuracy and stable performance. The main aim of this study is to support farmers in making better decisions, reduce risk, and improve productivity. The proposed approach is simple, understandable, and can be further developed into a mobile or web application for real-world use.

DOI: http://doi.org/10.5281/zenodo.17708787

Published by: vikaspatanker

Authors: Nandini Iyer

Abstract: Artificial Intelligence (AI) has emerged as a transformative force in the field of distributed computing, particularly in enhancing fault tolerance mechanisms. Fault tolerance, the ability of a system to continue operating properly in the event of the failure of some of its components, is critical in distributed systems that involve numerous interconnected nodes and components. AI brings new capabilities to fault tolerance by enabling systems to predict, detect, and respond to faults more efficiently and accurately than traditional methods. By leveraging machine learning algorithms, anomaly detection techniques, and predictive analytics, AI enhances the robustness and resilience of distributed computing environments. This article explores the integration of AI into fault tolerance strategies within distributed computing systems. It discusses the key challenges faced in maintaining fault-tolerant distributed systems, the role of AI-driven predictive maintenance, and anomaly detection, and the application of reinforcement learning to dynamic resource allocation and recovery processes. It also covers AI-assisted decision-making in fault diagnosis and recovery, and how AI helps optimize system performance while minimizing downtime and operational costs. Additionally, the article evaluates case studies from cloud computing, edge computing, and critical infrastructures where AI-based fault tolerance has been successfully implemented. By synthesizing current research and technological advancements, this article aims to provide a comprehensive understanding of the potential and limitations of AI in improving the reliability and fault tolerance of distributed computing systems. The outlook on future trends and challenges highlights ongoing research directions and emerging technologies that promise to further transform this area. Keywords include fault tolerance, distributed computing, artificial intelligence, predictive maintenance, and anomaly detection.

DOI: https://doi.org/10.5281/zenodo.17708723

Published by: vikaspatanker

Authors: Rohan Verma

Abstract: Predictive analytics has emerged as a transformative force in the field of cybersecurity, enabling organizations to proactively identify, assess, and mitigate cyber threats before they materialize into severe security breaches. This article explores the evolving role of predictive analytics in enhancing cybersecurity readiness by leveraging historical data, machine learning algorithms, and real-time information to anticipate potential vulnerabilities and attack vectors. The integration of advanced analytics tools in cybersecurity frameworks has revolutionized threat detection and response strategies, shifting the paradigm from reactive to proactive defense. Predictive models analyze diverse data sources—including network traffic, user behavior, and threat intelligence feeds—to identify anomalous patterns and predict future attacks with increasing accuracy. This capability supports not only the detection of known threats but also the anticipation of novel, sophisticated cyberattacks. Additionally, predictive analytics facilitates better resource allocation, enabling organizations to prioritize cybersecurity efforts based on risk assessments and probabilistic forecasts. The article also addresses challenges such as data privacy, model accuracy, and the evolving landscape of cyber threats, emphasizing the need for continuous innovation and adaptation. By comprehensively examining the technological foundations, applications, benefits, and limitations of predictive analytics, this exploration highlights how predictive techniques contribute significantly to strengthening cybersecurity posture in a digital-first world. The discussion extends to case studies illustrating successful implementations, underscoring a transition towards dynamic, intelligence-driven security operations. Overall, predictive analytics stands as a critical enabler of cybersecurity readiness, providing a competitive edge in defending against ever-evolving threats.

DOI: https://doi.org/10.5281/zenodo.17708624

Published by: vikaspatanker

Authors: Mayuri R. Tone, Mohammad Abdul Razzaq, Syed Abdur Rasheed, Saud Ahamed

Abstract: Artificial Intelligence (AI) has become the cornerstone of autonomous vehicle (AV) technology, enabling self-driving systems to make complex, real-time decisions with minimal human intervention. By integrating machine learning, deep neural networks, computer vision, and sensor fusion, AI allows vehicles to interpret their surroundings, predict potential hazards, and plan safe and efficient routes. Decision-making in AVs relies on continuous data analysis from LiDAR, radar, cameras, and GPS to assess dynamic traffic conditions and respond adaptively to unpredictable environments. AI algorithms learn from vast datasets to improve accuracy, reliability, and safety, ensuring context-aware and ethical decision processes. This paper explores the pivotal role of AI in enhancing the perception, reasoning, and decision-making capabilities of autonomous vehicles, highlighting current advancements, challenges, and the potential impact of intelligent systems on the future of transportation.

DOI: https://doi.org/10.5281/zenodo.17708497

Published by: vikaspatanker

Authors: Meera Kulkarni

Abstract: Natural Language Processing (NLP) has emerged as a transformative technology within enterprise service management (ESM), fundamentally altering how organizations interact with users, handle service requests, and optimize workflows. Leveraging AI-driven NLP enables enterprises to interpret unstructured human language input, automate routine processes, and generate actionable insights from vast and complex data sets. This article explores the multi-dimensional impact of NLP on ESM, illustrating how it enhances efficiency, accuracy, and user experience across organizational service functions. Through intelligent ticket classification, conversational agents, predictive analytics, and workflow orchestration, NLP empowers enterprises to shift from reactive to proactive service models. The seamless understanding and generation of natural language improve communication fluidity, reducing resolution times and minimizing human workload. Furthermore, NLP-driven self-service platforms enable employees and customers to resolve issues autonomously, elevating satisfaction levels and operational scalability. This integrated approach not only accelerates service delivery but also fosters data-driven decision making for continuous improvement. The vast applicability of NLP in domains such as IT service management, HR, facilities, and customer support underscores its strategic value. This article comprehensively examines these facets, highlighting the evolving landscape of ESM fueled by NLP innovations and its future trajectory towards more intelligent, autonomous enterprise ecosystems.

DOI: https://doi.org/10.5281/zenodo.17707841

Published by: vikaspatanker

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