Category Archives: Uncategorized

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

Authors: Ms. Pooja Jagatnarayan Dixit

Abstract: Conflict is an unavoidable component of organizational life, particularly in large organizational settings characterized by interdependence, complex hierarchies, and resource competition. Effective conflict management is essential for sustaining productivity, fostering collaboration, and promoting positive workplace relationships. This study examines conflict management techniques used in large organizations, with a specific focus on how the choice of technique influences employee satisfaction and organizational performance outcomes. A mixed-methods approach involving literature synthesis and analysis of a representative dataset was used. Findings highlight that collaborative and compromising approaches tend to correlate most strongly with improved employee satisfaction and performance outcomes, while avoidance and competitive approaches exhibit weaker or inconsistent effects. The study provides implications for managers and organizational leaders regarding selecting appropriate conflict management strategies, and suggests future directions for organizational research on adaptive conflict intervention models.

Published by: vikaspatanker

Authors: Manoj Parasa

Abstract: This study examines how compensation design can be strengthened through data driven fairness analysis and performance aligned pay structures within SAP SuccessFactors, addressing long standing challenges associated with subjective decision patterns, inconsistent pay progression, and limited transparency in traditional compensation cycles. The research focuses on the need for organizations to transition from static guideline based models toward intelligent, analytically supported frameworks that enhance equitable decision making across diverse workforce groups. Using a mixed methods approach that combines quantitative compensation modeling with qualitative analysis of organizational pay practices, the study evaluates how fairness indicators, performance signals, and demographic patterns can be incorporated into structured compensation decisions without disrupting existing operational workflows. The findings demonstrate that early stage predictive techniques available in 2020 improve insight into pay disparities, support more consistent budget allocation, and enhance alignment between performance outcomes and reward decisions. The proposed framework introduces a future ready compensation model that integrates data inputs, analytical layers, and governance controls, enabling organizations to improve pay equity, strengthen performance differentiation, and reinforce strategic workforce planning. The study contributes to academic literature by outlining a practical methodology for embedding fairness analytics into enterprise compensation systems and offers industry practitioners a scalable pathway for modernizing pay structures while maintaining compliance and operational stability. The overall conclusion affirms that data informed compensation design in SAP SuccessFactors provides a more transparent, equitable, and strategically aligned foundation for long term organizational readiness.

DOI: https://doi.org/10.5281/zenodo.17698304

Published by: vikaspatanker

Authors: Ms. Haripriya, Vishva P, Varunkumar V.

Abstract: The Moving Object Detection System is a project that is based on vision and is aimed at the real-time identification and tracking of moving objects by applying the processing of images. The system can either analyze live video streams or recorded footage in order to detect the motion by the method of comparing the differences between two consecutive frames. The system that is executed on Python together with the OpenCV library performs background subtraction, frame differencing, and contour detection to find and draw the moving objects accurately. The technology has great potential in fields like surveillance, traffic management, and automation of security systems. The system is capable of detecting motion with high efficiency, requiring very little computational resources, and at the same time it is very easy to integrate with IoT and alert systems for added functionality. The system in question represents an efficient solution that can be applied in various contexts not only for motion detection but also for its cost-effectiveness and versatility. Moreover, the proposed system can handle indoor and outdoor setup variations like light changes and background noises through the use of filtering and thresholding techniques. Thus, it can already be a low-budget, real-time, and powerful solution for motion tracking in smart surveillance and safety applications. Further improvements can introduce the application of deep learning for the classification of detected objects and the provision of cloud-based alert systems that would further extend its range of use across various domains.

Published by: vikaspatanker

Authors: Shashikant, Vinay Saxena

Abstract: MRI and X-ray images are widely used for detecting various prevalent diseases, and accurate diagnosis heavily depends on image quality. Often, images suffer from low contrast due to factors such as uneven or insufficient illumination, mo- tion of the subject or imaging device, and device-related imperfections, some- times accompanied by noise or artifacts. In such cases, an effective contrast enhancement algorithm that improves visibility without amplifying noise or ar- tifacts becomes crucial. In this work, we propose a simple yet efficient contrast enhancement method for medical images, based on dividing a modified entropy curve into two sub-entropy curves and combining it with homomorphic filtering. The proposed approach enhances image contrast while preserving important details and minimizing noise amplification. The effectiveness of the method is demonstrated through comprehensive qualitative and quantitative evaluations, highlighting its potential for improving medical image analysis.

DOI: http://doi.org/10.5281/zenodo.17697301

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Authors: Rohit.N, Dr.R. Kannadasan

Abstract: In human anatomy, the liver has a special feature called regeneration, and this feature helps the liver to grow back even after a large part of its organ is removed. Like regeneration, it helps maintain bile salts, protein synthesis, and detoxification. Irregular eating habits, sleep, and alcohol consumption increase liver function and cause various diseases such as fatty liver and other liver problems. Hepatocellular carcinoma is one of the liver diseases, and it is caused by the abnormal growth of cells in the liver. In such cases, liver regeneration is possible if the disease is identified at an early stage. To support this early identification, several research works have been carried out using both artificial intelligence and deep learning techniques. Therefore, this paper proposed an automated approach to identify liver cancer at an early stage through a segmentation and classification using deep learning techniques. The early-stage identification becomes possible with the dual stage of segmentation using U-NET and XAI. The U-NET helps to segment the image through its various texture properties of the image. Then, the XAI is used to analyze the individual regions of the image. This special feature of this approach is that it uses descriptive AI for classification, and this helps in identifying critical regions through heat maps and saliency maps. This technique was tested on a computed tomography dataset of liver images and its performance was evaluated in terms of precision, accuracy, recall and F1-score.

DOI: https://doi.org/10.5281/zenodo.17696117

Published by: vikaspatanker

Authors: V.Gomathi priya

Abstract: Disaster management has transformed from a reactive emergency-centred practice to a proactive, technology-driven discipline focused on minimizing the impact of both natural and human-induced hazards. This research highlights the rising frequency and severity of disasters such as earthquakes, floods, and cyclones, emphasizing the demand for integrated, systematic, and multi-sectoral strategies. The study examines how contemporary frameworks now prioritize preparedness through scenario planning, prevention, and capacity-building, supplemented by robust early-warning systems and risk reduction measures. The role of advanced technology—including decision support systems powered by artificial intelligence, geographic information systems, and, more recently, quantum computing—is analysed for its effectiveness in rapid data processing, predictive modelling, and optimized resource allocation. The research also reviews key disaster events from the past five years to illustrate ongoing challenges and responses, demonstrating the need for resilient infrastructure and coordinated recovery operations. Findings suggest that modern disaster management not only saves lives and reduces economic loss during crises but also promotes societal resilience and sustainable development through continuous innovation, capacity enhancement, and collaborative governance. This evolution ensures that disaster management remains a critical element for safeguarding communities in an era of mounting environmental and technological risk.

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Authors: Priyanka K, Sasiprabha V, Shobaa P, Shreelinganaathan M, Srimukhi TG

Abstract: Low‑voltage distribution lines are widely used in residential and semi‑urban areas, where accidental conductor breakage or unintended grounding can pose severe safety hazards, result in prolonged power outages, and damage equipment. Current detection methods rely heavily on manual handling or high‑cost protection systems, resulting in delayed responses and increased operational risks. To address these gaps, this work proposes a cost‑effective Smart LV Fault Detection System capable of identifying conductor breakage and earth faults in real time. The system integrates voltage and current sensing units with an ESP32 controller that continuously analyses line behaviour, recognises abnormalities, and triggers immediate alerts. Visual and audio warnings are activated on-spot, while remote fault notifications are delivered via GSM‑based communication. A solar‑powered backup ensures uninterrupted operation during power failures. Experimental tests show more than 95% detection accuracy with a response time under one second. The proposed solution is lightweight, scalable, and suitable for both rural and urban power networks, providing improved safety, reduced downtime, and faster maintenance response.

Published by: vikaspatanker