Daily Archives: July 4, 2025

Authors: Indra Bhuwan Yadav, B.Tech (C.S.E),, Neeharika Sengar, Assistant Professor, SOET, Dr. Rajendra Singh, HOD, SOET

Abstract: With the surge in digital services, backend infrastructure must manage high-volume transactions across diverse platforms with minimal latency and high reliability. Synchronous systems often struggle under concurrent loads, leading to performance bottlenecks. To address these limitations, this research introduces TRANSACTINET, a backend framework developed using the Tornado Python library. Designed with an event-driven model, the system supports asynchronous processing, modular architecture, and robust security protocols. This paper outlines its design, deployment strategy, and performance evaluation across real-world applications such as banking and agent-based commerce.

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Published by: vikaspatanker

Authors: Ms. Komala R, Shreya Sankannavar,

Abstract: The farming industry is a mainstay of the world economy, with potato cultivation contributing immensely to food security. Despite this, potato plants are very prone to several diseases like Earl y Blight, Late Blight, and bacterial infections, causing them to experience tremendous losses in yields. Conventional methods of disease detection involve the use of manual checking, which is time-consuming, labor-intensive, and inaccurate because of human error. To overcome such challenges, the project suggests a mac hine learning-based automatic potato disease detection system. The suggested system applies image processing and deep learning models to identify and classify diseases from leaf images with high accuracy. A dataset of healthy and diseased potato leaf images is preprocessed and utilized for training a convolutional neural network (CNN) model. The model is trained to classify different diseases and healthy leaves by learning from visual attributes. After training, the model detects diseases with high accuracy in real- time, allowing timely intervention and minimizing crop loss. This framework can help farmers and agricultural professionals keep track of crop health more effectively, increasing productivity and encouraging sustainable agriculture. The project indicates the use of machine learning in precision farming and how it can revolutionize conventio nal farming practices.

DOI: http://doi.org/

 

 

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Authors: Professor Dr. Satya Singh, Ratnesh Kumar Sharma

Abstract: COVID-19 harmed the lives of people in every region of the world. It has been established that, in addition to the physical symptoms, it significantly influences the patient’s mental health. Depression has been identified as one of the most widespread disorders that can hasten a person’s mortality at an early age. This is one of the conditions that has been singled out for this distinction. The trajectory of life for millions of people has been altered as a result of this illness. We conducted a survey that consisted of 21 questions based on the Hamilton instrument and the advice of a psychiatrist. This was done so that we could continue forward with the inquiry into the identification of depression in individuals. After the data were compiled and analysed, it became clear that people younger than 45 years of age had a higher risk of suffering from depression when compared to those older than 45 years of age. This is because most people at this age are concerned about getting married or schooling their children. On the other side, research has revealed that those whose ages fall between 18 and 25 are also at an increased risk of suffering from depression. This is likely because, at this stage in their lives, these individuals are more conscious of the potential outcomes of their lives. Based on all of the replies received, the findings of the survey were put through several different machine learning algorithms, including Decision Tree, KNN, and Naive Bayes. These algorithms were used to analyse the results. Further investigation is being done into how these two techniques are similar to and different from one another. According to the findings of the research, KNN has produced better results than other approaches in terms of accuracy, whereas decision trees have produced better results in terms of the amount of time needed to detect depression in a person. In conclusion, to overcome the traditional approach to a depression diagnosis, which is made up of affirmative questions and constant feedback from individuals, a model that is based on machine learning is offered as a potential alternative.

DOI: https://doi.org/10.5281/zenodo.15804697

 

Published by: vikaspatanker

Authors: Felix Corvin

Abstract: In the modern era of digital infrastructure, organizations are increasingly adopting Infrastructure as Code (IaC) to manage and automate the provisioning and configuration of resources across both on-premises and cloud environments. IaC ensures consistency, repeatability, and efficiency by allowing infrastructure to be defined and maintained through version-controlled code. Among the many tools available, Puppet and Ansible have emerged as two of the most widely adopted solutions, each bringing distinct advantages to the automation landscape. Puppet is based on a declarative model and is particularly suited for policy enforcement and large-scale system state management. Ansible, by contrast, follows a procedural model and is known for its flexibility, simplicity, and agentless operation. This review examines the rationale, architecture, and best practices behind co-deploying Puppet and Ansible within hybrid environments. Rather than viewing these tools as mutually exclusive, the paper explores how they can be used in complementary roles to achieve higher degrees of automation maturity, compliance, and infrastructure resilience. The review discusses how Puppet can handle base operating system configurations and enforce long-term system states, while Ansible is better suited for orchestration tasks, application deployment, and change management.

DOI: https://doi.org/10.5281/zenodo.15804110

Published by: vikaspatanker

Authors: Kael Veridian

Abstract: The demand for resilient virtual machine (VM) architectures has grown exponentially with the adoption of cloud computing across enterprise sectors. Ensuring continuity of services in the face of infrastructure failures, cyber threats, and unpredictable workloads requires a robust, automated, and secure cloud environment. This review article presents a comprehensive analysis of how Red Hat’s technology stack—including Red Hat Enterprise Linux (RHEL), Kernel-based Virtual Machine (KVM), OpenStack, OpenShift, Ansible Automation Platform, and Red Hat Satellite—enables the design and deployment of resilient VM infrastructures in public, private, and hybrid cloud environments. The paper begins by outlining the foundational elements of Red Hat’s ecosystem and its integration into virtualization and cloud orchestration platforms. It then explores architectural design principles for fault tolerance, high availability, and elastic scalability, including clustering solutions using Pacemaker/Corosync and automated lifecycle management through Ansible and CloudForms. Red Hat’s support for secure VM configurations, enabled by SELinux, SCAP compliance, and FIPS-certified modules, is discussed as a critical pillar of operational resilience. The review categorizes common resiliency patterns such as active-active clustering, multi-region redundancy, and hybrid cloud deployments that leverage Red Hat Cloud Access and Image Builder. It further evaluates storage and data protection strategies through Ceph, GlusterFS, LVM snapshots, and integration with backup solutions like Veeam and Commvault. Observability and monitoring capabilities are addressed through Red Hat Performance Co-Pilot, Prometheus/Grafana, and centralized logging via EFK stacks. Several real-world case studies are presented from finance, healthcare, and government sectors to illustrate the deployment of Red Hat-based resilient VM infrastructures in production environments. The article concludes by identifying emerging trends, including AI-driven self-healing automation, serverless VM workloads via KubeVirt and MicroShift, and zero-trust security architectures powered by service mesh and mTLS. While challenges such as cross-cloud compatibility and ecosystem complexity persist, Red Hat’s comprehensive, open-source platform offers a strategic foundation for building scalable, fault-tolerant, and secure virtual infrastructures in cloud-native ecosystems.

DOI: https://doi.org/10.5281/zenodo.15804019

Published by: vikaspatanker

Authors: Ariane Solis

Abstract: The ongoing reliance on legacy UNIX systems such as Solaris, AIX, and HP-UX in mission-critical enterprise environments poses significant challenges to maintaining high availability (HA) as these platforms age. Traditional HA clustering techniques—rooted in physical infrastructure, proprietary clustering software, and tightly coupled storage systems—struggle to adapt to the elasticity, fault tolerance, and operational flexibility offered by cloud environments like Amazon Web Services (AWS). This review explores the architectural shift from legacy on-premises HA clusters to AWS-based and hybrid high availability designs for UNIX workloads. It evaluates key AWS services such as EC2, Elastic Load Balancer (ELB), CloudWatch, Auto Scaling, and Route 53 in building redundant and failover-capable environments tailored for UNIX applications. The paper highlights the challenges of migrating UNIX workloads to AWS, including hardware-bound licensing, kernel-level dependencies, shared storage constraints, and clustering heartbeat mechanisms. Strategies for bridging these limitations—through hybrid models, emulation platforms (e.g., Charon-SSP for Solaris and AIX), and containerized service proxies—are analyzed. Key components of AWS-native HA design are reviewed, including EC2 auto-recovery, cross-AZ EBS, elastic IP remapping, and application-aware health monitoring via CloudWatch and Lambda functions. Hybrid clustering configurations linking on-prem systems to AWS—emerge as transitional models, allowing legacy workloads to benefit from cloud-based failover and storage resiliency while maintaining control over core services. The review includes real-world case studies across finance, healthcare, and manufacturing that demonstrate the feasibility and impact of AWS-based HA clustering for UNIX systems. It concludes with a comparative analysis of traditional versus cloud-based HA architectures, along with future directions involving serverless orchestration and AI-driven failover decision-making. Overall, the review provides a structured roadmap for IT architects seeking to modernize legacy UNIX platforms with the resilience and scalability of AWS.

DOI: https://doi.org/10.5281/zenodo.15803836

Published by: vikaspatanker

Authors: Ms. Samyadevi V Assistant Professor, GuruPrakash VM, Nishbha R, Raagul R

Abstract: A voice-controlled robot is developed to perform accurate and reliable movements in response to spoken commands. The system combines a machine learning-based speech recognition module with an advanced motor control algorithm to enable natural human-robot interaction. The speech module converts voice to text, accurately interpreting commands even in noisy environments and across various accents. Recognized commands are processed and translated into actions, which are executed through a motor control system using pulse-width modulation (PWM) and directional control to manage motor speed and direction. This ensures smooth, synchronized movements, adapting to load changes without delays or jerks. Challenges like background noise and command errors are minimized through noise filtering, adaptive models, and an optimized processing pipeline. The system’s performance—measured by response time, accuracy, and movement reliability—confirms fast and precise execution, showcasing the robot’s effectiveness in real-world scenarios.

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Published by: vikaspatanker