Vyshnavi Thanneeru1 and Murali Mohan Reddy Seelam2, 1Senior DevOps Engineer, Fidelity Investments, Westlake, USA, 2Senior Software Engineer, Cisco Systems, Dallas, USA
This Research article explores the changing impact of AI (Artificial Intelligence) on automation capabilities of Copado, improving the deployment and change management in Salesforce DevOps. In this paper we have outlined the AI- based methodologies that automate the version control, optimize change management workflows, and improve accuracy of the deployments. The traditional Salesforce DevOps pipelines face many challenges like deployment errors, merge conflicts, roll back issues, and dependencies between the components. By implementing predictive analytics, machine learning, and automated risk assessment, Copado automation provides improved efficiency in deployments, decrease in errors, and optimized release velocity in complex salesforce environments. The results from integrating these AI-driven improvements across different salesforce instances highlight the critical value of integrating Artificial Intelligence in Salesforce DevOps Pipelines. This research paper shows Copado’s AI-powered automation as an important advancement towards scalable, robust, and adaptive Salesforce DevOps implementation.
Salesforce, Artificial Intelligence, Copado, Release Management, Deployment, Change management.
Yurii Tulashvili, Iurii Lukianchuk and Viktor Kosheliuk, Department of Computer Sciences, Lutsk National Technical University, Lutsk, Ukraine
Over the past decade, blockchain technology has undergone rapid development and is recognized as one of the pivotal information technologies driving industrial transformation. Today, blockchain technology offers a promising solution to the problems faced by corporate information systems. Namely, with the help of appropriate measures of anonymization and preservation of confidentiality, the blockchain enhances data security, reduces the risk of unauthorized access, and ensures user privacy in corporate systems. Blockchain technology increases transparency, allowing users to monitor and verify the content of information, assess the integrity of data stores. In recent years, blockchain has become a subject of interest for state governments, multinational corporations, and major financial institutions. Today, considerable attention is paid to the development of private (corporate) blockchains. This article examines the prospects and development of blockchain technology in corporate information systems. The study is aimed at providing additional clarity regarding the concept of blockchain applications in corporate information systems. Existing enterprise applications cannot operate seamlessly with traditional transactional requirements when integrating blockchain technology. Therefore, they will have to be modified with inclusion in the existing data storage for asynchronous interaction with distributed nodes of the blockchain. A scheme of the principle of interaction of nodes - storages that support data synchronization and their current states are proposed. This scheme is based on a blockchain structure centered around cloud storage as a corporate document circulation system. Consensus to include a new entry - Byzantine fault tolerance. Blockchain nodes receive a parametric weight for decision making.
Blockchain, Consensus, Corporate Systems, Security.
SVAdesh, SanjayMP, GPrerithSShetty and Priyanshu, DepartmentofComputerScienceandEngineering,SahyadriCollegeofEngineering and Management Mangalore , Karnataka , India
In modern combat scenarios, the health and safety of soldiers are of paramount importance. Rapid and accurate health monitoring can significantly enhance the ability to provide timely medical interventions, potentially saving lives. This project centers on creating a cutting-edge health monitoring system for soldiers,leveragingArtificialIntelligenceandMachineLearning(AI/ML)technologiestoanalyzecritical healthparametersinreal-time.Oursystemintegratesmultiplesensorsintoawearablejacketthatsoldiers can comfortably wear during combat operations. These sensors continuously collect vital health data, including Electrocardiogram (ECG) readings, heart rate, and body temperature. The collected data is transmitted to a AWS Cloud, where it is analyzed using sophisticated AI/ML algorithms.The primary objective of the AI/ML component is to determine whether a soldier requires medical attention based on the analyzed health parameters. The system leverages historical health data and patterns to trainmachine learning models capable of identifying anomalies and predicting health issues. By employing a combination of supervised and unsupervised learning techniques, the system can detect irregularities in real-time and alert medical personnel immediately.
Health Monitoring System, Soldier Safety, Artificial Intelligence (AI), Real-Time Analysis, Wearable Technology, Electrocardiogram (ECG), Heart Rate Monitoring, Body Temperature Tracking, Sensor Integration, AWS Cloud, Anomaly Detection, Supervised Learning, Medical Alert System, Cloud-Based Processing.
