Best AWS Marketplace Software - Page 99

Linx Security

Linx Security is a modern identity governance and access management platform built for modern enterprises. We help organizations stay governed, meet compliance, and prevent identity-based breaches by giving security teams deep visibility and precise control over who has access to what, across every application, environment, and identity type - human or non-human. Unlike legacy IGA systems that are complex, slow to deploy, and rigid, Linx is modular, API-first, and purpose-built for hybrid cloud environments. Our platform unifies identity security and access governance in one intuitive solution, enabling enterprises to automate access requests, approvals, certifications, and least privilege enforcement at scale. With Linx, you can: Discover and monitor all identities, including service accounts and shadow admins. Automate access reviews and entitlement workflows with context-rich intelligence. Enforce least privilege across SaaS, cloud infrastructure, and on-prem systems. Streamline onboarding and offboarding across the identity lifecycle. Meet compliance standards like NIST, CIS, SOX, and PCI with confidence. Leading enterprises trust Linx to keep their identity security and governance at its highest standard—proactive, precise, and always aligned with evolving risk. We integrate seamlessly with your existing stack—Okta, Azure AD, AWS, Salesforce, ServiceNow, Workday, and more - so you can get started in days, not months. Because attackers don’t break in anymore, they log in. And Linx makes sure only the right people - and only the right machines - can.

Average Rating: 5.0/5.0

Total Reviews: 1

LMS powered by Moodle¨ With CentOS 7.8

Moodle is a open-source software learning management system

Average Rating: 4.0/5.0

Total Reviews: 3

Load-balanced Dockerized Security Design Template by DeepCyber

The Load-balanced Dockerized Security Design Template by DeepCyber is a comprehensive solution designed to enhance the security and scalability of applications deployed on Amazon Web Services (AWS). This template provides a pre-configured, load-balanced architecture that leverages Docker containers to ensure consistent and secure application deployment. Key Features and Functionality: - Dockerized Deployment: Utilizes Docker containers to encapsulate applications, ensuring consistency across different environments and simplifying the deployment process. - Load Balancing: Incorporates load balancing mechanisms to distribute incoming traffic evenly across multiple instances, enhancing application availability and performance. - Security Best Practices: Implements security measures aligned with industry standards, including network isolation, access controls, and monitoring, to protect applications from potential threats. - Scalability: Designed to scale horizontally, allowing for the addition of more instances as demand increases, ensuring that applications remain responsive under varying loads. Primary Value and Problem Solved: This design template addresses the challenges of deploying secure and scalable applications on AWS by providing a ready-to-use framework that integrates Docker containerization with load balancing and security best practices. It simplifies the deployment process, reduces the time required to set up a robust infrastructure, and ensures that applications are both secure and capable of handling varying levels of traffic efficiently.

Load balancer With Almalinux 8

A TCP/HTTP Load balancing Proxy Server

Local Photo ID (Singapore)

Local Photo ID (Singapore is a specialized dataset designed to facilitate the development and testing of identity verification systems within the Singaporean context. It provides a comprehensive collection of high-quality images of local identification documents, including passports, national identity cards, and driver's licenses. This dataset is invaluable for organizations aiming to enhance their document recognition and authentication processes, ensuring compliance with local regulations and improving the accuracy of their verification systems. Key Features and Functionality: - Diverse Document Collection: Includes a wide range of Singaporean identification documents, such as passports, national identity cards, and driver's licenses, offering a broad spectrum of data for training and testing purposes. - High-Resolution Images: Provides clear and detailed images, enabling precise analysis and recognition of security features, text, and other critical elements present in official IDs. - Regulatory Compliance: Assists in developing systems that adhere to Singapore's legal standards for identity verification, ensuring that applications meet local compliance requirements. - Enhanced Security Measures: Supports the creation of robust verification systems capable of detecting fraudulent documents and reducing the risk of identity theft. Primary Value and User Solutions: Local Photo ID (Singapore addresses the critical need for accurate and efficient identity verification in various sectors, including banking, telecommunications, and e-commerce. By leveraging this dataset, organizations can: - Improve Verification Accuracy: Train machine learning models to recognize and authenticate Singaporean IDs with higher precision, reducing false positives and negatives. - Streamline Onboarding Processes: Automate identity verification steps, leading to faster customer onboarding and enhanced user experience. - Ensure Regulatory Compliance: Develop systems that comply with Singapore's identity verification regulations, mitigating legal risks and building trust with customers. - Enhance Fraud Detection: Implement advanced detection mechanisms to identify counterfeit or altered documents, thereby strengthening security protocols. In summary, Local Photo ID (Singapore serves as a vital resource for organizations seeking to develop or refine identity verification systems tailored to the Singaporean market, offering a reliable foundation for building secure and compliant applications.

Locker CIS Hardened Ubuntu 18.04 AMI

Locker Labs has created this specially hardened CIS Ubuntu 18.04 AMI for security minded users. Our AMIs are compiled with our patented Zero Vulnerability Technology, mitigating both known and unknown vulnerabilities in the software supply chain.

Locker Hardened Ubuntu 18.04 AMI

Locker Labs has created this specially hardened Ubuntu 18.04 AMI for security minded users. Our AMIs are compiled with our patented Zero Vulnerability Technology, mitigating both known and unknown vulnerabilities in the software supply chain.

LogicMonitor Edwin AI

Edwin AI by LogicMonitor is an advanced AI agent designed to revolutionize IT operations by automating incident investigation, reducing alert noise, and accelerating resolution times. Built on Amazon Bedrock, Edwin AI integrates seamlessly into existing workflows, enabling IT teams to transition from reactive firefighting to proactive management without increasing headcount. Organizations have reported significant improvements, including up to 80% noise reduction, 30% fewer ITSM incidents, 60% faster mean time to resolution (MTTR), and a 20% boost in operational efficiency. Key Features and Functionality: - Agentic AIOps: Edwin AI is purpose-built for IT operations, offering contextual understanding of the ITOps environment to correlate events, identify root causes, and drive remediation. - Unified Data Context: It integrates observability telemetry, CMDB data, topology, and ITSM context into a comprehensive ITOps knowledge graph, facilitating explainable, data-driven decisions across hybrid environments through over 3,000 integrations. - Real-Time Alert Correlation: Edwin AI ingests cross-domain alerts, applies AI-powered correlation to eliminate noise and false positives, and links events to performance degradations and past incidents to identify likely causes swiftly. - Automated Triage and Escalation: Combining insights with incident context and resolver group logic, Edwin AI routes incidents to the appropriate teams and initiates remediation workflows automatically. Primary Value and Problem Solved: Edwin AI addresses the challenges of modern IT operations by significantly reducing alert fatigue and operational noise, enabling faster incident resolution, and enhancing overall productivity. By automating the correlation of alerts, identifying root causes, and recommending remediation steps, Edwin AI empowers IT teams to manage complex environments more effectively, ensuring system reliability and minimizing downtime.

Long-Memory Dynamic Factor Model (LMDFM)

Long-Memory Dynamic Factor Model (LMDFM) to analyze and forecast large number of time-series influenced by evolutions of unobserved factors.

LoRaWAN sensor library

Sensor library converts more than 500 sensor binary data to json readable messages. As a software vendor, you can use sensor library to make your software compatible with all our supported sensors very quickly. As a telco, you can use sensor library on top of your connectivity to bring more value to your customers.

Lucee CFML Server (Ubuntu+Nginx+Tomcat)

The Lucee CFML Server (Ubuntu+Nginx+Tomcat is a pre-configured Amazon Machine Image (AMI) designed to facilitate the rapid deployment of ColdFusion Markup Language (CFML applications on a robust and secure platform. This AMI integrates the Lucee 5.4.6.9 CFML engine with an optimized Ubuntu Server 24.04 LTS operating system, Nginx web server, and Tomcat 9.x application server, providing a comprehensive environment for developing and hosting dynamic web applications. Key Features and Functionality: - Lucee CFML Engine 5.4.6.9: Offers a lightweight, open-source CFML engine compatible with Adobe ColdFusion, enabling efficient development of web applications. - Ubuntu Server 24.04 LTS: Provides a stable and secure operating system foundation, ensuring long-term support and reliability. - Nginx Web Server: Configured with performance optimizations to handle high traffic loads efficiently. - Tomcat 9.x Application Server: Serves as the underlying application server for the CFML runtime, ensuring robust application performance. - CommandBox 6 CLI: Included for streamlined development and management tasks, enhancing developer productivity. - Security Hardening: Configured using Linux CIS Benchmark Level 1 and Adobe ColdFusion Lockdown Guide security protocols to ensure a secure environment. - Performance Monitoring: Integrated toolset for identifying and eliminating application performance bottlenecks, ensuring optimal performance. Primary Value and User Solutions: This AMI simplifies the deployment process for developers and organizations by providing a ready-to-use, secure, and high-performance environment for CFML applications. It eliminates the complexities associated with manual configuration and integration of individual components, allowing users to focus on application development and deployment. The inclusion of security hardening and performance monitoring tools ensures that applications run securely and efficiently, addressing common challenges in web application hosting.

LucidVueCX

LucidVueCX is an advanced customer analytics platform that aggregates and analyzes interactions across multiple channels—including voice calls, social media posts, online reviews, emails, and text messages—to provide businesses with a comprehensive, 360-degree view of their customer base. By leveraging state-of-the-art speech recognition and natural language understanding technologies, LucidVueCX transforms unstructured customer conversations into actionable insights, enabling organizations to enhance decision-making, improve customer experience, and drive business growth. Key Features and Functionality: - Cross-Channel Aggregation: Collects and consolidates customer interactions from various platforms, including phone calls, social media, emails, and online reviews, into a unified dashboard. - Advanced Speech Recognition: Utilizes cutting-edge AI-powered speech recognition to accurately transcribe and analyze voice interactions, ensuring high-quality data extraction. - Real-Time Trend Analysis: Identifies emerging trends, sentiments, and critical keywords within customer conversations, providing early warnings for shifts in buyer behavior and potential issues. - Compliance Monitoring: Automates the monitoring of 100% of customer interactions to ensure adherence to compliance standards and quality assurance protocols. - Intuitive Dashboard: Offers a user-friendly interface that presents high-level insights, relevant topics, and individual conversations, facilitating informed decision-making. Primary Value and Problem Solved: LucidVueCX addresses the challenge of capturing and analyzing vast amounts of customer feedback that often go unnoticed or are lost within siloed data systems. By automating the extraction and analysis of customer interactions across all channels, it provides businesses with actionable insights to: - Enhance Customer Experience: Understand customer sentiments and feedback to tailor services and products that meet evolving needs. - Improve Operational Efficiency: Identify and address operational issues, reduce product returns, and solve manufacturing problems by uncovering root causes through customer conversations. - Drive Revenue Growth: Discover new revenue opportunities by analyzing customer feedback for product insights and buying experiences. - Ensure Compliance: Monitor all customer interactions to maintain compliance with legal and quality standards, mitigating risks associated with non-compliance. By providing a non-intrusive, cost-efficient, and comprehensive solution, LucidVueCX empowers businesses to make data-driven decisions that enhance customer satisfaction and drive business success.

Lung Cancer - Classifier

The vLife Lung Cancer Classifier is an advanced machine learning model designed to assist healthcare professionals in the early detection and classification of lung cancer. By analyzing complex medical data, this classifier provides accurate assessments of lung nodules, distinguishing between benign and malignant cases. Its integration into clinical workflows aims to enhance diagnostic precision, reduce unnecessary invasive procedures, and improve patient outcomes through timely intervention. Key Features and Functionality: - Machine Learning-Based Classification: Utilizes sophisticated algorithms to analyze medical imaging and patient data, offering reliable classifications of lung nodules. - Early Detection Capabilities: Facilitates the identification of malignant nodules at an early stage, enabling prompt and appropriate treatment. - Non-Invasive Assessment: Provides a non-invasive method for evaluating lung nodules, potentially reducing the need for surgical biopsies. - Integration with Clinical Workflows: Designed to seamlessly integrate into existing healthcare systems, supporting clinicians in making informed decisions. Primary Value and Problem Solved: The vLife Lung Cancer Classifier addresses the critical need for accurate and early detection of lung cancer, the leading cause of cancer-related deaths worldwide. By leveraging machine learning to analyze medical data, it enhances diagnostic accuracy, minimizes unnecessary invasive procedures, and supports clinicians in delivering timely and effective patient care.

Lung Cancer Disease State Predictor

The Lung Cancer Disease State Predictor is a sophisticated machine learning solution designed to predict the survival outcomes of patients diagnosed with non-small cell lung cancer (NSCLC. By integrating and analyzing diverse health data modalities—including medical imaging, genomic information, and clinical records—this tool offers a comprehensive approach to understanding and forecasting patient prognoses. Leveraging the capabilities of Amazon SageMaker JumpStart, it provides healthcare professionals and researchers with a scalable and efficient means to develop, train, and deploy predictive models, thereby enhancing decision-making processes and personalized treatment strategies. Key Features and Functionality: - Multimodal Data Integration: Combines medical imaging, genomic data, and clinical information to create a holistic view of each patient's health status. - Pre-Built Solution Templates: Offers ready-to-use templates within Amazon SageMaker JumpStart, facilitating quick deployment and customization of predictive models. - Scalable Machine Learning Pipelines: Utilizes Amazon SageMaker's infrastructure to build and scale machine learning pipelines tailored to healthcare data analysis. - Comprehensive Notebooks: Provides a series of Jupyter notebooks guiding users through data preprocessing, model training, and inference, ensuring a seamless workflow. Primary Value and Problem Solved: The Lung Cancer Disease State Predictor addresses the critical need for accurate and personalized survival predictions in NSCLC patients. By harnessing the power of machine learning and integrating multiple data sources, it empowers healthcare providers to make informed decisions regarding treatment plans and patient management. This solution not only enhances the precision of prognostic assessments but also streamlines the development and deployment of predictive models, ultimately contributing to improved patient outcomes and more efficient healthcare delivery.

Lymphoma Disease State Predictor

The Lymphoma Disease State Predictor is an advanced machine learning model designed to assess and predict the progression of lymphoma by analyzing patient-specific data. This tool leverages deep learning algorithms to evaluate various clinical and histological parameters, providing healthcare professionals with a risk score that indicates the likelihood of disease progression. By integrating this predictor into clinical workflows, medical practitioners can make more informed decisions regarding treatment strategies, potentially improving patient outcomes. Key Features and Functionality: - Risk Assessment: Generates a histologic risk score (HRS by analyzing whole slide images from hematoxylin and eosin-stained lymphoma tissues collected prior to therapy. - Deep Learning Integration: Utilizes a convolutional neural network (CNN foundation model, pre-trained via self-supervised learning on a diverse set of over 1 million whole-slide image tiles from various benign and malignant tissue types. - Survival Prediction: Employs a regression head trained using supervised learning to optimize the Cox partial likelihood, using progression-free survival (PFS as a label, thereby predicting the risk of disease progression. - Clinical Validation: Tested on independent cohorts, including patients enrolled in Phase III clinical trials, to ensure accuracy and reliability in real-world scenarios. Primary Value and Problem Solved: The Lymphoma Disease State Predictor addresses the critical need for precise and individualized risk assessment in lymphoma patients. Traditional methods of evaluating disease progression often rely on generalized criteria, which may not capture the nuances of individual cases. By providing a personalized risk score based on deep learning analysis of histological data, this tool enables clinicians to tailor treatment plans more effectively, potentially enhancing patient survival rates and optimizing resource allocation in healthcare settings.