Best AWS Marketplace Software - Page 122

PowerWAF Enterprise (8 vCPU)

PowerWAF Enterprise (8 vCPU is a robust Web Application Firewall (WAF solution designed to protect web applications from a wide range of cyber threats. Tailored for enterprises requiring high-performance security, this offering leverages 8 virtual CPUs to deliver efficient and scalable protection. Key Features and Functionality: - Comprehensive Threat Protection: Safeguards against common web exploits, including SQL injection, cross-site scripting (XSS, and other OWASP Top Ten vulnerabilities. - High Performance: Utilizes 8 vCPUs to ensure rapid processing of web traffic without compromising application performance. - Scalability: Designed to handle varying traffic loads, making it suitable for growing enterprises. - Easy Deployment: Seamlessly integrates with existing AWS infrastructure, facilitating quick setup and management. Primary Value and User Solutions: PowerWAF Enterprise (8 vCPU addresses the critical need for robust web application security in the face of evolving cyber threats. By providing comprehensive protection and high performance, it ensures that enterprises can maintain the availability, integrity, and confidentiality of their web applications. This solution is particularly beneficial for organizations seeking to enhance their security posture without sacrificing application speed or scalability.

PowerWAF Pro (2 vCPU)

PowerWAF Pro is a comprehensive web application firewall designed to protect websites and web applications from a wide range of cyber threats. It safeguards against DDoS attacks, OWASP Top 10 vulnerabilities, data theft, server infiltration, malware installation, and zero-day attacks. Additionally, PowerWAF Pro enhances website performance through features like static and dynamic content caching, automatic image optimization, and integration with a world-class Content Delivery Network . This ensures faster content delivery and improved user experience. Key Features and Functionality: - Comprehensive Security: Offers protection against DDoS attacks, OWASP Top 10 vulnerabilities, data theft, server infiltration, malware installation, and zero-day attacks. - Website Acceleration: Enhances website speed through static and dynamic content caching, automatic image optimization, and integration with a world-class CDN. - Content Delivery Network : Utilizes a global CDN to ensure fast and reliable content delivery. - High-Speed DNS: Employs a high-speed anycast DNS network with over 40 points in 22 countries for efficient domain name resolution. - Automatic Image Optimization: Optimizes images on-the-fly without compromising quality, leading to faster page loads. - Developer Tools: Provides specialized tools for enhanced security, including bot recognition, network recognition, device recognition, and user fingerprinting. Primary Value and User Benefits: PowerWAF Pro addresses critical security and performance challenges faced by website owners and administrators. By offering robust protection against a variety of cyber threats, it ensures the integrity and availability of web applications. The integration of performance-enhancing features like content caching and image optimization not only secures the website but also significantly improves load times and user experience. This dual approach of security and performance optimization makes PowerWAF Pro an essential solution for businesses aiming to maintain a strong and reliable online presence.

PPE detection algorithm (Facemask)

Video-based AI that detects degree of COVID-19 facemask policy compliance. Analyzes statistical patterns at scale. See also separate Room Occupancy & Social Distance compliance detection module. Created by Providence Health Care, working with multidisciplinary teams from the University of British Columbia, including the Dept. of Computing Science & Computer Vision Lab. Our mission is to improve patient outcomes, particularly the most vulnerable in our Long-Term Care and Acute Care settings.

Pragmavant API

Pragmavant API is a practical solution designed to streamline business and cloud applications by offering asset type identification, asset tracking, and snapshot capabilities. It enables users to easily determine the content type within a provided asset, ensuring accurate classification and management. The API also facilitates the creation of checksums and digests, allowing for reliable tracking of any alterations to an asset's content. Additionally, it provides the functionality to generate visual snapshots of assets, which can be utilized for feedback and persistence purposes. Key Features and Functionality: - Asset Content Type Identification: Efficiently identifies the content type within a given asset, aiding in proper classification and management. - Asset Checksum and Digest Creation: Generates checksums and digests to reliably track and detect any changes or alterations in an asset's content. - Visual Asset Snapshot: Creates visual snapshots of assets, facilitating feedback collection and ensuring content persistence. Primary Value and Problem Solved: Pragmavant API addresses the challenges associated with managing and monitoring digital assets by providing tools for accurate content identification, change detection, and visual documentation. This enhances operational efficiency, ensures data integrity, and supports effective asset management within business and cloud environments.

PreCrime Defense

PreCrime™ Defense is a preemptive scam, fraud, and impersonation protection solution that can prevent account takeover and credit card or credential stealing from customers’ customers, or protect our customers’ suppliers from being attacked in email compromises. This mitigates fraud, reputational harm risk from negative brand damage, or customer credential stealing. PreCrime Defense identifies a malicious infrastructure only minutes after its creation, puts a network disruption in place within minutes of identification, and requests action to various takedown operators, who subsequently disturb DNS resolution or content removal, resulting in infrastructure takedown. BforeAI’s privileged access to takedown operators (including domain and DNS, content, industry alliances for abuse and malware prevention, law enforcement agencies, and independent response bodies) ensures malicious infrastructure is promptly removed. Our predictive technology is so effective that more than 80% of our takedowns are completed before there’s content on the infrastructure. BforeAI’s disruption partners, including VirusTotal, Quad9, Spamhaus, and Google Safe Browsing, subsequently put the identified malicious domain in a DNS resolution blocklist. Within 10 minutes on average, up to 75% of the traffic to the malicious infrastructure is already blocked. A step-by-step breakdown of how PreCrime works: Data Collection and Pre-processing PreCrime begins by collecting multiple network data points from thousands of sensors deployed across 98% of the surface internet. The tool observes more than 1 billion infrastructures and 500 million domains on a continuous basis – of them, 500,000 are created every day. Data is collected between 5 to 10 times per hour which enables observing any changes on a continuous and precise basis. This data is then preprocessed to remove noise and irrelevant information, ensuring that only meaningful interactions are considered. In total, PreCrime collects several terabytes of data on a daily basis. Graph Construction and Feature Extraction Next, PreCrime constructs a graph from the preprocessed data. Features such as query frequency, temporal patterns, and resolution paths are extracted and incorporated into the graph. Over 400 billion behaviors and edges are mapped in the graph database. These features provide a detailed view of domain interactions, which is crucial for accurate inference. Application of Graph Inference Techniques PreCrime applies various graph inference techniques to analyze the constructed graph. Four billion malicious behaviors are mapped in PreCrime. Community detection algorithms identify clusters of domains that exhibit similar behavior, while anomaly detection algorithms highlight nodes with abnormal patterns. Link prediction algorithms are used to forecast potential future connections, helping to identify emerging threats. Identification and Mitigation: Disruption and Takedown PreCrime re-scores over 20 million suspicious infrastructures on a daily average out of which it predicts 100,000 future attack infrastructures. Based on the results of the graph inference analysis, PreCrime detects infrastructures that exhibit characteristics of malicious behavior. They are flagged for further investigation and mitigation. PreCrime's preemptive approach ensures that potential threats are identified and neutralized before they can cause harm.

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Predicting Asthma Admissions

The "Predicting Asthma Admissions" solution leverages advanced machine learning algorithms to forecast asthma-related hospital admissions by analyzing environmental factors, patient demographics, and historical health data. This predictive capability enables healthcare providers to proactively manage resources and implement targeted interventions, ultimately improving patient outcomes and reducing healthcare costs. Key Features and Functionality: - Data Integration: Combines diverse datasets, including environmental conditions, patient demographics, and historical health records, to create a comprehensive analysis framework. - Machine Learning Models: Utilizes sophisticated algorithms to identify patterns and correlations that may not be evident through traditional analysis methods. - Real-Time Predictions: Provides timely forecasts of potential asthma admissions, allowing for proactive resource allocation and patient care strategies. - Scalability: Designed to handle large volumes of data, making it suitable for healthcare systems of varying sizes and complexities. Primary Value and Problem Solved: By accurately predicting asthma-related hospital admissions, this solution addresses several critical challenges in healthcare: - Resource Optimization: Enables hospitals to allocate staff and equipment more efficiently, reducing operational costs and improving service delivery. - Preventive Care: Identifies high-risk periods and patient populations, facilitating early interventions that can prevent severe asthma episodes and hospitalizations. - Enhanced Patient Outcomes: Supports personalized care plans by providing insights into potential triggers and risk factors, leading to better management of asthma and improved quality of life for patients. In summary, the "Predicting Asthma Admissions" solution empowers healthcare providers with actionable insights, fostering a proactive approach to asthma management and contributing to more efficient and effective healthcare delivery.

Predicting Cost Bloomers / Rising Risk

Predicting Cost Bloomers / Rising Risk is a sophisticated solution designed to help organizations proactively identify and manage potential cost overruns and financial risks within their operations. By leveraging advanced predictive analytics and machine learning algorithms, this tool enables businesses to forecast material prices, anticipate supply chain disruptions, and optimize procurement strategies, thereby enhancing financial stability and operational efficiency. Key Features and Functionality: - Machine Learning Algorithms: Utilizes advanced models to forecast material prices up to three months in advance, considering historical trends, market data, and external factors. - Integration with Business Intelligence Tools: Seamlessly connects with existing BI platforms to provide strategic purchasing recommendations, aiding in informed decision-making. - Time-Series Analysis and Regression Models: Employs sophisticated analytical techniques to improve the accuracy of price predictions, ensuring reliable procurement strategies. - Automated Dashboards: Offers real-time monitoring of price changes and recommendations through intuitive dashboards, enhancing decision-making efficiency. Primary Value and Problem Solved: Predicting Cost Bloomers / Rising Risk addresses the critical challenge of managing financial risks associated with fluctuating material costs and supply chain uncertainties. By providing accurate forecasts and strategic insights, it empowers organizations to make proactive decisions, optimize procurement processes, and mitigate potential cost overruns. This leads to improved financial planning, reduced operational risks, and enhanced overall business performance.

Predicting Diabetes Onset

The "Predicting Diabetes Onset" solution is a machine learning-based tool designed to forecast the likelihood of an individual developing diabetes. By analyzing various health metrics and patient data, it provides early warnings, enabling timely interventions to prevent or delay the onset of diabetes. Key Features and Functionality: - Machine Learning Algorithms: Utilizes advanced machine learning models to analyze patient data and predict diabetes risk. - Data Integration: Combines multiple health indicators, such as blood glucose levels, BMI, and family history, for comprehensive risk assessment. - User-Friendly Interface: Offers an intuitive platform for healthcare providers to input data and receive predictive insights. - Scalability: Designed to handle large datasets, making it suitable for both individual assessments and population-level studies. Primary Value and Problem Solved: This solution addresses the critical need for early detection of diabetes risk. By providing accurate predictions, it empowers healthcare professionals to implement preventive measures, reducing the incidence of diabetes-related complications and improving patient outcomes. Early intervention can lead to better management of health resources and a decrease in healthcare costs associated with diabetes treatment.

Predicting ED Utilization

ClosedLoop's Predicting ED Utilization solution leverages advanced artificial intelligence and machine learning to identify individuals at high risk of avoidable emergency department (ED) visits. By analyzing diverse healthcare data sources, the platform provides accurate, explainable, and actionable predictions, enabling healthcare organizations to proactively intervene and reduce unnecessary ED utilization. Key Features and Functionality: - Data Integration: Ingests, normalizes, and blends data from various health data sources, including electronic health records, medical claims, lab test results, and social determinants of health. - Risk Prediction: Utilizes machine learning models to predict the likelihood of avoidable ED visits within a specified timeframe, such as the next six months. - Explainable AI: Provides transparent insights into contributing factors affecting individual risk scores, such as chronic conditions, access to primary care, and previous ED visits. - Actionable Insights: Surfaces specific risk factors, enabling targeted interventions like strengthening continuity of care, enhancing enrollment in chronic care management programs, and addressing individual barriers to care. Primary Value and Problem Solved: The Predicting ED Utilization solution addresses the challenge of reducing unnecessary emergency department visits, which often result in higher healthcare costs and fragmented care. By accurately identifying individuals at risk, healthcare organizations can implement proactive measures to improve care continuity, enhance patient outcomes, and achieve significant cost savings.

Predicting Hospital Readmissions

Product Description: ClosedLoop's Predicting Hospital Readmissions solution leverages advanced artificial intelligence to identify patients at high risk of 30-day readmission. By analyzing diverse healthcare data sources, it provides actionable insights to healthcare providers, enabling proactive interventions that enhance patient outcomes and reduce unnecessary costs. Key Features and Functionality: - Comprehensive Data Integration: Aggregates and normalizes data from multiple sources, including electronic health records, clinical notes, e-prescribing data, vital signs, remote monitoring data, medical and prescription claims, ADT records, lab results, social needs assessments, and social determinants of health. - Explainable AI Predictions: Generates transparent predictions with detailed contributing factors, allowing clinicians to understand and trust the risk assessments. - Actionable Risk Factors: Identifies specific factors contributing to readmission risk, such as previous admissions for congestive heart failure, medication adherence levels, changes in medication regimen at discharge, and caregiver support levels. - Proactive Intervention Support: Enables healthcare providers to enhance pre-discharge evaluations, promote continuity of care, and educate patients on chronic disease management to prevent readmissions. Primary Value and Problem Solved: Hospital readmissions are a significant challenge, with approximately 4.2 million adult readmissions annually, costing CMS $26 billion each year. ClosedLoop's solution addresses this issue by providing healthcare organizations with the tools to predict and mitigate unplanned readmissions. By pinpointing high-risk individuals and surfacing actionable risk factors, it empowers providers to implement targeted interventions, improve patient care transitions, and reduce financial penalties associated with excessive readmissions.

Predicting Hypertension Onset

The "Predicting Hypertension Onset" solution leverages advanced machine learning techniques to analyze longitudinal electronic health records (EHRs and predict the onset of hypertension. By integrating deep learning models, particularly Long Short-Term Memory (LSTM networks, this tool processes temporal sequences of patient data to identify patterns indicative of future hypertension development. This predictive capability enables healthcare providers to implement early interventions, potentially reducing the risk of cardiovascular diseases associated with high blood pressure. Key Features and Functionality: - Advanced Machine Learning Models: Utilizes LSTM networks to capture temporal dependencies in patient data, enhancing prediction accuracy. - Comprehensive Data Integration: Combines various EHR components, including laboratory results, vital signs, demographics, diagnosis codes, medications, and procedures, to provide a holistic analysis. - Performance Metrics: Demonstrates high predictive performance with an Area Under the Receiver Operating Characteristic Curve (AUROC of up to 0.94, indicating strong discriminative ability. - Feature Importance Analysis: Employs SHapley Additive exPlanations (SHAP to interpret model predictions, highlighting key factors such as triglyceride levels and body mass index (BMI that contribute to hypertension risk. Primary Value and User Benefits: This solution addresses the critical need for early detection of hypertension by providing healthcare professionals with a predictive tool that analyzes patient data over time. By identifying individuals at risk before the onset of hypertension, it facilitates proactive management strategies, personalized treatment plans, and targeted lifestyle interventions. Ultimately, this approach aims to improve patient outcomes, reduce the incidence of hypertension-related complications, and optimize healthcare resource utilization.

PredictionIO ML Pre-configured by Terracloudx

Terracloudx offers this software product repackaged where additional charges apply for technical support include Apache PredictionIO® is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task.

Predixit

Predixit was an AI-powered marketing automation platform designed to enhance e-commerce and digital agency operations by delivering personalized shopping experiences. Utilizing predictive affinity algorithms, it analyzed and anticipated customer behaviors to present the most relevant content to each user. This approach aimed to increase conversions, sales, and user engagement by tailoring the online shopping journey to individual preferences. Key Features and Functionality: - Predictive Behavior Analysis: Employed advanced algorithms to understand and forecast customer interests, enabling proactive content delivery. - Personalized Content Recommendations: Provided individualized product suggestions based on user behavior and preferences, enhancing the relevance of displayed items. - Cross-Device and Cross-Site Tracking: Monitored user interactions across various devices and platforms to maintain a cohesive and personalized shopping experience. - Marketing Automation: Automated the creation and delivery of targeted promotions and messages, streamlining marketing efforts and improving efficiency. Primary Value and User Solutions: Predixit addressed the challenge of delivering personalized online shopping experiences, a capability often limited to larger e-commerce entities. By equipping smaller retailers and digital agencies with AI-driven tools, it enabled them to compete more effectively by offering tailored content and promotions. This personalization not only enhanced customer satisfaction but also drove higher conversion rates and sales, providing a significant competitive advantage in the digital marketplace.

Preinstalled SAP ERP 6.0 EHP8 Linux/MaxDb (SAP License not included)

Our pre-installed SAP systems come with a complete installation of both SAP NetWeaver software and SAP Maxdb Database. You just need to perform the post-installation tasks. This AMI will speed up your SAP deployments in the Cloud. Please note, you must have an existing SAP license in order to use the SAP software included in this AMI

Preinstalled SAP HANA 2.0 SP05 Suse (SAP License not included)

Our pre-installed SAP systems come with a complete installation HANA Server. You just need to perform the post-installation tasks. This AMI will speed up your HANA deployments in AWS. Please note, you must have an existing SAP license in order to use the SAP software included in this AMI