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prediction Product List and Ranking from 34 Manufacturers, Suppliers and Companies

Last Updated： Aggregation Period:Dec 24, 2025～Jan 20, 2026
This ranking is based on the number of page views on our site.

prediction Manufacturer, Suppliers and Company Rankings

Last Updated： Aggregation Period:Dec 24, 2025～Jan 20, 2026
This ranking is based on the number of page views on our site.

  1. AI CROSS Tokyo//Information and Communications
  2. パトコア Tokyo//IT/Telecommunications
  3. SCSK デジタルエンジニアリング事業本部 Tokyo//software
  4. 4 ソホビービー Tokyo//IT/Telecommunications
  5. 5 null/null
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prediction Product ranking

Last Updated： Aggregation Period:Dec 24, 2025～Jan 20, 2026
This ranking is based on the number of page views on our site.

  1. AI-based analysis result prediction Neural Concept Shape SCSK デジタルエンジニアリング事業本部
  2. LogP prediction, LogD prediction 'Partitioning' パトコア
  3. Achieving Maximum Profit! Operating Forecast for New Pachinko Machines Utilizing AI ソホビービー
  4. [Technical Information] Prediction of Melting Point by Molecular Dynamics Calculation
  5. 5 pKa prediction, molecular species prediction, isoelectric point prediction 'Protonation' パトコア

prediction Product List

16～30 item / All 83 items

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pKa prediction, molecular species prediction, isoelectric point prediction 'Protonation'

pKa prediction, molecular species prediction, isoelectric point prediction 'Protonation'

Calculate the pKa of ionizable groups! It can also be used for proteins and others.

Our company handles "Protonation groups" suitable for predictions such as pKa (acid-base dissociation constant). We offer a lineup including the high-precision pKa calculation program "pKa," "Microspecies" for predicting major structures at specific pH levels, and "Isoelectric point" for calculating isoelectric points. 【Lineup】 ■ pKa: High-precision pKa calculation program ■ Microspecies: Predicts major structures at specific pH levels ■ Isoelectric point: Calculation of isoelectric points *For more details, please refer to the PDF materials or feel free to contact us.

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  • Software (middle, driver, security, etc.)

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LogP prediction, LogD prediction 'Partitioning'

LogP prediction, LogD prediction 'Partitioning'

Predictions will be made based on the improved methods of Viswanadhan for both logP and logD!

The "Partitioning Group" can predict the pH-dependent logD value in addition to the water/octanol partition coefficient. Predictions are based on the improved methods of Viswanadhan et al. for both logP and logD. The modifications applied include the redefinition of selected atom types (particularly sulfur, carbon, nitrogen, and metal atoms) to adjust for electronic delocalization and consideration of ionization forms. 【Features】 ■ Predicts pH-dependent logD values in addition to the water/octanol partition coefficient ■ Predictions are based on the improved methods of Viswanadhan et al. for both logP and logD ■ The logP value of zwitterions is calculated from logD at the isoelectric point ■ Just as logD values are pH-dependent, logD calculations rely on methods for predicting pKa *For more details, please refer to the PDF document or feel free to contact us.

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  • Software (middle, driver, security, etc.)

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1H and 13C NMR spectrum prediction 'NMR'

1H and 13C NMR spectrum prediction 'NMR'

Predicting 1H and 13C NMR spectra of organic compounds!

The prediction of NMR spectra plays an important role in the validation of structures and the description of molecules. The "NMR Group" predicts 1H and 13C NMR spectra for organic compounds. 【Features】 ■ The prediction of NMR spectra plays an important role in the validation of structures and the description of molecules. ■ Predicts 1H and 13C NMR spectra for organic compounds. *For more details, please refer to the PDF document or feel free to contact us.

  • Software (middle, driver, security, etc.)

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Predicting the isothermal condition CL strength curve from the heating data of polypropylene powder.

Predicting the isothermal condition CL strength curve from the heating data of polypropylene powder.

Predicting OIT at isothermal measurements of 140, 150, and 160°C! Includes how to determine the endpoint temperature for peak integration.

This document is a technical note explaining how to predict the isothermal condition CL strength curve from the heating data of polypropylene powder. Using measurement data of 200 mg of PP (powder) with heating rates of 0.2 to 0.8 K/min, the OIT at isothermal measurements of 140, 150, and 160°C is predicted. When predicting OIT values from heating measurement data, it is crucial to appropriately select the starting and ending points of the CL data. We encourage you to read it. [Published Data (Excerpt)] ■ CL strength curves at heating rates of 0.2, 0.4, and 0.8 K/min (Normal Scale) ■ How to determine the temperature endpoint for peak integration ■ CL strength curves of 0.2, 0.4, and 0.8 K/min CL data (Log Scale) ■ Transition of activation energy concerning reaction rate ■ OIT predictions for PP (powder) at 140 to 160°C *For more details, please download the PDF or feel free to contact us.

  • Business Intelligence and Data Analysis

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OIT prediction based on accelerated test data of polyamide 6 powder chemiluminescence.

OIT prediction based on accelerated test data of polyamide 6 powder chemiluminescence.

Explore the reaction model formula for oxidation induction reactions using thinning data with TKsd software!

This document is a technical note explaining the OIT prediction based on the chemiluminescence accelerated test data for polyamide 6 powder (without added antioxidants). Using the TKsd software, we explored the reaction model equations for the oxidation induction reaction based on the downsampled data (accelerated test data). The resulting reaction model equations were divided into two stages: Equation A and Equation B. What do each of the reaction model equations A and B represent? Please take a look. 【Published Data (Excerpt)】 ■ Conducted accelerated tests on multiple test specimens over a long period using several constant temperature baths. ■ Reaction rate curves under isothermal conditions of 60-80°C from CL data at 0.2-0.8 K/min. ■ Reaction rate curves downsampled to 1/150 from the predicted reaction rate curves. ■ Explored reaction model equations from the downsampled reaction rate curves under isothermal conditions of 60-80°C. ■ Created a log-log plot with the Y-axis as CL intensity and the X-axis as time from the reaction model equations. *For more details, please download the PDF or feel free to contact us.

  • Business Intelligence and Data Analysis

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10 Examples of Demand Forecasting Using AI! An Explanation of Methods and Benefits

10 Examples of Demand Forecasting Using AI! An Explanation of Methods and Benefits

How about taking a step towards AI implementation by learning about examples of demand forecasting?

Many people may want to know how to effectively utilize AI for demand forecasting, along with specific examples. In this article, we will introduce eight examples of demand forecasting using AI and provide a detailed explanation of the methods and benefits. For companies looking to improve the accuracy of their demand forecasts and enhance operational efficiency, the implementation of AI can be a significant help. By reading this article, you will understand how AI performs demand forecasting and what benefits it brings. Furthermore, through specific examples, you can confirm how practical the use of AI can be. *For more detailed content of the blog, you can view it through the related links. For more information, please download the PDF or feel free to contact us.*

  • Other operation management software

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What is a demand forecasting algorithm? Explanation of AI implementation examples, mechanisms, and benefits.

What is a demand forecasting algorithm? Explanation of AI implementation examples, mechanisms, and benefits.

You can understand the steps for introducing demand forecasting AI and the key points for successful selection.

In today's market environment, the accuracy of demand forecasting significantly influences a company's performance. However, accurately capturing complex consumer trends and external factors is not easy. This is where AI-driven demand forecasting comes into focus. This article clarifies the mechanisms and advantages of AI-driven demand forecasting, as well as the algorithms used, and explains its effectiveness through actual implementation examples. *For more detailed information, please refer to the related links. You can download the PDF for more details or feel free to contact us.*

  • Other operation management software

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[SOINN] Time Series Forecasting AI

[SOINN] Time Series Forecasting AI

Simple and versatile! Can be executed in any environment, whether edge PC, server, or cloud.

We have started selling a forecasting AI module that has a proven track record with major companies in the fields of disaster prediction and energy demand forecasting. It allows for lightweight computation, enabling learning and operation on standard CPUs. With automatic noise removal from data and automatic adjustment of learning parameters, it requires no hassle. Additionally, it can be executed in any environment, whether edge PCs, servers, or the cloud, and allows for the repurposing of trained AI models. 【Features】 ■ Customers can perform learning, forecasting, and additional learning on their side. ■ Lightweight computation enables learning and operation on standard CPUs. ■ Automatic noise removal from data and automatic adjustment of learning parameters make it hassle-free. ■ Utilizing commercially available equipment keeps both implementation and running costs low. ■ Can be executed in any environment, whether edge PCs, servers, or the cloud. *For more details, please refer to the PDF document or feel free to contact us.

  • Company：SOINN
  • Price：Other
  • Other information systems

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[Information] How on-site personnel can start AI demand forecasting.

[Information] How on-site personnel can start AI demand forecasting.

Practical application of highly accurate AI to achieve reductions in waste loss, stockouts, storage costs, and optimization of personnel.

The use of "AI" as a means for demand forecasting is increasing. However, it is often perceived that utilizing this "AI" is not easy at all. In this document, we introduce "methods and examples of building high-accuracy, automated AI using AI" aimed at "those who feel challenges with the accuracy, labor intensity, and reliance on individuals in demand forecasting," specifically for practitioners and planning departments in manufacturing, retail, and distribution. We have summarized the information in a way that is easy to understand even for those without AI knowledge, and we hope that after reading this document, you will be able to actually utilize it as a method for demand forecasting. [Contents (partial)] ■ Understanding demand forecasting with AI ■ How to create demand forecasting AI ■ Current status and key points of AI utilization (appendix) *For more details, please refer to the PDF document or feel free to contact us.

  • Other production management systems

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AI Integration Services

AI Integration Services

We will re-examine what machines can do and what people can do, and assist in creating new forms of work.

We offer an "AI Integration Service" that contributes to solving resource and labor challenges. Utilizing past sales data and publicly available data, we provide "demand forecasting" to predict sales and management indicators, and we implement various machine learning solutions such as "automation through image recognition," which uses voice and image recognition technology to improve operational efficiency, reduce labor, and achieve cost savings. Please feel free to contact us if you have any inquiries. 【Service Contents】 ■ Demand Forecasting ■ Automation through Image Recognition ■ Quality Inspection / Anomaly Detection ■ Security Enhancement *For more details, please download the PDF or feel free to contact us.

  • Other production and development software and systems
  • Other Software
  • Other contract services

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[Blog] December 2021: What We Predict

[Blog] December 2021: What We Predict

Insights into the future of observability and digital experience monitoring! Introducing Catchpoint news.

In December, AWS experienced an unprecedented situation with three outages in three weeks. Catchpoint conducted an incident review that delved into the details of these three outages and their commercial impact. - Major online services, including Amazon, crashed due to AWS outages - AWS outages occurred again this week - Third AWS outage in December – adding insult to injury At the same time, we released a series of forward-looking insights on trends in observability and digital experience monitoring, acting as newsmakers and prophets. [Overview] ■ Trends and developments in top networks 2022 ■ 2022 Application Performance Management Predictions - Part 1 ■ 2022 Application Performance Management Predictions - Part 2 ■ Catchpoint 2022 Predictions: Total Experience Orchestration ■ During Cyber 5, dozens of retailers faced website outages and slowdowns *For more details on the blog, please refer to the related links. Feel free to contact us for more information.

  • Other information systems
  • Other network tools
  • Other analyses

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Analysis of the global automotive seat belt industry, including size, share, growth, trends, and forecasts.

Analysis of the global automotive seat belt industry, including size, share, growth, trends, and forecasts.

Provide detailed insights into market dynamics and offer stakeholders a comprehensive analysis!

Persistence Market Research has published a comprehensive analysis that delves deeply into the dynamics of the global automotive seatbelt market. Focusing on critical market forces, growth catalysts, hurdles, and rapidly growing trends, this report provides valuable insights. It meticulously outlines the trajectory of the automotive seatbelt market from 2024 to 2033, presenting abundant data and statistics to offer stakeholders a solid foundation for strategic decision-making and informed market navigation. [Contents (Excerpt)] ■ Summary ■ Global Market Outlook ■ Demand-Side Trends ■ Supply-Side Trends ■ Technology Roadmap The report can also be customized to fit the data you are looking for. Please feel free to consult us if you would like to know more about "the market outlook for □□ type products" or if you need an analysis of "Company ○○."

  • Interior parts

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What is sales forecasting?

What is sales forecasting?

Overview of sales forecasting, examples of how to create sales forecasts, etc.! A column explaining with a focus on sales forecasting for sales organizations.

In companies and organizations operated for profit, it goes without saying that "sales" significantly influence the management situation. In this article, I would like to take a moment to reflect on sales-related forecasts. Please note that this article is not a general explanation for financial professionals, but rather focuses on sales forecasting within sales organizations. *For detailed content of the column, you can view it through the related links. For more information, please feel free to contact us.*

  • SFA/Sales Support System

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Semiconductor Market Forecast 'SRL Quarterly Forecast'

Semiconductor Market Forecast 'SRL Quarterly Forecast'

Semiconductors are destined for long-term growth, but they will experience fluctuations in the short term. Understanding these changes and trends, and riding the wave of growth, is the key to your company's success.

A one-year forecast for the rapidly changing semiconductor market. The semiconductor market is expected to grow steadily in the medium to long term. However, in the short term, it will show significant fluctuations. This can be seen as "growing pains," which are unavoidable as we transition to the next stage. The important thing is to understand these short-term fluctuations, overcome them, and leverage them for long-term growth. There is a wealth of information regarding semiconductors, but this publication provides clear and concise explanations of important movements, primarily through diagrams, to help you understand this field.

  • Company：SRL
  • Price：10,000 yen-100,000 yen
  • Other services
  • Other contract services
  • Other semiconductors

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