NonprofitScience and engineering research institutesJapan
Nonprofit Japan
Science and engineering research institutesCenter for Information and Neural Networks is a nonprofit in science and engineering research institutes founded in 2011[1] and based in Suita, Japan[1].
Each with its fit to Center for Information and Neural Networks, from 0 to 100%: how close it is in what it does, its industry, the distinctive parts both public maps hold, and size and region. 85% and above is a direct peer, 70 to 85 close, 60 to 70 related; under 65 is left out, so there may be few. Open a row for its breakdown.
Nonprofit · Science and engineering research institutes · Japan
| Component | Match | Weight | Adds |
|---|---|---|---|
| What it doesHow close its public description reads to Center for Information and Neural Networks's, against an ordinary organization. | 95% | 61% | +58.2 |
| IndustrySame segment | 70% | 33% | +23.5 |
| Shared partsNeeds a public map on both sides: left out. | n/a | — | — |
| Size & regionCountry same · size not known for both · model not known for both. | 100% | 6% | +5.6 |
| Fit | 87% | ||
Fit = Σ weight × match over the components known for both, the weights re-scaled to 100%. Unknown is left out, never counted as a difference.
Nonprofit · Science and engineering research institutes · Japan
| Component | Match | Weight | Adds |
|---|---|---|---|
| What it doesHow close its public description reads to Center for Information and Neural Networks's, against an ordinary organization. | 76% | 61% | +46.2 |
| IndustrySame segment | 70% | 33% | +23.5 |
| Shared partsNeeds a public map on both sides: left out. | n/a | — | — |
| Size & regionCountry same · size not known for both · model not known for both. | 100% | 6% | +5.6 |
| Fit | 75% | ||
Fit = Σ weight × match over the components known for both, the weights re-scaled to 100%. Unknown is left out, never counted as a difference.
Nonprofit · Science and engineering research institutes · Japan
| Component | Match | Weight | Adds |
|---|---|---|---|
| What it doesHow close its public description reads to Center for Information and Neural Networks's, against an ordinary organization. | 75% | 61% | +45.9 |
| IndustrySame segment | 70% | 33% | +23.5 |
| Shared partsNeeds a public map on both sides: left out. | n/a | — | — |
| Size & regionCountry same · size not known for both · model not known for both. | 100% | 6% | +5.6 |
| Fit | 75% | ||
Fit = Σ weight × match over the components known for both, the weights re-scaled to 100%. Unknown is left out, never counted as a difference.
Nonprofit · Science and engineering research institutes · Japan
| Component | Match | Weight | Adds |
|---|---|---|---|
| What it doesHow close its public description reads to Center for Information and Neural Networks's, against an ordinary organization. | 73% | 61% | +44.5 |
| IndustrySame segment | 70% | 33% | +23.5 |
| Shared partsNeeds a public map on both sides: left out. | n/a | — | — |
| Size & regionCountry same · size not known for both · model not known for both. | 100% | 6% | +5.6 |
| Fit | 74% | ||
Fit = Σ weight × match over the components known for both, the weights re-scaled to 100%. Unknown is left out, never counted as a difference.
Nonprofit · Science and engineering research institutes · Japan
| Component | Match | Weight | Adds |
|---|---|---|---|
| What it doesHow close its public description reads to Center for Information and Neural Networks's, against an ordinary organization. | 73% | 61% | +44.3 |
| IndustrySame segment | 70% | 33% | +23.5 |
| Shared partsNeeds a public map on both sides: left out. | n/a | — | — |
| Size & regionCountry same · size not known for both · model not known for both. | 100% | 6% | +5.6 |
| Fit | 73% | ||
Fit = Σ weight × match over the components known for both, the weights re-scaled to 100%. Unknown is left out, never counted as a difference.
Nonprofit · Science and engineering research institutes · Japan
| Component | Match | Weight | Adds |
|---|---|---|---|
| What it doesHow close its public description reads to Center for Information and Neural Networks's, against an ordinary organization. | 69% | 61% | +42.3 |
| IndustrySame segment | 70% | 33% | +23.5 |
| Shared partsNeeds a public map on both sides: left out. | n/a | — | — |
| Size & regionCountry same · size not known for both · model not known for both. | 100% | 6% | +5.6 |
| Fit | 71% | ||
Fit = Σ weight × match over the components known for both, the weights re-scaled to 100%. Unknown is left out, never counted as a difference.
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