Abstract Let $$ {\mathcal {H}} $$ H be a connected subgraph of a graph G. The $${\mathcal {H}}$$ H -structure connectivity of G, denoted by $$ \kappa (G;{\mathcal {H}}) $$ κ ( G ; H ) , is the minimum cardinality of a set of connected subgraphs in G, whose removal either disconnects G or reduces it to a trivial graph, where each element in the set is isomorphic to $$ {\mathcal {H}} $$ H . The $${\mathcal {H}}$$ H -substructure connectivity of G, denoted by $$ \kappa ^s(G;{\mathcal {H}}) $$ κ s ( G ; H ) , is the minimum cardinality of a set of connected subgraphs in G, whose removal either disconnects G or reduces it to a trivial graph, where each element in the set is isomorphic to a connected subgraph of $$ {\mathcal {H}} $$ H . In this paper, we investigate the $$ {\mathcal {H}} $$ H -structure connectivity and $$ {\mathcal {H}} $$ H -substructure connectivity of folded divide-and-swap cube $$ FDSC_n $$ F D S C n for $$ {\mathcal {H}}\in \{K_1, K_{1,1}, K_{1,m} \text (2\le m \le d+2) \} $$ H ∈ { K 1 , K 1 , 1 , K 1 , m ( 2 ≤ m ≤ d + 2 ) } where $$ n=2^d $$ n = 2 d . We show that $$\kappa (FDSC_n;K_1)=\kappa ^s(FDSC_n;K_1)=d+2$$ κ ( F D S C n ; K 1 ) = κ s ( F D S C n ; K 1 ) = d + 2 , $$\kappa (FDSC_n;K_{1,1})=\kappa ^s(FDSC_n;K_{1,1})=d+1 $$ κ ( F D

Abstract The fashion industry’s traditional price-setting methods, based on historical sales and Fashion Week trends, are inadequate in the digital era. Rapid changes in collections and consumer preferences necessitate advanced Artificial Intelligence (AI) techniques. These AI methods should analyze data from various sources, including social media and e-commerce, to predict future fashion trends and prices. In this paper, we propose, apply, and assess a data analytics approach, i.e., FashionXpert, employing several image processing and machine learning techniques in an AI pipeline for garment price prediction. It integrates various heterogeneous data sources (e.g., textual and image data from e-stores, brand websites, and social media) to obtain more consistent, accurate, and beneficial information. We evaluated its effectiveness with an industrial data set obtained by a fashion search tool from the electronic commerce sites of clothing brands. FashionXpert predicted garment prices with an average Mean Absolute Error (MAE) of 15.31 EUR on a data set that has a standard deviation of 72.99 EUR.