/home/adminoer/public_html/lib/SearchEngine/DefaultEngine.php:615 "Search Engine Debug 🔎 🪲"
Engine Type ⚙️: "SLiMS\SearchEngine\DefaultEngine"
SQL ⚙️: array:2 [ "count" => "select count(distinct b.biblio_id) from biblio as b left join mst_publisher as mp on b.publisher_id=mp.publisher_id left join mst_place as mpl on b.publish_place_id=mpl.place_id where b.opac_hide=0 and (b.biblio_id in(select ba.biblio_id from biblio_author as ba left join mst_author as ma on ba.author_id=ma.author_id where ma.author_name like ?))" "query" => "select b.biblio_id, b.title, b.image, b.isbn_issn, b.publish_year, mp.publisher_name as `publisher`, mpl.place_name as `publish_place`, b.labels, b.input_date, b.edition, b.collation, b.series_title, b.call_number from biblio as b left join mst_publisher as mp on b.publisher_id=mp.publisher_id left join mst_place as mpl on b.publish_place_id=mpl.place_id where b.opac_hide=0 and (b.biblio_id in(select ba.biblio_id from biblio_author as ba left join mst_author as ma on ba.author_id=ma.author_id where ma.author_name like ?)) order by b.last_update desc limit 20 offset 0" ]
Bind Value ⚒️: array:1 [ 0 => "%Goebel, Randy%" ]
Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and …