Daniele Apiletti

96 total papers · 837 total citations
48 papers, 526 citations indexed

About

Daniele Apiletti is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Daniele Apiletti has authored 48 papers receiving a total of 526 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 10 papers in Information Systems and 9 papers in Computer Networks and Communications. Recurrent topics in Daniele Apiletti's work include Data Mining Algorithms and Applications (8 papers), Data Stream Mining Techniques (7 papers) and Anomaly Detection Techniques and Applications (6 papers). Daniele Apiletti is often cited by papers focused on Data Mining Algorithms and Applications (8 papers), Data Stream Mining Techniques (7 papers) and Anomaly Detection Techniques and Applications (6 papers). Daniele Apiletti collaborates with scholars based in Italy, France and China. Daniele Apiletti's co-authors include Tania Cerquitelli, Elena Baralis, Giulia Bruno, Paolo Garza, Enrico Macii, Giovanni Malnati, Silvia Chiusano, Luca Cagliero, Vincenzo D’Elia and Alberto Macii and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and IEEE Access.

In The Last Decade

Daniele Apiletti

46 papers receiving 503 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Daniele Apiletti	183	121	112	62	56	48	526
Aman Kataria	170 0.9×	154 1.3×	148 1.3×	49 0.8×	46 0.8×	32	569
Nicola Bicocchi	142 0.8×	163 1.3×	98 0.9×	105 1.7×	73 1.3×	52	590
Miltos Petridis	269 1.5×	123 1.0×	85 0.8×	52 0.8×	47 0.8×	63	530
Feng Gao	152 0.8×	241 2.0×	118 1.1×	80 1.3×	94 1.7×	45	594
Fahim Arif	128 0.7×	222 1.8×	155 1.4×	109 1.8×	81 1.4×	65	613
Kunal Gupta	140 0.8×	96 0.8×	58 0.5×	87 1.4×	126 2.3×	34	584
Vijender Kumar Solanki	125 0.7×	141 1.2×	85 0.8×	59 1.0×	110 2.0×	68	522
Bao Rong Chang	142 0.8×	113 0.9×	108 1.0×	89 1.4×	130 2.3×	113	604
Arif Ur Rahman	136 0.7×	146 1.2×	183 1.6×	67 1.1×	57 1.0×	25	524
Seyed Mahdi Jameii	159 0.9×	198 1.6×	138 1.2×	67 1.1×	107 1.9×	27	579

Countries citing papers authored by Daniele Apiletti

Since Specialization

Citations

This map shows the geographic impact of Daniele Apiletti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniele Apiletti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniele Apiletti more than expected).

Fields of papers citing papers by Daniele Apiletti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniele Apiletti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniele Apiletti. The network helps show where Daniele Apiletti may publish in the future.

Co-authorship network of co-authors of Daniele Apiletti

This figure shows the co-authorship network connecting the top 25 collaborators of Daniele Apiletti. A scholar is included among the top collaborators of Daniele Apiletti based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniele Apiletti. Daniele Apiletti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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