Min Jhon

1.1k citations
60 papers · 701 · 1 hit paper · h-index 15

Impact in

Papers in

Min Jhon

55 papers receiving 683 citations

Min Jhon's Hit Papers

A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis 2023 · 120 citations
1200+1+2Years since publication4080120

Peers

Min Jhon
Comparison fields: 5 of 108
  • Biological Psychiatry 75
  • Applied Psychology 75
  • Clinical Psychology 261
  • Behavioral Neuroscience 41
  • Experimental and Cognitive Psychology 115
Replace Richard Berman with:
Richard Berman United States
Ayşe Kurtulmuş Türkiye
Mrudula Utukuri United Kingdom
Jinfeng Miao China
Diogo Telles‐Correia Portugal
Katrina A. S. Davis United Kingdom
Ziwei Teng China
Yuhua Liao China
Hyewon Kim South Korea
Won Sub Kang South Korea
Min Jhon relative to Richard Berman United States Richard Berman's profile →
Citations per field
00.5×
Richard Berman · 1×
Citations per year

Countries citing papers authored by Min Jhon

Since Specialization
Citations

This map shows the geographic impact of Min Jhon'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 Min Jhon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min Jhon more than expected).

Fields of papers citing papers by Min Jhon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Min Jhon. 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 Min Jhon. The network helps show where Min Jhon may publish in the future.

Co-authors

The 25 scholars most cited alongside Min Jhon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Min Jhon Line = papers co-authored together Min Jhon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 60 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis
Hit paper breakdown →
2023120
2 2019104
3 201839
4 201938
5 202231
6 202129
7 202025
8 201625
9 202021
10 202019
11 202119
12 201819
13 202017
14 202214
15 202114
16 202214
17 202414
18 201813
19 202112
20 202212

About Min Jhon

Min Jhon is a scholar working on Clinical Psychology, Social Psychology, Experimental and Cognitive Psychology, Psychiatry and Mental health and Pharmacology, having authored 60 papers that have together received 701 indexed citations. Recurring topics across this work include Mental Health Research Topics (12 papers), Schizophrenia research and treatment (10 papers), COVID-19 and Mental Health (10 papers), Mental Health Treatment and Access (9 papers), Treatment of Major Depression (8 papers), Tryptophan and brain disorders (7 papers), Child and Adolescent Psychosocial and Emotional Development (7 papers) and Stress Responses and Cortisol (6 papers). The work is most often cited by research in Biological Psychiatry (75 citations), Applied Psychology (75 citations), Clinical Psychology (261 citations), Behavioral Neuroscience (41 citations) and Experimental and Cognitive Psychology (115 citations). Min Jhon has collaborated with scholars based in South Korea, United Kingdom and Australia. Frequent co-authors include Sung‐Wan Kim, Jae‐Min Kim, Ju‐Yeon Lee, Ju‐Wan Kim, Soo-Hyung Kim, Hyung-Jeong Yang, Seunghyong Ryu, Hee‐Ju Kang, Mina Kim and Il‐Seon Shin. Their work appears in journals such as Frontiers in Psychiatry, Journal of Affective Disorders, Journal of Korean Medical Science, PLoS ONE and Early Intervention in Psychiatry.

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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