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Ranjan Mondal - IEEE Xplore Author Profile

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Author details

Ranjan Mondal

Publications

7

Citations

294

Publications by Year

20172021

Co-Authors:

Omer Faruk AdilAitor Alvarez-GilaCodruta O. AncutiCosmin AncutiHarsh Aulakh

Show All Co-Authors (120)

Ranjan Mondal

Affiliation

Electronics and Communication Sciences Unit

Indian Statistical Institute

Kolkata, India

Publication Topics

Convolutional Neural Network,
Image Dehazing,
Input Image,
Transmission Map,
Advanced Driver Assistance Systems,
Cloudy Days,
Convolutional Layers,
Dark Channel,
Erosion Operation,
Feature Maps,
Generative Adversarial Networks,
Ground Truth Image

Biography

Ranjan Mondal received the master’s degree from the Indian Statistical Institute, Kolkata, India, where he is currently pursuing the Ph.D. degree. His research interests lie in the intersection of deep learning and reinforcement learning with computer vision and image processing.(Based on document published on 28 May 2018).

Publications

7

Citations

294

Publications by Year

20172021

Co-Authors:

Omer Faruk Adil
Aitor Alvarez-Gila
Codruta O. Ancuti
Cosmin Ancuti
Harsh Aulakh

Show All Co-Authors (120)

Author's Published Works

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Showing 1-16 of 16 results

Conferences (13)

Journals (3)

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Jun Meng(16)
Yushi Luan(7)
Xiukun Wang(4)
Jing Zhang(3)
Tsauyoung Lin(3)
School of Computer Science and Technology, Dalian University of Technology, Dalian, China(11)
School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China(4)
School of Bioengineering, Dalian University of Technology, Dalian, Liaoning, China(3)
Department of Computer Science, San Jose State University, San Jose, USA(2)
School of Bioengineering, Dalian University of Technology, Dalian, China(2)
IEEE/ACM Transactions on Computational Biology and Bioinformatics(3)
2010 IEEE International Conference on Granular Computing(2)
2013 IEEE International Conference on Granular Computing (GrC)(2)
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(2)
2011 IEEE International Conference on Granular Computing(1)
IEEE(16)
Beijing, China(2)
Istanbul, Turkiye(2)
San Jose, CA, USA(2)
Hangzhou, China(1)
Kaohsiung, Taiwan(1)
Neighborhood System(6)
Binary Relation(4)
Equivalence Relation(4)
Support Vector Machine(4)
Classification Accuracy(3)

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Results

A multi-granularity information-enhanced pre-training method for predicting the coding potential of sORFs in plant lncRNAs

Shihao Xia;Jun Meng;Zhaowei Wang;Yu Wang;Haibin Li;Zhaojing Qin;Yushi Luan

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2023 | Conference Paper |

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Small open reading frames (sORFs) are nucleotide sequences that may be translated into small peptides. Recently, increasing studies have demonstrated that peptides encoded by sORFs in plant long noncoding RNAs (lncRNAs) play a vital role in growth regulation and disease treatment. To accelerate the discovery of lncRNA-encoded peptides, it is essential to predict translatable sORFs in lncRNAs (lncR...Show More

A multi-granularity information-enhanced pre-training method for predicting the coding potential of sORFs in plant lncRNAs

Shihao Xia;Jun Meng;Zhaowei Wang;Yu Wang;Haibin Li;Zhaojing Qin;Yushi Luan

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2023 | Conference Paper |

TGAAL: Combining Transformer-based GAN and active learning to identify the coding potential of sORFs in plant lncRNAs

Yu Wang;Jun Meng;Shihao Xia;Haibin Li;Zhaowei Wang;Yushi Luan

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2023 | Conference Paper |

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Some small open reading frames (sORFs) in plant long non-coding RNAs (lncRNAs) are capable of encoding small peptides, which play key roles in the growth and development of organisms. Therefore, it is particularly important to identify the coding potential of sORFs in plant lncRNAs. However, existing methods often ignore the differences in length distribution between coding sORFs (csORFs) and non-...Show More

TGAAL: Combining Transformer-based GAN and active learning to identify the coding potential of sORFs in plant lncRNAs

Yu Wang;Jun Meng;Shihao Xia;Haibin Li;Zhaowei Wang;Yushi Luan

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2023 | Conference Paper |

Predicting the interactions between plant lncRNA-encoded peptide and protein using domain knowledge-based prototypical network

Siyuan Zhao;Jun Meng;Yushi Luan

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2022 | Conference Paper |

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Long noncoding RNA(lncRNA) has been reported to encode small peptides which play key roles in life activities through their functions by binding to proteins. It is crucial to predict the interactions between the lncRNA-encoded peptide and protein. However, no computational methods have been designed for predicting this type of interactions directly, owing to the few-shot problem causing poor gener...Show More

Predicting the interactions between plant lncRNA-encoded peptide and protein using domain knowledge-based prototypical network

Siyuan Zhao;Jun Meng;Yushi Luan

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2022 | Conference Paper |

Identifying LncRNA-Encoded Short Peptides Using Optimized Hybrid Features and Ensemble Learning

Siyuan Zhao;Jun Meng;Qiang Kang;Yushi Luan

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2022 | Volume: 19, Issue: 5 | Journal Article |

Cited by: Papers (5)

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Long non-coding RNA (lncRNA) contains short open reading frames (sORFs), and sORFs-encoded short peptides (SEPs) have become the focus of scientific studies due to their crucial role in life activities. The identification of SEPs is vital to further understanding their regulatory function. Bioinformatics methods can quickly identify SEPs to provide credible candidate sequences for verifying SEPs b...Show More

Identifying LncRNA-Encoded Short Peptides Using Optimized Hybrid Features and Ensemble Learning

Siyuan Zhao;Jun Meng;Qiang Kang;Yushi Luan

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2022 | Volume: 19, Issue: 5 | Journal Article |

LPI-DL: A recurrent deep learning model for plant lncRNA-protein interaction and function prediction with feature optimization

Jael Sanyanda Wekesa;Yushi Luan;Jun Meng

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2020 | Conference Paper |

Cited by: Papers (6)

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Predicting lncRNA-protein association is essential for insights into fundamental biological processes and disease etiology in plants and animals. There has been an enormous increment in the number of identified long noncoding RNAs (lncRNAs). However, less efforts has been directed towards lncRNA-protein interaction (LPI) prediction to help in characterizing the huge array of plant lncRNAs. This st...Show More

LPI-DL: A recurrent deep learning model for plant lncRNA-protein interaction and function prediction with feature optimization

Jael Sanyanda Wekesa;Yushi Luan;Jun Meng

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2020 | Conference Paper |

Biomedical Event Trigger Detection Based on BiLSTM Integrating Attention Mechanism and Sentence Vector

Xinyu He;Lishuang Li;Jia Wan;Dingxin Song;Jun Meng;Zhanjie Wang

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2018 | Conference Paper |

Cited by: Papers (10)

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As the crucial and prerequisite step in biomedical event extraction, trigger detection has attracted much attention. Most of the existing trigger detection methods either rely on elaborately designed features or consider features only within a window. Another challenge is that the existing methods treat each word in sentence equally. Also, most methods ignore the sentence-level semantic informatio...Show More

Biomedical Event Trigger Detection Based on BiLSTM Integrating Attention Mechanism and Sentence Vector

Xinyu He;Lishuang Li;Jia Wan;Dingxin Song;Jun Meng;Zhanjie Wang

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2018 | Conference Paper |

A Two-Stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings

Xinyu He;Lishuang Li;Yang Liu;Xiaoming Yu;Jun Meng

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2018 | Volume: 15, Issue: 4 | Journal Article |

Cited by: Papers (27)

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Extracting biomedical events from biomedical literature plays an important role in the field of biomedical text mining, and the trigger detection is a key step in biomedical event extraction. We propose a two-stage method for trigger detection, which divides trigger detection into recognition stage and classification stage, and different features are selected in each stage. In the first stage, we ...Show More

A Two-Stage Biomedical Event Trigger Detection Method Integrating Feature Selection and Word Embeddings

Xinyu He;Lishuang Li;Yang Liu;Xiaoming Yu;Jun Meng

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2018 | Volume: 15, Issue: 4 | Journal Article |

Parallel information fusion method for microarray data analysis

Jun Meng;Rui Li;Jing Zhang

2015 IEEE International Conference on Big Data (Big Data)

Year: 2015 | Conference Paper |

Cited by: Papers (1)

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Classification of microarray data has always been a challenging task due to the enormous number of genes. Finding a small, closely related gene set to accurately classify disease cells is an important research problem. Integrating biological knowledge into genomic analysis to help to improve the interpretation of the results is an effective approach. In this paper, affinity propagation (AP) cluste...Show More

Parallel information fusion method for microarray data analysis

Jun Meng;Rui Li;Jing Zhang

2015 IEEE International Conference on Big Data (Big Data)

Year: 2015 | Conference Paper |

Gene Selection Integrated with Biological Knowledge for Plant Stress Response Using Neighborhood System and Rough Set Theory

Jun Meng;Jing Zhang;Yushi Luan

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2015 | Volume: 12, Issue: 2 | Journal Article |

Cited by: Papers (19)

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Mining knowledge from gene expression data is a hot research topic and direction of bioinformatics. Gene selection and sample classification are significant research trends, due to the large amount of genes and small size of samples in gene expression data. Rough set theory has been successfully applied to gene selection, as it can select attributes without redundancy. To improve the interpretabil...Show More

Gene Selection Integrated with Biological Knowledge for Plant Stress Response Using Neighborhood System and Rough Set Theory

Jun Meng;Jing Zhang;Yushi Luan

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Year: 2015 | Volume: 12, Issue: 2 | Journal Article |

Regarding covering as a collection of binary relations

Jun Meng;Tsauyoung Lin

2014 IEEE International Conference on Granular Computing (GrC)

Year: 2014 | Conference Paper |

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Covering-based rough set is an important generalization of Pawlak's rough set theory. By the fact that there have defined eight pairs lower and upper approximation operations in the case of covering-based rough set. Checking which pair of upper and lower approximations is the best definition among them is very necessary. We took advantage of the 2nd GrC Model and 6th pairs of upper and lower appro...Show More

Regarding covering as a collection of binary relations

Jun Meng;Tsauyoung Lin

2014 IEEE International Conference on Granular Computing (GrC)

Year: 2014 | Conference Paper |

Protein function prediction based on physiochemical properties and protein granularity

Wanlu Wang;Xin Zhang;Jun Meng;Yushi Luan

2013 IEEE International Conference on Granular Computing (GrC)

Year: 2013 | Conference Paper |

Cited by: Papers (3)

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Assigning biological function to uncharacterized proteins is a fundamental problem in the post-genomic age. The increasing availability of large amounts of data on protein sequences has led to the emergence of developing effective computational methods for quickly and accurately predicting their functions. In this work, we extract 353 numerical features from sequences based not only on physiochemi...Show More

Protein function prediction based on physiochemical properties and protein granularity

Wanlu Wang;Xin Zhang;Jun Meng;Yushi Luan

2013 IEEE International Conference on Granular Computing (GrC)

Year: 2013 | Conference Paper |

Construct rough approximation based on GAE

Lin Shi;Jun Meng;Yang Zhou;Tsauyoung Lin

2013 IEEE International Conference on Granular Computing (GrC)

Year: 2013 | Conference Paper |

Cited by: Papers (1)

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Recently cloud computing has emerged as a new paradigm which focuses on web-scale problems, large data centers, multiple models of computing and highly-interactive web applications. It is high available and scalable for distributed and parallel data storage and computing based on a large amount of cheap PCs. As the representative product, Google app engine (GAE), which acts a platform as a service...Show More

Construct rough approximation based on GAE

Lin Shi;Jun Meng;Yang Zhou;Tsauyoung Lin

2013 IEEE International Conference on Granular Computing (GrC)

Year: 2013 | Conference Paper |

A comparison of some approximation definitions about neighborhood system based rough sets

Jun Meng;Jing Zhang;Xiukun Wang

2012 IEEE International Conference on Granular Computing

Year: 2012 | Conference Paper |

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This paper presents two kinds of rough set approximation definition based on neighborhood system, comparison of some kinds of approximation, and analysis of their related properties. Adopting Pawlak's method to measure the accuracy in three approximation definitions, that is, Lin's definition and two definitions presented in this paper. The conclusion is that the first approximation definition pre...Show More

A comparison of some approximation definitions about neighborhood system based rough sets

Jun Meng;Jing Zhang;Xiukun Wang

2012 IEEE International Conference on Granular Computing

Year: 2012 | Conference Paper |

Rule induction for tolerance relation-based rough sets

Jun Meng;Peng Wang;Xiukun Wang;Tsau Young Lin

2011 IEEE International Conference on Granular Computing

Year: 2011 | Conference Paper |

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Tolerance relation, derived partition, quotient set and named quotient space are very important concepts based on tolerance relations. In this paper, we review them at first. Then we present the construction of three spaces, namely, granular, quotient and knowledge spaces. We define the concepts of knowledge dependency in rough set models based on tolerance relations, such as weak dependency, stro...Show More

Rule induction for tolerance relation-based rough sets

Jun Meng;Peng Wang;Xiukun Wang;Tsau Young Lin

2011 IEEE International Conference on Granular Computing

Year: 2011 | Conference Paper |

Isomorphism Between Information Tables for Tolerance and Classical Rough Sets

Jun Meng;Tsauyoung Li;Zehua Chen;Xiukun Wang;Peng Wang

2010 IEEE International Conference on Granular Computing

Year: 2010 | Conference Paper |

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Tolerance relations are more generic than equivalence relations from viewpoint of Granular Computing. But it cannot to be used to divide a set directly. Based on neighborhood system of tolerance relation, we can get the induced partition. We propose the extended information table of tolerance rough sets. An example is given to illustrate the continuity of named quotient spaces, so the information ...Show More

Isomorphism Between Information Tables for Tolerance and Classical Rough Sets

Jun Meng;Tsauyoung Li;Zehua Chen;Xiukun Wang;Peng Wang

2010 IEEE International Conference on Granular Computing

Year: 2010 | Conference Paper |

Face Recognition Based on Local Binary Patterns with Threshold

Jun Meng;Yumao Gao;Xiukun Wang;Tsauyoung Lin;Jianying Zhang

2010 IEEE International Conference on Granular Computing

Year: 2010 | Conference Paper |

Cited by: Papers (22) | Patents (4)

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This paper presents a novel and efficient face recognition technique based on Local Binary Pattern (LBP) with threshold for resolving traditional LBP's weakness of extracting global features. By setting a threshold to enhance the robustness to noise such as light and extract the global features of face preferably. Combining the local features by LBP with global features as the total features of th...Show More

Face Recognition Based on Local Binary Patterns with Threshold

Jun Meng;Yumao Gao;Xiukun Wang;Tsauyoung Lin;Jianying Zhang

2010 IEEE International Conference on Granular Computing

Year: 2010 | Conference Paper |

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