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Daniel Neagu
Ciprian-Daniel Neagu
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- affiliation: University of Bradford, UK
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2020 – today
- 2024
- [j31]Luca Parisi
, Ciprian-Daniel Neagu
Optimal evolutionary framework-based activation function for image classification. Knowl. Based Syst. 299: 112025 (2024) - 2023
- [j30]Ahsanullah Yunas Mahmoud
Early diagnosis and personalised treatment focusing on synthetic data modelling: Novel visual learning approach in healthcare. Comput. Biol. Medicine 164: 107295 (2023) - 2022
- [j29]Luca Parisi
Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson's disease and COVID-19. Expert Syst. Appl. 187: 115892 (2022) - [c71]Ahsanullah Yunas Mahmoud
Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective. FRUCT 2022: 152-161 - [c70]Ahsanullah Yunas Mahmoud
Machine Learning Experiments with Artificially Generated Big Data from Small Immunotherapy Datasets. ICMLA 2022: 986-991 - [e1]M. Arif Wani, Mehmed M. Kantardzic, Vasile Palade, Daniel Neagu, Longzhi Yang, Kit Yan Chan:
21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022, Nassau, Bahamas, December 12-14, 2022. IEEE 2022, ISBN 978-1-6654-6283-9 [contents] - 2021
- [j28]Daniel Neagu, Felician Campean
Engineering Data- & Model-Driven Applications: EDMA-2017 special issue editorial. Expert Syst. J. Knowl. Eng. 38(7) (2021) - [j27]Morteza Soleimani
Diagnostics and prognostics for complex systems: A review of methods and challenges. Qual. Reliab. Eng. Int. 37(8): 3746-3778 (2021) - [j26]Morteza Soleimani, Felician Campean
Integration of Hidden Markov Modelling and Bayesian Network for fault detection and prediction of complex engineered systems. Reliab. Eng. Syst. Saf. 215: 107808 (2021) - [j25]Ahmad Idris Tambuwal, Daniel Neagu:
Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series. SN Comput. Sci. 2(6): 475 (2021) - [c69]Souhaiel Khalfaoui, Eric Manouvrier, Alexandre Briot, David Delaux, Stéphane Butel, Jesutofunmi Ibrahim, Tatenda Kanyere, Bola Orimogunje, Amr Abdullatif
Defect Prediction on Production Line. UKCI 2021: 532-544 - 2020
- [j24]Attila Csenki, Daniel Neagu
Proximity Curves for Potential-Based Clustering. J. Classif. 37(3): 671-695 (2020) - [j23]Bhupesh Kumar Mishra
A novel application of deep learning with image cropping: a smart city use case for flood monitoring. J. Reliab. Intell. Environ. 6(1): 51-61 (2020) - [i5]Luca Parisi
QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics. CoRR abs/2010.08031 (2020)
2010 – 2019
- 2019
- [j22]Shehla Khalid, Neil Small, Daniel Neagu, Claire Surr:
A Study Proposing a Data Model for a Dementia Care Mapping (DCM) Data Warehouse for Potential Secondary Uses of Dementia Care Data. Int. J. Heal. Inf. Syst. Informatics 14(1): 61-79 (2019) - [c68]Bhupesh Kumar Mishra
Using Deep Learning for IoT-enabled Camera: A Use Case of Flood Monitoring. DESSERT 2019: 235-240 - [c67]Omololu Makinde, Abimbola Sangodoyin
Distributed Network Behaviour Prediction Using Machine Learning and Agent-Based Micro Simulation. FiCloud 2019: 182-188 - [c66]Omesaad Rado, Daniel Neagu:
On Selection of Optimal Classifiers. SGAI Conf. 2019: 494-499 - [c65]Denis Torgunov, Paul R. Trundle, Felician Campean
Vehicle Warranty Claim Prediction from Diagnostic Data Using Classification. UKCI 2019: 483-492 - [c64]Gaurav Pant, Felician Campean
Co-modelling Strategy for Development of Airpath Metamodel on Multi-physics Simulation Platform. UKCI 2019: 504-516 - [c63]Natasha Micic, Ci Lei, Daniel Neagu, Felician Campean
Queries on Synthetic Images for Large Multivariate Engineering Data Base Searches. UKCI 2019: 517-528 - 2018
- [j21]Caroline Duvier, Daniel Neagu, Crina Oltean-Dumbrava, Dave Dickens:
Data quality challenges in the UK social housing sector. Int. J. Inf. Manag. 38(1): 196-200 (2018) - [c62]Órla Murphy, Esmaeil Habib Zadeh, Felician Campean
Robustness of Automotive SOTA: State-of-the-Art in Uncertainty Modelling. HPCC/SmartCity/DSS 2018: 1506-1513 - [c61]Natasha Micic, Daniel Neagu, Denis Torgunov, Felician Campean
Exploring Methods for Comparing Similarity of Dimensionally Inconsistent Multivariate Numerical Data. HPCC/SmartCity/DSS 2018: 1528-1535 - [c60]Aisyah Mat Jasin
The Wild Bootstrap Resampling in Regression Imputation Algorithm with a Gaussian Mixture Model. MLDM (2) 2018: 218-230 - [c59]Habiba Muhammad Sani, Ci Lei, Daniel Neagu:
Computational Complexity Analysis of Decision Tree Algorithms. SGAI Conf. 2018: 191-197 - [c58]Omololu Makinde, Daniel Neagu, Marian Gheorghe
Agent Based Micro-simulation of a Passenger Rail System Using Customer Survey Data and an Activity Based Approach. UKCI 2018: 123-137 - [c57]Najat Ali, Daniel Neagu, Paul R. Trundle:
Classification of Heterogeneous Data Based on Data Type Impact on Similarity. UKCI 2018: 252-263 - [c56]Ebtesam Taktek, Dhavalkumar Thakker, Daniel Neagu:
Comparison between Range-based and Prefix Dewey Encoding. WEBIST 2018: 364-368 - 2017
- [j20]Daniel Neagu
Special issue on innovative techniques and applications of artificial intelligence guest editorial. Expert Syst. J. Knowl. Eng. 34(5) (2017) - [c55]Natasha Micic, Daniel Neagu, Felician Campean
Towards a Data Quality Framework for Heterogeneous Data. iThings/GreenCom/CPSCom/SmartData 2017: 155-162 - [c54]Ahmad Idris Tambuwal, Daniel Neagu, Marian Gheorghe
An Experimental Comparison of Ensemble Classifiers for Evolving Data Streams. SGAI Conf. 2017: 156-162 - [i4]Mohammad Azzeh, Peter I. Cowling, Daniel Neagu:
Software stage-effort estimation based on association rule mining and fuzzy set theory. CoRR abs/1703.04539 (2017) - 2016
- [j19]Ali Bou Nassif
Guest editorial: special issue on predictive analytics using machine learning. Neural Comput. Appl. 27(8): 2153-2155 (2016) - [j18]Daniel Neagu:
Special issue on computational intelligence algorithms and applications. Soft Comput. 20(8): 2921-2922 (2016) - [j17]Pritesh Mistry, Daniel Neagu
Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology. Soft Comput. 20(8): 2967-2979 (2016) - 2015
- [c53]Haruna Isah
Bipartite Network Model for Inferring Hidden Ties in Crime Data. ASONAM 2015: 994-1001 - [i3]Haruna Isah, Daniel Neagu, Paul R. Trundle:
Bipartite Network Model for Inferring Hidden Ties in Crime Data. CoRR abs/1510.02343 (2015) - [i2]Haruna Isah, Daniel Neagu, Paul R. Trundle:
Social Media Analysis for Product Safety using Text Mining and Sentiment Analysis. CoRR abs/1510.05301 (2015) - 2014
- [j16]Daniel Neagu
Special issue on innovative techniques and applications of artificial intelligence. Expert Syst. J. Knowl. Eng. 31(5): 409-410 (2014) - [c52]Yousef Alqasrawi, Daniel Neagu
Investigating the relationship between the distribution of local semantic concepts and local keypoints for image annotation. UKCI 2014: 1-7 - [c51]Haruna Isah
Social media analysis for product safety using text mining and sentiment analysis. UKCI 2014: 1-7 - [c50]Pritesh Mistry, Anna Palczewska, Daniel Neagu
Using computational methods for the prediction of drug vehicles. UKCI 2014: 1-7 - [c49]Longzhi Yang
Integration strategies for toxicity data from an empirical perspective. UKCI 2014: 1-8 - 2013
- [j15]Anna Palczewska, Xin Fu, Paul R. Trundle, Longzhi Yang
Towards model governance in predictive toxicology. Int. J. Inf. Manag. 33(3): 567-582 (2013) - [j14]Anna Palczewska, Daniel Neagu
Using Pareto points for model identification in predictive toxicology. J. Cheminformatics 5: 16 (2013) - [j13]Yousef Alqasrawi, Daniel Neagu
Fusing integrated visual vocabularies-based bag of visual words and weighted colour moments on spatial pyramid layout for natural scene image classification. Signal Image Video Process. 7(4): 759-775 (2013) - [j12]Yasmina Bashon, Daniel Neagu
A framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributes. Soft Comput. 17(9): 1595-1615 (2013) - [j11]Ruqayya Abdulrahman
Data Extraction from Online Social Networks Using Application Programming Interface in a Multi Agent System Approach. Trans. Comput. Collect. Intell. 11: 88-118 (2013) - [c48]Longzhi Yang
Toxicity risk assessment from heterogeneous uncertain data with possibility-probability distribution. FUZZ-IEEE 2013 - [c47]Anna Palczewska, Jan Palczewski
Interpreting random forest models using a feature contribution method. IRI 2013: 112-119 - [c46]Anna Palczewska, Jan Palczewski
Interpreting Random Forest Classification Models Using a Feature Contribution Method. IRI (best papers) 2013: 193-218 - [i1]Anna Palczewska, Jan Palczewski, Richard Marchese Robinson, Daniel Neagu:
Interpreting random forest classification models using a feature contribution method. CoRR abs/1312.1121 (2013) - 2012
- [j10]Ruqayya Abdulrahman
Multi agent system approach for vulnerability analysis of online social network profiles over time. Int. J. Knowl. Web Intell. 3(3): 256-286 (2012) - [c45]Xin Fu, Kim Travis, Daniel Neagu
Fuzzy complex number aided evaluation of predictive toxicology models. FUZZ-IEEE 2012: 1-8 - [c44]Longzhi Yang
Towards the integration of heterogeneous uncertain data. IRI 2012: 295-302 - [c43]Mokhairi Makhtar
Optimisation of Classifier Ensemble for Predictive Toxicology Applications. UKSim 2012: 236-241 - 2011
- [j9]Mona Awad Alkhattabi
Assessing information quality of e-learning systems: a web mining approach. Comput. Hum. Behav. 27(2): 862-873 (2011) - [j8]Xin Fu, Anna Wojak, Daniel Neagu
Data governance in predictive toxicology: A review. J. Cheminformatics 3: 24 (2011) - [j7]Mohammad Azzeh
Analogy-based software effort estimation using Fuzzy numbers. J. Syst. Softw. 84(2): 270-284 (2011) - [c42]Mokhairi Makhtar
Comparing Multi-class Classifiers: On the Similarity of Confusion Matrices for Predictive Toxicology Applications. IDEAL 2011: 252-261 - [c41]Yasmina Bashon, Daniel Neagu
Fuzzy set-theoretical approach for comparing objects with fuzzy attributes. ISDA 2011: 754-759 - [c40]Mokhairi Makhtar
Binary Classification Models Comparison: On the Similarity of Datasets and Confusion Matrix for Predictive Toxicology Applications. ITBAM 2011: 108-122 - [c39]Ruqayya Abdulrahman
Multi Agent System for Historical Information Retrieval from Online Social Networks. KES-AMSTA 2011: 54-63 - [c38]Ruqayya Abdulrahman
Formal Specification of Multi Agent System for Historical Information Retrieval from Online Social Networks. KES-AMSTA 2011: 84-93 - 2010
- [j6]Mohammad Azzeh
Fuzzy grey relational analysis for software effort estimation. Empir. Softw. Eng. 15(1): 60-90 (2010) - [c37]Mohammad Azzeh
Software Stage-Effort Estimation Based on Association Rule Mining and Fuzzy Set Theory. CIT 2010: 249-256 - [c36]Ruqayya Abdulrahman
Algorithms for Data Retrieval from Online Social Network Graphs. CIT 2010: 1660-1666 - [c35]Yasmina Bashon, Daniel Neagu
A New Approach for Comparing Fuzzy Objects. IPMU (2) 2010: 115-125 - [c34]Yousef Alqasrawi, Daniel Neagu
Spatial pyramid local keypoints quantization for bag of visual patches image representation. ISDA 2010: 1270-1274 - [c33]Shehla Khalid, Claire Surr, Daniel Neagu
DCM Data Management Framework: A Data Warehousing Approach. ITBAM 2010: 45-56
2000 – 2009
- 2009
- [c32]Sophia Alim, Ruqayya Abdulrahman
Data retrieval from online social network profiles for social engineering applications. ICITST 2009: 1-5 - [c31]Yousef Alqasrawi, Daniel Neagu
Natural Scene Image Recognition by Fusing Weighted Colour Moments with Bag of Visual Patches on Spatial Pyramid Layout. ISDA 2009: 140-145 - [c30]Mohammad Azzeh
Software effort estimation based on weighted fuzzy grey relational analysis. PROMISE 2009: 8 - [c29]Rita E. Ochuko, Andrea J. Cullen, Daniel Neagu
Overview of Factors for Internet Banking Adoption. CW 2009: 163-170 - 2008
- [c28]Muna Hatem, Daniel Neagu
RDF Repository of Experts Based on Context Oriented Automatic Annotation Framework. Asia International Conference on Modelling and Simulation 2008: 29-34 - [c27]Chunying Zhao, Gongde Guo, Xuming Huang, Tianqiang Huang, Daniel Neagu:
Data Reduction and Classification based on Multiple Classifier Behavior. DMIN 2008: 78-83 - [c26]Mohammad Azzeh, Daniel Neagu, Peter I. Cowling:
Adjusting Analogy Software Effort Estimation Based on Fuzzy Logic. ICSOFT (SE/MUSE/GSDCA) 2008: 127-132 - [c25]Ladan Malazizi, Daniel Neagu
Improving Imbalanced Multidimensional Dataset Learner Performance with Artificial Data Generation: Density-Based Class-Boost Algorithm. ICDM 2008: 165-176 - [c24]Mohammad Azzeh
Software Project Similarity Measurement Based on Fuzzy C-Means. ICSP 2008: 123-134 - [c23]Mohammad Azzeh
Improving analogy software effort estimation using fuzzy feature subset selection algorithm. PROMISE@ICSE 2008: 71-78 - [p1]Paul R. Trundle, Daniel Neagu
Development of Multi-output Neural Networks for Data Integration - A Case Study. Innovations in Hybrid Intelligent Systems 2008: 80-87 - 2007
- [c22]Yang Lan, Daniel Neagu
Applications of the Moving Average of n th -Order Difference Algorithm for Time Series Prediction. ADMA 2007: 264-275 - [c21]Xuming Huang, Gongde Guo, Daniel Neagu
Weighted kNNModel-Nased Data Reduction and Classification. FSKD (1) 2007: 689-695 - [c20]Yang Lan, Daniel Neagu
A new time series prediction algorithm based on moving average of nth-order difference. ICMLA 2007: 248-253 - [c19]Yu Huang, Gongde Guo, Daniel Neagu
A Partial Coverage Based Approach to Classification. ICTAI (1) 2007: 275-280 - [c18]Paul R. Trundle, Daniel Neagu
Multi-source Data Modelling: Integrating Related Data to Improve Model Performance. MLDM 2007: 32-46 - 2006
- [c17]Daniel Neagu
An Effective Combination Based on Class-Wise Expertise of Diverse Classifiers for Predictive Toxicology Data Mining. ADMA 2006: 165-172 - [c16]Gongde Guo, Daniel Neagu
An Effective Combination of Multiple Classifiers for Toxicity Prediction. FSKD 2006: 481-490 - 2005
- [b1]Mircea Gh. Negoita, Daniel Neagu
Computational Intelligence - Engineering of Hybrid Systems. Studies in Fuzziness and Soft Computing 174, Springer 2005, ISBN 978-3-540-23219-3, pp. 1-194 [contents] - [j5]Gongde Guo, Daniel Neagu
Fuzzy Knnmodel Applied to Predictive Toxicology Data Mining. Int. J. Comput. Intell. Appl. 5(3): 321-334 (2005) - [c15]Gongde Guo, Daniel Neagu
A Study on Feature Selection for Toxicity Prediction. FSKD (2) 2005: 31-34 - [c14]Gongde Guo, Daniel Neagu
Using kNN Model for Automatic Feature Selection. ICAPR (1) 2005: 410-419 - [c13]Shuai Zhang, Daniel Neagu
Refinement of Clustering Solutions Using a Multi-label Voting Algorithm for Neuro-fuzzy Ensembles. ICNC (3) 2005: 1300-1303 - [c12]Daniel Neagu
Hybrid Intelligent Systems for Predictive Toxicology - a Distributed Approach. ISDA 2005: 26-31 - [c11]Daniel Neagu
A Multi-Label Voting Algorithm for Neuro-Fuzzy Classifier Ensembles with Applications in Visual Arts Data Mining. ISDA 2005: 245-250 - [c10]Gongde Guo, Daniel Neagu:
Similarity-based classifier combination for decision making. SMC 2005: 176-181 - 2004
- [c9]Daniel Neagu:
A Study of Neural and Fuzzy Parameters for Explicit and Implicit Knowledge-based Systems. ANNs 2004: 51-61 - 2003
- [j4]Paolo Mazzatorta, Emilio Benfenati, Ciprian-Daniel Neagu
Tuning Neural and Fuzzy-Neural Networks for Toxicity Modeling. J. Chem. Inf. Comput. Sci. 43(2): 513-518 (2003) - [j3]Daniel Neagu
A neuro-fuzzy approach for functional genomics data interpretation and analysis. Neural Comput. Appl. 12(3-4): 153-159 (2003) - [c8]Marian Viorel Craciun, Daniel Neagu
A Study of Aquatic Toxicity Using Artificial Neural Networks. KES 2003: 911-918 - 2002
- [j2]Ciprian-Daniel Neagu
Neural and Neuro-Fuzzy Integration in a Knowledge-Based System for Air Quality Prediction. Appl. Intell. 17(2): 141-169 (2002) - [j1]Paolo Mazzatorta, Emilio Benfenati, Daniel Neagu
The Importance of Scaling in Data Mining for Toxicity Prediction. J. Chem. Inf. Comput. Sci. 42(5): 1250-1255 (2002) - [c7]Ciprian-Daniel Neagu, Emilio Benfenati, Giuseppina C. Gini, Paolo Mazzatorta, Alessandra Roncaglioni:
Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds. ECAI 2002: 498-502 - [c6]Ciprian-Daniel Neagu, Vasile Palade:
Modular Neuro-Fuzzy Networks Used in Explicit and Implicit Knowledge Integration. FLAIRS 2002: 277-281 - [c5]Emilio Benfenati, Paolo Mazzatorta, Daniel Neagu
Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach. Multiple Classifier Systems 2002: 293-303 - 2001
- [c4]Vasile Palade, Ciprian-Daniel Neagu
Interpretation of Trained Neural Networks by Rule Extraction. Fuzzy Days 2001: 152-161 - 2000
- [c3]Ciprian-Daniel Neagu, Vasile Palade:
Neural explicit and implicit knowledge representation. KES 2000: 213-216 - [c2]Vasile Palade, Ciprian-Daniel Neagu, Gheorghe Puscasu:
Rule extraction from neural networks by interval propagation. KES 2000: 217-220
1990 – 1999
- 1999
- [c1]Ciprian-Daniel Neagu
Fuzzy Computing in a MultiPurpose Neural Network Implementation. Fuzzy Days 1999: 697-700
Coauthor Index
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last updated on 2024-10-07 22:06 CEST by the dblp team
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