By Dr Hamid Alinejad-Rokny

Journal articles

Shirvani MH; Alinejad-Rokny H, 2012, 'Performance Assessment of Feasible Scheduling Multiprocessor Tasks Solutions by using DEA FDH method', Information Sciences Letters, 1, pp. 61 - 66, http://dx.doi.org/10.12785/isl/010106

Alizadeh H; Alinejad-Rokny H; Parvin H; Sohrabi B, 2012, 'A new inference engine: Surface Matching Degree', Applied Mathematical Modelling, http://dx.doi.org/10.1016/j.apm.2012.02.027

Esmaeili L; Minaei-Bidgoli B; Alinejad-Rokny H; Nasiri M, 2012, 'Hybrid recommender system for joining virtual communities', Research Journal of Applied Sciences, Engineering and Technology, 4, pp. 500 - 509

Parvin H; Alinejad-Rokny H; Asadi M, 2011, 'An ensemble based approach for feature selection', Australian Journal of Basic and Applied Sciences, 5, pp. 1153 - 1163

Minaei-Bidgoli B; Parvin H; Alizadeh H; Alinejad-Rokny H; Punch WF, 2011, 'Effects of resampling method and adaptation on clustering ensemble efficacy', Artificial Intelligence Review, pp. 1 - 22, http://dx.doi.org/10.1007/s10462-011-9295-x

Conference Papers

Argha A; Li J; Magdy J; Alinejad-Rokny H; Celler BG; Butcher K; Ooi SY; Lovell NH, 2023, 'Assessing the Generalizability of a Deep Learning-based Automated Atrial Fibrillation Algorithm', in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, http://dx.doi.org/10.1109/EMBC40787.2023.10341108

Montazerin M; Rahimian E; Naderkhani F; Atashzar SF; Alinejad-Rokny H; Mohammadi A, 2023, 'HYDRA-HGR: A Hybrid Transformer-Based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information', in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, http://dx.doi.org/10.1109/ICASSP49357.2023.10096192

Asgharnezhad H; Shamsi A; Bakhshayeshi I; Alizadehsani R; Chamaani S; Alinejad-Rokny H, 2023, 'Improving PPG Signal Classification with Machine Learning: The Power of a Second Opinion', in International Conference on Digital Signal Processing, DSP, http://dx.doi.org/10.1109/DSP58604.2023.10167869

Shahabikargar M; Beheshti A; Khatami A; Nguyen R; Zhang X; Alinejad-Rokny H, 2022, 'Domain Knowledge Enhanced Text Mining for Identifying Mental Disorder Patterns', in Proceedings - 2022 IEEE 9th International Conference on Data Science and Advanced Analytics, DSAA 2022, http://dx.doi.org/10.1109/DSAA54385.2022.10032346

Khozeimeh F; Roshanzamir M; Shoeibi A; Darbandy MT; Alizadehsani R; Alinejad-Rokny H; Ahmadian D; Khosravi A; Nahavandi S, 2022, 'Importance of Wearable Health Monitoring Systems Using IoMT; Requirements, Advantages, Disadvantages and Challenges', in IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics and 8th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, CINTI-MACRo 2022 - Proceedings, pp. 245 - 250, http://dx.doi.org/10.1109/CINTI-MACRo57952.2022.10029528

Danaei S; Bostani A; Moravvej SV; Mohammadi F; Alizadehsani R; Shoeibi A; Alinejad-Rokny H; Nahavandi S, 2022, 'Myocarditis Diagnosis: A Method using Mutual Learning-Based ABC and Reinforcement Learning', in IEEE Joint 22nd International Symposium on Computational Intelligence and Informatics and 8th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, CINTI-MACRo 2022 - Proceedings, pp. 265 - 270, http://dx.doi.org/10.1109/CINTI-MACRo57952.2022.10029403

Conference Abstracts

Gooneratne S; Alinejad-Rokny H; Mohammadi D; Bohn P; Wiseman R; O'Connor D; Davenport M; Kent S, 2015, 'LINKING PIGTAIL MACAQUE MHC I HAPLOTYPES AND CTL ESCAPE MUTATIONS IN SIV INFECTION', in JOURNAL OF MEDICAL PRIMATOLOGY, WILEY-BLACKWELL, Vol. 44, pp. 335 - 335, https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000361966000094&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=891bb5ab6ba270e68a29b250adbe88d1

Preprints

Rahmani AM; Haider A; Adeli M; Mzoughi O; Gemeay E; Mohammadi M; Alinejad-Rokny H; Khoshvaght P; Hosseinzadeh M, 2024, Enhanced Classification of Heart Sounds Using Mel Frequency Cepstral Coefficients: A Comparative Study of Single and Ensemble Classifier Strategies,

Truong P; Shen S; Joshi S; Islam MI; Zhong L; Raftery MJ; Afrasiabi A; Alinejad-Rokny H; Nguyen M; Zou X; Bhuyan GS; Sarowar CH; Ghodousi ES; Stonehouse O; Mohamed S; Toscan CE; Connerty P; Kakadia PM; Bohlander SK; Michie KA; Larsson J; Lock RB; Walkley CR; Thoms JAI; Jolly CJ; Pimanda JE, 2024, Genome-Wide CRISPR-Cas9 Screening Identifies a Synergy between Hypomethylating Agents and SUMOylation Blockade in MDS/AML, , http://dx.doi.org/10.1101/2024.04.17.589858

Jafari M; Sadeghi D; Shoeibi A; Alinejad-Rokny H; Beheshti A; García DL; Chen Z; Acharya UR; Gorriz JM, 2023, Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023, , http://dx.doi.org/10.48550/arxiv.2309.12202

Karami M; Alizadehsani R; Khadijeh ; Jahanian ; Argha A; Dehzangi I; Alinejad-Rokny H, 2023, Revolutionizing Genomics with Reinforcement Learning Techniques, , http://dx.doi.org/10.48550/arxiv.2302.13268

Abedini SS; Akhavan S; Heng J; Alizadehsani R; Dehzangi I; Bauer DC; Rokny H, 2023, A Critical Review of the Impact of Candidate Copy Number Variants on Autism Spectrum Disorders, , http://dx.doi.org/10.48550/arxiv.2302.03211

Roshanzamir M; Shamsi A; Asgharnezhad H; Alizadehsani R; Hussain S; Moosaei H; Mohammadi A; Acharya UR; Alinejad H, 2023, Quantifying Uncertainty in Automated Detection of Alzheimer’s Patients Using Deep Neural Network, , http://dx.doi.org/10.20944/preprints202301.0148.v1

Subramanian S; Subramanian S; Thoms JAI; Huang Y; Cornejo P; Koch F; Jacquelin S; Shen S; Song E; Joshi S; Brownlee C; Woll P; Fajardo DC; Beck D; Curtis D; Yehson K; Antonenas V; Brien TO; Trickett A; Powell J; Lewis I; Pitson S; Gandhi M; Lane S; Vafaee F; Wong E; Göttgens B; Rokny HA; Wong JWH; Pimanda J, 2023, Cell Type-Specific Regulation by a Heptad of Transcription Factors in Human Hematopoietic Stem and Progenitor Cells, , http://dx.doi.org/10.1101/2023.04.18.537282

Jafari M; Shoeibi A; Khodatars M; Ghassemi N; Moridian P; Delfan N; Alizadehsani R; Khosravi A; Ling SH; Zhang Y-D; Wang S-H; Gorriz JM; Rokny HA; Acharya UR, 2022, Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review, , http://dx.doi.org/10.48550/arxiv.2210.14909

Jafari M; Shoeibi A; Ghassemi N; Heras J; Ling SH; Beheshti A; Zhang Y-D; Wang S-H; Alizadehsani R; Gorriz JM; Acharya UR; Rokny HA, 2022, Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence, , http://dx.doi.org/10.48550/arxiv.2210.14611

Montazerin M; Rahimian E; Naderkhani F; Atashzar SF; Alinejad-Rokny H; Mohammadi A, 2022, HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information, , http://dx.doi.org/10.48550/arxiv.2211.02619

Nasab RZ; Ghamsari MRE; Argha A; Macphillamy C; Beheshti A; Alizadehsani R; Lovell NH; Lotfollahi M; Alinejad-Rokny H, 2022, Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View, , http://dx.doi.org/10.48550/arxiv.2210.04453

Rahaie Z; Rabiee HR; Alinejad-Rokny H, 2022, DeepGenePrior: A deep learning model to prioritize genes affected by copy number variants, , http://dx.doi.org/10.1101/2022.08.22.504862

Parhami P; Fateh M; Rezvani M; Rokny HA, 2022, A benchmarking of deep neural network models for cancer subtyping using single point mutations, , http://dx.doi.org/10.1101/2022.07.24.501264

Kazemi A; Hamidieh K; Dashti H; Ghareyazi A; Tahaei MS; Rabiee HR; Alinejad-Rokny H; Dehzangi I, 2022, Pan-cancer integrative analysis of whole-genome De novo somatic point mutations reveals 17 cancer types, , http://dx.doi.org/10.21203/rs.3.rs-1567157/v1

Band S; Ardabili S; Yarahmadi A; Pahlevanzadeh B; Kausar Kiani A; Beheshti A; Alinejad Rokny H; Dehzangi I; Chang A; Mosavi A; Moslehpour M, 2022, A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis, , http://dx.doi.org/10.20944/preprints202202.0083.v2

Sharifonnasabi F; Jhanjhi N; John J; Obeidy P; Shamshirband S; Alinejad Rokny H; Baz M, 2022, Hybrid HCNN-KNN Model Enhances Age Estimation Accuracy in Orthopantomography, , http://dx.doi.org/10.20944/preprints202108.0413.v3

Band S; Ardabili S; Yarahmadi A; Pahlevanzadeh B; Kausar Kiani A; Beheshti A; Alinejad Rokny H; Dehzangi I; Mosavi A, 2022, Machine Learning and Internet of Medical Things for Handling COVID-19: Meta-Analysis, , http://dx.doi.org/10.20944/preprints202202.0083.v1

Sharifonnasabi F; Jhanjhi N; John J; Obeidy P; Shamshirband S; Alinejad Rokny H, 2022, Hybrid HCNN-KNN Transfer Learning Model Enhances Age Estimation Accuracy in Orthopantomography, , http://dx.doi.org/10.20944/preprints202108.0413.v2

Rahman MM; Kamal Nasir M; A-Alam N; Islam Khan S; Band S; Dehzangi I; Beheshti A; Alinejad Rokny H, 2022, Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection, , http://dx.doi.org/10.20944/preprints202201.0258.v1

Kazemi A; Ghareyazi A; Hamidieh K; Dashti H; Tahaei M; Rabiee H; Alinejad Rokny H; Dehzangi A, 2021, Pan-Cancer Integrative Analysis of Whole-Genome <em>De novo</em> Somatic Point Mutations Reveals 17 Cancer Types, , http://dx.doi.org/10.20944/preprints202111.0266.v1

Rezaie N; Bayati M; Tahaei MS; Hamidi M; Khorasani S; Lovell N; Breen J; Rabiee H; Rokny HA, 2021, Somatic point mutations are enriched in long non-coding RNAs with possible regulatory function in breast cancer, , http://dx.doi.org/10.21203/rs.3.rs-827525/v1

Islam Khan MS; Rahman A; Karim MR; Bithi NI; Band SS; Dehzangi A; Alinejad-Rokny H, 2021, CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images, , http://dx.doi.org/10.1101/2021.05.19.21257430

Debnath T; Reza MM; Rahman A; Band S; Alinejad Rokny H, 2021, Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity, , http://dx.doi.org/10.20944/preprints202105.0424.v1

Hamidi H; Alinejad-Rokny H; Coorens T; Sanghvi R; Lindsay SJ; Rahbari R; Ebrahimi D, 2021, Signatures of Mutational Processes in Human DNA Evolution, , http://dx.doi.org/10.1101/2021.01.09.426041

Liu N; Low WY; Alinejad-Rokny H; Pederson S; Sadlon T; Barry S; Breen J, 2020, Seeing the forest through the trees: Identifying functional interactions from Hi-C, , http://dx.doi.org/10.1101/2020.11.29.402420

Sharifrazi D; Alizadehsani R; Hassannataj Joloudari J; Shamshirband S; Hussain S; Alizadeh Sani Z; Hasanzadeh F; Shoaibi A; Dehzangi A; Alinejad-Rokny H, 2020, CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering, , http://dx.doi.org/10.20944/preprints202007.0650.v1

Dashti H; Dehzangi A; Bayati M; Breen J; Lovell N; Ebrahimi D; Rabiee HR; Alinejad-Rokny H, 2020, Integrative analysis of mutated genes and mutational processes reveals seven colorectal cancer subtypes, , http://dx.doi.org/10.1101/2020.05.18.101022

Asgari Y; Heng JIT; Lovell N; Forrest ARR; Alinejad-Rokny H, 2020, Evidence for enhancer noncoding RNAs (enhancer-ncRNAs) with gene regulatory functions relevant to neurodevelopmental disorders, , http://dx.doi.org/10.1101/2020.05.16.087395

Alinejad-Rokny H; Modegh RG; Rabiee HR; Rezaie N; Tam KT; Forrest ARR, 2020, MaxHiC: robust estimation of chromatin interaction frequency in Hi-C and capture Hi-C experiments, , http://dx.doi.org/10.1101/2020.04.23.056226

Alinejad-Rokny H; Heng JIT; Forrest ARR, 2019, Brain-enriched coding and long non-coding RNA genes are overrepresented in recurrent autism spectrum disorder CNVs, , http://dx.doi.org/10.1101/539817

Bayati M; Rabiee HR; Mehrbod M; Vafaee F; Ebrahimi D; Forrest A; Alinejad-Rokny H, 2018, CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomes, , http://dx.doi.org/10.1101/424960

Alinejad-Rokny H; Zarepour E; Khadijeh Jahanian H; Beheshti A; Dehzangi A, A Multivariate Data Analytics Approach Revealed No Footprint of APOBEC3 Proteins in Hepatitis B Virus Genome, , http://dx.doi.org/10.2139/ssrn.3514647

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