Thursday , December 19 2024

Publications

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2023 Ranking

Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2022 and single recent year data pertain to citations received during calendar year 2022. The selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field. This version (6) is based on the October 1, 2023 snapshot from Scopus, updated to end of citation year 2022. This work uses Scopus data provided by Elsevier through ICSR Lab (https://www.elsevier.com/icsr/icsrlab). Calculations were performed using all Scopus author profiles as of October 1, 2023. If an author is not on the list it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work. PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases. The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, please read the 3 associated PLoS Biology papers that explain the development, validation and use of these metrics and databases. (https://doi.org/10.1371/journal.pbio.1002501, https://doi.org/10.1371/journal.pbio.3000384 and https://doi.org/10.1371/journal.pbio.3000918). Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a

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Publications Metrics

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ORCID = 0000-0003-1103-4938

Web of Sciences Researcher ID = C-7876-2012

Citations (23-09-2022) = 0950
Publications/Proceedings = 75Journal Publications = 60(1st author: 45 IF & 4 ISI indexed)
Cumulative ISI Impact Factor = 175.18Conference Proceedings = 10

Review Editor

Frontiers in Artificial Intelligence  

Book Chapter

Impact Factor Journals                    (Total IF: 215.18)

  1. Hussain L, Abbasi, A.A. (2022) AI-based non-deep learning and deep learning techniques used to accurately predict prostate cancer. Chapter-06.  Artificial Intelligence in Cancer Diagnosis and Prognosis, Volume 3. IOP Publishing. University of Louisville. Pp. 6.1-6.33, doi:10.1088/978-0-7503-3603-1ch6
  2. Abbasi, A. A., Hussain, L., & Ahmed, B. (2022, December). Improving Multi-class Brain Tumor Detection Using Vision Transformer as Feature Extractor. In International Conference on Intelligent Systems and Machine Learning (pp. 3-14). Cham: Springer Nature Switzerland.

The year 2017-2023

2023

  1. Hussain, et al. (2023). Deep convolutional neural networks accurately predict breast cancer using mammograms. Waves in Random and Complex Media, 1-24. I.F. 4.853
  2. Qureshi, S. A., Hussain, L., et al. (2023). Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans. Scientific reports, 13(1), 3291. I.F. 5.00
  3. Hussain, L., Alabdulkreem, E., Lone, K. J., Al-Wesabi, F. N., Nour, M. K., Hilal, A. M., … & Aziz, S. (2023). Feature ranking chi-square method to improve the epileptic seizure prediction by employing machine learning algorithms. Waves in Random and Complex Media, 1-27. I.F. 4.853
  4. Nawaz, &, Hussain, L. et al. (2023). Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection. Computers, Materials & Continua, 75(3). I.F. 3.860
  5. Ahmed, S., Raza, B., Hussain, L.,et al. (2023). The Deep Learning ResNet101 and Ensemble XGBoost Algorithm with Hyperparameters Optimization Accurately Predict the Lung Cancer. Applied Artificial Intelligence, 37(1), 2166222. I.F. 2.777.
  6. Yang, J., Yee, P. L., Khan, A. A., and,.. Hussain et al. (2023). Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features. Digital health, 9, 20552076231172632. I.F. 4.687

2022

  1. Hussain et al.  (2022) Bayesian Dynamic 1 Profiling and Optimization of Important Ranked Energy from Gray level co-occurrence (GLCM) Feature for Empirical Analysis of Brain MRI. Scientific Reports. I.F. 5.00
  2. Hussain, L., Alsolai, H., Hassine, S. B. H., Nour, M. K., Duhayyim, M. A., Hilal, A. M., … & Rizwanullah, M. (2022). Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features. Applied Sciences, 12(13), 6517. I.F. 2.838
  3. Akhtar, N., Ali, M., Hussain, L. (2022) Diverse Pose Lip-reading Framework. Applied Sciences. I.F. 2.838. 
  4. Hussain et al. (2022) Automated breast cancer detection by reconstruction independent component analysis (RICA) based hybrid features using machine learning paradigms. Connection Science. I.F. 3.53.https://www.tandfonline.com/doi/full/10.1080/09540091.2022.2151566https://www.tandfonline.com/doi/full/10.1080/09540091.2022.2151566
  5. Mir, A. A., CÇelebi, F. V., Rafique, M., Hussain, L., Almasoud, A. S., Alajmi, M., … & Hilal, A. M. (2022). An Improved Imputation Method for Accurate Prediction of Imputed Dataset Based Radon Time Series. IEEE Access. I.F. 3.367.
  6. Mengash H.A., Hussain, L. et al. (2022) Smart Cities-Based Improving Atmospheric Particulate Matters Prediction using Chi-Square Feature Selection Methods by Employing Machine Learning Techniques. Applied Artificial Intelligence. I.F. 2.777.
  7. Butt, F. M., Hussain, L., Jafri, S. H. M., Alshahrani, H. M., Al-Wesabi, F. N., Lone, K. J., … & Duhayyim, M. A. (2022). Intelligence-based Accurate Medium and Long Term Load Forecasting System. Applied Artificial Intelligence, 36(1), 2088452. I.F. 2.838
  8. Qureshi, S.A., Hussain, L. et al. (2022) Gunshots Localization and Classification Model based on Wind Noise Sensitivity Analysis using Extreme Learning Machine. IEEE Access. I.F. 3.367. 
  9. Khan, R. N., Hussain, L., Alluhaidan, A. S., Majid, A., Lone, K. J., Verdiyev, R., … & Duong, T. Q. (2022). COVID-19 lung infection detection using deep learning with transfer learning and ResNet101 features extraction and selection. Waves in Random and Complex Media, 1-24. I.F. 4.853
  10. Eltahir, M. M., Hussain, L., Malibari, A. A., K Nour, M., Obayya, M., Mohsen, H., … & Ahmed Hamza, M. (2022). A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure. Applied Sciences, 12(13), 6350. I.F. 2.838
  11. Qureshi, S. A., Raza, S. E. A., Hussain, L., Malibari, A. A., Nour, M. K., Rehman, A. U., … & Hilal, A. M. (2022). Intelligent Ultra-Light Deep Learning Model for Multi-Class Brain Tumor Detection. Applied Sciences, 12(8), 3715. I.F. 2.838
  12. Abdulrahman, A., Jaber A., Negm N., Hussain L., et al. (2022) uture Challenges of Particulate Matters (PMs) Monitoring by Computing Associations Among Extracted Multimodal Features Applying Bayesian Network Approach. Applied Artificial Intelligence. I.F. 2.777. doi. 10.1080/08839514.2022.2112545 
  13. Shim, S. O., Alkinani, M. H., Hussain, L., & Aziz, W. (2022). Feature Ranking Importance from Multimodal Radiomic Texture Features using Machine Learning Paradigm: A Biomarker to Predict Lung Cancer. Big Data Research, 100331. I.F. 3.739
  14. Attaullah, M., Ali, M., Hussain, L. et al. (2022) Initial stage Covid-19 Detection System based on Patients’ Symptoms and Chest X-ray Images. Applied Artificial Intelligence. I.F. 2.777
  15. Qureshi, S.A., Raza, S.E.A, Hussain, L. (2022) Intelligent Ultra-Light Deep Learning Model for Multi-Class Brain Tumor Detection. Applied Sciences. I.F. 2.679.
  16. Khan, S.M; .., Hussain, L. (2022). Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments. Applied Sciences. I.F. 2.838. 
  17. Hussain L. et al. (2022) Feature ranking methods to improve epileptic seizure prediction by employing machine learning algorithms. Waves in Random and Complex Media. I.F. 4.853. (Accepted)
  18. Qureshi, S.A., Hussain, L. (2022). Kalman Filtering and Bipartite Matching based Super-Chained Tracker Model for online Multi-Object Tracking in Video Sequences. Applied Sciences. I.F. 2.838.
  19. Rathore, F., Hussain, L. (2022) Survival Prediction of Glioma Patients from Integrated Radiology and Pathology Images using Machine Learning Ensemble Regression Methods. Applied Sciences. I.F. 2.838.
  20. Mussadiq, U. … Hussain, L. (2022). The Intelligent Modelling and Optimization of an Economic and Ecosystem-Friendly Model for Grid Connected Prosumer Community. Plus One. I.F. 3.752. (In Production.
  21. Rehman, M. ……, Hussain, L. (2022). Machine Learning Based Skin Lesion Segmentation Method with Novel Borders And Hair Removal Techniques. Plus One. I.F. 3.752. (In Production)
  22. Sana, L.,… Hussain, L. (2022). Anomaly detection for cyber internet of things attacks: A systematic review. Applied Artificial Intelligence. I.F. 2.777. (Accepted)

2021

  1. Hussain, L., Huang, P., Nguyen, T. et al. (2021) Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predict pathologic complete response. BioMed Eng OnLine 20, 63. https://doi.org/10.1186/s12938-021-00899-z. I.F. 2.819
  2. Iqbal, S., Hussain, L. (2021) Image Enhancement Methods on Extracted Texture Features to Detect Prostate Cancer by Employing Machine Learning Techniques. Waves in Random and Complex Media. I.F. 4.853.
  3. Lone, K.J., Hussain, L., et al. (2021) Detecting Basic Human Activities and Postural Transition using Robust Machine Learning Techniques by Applying Dimensionality Reduction Methods. Waves in Random and Complex Media. I.F. 4.853. doi: http://dx.doi.org/10.1080/17455030.2021.1971325
  4. Anjum, S., Hussain, L. et al. (2021)Detecting Brain Tumors using Deep Learning Convolutional Neural Network with Transfer Learning Approach, International Journal of Imaging Systems and Technology. I.F. 2.00. DOI: 10.1002/ima.22641
  5. Iqbal, S., Siddiqui, G.F., Rehman, A., Hussain, L. et al. (2021) Prostate Cancer Detection using Deep Learning and Traditional Techniques. IEEE Access.DOI:10.1109/ACCESS.2021.3057654. I.F. 4.098
  6. Rafique, M., Iqbal, J., Lone, K.J…. Rahman,S.R, Hussain, L. (2021) Multifractal Detrended Fluctuation Analysis of Soil Radon (222Rn) and Thoron (220Rn) Time Series. Journal of Radioanalytical and Nuclear Chemistry. I.F. 1.371.
  7. Hussain et al. (2021) Machine Learning Based Lungs Cancer detection using Reconstruction Independent Component Analysis and Sparse Filter Features. Waves in Random and Complex Media. I.F. 4.853.
  8. Anjum, S., Hussain, L. et al. (2021) Automated Multi-class Brain tumor types detection by extracting RICA based features and employing machine learning techniques. Mathematical Biosciences and Engineering. I.F. 2.080.

2020

  1. Hussain, L., Nguyen, T., Li, H. et al. (2020) Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection. BioMed Eng OnLine 19, 88. I.F. 2.819.
  2. Aziz, W., Hussain, L., et al. (2020) Machine learning based classification of normal, slow and fast walking by extracting multimodal features from stride interval time series.Mathematical Biosciences and Engineering. 18 (1): 495-517. I.F. 2.080.
  3. Hussain, L., et al. (2020) Machine learning based congestive heart failure detection using feature importance ranking of multimodal features. Mathematical Biosciences and Engineering.18(1): 69-91. doi: 10.3934/mbe.2021004. I.F. 2.080.
  4. Hussain, L. et al. (2020). Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust machine learning techniques. Waves in random and complex media. DOI:10.1080/17455030.2020.1810364. I.F. 4.853.
  5. Butt, F.M., Hussain, L., et al. (2020) Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands. Mathematical Biosciences and Engineering. 18 (1): 400-425. I.F. 2.080.
  6. Jawad, M., Nadeem, M.S.A.,Shim, S., Khan, I.R, Shaheen, A., Habib, N., Hussain, L., Aziz, W. (2020). Machine Learning based Cost Effective Electricity Load Forecasting Model using Correlate Meteorological Parameters. IEEE Access.DOI: 10.1109/ACCESS.2020.3014086. I.F. 4.098.
  7. Iqbal, J., Lone, K.J., Hussain, L. and Rafique, M., (2020). Detrended cross correlation analysis (DCCA) of radon, thoron, temperature and pressure time series data. Physica Scripta. https://doi.org/10.1088/1402-4896/ab9fb1. I.F. 2.487.
  8. Manshad A.M, Qumar A.A., Petrovich B.A., Hussain, L. (2020) Conflict matrix as a mechanism of identifying the conflict in emotions of written text. International Journal of Engineering & Technology, 9(2), 541-545. IF. 0.690
  9. Hussain, L., Aziz, W., Saeed, S. et al. (2020) Extracting mass concentration time series features for classification of indoor and outdoor atmospheric particulates. Acta Geophys. https://doi.org/10.1007/s11600-020-00443-y. I.F. 2.054.
  10. Abbasi, A.A., Hussain, L., Awan, I.A. et al.  (2020) Detecting prostate cancer using deep learning convolution neural network with transfer learning approach. Cogn Neurodyn (2020). https://doi.org/10.1007/s11571-020-09587-5 I.F. 5.082
  11. Hussain,L. et al. (2020) Analyzing the dynamics of sleep electroencephalographic (EEG) signals with different pathologies using threshold-dependent symbolic entropy. Waves in random and complex media:https://doi.org/10.1080/17455030.2020.1743378. I.F. 4.853
  12. Hussain, L., Awan, I. A., Aziz, W., Saeed, S., Ali, A., Zeeshan, F., & Kwak, K. S. (2020). Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques. BioMed Research International, 2020. F. 3.411
  13. Anjum S., Hussain L., Ali M., Abbasi A.A. (2020) Automated Multi-class Brain Tumor Types Detection by Extracting RICA Based Features and Employing Machine Learning Techniques. In: Kia S.M. et al. (eds) Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. MLCN 2020, RNO-AI 2020. Lecture Notes in Computer Science, vol 12449. Springer, Cham. https://doi.org/10.1007/978-3-030-66843-3_24

2019

  1. Hussain, L., Aziz, W., Alshdadi, A.A., Nadeem, M.S.A. and Khan, I.R.,(2019). Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features. IEEE Access, 7, pp.64704-64721. I.F. 4.098.
  2. Hussain, L., Saeed, S., Idris, A., Awan, I.A., Shah, S.A., Majid, A., Ahmed, B. and Chaudhary, Q.A., (2019). Regression analysis for detecting epileptic seizure with different feature extracting strategies. Biomedical Engineering/Biomedizinische Technik. I.F. 1.096.
  3. Hussain L. Aziz, W, Alshdadi, A.A.,, Abbasi, A.A., Majid, A., Marchal, A.R. (2019) Multiscaled entropy analysis to quantify the dynamics of motor movement signals with fist or feet movement using topographic maps. Technology and Health Care, pp. 1-15. I.F. 1.285
  4. Hussain, L. et. al. (2019). Applying Bayesian Network Approach to Determine the Association Between Morphological Features Extracted from Prostate Cancer Images. IEEE Access, 7, 1586-1601. DOI. 10.1109/ACCESS.2018.2886644. IF. 4.098.
  5. Hussain, L., Saeed, S, Awan, I.A, Idris, A, Nadeem, M.S.A, Chaudhary, Q. (2019). Detecting Brain Tumor using Machine Learning Techniques Based of Different Features Extracting Strategies. Current Medical Imaging Reviews, 14 (1). DOI: https://doi.org/10.2174/1573405614666180718123533 IF. 0.812.

2018

  1. Hussain, L. et. al. (2018) Arrhythmia Detection by Extracting Hybrid Features based on Refined Fuzzy Entropy (FuzEn) Approach and Employing Machine Learning Techniques. Waves in Random and Complex Media. DOI:10.1080/17455030.2018.1554926. IF. 4.853.
  2. Hussain, L., Aziz, W., Saeed, S., Idris, A., Awan, I.A., Shah, S.A., Nadeem, M.S.A., Rathore, S. (2018) Spatial Wavelet-based Coherence and Coupling in EEG signals with Eye open and close during resting states.  IEEE Access, 6(1), 37003-37022. DOI. 1109/ACCESS.2018.2844303. I.F. 4.098
  3. Hussain, L. (2018). A Deeper Analysis of Detecting Epileptic Seizure with Different Feature Extracting Strategies using Robust Machine Learning Classification Techniques by Applying Advance Parameter Optimization Approach. Cognitive Neurodynamic. DOI: 10.1007/s11571-018-9477-1. F. 5.082
  4. Hussain, L., Ahmed, A., Saeed, S., Rathore, S., Awan, IA., Shah, S.A., Majid, A. Idris, A., Awan, AA. (2018). Prostate Cancer Detection using Machine Learning Techniques by Employing Combination of Features Extracting Strategies. Cancer Biomarker. DOI: 10.3233/CBM-170643. F. 4.388.
  5. Asim, Y., Raza, B., Malik, AK., Rathore, S., Hussain, L. Iftikhar, MA. (2018). A multi-modal, multi-atlas based approach for Alzheimer detection via machine learning. International Journal of Imaging Systems and Technology. DOI: 10.1002/ima.22263. I.F. 2.00.

2017

  1. Hussain, L., Aziz, W., Alowibdi, J. S., Habib, N., Rafique, M., Saeed, S., & Kazmi, S. Z. H. (2017). Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states. Journal of Physiological Anthropology, 36(1), 21. I.F. 2.867.
  2. Hussain, L., Aziz, W., Saeed, S., et al. (2017). Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm. Biomedical Engineering / Biomedizinische Technik. Aug. 2017, from doi:10.1515/bmt-2017-0041. F. 1.458.
  3. Hussain, L., Aziz, W., Saeed, S., Shah, S. A., Nadeem, M. S. A., Awan, I. A., … & Kazmi, S. Z. H. (2017). Complexity analysis of EEG motor movement with eye open and close subjects using multiscale permutation entropy (MPE) technique. Biomedical Research, 28(16), 7104-7111. F. 0.236

2017 ISI indexed (Master Journal list)

  1. Hussain, L., Shafi, I., Saeed, S., Abbas, A., Awan, I.A., Nadeem, SA., Kazmi, S.Z.H., Shah, S.A., Iqbal, S., & Rahman, B. (2017). A radial base neural network approach for emotion recognition in Human speech. International Journal of Computer Science and Network Security, 17(8), 52-62. (ISI indexed).
  2. Hussain, L., Seed, S., Awan, I.A., & Idris, A. (2017). Multiscaled Complexity Analysis of EEG Epileptic Seizure using entropy based computational techniques. Archives of Neuroscience.  DOI: 10.5812/archneurosci. 61161. (ISI Indexed)
  3. Hussain, L., Aziz, W., & Saeed, S. (2017). Coupling functions between brain waves: Significance of opened/closed eyes. Journal Systemics, Cybernetics and Informatics (JSCI). 15 (4), 9-15. (Indexed by Scopus, EBSCO, Cabell, DOAJ).
  4. Saeed, S., Idris, A., Hussain, L., Awan, I.A. (2017). Analyzing the Dynamics of Indoor Particulate Matter using Nonlinear Time Series Techniques and Predicting the Behavior Based on Robust Regression Models. International Journal of Computer Science and Network Security, 17(11), 6-19. ISI indexed
  5. Saeed, S., Hussain, L., Awan, I.A, Idris, A. (2017). Comparative Analysis of different Statistical Methods for Prediction of PM2.5 and PM10 Concentrations in Advance for Several Hours. International Journal of Computer Science and Network Security, 17(11), 45-52. ISI indexed
  6. Hussain, L., Seed, S., Awan, I.A., & Idris, A. (2017). Multiscaled Complexity Analysis of EEG Epileptic Seizure using entropy based computational techniques. Archives of Neuroscience.  DOI: 10.5812/archneurosci. 61161. (ISI Indexed)

Year 2016

  1. Hussain, L., & Aziz, W. (2016). Time-Frequency Spatial Wavelet Phase Coherence Analysis of EEG in EC and EO During Resting State. Procedia Computer Science, 95, 297-302. (Scopus Indexed)
  2. Hussain, L., Anjum, S., Aziz, W., & Abbasi, M. M. (2016). Complexity Analysis of EEG Motor Movement with Eye open and close subjects using Multiscale Sample Entropy (MSE) techniques. PONTE. 72(7), 341-349. ISI Indexed; doi: 0.21506/j.ponte.2016.7.26

Year 2015

  1. Hussain, L., Aziz, W., Khan, S. A, Abbasi, A. Q., Hassan, S. Z., & Abbasi, M. M. (2015). Classification of Electroencephalography (EEG) Alcoholic and Control Subjects using Machine Learning Ensemble Methods. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 2(1), 126-131.
  2. Ajaz, R. A & Hussain, L. (2015). Seed Classification using Machine Learning Techniques. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 2(5), 1098-1102.

Year 2014

  1. Hussain, L., Aziz, W., Nadeem, S. A., Shah, S. A., & Majid, A. (2014). Electroencephalography (EEG) Analysis of Alcoholic and Control Subjects Using Multiscale Permutation Entropy. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 1(5), 380-387.
  2. Hussain, L., Aziz, W., Nadeem, S. A., & Abbasi, A.Q. (2014). Classification of Normal and Pathological Heart Signal Variability Using Machine Learning Techniques. INTERNATIONAL JOURNAL OF DARSHAN INSTITUTE ON ENGINEERING RESEARCH & EMERGING TECHNOLOGIES, 3(2), 13-19.
  3. Hussain, L., Aziz, W., Kazmi, Z. H, & Awan, I. A. (2014). Classification of Human Faces and Non-Faces Using Machine Learning Techniques. International Journal of Electronics and Electrical Engineering, 2(2), Vol. 2,116-123. doi: 10.12720/ijeee.2.2.116-123
  4. Hussain, L., Nadeem, S. A. & Shah, S. A. A. (2014). Short Term Load Forecasting System based on Support Vector Kernel Methods. International Journal of Computer Science & Information Technology (IJCSIT), 6(3), 93-102. Doi:10.5121/ijcsit.2014.6308.

Year 2013

  1. Abbasi, A. Q., Aziz, W., Seed, S., Awan, I., & Hussain, L. (2013). Comparative study of multiscale entropy analysis and symbolic time series analysis when applied to human gait dynamics. IEEE Transactions on, 126-132. Digital Object Identifier: 10.1109/ICOSST.2013.6720618

Conference Proceedings/ Papers presentations

  1. Paper titled “Automated Lung Cancer Detection based on Multimodal Features Extracting Strategy Using Machine Learning Techniques” to be held at Town & Country Resort and Convention Center, San Diego, California, United States from 16 – 21 February 2019 in collaboration with SIIM, IFCARS, MIPS and AAPM. (Accepted)
  2.  Presented a paper titled “Automated Breast Cancer Detection using Machine Learning Techniques by Extracting Different Feature Extracting Strategies” 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering, Proceedings of TrustCom/BigDataSE 2018 IEEE Catalog Number: CFP18TRU-USB, ISBN: 978-1-5386-4387-7,  July 31 to August 03, 2018, New Jersey, USA, IEEE Trustcom 2018 (pp.327-331).
  3. 17th International Conference FIT Dec. 16-18, 2019 at Islamabad title “Segmentation of white-matter lesions using deep learning algorithm: An application to multi-institutional data”.
    1. EasyChair status
    2. Conference link
  4. AAAI-20 thirty-fourth AAAI conference on AI, February 7-12, 2020, Hilton New York, USA
    1. Conference Link
    2. Paper submission
  5. Paper titled “Detecting Brain Tumor using Deep Learning Convolution Neural Network with Transfer Learning Approach”, MLDM19 – 15th International Conference on Machine Learning and Data Mining, July 20-25, 2019, New York, USA. 
    1. EasyChair status
    2. Conference link
  6. Presented a paper titled “Wavelet Phase Coherence in EEG with EC and EO during resting states. 2015 Complex Adaptive Systems, Missouri University of Science and Technology, November 2-4, 2016, Los Angles, California, USA
  7. Presented a paper titled “Coupling functions between brain waves: Significance of opened/closed eyes. 21st World Multiconference on Systemics, Cybernetics and Informatics (WMSCI 2017) Special track on Knowledge and Cognitive Science and Technologies: KCST 2017, July 8-11, 2017, Orlando, Florida, USA. Proceedings of the 21st World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI 2017). (Vol. II, pp. 275-280).
  8. Presented a paper titled “Real-Time Emotion Recognition in Human Speech Using Neural Networks”, 1st International Conference, on Engineering Sciences (ICES 2012), 28-29, 2012, Punjab University Lahore, Pakistan
  9. Presented a paper titled “Multiscale Entropy Analysis of Electroencephalogram (EEG) of Alcoholic and Control Subjects. 2nd Conference on Emerging Trends in Bioinformatics and Computational Biology, MAJU, Islamabad, Pakistan
  10. Asim, Y., Malik, A.K., Raza, B., Bilal, A., Hussain, L. (Dec. 2017)., “Ensemble of SVM Classifiers for Automated Multi-Modality Segmentation of White-Matter Lesions,” 2017 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2017, pp. 161-166. doi:10.1109/FIT.2017.00036

Seminars Made/ Participated

  1. Acted as Keynote Speaker at a Conference titled Cyber Security in next-generation technologies (AI, IoT, Big Data, Cloud Computing) organized by Pakistan Information Security Association (PISA) at AJK Medical College, Muzaffarabad on June 02, 2018.
  2. Acted as a resource person in conducting hands on training on Mendeley Desktop Software Seminar in collaboration with ORIC for M.Phil. and PhD students and Faculty members on May 10, 2018
  3. Made a Seminar titled “Writing Quality Research Papers and Research Theses for M.Phil. and PhD students” organized at Department of Computer Science, University of Azad Jammu and Kashmir, City Campus, Muzaffarabad on February 16, 2018.
  4. Made a seminar titled “Spatial Coherence and Coupling connectivity between phase of brain waves: Differences in resting states with eye open and closed” at Nonlinear and Biomedical Physics Research Group (N&BMPR group), Physics Building, Lancaster University, UK on June 12, 2015.
  5. Participated in a Seminar title “Bayesian Networks – Artificial Intelligence & Virtual Reality Research” organized by New York University, Kimmel Center, Classroom 912 60 Washington Square South New York, NY 10012, USA on November 14, 2017.
  6. Participated in two Days seminar/workshop on Outcome Prediction in Attention, Learning and Cognitive Control organized by the Department of Experimental Psychology, University of Oxford, UK on March 19-20, 2015
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