Sadeghian Group
Publication
Latent Variable Energy Based Model with Self-Supervised Approaches for Cancer Grading Problem
K. Tirdad, A. Dela Cruz, K. Wenger, and A. Sadghian, “Latent Variable Energy Based Model with Self-Supervised Approaches for Cancer Grading Problem,” Accepted, T-CARIEM AI in Medicine Conference, Oct. 2023.Challenges in Expert Labeling of Data to Leverage Machine Learning to Support Physiotherapy in the ICU
A. Ieraci, V. Porcilla, M. Davoudpour, S. Mathur, K. Wu, J. Batt, S. Gibson, and A. Sadeghian, “Challenges in Expert Labeling of Data to Leverage Machine Learning to Support Physiotherapy in the ICU” Accepted, T-CARIEM AI in Medicine Conference, Oct. 2023.Identifying trusted and ambiguous region in neural network predictions: high-fidelity AI for image pathology
K. Wenger, K. Hossein Abadi, D. Fozard, K. Tirdad, A. Dela Cruz, and A. Sadeghian, “Identifying trusted and ambiguous region in neural network predictions: high-fidelity AI for image pathology” Accepted, T-CARIEM AI in Medicine Conference, Oct. 2023.Supervised Machine Learning Pipeline to Classify Pain using sEMG and MMG during Neuromuscular Electrical Stimulation to Combat Intensive Care Unit Acquired Weakness
M. Sharma, and A. Sadeghian, “Supervised Machine Learning Pipeline to Classify Pain using sEMG and MMG during Neuromuscular Electrical Stimulation to Combat Intensive Care Unit Acquired Weakness,” Accepted, T-CARIEM AI in Medicine Conference, Oct. 2023.Iterative XAI Frameworks for Oncology Decision Making: Integrating Expert Feedback to Enhance Cancer Diagnosis
S. Ghasemi, and A. Sadeghian, “Iterative XAI Frameworks for Oncology Decision Making: Integrating Expert Feedback to Enhance Cancer Diagnosis,” Accepted, T-CARIEM AI in Medicine Conference, Oct. 2023.A novel application of XAI in squinting models: A position paper 
Expert Labelling of Data to Leverage Machine Learning to Administer Neuromuscular Electrical Stimulation in the Non-Responsive Patient
A. Ieraci, M. Davoudpour, S. Mathur, K. Wu, J. Batt, S. Gabison, and A. Sadeghian, “Expert Labelling of Data to Leverage Machine Learning to Administer Neuromuscular Electrical Stimulation in the Non-Responsive Patient,” Poster Presentation, KITE Research Institute, University Health Network (UHN) and Rehabilitation Sciences Institute (RSI), University of Toronto, International Conference on Aging, Innovation and Rehabilitation (ICAIR), Toronto, Ontario, Canada, May 2023.Physiological Data Annotation to Leverage Machine Learning to Better Administer Neuromuscular Electrical Stimulation in the Non-Responsive ICU Patient
A. Ieraci, M. Davoudpour, S. Mathur, K. Wu, J.Batt, S. Gabison, and A. Sadeghian, “Physiological Data Annotation to Leverage Machine Learning to Better Administer Neuromuscular Electrical Stimulation in the Non-Responsive ICU Patient,” Poster Presentation, iBest Research Conference on AI in Health, Toronto, Ontario, Canada, Apr. 2023.Multifractal Characterization and Modeling of Blood Pressure Signals
ABC: Artificial Intelligence for Bladder Cancer Grading System
A Semi-Supervised Learning Approach for Bladder Cancer Grading
A wavelet feature-based neural network approach to estimate electrical arc characteristics
A Perceptual Computer for Hierarchical Portfolio Selection Based on Interval Type-2 Fuzzy Sets
An Application of Machine Learning to Forecast Hypertension Signals in Intracranial Pressure: A Comparison of Various Algorithms
Retweet Prediction based on Topic, Emotion and Personality
Machine Learning-Based Approach to Analyze Saccadic Eye Movement in Patients with Mild Traumatic Brain Injury
A Deep Neural Network Approach for Sentiment Analysis of Medically Related Texts: An Analysis of Tweets Related to Concussions in Sports
Hybrid Neuro-Fractal Analysis of ECG Signal to Predict Ischemia
Application of Hybrid Wavelet-SVM Algorithm to Detect Broken Rotor Bars in Induction Motors
Implementation of remote data collection necessitated by physical distancing required in a research program developing smart textile garments for delivery of neuromuscular electrical stimulation therapy in the ICU
M. Sharma, Y. Sadat Nejad, A. Ieraci, S. Gabison, V. Porcilla, M. Davoudpour, S. Mathur, A. Sadeghian, and J. Batt, “Implementation of remote data collection necessitated by physical distancing required in a research program developing smart textile garments for delivery of neuromuscular electrical stimulation therapy in the ICU,” Poster presented at the Respirology Research Forum, Jun. 2020, Toronto, ON.Re: Artificial Intelligence for Diagnosis and Grading of Prostate Cancer in Biopsies: A Population-based, Diagnostic Study
A Fast and Accurate Method for Calculating the Center of Gravity of Polygonal Interval Type-2 Fuzzy Sets
Machine Learning Applications in Imaging Analysis for Patients with Pituitary Tumors: A Review of the Current Literature and Future Directions
Topic Specific Emotion Detection for Retweet Prediction
Toward learning intracranial hypertension through physiological features: A statistical and machine learning approach
Retweet: A popular information diffusion mechanism – A survey paper
A cluster-based dissimilarity learning approach for localized fault classification in Smart Grids
On the impact of topological properties of smart grids in power losses optimization problems
A PCA based feature reduction in intracranial hypertension analysis
A hybrid wavelet based K-means clustering approach to detect intracranial hypertension
Application of multilayer perceptron neural networks and support vector machines in classification of healthcare data
Spectral reconstruction of protein contact networks
Data-driven detrending of nonstationary fractal time series with echo state networks
An online demand response EMS with anomaly usage detection
A Smoothing Technique for the Multifractal Analysis of a Medium Voltage Feeders Electric Current
Hierarchical genetic optimization of a fuzzy logic system for energy flows management in microgrids
A learning intelligent System for classification and characterization of localized faults in Smart Grids
An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery
On the impact of topological properties of smart grids in power losses optimization problems
F. Possemato, M. Paschero, L. Livi, A. Rizzi, and A. Sadeghian, “On the impact of topological properties of smart grids in power losses optimization problems,” Int J Electr Power Energy Syst, vol. 78, pp. 755–764, 2016.Classification of type-2 fuzzy sets represented as sequences of vertical slices
L. Livi, H. Tahayori, A. Rizzi, A. Sadeghian, and W. Pedrycz, “Classification of type-2 fuzzy sets represented as sequences of vertical slices,” IEEE Trans Fuzzy Syst, vol. 24, no. 5, pp. 1022–1034, 2016.Discrimination and characterization of parkinsonian rest tremors by analyzing long-term correlations and multifractal signatures
L. Livi, A. Sadeghian, and H. Sadeghian, “Discrimination and characterization of parkinsonian rest tremors by analyzing long-term correlations and multifractal signatures,” IEEE Trans. Biomed. Eng., vol. 63, no. 11, pp. 4243–4249, 2016.On the Long-Term Correlations and Multifractal Properties of Electric Arc Furnace Time Series
L. Livi, E. Maiorino, A. Rizzi, and A. Sadeghian, “On the Long-Term Correlations and Multifractal Properties of Electric Arc Furnace Time Series,” Int. J. Bifurcation Chaos, vol. 26, no. 1, 2016.Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins
L. Livi, E. Maiorino, A. Pinna, A. Sadeghian, A. Rizzi, and A. Giuliani, “Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins,” Phys A Stat Mech Appl, vol. 441, pp. 199–214, 2016.A generative model for protein contact networks
L. Livi, E. Maiorino, A. Giuliani, A. Rizzi, and A. Sadeghian, “A generative model for protein contact networks,” J. Biomol. Struct. Dyn., vol. 34, no. 7, pp. 1441–1454, 2016.Characterization of graphs for protein structure modeling and recognition of solubility
L. Livi, A. Giuliani, and A. Sadeghian, “Characterization of graphs for protein structure modeling and recognition of solubility,” Curr. Bioinform., vol. 11, no. 1, pp. 106–114, 2016.An Extreme Learning Machine (ELM) Predictor for Electric Arc Furnaces’ v-i Characteristics
S. Ismaeel, A. Miri, A. Sadeghian, and D. Chourishi, “An Extreme Learning Machine (ELM) Predictor for Electric Arc Furnaces’ v-i Characteristics,” in Proc. - IEEE Int. Conf. Cyber Secur. Cloud Comput., CSCloud: IEEE Int. Symp. of Smart Cloud IEEE SSC, 2016, pp. 329–334.Retweet prediction considering user’s difference as an author and retweeter
S. N. Firdaus, C. Ding, and A. Sadeghian, “Retweet prediction considering user’s difference as an author and retweeter,” in Proc. IEEE/ACM Int. Conf. Adv. Soc. Netw. Anal. Min., ASONAM, 2016, pp. 852–859.A dissimilarity learning approach by evolutionary computation for faults recognition in smart grids
E. De Santis, F. M. F. Mascioli, A. Sadeghian, and A. Rizzi, A dissimilarity learning approach by evolutionary computation for faults recognition in smart grids, vol. 620. Springer Verlag, 2016.Granular Computing Techniques for Classification and Semantic Characterization of Structured Data
F. M. Bianchi, S. Scardapane, A. Rizzi, A. Uncini, and A. Sadeghian, “Granular Computing Techniques for Classification and Semantic Characterization of Structured Data,” Cognitive Comput., vol. 8, no. 3, pp. 442–461, 2016.Identifying user habits through data mining on call data records
F. M. Bianchi, A. Rizzi, A. Sadeghian, and C. Moiso, “Identifying user habits through data mining on call data records,” Eng Appl Artif Intell, vol. 54, pp. 49–61, 2016.A new fuzzy disjointing difference operator to calculate union and intersection of type-2 fuzzy sets
H. Tahayori and A. Sadeghian, “A new fuzzy disjointing difference operator to calculate union and intersection of type-2 fuzzy sets,” in Frontiers of High. Order Fuzzy Sets, Springer New York, 2015, pp. 1–17.Interval Type-2 Fuzzy Set Reconstruction Based on Fuzzy Information-Theoretic Kernels
H. Tahayori, L. Livi, A. Sadeghian, and A. Rizzi, “Interval Type-2 Fuzzy Set Reconstruction Based on Fuzzy Information-Theoretic Kernels,” IEEE Trans Fuzzy Syst, vol. 23, no. 4, pp. 1014–1029, 2015.Frontiers of higher order fuzzy sets
A. Sadeghian and H. Tahayori, Frontiers of higher order fuzzy sets. Springer New York, 2015.Quantitative ultrasound spectroscopic imaging for characterization of disease extent in prostate cancer patients
A. Sadeghi-Naini et al., “Quantitative ultrasound spectroscopic imaging for characterization of disease extent in prostate cancer patients,” Transl. Oncol., vol. 8, no. 1, pp. 25–34, 2015.Multifractal characterization of protein contact networks
E. Maiorino, L. Livi, A. Giuliani, A. Sadeghian, and A. Rizzi, “Multifractal characterization of protein contact networks,” Phys A Stat Mech Appl, vol. 428, pp. 302–313, 2015.Entropic one-class classifiers,” IEEE Trans. Neural Networks Learn
L. Livi, A. Sadeghian, and W. Pedrycz, “Entropic one-class classifiers,” IEEE Trans. Neural Networks Learn. Sys., vol. 26, no. 12, pp. 3187–3200, 2015.Data granulation by the principles of uncertainty
L. Livi and A. Sadeghian, “Data granulation by the principles of uncertainty,” Pattern Recogn. Lett., vol. 67, pp. 113–121, 2015.Granular modeling and computing approaches for intelligent analysis of non-geometric data
L. Livi, A. Rizzi, and A. Sadeghian, “Granular modeling and computing approaches for intelligent analysis of non-geometric data,” Appl. Soft Comput. J., vol. 27, pp. 567–574, 2015.Classifying sequences by the optimized dissimilarity space embedding approach: A case study on the solubility analysis of the E. coli proteome
L. Livi, A. Rizzi, and A. Sadeghian, “Classifying sequences by the optimized dissimilarity space embedding approach: A case study on the solubility analysis of the E. coli proteome,” J. Intelligent Fuzzy Syst., vol. 28, no. 6, pp. 2725–2733, 2015.A learning intelligent system for fault detection in Smart Grid by a One-Class Classification approach
E. De Santis, A. Rizzi, A. Sadeghian, and F. M. F. Mascioli, “A learning intelligent system for fault detection in Smart Grid by a One-Class Classification approach,” in Proc Int Jt Conf Neural Networks, 2015, vol. 2015-September.Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification
E. De Santis, L. Livi, A. Sadeghian, and A. Rizzi, “Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification,” Neurocomputing, vol. 170, pp. 368–383, 2015.Synthesizing social context for making Internet of Things environments more immersive
M. Davoudpour, A. Sadeghian, and H. Rahnama, “Synthesizing social context for making Internet of Things environments more immersive,” in Int. Conf. Netw. Future, NOF, 2015.CANthings’ (Context Aware Network for the Design of Connected Things) service modeling based on Timed CPN
M. Davoudpour, A. Sadeghian, and H. Rahnama, “‘CANthings’ (Context Aware Network for the Design of Connected Things) service modeling based on Timed CPN,” in Proc. IEEE Int. Conf. Semantic Comput., IEEE ICSC, 2015, pp. 127–130.Prediction of telephone calls load using Echo State Network with exogenous variables
F. M. Bianchi, S. Scardapane, A. Uncini, A. Rizzi, and A. Sadeghian, “Prediction of telephone calls load using Echo State Network with exogenous variables,” Neural Netw., vol. 71, pp. 204–213, 2015.Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition
F. M. Bianchi, E. De Santis, A. Rizzi, and A. Sadeghian, “Short-Term Electric Load Forecasting Using Echo State Networks and PCA Decomposition,” IEEE Access, vol. 3, pp. 1931–1943, 2015.Recommender systems in e-commerce
S. Sivapalan, A. Sadeghian, H. Rahnama, and A. M. Madni, “Recommender systems in e-commerce,” in World Autom. Congress Proc., 2014, pp. 179–184.Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction
M. Moharrer, H. Tahayori, L. Livi, A. Sadeghian, and A. Rizzi, “Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction,” Soft Comput., vol. 19, no. 1, pp. 237–250, 2014.Distinguishability of interval type-2 fuzzy sets data by analyzing upper and lower membership functions
L. Livi, H. Tahayori, A. Sadeghian, and A. Rizzi, “Distinguishability of interval type-2 fuzzy sets data by analyzing upper and lower membership functions,” Appl. Soft Comput. J., vol. 17, pp. 79–89, 2014.Optimized dissimilarity space embedding for labeled graphs
L. Livi, A. Rizzi, and A. Sadeghian, “Optimized dissimilarity space embedding for labeled graphs,” Inf Sci, vol. 266, pp. 47–64, 2014.Fault recognition in smart grids by a one-class classification approach
E. De Santis, L. Livi, F. M. F. Mascioli, A. Sadeghian, and A. Rizzi, “Fault recognition in smart grids by a one-class classification approach,” in Proc Int Jt Conf Neural Networks, 2014, pp. 1949–1956.Evolutionary optimization of a one-class classification system for faults recognition in smart grids
E. De Santis, G. Distante, F. M. F. Mascioli, A. Sadeghian, and A. Rizzi, “Evolutionary optimization of a one-class classification system for faults recognition in smart grids,” in ECTA - Proc. Int. Conf. Evol. Comput. Theory Appl., 2014, pp. 95–103.‘CANthings’: Context-Aware Networks for the Design of Connected Things
M. Davoudpour, A. Masoumi, A. Sadeghian, and H. Rahnama, “‘CANthings’: Context-Aware Networks for the Design of Connected Things,” in World Autom. Congress Proc., 2014, pp. 468–473.A formal ontology alignment for canthings (context aware network for the connected things)
M. Davoudpour, A. Masoumi, A. Sadeghian, and H. Rahnama, “A formal ontology alignment for canthings (context aware network for the connected things),” in Proc. - Int. Conf. Next Gener. Mob. Appl., Serv. Technol., NGMAST, 2014, pp. 175–180.A Granular Computing approach to the design of optimized graph classification systems
F. M. Bianchi, L. Livi, A. Rizzi, and A. Sadeghian, “A Granular Computing approach to the design of optimized graph classification systems,” Soft Comput., vol. 18, no. 2, pp. 393–412, 2014.Induction of shadowed sets based on the gradual grade of fuzziness
H. Tahayori, A. Sadeghian, and W. Pedrycz, “Induction of shadowed sets based on the gradual grade of fuzziness,” IEEE Trans Fuzzy Syst, vol. 21, no. 5, pp. 937–949, 2013.Shadowed fuzzy sets: A framework with more freedom degrees for handling uncertainties than interval type-2 fuzzy sets and lower computational complexity than general type-2 fuzzy sets
H. Tahayori and A. Sadeghian, Shadowed fuzzy sets: A framework with more freedom degrees for handling uncertainties than interval type-2 fuzzy sets and lower computational complexity than general type-2 fuzzy sets, vol. 417. Springer Verlag, 2013.Low-frequency quantitative ultrasound imaging of cell death in vivo
A. Sadeghi-Naini et al., “Low-frequency quantitative ultrasound imaging of cell death in vivo,” Med. Phys., vol. 40, no. 8, 2013.Matching general type-2 fuzzy sets by comparing the vertical slices
A. Rizzi, L. Livi, H. Tahayori, and A. Sadeghian, “Matching general type-2 fuzzy sets by comparing the vertical slices,” in Proc. Jt. IFSA World Congr. NAFIPS Annual Meet., IFSA/NAFIPS, 2013, pp. 866–871.Modeling complex concepts with type-2 fuzzy sets: The case of user satisfaction of online services
M. Moharrer, H. Tahayori, and A. Sadeghian, Modeling complex concepts with type-2 fuzzy sets: The case of user satisfaction of online services, vol. 301. 2013.Drivers of customer satisfaction in online tourism-the case of European countries
M. Moharrer, H. Tahayori, and A. Sadeghian, “Drivers of customer satisfaction in online tourism-the case of European countries,” Middle East J. Sci. Res., vol. 13, no. 9, pp. 1172–1179, 2013.Bouncing and raindrop image search algorithms, two novel feature detection mechanisms
H. Mohammadi, A. N. Venetsanopoulos, and A. Sadeghian, “Bouncing and raindrop image search algorithms, two novel feature detection mechanisms,” in Int. Conf. Digit. Signal Process., DSP, 2013.Aggregating α-planes for Type-2 fuzzy set matching
L. Livi, H. Tahayori, A. Sadeghian, and A. Rizzi, “Aggregating α-planes for Type-2 fuzzy set matching,” in Proc. Jt. IFSA World Congr. NAFIPS Annual Meet., IFSA/NAFIPS, 2013, pp. 860–865.Dissimilarity space embedding of labeled graphs by a clustering-based compression procedure
L. Livi, F. M. Bianchi, A. Rizzi, and A. Sadeghian, “Dissimilarity space embedding of labeled graphs by a clustering-based compression procedure,” in Proc Int Jt Conf Neural Networks, 2013.Ontology enhancement through inductive decision trees
B. Gajderowicz, A. Sadeghian, and M. Soutchanski, Ontology enhancement through inductive decision trees, vol. 7123 LNAI. 2013.Median interval approach to model words with interval type-2 fuzzy sets
H. Tahayori and A. Sadeghian, “Median interval approach to model words with interval type-2 fuzzy sets,” Int. J. Adv. Intell. Paradigms, vol. 4, no. 3–4, pp. 313–336, 2012.Handling uncertainties of membership functions with Shadowed Fuzzy Sets
H. Tahayori and A. Sadeghian, “Handling uncertainties of membership functions with Shadowed Fuzzy Sets,” in World Autom. Congress Proc., 2012.On the accessibility/controllability of fuzzy control systems
M. Biglarbegian, A. Sadeghian, and W. Melek, “On the accessibility/controllability of fuzzy control systems,” Inf Sci, vol. 202, pp. 58–72, 2012.A hybrid approach for adaptive car navigation
S. Barzegar, M. Davoudpour, and A. Sadeghian, A hybrid approach for adaptive car navigation, vol. 7665 LNCS. 2012.Dynamic reconstruction of nonlinear v-i characteristic in electric arc furnaces using adaptive neuro-fuzzy rule-based networks
A. Sadeghian and J. D. Lavers, “Dynamic reconstruction of nonlinear v-i characteristic in electric arc furnaces using adaptive neuro-fuzzy rule-based networks,” Appl. Soft Comput. J., vol. 11, no. 1, pp. 1448–1456, 2011.Relational attribute integrated matching analysis (RAIMA): A framework for the design of self-adaptive egocentric social networks
H. Rahnama, A. Sadeghian, and A. M. Madni, “Relational attribute integrated matching analysis (RAIMA): A framework for the design of self-adaptive egocentric social networks,” IEEE Syst. J., vol. 5, no. 1, pp. 80–90, 2011.Self-adaptive middleware for the design of context-aware software applications in public transit systems
H. Rahnama, P. Kramaric, A. Sadeghian, and A. Shepard, “Self-adaptive middleware for the design of context-aware software applications in public transit systems,” in UbiComp - Proc. ACM Conf. Ubiquitous Comput., 2011, pp. 491–492.Modeling innovation in international business with respect to the cultural distance using interval type-2 fuzzy sets
M. Moharrer, H. Tahayori, and A. Sadeghian, “Modeling innovation in international business with respect to the cultural distance using interval type-2 fuzzy sets,” in Annu Conf North Am Fuzzy Inf Process Soc NAFIPS, 2011.iFAST: An intelligent Fire-threat Assessment and Size-up Technology for first responders
H. Mohammadi and A. Sadeghian, “iFAST: An intelligent Fire-threat Assessment and Size-up Technology for first responders,” in IEEE SSCI: Symp. Ser. Comput. Intell. - CIDUE: IEEE Symp. Comput. Intell. Dyn. Uncertain Environ., 2011, pp. 33–40.Accessibility of fuzzy control systems
M. Biglarbegian, A. Sadeghian, and W. W. Melek, Accessibility of fuzzy control systems, vol. 6752 LNAI. 2011.Formalized learning automata with adaptive fuzzy coloured Petri net; an application specific to managing traffic signals
S. Barzegar, M. Davoudpour, M. R. Meybodi, A. Sadeghian, and M. Tirandazian, “Formalized learning automata with adaptive fuzzy coloured Petri net; an application specific to managing traffic signals,” Sci. Iran., vol. 18, no. 3 D, pp. 554–565, 2011.Spam detection system: A new approach based on interval type-2 fuzzy sets
R. Ariaeinejad and A. Sadeghian, “Spam detection system: A new approach based on interval type-2 fuzzy sets,” in Can Conf Electr Comput Eng, 2011, pp. 000379–000384.A bidirectional associative memory based on cortical spiking neurons using temporal coding
M. Zamani, A. Sadeghian, and S. Chartier, “A bidirectional associative memory based on cortical spiking neurons using temporal coding,” in Proc Int Jt Conf Neural Networks, 2010.Hopfield neural networks as pseudo random number generators
K. Tirdad and A. Sadeghian, “Hopfield neural networks as pseudo random number generators,” in Annu Conf North Am Fuzzy Inf Process Soc NAFIPS, 2010.Operations on type-2 fuzzy sets based on the set of pseudo-highest intersection points of convex fuzzy sets
H. Tahayori, A. Sadeghian, and A. Visconti, “Operations on type-2 fuzzy sets based on the set of pseudo-highest intersection points of convex fuzzy sets,” in Annu Conf North Am Fuzzy Inf Process Soc NAFIPS, 2010.Reinforcement learning using associative memory networks,” in Proc Int Jt Conf Neural Networks
R. Salmon, A. Sadeghian, and S. Chartier, “Reinforcement learning using associative memory networks,” in Proc Int Jt Conf Neural Networks, 2010.Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS: A message from the conference organizers
A. Sadeghian, B. Tastle, W. Melek, and H. Ying, “Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS: A message from the conference organizers,” Annu Conf North Am Fuzzy Inf Process Soc NAFIPS, 2010.Application of artificial neural networks in controlled drug delivery systems
M. Rafienia, M. Amiri, M. Janmaleki, and A. Sadeghian, “Application of artificial neural networks in controlled drug delivery systems,” Appl Artif Intell, vol. 24, no. 8, pp. 807–820, 2010.Modeling linguistic label perception in tourism E-satisfaction with type-2 fuzzy sets
M. Moharrer, H. Tahayori, and A. Sadeghian, “Modeling linguistic label perception in tourism E-satisfaction with type-2 fuzzy sets,” in Annu Conf North Am Fuzzy Inf Process Soc NAFIPS, 2010.Invited paper: Self-adaptive middleware for the design of context-aware software applications in public transit systems
A. M. Madni, H. Rahnama, and A. Sadeghian, “Invited paper: Self-adaptive middleware for the design of context-aware software applications in public transit systems,” in Final Program Abstr. Book - Int. Symp. Commun., Control, Signal Process., ISCCSP, 2010.A new approach to improve the accuracy of online clustering algorithm based on scatter/gather mode
K. Farsandaj, C. Ding, and A. Sadeghian, “A new approach to improve the accuracy of online clustering algorithm based on scatter/gather model,” in Annu Conf North Am Fuzzy Inf Process Soc NAFIPS, 2010.Traffic signal control with adaptive fuzzy coloured petri net based on learning automata
S. Barzegar, M. Davoudpour, M. R. Meybodi, A. Sadeghian, and M. Tirandazian, “Traffic signal control with adaptive fuzzy coloured petri net based on learning automata,” in Annu Conf North Am Fuzzy Inf Process Soc NAFIPS, 2010.One-shot training algorithm for self-feedback neural networks
M. Amiri, A. Sadeghian, and S. Chartier, “One-shot training algorithm for self-feedback neural networks,” in Annu Conf North Am Fuzzy Inf Process Soc NAFIPS, 2010.Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks
M. Amiri, H. Davande, A. Sadeghian, and S. Chartier, “Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks,” Neural Netw., vol. 23, no. 7, pp. 892–904, 2010.Ontology Granulation Through Inductive Decision Trees
B. Gajderowicz and A. Sadeghian, "Ontology Granulation Through Inductive Decision Trees," Accepted, USRW 2009.A Context-Aware Development Framework for Building Self-Adaptive Mobile Software for Public Transport Systems
H. Rahnama, A. Sadeghian, A. Ferworn, and X. Aubry, "A Context-Aware Development Framework for Building Self-Adaptive Mobile Software for Public Transport Systems," in Proc. of the 2009 16th World Congress on Intelligent Transport Systems, Sept. 2009.A Knowledge Based Decision Support Architecture for Designing Brushless DC Motors
A. Akbarzadeh, and A. Sadeghian, "A Knowledge Based Decision Support Architecture for Designing Brushless DC Motors," Accepted, IEEE ETFA 2009.Intelligent agent control using simple logic-based hierarchical planning
H. Pham, G. H. Mahmoud, A. Ferworn and A. Sadeghian, "Intelligent agent control using simple logic-based hierarchical planning," Accepted, SoSE 2009.Expectation Maximization Enhancement with Evolution Strategy for Stochastic Ontology Mapping
B. Gajderowicz, A. Sadeghian, and M. Santos, "Expectation Maximization Enhancement with Evolution Strategy for Stochastic Ontology Mapping," Accepted, GECCO 2009.Inducing Relational Fuzzy Classification Rules by means of Cooperative Coevolution
V. Akbarzadeh, A. Sadeghian, and M. Santos, "Inducing Relational Fuzzy Classification Rules by means of Cooperative Coevolution," in Foundations of Computational Intelligence, vol 4: Bio-Inspired Data Mining Theoretical Foundations and Applications, Studies in Computational Intelligence, Chapter 6. Springer Verlag, Germany, 2009.Online Detection of Broken Rotor Bars in Induction Motors by Wavelet Packet Decomposition and Neural Networks
A. Sadeghian, Z. Ye and B. Wu, "Online Detection of Broken Rotor Bars in Induction Motors by Wavelet Packet Decomposition and Neural Networks," IEEE Trans. Instrumentation and Measurement, vol. 58, no. 7, pp. 2253-2263, Jul. 2009.Survivability in Existing ATM-Based Mesh Networks
I. Woungang, G. Ma, M. K. Denko, A. Sadeghian, S. Misra and A. Ferworn, "Survivability in Existing ATM-Based Mesh Networks", Proceedings of IEEE 23rd International Conference on Advanced Information Networking and Applications (AINA-09), Bradford, UK, May 26-29, 2009.Computer simulation of developing abrasive jet machined profiles including particle interference
N. Shafiei, H. getu, A. Sadeghian, and M. Papini, "Computer simulation of developing abrasive jet machined profiles including particle interference," Journal of Materials Processing Technology, vol. 209, no. 9, pp. 4366-4378, May 2009.A Theoretic Framework for Intelligent Expert Systems in Medical Encounter Evaluation
W. Melek, and A. Sadeghian,"A Theoretic Framework for Intelligent Expert Systems in Medical Encounter Evaluation," Expert System, vol. 26, no. 1, pp. 82-99, 2009.An analytical estimation of dependence of copper loss in high frequency transformers on winding cross section
K. V. Namjoshi, A. Sadeghian and J. D. Lavers, "An analytical estimation of dependence of copper loss in high frequency transformers on winding cross section," in Proc. of the 53rd Magnetism and Magnetic Materials Conf, 2008.Neural Network Modeling of Magnetic Hysteresis
V. Akbarzadeh, M. Davoudpour, and A. Sadeghian, "Neural Network Modeling of Magnetic Hysteresis," in Proc. of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1267-1270, 2008.A Review of Applications of Artificial Neural Networks in Cryptosystems
T. Schmidt, H. Rahnama and A. Sadeghian, "A Review of Applications of Artificial Neural Networks in Cryptosystems," in Proc. of the International Symposium on Soft Computing for Industry (ISSCI 2008).A Neural Network-Based Surface Roughness Discrimination Algorithm
M. Talbot, M. Arvandi, and A. Sadeghian, "A Neural Network-Based Surface Roughness Discrimination Algorithm," in Proc. of the International Symposium on Intelligent Automation and Control (ISIAC 2008).On the Use of Recurrent Neural Networks to Design Symmetric Ciphers
M. Arvandi, S. Wu, and A. Sadeghian, "On the Use of Recurrent Neural Networks to Design Symmetric Ciphers," IEEE Computational Intelligence Magazine, vol. 3, no. 2, pp. 42-53, May 2008.Tangential Magnetic Field at the Surface of Pot core Transformers
K. V. Namjoshi, J. D. Lavers, and A. Sadeghian, "Tangential Magnetic Field at the Surface of Pot core Transformers," American Journal of Physics, vol. 103, no. 7, April 2008.Novel Low-Frequency Ultrasound Detection of Apoptosis in vitro and in vivo
N. Papanicolau, J. Lee, B. Banihashemi, B. Debeljevic, S. Ranieri, M. Azrif, R. Karshafian, A. Giles, M. C. Kolios, A. Sadeghian, and G. J. Czarnota, "Novel Low-Frequency Ultrasound Detection of Apoptosis in vitro and in vivo," Accepted, Ultrasonic Imaging and Tissue Characterization Symposium, UITC 2008.Derivation Of Relational Fuzzy Classification Rules Using Evolutionary Computation
V. Akbarzadeh, A. Sadeghian, and M.V. dos Santos,"Derivation Of Relational Fuzzy Classification Rules Using Evolutionary Computation," Accepted, IEEE International Conference on Fuzzy Systems (IEEE-FUZZ'08), 2008.Auto-Associative Memory Based on a New Hybrid Model of SFNN and GRNN: Performance Comparison with NDRAM, ART2 and MLP
H. Davande, M. Amiri, A. Sadeghian, and S. Chartier,"Auto-Associative Memory Based on a New Hybrid Model of SFNN and GRNN: Performance Comparison with NDRAM, ART2 and MLP," Accepted, IEEE International Joint Conference on Neural Networks, 2008.Adaptive Context for Generic Pattern Matching in Ad Hoc Social Networks
H. Rahnama, A. M. Madni, A. Sadeghian , B. Gajderowicz, and C. Mawson,"Adaptive Context for Generic Pattern Matching in Ad Hoc Social Networks," Accepted, the 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP 2008), 2008.A Method for Quick Estimation of Tangential Magnetic Field at the Surface of Pot Core Transformers
K. V. Namjoahi, J. D. Lavers, and A. Sadeghian, "A Method for Quick Estimation of Tangential Magnetic Field at the Surface of Pot Core Transformers," Accepted, 52nd Magnetism and Magnetic Materials Conference, 2007.Auto-Associative Neural Network Based on New Hybrid Model of SFNN and GRNN
M. Amiri, H. Davande, A. Sadeghian and S. A. Seyyedsalehi, "Auto-Associative Neural Network Based on New Hybrid Model of SFNN and GRNN," in Proc. of the Int'l Joint Conf. on Neural Networks, pp. 2664-2670, 2007.Chosen Plaintext Attack Against Neural Network-Based Symmetric Cipher
M. Arvandi, and A. Sadeghian, "Chosen Plaintext Attack Against Neural Network-Based Symmetric Cipher," in Proc. of the Int'l Joint Conf. on Neural Networks, pp. 847-851, 2007.On Efficient Tuning of LS-SVM Hyper-Parameters in Short-Term Load Forecasting: A Comparative Study
M. Afshin, A. Sadeghian,and K. Raahemifar, "On Efficient Tuning of LS-SVM Hyper-Parameters in Short-Term Load Forecasting: A Comparative Study," in Proc. of the 2007 IEEE Power Engineering Society General Meeting (IEEE-PES).A Minimum Distance Bound on 1-Generator Quasi-Cyclic Codes
I. Woungang, S. Misra, A. Sadeghian, and A. Ferworn, "A Minimum Distance Bound on 1-Generator Quasi-Cyclic Codes," in Proc. of the 10th Canadian Workshop on Information Theory (CWIT 2007), pp. 156-159, 2007.Applying Model-Driven Development to Pervasive System Engineering
H. Pham, Q. H. Mahmoud, A. Ferworn, and A. Sadeghian, "Applying Model-Driven Development to Pervasive System Engineering," in Proc. of the First Workshop on Software Engineering for Pervasive Computing Applications, Systems and Environments 2007.Social Context Awareness in Ad Hoc System of Systems
H. Rahnama, A. Sadeghian, and A. Madni, "Social Context Awareness in Ad Hoc System of Systems," in Proc. of IEEE System of Systems 2007.Rubble Search with Canine Augmentation Technology
A. Ferworn, A. Sadeghian, K. Barnum, H. Rahnama, and I. Woungang, "Rubble Search with Canine Augmentation Technology," in Proc. of IEEE System of Systems 2007.Canine as Robot in Directed Search
A. Ferworn, A. Sadeghian, K. Barnum, H. Rahnama, and I. Woungang,"Canine as Robot in Directed Search" in Proc. of IEEE System of Systems 2007.Applying Model-Driven Development Techniques to the Development of Search and Rescue Systems
H. Pham, A. Ferworn, Q. H. Mahmoud, and A. Sadeghian, "Applying Model-Driven Development Techniques to the Development of Search and Rescue Systems," in Proc. of IEEE System of Systems 2007.PCA-based Least Squares Support Vector Machines in Week-Ahead Load Forecasting
M. Afshin,and A. Sadeghian, "PCA-based Least Squares Support Vector Machines in Week-Ahead Load Forecasting," in Proc. of the 2007 IEEE Industrial and Commercial Power Systems Technical Conference (IEEE-I&CPS).An Algebraic Characterization of a class of Quasi-Cylic Code
I. Woungang, A. Tsemo, A. Sadeghian, and S. Misra, "An Algebraic Characterization of a class of Quasi-Cylic Code," in Proc. of Blackwell-Tapia conference in Mathematics, Institute for Mathematics and its Applications, Minneapolis, Nov. 2006.Fuzzy Bayesian Moels for Classification and Diagnosis in generalized Cardiology Discipline
H. Rahnama, A. Sadeghian and W. Melek, "Fuzzy Bayesian Moels for Classification and Diagnosis in generalized Cardiology Discipline"in Proc. of the World Automation Conf., Jul. 2006.Symmetric Cipher Design Using Recurrent Neura Networks
M. Arvandi, S. Wu, A. Sadeghian, W. Melek, and I. Woungang, "Symmetric Cipher Design Using Recurrent Neura Networks" in the Proc. of the 2006 International Joint Conference on Neural Networks (IJCNN 2006), pp. 2039 - 2046.Generic Expert System with Fuzzy Bayesian Inference for Medical Classification and Diagnosis in Cardiology
H. Rahnama, and A. Sadeghian, "A Generic Expert System with Fuzzy Bayesian Inference for Medical Classification and Diagnosis in Cardiology,"in Proc. of the 5th ICUE 2006.A Lower Bound on the Minimum Distance of a 1-Generator Quasi-Cyclic Code
I. Woungang, S. Misra, and A. Sadeghian, "A Lower Bound on the Minimum Distance of a 1-Generator Quasi-Cyclic Code,"in Proc. of the 23rd Queen's Biennial Symposium on Communications, pp. 63-65, May 2006.Urban Search and Rescue with Canine Augmentation Technology
Wireless Web Security Using a Neural Network-Based Cipher
I. Woungang, A. Sadeghian, S. Wu, S. Misra, and M. Arvandi, "Wireless Web Security Using a Neural Network-Based Cipher," Chapter II in Web Services Security and E-Business, G. Radhammani and G. S.V. Radha Krishna Rao (Eds.), Idea Group Publishing Inc., USA, ISBN: 1-59904-168-5, pp. 32-56, 2006.Mechanical Fault Diagnosis for Induction Motor with Variable Speed Drives using Adaptive Neuro-Fuzzy Inference System
Z. Ye, A. Sadeghian, and B. Wu, "Mechanical Fault Diagnosis for Induction Motor with Variable Speed Drives using Adaptive Neuro-Fuzzy Inference System," Electric Power Systems Research, vol. 76, no. 9, pp. 742-752, Jun. 2006.A Neuro Fuzzy-based Expert System for Disease Diagnosis
W. Melek, A. Sadeghian, H. Najjaran, and M. Noorfar, "A Neuro Fuzzy-based Expert System for Disease Diagnosis," in Proc. of the 2005 IEEE International Conference on Systems, Man and Cybernetics, pp. 3736-3741, Oct. 2005.Implementation of Knowledge-Based System for Iron Core Inductor Design
A. Sadeghian and J. D. Lavers, "Implementation of Knowledge-Based System for Iron Core Inductor Design," IEEE Trans. on Magnetics, vol. 40, no. 6, pp. 3495 - 3504, Nov. 2004.Design of Broadband Networks Based on the Virtual Network Concept
I. Woungang, D. Sa, A. Sadeghian, and W. Melek, "Design of Broadband Networks Based on the Virtual Network Concept," in Proc. of the 3rd ICUE 2004.Bounds on the Minimum Distances of a Class of q-ary Images of q^m-ary Irreducible Cyclic Codes
I. Woungang, A. Sadeghian, and W. Melek, "Bounds on the Minimum Distances of a Class of q-ary Images of q^m-ary Irreducible Cyclic Codes," in Proc. of the 2004 IEEE International Symposium on Information Theory, Jul. 2004Study of the Generalization Capability of the Sugeno-Yasukawa Class of Fuzzy Inference Systems
W. Melek, A. Sadeghian, and A. Goldenberg, "Study of the Generalization Capability of the Sugeno-Yasukawa Class of Fuzzy Inference Systems," in Proc. of the IPMU (Information Processing and Management of Uncertainty in Knowledge-Based Systems) 2004.Current Signature Analysis of Induction Motor Mechanical Faults by Wavelet Packet Decomposition
Z. Ye, B. Wu and A. Sadeghian, "Current Signature Analysis of Induction Motor Mechanical Faults by Wavelet Packet Decomposition,"IEEE Trans. on Industrial Electronics, vol. 50, no. 6, pp. 1217-1228, Dec. 2003.An Interpretation of Preisach-Krasnoselskii Hysteresis Model with the use of Artificial Neural Networks
A. Sadeghian,"An Interpretation of Preisach-Krasnoselskii Hysteresis Model with the use of Artificial Neural Networks," in Proc. of the IEEE International Conference on Magnetics (INTERMAG'2002).Evaluation of a Fuzzy Logic Controller for Laser Thermal Therapy
V. W. Choy, A. Sadeghian, M. D. Sherar and W. M. Whelan, "Evaluation of a Fuzzy Logic Controller for Laser Thermal Therapy," in Proc. of SPIE, Vol. 4617, p. 77-86, Laser Tissue Interaction XIII, Steven Jacques; Ed., 2002.Nonlinear Neuro-Fuzzy Prediction: Methodology, Design & Application
A. Sadeghian, "Nonlinear Neuro-Fuzzy Prediction: Methodology, Design & Application,"in Proc. of the 10th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'2001), vol. 2, pp. 1022-1026, Dec. 2001.On the Use of Recurrent Neuro-Fuzzy Networks for Predictive Control
A. Sadeghian and J. D. Lavers, "On the Use of Recurrent Neuro-Fuzzy Networks for Predictive Control," in Proc. of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference (IFSA/NAFIPS'2001), pp. 233-239,Jul. 2001.Electrical Machine Fault Detection using Adaptive Neuro-Fuzzy Inference
Z. Ye, B. Wu and A. Sadeghian, "Electrical Machine Fault Detection using Adaptive Neuro-Fuzzy Inference," in Proc. of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference (IFSA/NAFIPS'2001),pp. 390-395, Jul. 2001.Signature Analysis of Induction Motor Mechanical Faults by Wavelet Packet Decomposition
Z. Ye, B. Wu and A. Sadeghian, "Signature Analysis of Induction Motor Mechanical Faults by Wavelet Packet Decomposition," in Proc. of the Applied Power Electronics Conference (APEC'2001), pp. 1022-1029, Mar. 2001.Application of Feedforward Neuro-Fuzzy Networks for Current Prediction in Electric Arc Furnaces
A. Sadeghian and J. D. Lavers, "Application of Feedforward Neuro-Fuzzy Networks for Current Prediction in Electric Arc Furnaces," Proc. of The IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'2000), vol. 4, pp. 420-425, July 2000.Neuro-Fuzzy Predictors for the Approximate Prediction of v-i Characteristic of Electric Arc Furnaces
A. Sadeghian and J. D. Lavers, "Neuro-Fuzzy Predictors for the Approximate Prediction of v-i Characteristic of Electric Arc Furnaces," Proc. of The 19th International Meeting of the North American Fuzzy Information ProcessingSociety (NAFIPS'2000), pp. 183-187, July 2000.Recurrent Neuro-Fuzzy Predictor for the Prediction of vi characteristics of Electric Arc Furnaces
A. Sadeghian and J. D. Lavers, "Recurrent Neuro-Fuzzy Predictor for the Prediction of vi characteristics of Electric Arc Furnaces," in Proc. of The 9th IEEE International Conference on Fuzzy Systems (IEEE-FUZZ'2000), Vol. 1, pp. 110-115, May 2000.Implementation of a Knowledge-Based System Approach for Inductor and Transformer Design
A. Sadeghian and J. D. Lavers, "Implementation of a Knowledge-Based System Approach for Inductor and Transformer Design," in Proc. of The 8th European Conference on Power Electronics and Applications (EPE'99), Sept. 99.Nonlinear Black-Box Modeling of Electric Arc Furnace: An Application of Fuzzy Logic Systems
A. Sadeghian and J. D. Lavers, "Nonlinear Black-Box Modeling of Electric Arc Furnace: An Application of Fuzzy Logic Systems," in Proc. of The 8th IEEE International Conference on Fuzzy Systems (IEEE-FUZZ'99), vol. 1, pp. 234-239, Aug. 99.Application of Radial Basis Functions to Model Electric Arc Furnaces
Application of Adaptive Fuzzy Logic Systems to Model Electric Arc Furnaces