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166 results on '"Khashman, Adnan"'

1. Using Deep Learning as Prediction Model in Poll-Driven Social Science Applications

Author

Khashman, Adnan, Sadikoglu, Gunay, Khashman, Zeliha, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Aliev, Rafik A., editor, Pedrycz, Witold, editor, Jamshidi, Mo, editor, Babanli, Mustafa, editor, and Sadikoglu, Fahreddin M., editor

Published
2021
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2. Metabolic Syndrome Risk Evaluation Based on VDR Polymorphisms and Neural Networks

Author

Khashman, Adnan, Serakinci, Nedime, Kizilkanat, Meral, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Aliev, Rafik A., editor, Pedrycz, Witold, editor, Jamshidi, Mo, editor, Babanli, Mustafa B., editor, and Sadikoglu, Fahreddin M., editor

Published
2020
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3. Banknote Issuing Country Identification Using Image Processing and Neural Networks

Author

Khashman, Adnan, Ahmed, Waleed, Mammadli, Sadig, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Aliev, Rafik A., editor, Pedrycz, Witold, editor, Jamshidi, Mo., editor, and Sadikoglu, Fahreddin M., editor

Published
2019
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4. Data Coding and Neural Network Arbitration for Feasibility Prediction of Car Marketing

Author

Khashman, Adnan, Sadikoglu, Gunay, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Aliev, Rafik A., editor, Pedrycz, Witold, editor, Jamshidi, Mo., editor, and Sadikoglu, Fahreddin M., editor

Published
2019
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16. Acute Lymphoblastic Leukemia Identification Using Blood Smear Images and a Neural Classifier

Author

Khashman, Adnan, Abbas, Hayder Hassan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Rojas, Ignacio, editor, Joya, Gonzalo, editor, and Cabestany, Joan, editor

Published
2013
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17. Support Vector Machines versus Back Propagation Algorithm for Oil Price Prediction

Author

Khashman, Adnan, Nwulu, Nnamdi I., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Zhang, Huaguang, editor, Polycarpou, Marios, editor, Alippi, Cesare, editor, and He, Haibo, editor

Published
2011
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20. Intelligent Detection of Voltage Instability in Power Distribution Systems

Author

Khashman, Adnan, Buruncuk, Kadri, Jabr, Samir, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sandoval, Francisco, editor, Prieto, Alberto, editor, Cabestany, Joan, editor, and Graña, Manuel, editor

Published
2007
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21. Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques.

Author

Olaniyi, Ebenezer Obaloluwa, Komolafe, Temitope Emmanuel, Oyedotun, Oyebade Kayode, Oyemakinde, Tolulope Tofunmi, Abdelaziz, Mohamed, and Khashman, Adnan

Subjects
SOFT computing, MELANOMA diagnosis, RADIAL basis functions, FEATURE extraction, IRIS (Eye), EYE tracking, TEXTURE analysis (Image processing), DIAGNOSIS
Abstract

Background: Eye melanoma is deforming in the eye, growing and developing in tissues inside the middle layer of an eyeball, resulting in dark spots in the iris section of the eye, changes in size, the shape of the pupil, and vision. Objective: The current study aims to diagnose eye melanoma using a gray level cooccurrence matrix (GLCM) for texture extraction and soft computing techniques, leading to the disease diagnosis faster, time-saving, and prevention of misdiagnosis resulting from the physician's manual approach. Material and Methods: In this experimental study, two models are proposed for the diagnosis of eye melanoma, including backpropagation neural networks (BPNN) and radial basis functions network (RBFN). The images used for training and validating were obtained from the eye-cancer database. Results: Based on our experiments, our proposed models achieve 92.31% and 94.70% recognition rates for GLCM+BPNN and GLCM+RBFN, respectively. Conclusion: Based on the comparison of our models with the others, the models used in the current study outperform other proposed models. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Intelligent Face Recognition: Local Versus Global Pattern Averaging

Author

Khashman, Adnan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Sattar, Abdul, editor, and Kang, Byeong-ho, editor

Published
2006
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24. Multi-expression Face Recognition Using Neural Networks and Feature Approximation

Author

Khashman, Adnan, Garad, Akram A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Esposito, Floriana, editor, Raś, Zbigniew W., editor, Malerba, Donato, editor, and Semeraro, Giovanni, editor

Published
2006
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25. Intelligent Face Recognition Using Feature Averaging

Author

Khashman, Adnan, Garad, Akram A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mehrotra, Sharad, editor, Zeng, Daniel D., editor, Chen, Hsinchun, editor, Thuraisingham, Bhavani, editor, and Wang, Fei-Yue, editor

Published
2006
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26. Face Recognition Using Neural Networks and Pattern Averaging

Author

Khashman, Adnan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Yi, Zhang, editor, Zurada, Jacek M., editor, Lu, Bao-Liang, editor, and Yin, Hujun, editor

Published
2006
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28. Multi-banknote Identification Using a Single Neural Network

Author

Khashman, Adnan, Sekeroglu, Boran, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Blanc-Talon, Jacques, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2005
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35. A modified backpropagation learning algorithm with added emotional coefficients

Author

Khashman, Adnan

Subjects
Machine learning -- Research, Algorithms -- Usage, Artificial intelligence -- Research, Neural networks -- Design and construction, Data mining -- Methods, Data warehousing/data mining, Algorithm, Artificial intelligence, Neural network, Business, Computers, Electronics, Electronics and electrical industries
Abstract

Much of the research work into artificial intelligence (AI) has been focusing on exploring various potential applications of intelligent systems with successful results in most cases. In our attempts to model human intelligence by mimicking the brain structure and function, we overlook an important aspect in human learning and decision making: the emotional factor. While it currently sounds impossible to have 'machines with emotions,' it is quite conceivable to artificially simulate some emotions in machine learning. This paper presents a modified backpropagation (BP) learning algorithm, namely, the emotional backpropagation (EmBP) learning algorithm. The new algorithm has additional emotional weights that are updated using two additional emotional parameters: anxiety and confidence. The proposed 'emotional' neural network will be implemented to a facial recognition problem, and the results will be compared to a similar application using a conventional neural network. Experimental results show that the addition of the two novel emotional parameters improves the performance of the neural network yielding higher recognition rates and faster recognition time. Index Terms--Anxiety and confidence, backpropagation (BP), emotional neural networks, face recognition.

Published
2008

43. Heart Diseases Diagnosis Using Neural Networks Arbitration

Author

Khashman Adnan, Oyebade K. Oyedotun, and Ebenezer O. Olaniyi

Subjects
Control and Optimization, Heart disease, Artificial neural network, Computer Networks and Communications, Computer science, business.industry, Mortality rate, Feed forward, Machine learning, computer.software_genre, medicine.disease, Field (computer science), Computer Science Applications, Human-Computer Interaction, Support vector machine, Artificial Intelligence, Modeling and Simulation, Multilayer perceptron, Signal Processing, medicine, Data mining, Artificial intelligence, Set (psychology), business, computer
Abstract

There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.

Published
2015

48. Prototype Incorporated Emotional Neural Network (PI-EmNN)

Author

Oyedotun, Oyebade and Khashman, Adnan

Subjects
Computer science [C05] [Engineering, computing & technology], classification, Sciences informatiques [C05] [Ingénierie, informatique & technologie], Emotional neural network, prototype learning
Abstract

Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ‘engineering’ prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, prototype learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype and adaptive learning theories. We refer to our new model as “PI-EmNN” (Prototype-Incorporated Emotional Neural Network). Furthermore, we apply the proposed model to two real-life challenging tasks, namely; static hand gesture recognition and face recognition, and compare the result to those obtained using the popular back propagation neural network (BPNN), emotional back propagation neural network (EmNN), deep networks and an exemplar classification model, k-nearest neighbor (k-NN).

Published
2017

50. Neural network diagnosis of heart disease

Author

Abdulkader Helwan, Khashman Adnan, Ebenezer O. Olaniyi, and Oyebade K. Oyedotun

Subjects
Artificial neural network, Heart disease, business.industry, Mortality rate, medicine, Feedforward neural network, Artificial intelligence, medicine.disease, business, Backpropagation
Abstract

Mortality rate increases all over the world on a daily basis. The reason for this could be largely adduced to the increase in the number of patients with cardiovascular diseases. To worsen the case, many physicians have been known for misdiagnosis of patients reporting heart related ailment. In this paper, an intelligent system has been design which will help in effective diagnosis of the patient to avoid misdiagnosis. The dataset of UCI statlog heart disease has been used in this experiment. The dataset is comprises thirteen features which are vital in diagnosis of heart diseases. The system is model on a multilayer neural network trained with backpropagation and simulated on feedforward neural network. The recognition of 85% was obtained from testing of the network.

Published
2015

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