The significance of food in human health and well-being cannot be overemphasized. Nowadays, in ou... more The significance of food in human health and well-being cannot be overemphasized. Nowadays, in our dynamic life, people are increasingly concerned about their health due to increased nutritional ailments. For this reason, mobile food-tracking applications that require a reliable and robust food classification system are gaining popularity. To address this, we propose a robust food recognition model using deep convolutional neural networks with a self-attention mechanism (FRCNNSAM). By training multiple FRCNNSAM structures with varying parameters, we combine their predictions through averaging. To prevent over-fitting and under-fitting data augmentation to generate extra training data, regularization to avoid excessive model complexity was used. The FRCNNSAM model is tested on two novel datasets: Food-101 and MA Food-121. The model achieved an impressive accuracy of 96.40% on the Food-101 dataset and 95.11% on MA Food-121. Compared to baseline transfer learning models, the FRCNNSAM model surpasses performance by 8.12%. Furthermore, the evaluation on random internet images demonstrates the model's strong generalization ability, rendering it suitable for food image recognition and classification tasks.
Fuzzy Neural Networks for Detection Kidney Diseases
Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation, 2021
This study presents a learning mode-base Fuzzy Neural Networks (FNN) to detect chronic kidney dis... more This study presents a learning mode-base Fuzzy Neural Networks (FNN) to detect chronic kidney disease (CKD). Combining the fuzzy set theory with the NN structure helps the proposed system to learn sensor data and adjust network parameters. The structure and algorithms of multi-input multi-output FNN are presented. The FNN algorithms implement the TSK type fuzzy rules. The learning of the system is executed by utilizing a gradient descent algorithm and c-means clustering. The presented system is trained using kidney datasets. The performance of the system is evaluated using mean accuracy, sensitivity, specificity and precision which were obtained as 99.75%, 100%, 99.34% and 99.9% correspondingly. The comparison of the results of simulation of the proposed model with the results of other existing algorithms demonstrates the efficiency of the presented FNN model. The experimental results indicate that the approach proposed offers reasonable accuracy of detection and has the potential t...
11th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions and Artificial Intelligence - ICSCCW-2021, 2022
This paper presents the development of recurrent neural network based fuzzy inference system for ... more This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The neuro-fuzzy system is used for the identification and control of nonlinear dynamic plant. The simulation results of identification and control systems based on recurrent neuro-fuzzy network are compared with the simulation results of other neural systems. It is found that the recurrent neuro-fuzzy based system has better performance than the others.
Decision Making and Obstacle Avoidance for Soccer Robots
Robot-soccer game is characterized by a rapidly changing dynamic environment with moving obstacle... more Robot-soccer game is characterized by a rapidly changing dynamic environment with moving obstacles. In this environment making a decision in a very short time, on-line adaptation and flexibility are very important parameters used in decision-making. Based on the decision made by a robot, the collision-free navigation of the mobile robot in this dynamic environment is also important. In the paper, the development of decision-making and navigation algorithms of a soccer robot are considered. Behavior-tree algorithm is presented and implemented for control of the mobile robots. At the same time, the type-2 fuzzy logic system is used for solving the obstacle avoidance problem. The use of the presented algorithm allows the decreasing of the path length of the robot. The efficiency of the presented algorithms has been tested with simulations and practical experiments. The obtained results demonstrate the suitability of using presented algorithms in the navigation of holonomic robots.
WSEAS Transactions on Signal Processing archive, 2017
Skin colour detection has been a commendable technique due to its wide range of application in bo... more Skin colour detection has been a commendable technique due to its wide range of application in both analyses based on diagnostic and human computer interactions. Various problems could be solved by simply providing an appropriate method for pixel-like skin parts. Presented in this study is a colour segmentation algorithm that works directly in RGB colour space without converting the colour space. Genfis function as explored in this study formed the Sugeno fuzzy network and utilizing Fuzzy C-Mean (FCM) clustering rule, clustered the data and for each cluster/class a rule is generated. Also, the Radial Basis Function (RBF) utilized Gaussian function for grouping. Finally, corresponding output from data mapping of pseudopolynomial is obtained from input dataset to the adaptive neuro fuzzy inference system (ANFIS), while the Euclidean distance performed data mapping in the RBF model. The result obtained from these two algorithms depicts the RBFN outperforming ANFIS with remarkable margins.
International Journal of Advanced Computer Science and Applications, 2018
In this paper the detection of the climate crashes or failure that are associated with the use of... more In this paper the detection of the climate crashes or failure that are associated with the use of climate models based on parameters induced from the climate simulation is considered. Detection and analysis of the crashes allows one to understand and improve the climate models. Fuzzy neural networks (FNN) based on Takagi-Sugeno-Kang (TSK) type fuzzy rule is presented to determine chances of failure of the climate models. For this purpose, the parameters characterising the climate crashes in the simulation are used. For comparative analysis, Support Vector Machine (SVM) is applied for simulation of the same problem. As a result of the comparison, the accuracy rates of 94.4% and 97.96% were obtained for SVM and FNN model correspondingly. The FNN model was discovered to be having better performance in modelling climate crashes.
Sensory evaluation of customer satisfaction using type-2 fuzzy logic
Journal of Intelligent & Fuzzy Systems
Sensory experiences that include vision, hearing, touching, smelling and tasting are important pa... more Sensory experiences that include vision, hearing, touching, smelling and tasting are important parameters that enable people to trade effectively in retail stores. In this study, based on multisensory attributes the evaluation of customer satisfaction is considered using fuzzy set theory and conjoint analysis. Fuzzy set theory is one of the best methodologies for describing the meaning of linguistic values that express customer preferences. However, there may be different customer and expert opinions in the evaluation of preferences by expressing linguistic values. In the paper, a type-2 fuzzy set is used to handle these uncertainties. This paper proposes the combination of type-2 fuzzy sets and conjoint analysis in order to evaluate customer satisfaction using customer opinions about sensory variables such as sight, sound, taste, touch and smell when purchasing goods in retail stores. For this purpose, using statistical survey results and type-2 fuzzy sets the customer satisfaction...
Proceedings of the 2017 9th International Conference on Education Technology and Computers, 2017
Fuzzy logic science is the fundamental factor of any implementation and design of an intelligent ... more Fuzzy logic science is the fundamental factor of any implementation and design of an intelligent system of traffic lights controller (traffic signal control), and according to this science, many applications have been designed and sat up to emulate the circumstances and conditions of an isolated traffic junction, where this emulation provides some results that illustrates the fuzzy logic controller which has got better achievement such as neural computations in which provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy. Throughout the last several years, fuzzy control which based on based on fuzzy logical system has proved that is one of the most effective area in fuzzy system, principally in the major of manufacturing processes and also it has a huge importance in human brain analysis or what is called 'human thinking', moreover, it is closer to natural language.
In the paper, a vision-based vehicle identification system is proposed for autonomous intelligent... more In the paper, a vision-based vehicle identification system is proposed for autonomous intelligent car driving. The accurate detection of obstacles (vehicles) during intelligent car driving allows avoiding crashes, preventing accidents, saving people’s lives and reducing harm. The vehicle detection system, which uses low-quality images captured by a monocular video camera mounted at the front of the car, is based on convolutional neural networks (CNN). The CNN can extract global features of the images using convolutional layers and achieves more accurate, and faithful contours of vehicles. The CNN structure proposed in the paper provides high-accuracy detection of vehicle images. The experiments that have been performed using GTI dataset demonstrate that the CNN-based vehicle detection system achieves very accurate results and is more robust to different variations of images.
Diagnosis of fetal health is a difficult process that depends on various input factors. Depending... more Diagnosis of fetal health is a difficult process that depends on various input factors. Depending on the values or the interval of values of these input symptoms, the detection of fetal health status is implemented. Sometimes it is difficult to determine the exact values of the intervals for diagnosing the diseases and there may always be disagreement between the expert doctors. As a result, the diagnosis of diseases is often carried out in uncertain conditions and can sometimes cause undesirable errors. Therefore, the vague nature of diseases and incomplete patient data can lead to uncertain decisions. One of the effective approaches to solve such kind of problem is the use of fuzzy logic in the construction of the diagnostic system. This paper proposes a type-2 fuzzy neural system (T2-FNN) for the detection of fetal health status. The structure and design algorithms of the T2-FNN system are presented. Cardiotocography, which provides information about the fetal heart rate and uter...
Diagnosis of Common Diseases Using Type-2 Fuzzy System
Proceedings of the 3rd International Conference on Machine Learning and Soft Computing
High level of expertise is required for human disease diagnosis which is a complicated and diffic... more High level of expertise is required for human disease diagnosis which is a complicated and difficult process. Each disease is characterised with the set of observable sign and symptoms. Based on these symptoms to understand patient health problems and to make a diagnosis of these diseases with their clear definition is difficult. The diagnosis of the disease is based on knowledge of doctor physicians. Fuzzy logic is one of the best approaches to design knowledge-based system for diagnosis of the diseases. In this paper, the design of a type-2 fuzzy system is performed for diagnosis of the common diseases using proper values of the inputs. The input symptoms and output diseases are defined for construction of the fuzzy rule base. The relationships are presented using type-2 IF-Then rules. Based on the fuzzy rules the design of type-2 fuzzy inference system is performed. The designed system will help the physician to diagnose common diseases such as common cold and flu.
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Papers by Rahib Abiyev