Background: Assessment of root development and apical closure is critical in dental disciplines, including endodontics, trauma management, and age estimation. This study aims to leverage advances in deep learning Convolutional Neural Networks (CNNs) to automatically evaluate the apical region status of permanent first molars, highlighting a digital health application of AI in dentistry. Methods: In this retrospective study, 262 Cone Beam Computed Tomography (CBCT) scans were reviewed, and 147 anonymized dental images were cropped from pseudopanoramic radiographs, including standard measurements. Tooth regions were resized to 471 × 1075 pixels and split into training (80%) and test (20%) sets. CNN performance was assessed using accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curves with area under the curve (AUC), demonstrating AI-based image analysis in a dental context. Results: Precision, recall, and F1-scores were 0.79 for open roots and 0.81 for closed roots, with a macro average of 0.80 across all metrics. The overall accuracy and AUC were also 0.80. Conclusions: These results suggest that CNNs can be effectively used to assess apical patency from ROI images derived from pseudopanoramic radiographs.

Identity and Access Management (IAM) that can be both secure and flexible has become critical as corporate networks continue expanding in size and complexity. The agility required to address changing cybersecurity threats like phishing attempts, insider attacks, and behavioral anomalies that circumvent rule-based controls is frequently lacking in traditional IAM systems. This article presents MyD, an AI-driven IAM framework that incorporates supervised machine learning and hybrid deep-learning approaches for realtime anomaly detection in order to overcome these constraints. Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) layers have been utilized in the system architecture to process real-world behavioral data that was obtained by enterprisewide monitoring agents and trained on Random Forest classifiers. This design supports the detection of minute departures from normal behavior by allowing the system to learn both temporal and spatial patterns in user activities. An analysis of a dataset with more than 8,900 labeled records generated an AUC value of 0.3135, an F1 score of 30.78%, and a recall rate of 78.14%. These results indicate the sensitivity of the framework to anomalous behavior and its ability to perform in situations with unbalanced data. The MyD system shows great promise for real-time, user-specific, scalable threat detection in business settings. All things considered, the experimental results confirm its significance as a cutting-edge solution for intelligent IAM infrastructure.

Generalized Zero-Shot Learning (GZSL) aims to recognize classes in the test dataset that do not have image samples in the training dataset. GZSL tasks typically place all classes into a semantic space using predefined semantic attributes for seen and unseen classes. Since there are no real images for unseen classes, classifying these classes correctly is a challenging task. To overcome this challenge, synthetic features for unseen classes are generated using generative networks. Based on this approach, we proposed a generator-based GZSL model that selects the best samples using machine learning methods for the generated synthetic features. In our proposed model, we preferred semantically rich representations instead of traditional semantic attributes for semantic information representation. Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) were used together to generate synthetic features. We applied k-means and DBSCAN clustering algorithms to the generated synthetic features and then classified them. To evaluate our proposed model, we conducted experiments on well-known GZSL benchmark datasets AWA2, CUB, and FLO. We extended our experiments to include open-set classes. Comprehensive experiments showed GZSL classification performances of 67.8% on AWA2, 77.0% on CUB, and 92.4% on FLO. Additionally, we observed the improving effect of k-means and DBSCAN clustering algorithms on GZSL classification performance.

Video and image denoising techniques aim to eliminate noise while preserving image details. However, in the process of noise reduction, while some image texture may get lost, residual noise artifacts can persist. So far, no single model has achieved universal success across all types and levels of noise. In this study, we present a noise reduction model that combines deep learning methods using limited hardware resources. Specifically, we leverage a combination of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) architecture, which has demonstrated effectiveness in preserving image structure as the depth of the model increases. Additionally, we enhance visual quality by incorporating GAN model after the CNN network. Our evaluation of the proposed approach reveals superior performance at low noise levels compared to previous neural network-based methods. By utilizing the findings of this study, we gain insights into the more efficient utilization of deep learning and traditional noise reduction methods, explore their behavior at different noise levels, and compare results obtained from diverse datasets. The proposed method outperforms other CNN-based methods at certain noise levels, thereby providing valuable prior knowledge to researchers in this field.

In this paper, we propose a novel hybrid deep learning model designed for predicting the contribution of SMEs to the economy. Our hybrid model aims to address the challenge of accurately forecasting SMEs' economic contribution by leveraging the strengths of CNN in capturing spatial relationships and patterns and LSTM's ability to capture sequential dependencies. We present the architecture of the hybrid model and describe the data preprocessing steps, feature extraction, and model training. To evaluate the effectiveness of the proposed model, we conducted experiments on a comprehensive dataset of SMEs' economic indicators in Türkiye. The results demonstrate that the hybrid deep learning model outperforms traditional forecasting methods and standalone neural network models. The incorporation of CNN and LSTM enables the model to capture intricate relationships within the data, leading to more accurate and robust predictions.

In recent years, the use of artificial intelligence in medicine, as in many other fields, has begun to increase considerably. Creating magnetic resonance (MR) reports manually by medical doctors is a very difficult, time-consuming, and potentially error-prone process. In order to address these problems, a deep learning-based image captioning model is proposed in this study to automatically generate reports from brain MRIs. In the developed model, image processing, natural language processing, and deep learning methods are used together to produce text for the content and diagnoses in the medical image. First, pre-processing, such as rotating at random angles, changing size, cropping, changing brightness and contrast, adding shadows, and mirroring, were performed for MR images. Then, a model that generates reports was developed by utilizing the Bootstrapping Language Image Pre-Training (BLIP) model and the transformer architecture of the model. The experimental studies showed that the proposed model had successful results; the produced reports were highly similar to the original reports and could be used as a supplementary tool in medicine.

Electricity has been considered one of the most important resources in the world since its invention. Due to its nature, it is not possible to store it in large quantities after production. Due to this structure, it is extremely important to produce electricity at the right time and in the right quantities. The day-ahead electricity market, which is one of the electricity markets in Turkey, is a market where agreements are made one day before physical electricity trading. Participants in the market bid for the electricity they can produce the next day for a certain time period. The submitted bids are compared with the market clearing price (PTF) and the bids below the PTF are accepted. Since the PTF is not known from the previous day in this structure, it creates uncertainty. Correct PTF estimation helps market participants to make strategic decisions by minimizing their risks.

A deep learning approach based on Recurrent Neural Networks (RNN) was applied in this study due to the suitability of the data for time series analysis for the estimation of PTF. The data sets created in the study were created and compared by analyzing different sizes and unusual situations such as holidays and weekends. The performance of the model was evaluated with common error metrics such as Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Percentage Error (MAPE). In the model training, optimization studies were performed on hyperparameters such as epoch, batch size, activation function, optimizer selection, time step value and the success of the model was compared on different data sets. When the model results were examined, it was shown that the accuracy rates increased significantly especially in the data set where the data pre-processing step was performed by removing weekend and official holiday data. According to the results obtained as a result of hyperparameter optimizations, it is seen that the performance of the proposed RNN model is at a level that can compete with the results obtained in previous studies conducted with similar methods when compared with the literature.

As a result, this study shows that the RNN-based deep learning approach can be an alternative method for PTF estimation in the day-ahead electricity market and reveals that it can be an effective estimation tool that market participants can use in their decision-making processes. In future studies, it is aimed to work with larger data sets of the model and to further increase the model performance with different deep learning architectures.

In recent years, high bandwidth and cost-efficient wireless network technologies have emerged as a competition factor in process of constituting own infrastructure. On the other hand, today's challenges of designing an effective layout have become an important problem for public institutions and private companies with increasing requests. There are some methods to solve this problem. In this study, a new approach based on a genetic algorithm is proposed to solve the mentioned problem. A simulation is developed to test the success of the algorithm. The most effective layout design of access points is constituted by the distance between access points and the communication channels used in the developed simulation. The obtained experimental results showed that the proposed algorithm successfully achieved the challenge of designing an access point layout in terms of total coverage area and average bandwidth per user.

The COVID-19 pandemic has profoundly impacted healthcare systems and economies worldwide, leading to the implementation of travel restrictions and social measures. Efforts such as vaccination campaigns, testing, and surveillance have played a crucial role in containing the spread of the virus and safeguarding public health. There needs to be more research exploring the transmission dynamics of COVID-19, particularly within European nations. Therefore, the primary objective of this research was to examine the spread patterns of COVID-19 across various European countries. Doing so makes it possible to implement preventive measures, allocate resources, and optimize treatment strategies based on projected case and mortality rates. For this purpose, a hybrid prediction model combining CNN and LSTM models was developed. The performance of this hybrid model was compared against several other models, including CNN, k-NN, LR, LSTM, MLP, RF, SVM, and XGBoost. The empirical findings revealed that the CNN-LSTM hybrid model exhibited superior performance compared to alternative models in effectively predicting the transmission of COVID-19 within European nations. Furthermore, examining the peak of case and death dates provided insights into the dynamics of COVID-19 transmission among European countries. Chord diagrams were drawn to analyse the inter-country transmission patterns of COVID-19 over 5-day and 14-day intervals.

Big data, generated by various sources such as mobile devices, sensors, and the Internet of Things (IoT), has many characteristics such as volume, velocity, variety, variability, veracity, validity, vulnerability, volatility, visualization, and value. An Intrusion Detection System (IDS) is essential for cybersecurity to detect intrusions before or after attacks. Traditional software methods struggle to store, manage, and analyze big data, developing new techniques for effective and rapid intrusion detection in organizations and enterprises. This study introduces the IDS Random Forest (RF) model in binary and multiclass classification for intrusion detection. In this model, we used the Synthetic Minority Oversampling TEchnique (SMOTE) to address class imbalances, and the RF classifier to classify attacks using the Network Security Laboratory (NSL)-KDD dataset. In the experiment, we compared the IDS-RF model with the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Logistic Regression (LR) classifiers in terms of accuracy, precision, recall, f1-score, and times for training and testing. The experimental results showed that the IDS-RF model achieved high performance in binary and multiclass classification compared to others. In addition, the proposed model also achieved high accuracies for each class (Normal, DoS, Probe, U2R, or R2L) and obtained 98.69%, 99.72%, 98.93%, 95.13%, and 89%, respectively.

Identity and access management (IAM) systems are essential for securing enterprise environments by ensuring that only authorized users can access critical resources. However, traditional IAM systems often fail to address the complexity of evolving cyber threats. This paper introduces an AI-driven IAM system that enhances security protocols through real-time anomaly detection. By leveraging a hybrid architecture consisting of convolutional neural networks (CNN) and long short-term memory (LSTM) layers, the system provides real-time analysis of user behavior to detect identity-related anomalies. The data was collected from real-world environments using a .NET worker service and preprocessing involved user-specific normalization techniques. The proposed model achieved test accuracy of 85.44%, precision of 87.95%, recall of 85.44%, and area under curve (AUC) score of 0.8578. These results demonstrate the model’s ability to provide scalable and adaptive solutions for modern IAM challenges.

The aim of this paper is to present a creative approach to generate test case outputs for a given input automatically for software testing. Sequence-to-sequence (seq2seq) model is applied. Our approach aims to address the challenge of creating meaningful test case outputs for input variations in software testing, improving efficiency and accuracy in test automation. With the help of natural language processing techniques, the model is trained on an original dataset of test inputs and their corresponding outputs, predicting the output for a given test case input. We employ evaluation metrics including BLEU, ROUGE, and JACCARD similarity scores to assess the quality of generated outputs, comparing them against reference outputs. Our initial results show that the seq2seq model has a huge potential of producing accurate test case outputs, significantly reducing manual effort in test case generation. This work demonstrates the potential for integrating Recurrent Neural Network techniques into software testing and providing a scalable solution for automated test case output generation.

Recently, one of the most common approaches used in speech recognition is deep learning. The most advanced results have been obtained with speech recognition systems created using convolutional neural network (CNN) and recurrent neural networks (RNN). Since CNNs can capture local features effectively, they are applied to tasks with relatively short-term dependencies, such as keyword detection or phoneme- level sequence recognition. This paper presents the development of a deep learning and speech command recognition system. The Google Speech Commands Dataset has been used for training. The dataset contained 65.000 one-second-long words of 30 short English words. That is, %80 of the dataset has been used in the training and %20 of the dataset has been used in the testing. The data set consists of one-second voice commands that have been converted into a spectrogram and used to train different artificial neural network (ANN) models. Various variants of CNN are used in deep learning applications. The performance of the proposed model has reached %94.60.

Identity and Access Management (IAM) systems are essential for securing enterprise environments, ensuring that only authorized users can access critical resources. However, traditional IAM systems often fall short in addressing the evolving complexity of cyber threats, making it challenging for organizations to maintain robust security measures. This paper introduces an AI-driven IAM system designed to tackle these challenges by enhancing security protocols and optimizing user authentication processes through real-time anomaly detection. The proposed system leverages advanced machine learning algorithms to identify and mitigate identity-related anomalies, preventing unauthorized access and potential security breaches. Initial evaluations reveal that the system significantly improves detection accuracy, demonstrating its effectiveness across various sectors, including finance, healthcare, and technology. The model not only addresses the limitations of traditional IAM systems but also provides a robust, adaptive, and scalable solution tailored for modern enterprises. In experimental studies, the model achieved an accuracy of 0.73, a precision of 0.70, and a recall of 0.80. These results indicate the system's strong capability to accurately detect anomalies, reinforcing its potential to redefine IAM practices and enhance security measures in dynamic enterprise environments. By integrating AI-driven anomaly detection, the developed IAM system offers a forward-looking and innovative approach to managing access controls.

Increasing in the data volume and complexity make log analysis mandatory for security and performance management in server systems. In this new era, where traditional manual methods are insufficient, the automatic log analysis potential of artificial intelligence and deep learning techniques comes to the fore. In this study, a deep learning model is developed to detect anomalies by analyzing log data collected from servers and devices. This log anomaly detection model, developed using Convolutional Neural Network (CNN), uses structured log data processed with the Drain log parsing algorithm and effectively classifies anomalies by extracting features from this data. In the experimental studies conducted on Hadoop Distributed File System (HDFS) log data, it is observed that the model reaches up to 99% accuracy rates and improves both debugging processes and operating efficiency.

Lately, Unmanned aerial vehicles, especially drones, are mainly used for transportation, communication and military purposes. Using only one drone to accomplish a mission leads to a solution that is costly and has low error tolerance. For this reason, a network structure called as Flying Ad-Hoc Networks (FANET) has been created which consisting of organized drones with lower costs and task sharing mechanism. However, these networks remain vulnerable to various attacks due to various vulnerabilities such as the use of civilian drones, use of unencrypted GPS signals, physical attacks using malicious drones and so on. Although efforts are being made to find solutions to these attacks, an effective solution cannot be produced due to the limited memory and calculation capabilities of drones. Encryption of drone communications is important for security. However, computational costs associated with encryption cause a decrease in drone battery life. In the literature, Eliyptic Curve Cryptography (ECC) algorithm is mostly used due to its low computational cost. Even though the algorithm has low computational cost when compared to other cryptographic algorithms, it also increases power consumption. In this study, drone detection and subsequent classification of malicious drones which are potential menaces for man-in-the-middle or physical attacks for the network were implemented by using real time frames taken in real time from the drone camera. YOLO (You Only Look Once) detection algorithm was used in the drone detection phase and Convolutional Neural Networks (CNN) was used in the classification phase. While communication between drones is normally carried out unencrypted, communication has been enabled to be encrypted with the help of ECC after detecting a malicious drones. Thus, it is aimed to increase the drone battery life and switch to encrypted communication only in case of doubt. In the study, a dataset which is consisting of 4 classes whose names are Yuneec Typhon, DJI Tello, DJI Phantom 4 and Other types of Drones was created using internet resources and YouTube videos, and the classification success was measured as 88.78%.

Artificial intelligence (AI) is computer systems that can perform tasks that require human intelligence. It consists of data based on machine learning, deep learning and artificial neural networks. AI; with the increase in data collection and the ability to store large numbers of data, its use in the field of health has increased. It has been increasing rapidly recently. AI is being used more and more frequently with its features that help physicians in diagnosis, treatment planning, prognosis prediction and application of treatments. In this review, it is aimed to specify AI and its areas of use in the healthcare system.

Customer churn refers to disconnection of a customer from a business. The cost of customer churn includes both lost revenue and marketing costs to acquire new customers. Reducing customer churn is the primary goal for every business. Customer churn prediction can contribute to the development of strategies that enable businesses to retain the customers with high risk of loss. Nowadays, the importance of customer churn prediction models is increasing day by day. In this study, a multi-layer perceptron (MLP) based model has been developed for prediction of customer churn by using dataset of an anonymous telecommunications company. The developed model has been compared with k-Nearest Neighbors (kNN), logistic regression (LR), naive bayes (NB), random forest (RF) and support vector machine (SVM) extensively. The experimental results have shown that the developed MLP-based model has more successful than others with respect to accuracy, precision, recall, sensitivity, balanced classification rate, Matthews’s correlation coefficient and area under ROC curve (AUC).

SME is a term used for businesses based on the number of employees, size and turnover. SMEs form the basis of the economy and are indispensable organizations of business life around the world. In this study, ConvLSTM model was created to evaluate the financial performance of SMEs operating in the software sector in Turkey. The motivation of the study is to analyze the performance of SMEs operating in the software sector in Turkey. In the study, data from the Turkish Small and Medium Enterprises Development and Support Institution for the period 2018-2022 was used. ConvLSTM was compared with LR, LSTM, SVM, CNN, RF and MLP. Experiments showed that ConvLSTM outperformed other models, with performance above 0.8 R2 for all parameters.

Earthquakes are one of the most dangerous natural disasters that have constantly threatened humanity in the last decade. Therefore, it is extremely important to take preventive measures against earthquakes. Time estimation in these dangerous events is becoming more specific, especially in order to minimize the damage caused by earthquakes. In this study, a hybrid deep learning model is proposed to predict the time of the next earthquake to potentially occur. The developed CNN+GRU model was compared with RF, ARIMA, CNN and GRU. These models were tested using an earthquake dataset. Experimental results show that the CNN+GRU model performs better than others according to MSE, RMSE, MAE and MAPE metrics. This study highlights the importance of predicting earthquakes, providing a way to help take more effective precautions against earthquakes and potentially minimize loss of life and material damage. This study should be considered an important step in the methods used to predict future earthquakes and supports efforts to reduce earthquake risks.

In today's information and technology age, search engines have become an important part of our lives. Although search engines are the first to be used to access information, old and unnecessary information is included in the content offered to users. In terms of providing up-to-date data, today's search engines often cannot offer the desired success. In order to keep the data presented by web browsers up-to-date, the time of return visits must be accurately estimated. In this study, EMACrawler based on exponential moving average is proposed to determine the revisit times, which is the most important feature that affects the performance of search engines. The proposed method is tested using precision, total coverage and efficiency metrics. It has been seen that EMACrawler obtains the current data on the web pages in an accurate and quick manner. As a result of the experimental studies, it has been seen that EMACrawler is more successful than other methods in obtaining up-to-date data and maintaining the freshness of the browser database.

Although Knowledge Graphs (KGs) are widely used, they suffer from hosting false information. In the literature, many studies have been carried out to eliminate this deficiency. These studies correct triples, relations, relation types, and literal values or enrich the KG by generating new triples and relations. The proposed methods can be grouped as closed-world approaches that take into account the KG itself or open-world approaches using external resources. The recent studies also considered the confidence of triples in the refinement process. The confidence values calculated in these studies affect either the triple itself or the ground rule for rule-based models. In this study, a propagation approach based on the confidence of triples has been proposed for the refinement process. This method ensures that the effect of confidence spreads over the KG without being limited to a single triple. This makes the KG continuously more stable by strengthening strong relationships and eliminating weak ones. Another limitation of the existing studies is that they handle refinement as a one-time operation and do not give due importance to process performance. However, real-world KGs are live, dynamic, and constantly evolving systems. Therefore, the proposed approach should support continuous refinement. To measure this, experiments were carried out with varying data sizes and rates of false triples. The experiments have been performed using the FB15K, NELL, WN18, and YAGO3-10 datasets, which are commonly used in refinement studies. Despite the increase in data size and false information rate, an average accuracy of 90% and an average precision of 98% have been achieved across all datasets.

With the widespread use of social networks, blogs, forums and e-commerce web sites, the volume of user generated textual data is growing exponentially. User opinions in product reviews or in other textual data are crucial for manufacturers, retailers and providers of the products and services. Therefore, sentiment analysis and opinion mining have become important research areas. In user reviews mining, topic modeling based approaches and Latent Dirichlet Allocation (LDA) are significant methods that are used in extracting product aspects in aspect based sentiment analysis. However, LDA cannot be directly applied on user reviews and on other short texts because of data sparsity problem and lack of co-occurrence patterns. Several studies have been published for the adaptation of LDA for short texts. In this study, a novel method for aspect based sentiment analysis, Sentence Segment LDA (SS-LDA) is proposed. SS-LDA is a novel adaptation of LDA algorithm for product aspect extraction. The experimental results reveal that SS-LDA is quite competitive in extracting products aspects.

Day by day the usage of infrared cameras has been increasing in the world. With the increasing use of thermal infrared cameras and images, especially in military, security and medicine, the need for coloring thermal infrared images to visible spectrum has arisen. In this study, a deep based model has been developed to generate visible spectrum images (RGB - Red Green Blue) from thermal infrared (TIR) images. In the proposed model, an encoder-decoder architecture with skip connections has been used to generate RGB images. KAIST-MS (Korea Advanced Institute of Science and Technology-Multispectral) dataset used for training and test the developed model. The experimental results extensively tested using Least Absolute Deviations (L1), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM).

A new weighted multi-attribute based recommender system (WMARS) has been developed using extended user behavior analysis. WMARS obtained data from number of clicked items in the recommendation list, sequence of the clicked items in recommendation the list, duration of tracking, number of tracking same item, likes/dislikes, association rules of clicked items, remarks for items. WMARS has been applied to a movie web site. The experimental results have been obtained from a total of 567 heterogeneous users, including employers in different sectors, different demographic groups, and undergraduate and graduate students. Using different weighted sets of the attributes’ parameters, WMARS has been tested and compared extensively with collaborative filtering. The experimental results show that WMARS is more successful than collaborative filtering for the data set that was used.

This paper proposes an artificial neural network (ANN) to obtain the electron energy distribution functions (EEDFs) in SF₆ and argon from the following: 1) mean energies; 2) the drift velocities; and 3) other related swarm data. In order to obtain the required swarm data, the electron swarm behavior in SF₆ and argon is analyzed over the range of the density-reduced electric field strength E/N from 50 to 800 Td from a Boltzmann equation analysis based on the finite difference method under a steady-state Townsend condition. A comparison between the EEDFs calculated by the Boltzmann equation and by ANN for various values of E/N suggests that the proposed ANN yields good agreement of EEDFs with those of the Boltzmann equation solution results.

Fuzzy logic controllers (FLC) have gained popularity in the past few decades with successful implementation in many areas, including electrical machines’ drive control. Many colleges are now offering fuzzy logic courses due to successful applications of FLCs in nonlinear systems. However, teaching students a fuzzy logic controlled drive system in a laboratory, or training technical staff, is time consuming and may be an expensive task. This paper presents an educational tool for fuzzy logic controlled brushless direct current motor (BDCM), which is a part of a virtual electrical machinery laboratory project. The tool has flexible structure and graphical interface. Motor and controller parameters of the drive system can be changed easily under different operating conditions.

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been presented to speed control of a switched reluctance motor (SRM). SRMs have become an attractive alternative in variable speed drives due to their advantages such as structural simplicity, high reliability, high efficiency and low cost. But, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter variations. In this study, to tackle these problems, an adaptive neurofuzzy controller is proposed. Heuristic rules are derived with the membership functions then the parameters of membership functions are tuned by ANFIS. The algorithm has been implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the proposed ANFIS controller under different operating conditions of the SRM.

Switched reluctance motor (SRM) is increasingly employed in industrial applications where variable speed is required because of their simple construction, ease of maintenance, low cost and high efficiency. However, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter uncertainties. In this paper, to overcome this problem, a neuro fuzzy controller (NFC) is proposed. Heuristic rules are derived with the membership functions of the fuzzy variables tuned by a neural network (NN). The algorithm is implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the NFC with various working conditions of the SRM.