Using fuzzy logic and particle swarm optimization to design a decision-based filter for cDNA microarray image restoration

Background/Objectives: This study presents an external and internal prior guided patch based filter for minimizing Gaussian noise in complementary deoxyribonucleic acid (cDNA) microarray images. Methods/Statistical analysis: The proposed denoising filter is capable of taking into consideration both external and internal prior. It employs combined prior guided patch based denoising considering various distance based patch-matching methods. Findings: Experimental results demonstrate that the combined prior guided patch based filter outperforms the existing well-known filters in minimizing noise of cDNA microarray images. The outcome of the proposed scheme found to offer better peak signalto-noise ratio and structural similarity index in contrast to existing filtering techniques. Effectiveness of the proposed denoising method is also assessed by estimating the spot intensities of cDNA microarray image that reflects the effect of noise reduction in the image. Application/Improvements: Minimization of noise is a crucial step of cDNA microarray image processing and it aids in microarray analysis by extracting valid and good quality gene expression measurements. Keywords: Denoising, External Prior, Internal Prior, K- nearest Neighbor, Microarray Image, Singular Value Decomposition

cDNA microarray technology enables whole genome study of gene expressions by measuring the differential expression of genes in microarray images. An important first step in analyzing microarray image is the accurate delineation of the cDNA spots in the image. We report here a fully automated spot segmentation algorithm for cDNA microarray images. The algorithm makes use of morphological operations, adaptive multi-level thresholding, and statistical intensity modeling to perform automatic grid addressing and spot segmentation. Our algorithm is robust for even poor quality cDNA microarray images.

A finite impulse response (FIR) filter design method is presented by truncating the constrained least squares filter for real-time, spatially adaptive image restoration. The proposed method truncates the original constrained least squares image restoration filter using the Maxwell-Boltzmann distribution kernel. For the edge preserving image restoration, the orientation of local edge is analyzed based on the covariance matrix, and the edge orientation-adaptive restoration filters are generated. The reduced size of the FIR type restoration filter makes hardware implementation easier for real-time image enhancement. Experimental results show that the proposed method provide more detail and less restoration artifacts than existing methods. As a result, the proposed restoration filter can be applied to realtime image enhancement systems, such as high-definition televisions and video surveillance systems.

Image filtering techniques have potential applications in biomedical image processing such as image restoration and image enhancement. The potential of traditional filters largely depends on the apriori knowledge about the type of noise corrupting the image. This makes the standard filters to be application specific. For example, the well-known median filter and its variants can remove the salt-and-pepper (or impulse) noise at low noise levels. Each of these methods has its own advantages and disadvantages. In this paper, we have introduced a new finite impulse response (FIR) filter for image restoration where, the filter undergoes a learning procedure. The filter coefficients are adaptively updated based on correlated Hebbian learning. This algorithm exploits the inter pixel correlation in the form of Hebbian learning and hence performs optimal smoothening of the noisy images. The application of the proposed filter on images corrupted with Gaussian noise, results in restorations which are better in quality compared to those restored by average and Wiener filters. The restored image is found to be visually appealing and artifact-free

A wavelet-based Markov random field segmentation model in segmenting microarray experiments

This paper proposes an improvement of the direct power control (DPC) scheme of a grid connected three phase voltage source inverter based on artificial neural networks (ANN) and fuzzy logic (FL) techniques for the renewable energy applications. This advanced control strategy is based on two intelligent operations, the first one is the replacement of the conventional switching table of a three phase voltage source inverter (VSI) by a selector based on artificial neural networks approach, and the second one is the replacement of the hysteresis comparators by fuzzy logic controllers for the instantaneous active and reactive power errors. These operations enable to reduce the power ripples, the harmonic disturbances and increase the response time period of the system. Finally, the simulation results were obtained by Matlab/Simulink environment, under a unity power factor (UPF). These results verify the transient performances, the validity and the efficiency of the proposed DPC scheme.

We describe in this book, new methods for building intelligent systems using type-2 fuzzy logic and soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including type-1 fuzzy logic, neural networks, and genetic algorithms, which can be used to create powerful hybrid intelligent systems. In this book, we are extending the use of fuzzy logic to a higher order, which is called type-2 fuzzy logic. Combining type-2 fuzzy logic with traditional SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers. We consider in this book the use of type-2 fuzzy logic and traditional SC techniques to solve pattern recognition problems in real-world applications. We consider in particular the problems of face, fingerprint and voice recognition. We also consider the problem of recognizing a person by integrating the information given by the face, fingerprint and voice of the person. Other types of applications solved with type-2 fuzzy logic and SC techniques, include intelligent control, intelligent manufacturing, and adaptive noise cancellation.

Introduction: This paper presents a novel approach for classifying different types of steel rods using a combination of pre-trained convolutional neural networks (CNNs) and fuzzy logic systems. We utilize VGG16, ResNet50, and InceptionV3 models to extract features from grayscale images of steel rods, which are then classified using Support Vector Machines (SVM). Furthermore, we incorporate a fuzzy logic system to improve the final classification accuracy by combining the individual accuracies of each CNN model. Experimental results demonstrate the effectiveness of our approach in achieving high classification accuracy compared to traditional methods. Objectives: This research presents a novel multi-model approach for steel rod classification utilizing three distinct fuzzy logic systems: Fuzzy Recurrent Neural Network (RNN), Fuzzy Convolutional Neural Network (CNN), and Fuzzy Neural Network. The proposed system addresses the critical challenge of accurate defect detection and classification in steel manufacturing processes. By combining these complementary approaches, we achieve enhanced feature extraction capabilities and improved classification accuracy. Experimental results demonstrate that our multi-model system achieves a classification accuracy of 97.8%, surpassing single-model approaches by an average of 8.2%. The system successfully identifies surface defects, structural anomalies, and dimensional variations with high precision, making it suitable for real-time industrial applications. Methods: This paper introduces a novel approach for the feature extraction and classification of steel rods by integrating multiple models and utilizing fuzzy logic systems. The methodology leverages pre-trained convolutional neural networks (CNNs) for extracting comprehensive feature representations from steel rod images. These extracted features are then combined and integrated using fuzzy logic to enhance classification accuracy and reliability. By addressing the limitations of traditional manual inspection methods and single-model approaches, this integrated multi-model system offers a robust solution for real-time industrial implementation. The proposed system aims to improve the efficiency and effectiveness of quality control processes in steel rod manufacturing, ensuring consistent and accurate classification even under varying conditions and complexities. This innovative approach paves the way for more advanced and adaptable automated inspection systems in the industry. Results: The paper aims to develop an integrated multi-model system that combines three distinct fuzzy logic approaches to enhance feature extraction capabilities through complementary methodologies. By improving the classification accuracy and reliability, the system addresses the limitations of existing methods. The goal is to create a robust and adaptable system that is suitable for real-time industrial implementation for steel rod analysis and classification. This comprehensive approach leverages the strengths of multiple models and fuzzy logic techniques, ensuring accurate and efficient classification processes, even under complex and varying conditions. The proposed multi model system is designed to meet the stringent quality control requirements of industrial applications, ultimately improving productivity and product quality through reliable automated inspection and classification for steel rod with accuracy of 97.80%, precision of 97.30% and recall 97.50%. Conclusions: This paper presents a novel approach for the classification of steel rods using a combination of pre-trained CNN models and fuzzy logic systems. The experimental results demonstrate the effectiveness of our method in achieving high classification accuracy. This research demonstrates the effectiveness of a multi-model fuzzy logic approach for steel rod classification. The system achieves significant improvements over single-model approaches while maintaining real-time processing capabilities. Our approach provides a foundation for further research and development in automated industrial image classification.

This study focuses on a technical and economic analysis of designing and operating an off-grid hybrid renewable energy system (HRES) in a rural community called Olooji, situated in Ogun state, Nigeria, as a case study. First, a size optimization model is developed on the basis of the novel metaheuristic particle swarm optimization (PSO) technique to determine the optimal configuration of the proposed off-grid system on the basis of the minimization of the levelized cost of electricity, by factoring in the local meteorological and electricity load data and details on the technical specification of the main components of the HRES. Second, a fuzzy-logic-controlled energy management system (EMS) is developed for the dynamic power control and energy storage of the proposed HRES, ensuring the optimal energy balance between the different multiple energy sources and the load at each hour of operation. The result of the size optimization model showed that an LCOE for implementing an HRES in the community would be 0.48 USD/kWh in a full-battery-capacity scenario and 1.17 USD/kWh in a half-battery-capacity scenario. The result from this study is important for quick decision-making and effective feasibility studies on the optimal technoeconomic synopsis of implementing minigrids in rural communities.

A new technique is described which couples median filtering and image deblurring techniques to filter noisy images without introducing defocusing side effects. To deal with colour images a vector median filtering procedure is proposed. Using this procedure a better edge preserving filter is obtained which does not introduce new colours. The deblurring operation is performed componentwise by fitting an ARMA model to the image. The AR part of the model estimates the image and the MA part estimates and blurring function. Finally the MA part is inverted and applied to remove the blur introduced by the median filter.

Gridding is the primary task of cDNA Microarray images processing. Among the present methods, those based on projection are simple, rapid, and with high accuracy. Several techniques based on projection are analyzed in this paper. Base on these techniques, an advanced method is proposed. The parameters are irrelevant to spot size, spacing, number of rows, columns, and applicable to various types of cDNA Microarray images. Experiment shows that the proposed method is also easy, fast, easily implemented, and with high accuracy. At the same time, the method has better robustness to images with a lot of noise.

Through this research, we shows a study on the identification of poverty in a great city with over a million of habitants -and featured with a radical violence which costs 32000 deaths-and analyze vulnerability with homeless and poor people in the municipality of Ciudad Juarez - a largest city in México, the seventh most largest-, trying the questionnaire used for the study, with fuzzy logic techniques. With this new method of treatment about questionnaires using fuzzy logic techniques, we can obtain the equivalent response of a specific group, as well as membership and uncertainty of it. This will reduce the subjective interpretation of the pollsters, favoring decision to propose public policies related to improving the quality of life of people in this society.

In Chapter 4, fuzzy logic in narrow sense has been presented at length. A lot of arguments have been given in favour of this logic, among which the most important is its role in providing tools for modelling of the vagueness phenomenon via graded approach. Consequently, FLn should be a theoretical basis standing beyond methods and techniques of fuzzy logic. The mediator between the latter and FLn is natural language. To be more specific, most of the applications of fuzzy logic are based on the generalized modus ponens mentioned in Chapter 1, which is a model of one of the fundamental principles of human reasoning. Recall that it typically uses fuzzy IF-THEN rules, which are conditional statements containing expressions of natural language and interpreted using fuzzy sets.

Text summarization is an approach to summarize the original document with better efficiency than human. Mining prominent words from the original document and connecting those to make a semantically correct sentence in the best way are, the ultimate goal. This paper focuses on making the summary more reliable and understandable with the techniques of fuzzy logic. The extractive method of text summarization and the fuzzy logic method along with algorithms have enhanced the power of unsupervised learning which is proved by comparison with the algorithms working alone.

Forest fires pose severe threats to ecosystems and communities globally, especially in vulnerable semi-arid regions like North Africa. Understanding the key factors influencing forest fire dynamics is essential for effective management and mitigation. This study aims to comprehensively analyze forest fire risk patterns in Djebel El Ouahch's massif (Algeria), focusing on integrating bioclimatic, fuel, geomorphological, and human factors through advanced fuzzy logic and geographic information system (GIS) techniques. Climatic station data, satellite imagery, and GIS were employed to map bioclimatic parameters, land cover, and geomorphological features. Fuzzy logic systems were applied to integrate these factors, assigning appropriate weights based on their significance. The resulting forest fire prediction model was defuzzified to generate predictive maps indicating varying vulnerability levels within the study area. Predictive maps delineated areas of low to high forest fire risk. Low-risk zones were characterized by sparse vegetation, while high-risk regions featured densely vegetated slopes near human settlements. The study identified critical factors influencing vulnerability, emphasizing the impact of climate, terrain, and human activities. Urgent attention was directed toward high-risk areas, necessitating tailored fire prevention measures and strategic urban planning to minimize human-induced risks. The results underscored the complex interaction of natural and anthropogenic factors in shaping forest fire susceptibility. Understanding these dynamics facilitates evidence-based policymaking, enhancing forest fire preparedness, biodiversity preservation, and community safety. Additionally, the study emphasized the need for continuous research incorporating real-time climate data and socio-economic factors to refine predictive models. This research provided valuable insights into forest fire risk patterns in Djebel El Ouahch, serving as a foundation for targeted fire management strategies. By bridging the gap between theoretical knowledge and practical application, this study contributes significantly to sustainable forest management and disaster mitigation efforts globally, emphasizing the importance of proactive measures in safeguarding vulnerable ecosystems and communities.