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This paper has proposed a hybrid electric vehicle that uses intelligent energy management strategy to decrease the energy consumption of the vehicle. Here, the total energy consumption of the vehicle is initially modelled and further investigated to reduce the amount of energy used to be identified as a sum of electrical energy provided by consumed fuels and on-board batteries. In particular, an intelligent controller is proposed in this work to execute its ability to decrease the total amount of energy consumed and improve the energy efficiency of the vehicle. A fuzzy system is utilized in an account supervisory controller to decide the appropriate mode of operation for the system. The part of the proposed work involves development of optimal control strategies by using neuro-fuzzy logic. In order to obtain optimal performance, the controllers are used to regulate vehicle subsystems and set points. The biggest advantage of this work is the reduction in energy consumption and their ability to execute the operation online. Simulink/MATLAB is used to simulate and validate the performance of the proposed work under various conditions and under several dataset values.

Due to the drastic exploitation of mobile devices and mobile apps in the day-to-day activities of people, the enhancement in hardware and software tools for mobile devices is also rising rapidly to cater to the requirements of mobile users. However, the progress of resource-intensive mobile applications is still inhibited by the limited battery power, restricted memory, and scarce resources of mobile devices. By employing mobile cloud computing, mobile edge computing, and fog computing, many researchers are providing their frameworks and offloading algorithms to augment the resources of mobile devices. In the existing solutions, offloading resource-intensive tasks is adopted only for specific scenarios and also not supporting the flexible exploitation of IoT-based smart mobile applications. So, a novel neuro-fuzzy modeling framework is proposed to augment the inadequate resources of a mobile device by offloading the resource-intensive tasks to external entities, and also a Bat optimization algorithm is exploited to schedule as many tasks as possible to the augmentation entities thereby improving the total execution time of all tasks and minimizing the resource exploitation of the mobile device. In this research work, external augmentation entities like distant cloud, edge cloud, and microcontroller devices are providing Resource augmentation as a Service (RaaS) to mobile devices. An IoT-based smart transport mobile app is implemented based on the proposed framework which depicts a significant reduction in execution time, energy consumption, bandwidth utilization, and average delay. Performance analysis depicts that the neuro-fuzzy hybrid model with Bat optimization provides a significant improvement compared with proximate computing and web service frameworks on the Quality of Service (QoS) parameters namely energy consumption, execution time, bandwidth utilization, and latency. Thus, the proposed framework exhibits a feasible solution of RaaS to resource-constrained mobile devices by exploiting edge computing.

Energy performance of buildings is an important issue to estimate the energy waste of buildings and their impact on the environment, so designing energy-efficient buildings could improve their energy performance. In this case, the estimation of heating and cooling loads plays an important role in this regard. However, there are few factors with unpredictable influences on the heating and cooling loads. This study has attempted to analyze the eight parameters that can significantly affect the heating and cooling loads through the Neuro-fuzzy logic approach. Accordingly, the eight parameters of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution were considered as inputs and predicting the cooling and heating load changes was regarded as the output of this study. The model was developed and its results were measured in two regression indicators of r and RMSE. Based on the obtained results it was found that roof has the strongest impact on heating and cooling loads (RMSE: 4.3596), moreover, if two factors were concurrently changed, then the combination of relative compactness and wall area can significantly affect the heating and cooling loads (RMSE: 2.6312). The most influential combination of three factors is observed as well for the heating and cooling load and these factors are relative compactness, wall area and glazing area (0.5948). The most influential combination of three factors is observed as well for the hearing load and these factors are relative compactness, wall area and glazing area (RMSE: 0.5948 and RMSE: 1.5769 for heating and cooling load, respectively). However, Neuro-fuzzy logic showed overfitting for more than two inputs, therefore it is not recommended for more than two inputs.

The growing gap between electric power generated and that demanded is of utmost concern especially in developing economy, hence calling for measures to argument the existing power generated of which DG is a more viable aspect to explore in curtailing this challenges; although been confronted with issue of location and sizing. This research applied Adaptive neuro fuzzy logic technique to optimize DG location and size. A 24 bus radial network was used to demonstrate this process and having a suitable location and size at optimal position reduces power losses and also improves the voltage profile at the buses. The method was simulated using ANFIS toolbox MATLAB R2013b (8.2.0.701) 64-bit software and tested using Gwagwalada injection sub-station feeder 1 system. The results obtained were compared to that obtained using ANN. It was observed that adaptive neuro fuzzy logic technique performed better in terms of reducing power losses compared to ANN technique. The percentage reduction in the power loss at the buses cumulatively is 48.96% for ANN while adaptive neuro fuzzy logic technique is 49.21%. The voltage profile of the networks after optimizing the DG location and sizes using adaptive neuro fuzzy logic technique were also found to be much improved with the lowest bus voltage improved from 0.9284 to 1.05pu.

To analyze the crop yield there is need to estimate the crop production. However, it is challenging task to control the crop production response because of different inputs. Fertilizer has a notable impact on crop yield. In order to analyze the fertilizers, it is suitable to establish a predictive approach to obtain optimal parameter for the best fertilizers. The main goal of the study was to establish a predictive approach by adaptive neuro fuzzy inference system (ANFIS) to determine the impact of temperature, humidity, moisture, soil type, crop type, nitrogen, potassium and phosphorous on the fertilizers prediction. There are five fertilizers which should be predicted by the ANFIS. The used fertilizers are: urea, DAP, 14-35-14, 28-28, 17-17-17, 20-20, 10-26-26. It was found that the “phosphorous” and “nitrogen” is the optimal combination of two parameters for the fertilizer prediction. The results could be useful for optimization of the crop yield response in order to reduce the cost of the process.

This paper utilizes clustering tool in MATLAB to find an optimal set of input parameters for the detection of human emotions using a neuro-fuzzy logic system. Previous studies have relied on a total of 14 physiological factors to detect one or more of 22 different human emotions. In this paper, we use clustering techniques to rank the factors in terms of their significance and impact on the system, and thus find a smaller subset of the factors for the detection of emotions. The clustering method shows that the stroke volume factor (SV) has the lowest impact in the model and as such can be eliminated from the set of factors. The electroencephalography (EEG), heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) are shown to have the highest impact on the model, and must be include in the input set of the model. We compare the clustering method with exhaustive methods for finding the optimal set of factors.

Tuberculosis [TB] has afflicted numerous nations in the world. As per a report by the World Health Organization [WHO], an estimated 1.4 million TB deaths in 2015 and an additional 0.4 million deaths resulting from TB disease among people living with HIV, were observed. Most of the TB deaths can be prevented if it is detected at an early stage. The existing processes of diagnosis like blood tests or sputum tests are not only tedious but also take a long time for analysis and cannot differentiate between different drug resistant stages of TB. The need to find newer prompt methods for disease detection has been aided by the latest Artificial Intelligence [AI] tools. Artificial Neural Network [ANN] is one of the important tools that is being used widely in diagnosis and evaluation of medical conditions. This review aims at providing brief introduction to various AI tools that are used in TB detection and gives a detailed description about the utilization of ANN as an efficient diagnostic technique. The paper also provides a critical assessment of ANN and the existing techniques for their diagnosis of TB. Researchers and Practitioners in the field are looking forward to use ANN and other upcoming AI tools such as Fuzzy-logic, genetic algorithms and artificial intelligence simulation as a promising current and future technology tools towards tackling the global menace of Tuberculosis. Latest advancements in the diagnostic field include the combined use of ANN with various other AI tools like the Fuzzy-logic, which has led to an increase in the efficacy and specificity of the diagnostic techniques.

The speed-traffic flow density interdependence diagram has a number of variations, starting with the theoretical model, through various empirical models that were developed and models based on actual research done on traffic flow. The functional interdependence is obtained using the Sugeno fuzzy logic system, where representative values proposed in HCM 2010 have been adopted as parameters of output association functions. Subsequently the neural network is trained based on actual traffic flow data, which by adjusting the association function of the fuzzy logic system yields an output form of the basic traffic flow diagram. It was noticed that this hybrid expert system produces better output results by applying the “subtractive clustering“ method on data that are used for training a neural network. Finally, the model was tested on several input data groups, and the interdependence between speed and traffic flow density is shown in graphical form.

In this paper, adaptive neuro-fuzzy inference system ANFIS is used to assess conditions required for aquatic systems to serve as a sink for metal removal; it is used to generate information on the behavior of heavy metals (mercury) in water in relation to its uptake by bio-species (e.g. bacteria, fungi, algae, etc.) and adsorption to sediments. The approach of this research entails training fuzzy inference system by neural networks. The process is useful when there is interrelation between variables and no enough experience about mercury behavior, furthermore it is easy and fast process. Experimental work on mercury removal in wetlands for specific environmental conditions was previously conducted in bench scale at Concordia University laboratories. Fuzzy inference system FIS is constructed comprising knowledge base (i.e. premises and conclusions), fuzzy sets, and fuzzy rules. Knowledge base and rules are adapted and trained by neural networks, and then tested. ANFIS simulates and predicts mercury speciation for biological uptake and mercury adsorption to sediments. Modeling of mercury bioavailability for bio-species and adsorption to sediments shows strong correlation of more than 98% between simulation results and experimental data. The fuzzy models obtained are used to simulate and forecast further information on mercury partitioning to species and sediments. The findings of this research give information about metal removal by aquatic systems and their efficiency.

This paper presents an approach of computer-aided diagnosis for early prediction of cancer cells in brain. It extracts the texture from the given brain MRI sample.It uses image processing techniques followed by neuro classification for prediction of Cancer for a given MRI sample. A neuro fuzzy approach is used for the recognition of the extracted region. The implementation is observed on various types of MRI images with different types of cancer regions.

Predicting the remaining useful life of the partially degraded components and putting them to use will help to save natural resources to a great extent. This will reduce the overall cost, energy and protects environment. High productivity cutting tools used in manufacturing industry are generally expensive. As such, the accurate assessment of remaining useful life (for reuse) of any given tool is of great significance in any manufacturing industry. The main objective of this research is to develop a comprehensive methodology to assess the reuse potential of carbide-tipped tools. This paper presents the use of three soft computing methods, namely, artificial neural network, neuro fuzzy logic and support vector regression methods for the assessment of remaining useful life (RUL) of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Tool life values are predicted using the aforesaid three soft computing methods and RUL obtained from these values are compared. It is found that the predictive neuro fuzzy method is capable of giving a better prediction of remaining useful tool life than the other methods.

The chapter considers the problem of recognition of visual and sound information by constructing a virtual environment, which allows a qualitatively simple system to carry out experiments and create an algorithmic model of pattern recognition comparable to human capabilities. The aim of the research is to obtain an algorithmic model that can extract from the surrounding world, “meaningful” (visual and sound) objects to link with the relevant lexical concepts which are the atomic building blocks of intelligence. The research is dedicated to the development of machine intelligence with the phased increase in the complexity of the behavioral model of artificial personality (AP), with the goal being experimental research in the problem of artificial intelligence.

This paper demonstrates the control parameter optimization using a hybrid Differential Evolution (DE) — Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for permanent-magnet brushless DC (BLDC) motor drive system ensuring deadbeat response. The parameter settings are further used in adaptive current and speed controllers to attain the objective in a BLDC drive system. The parameters of both inner-loop and outer-loop PI controllers, which vary with the operating conditions of the system, are adapted in order to maintain deadbeat response for current and speed. The ANFIS is modeled using the evenly distributed operating points selected within preset regions of system loading. The optimized data from DE are used to train the ANFIS that could deduce the controller parameters at any other loading condition within the same region of operation.

Data mining is one of the most optimal methods to analyze the data .Nowadays most of the people will be reluctant to invest money in shares and options because of the global economic crisis. In this research paper, a proposal is made in order to analyze the stocks/sectors and options based on various financial parameters using data mining techniques. The neuro-fuzzy logic technique is proposed to use in this proposal, to develop this work and the mining technique is applied to classify the result obtained

Fuzzy and Neuro Fuzzy logic concepts have been used for variety of applications, where machine intelligence can be used for the system. The fuzzy logic concept has been used for a number of biomedical applications also. We have used fuzzy and neuro fuzzy approach for tackling surgery procedures of a patient suffering from stress urinary incontinence (SUI). We have further extended the application of Fuzzy and Neuro Fuzzy logic for predicting the approximate recovery time of a patient under physical therapy suffering from stroke, paraplegia, quadriplegia etc. Here, we have outlined methods for above mentioned biomedical applications based on a knowledge base created from the patients of different age groups. We have shown our simulation results with input data and possible outcomes with the Fuzzy and Neuro Fuzzy system.

Purpose : To investigate the impact of critical quality attributes (CQAs) and critical process parameters (CPPs) on quality target product profile (QTPP) attributes of orally disintegrating tablet (ODT) containing ondansetron (OND) using two artificial neural network (ANN) programs. Methods : Different amounts of two different commercial superdisintegrants commonly used in ODT formulations (Ludiflash® and Parteck®) were examined as CQAs, while three different tablet-pressing forces were evaluated as CPPs for an orally disintegrating tablet (ODT) formulation. The impact of CQAs, and CPPs on the target product profile (tablet hardness, friability and disintegration time) were analysed using gene expression programming (GEP) and neuro-fuzzy logic (NFL) models. Results : NFL model defined the relations between CQAs, CPPs and QTPP, while GEP model favoured the use of an ODT formulation with suitable QTPP features which contained 4 mg ondansetron, 21.30 mg Parteck®, and 119 mg Avicel, fabricated with a compression force of 515 psi. In this regard, the tablet formulation demonstrated the required specifications. Conclusion : ANN programs are a useful tool for research and development (R&D) studies in the pharmaceutical industry and the use of ANNs can be beneficial in terms of raw materials, time and cost, as demonstrated for ondansetron ODT tablets. Keywords : Ondansetron, Critical quality attributes, Critical process parameters, Quality target product profile, Gene expression programming, Neuro-fuzzy logic, Artificial neural network

The original approach for the optical information processing for the hyperspectral remote sensing systems is developed on the union basis of the two mathematical tools: fuzzy logic and neural network. The optical information processing includes the complicated calculations and final results can give a large error. It is well known that there are large number of input parameters and some there uncertainty in the case of information processing of hyperspectral remote sensing systems. The using of statistical and determined models give the result having quite a large error of optical information processing and the given calculations take a lot of time to compute. Therefore the neoro-fuzzy logic application can be more expediency for processing of opto-electronic signals.

This chapter presents the work done in collaboration between ENEA and the OECD Halden Reactor Project (HRP), which resulted in the application of HRP neuro-fuzzy tools- PEANO [1, 2, 3, 4] and ALADDIN [5, 6] to problems of gas plant control, either related to measurement calibration or to process instability.

Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as healthcare systems integrate patient information for research and utilisation objectives. Data mining techniques can be used to discover useful patterns by exploring and analysing clinical repositories. It is feasible to incorporate data mining techniques into the classification process to discover useful patterns for classification rules from training samples. This paper thus assess the role of the data mining techniques namely fuzzy logic-based classifier and a back propagation-based neural networks in the diagnosis of severity of appendicitis in patients presenting with right iliac fossa (RIF) pain. It is based on the statistics already collected about the presence of appendicitis from patients dataset of around 2,230 datasets collected from BHEL Hospital, Tiruchirappalli. The conclusion is that neuro fuzzy logic-based classifiers can be used an effective tool for accurately diagnosing the severity of appendicitis.