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The first Zagreb index is equal to the sum of the squares of the degrees at each vertex of G. In this study, we create four distinct types of fuzzy transformation graphs and investigate the fundamental characteristics shared by them. Additionally, upper bounds on the first Zagreb index of fuzzy transformation graphs in terms of fuzzy graph G elements have been discovered.

Brain tumors used to be uncommon, but they are increasingly becoming more common. Magnetic Resonance Images (MRI) scans may also be used by the medical specialist to detect brain malignancies. One of the most critical concerns in artificial intelligence systems is medical diagnosis via image processing and machine learning. Artificial intelligence based machine learning has enabled the digitalized the process of health monitoring. Diagnostic Images are a valuable source of information for determining the prognosis and treatment of certain cancers. It takes shape when human cells do not develop, split, or perish in the same way as conventional cells do. All voluntary and involuntary actions in the human body are controlled by the brain. Maintaining a healthy brain is critical for living a longer life. This paper proposes brain tumor detection using modified particle swarm optimization (MPSO) segmentation with ensemble classification. Initially image noise is suppressed using wiener filter. Then the feature extraction process is implemented using Haralick features. The SVM classifier with improved fuzzy segmentation is compared to the proposed work. With an accuracy of 98.2 percent, the new model outperformed the previous model.

An inductive-capacitive-inductive (LCL) type filters are broadly utilized in grid-connected voltage source inverters (VSIs) since they can give substantially improved attenuation of switching harmonics in currents injected into the grid with lower cost, weight and power losses than their L and LC type counterparts. This paper discusses an improved model reference adaptive control (MRAC) strategy for designing the control parameters to voltage source converter and improves stability of photovoltaic (PV) generation in different grid capabilities. An improved damping strategy is commonly referred to as MRAC, which has been developed using the proportional resonant (PR) controller and its gain parameters are optimized by Ant Lion optimization (ALO) algorithm. The error elimination at steady-state and gain at particular frequency were provided by the PR current controller. The main focus is set on the mathematical modelling of grid-connected PV, LCL filter and PR current controller and its parameters specifically, resonant and proportional gains. Further, the phase margin and gain of the controller transfer function are presented by means of bode plot to evaluate the operating condition of the controller for different gain parameters. Moreover, a large number of control strategies are mandatory for optimizing the controller parameters and to stabilize the system with zero steady-state error. The obtained results of improved MRAC strategy is presented and its effectiveness is compared with some existing controllers.

The power converters which are connected to main grids having various challenges like parametric uncertainty, instability and voltage fluctuations. To solve these issues adaptive control strategies...

Energy plays a major role in the manufacturing process. Technologies used for energy generation and resource utilization were changing very fast. Conventional and nonconventional energy generation ...

Increasing population in many countries consumed natural resources and generates secondary product. These secondary products may be in the form of pollutants and liberated in the atmosphere. In thi...

Model order reduction (MOR) has been proved robust and widely applicable to simulating large mathematical models in engineering and sciences. Such reduced order models (ROM) are useful not only for system analysis and simulation but also for reduced complex controller design. Wide application of MOR has been also found in optimizing and control. There are many reduction methods present for reducing the order of large-scale linear SISO and MIMO systems in time domain and frequency domain. Some mixed methods have been developed by considering the algorithm of two different reduction methods. The aim of the paper would be to develop the strong reduction method to reduce the large-scale linear system into reduced model, which approximates the original system effectively and some important properties of the original system are also preserved.

Most countries of the world are urbanized at a fast pace. Solid waste is one of the auxiliary products of this urbanization. It requires an efficient way to assess the waste, type of waste, waste characteristics, technology for waste utilization, and waste recovery economics. In this paper, a review study of waste to energy was done for different countries/continents, both developing and developed countries. The quality or content of waste differs in various countries, due to different climatic condition and biological factors. In various previous literatures, this topic is specifically discussed related to either waste to recycle, to energy, to landfill, or to by-product development. A specific model incorporating all these systems together is lacking. Hence, this review is conducted for different countries to find out a suitable model in a developing country like India. In this paper, all continents of the world are covered to understand the technology being used there for waste to energy system. Around 20 years of literature (1996–2017) related to waste to energy system is considered for review. In this paper, energy can be recovered from waste or materials like compost fertilizer refuse derived fuel (RDF), bio-oil, char, etc. The quality of waste generated depends on the climatic conditions, food pattern, etc. in different countries. Hence, it is needed to find the waste characteristics in order to find a suitable technology for a feasible waste to energy conversion system. Effective utilization of waste will result in a better environment. A systematic review of the literature was done for different continents rather than meta-analysis review for the individual country for the waste management system. After reviewing the existing model followed for energy recovery system in different countries, a suitable model for a country like India is to be developed.

In today’s scenario, energy security and global warming are considered as vital parameters of concern. The use of large number of conventional vehicles on roads has caused serious environmental pollution and they consume considerable amount of energy resources. Thus solution lies in switching to cleaner and greener energy alternatives, such as electrical energy. In this paper, conventional, electric and hybrid electric vehicles are compared on the basis of efficiency and greenhouse gas emissions. For efficient comparison, three different cycles, namely, Manhattan, Urban Dynamometer Driving Schedule and Arterial, are chosen. The simulation and computer modelling is implemented using the Advanced vehicle simulator (ADVISOR) platform. When Electric vehicle is tested on three different types of driving cycles, it exhibits highest efficiency and no tail pipe emission as compared to other vehicles. Thus the use of Pure electric vehicle eliminates both the problem of depleting energy resources and environmental pollution.

This paper presents a distribution network reconfiguration based on bacterial foraging optimization algorithm (BFOA) along with backward-forward sweep (BFS) load flow method and geographical information system (GIS). Distribution network reconfiguration (DNR) is a complex, non-linear, combinatorial, and nondifferentiable constrained optimization process aimed at finding the radial structure that minimized network power loss while satisfying all operating constraints. BFOA is used to obtain the optimal switching configuration which results in a minimum loss, BFS is used to optimize the deviation in node voltages, and GIS is used for planning and easy analysis purposes. Simulation is performed on the 33-bus system and results are compared with the other approaches. The obtained results show that the proposed approach is better in terms of efficiency and having good convergence criteria.

In the power system, voltage instability is one of the important issues and it may cause voltage sag, voltage swell, change in load and generation of harmonics. To tackle all these problems, the transmission power system makes use of an intelligent control model to improve the stability of power system. In this paper, a new adaptive neuro fuzzy inference system (ANFIS) based power system stabiliser (PSS) for the enhancement of voltage stability in power systems is proposed. A hybrid learning algorithm, which combines the least squares method and the back propagation algorithm, is used to train the ANFIS. The performance of the power system by using fuzzy based PSS is about 60.1% when compared to that of the conventional PSS. To evaluate the effectiveness of the proposed technique the performance of the proposed technique was compared with the results obtained by using fuzzy PSS model and its performance was analysed. Simulation results described in the paper demonstrate that the adaptive neuro-fuzzy based PSS can give 33.43% of improvement in voltage stability when compared to that of fuzzy based PSS over a wide range of operating conditions.

It is noticed, a hike in percentage over the smart grid interests in producers and consumers. In order to meet the requirements of the consumption of power we need to increase the use of renewable energy resources. Distributed Energy Sources (DER) is quite attractive way of distributing energy. It has got great future scope in distribution area. In the present paper the characteristics, advantages and disadvantages of various DER sources is discussed. The challenges and issues of DER are also described and addressed the problems that arise in existing grid.

With the introduction of the Web 3.0 standards on the World Wide Web, there is a need to include semantic techniques and ontologies in the Web based Recommendation Systems. In order to build query relevant domains and make information retrieval more efficient, it required recommending ontologies based on the query. Most ontology recommendation systems do not preserve the associations and axioms between them rather ontology matching and clustering algorithms tend to deduce logics dynamically. In this paper, a semantic algorithm for ontology recommendation has been proposed, where query-relevant ontologies are recommended by preserving the relationships between the ontological entities. The semantic similarity is computed using the query and the concepts initially and further between the query and description logics which makes it a context-based ontology recommendation system. A strategic approach called as SemantoSim is proposed to compute the semantic similarity.

This paper considers the problem of designing a class of stabilizing proportional derivative (PD) controllers for multi-input multi-output (MIMO) nonlinear systems. For a MIMO nonlinear system a set of stabilizing PD controllers were designed for each subsystem. The design approach is based on PD stabilization theorem derived from generalized result of the classical Hermite Biehler theorem. By considering the nonlinear terms as interconnections, a linear matrix inequalities optimization problem is formulated to ensure the stability of the composite nonlinear system with the designed decentralized controller parameters. A genetic algorithm based search technique is adopted to select an optimal PD controller gain from a search space of stabilizing controllers in order to have an optimum value of performance index. A two-link robot manipulator system is considered to show the effectiveness of the design procedure.

Inverted Pendulum is a well established benchmark problem that produces many challenges to a control engineer. It is a nonlinear, unstable, non minimumphase and under actuated system.Itrequires a controller which canadapt in different disturbance conditions and work appreciably well when compared to conventional controllers. In this paper Interval type-2 Fuzzy logic controllerfor inverted pendulum isdesigned.The objective of the proposed control technique is to develop the stability position of the pendulum. The optimal membership functions and the interference system are developed using IT2FLS. Using the IT2FLS, the position of the inverted pendulum is maintained towards the reference position. The proposed control techniqueis implemented in MATLAB/Simulink working platform and the control performances are evaluated. Then, the performance of proposed controller is evaluated and compared with the PI controller, Fuzzy controller andABC- FLC.

In this paper, a hybrid control technique is proposed for managing the variation of angle and velocity of the inverted pendulum. The proposed hybrid technique is the combination of ABC algorithm and interval type-2 Fuzzy Logic System (IT2FLS). The objective of the proposed hybrid control technique is to achieve the stability position of the pendulum. Here, the ABC algorithm is used to optimize the change of angle and change of velocity of the pendulum. With the optimized value, the optimal membership functions and the interference system are developed using IT2FLS. Using the ABC based IT2FLS, the position of the inverted pendulum is maintained towards the reference position. The proposed hybrid control technique is implemented in MATLAB/Simulink working platform and the control performances are evaluated.