Deer hunting, a novel meta-heuristic algorithm is proposed from the inspiration of humans hunting behaviour towards deer. The deer attaching method is based on the developed hunting strategies which depend on the movement of two hunters in their best positions, termed as leader and successor. Each hunter updates his position until they reach the deer. This algorithm is experimented in real-time engineering applications.

Deer hunting

A technique for the fault diagnosis in analog circuits is designed by rider optimization algorithm (ROA). Its development is based on a group of riders, racing toward a target location. In addition, RideNN classifier is developed by training algorithm for the neural network (NN). This is experimented by means of triangular wave generator (TWG), low noise bipolar transistor amplifier (BTA), and differentiator (DIF) and an application circuit, solar power converter (SPC).

Rider optimization

A Nature-inspired optimization algorithm, especially swarm intelligence-based algorithms an evolutionary computation-based are used to solve a variety of optimization problems. Here, a novel optimization algorithm presented on the basis of lion unique social behaviour. Most popular social behaviours of lion are territorial defence and territorial takeover. The performance of this algorithm is investigated by several test suites and engineering optimization problems. Test suits are used to reveal the versatility of the

Lion optimization

A hybrid algorithm called MKF-Cuckoo is presented in which it is the hybridization of cuckoo search algorithm with multiple kernel-based fuzzy c means algorithm. Here, MKFCM objective is solved by the cuckoo search algorithm which is one of the recent optimization algorithms proved effective in many optimization problems. The performance is comparatively analyzed with some other algorithm using clustering accuracy, rand coefficient, and computational time with iris and wine datasets to prove effectiveness.

Multiple kernel-based fuzzy c-means algorithm for clustering

The main intension of the research is generating rules from data as well as for the selection of optimized rules, adapting of genetic algorithm is done and to explain the exploration problem in genetic algorithm, introduction of new operator, called systematic addition is done, then proposing a simple technique for scheming of membership function and discretization, and for designing a fitness function by allowing the frequency of occurrence of the rules in the training data.

Genetic Fuzzy System for medical data classification

This is subjected to classify the tumor and non-tumor images, followed by segmentation of tumor region in MRI images. Classification methodologies such as feed forward back propagation neural network, radial basis neural network, support vector machine with quadratic programming and adaptive neuro-fuzzy inference system are considered for experimental investigation in which support vector machine with quadratic programming is found to be dominant.

Threshold prediction for segmenting tumour from brain MRI scans

The heuristic image restoration method is organized by two steps that are core processing and post processing. The local and global features of each pixel values of noisy image are extracted and restored it by exploiting an extracted feature and Markov Random Field (MRF) in core processing and the restored image quality and boundary edges are sharpened using post processing function. The implementation shows the effectiveness in restoring the noisy images.

Markov Random Field based Image Restoration

An optimized flight booking and transportation terminal open/close decision system is presented using Genetic Algorithm to maximize the revenue of airline. Consequently, its frequency is generated with linguistic variable and deviation of booking is interpreted. The open/close decision system is optimized by an observed data and genetic algorithm. Experimentation is performed with the synthetic data to prove the significance of the system.

Airlines booking terminal open/close decision system

A Facial Emotion Recognition (FER) system is developed to analyse the elemental facial expressions of human, such as normal, smile, sad, surprise, anger, fear, and disgust. The process of recognition is categorised into four processes, namely pre-processing, feature extraction, feature selection, and classification. GWO-based neural network (NN) is used to classify the emotions from the selected features.

Facial emotion recognition system

This system aims to predict an optimal sizing for hybrid wind and photovoltaic (PV) power generation under minimized cost. This optimal sizing of hybrid Wind-PV is accomplished by satisfying the average annual load demand. This process happens via opposition based genetic algorithm with Cauchy mutation (OGA-CM) and its performance measure is compared with Opposition based Genetic Algorithm and Genetic Algorithm.

Hybrid wind and photovoltaic power system

An approach to mine user buying patterns using PrefixSpan algorithm is proposed and place the products on shelves based on the order of mined purchasing patterns. In first stage, sequences of product categories are mined to place the product categories on shelves based on the sequence order of mined patterns and in the second stage, the patterns are mined for each category and then, rearrange the products within the category by incorporating the profit measure on the mined patterns.

Products placement in supermarkets

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color: #037cff; } .composer .milestone-alt { background: #037cff; color: #fff; } .composer .pricing-table .col { border: 1px solid #037cff; } .composer .pricing-table .head { background: #037cff; } .composer .pricing-table .featured .price { color: #037cff; } .composer .progress-bar .bar span { background: #037cff; } .composer .progress-bar-alt .bar { border-color: #037cff; } .composer .progress-bar-alt .bar em { background: #037cff; } .composer .progress-bar-alt .bar em:after { border-top: 4px solid #037cff; } .composer .progress-bar-alt .bar span { border-right: 1px solid #037cff; } .composer .process:after { border-color: #037cff; } .composer .process .done:after { border-color: #037cff; color: #037cff; } .composer .process .done:before { border-color: #037cff; } .composer .process .done i { border-color: #037cff; color: #037cff; } .composer .services .icon { background: #037cff; } .composer .services-alt .icon { background: none; } .composer .tabs > label { background: #037cff; } .composer .tabs > label:nth-child(2) { border-left-color: #037cff; } .composer .tabs > div { border-color: #037cff; } .composer .tabs > input:checked + label { border-top: 1px solid #037cff; border-right-color: #037cff; border-left-color: #037cff; color: #037cff; } .composer .team-alt li > div { border-color: #037cff; } .composer .team-alt .social { background: #037cff; } .composer .testimonial-alt .text { background: #037cff; } .composer .testimonial-alt .text:after { border-right-color: #037cff; } .composer .testimonial-group .author div { background: #037cff; } .composer .testimonial-group .author.active:before { border-top: 6px solid #037cff; } .composer .toggle dt:hover { color: #037cff; } .composer .toggle dt i { background: #037cff; } .composer .toggle-alt dt { box-shadow: inset 0 0 0 1px #037cff; color: #037cff; } .composer .twitter-feed li { background: #037cff; } .composer .twitter-feed-alt p { border: 1px solid #037cff; } .composer .twitter-feed-alt a { color: #037cff; } .composer .twitter-carousel .fa-twitter { background: #037cff; } .composer .twitter-carousel .owl-buttons div { border-color: #037cff; 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