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Nowadays, optimal and intelligent design approaches are vital in almost all areas of engineering. Scientists and engineers are attempting to make frameworks and models more proficient and intelligent. This paper deals with a detailed investigation on design of various digital filters using optimization algorithms. Generally digital filters are classified into two types which are FIR and IIR filters and are again classified into one dimensional, two dimensional and three dimensional filters for signal, image and video respectively. The design of a digital filter that satisfies all the required conditions perfectly is a challenging factor. So, apart from the conventional mathematical methods, optimization algorithms can be used to design optimal digital filters. IIR Filters are infinite impulse response filter; they have impulse response of infinite duration. FIR Filters are finite impulse response filters; they have impulse response of finite duration. In this paper we have discussed the design of various optimal digital filters based on various optimization algorithms, for processing of signal, image and video. The design of digital filters based on Evolutionary algorithms and swarm intelligence algorithms like Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization, Cuckoo Search Algorithm, Differential Evolution, Gravitational Search, Harmony Search, Spiral Optimization, teaching–learning based optimization, wind driven optimization, hybridization of optimization algorithm are presented.

Indirect immunofluorescence (IIF) method with human epithelial type-2 (HEp-2) cell as substrates is recommanded in anti nuclear antibody (ANA) test. These IIF slides are observed under microscope by pathologists to prepare the report. So, the ANA test is subjective and requires systematic automation. This paper proposes a novel algorithm for HEp-2 cellcategorization, which can be implanted in the ANA test automation system. A hybrid descriptor which represents the textural and morphological characters of the objects of interest was used along with Binary tree. The hybrid descriptors were generated by decomposing the image into binary images by using lower and upper thresholds. The performance of the proposed algorithm was evaluated on the ICPR 2016 IIF HEp-2 cell image dataset, the results concluded that Hybrid Descriptor with Binary Tree approach achieved the best performance with “97.5%” mean class accuracy.

In recent days, total hip arthroplasty (THP) become a very victorious process which relieve the pain and enhances its functioning. The main motive of the paper is to design and assess a synergic deep learning (SDL) for learning and predicting various metrics like duration of stay (DOS), discharge disposition (DD) and inpatient expenses for THA. The next motive is to develop a patient specific payment (PSP) model reporting the complexity level of the patient. By the use of 15 preoperative parameters from 78,335 THA patients for osteoarthritis from National Inpatient Sample and OME databases, the prediction of DOS, DS and inpatient expenses takes place. A set of two evaluation parameters namely accuracy and receiver operating characteristic curve is used for experimentation. In addition, a predictive uncertainty is employed. All patient refined comorbidity cohort for establishing the PSP model. The presented SDL model exhibited better learning with high trustworthiness, receptiveness, and validity in its prediction outcome. The presented model can be applied for the implementation of PSP model for tiered payments depending upon the case complexities.

This investigation practices a grey wolf optimization algorithm to optimize the filter coefficients of the two-dimensional wavelet transform to increase the quality of the decompressed image with lossy compression. The reason for the decrease in image quality in wavelet-based compression is nothing more than a quantization error. Our approach reduces the effect of this quantization error by using an adaptive filtering technique with optimization of the wavelet filter coefficients. Experimental results showed that this optimized filter outperforms existing wavelet filters.

The modern world uses digital data invariably and the advent of smart phones and cameras add to images and communicating through images in social media is a very common happening due to the exposure to internet and the economical availability of data. Moreover, people explore the web for much news and on different topics. They feel satisfied to look into image type info than textual information. So, image retrieval is paramount nowadays and it is very much useful in many societal and defence applications. The methods used for retrieval started through text based and query based is popularly used now. The content based retrieval uses many stages, approaches, technologies, algorithms etc. It is intended to provide a broad perspective about CBIR in this work. Moreover, the computational and time complexities involved during retrieval relies on the performance of the entire system. One of the aspects that can improve the precision is the reduction of semantic gap. Taking this as the base, we have explored a literature and presented the points related to various points including relevance feedback so as to enable other researchers to carry out experimental work on the suggested areas.