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In this study, the integrated modified whale optimization and modified fuzzy c-means clustering algorithm using morphological operations are developed and implemented for appropriate knowledge extraction of a cyst from computer tomography (CT) images of the liver to facilitate modern intelligent healthcare systems. The proposed approach plays an efficient role in diagnosing the liver cyst. To evaluate the efficiency, the outcomes of the proposed approach have been compared with the minimum cross entropy based modified whale optimization algorithm (MCE and MWOA), teaching-learning optimization algorithm based upon minimum cross entropy (MCE and TLBO), particle swarm intelligence algorithm (PSO), genetic algorithm (GA), differential evolution (DE) algorithm, and k-means clustering algorithm. For this, various parameters such as uniformity (U), mean structured similarity index (MSSIM), structured similarity index (SSIM), random index (RI), and peak signal-to-noise ratio (PSNR) have been considered. The experimental results show that the proposed approach is more efficient and accurate than others.

With the advancement in technology, Computer and machine vision system is getting involved in the agriculture sector for the last few years. Deep Learning is a recent advancement in the Artificial Intelligence field. In the present era, many researchers have used deep learning applications for the classification of images, and is found to be one of the emerging areas in computer vision. In the classification of fruit images, the main goal is to improve the accuracy of the classification system. The accuracy of the classifier depends on various factors like the nature of acquired images, the number of features, types of features, selection of optimal features from extracted features, and type of classifiers used. In the proposed article, integration of CNN, RNN, and LSTM for the classification of fruit images are defined. In this approach, CNN and RNN are employed for the development of discriminative characteristics and sequential-labels respectively. LSTM presents an explanation by integrating a memory cell to encode learning at each interval of classification. Key parameters: accuracy, F-measure, sensitivity, and specificity are applied to assess the achievement of the proposed scheme. From empirical results, it has been declared that the offered classification method provides efficient results.

Fruit classification is noticed as the one of the looming sectors in computer vision and image classification. A fruit classification may be adopted in the fruit market for consumers to determine the variety and grading of fruits. Fruit quality is a prerequisite property from health view position. Classification systems described so far are not adequate for fruit classification during accuracy and quantitative analysis. Thus, the examination of new proposals for fruit classification is worthwhile. In the present time, automatic fruit classification is though a demanding task.Deep learning is a powerful state of the art approach for image classification [1] This task incorporates deep learning models: Convolution Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) for classification of fruits based on chosen optimal and derived features. As preliminary arises, it has been recognized that the recommended procedure has effective accuracy and quantitative analysis results. Moreover, the comparatively high computational momentum of the proposed scheme will promote in the future for the real time classification operations

Artificial Intelligence, Machine Learning, Fuzzy Logic, Neural Network, Genetic Algorithm and their hybrid systems play vital role in the medical sciences to diagnose various diseases efficiently in the patients. The problems related to the heart are widely comon in today’s world. The risk of heart failure develops due to the narrowness and blockage in the coronary arteries of the heart as excess cholesterol deposits in the arteries and blood vessels that results in fatigue, chest pain, dyspnoea, sleeping difficulties and depression. This research aims to explore diverse work done on FL and Hybrid-based techniques to identify the risk of heart disease among the patients. The present study reveals publications along with the strength, operating system, accuracy rate and other specifications used in the identification of heart disease based on FL and Hybrid-based approaches since 2010. This survey contributes motivation for research scholars to generate more innovative ideas and continue their research work in the respective field. Moreover, the future model for direct service of the patients from old age homes to the Intensive Care Unit through ambulance services is also presented in this paper.

With an advancement in artificial intelligence (AI) applications, the use of smart imaging devices has been increased at a rapid rate. Recently, many researchers have utilized deep learning models such as convolutional neural networks (CNN) for image classification models. Compared to the traditional machine learning models, CNN does not require any kind of handcrafted features. It utilizes various filters to extract the potential features of images automatically. Inspired from this, in this paper, we have proposed a novel fruit classification model which utilizes the features of CNN, Long short Term Memory (LSTM) and Recurrent Neural Network (RNN) architectures. Type-II fuzzy enhancement is also used as pre-processing tool to enhance the images. Additionally, to tune the hyper-parameters of the proposed model, TLBO-MCET is also utilized. Extensive experiments are drawn by considering the existing and the proposed fruit classification models. Comparative analysis reveals that the proposed model outperforms the competitive fruit classification models.

Image Enhancement is used as a preprocessing step in many computer vision applications. It provides enhanced input for other computerized image processing methods. Many preprocessing techniques can be applied to images depending on the application domain. In this paper we are proposing an image enhancement technique for color images that can be used as preprocessing step in many computer vision applications. It can also be used as a data augmentation technique in object detection. Luminance component of images is sometimes not captured by cameras and displayed by monitors properly. To remove this drawback of devices we have used gamma encoding. Four different values of gamma are evaluated depending on the quality of images. Image is then converted into YUV Color space. Y component represents the luminance. U and V components represent color. After that Contrast Limited Adaptive Histogram Equalization is applied to the Y component to improve the contrast of the image. The results are compared with the state-of-the-art methods on the basis of Peak Signal to noise Ratio (PSNR) and Mean Square Error (MSE). Quantitative results show that proposed algorithm results in improved value of PSNR and decreased value of MSE as compared to existing methods. Qualitative comparison is also done and results show improvement over the existing techniques.

Fruit image classification is an ill-posed problem. Many machine learning techniques have been developed until now to improve the classification problem of fruit images. However, the performance of these techniques depends upon the quality of acquired fruit images. Thus, the performance of competitive fruit classification techniques reduces for images captured under poor environmental conditions, such as haze, fog, smog etc. To overcome this issue, type-II fuzzy-based fruit image improvement approach is employed to improve the visibility of weather degraded fruit images. After that, fruit images will be classified using an integrated classification model. The integrated model combines two well-known models (i.e. CNN and RNN). CNN is utilised to evaluate the discriminative features of fruit images. RNN is utilised to asses sequential labels. Extensive analysis shows that the proposed integrated classification model outperforms competitive fruit image classification techniques in terms of accuracy and coefficient of correlation.

We suggested a Fuzzy image enhancement technique using Type-II to improvise visibly weather-degraded fruit images. The acquired fruit picture is divided into dark and light regions with Fuzzy partitioned. Fuzzy-sure entropy was used to choose the optimum visibility enhancement threshold values. Compared to the ATCE, BPDHE, GC and CLAHE image enhancement frameworks, the proposed scheme offers improved image quality. Experimental findings show that the approach in terms of visual and quantitative analysis have a lower percentage of saturated pixels, better output of visible edges and color gradients.

Deep learning has been a game changer in the field of object detection in the last decade. But all the deep learning models for computer vision depend upon large amount of data for consistent results. For real life problems especially for medical imaging, availability of enough amounts of data is not always possible. Data augmentation is a collection of techniques that can be used to extend the dataset size and improve the quality of images in the dataset by a required amount. Logically it is used to make the deep learning model independent of the counterfeit features of the data space. In this paper a comprehensive review of data augmentation techniques for object detection is done. Problem of class imbalance is also outlined with possible solutions. In addition to train time augmentation techniques an overview of test time augmentations is also presented.

The design of distributed database has became demanding with the increase in use of IoT and cloud based services. Distributed database system’s performance is totally relies on its design. Allocation of data is one of the major design issues while designing distributed databases. This paper presents a new technique for non-redundant allocation of data in distributed database design. The proposed approach allocates the data based on Simplified Biogeography Based Optimization (Simplified-BBO). The performance comparison of Simplified-BBO based approach is done against the GA and BBO based approaches. The proposed approach helps in decreasing the data communication cost during query execution which results in increasing the overall performance of distributed database systems.

Image segmentation play a vital role in classification. In this paper, to minimize cross entropy, Teacher-learner optimization approach is engaged to explore an optimal sequence of threshold values at distinct levels. One of the most prominent ideas in image segmentation is image thresholding. The suggested scheme is influenced by the shift of advice in the classroom framework, where a leaner hears from the scholar and later corresponds with each alternative.This idea is practiced to search optimal threshold values from fruit images at various levels.The proposed approach exploited the information theory approach called minimum cross entropy. For experiment work, fruit images (red, green, and golden apple) are adopted. PSNR and uniformity performance procedures are exploited to match the evaluation of TLBO - MCET with GA - MCET, and HBMO - MCET.

Image thresholding is one of the most important approaches for image segmentation. Among multilevel thresholding techniques, cross entropy has been widely used by researchers to find multilevel threshold values. In multilevel cross entropy thresholding techniques, main target is to find an optimal combination of threshold values at different levels for minimizing the cross entropy. In this paper, Teaching-Learning-based Optimization (TLBO) algorithm is used to find an optimal combination of threshold values at different levels for minimizing the cross entropy. TLBO algorithm is inspired by passing on knowledge within a classroom environment where students first gain knowledge from a teacher and then through mutual interaction. This new proposed approach is called the TLBO-based minimum cross entropy thresholding (TLBO-based MCET) algorithm. The performance of the proposed algorithm is tested on a number of standard test images. For comparative analysis, the results of TLBO-based MCET algorithm are compared with the results of Firefly-based minimum cross entropy thresholding (FF-based MCET), Honey Bee Mating Optimization-based minimum cross entropy thresholding (HBMO-based MCET) and Quantum Particle Swarm Optimization-based minimum cross entropy thresholding (Quantam PSO-based MCET). Comparative analysis is done based on two most popular objective performance measures, Peak Signal to Noise Ratio (PSNR) and Uniformity. From experimental results, it is observed that the proposed method is an efficient and feasible method to search an optimal combination of threshold values at 2nd, 3rd, 4th and 5th levels. Keywords: Cross entropy, Teacher-Learning-based Optimization (TLBO), Thresholding, PSNR, Uniformity

Object detection is being widely used in many fields, and therefore, the demand for more accurate and fast methods for object detection is also increasing. In this paper, we propose a method for object detection in digital images that is more accurate and faster. The proposed model is based on Single-Stage Multibox Detector (SSD) architecture. This method creates many anchor boxes of various aspect ratios based on the backbone network and multiscale feature network and calculates the classes and balances of the anchor boxes to detect objects at various scales. Instead of the VGG16-based deep transfer learning model in SSD, we have used a more efficient base network, i.e., EfficientNet. Detection of objects of different sizes is still an inspiring task. We have used Multiway Feature Pyramid Network (MFPN) to solve this problem. The input to the base network is given to MFPN, and then, the fused features are given to bounding box prediction and class prediction networks. Softer-NMS is applied instead of NMS in SSD to reduce the number of bounding boxes gently. The proposed method is validated on MSCOCO 2017, PASCAL VOC 2007, and PASCAL VOC 2012 datasets and compared to existing state-of-the-art techniques. Our method shows better detection quality in terms of mean Average Precision (mAP).

In digital image processing, image improvement is an essential step. In this paper, Gaussian, S-shaped and Z-shaped membership functions based on Type-II are used to enhance the image of the fruit. The issue of over and under enhancement can be encountered by fuzzy techniques. Type-II fuzzy has the ability to solve this problem. The suggested approach was applied to photographs of red apple orange and watermelon fruit and found that analytical results were stronger.

Medical images form a vital part of a patient’s record in medical centers. Medical imaging devices generate data with huge memory requirements. Medical image compression is mandatory for storage and communication of medical data for the purpose of diagnosis. Compression removes the extraneous and redundant data in an image to reduce the storage cost as well as data transmission cost. Compression involves removing coding, interpixel or psychovisual redundancy in an image and, at the same time, retaining the integrity of the information required for the diagnosis in medical images. Lossless compression assures exact reconstruction of the original image after decompressing it and provides greater quality but lesser compression ratio. This paper presents two approaches for lossless compression of medical images. In the first approach, Huffman coding is implemented directly on medical images, whereas in the second approach, differential coding is applied on medical images before implementing Huffman coding. Experimental results show that differential Huffman coding improves the compression ratio.

In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. Detection and tracking includes extraction of moving object from frames and continuous tracking it thereafter forming persistent object trajectories over time. There are some really smart techniques proposed by researchers for efficient and robust detection or tracking of objects in videos. A comprehensive coverage of such innovative techniques for which solutions have been motivated by theories of soft computing approaches is proposed. The main objective of this research investigation is to study and highlight efforts of researchers who had conducted some brilliant work on soft computing based detection and tracking approaches in video sequence. The study is novel as it traces rise of soft computing methods in field of object detection and tracking in videos which has been neglected over the years. The survey is compilation of studies on neural network, deep learning, fuzzy logic, evolutionary algorithms, hybrid and recent innovative approaches that have been applied to field of detection and tracking. The paper also highlights benchmark datasets available to researchers for experimentation and validation of their own algorithms. Major research challenges in the field of detection and tracking along with some recommendations are also provided. The paper provides number of analyses to guide future directions of research and advocates for more applications of soft computing approaches for object detection and tracking approaches in videos. The paper is targeted at young researchers who will like to see it as platform for introduction to a mature and relatively complex field. The study will be helpful in appropriate use of an existing method for systematically designing a new approach or improving performance of existing approaches.

Background subtraction (BS) is one of most commonly used methods for detection of moving objects in videos that works by subtracting current frame from a background frame. Effective background modeling and threshold plays a crucial role in BS and can govern accuracy and preciseness of object boundaries. This paper proposes a fuzzy entropy based approach modified BS algorithm for moving object detection with Kalman tracker. The standard BS method has been enhanced using concept of fuzzy 2-partition entropy and big bang big crunch optimization (BBBCO). BBBCO has been used to enhance standard BS algorithm for extracting various parameters required in BS algorithm by framing the problem of parameters detection as optimization problem which is solved using concept of fuzzy partition entropy. The proposed algorithm generates optimal threshold values along with various other measures for background modeling. The detected objects are further tracked using Kalman filter based tracker. The evaluation of proposed method has been done on videos from benchmark datasets and statistical parameters have been calculated. The method is also compared with standard BS and another recent study in the field. The results show promise of the proposed method.

Enhancement of hazy images or video is challenging task because of low contrast exhibited in them. Global contrast stretching methods have been successful in restoring contrasts but problems like overcompensation, truncation of pixel values amounting to loss of information tends to creep in. Artifacts may be introduced and images may loose its colorfulness. This paper presents an evolutionary enhancement method for restoring contrast in images or videos while preserving its colorfulness and brightness. The study proposes a novel histogram equalization method inspired by principles of water cycle algorithm. The proposed method first smoothes Y channel of YCbCr color space and divides input frame into two components using Otsu's 2D thresholding. A set of weighing constraints have been formulated and applied to both components individually in a controlled manner. Water cycle algorithm has been employed to exploit an optimal value of weighing factors for enforcement of constraints on individual components. A three dimension objective function has been designed to suitably perform equalization and control enhancement process. Experimental results show that proposed method is effective in removing haze like patterns in images and videos.

Background subtraction (BS) is one of the most commonly used methods for detecting moving objects in videos. In this task, moving objectpixels are extracted by subtracting the current frame from a background frame. The obtained difference is compared against a threshold value to classify pixels as belonging to the foreground or background regions. The threshold plays a crucial role in this categorization and can impact the accuracy and preciseness of the object boundaries obtained by the BS algorithm. This paper proposes an approach for enhancing and optimizing the performance of the standard BS algorithm. This approach uses the concept of fuzzy 2-partition entropy and Big Bang–Big Crunch Optimization (BBBCO). BBBCO is a recently proposed evolutionary optimization approach for providing solutions to problems operating on multiple variables within prescribed constraints. BBBCO enhances the standard BS algorithm by framing the problem of parameter detection for BS as an optimization problem, which is solved using the concept of fuzzy 2-partition entropy. The proposed method is evaluated using videos from benchmark datasets and a number of statistical metrics. The method is also compared with standard BS and another recently proposed method. The results show the promise of the proposed method.

Image segmentation has been used widely for detection and extraction of objects in digital images. Thresholding is one of the effective image segmentation techniques. Fuzzy 2-partition entropy is used effectively for the selection of threshold value. Fuzzy 2-partition entropy required the optimization of some parameters for the selection of threshold value. Teaching-Learning-Based Optimization (TLBO) algorithm has been applied on fuzzy 2-partition entropy to find these parameters and subsequent threshold value. Normally, fuzzy 2-partition Shannon entropy is used for thresholding. In the proposed research work, fuzzy 2-partition Kapur entropy is explored for the evaluation of its potential for selecting optimal threshold value using TLBO algorithm. The present work measures the performance comparison of fuzzy 2-partition Kapur entropy using TLBO with fuzzy 2-partition Shannon entropy using TLBO. The experiments are carried out on standard test images from benchmark dataset. Peak signal to noise ratio, uniformity and structural similarity index are the three different measures which have been used to compare the performance. Results demonstrate that the performance of fuzzy 2-partition Kapur entropy using TLBO is quite promising.

Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

Growth of mobile-based applications and traffic on web has made the role of schedulers in communication networks very challenging. The continuous pressure asserted by these applications is so large that even versatile WiMAX networks are facing stiff challenges in fair distribution of resources to these real- and non-real-time applications. Maintenance of fairness and flagrant quality of service levels among various applications is only possible with adaptive and intelligent scheduling mechanism. Design of such system is only possible if artificial intelligence is embedded in base station scheduler. This paper proposes mechanism for allocation of resources in WiMAX networks utilizing fuzzy logic principles. The proposed mechanism guarantees that latency and throughput requirements of real- and non-real-time traffic classes are met. Variations in incoming traffic have been exploited and used in decision making for offering bandwidth to maintain effective performance levels for all traffic classes. The proposed scheduler considers requests from subscribers and exploits powers of fuzzy logic to extract new weights for queues serving various traffic classes in a WiMAX network on the basis of most recent values of latency, throughput and share of traffic. The scheduling framework has been rigorously tested by performing versatile experiments for ensuring quality of service levels under hard hitting conditions. Fairness of scheduler has also been explored and results obtained are quite promising.

The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO) is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA)-based, Biogeography-based Optimization (BBO)-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.

Many organizations like hospitals for telemedicine, journalism for live-telecast and academias are using a service video-on-demand for delivering the lectures and research contents to the remote locations across the globe. The videos to be broadcasted are time and resource consuming due to the large amount of data and due to these constraints, for getting fast access over Internet and mobile devices, such video applications need to be compressed into another format. The usage of videos is occasional so to save huge infrastructure cost and time, the Infrastructure as a Service (IaaS) Cloud systems can be leveraged. In this paper, an attempt has been made to design, implement and optimize the performance of Digital Video to MPEG4 transcoding in the Cloud environment using Meghdoot (an Open-Source Cloud stack). The classical MapReduce approach is used to rationalize the use of resources by exploring on demand computing and performs parallel video conversion thereby reducing the video encoding times. Experimental results point out to suitability of better performance that by varying the technique of splitting the video file size of fragments that is through Mencoder and through default Hadoop Splitting. The comparison of both the systems to get the best compression times will help us to optimize the Cloud resources that further helps in trade-off between time, cost and quality.

Segmentation of an image is used to separate the image into several significant parts based on properties of discontinuity and similarity. Segmentation of an image is generally done with the help of thresholding technique. Thresholding is used to turn an image from gray scale to binary. The selection of suitable threshold value in the image is a challenging task. Thresholding value depends upon the randomness of intensity distribution of the image. Entropy is a parameter that is used to measure the randomness of intensity distribution of the image. In this work, Shannon-entropy-based and Non-Shannon (Renyi, Collision and Min) entropy-based approaches are used to select suitable threshold value. After this, thresholding values obtained from different approaches are tested on 6 standard test images. For evaluating, peak signal-to-noise ratio (PSNR) and uniformity (U) parameters are used. From the results, it is observed that Renyi-entropy-based approach is a better approach than other approaches.

Mammography is the most reliable, effective, low cost and high sensitive method for early detection of breast cancer. In breast cancer diagnosis, it is difficult for radiologists to detect the typical diagnostic signs because mammograms are low contrast and noisy images. In order to improve diagnosis accuracy, mammogram contrast enhancement technique is often used to enhance the contrast of mammogram and aid the radiologists. In this paper, a combined approach with fuzzy and wavelet towards improved enhancement of details and subtle features in digital mammogram images. The proposed contrast enhancement approach utilizes wavelet transform to decompose the mammogram, and then approximation coefficients are modified by fuzzy contrast enhancement approach and detail coefficients are by non-linear transformation. After that, inverse wavelet transform is applied on modified coefficients to obtain the enhanced mammogram. The proposed algorithm has been tested on a number of images in Digital Database for Screening Mammography (DDSM), comparing the results with histogram equalization which is a well-established image enhancement technique and Cheng’s enhancement algorithm based on wavelet transform. In order to accurately assess the proposed approach, enhanced mammogram images are evaluated through objective image quality assessment parameters: information entropy, contrast and peak signal-to-noise ratio (PSNR). Experimental results of the proposed approach are quite promising.

Images taken in poor environmental conditions decrease the visibility and hidden information of digital images. Therefore, image enhancement techniques are necessary for improving the significant details of these images. An extensive review has shown that histogram-based enhancement techniques greatly suffer from over/under enhancement issues. Fuzzy-based enhancement techniques suffer from over/under saturated pixels problems. In this paper, a novel Type-II fuzzy-based image enhancement technique has been proposed for improving the visibility of images. The Type-II fuzzy logic can automatically extract the local atmospheric light and roughly eliminate the atmospheric veil in local detail enhancement. The proposed technique has been evaluated on 10 well-known weather degraded color images and is also compared with four well-known existing image enhancement techniques. The experimental results reveal that the proposed technique outperforms others regarding visible edge ratio, color gradients and number of saturated pixels.

This paper addresses p-processes single processor scheduling problem with a common deadline, to minimize the total execution time and reduce the penalty costs. Process scheduling is one of the most essential factor on which the efficiency and the performance of the work done by the CPU depends. Earliness and tardiness of the processes degrades the efficiency of the processor as they carry penalty costs with them. Thus, the scheduling problem of minimizing the total sum of earliness and tardiness with a common deadline on a single processor is important and competitive. Scheduling is particularly one of the subset of combinatorial optimization problems, which are in fact NP-Hard problems. This problem can be solved using heuristic and meta-heuristic approach such as genetic algorithm for optimal results. In the experiment performed, the Genetic Algorithm outperforms the results in comparison with a simple heuristic method.