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The humankind had faced several pandemic outbreaks, and coronavirus illness (COVID-19) caused by severe, acute respiratory syndrome coronavirus 2, is designated an emergency by the World Health Organization (WHO). Recognition of COVID-19 is a challenging task. The most commonly used methods are X-ray and CT scans images to inspect COVID-19 patients. It requires specialised medical professionals to report each patient's health manually. It is found that COVID-19 shows considerable similarity to pneumonia lung disease. Thus, knowledge learned from a model to diagnose pneumonia can be translated to identify COVID-19. Transfer learning method offers a drastic performance when compared with results from conventional classification. In this study, Image pre-processing is done to alleviate intensity variations between medical images. These processed images undergo a feature extraction which is accomplished using Q-deformed entropy and deep learning extraction. The feature extraction techniques are employed to remove abnormal markers from images, noise impedance from tissues and lesions. The traits acquired are integrated to differentiate between COVID-19, pneumonia and healthy cases. The primary aim of this model is to produce an image processing tool for medical professionals. The model results to inspect how a healthy or COVID-19 individual outperforms conventional models. The maximum accuracy of the collected data set is 99.68%. [ABSTRACT FROM AUTHOR]

Wireless nodes are one of the main components in different applications that are offered in a smart city. These wireless nodes are responsible to execute multiple tasks with different priority levels. As the wireless nodes have limited processing capacity, they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity. Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications. This execution delay is reduced by placing fog computing nodes near these application nodes. A fog node has limited processing capacity and is sometimes unable to execute all the requested tasks. In this work, an optimal task offloading scheme that comprises two algorithms is proposed for the fog nodes to optimally execute the time-sensitive offloaded tasks. The first algorithm describes the task processing criteria for local computation of tasks at the fog nodes and remote computation at the cloud server. The second algorithm allows fog nodes to optimally scrutinize the most sensitive tasks within their task capacity. The results show that the proposed task execution scheme significantly reduces the execution time and most of the time-sensitive tasks are executed. [ABSTRACT FROM AUTHOR]

Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment. The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection. The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing. The proposed approach outperformed considered algorithms in terms of accuracy. Additionally, we augmented the newly created dataset to make it balanced to increase performance. A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm. It shows that augmentation improves the overall accuracy of tweet analysis. After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVM with 10-fold cross-validation significantly improved compared to the primary dataset. [ABSTRACT FROM AUTHOR]

We study the capacitated vehicle routing problem (CVRP) which is a well-known NP-hard combinatorial optimization problem (COP). The aim of the problem is to serve different customers by a convoy of vehicles starting from a depot so that sum of the routing costs under their capacity constraints is minimized. Since the problem is very complicated, solving the problem using exact methods is almost impossible. So, one has to go for the heuristic/metaheuristic methods and genetic algorithm (GA) is broadly applied metaheuristic method to obtain near optimal solution to such COPs. So, this paper studies GAs to find solution to the problem. Generally, to solve a COP, GAs start with a chromosome set named initial population, and then mainly three operators-selection, crossover and mutation, are applied. Among these three operators, crossover is very crucial in designing and implementing GAs, and hence, numerous crossover operators were developed and applied to different COPs. There are two major kinds of crossover operators-blind crossovers and distance-based crossovers. We intend to compare the performance of four blind crossover and four distance-based crossover operators to test the suitability of the operators to solve the CVRP. These operators were originally proposed for the standard travelling salesman problem (TSP). First, these eight crossovers are illustrated using same parent chromosomes for building offspring(s). Then eight GAs using these eight crossover operators without any mutation operator and another eight GAs using these eight crossover operators with a mutation operator are developed. These GAs are experimented on some benchmark asymmetric and symmetric instances of numerous sizes and various number of vehicles. Our study revealed that the distance-based crossovers are much superior to the blind crossovers. Further, we observed that the sequential constructive crossover with and without mutation operator is the best one for the CVRP. This estimation is validated by Student’s t-test at 95% confidence level. We further determined a comparative rank of the eight crossovers for the CVRP. [ABSTRACT FROM AUTHOR]

Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to expansions, several new clustering methods and extensions have been proposed in recent years. ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them. This paper aims to modify the algorithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures. The parameterized ROCK algorithm is the name given to the modified algorithm (P-ROCK). The proposed modification makes the original algorithm more flexible by using user-defined parameters. A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm. A comparison with the original ROCK algorithm is also provided. Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%. The proposed P-ROCK algorithm has improved the runtime and is more flexible and scalable. [ABSTRACT FROM AUTHOR]

Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with adequate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%. [ABSTRACT FROM AUTHOR]

Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI) technologies are widely used in medical field. Within the last few months, due to the increased use of CT scans, millions of patients have had their CT scans done. So, as a result, images showing the Corona Virus for diagnostic purposes were digitally transmitted over the internet. The major problem for the world health care system is a multitude of attacks that affect copyright protection and other ethical issues as images are transmitted over the internet. As a result, it is important to apply a robust and secure watermarking technique to these images. Notably, watermarking schemes have been developed for various image formats, including .jpg, .bmp, and .png, but their impact on NIfTI (Neuroimaging Informatics Technology Initiative) images is not noteworthy. A watermarking scheme based on the Lifting Wavelet Transform (LWT) and QR factorization is presented in this paper. When LWT and QR are combined, the NIfTI image maintains its inherent sensitivity and mitigates the watermarking scheme's robustness. Multiple watermarks are added to the host image in this approach. Measuring the performance of the graphics card is done by using PSNR, SSIM, Q (a formula which measures image quality), SNR, and Normalized correlation. The watermarking scheme withstands a variety of noise attacks and conversions, including image compression and decompression. [ABSTRACT FROM AUTHOR]

Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compression using neural network, fuzzy logic and neurofuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments. [ABSTRACT FROM AUTHOR]