On cloud security requirements, threats, vulnerabilities and countermeasures: A survey

A systematic literature review is presented that surveyed the topic of cloud testing over the period 2012--2017. Cloud testing can refer either to testing cloud-based systems (testing of the cloud) or to leveraging the cloud for testing purposes (testing in the cloud): both approaches (and their combination into testing of the cloud in the cloud) have drawn research interest. An extensive paper search was conducted by both automated query of popular digital libraries and snowballing, which resulted in the final selection of 147 primary studies. Along the survey, a framework has been incrementally derived that classifies cloud testing research among six main areas and their topics. The article includes a detailed analysis of the selected primary studies to identify trends and gaps, as well as an extensive report of the state-of-the-art as it emerges by answering the identified Research Questions. We find that cloud testing is an active research field, although not all topics have received enough attention and conclude by presenting the most relevant open research challenges for each area of the classification framework.

/pdf/a-systematic-review-on-cloud-testing-9ta2209gsb.pdf

A Systematic Review on Cloud Testing

The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detection system is essential for accelerating crop diagnosis. Using machine learning and image processing, this paper describes a framework for detecting leaf illness. An image of a leaf can be used as an input for this framework. To begin, leaf photographs are preprocessed in order to remove noise from their images. The mean filter is used to filter out background noise. Histogram equalization is used to enhance the quality of the image. The division of a single image into multiple portions or segments is referred to as segmentation in photography. It assists in establishing the boundaries of the image. Segmenting the image is accomplished using the K-Means approach. Feature extraction is carried by using the principal component analysis. Following that, images are categorized using techniques such as RBF-SVM, SVM, random forest, and ID3.

Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection

Context: Software has become more complicated, dynamic, and asynchronous than ever, making testing more challenging. With the increasing interest in the development of cloud computing, and increasing demand for cloud-based services, it has become essential to systematically review the research in the area of software testing in the context of cloud environments. Objective: The purpose of this systematic mapping study is to provide an overview of the empirical research in the area of software cloud-based testing, in order to build a classification scheme. We investigate functional and non-functional testing methods, the application of these methods, and the purpose of testing using these methods. Method: We searched for electronically available papers in order to find relevant literature and to extract and analyze data about the methods used. Result: We identified 69 primary studies reported in 75 research papers published in academic journals, conferences, and edited books. Conclusion: We found that only a minority of the studies combine rigorous statistical analysis with quantitative results. The majority of the considered studies present early results, using a single experiment to evaluate their proposed solution.

/pdf/a-systematic-mapping-study-of-empirical-studies-on-software-9v4x2nrpx2.pdf

A Systematic Mapping Study of Empirical Studies on Software Cloud Testing Methods

Many approaches for crop yield prediction were analyzed by countries using remote sensing data, but the information obtained was less successful due to insufficient data gathered due to climatic variables and poor image resolution. As a result, current crop yield estimation methods are obsolete and no longer useful. Several attempts have been made to overcome these difficulties by combining high precision remote sensing images. Furthermore, such remote sensing-based working models are better suited to extraterrestrial farmers and homogeneous agricultural areas. The development of this innovative framework was prompted by a scarcity of high-quality satellite imagery. This intelligent strategy is based on a new theoretical framework that employs the energy equation to improve crop yield predictions. This method was used to collect input from multiple farmers in order to validate the observation. The proposed technique’s excellent reliability on crop yield prediction is compared and contrasted between crop yield prediction and actual production in different areas, and meaningful observations are provided.

/pdf/multisensor-data-and-cross-validation-technique-for-merging-2ahiw6br.pdf

Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System

One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not.

Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System

https://cyberleninka.org/article/n/1197745.pdf

Cloud Testing Tools and its Challenges: A Comparative Study

Big Data is already there and developers and analytics are working with it using several support upcoming frameworks and technologies. The storage and retrieval systems, the access layers and processes for Big Data are evolving day by day. Test Architects and Testing teams are not excluded in this Big scenario. This literature focuses on some of the challenges test teams would be facing in the near future. Two specialized testings are considered to learn the intricacies, and thoughts on Big Test Data Management are also presented.

http://ieeexplore.ieee.org/iel7/6715259/6726462/06726822.pdf

Getting ready for BigData testing: A practitioner's perception

https://doi.org/10.1016/j.measurement.2022.111054

Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence

Wireless sensor networks are extensively utilized as a communication mechanism in the field of the Internet of Things (IoT). Along with these services, numerous IoT based applications need stabilized transmission or delivery over unbalanced wireless connections. To ensure the stability of data packets delivery, prevailing works exploit diverse geographical routing with multi-hop forwarders in WSNs. Furthermore, critical Denial of Service (DoS) attacks frequently has an impact on these techniques, where an enormous amount of invalid data starts replicating and transmitted to receivers to prevent Wireless Sensor Networks (WSN) communication. In this investigation, a novel adaptive endorsement method is designed by combining dimensionality reduction based Hilbert-Huang Transformation (DR-HHT) and authentication trust mode (ATM). DR-HHT and ATM defend against the severity of DoS attacks, by fulfilling trust, reliability, stability requirements. ATM also examines the state information (SI) of nodes in wireless links; this SI leverages the performance of ATM to enhance data delivery effectually. Dissimilar to existing routing protocols, DR-HHT and ATM guarantee data integrity by building Kolmogorov_Smirnov based authentication algorithms. Concerning the correlation coefficient, this model isolates DoS attacks and diminishes computational cost. This strategy also eliminates duplicate data transmission and redundant information, offering an effectual trust-based evaluation model from adaptive authentication. Extensive simulation demonstrates that the anticipated model shows a better trade-off than the prevailing techniques and the simulation is carried out in a MATLAB environment. 

S. Nachiyappan

Papers

Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System

Cloud Testing Tools and its Challenges: A Comparative Study

Getting ready for BigData testing: A practitioner's perception

Integrating bio medical sensors in detecting hidden signatures of COVID-19 with Artificial intelligence

Novel DoS Attack Detection Based on Trust Mode Authentication for IoT