Ensuring the aging resilient software design can be of paramount significance to enable faultless software system. Particularly assessing reusability extent of the software components can enable efficient software design. The probability of aging proneness can be characterized based on key OO-SM like cohesion, coupling and complexity of a software component. In this paper, aging resilient software reusability prediction model is proposed for object oriented design based Web of Service (WoS) software systems. This work introduces multilevel optimization to accomplish a novel reusability prediction model. Considering coupling, cohesion and complexity as the software characteristics to signify aging proneness, six CK metrics; WMC, CBO, DIT, LCOM, NOC, and RFC are obtained from 100 WoS software. The extracted CK metrics are processed for min–max normalization that alleviates data-unbalancing and hence avoids saturation during learning. The 10-fold Cross-validation followed by outlier detection is considered to enrich data quality for further feature extraction. To reduce computational overheads RSA algorithm is applied. SoftAudit tool is applied to estimate reusability of each class, while binary ULR estimates calculates (reuse proneness) threshold. Applying different classification algorithms such as LM, ANN algorithms, ELM, and evolutionary computing enriched ANN reuse-proneness prediction has been done. The performance assessment affirms that AGA based ANN model outperforms other techniques and hence can be used for earlier aging-resilient reusability optimization for WoS software design.

Enhanced evolutionary computing based artificial intelligence model for web-solutions software reusability estimation

Security policy speculation of user uploaded images on content sharing sites

In today's world, large group migration of applications to the fog computing is registered in the information technology world. The main issue in fog computing is providing enhanced quality of service (QoS). QoS management consists of various method used for allocating fog‐user applications in the virtual environment and selecting suitable method for allocating virtual resources to physical resource. The resources allocation in effective manner in the fog environment is also a major problem in fog computing; it occurs when the infrastructure is build using light‐weight computing devices. In this article, the allocation of task and placement of virtual machine problems is explained in the single fog computing environment. The experiment is done and the result shows that the proposed framework improves QoS in fog environment.

Improving quality‐of‐service in fog computing through efficient resource allocation

This paper intends to perform de-duplication for enhancing the storage optimization. Hence, this paper contributes by proposing a hybrid fingerprint extracting using simhash (SH) and Huffman coding (HC) algorithms. Secondly, the data is clustered using the latest technique called as grey wolf optimization (GWO) to extract the metadata. The extracted metadata is stored in metadata server which provides better storage optimization and de-duplication. Euclidean distance based GWO is adopted as it provides minimum Euclidean distance in the GWO based clustering for de-duplication. The proposed GWO based clustering method is compared with the existing methods such as k-means, k-mode, Euclidean distance based Particle Swarm Optimization and Euclidean distance based genetic algorithm in terms of accuracy, True Positive Rate (TPR), True Negative Rate (TNR) and performance time and the significance of the GWO based clustering method is described.

Grey wolf optimization based clustering of hybrid fingerprint for efficient de-duplication

The development of encryption and decryption plays a vital role in the field of security. Recently, Chaos-based security has suggested the reliable and efficient way of securing the images. In this paper, Self-Adaptive Grey Wolf Optimization (GWO) is proposed for optimizing 2-dimensional Logistic Chaotic Mapping (2DCM). Further, the security analysis of the proposed method is performed using different comparison such as key sensitivity, histogram analysis, adjacent pixel autocorrelation, information entropy, attacks, quality of encryption, Chi square test etc. Moreover, analytical outcomes are compared with the conventional algorithms like standard encryption and decryption, Genetic Algorithm (GA) and GWO. The experimental result stated that the proposed method is key sensitive and opposed to the general attacks during the encryption and decryption of images.

https://link.springer.com/content/pdf/10.1007%2Fs12065-018-0159-z.pdf

Medical image security enhancement using two dimensional chaotic mapping optimized by self-adaptive grey wolf algorithm

Software development has nowadays evolved into a extreme change that uses the best modules being run in various closed and open source software. Extracting the best component and fit them into an ongoing component based development software poses great challenges to run the software error free with desired outcomes. In this proposal we have introduced an `Intelligent Risk Analysis Model (IRAM) for reusability in developing component service oriented software, which are developed using eminent object oriented programming language paradigm Java. This model performs surfing operation into Java object oriented programming modules warehouse, which consists of several availability modules of various projects, and gives a list of suitable module. Those modules are tested on cohesion /coupling testing analysis to determine the individual strength and binding capacity of modules before performing Regression test. Final desirable outcomes are tested on performing regression test while integrating successful level module with domain being developed and proper deployment is made on successful expected outcome of the product. Risk analysis of software project is analyzed for transformation of reusability components risk before transformation Analysis, adaptable risk Analysis, and reusability risk Analysis for package in Java programming. In this paper IRAM model to focus Java package the domain and size of package, integration and dependency relationship of package, measuring the coupling and cohesion in risk metric for development and implementation of reusability package without any risk factor.

Coupling and cohesion metrics in Java for adaptive reusability risk reduction

Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis on water management techniques that are applied across various categories of the applications. Keeping in mind the population density index, there is a dire need to implement intelligent water management mechanisms for effective distribution, conservation and to maintain the water quality standards for various purposes. The prescribed work discusses about few major areas of applications that are required for efficient water management. Those are recent trends in wastewater recycle, water distribution, rainwater harvesting and irrigation management using various Artificial Intelligence (AI) models. The data acquired for these applications are purely unique and also differs by type. Hence, there is a dire need to use a model or algorithm that can be applied to provide solutions across all these applications. Artificial Intelligence (AI) and Deep Learning (DL) techniques along with the Internet of things (IoT) framework can facilitate in designing a smart water management system for sustainable water usage from natural resources. This work surveys various water management techniques and the use of AI/DL along with the IoT network and case studies, sample statistical analysis to develop an efficient water management framework.

/pdf/smart-water-resource-management-using-artificial-3rxw2qii.pdf

Smart Water Resource Management Using Artificial Intelligence—A Review

In today’s quandary, the effort required to prevent the disease occurrence should be more as compared to the methodologies followed for identifying it. In this paper list down the different number of diseases are affected in distinct regions are extracted and by fixing disease based coupling and cohesion range metric the severity level of each disease and its dependence level will be confirmed. By preparing the coupling and cohesion metrics, additional tests are performed to measure its updating as the value is ‘1’ then the disease severity level will be very high otherwise consider as low.

M. Iyapparaja

Papers

Security policy speculation of user uploaded images on content sharing sites

Coupling and cohesion metrics in Java for adaptive reusability risk reduction

Smart Water Resource Management Using Artificial Intelligence—A Review

Metrics Based Evaluation for Disease Affection in Distinct Cities

Augmenting SCA project management and automation framework