The extension of the sensor node's life span is an essential requirement in a Wireless Sensor Network. Cluster head selection algorithms undertake the task of cluster head election and rotation among nodes, and this has significant effects on the network's energy consumption. The objective of this paper is to analyze existing cluster head selection algorithms and the parameters they implement to enhance energy efficiency. To achieve this objective, systematic literature review methodology was used. Relevant papers were extracted from major academic databases Elsevier, Springer, Wiley, IEEE, ACM Digital Library, Citeseer Library, and preprints posted on arXiv. The results show that there are many existing Cluster Head Selection Algorithms and they are categorized as deterministic, adaptive and hybrid. These algorithms use different parameters to elect Cluster Heads. In future the researchers should derive more parameters that can be used to elect cluster heads to improve on energy consumption.

Many front-end web developers are nowadays increasingly using sassy cascading stylesheets (SCSS) instead of the regular cascading style sheets (CSS). Despite its increased demand, SCSS has inherent complexity which arises from its features such as the use of nesting, inheritance, variables, operators, and functions. In addition, SCSS complexity, like all other software, continually increases with age. High complexity is undesirable because it leads to software that is difficult to understand, modify and test. Although there has been some metrics proposed to measure stylesheets complexity, these were defined in the context of regular CSS, and cannot be used to measure SCSS due to differences in their syntax. This paper proposes four metrics for measuring the complexity of SCSS code. The metrics have been used to calculate the complexity of three code snippets and three real-world projects and were found to be intuitional. The metrics were also evaluated using the Kaner framework and satisfied all the evaluation questions, indicating that they are sufficiently practical as required in the industry. In addition, the metrics were evaluated using Weyuker's properties, and results show that all the four metrics satisfied seven out of the nine properties, implying that they are theoretically sound.

The paper presents feature extraction methods and classification algorithms used to classify maize leaf disease images. From maize disease images, features are extracted and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction method to see which feature extraction method performs best with image classification algorithms. Based on the evaluation, the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The random forest classifier emerged the best in terms of image classification, based on four performance metrics which are accuracy, precision, recall, and F1-score. The experimental outcome indicated that the random forest had 0.74 accuracy, 0.77 precision, 0.77 recall, and 0.75 F1-score.

The paper presents feature extraction methods and classification algorithms used to classify maize leaf disease images. From maize disease images, features are extracted and passed to the machine learning classification algorithm to identify the possible disease based on the features detected using the feature extraction method. The maize disease images used include images of common rust, leaf spot, and northern leaf blight and healthy images. An evaluation was done for the feature extraction method to see which feature extraction method performs best with image classification algorithms. Based on the evaluation, the outcomes revealed Histogram of Oriented Gradients performed best with classifiers compared to KAZE and Oriented FAST and rotated BRIEF. The random forest classifier emerged the best in terms of image classification, based on four performance metrics which are accuracy, precision, recall, and F1-score. The experimental outcome indicated that the random forest had 0.74 accuracy, 0.77 precision, 0.77 recall, and 0.75 F1-score.

Modern organizations are adopting new ways of measuring their level of security for compliance and justification of security investments. The highly interconnected environment has seen organizations generate lots of personal information and sensitive organizational data. Easiness in automation provided by open-source enterprise resource planning (ERP) software has accelerated its acceptability. The study aimed at developing a security measurement framework for open-source ERP software. The motivation was twofold: paradigm shift towards open-source ERP software and the need for justified investment on information security. Product quality evaluation method based on ISO 25010 framework guided the selection of attributes and factors. A security measurement framework with security posture at the highest level, attributes and factors was developed presenting a mechanism for assessing organization’s level of security. Security posture promotes customers’ confidence and gives management means to leverage resources for information security investment. The future work includes definition of metrics based on the framework.