In the last two decades, illicit activities have dramatically increased on the Dark Web. Every year, Dark Web witnesses establishing new markets, in which administrators, vendors, and consumers aim to illegal acquisition and consumption. On the other hand, this rapid growth makes it quite difficult for law and security agencies to detect and investigate all those activities with manual analyses. In this paper, we introduce our approach of utilizing data mining techniques to produce useful patterns from a dark web market contents. We start from a brief description of the methodology on which the research stands, then we present the system modules that perform three basic missions: crawling and extracting the entire market data, data pre-processing, and data mining. The data mining methods include generating Association Rules from products’ titles, and from the generated rules, we infer conceptual compositions vendors use when promoting their products. Clustering is the second mining aspect, where the system clusters vendors and products. From the generated clusters, we discuss the common characteristics among clustered objects, find the Top Vendors, and analyze products promoted by the latter, in addition to the most viewed and sold items on the market. Overall, this approach helps in placing a dark website under investigation.

Producing skilled workforce according to industry required skills is quite challenging. Knowledge of trainee’s enrollment behavior and trainee’s course selection variables can help to address this issue. Prior knowledge of both can help to plan and target right geographic locations and right audience to produce industry required skilled workforce. Globally Technical and Vocational Education Training (TVET) is used to provide skilled workforce for the industry. TVET is an educational stream which focus learning through more practicing with less theory knowledge.
In this article, we have analyzed TVET actual enrollment data of 2017 – 2018 session from a TVET training provider organization of Punjab, Pakistan. The purpose of this analysis is to understand trainee’s enrollment behavior and course selection variables which plays an important role in TVET course selection by the trainees. This enrollment behavior and course selection variables can be used to monitor and control industry required and produced skilled TVET workforce. We developed a framework which contain series of steps to perform this analysis to extract knowledge. We used educational data mining techniques of association, clustering and classification to extract knowledge. The analysis reveals that central Punjab youth is getting more TVET education as compare to south and north Punjab, Pakistan. Similarly, trainee’s ‘age group’, ‘qualification’, ‘gender’, ‘religion’ and ‘marital status’ are potential variables which can play important role in TVET course selection. By controlling these variables and integrating TVET training provider institutes, funding agencies and industry, we can smartly produce TVET skilled workforce required for industry nationally and internationally.

Application of Education Management Information System for administering school academic activities is widely recognized as an essential tool of improving quality of education for sustainable development. However, in developing countries including Tanzania, most secondary schools use manual system for collecting, storing and disseminating education information. The Manual system limits schools to have accurately, timely and reliable dissemination of education information. Moreover, when parents want to monitor student’s academic progress, the manual system requires them to visit schools physically and sometimes to wait until the end of the terminal and annual examination to get student academic report. Social and economic activities are one of the factors which limit parents to monitor student’s academic progress effectively. Poor parental involvement for monitoring and tracking student’s academic progress leads to poor student academic achievement. To address the solution, the study used structured interview and questionnaires to collect data from secondary schools education stakeholder. The collected data was analyzed using Pandas Python data analysis package. Findings from the study revealed that, poor student academic achievement in Tanzanian secondary schools is being caused by poor parental involvement in monitoring and tracking student’s academic progress. However, the study developed and implemented a centralized Education Management Information System for enhancing parental involvement in monitoring and tracking student’s academic progress. The significance of this study was to enhance parental involvement for student academic achievement by improving delivery of quality education for sustainable development.

Process scheduling is considered as a momentous and instinct task accomplished by operating system. Round robin is one of the extensively utilized algorithms for scheduling. Various noticeable scheduling algorithms based on round robin strategy have been introduced in last decade. The most sensitive issue of round robin algorithm is time quantum because it determines and controls the time of achieving resources for a process during execution. Different types of approaches are available for determining time quantum related to round robin. This paper represents a new round robin algorithm having proficient time quantum that has been determined by considering the maximum difference among differences of adjacent consecutive processes into the ready queue. The proposed methodology is an endeavor to increase the outcomes of round robin as well as system performance. The algorithm is experimentally and comparatively better than the mentioned round robin algorithms in this paper. From the consideration against the referred algorithms, it decreases average turn-around-time, average waiting-time and the number of context-switching along with other CPU scheduling criteria.

Web Usage Mining provides efficient ways of mining the web logs for knowing the user’s behavioral patterns. Existing literature have discussed about mining frequent pages of web logs by different means. Instead of mining all the frequently visited pages, if the criterion for mining frequent pages is based on a weighted setting then the compilation time and storage space would reduce. Hence in the proposed work, mining is performed by assigning weights to web pages based on two criteria. One is the time dwelled by a visitor on a particular page and the other is based on recent access of those pages. The proposed Weighted Window Tree (WWT) method performs Weighted Association Rule mining (WARM) for discovering the recently accessed frequent pages from web logs where the user has dwelled for more time and hence proves that these pages are more informative. WARM’s significance is in page weight assignment for targeting essential pages which has an advantage of mining lesser quality rules.

In the present era of miniaturization, higher power dissipation in form of heat has become a very critical issue for the digital Circuits. This excessive heat may result in the lower chip reliability and even destroy it. Due to this reason a substitute is required for the traditional CMOS technology, Reversible logic is a paradigm in this direction. This paper encompasses of the newly proposed SA reversible logic and basic combinational implementations using a single SA building block only resulting in lower circuit level complexity as well as hardware requirement. The output responses and energy dissipation of proposed SA reversible logic are verified and calculated with the help of QCADesigner and QCADesigner-E simulation tools respectively.