Higher education institutions are catching up on their high competition and challenges are in their analysis productivity. The major challenge is to monitor and analyze student progress through learning outcomes in the curriculum. One of the approaches is the outcome-based education (OBE) model to deal with learning outcomes. OBE is an integral part of higher education institutions. The OBE system is a key step for accreditation in engineering education. OBE focuses on a student-centered approach. The OBE is not restricted to welldefined teaching strategies or direct evaluations but also encompasses indirect evaluations to help students achieve the intended outcomes. In this investigation, engineering students' data have been analyzed forming three distinct clusters to group students according to best, average, and worst achievement of learning outcomes in two different computer engineering courses generally taught in the early semesters in higher education institutions. A data mining clustering approach is used to segment students using k-means and k-medoids techniques. Clustering can be regarded as a data modeling technique that provides summary data that interact with multiple disciplines and plays an important role in a wide range of computer applications. The investigation comprises of two parts for analysis: one part of the analysis is the mid-term and final exam scores, the quiz and assignment results, the laboratory results, and the evaluation, together with the learning outcomes achieved, and the other part is the comparative analysis of learning outcomes achieved in both engineering courses clustering with the best, average, and worst attainments, respectively. In this investigation, the results obtained from clustering data points show that the same group of clusters with the best, average, and worst learning outcomes achievements formed using both k-means and k-medoid clustering for one course. On the other hand, a diverse group of clusters with the best, average, and worst learning outcomes achievements formed using both k-means and k-medoids clustering for another course.

Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.

Abstract-Students' pedagogical progress plays a pivotal role in any educational institute in order to pursue imperative education. Educational institutes, Universities, Colleges implement various performance measures in order to keep analyzing and tracking progress of students to cultivate benefits of education in a better way. There are several data mining techniques to apply on education in order to build constructive educational strategies and solutions. This study aims to analyze and track engineering under graduate student's records to judge quality education, student motivation towards learning, and student pedagogical progress to maintain education at high quality level and predicting engineering student's forthcoming progress. Different engineering discipline students' (of three different cohorts) data have been analyzed for tracing current as well as future pedagogical progress based on their sessional (preexamination) marks. In this research, the classification techniques by k-nearest neighbor, Naïve Bayes and decision trees are applied to evaluate different engineering technologies student's performance and also there are different methodologies that can be used for data classification.

With the advent of online social media, such as articles, websites, blogs, messages, posts, news channels, and by and large web content has drastically changed the way individuals take a glimpse at different things around them. Today, it's an everyday practice for some individuals to read the news on the web. Sentiment analysis (also called opinion mining) alludes to the utilization of natural language processing, content investigation, and computational linguistics to distinguish and separate subjective data in source materials. Sentiment analysis is broadly applied to online reviews, news feeds and social networking for a wide variety of applications, ranging from marketing to client services. Sentiment analysis emphasizes on the classification of textual data into positive, negative and neutral categories. This research is an endeavor to the case study that calculates news polarity or emotions on different sports feeds which may influence changes in sports news development patterns. The interest of this approach is to generate various text analytics that computes feelings from all pertinent ongoing sports news accessible out in the public domain. The significance and application value of sentiment analysis of RSS feeds in this study is to distinguish between positive feeds and negative feeds on sports that could affect readers or users minds in order to improve RSS feeds messaging broadcast among folks. The methodology utilizes the sentiment analysis techniques using two different online open-source sentiment analysis tools in Rich Site Summary (RSS) news feeds that have an influence on sports-related broadcast esteems.

Accurate prediction of students' academic performance is one of the challenges in maintaining quality standards in any Higher Education Institution (H.E.I.). To ensure the quality of teaching and learning, H.E.I.s often employ Self-Assessment Reports (S.A.R.s) in which identifying a student drop-out ratio is important. Hence, it is essential to identify at-risk students in a given academic program. This article aims to identify at-risk students early by proposing a data mining-based predictive framework to improve the student's learning experience and minimize the dropped-out ratio. The academic sub-attributes or indicators in each course that may affect the performance of students in higher education institutions used in this study to examine students' academic achievement and predict students' performance to distinguish at-risk students are the marks of assignments, mid-term, lab exams, semester marks, total, grade, grade point (G.P.), quality point (Q.P.), grade point average (G.P.A.), and credit hours data of multiple courses categorized according to three knowledge areas defined by Higher Education Commission (H.E.C), Pakistan using data mining predictive techniques. The results indicate that the proposed methods can achieve maximum accuracy in predicting and identifying at-risk students in different courses.

The automobile industry is currently looking at the technology needed to move from today's original autonomous autos to a self-contained and safe driving solution. The automobile industry has been remarkably successful in producing reliable, safe, and affordable cars over the past century. Due to the significant progress made in computers and telecommunications, an autonomous car became a reality. In this regard, an android driver-less car is a vehicle that uses a combination of motors, software, and sensors to park cars between destinations without a human operator. To be fully autonomous, vehicles must be able to travel unmanned to a pre-determined destination on roads that are not fit for use. In this paper, the android controlled Arduino based intelligent car parking development stages and functionalities has been discussed. The motor system will be composed of the dc motors that run the car as well as the wheels and body of the car. The DC motor controls the circuit and a software driver. The android application will drive the car forward, reverse, left, and right (stopping will be the absence of a forward or backward command). It will do this by means of the software driver. There is also one motor which holds the brake and release. The significance of this system is that it has a distinctiveness to control real cars in real-time with android applications including steering control, gear shifting, horn control, and engine on/off. It has a self-parking system in a narrow crowded system through the sensors reading the environment and with actuators, a car could be park itself. Finally, on enabling effective automobile safety and efficient automotive cars, some of the challenges are needed to be addressed (and to provide) useful suggestions for approval by car manufacturers, designers, policymakers, and regulatory bodies.

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Software development procedures are constantly evolving to monitor and evaluate the quality and to save money and time. Numerous organizations are migrating away from traditional business models and toward agile methodologies. The purpose of this paper is to illustrate both traditional and agile software development approaches and the numerous techniques associated with them. This analysis conducted identical investigations into two radically different ways of thinking about product development: traditional methods and agile approaches. The paper is supported by several studies commissioned by various organizations to determine the transition from the traditional to the agile paradigm. The follow-up and data analysis explore and reveal changes and emerging trends that arose announcement of the agile technique which is mentioned in this paper, and also has gained increasing prominence in agile in product development over the last couple of years.