Engineering education plays a prominent role in the development of technologies, society, nation, production, economy and employment. It is the art of applying scientific and mathematical principles, and experience to produce a technical product or system to meet out a specific need in the society. Based on the literature, it was thought that implementation of a cooperative project-based education on electrical-electronics engineering students could contribute to their basic engineering skills, their cooperative learning, and their attitudes towards engineering education and occupation. The aim of this study was to reveal the effect of project-based cooperative studio studies on the occupational basic skills of electrical-electronics engineering students, cooperative learning, and their attitudes towards engineering occupation. The research is designed to be a study that is half-experimental and half-quantitative study and was composed of 42 students. Within the research, project-based cooperative studio studies were utilized by the experimental group while the control group had similar course requirements for six weeks, but their practice solely included the content of the Lab II course in the official curriculum. The resulting data was gathered using the Basic Electronics Skills Self-Effacement Scale, the Scale for Attitude towards Cooperative Learning, and the Scale for Attitude towards Engineering and Engineering Education. The results indicate that the project-based cooperative studio studies are contributing more meaningfully to students’ intermediate level electronics skills, and their attitudes towards cooperative learning and towards engineering occupation.

Artificial neural network is an information processing system which is influenced by the human brain and works on the same principles of the biological nervous system. They possess the ability to extract meaning from complex and intricate data, by detecting trends and extracting patterns from it. This paper illustrates the ability of neural network model and linear regression model constructed to predict the creditworthiness of an application accurately and precisely with minimal false predictions and errors. The results are shown to be similar for both the models, thus, models are efficient to use depending on the type of application and attributes.

The amount of research reports on how to properly teach, in conjunction with technical topics, agile skills in undergraduate courses is a good indicator of how important are such skills in academy and industry nowadays. Such investigations have addressed challenges like how to engage students with agile principles and values without getting distracted by technology, or how to balance theory and practice to get students to meet learning objectives through practical experience. This paper intends to contribute to this research topic by describing new strategies for our particular needs for teaching agile in an introductory software engineering course, including better evaluation criteria for agile values and practices, and higher quality projects. The described strategies include a new approach for theoretical, laboratory, and project sessions arrangement, as well as a ‘Continuous Delivery Pipeline’ adapted to our educational context, with very promising results.

Personalised learning is a way of organising the learning content and to be accessed by the individual learner in a manner that is suitable to learner’s requirements. There are existing related works on personalised e-learning systems that focused on learner’s preference without considering the difficulty level and the relationship degree that exists between various course concepts. Hence, these affect the learning ability and the overall performance of learners. This research paper presents a genetic algorithm-based curriculum sequencing model in a personalised e-learning environment. It helps learners to identify the difficulty level of each of the curriculum or course concepts and the relationship degree that exists between the course concepts in order to provide an optimal personalised learning pattern to learners based on curriculum sequencing to improve the learning performance of the learners. The result of the implementation showed that the genetic algorithm is suitable to generate the optimal learning path using the values of difficulty level and relationship degree of course concepts. Furthermore, the system classified the learners into three different understanding levels of the course concepts such as partially, moderately and highly successful.

Indigenous Knowledge (IK), can be preserved using Information Systems in order to protect cultural heritage and disseminate local knowledge for development. This knowledge often passed on orally for generations has become significant in searching for answers to several world's critical problems, are at risk of becoming extinct. This "traditional wisdom" is highly useful in solving complex problems of health, agriculture, education, use of natural resources and the environment. The main challenges of IK are inadequate documentation and diminishing transmission channels. Both descriptive and quantitative methods are used in this study that focuses on highlighting the importance of indigenous knowledge in the sustainable development process and illustrating ways in which technology can be used to preserve it, thereby enriching the development process from a holistic perspective. This research strengthens the preservation of local IK, enhances its adoption in the formal educational settings, leads to improvement in scientific knowledge development and inspire sustainable community development using a holistic approach.

Gray level transformation is a significant part of image enhancement techniques which deal with images composed of pixels. The outcomes of this process can be either images or a set of representative characteristics. It effects is simple but complicated in its implementation. Recently much work is completed in the field of images enhancement with varying observable techniques. This paper describes how to enhance an image using different gray level techniques and a demonstration of its implementation. PPI Analyzer, a kind of software created to implement the various techniques is based on explosive phenomenon of MATLAB. The implemented program with interactive interface to allow for relaxed modification, presented encouraging results.

Sentiment Classification is a special task of Sentiments Analysis in which a text document is assigned into some category like positive, negative, and neutral on the basis of some subjective information contained in documents. This subjective information called as sentiment features are highly responsible for efficient sentiment classification. Thus, Feature extraction is essentially an important task for sentiment classification at any level. This study explores most relevant and crucial features for sentiment classification and groups them into seven categories, named as, Basic features, Seed word features, TF-IDF, Punctuation based features, Sentence based features, N-grams, and POS lexicons. This paper proposes two new sentence based features which are helpful in assigning the overall sentiment of contrastive sentences and on the basis of proposed features; two algorithms are developed to find the sentiment of contrastive sentences. The dataset of TripAdvisor is used to evaluate our proposed features. Obtained results are compared with several state-of-the-art studies using various features on the same dataset and achieve superior performance.