The problem in the learning community in the culinary field is that there is still a lot of cooking knowledge that is lost because the person concerned dies before being inherited, the many types of dishes that are still separated in small groups, the emergence of various types of fast food that do not pay attention to the correct way of cooking with recipes, methods, and processes according to Health standards. Culinary is an alternative business that is very promising and supports the community's economy and a culinary tourism trend that is currently in great demand by all groups. From these problems, we need a learning community in the field of culture that can be a solution. The learning community components are seen from four perspectives, namely, explore where the author uses the SECI model, and explain where knowledge is disseminated through mobile learning with features tailored to user needs. The practice begins with selecting the recipe, the ingredients used, the cooking process, validation, and feedback from the participants. And the engage perspective obtained good interaction from participants and to produce new recipes requires learning that is done repeatedly by combining various recipes, ingredients, and methods so that new recipes are produced that have unique and interesting characteristics and have taste, aroma and deliciousness and good nutritional value. The number of references used were 72 papers sourced from journals as many as 54 papers (75%) and the remaining 18 papers (25%) sourced from conferences, websites & white papers, and research reports. There were 15 papers published in 1991 – 2010 and 2011 – 2021 as many as 57 papers. This research is still limited to the learning community component in the culinary field, seen from four perspectives, and has not discussed the model that has been validated by experts. Participant motivation and learning outcomes will be discussed in the next research

Requirement elicitation process requires collaboration with people of different backgrounds and expertise. Collaboration between diverse teams such as developers, testers, designers, requirement engineers, and stakeholders makes requirement elicitation process highly human dependent. The main goal of this research is to find out the role and importance of “human aspects” such as domain knowledge, motivation, communication skills, gender, age personality, attitude, geographical distribution, emotions, and cultural diversity in requirement elicitation activities. The purpose of this study is to identify the industrial perspectives of key human aspects that will help organizations to carry out RE-related activities more effectively. To fulfill that purpose, we surveyed 165 software practitioners and elicited the industrial perspective through their responses. Practitioner’s data revealed that requirement elicitation activities are highly human-dependent, 90% of practitioners were of the view that the success of requirement engineering activities depends on the individuals engaged in those activities. Software practitioner’s data revealed that domain knowledge (84%), motivation (68%), communication skills (61%), and personality (41%) are the highly important aspect for the individual engaged in requirement engineering activities. Furthermore, the data revealed that the correctness (73%) of identified requirements is a highly important factor in measuring the performance of the person involved in the RE process. Simultaneously, the clarity (78%) and the completeness (75%) of identified requirements are also important. Our results suggest that the individual engaged in the requirement engineering process should have the social and collaborative (89%), enthusiastic (94%), altruistic (kind, generous, trustworthy, and helpful) (67%) qualities to be able to carry out RE activities effectively. Our survey suggests that the practitioners may consider the findings of this research appropriately when forming, managing teams, and conducting software requirement elicitation activities.

Social media usage has increased due to the rate at which technologies are emerging and it is less likely to detect false news/information manually as it aims to capture the human mind. The spread of false news can cause havoc; therefore, detection of false news becomes paramount where almost everyone has access to social media. Our proposed system optimizes the false news detection process. The system combines advantages of two textual feature extraction methods and two machine learning algorithms for text classification. Basic pre-processing methods were employed. Feature extraction was carried out using Term Frequency-Inverse Document Frequency with Word2Vector. K-Nearest Neighbour (KNN) and Naïve Bayes (NB) algorithms are combined to give KNN Bayesian. The most available systems made use of a single feature extraction method but in our system, two feature extraction methods are combined. The evaluation metrics used were accuracy, precision, recall, f1score and KNN Bayesian performed better than KNN. To further evaluate our model, the Area under the Curve-Receiver Operator Characteristics (AUC-ROC) revealed that AUC of KNN Bayesian ROC curve is higher than that of KNN.

The data mining classification techniques and analysis can enable banks to move precisely classify consumers into various credit risk group. Knowing what risk group a consumer falls into would allows a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued on terms commensurate with  the risk of default. So research en for classification and prediction of loan grants. The attributes are determined that have greatest effect in the loan grants. For this purpose C4.5, CART and Naïve Bayes are compared and analyzed in this research. This concludes that a bank should not only target the rich customers for granting loan but it should assess the other attributes of a customer as well which play a very important part in credit granting decisions and predicting the loan defaulters.

In most developing countries, the majority of the population heavily rely on agriculture for their livelihood. The yield of agriculture is heavily dependent on uncertain weather conditions like monsoon, soil fertility, availability of irrigation facilities and fertilizers as well as support from Govt. The main challenge in this study if the agricultural yield which is quite less compared to the effort put in due to inefficient agricultural implements and lack of knowledge on the other hand. It is therefore essential for the farmers to improve their harvest yield by acquisition of related data such as soil condition, temperature, humidity, availability of irrigation facilities, availability of manure etc and adopt smart farming techniques using modern agricultural equipment. A trend has started amongst the farmers to shift from traditional conventional farming to smart farming using the Internet of Things (IoT) technology, which can help improve yield with reduced effort at economic cost. The main focus of this paper is to present work related to these technologies in the agriculture field. This also presented their challenges & benefits related to smart farming. For improving the system, IoT will interact with other useful systems like Wireless Sensor Networks.  It can help for understanding the job of data by using IoT and correspondence advancements in horticulture division. This will help to motivate and educate the unskilled farmers to comprehend the best bits of knowledge given by the huge information investigation utilizing smart technology and also provide data analysis in terms of temperature, humidity that can help farmers to reduce computation time. It will also help to identify water utilization in prior.