Machine learning and artificial intelligence techniques are more and more in our lives and studies in this field are increasing day by day. Data is vital for these studies. In order to draw meaningful conclusions from the available data, new methods are proposed and successful results are obtained. The preparation of the obtained data is very important in the studies to be carried out. Data preprocessing is very important in the preparation of data. The most critical stage of the data preprocessing process is the scaling or normalization of the data. Machine learning libraries such as scikit-learn and programming languages such as R provide the necessary libraries to scale data. However, it is not known exactly which normalization method will be applied and which will yield more successful results. The success of these normalization methods has been investigated on many different methods, but such a study has not been done on the adaptive neural fuzzy inference system (ANFIS). The aim of this study is to examine the success of normalization methods on ANFIS in terms of both classification and regression problems. So, for studies using the Anfis method, guidance will be provided on which normalization process will give better results in the data preprocessing stage. Four different normalization methods in the scikit-learn library were applied on the Diabets and Forestfire datasets in the UCI database. The results are presented separately for both classification and regression. It has been determined that min-max normalization in classification problems and working with original data in regression problems are more successful.

Decentralized self-adaptive systems consist of multiple control loops that adapt some local and system-level global goals of each locally managed system or component in a decentralized setting. As each component works together in a decentralized environment, a control loop cannot take adaptation decisions independently. Therefore, all the control loops need to exchange their adaptation decisions to infer a global knowledge about the system. Decentralized self-adaptation approaches in the literature uses the global knowledge to take decisions that optimize both local and global goals. However, coordinating in such an unbounded manner impairs scalability. This paper proposes a decentralized self-adaptation technique using reinforcement learning that incorporates partial knowledge in order to reduce coordination overhead. The Q-learning algorithm based on Interaction Driven Markov Games is utilized to take adaptation decisions as it enables coordination only when it is beneficial. Rather than using unbounded number of peers, the adaptation control loop coordinates with a single peer control loop. The proposed approach was evaluated on a service-based Tele Assistance System. It was compared to random, independent and multiagent learners that assume global knowledge. It was observed that, in all cases, the proposed approach conformed to both local and global goals while maintaining comparatively lower coordination overhead.

Informal education will be successful as an alternative for the community because not all people are able to receive formal education. This study uses a qualitative method with a systematic literature review (SLR) technique to look for learning community components in informal education to support learning in the culinary community in the new normal era of Covid-19. The author collects, studies, and analyzes reference sources according to the specified keywords. Found 53 papers from 2002 to 2021 with background authors from academia, industry, and the public sector with reference sources from journals, conferences, white papers, and research reports. Systematic literature review results obtained 6 components of learning community in informal education, namely content, forum, method, technology, figure/layout, and human/social resources. The six components as a reference and the author's first step in the next research through searching for the characteristics of the learning community in the culinary field, then making a learning model of the culinary community. Because of the importance of the learning community component in informal education to help community members share knowledge, solve problems, share common goals and interests among community members.

The success of any software product could be measured by its uses and adoption of that technology by the end-users. In this study, we investigate the factors on which bank user intents to adopt internet banking in Pakistan. A survey was conducted on Pakistani banking industry customers using the unified theory of acceptance and use of technology (UTAUT) model which explains the intention of bank users to use the banking systems. The four predictors of UTAUT which were facilitating conditions, social influence, effort expectancy and performance expectancy were significant in predicting the intention of bank users to adopt the banking systems. Finally, we discuss the results, restrictions, implications and future recommendations. The findings of the study may help to provide insights into a better approach to promote e-banking acceptance.

Leaf disease of plants causes great loss in productivity of crops. So proper take care of plants is mandatory. Plants can be affected by various diseases. So Early diagnosis of leaf disease is a good practice. Computer vision-based classification of leaf disease can be a great way in diagnosing diseases early. Early detection of diseases can lead to better treatment. Vision based technology can identify disease quickly. Though deep learning is trending and using vastly for recognition task, but it needs very large dataset and also consumes much time. This paper introduced a method to classify leaf diseases using Gist and LBP (Local Binary Pattern) feature. These manual feature extraction process need less time. Combination of gist and LBP features shows significant result in classification of leaf diseases. Gist is used as global feature and LBP as local feature. Gist can describe an image very well as a scene. LBP is robust to illumination changes and occlusions and computationally simple. Various diseases of different plants are considered in this study. Gist and LBP features from images are extracted separately. Images are pre-processed before feature extraction. Then both feature matrix is combined using concatenation method. Training and testing is done on different plants separately. Different machine learning model is applied on the feature vector. Result from different machine learning algorithms is also compared. SVM performs better in classifying plant’s leaf dataset.