Literature Review on Big Data Analytics Methods

Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed

Pengaruh Stemmer Bahasa Indonesia Terhadap Peforma Analisis Sentimen Terjemahan Ulasan Film

Bahasa Indonesia memiliki banyak variasi akhiran, awalan, dan sisipan. Stemming adalah bagian dari prapengolahan dari analisis sentimen yang mendeteksi dan menghilangkan imbuhan tersebut. Pengaruh dari stemming pada analisis sentimen masih belum jelas karena dataset yang digunakan tidak terdistribusi secara bebas. Untuk mendapatkan pengaruh dari stemming terhadap analisis sentimen maka dilakukan percobaan dengan dataset ulasan film yang sudah diterjemahkan ke Bahasa Indonesia. Stemmer Sastrawi sebagai algoritma stemming terbaru digunakan pada penelitian ini. Dataset dibagi menjadi 5 (lima) kategori yang mana 100 data, 250 data, 500 data, 750 data, dan 1000 data. Hasil yang diperoleh menunjukan bahwa stemmer tidak memberikan peningkatan akurasi yang stabil. Bahkan waktu yang diperlukan untuk menyelesaikan analisis sentimen memerlukan waktu meningkat hingga 310 kali lipat. Kenyataan ini sangat buruk karena stemming dapat mengurangi efisiensi dari analisis sentimen

Investigation of Heterogeneous Approach to Fact Invention of Web Users’ Web Access Behaviour

World Wide Web consists of a huge volume of different types of data. Web mining is one of the fields of data mining wherein there are different web services and a large number of web users. Web user mining is also one of the fields of web mining. The web users’ information about the web access is collected through different ways. The most common technique to collect information about the web users is through web log file. There are several other techniques available to collect web users’ web access information; they are through browser agent, user authentication, web review, web rating, web ranking and tracking cookies. The web users find it difficult to retrieve their required information in time from the web because of the huge volume of unstructured and structured information which increases the complexity of the web. Web usage mining is very much important for various purposes such as organizing website, business and maintenance service, personalization of website and reducing the network bandwidth. This paper provides an analysis about the web usage mining techniques. Â

A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations

With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. China, the United States, the European Union, and others have put forward the importance of personalized learning in recent years, emphasizing the realization of the organic combination of large-scale education and personalized training. The development of a personalized learning system oriented to learners' preferences and suited to learners' needs should be accelerated. This review provides a comprehensive analysis of the current situation of personalized learning and its key role in education. It discusses the research on personalized learning from multiple perspectives, combining definitions, goals, and related educational theories to provide an in-depth understanding of personalized learning from an educational perspective, analyzing the implications of different theories on personalized learning, and highlighting the potential of personalized learning to meet the needs of individuals and to enhance their abilities. Data applications and assessment indicators in personalized learning are described in detail, providing a solid data foundation and evaluation system for subsequent research. Meanwhile, we start from both student modeling and recommendation algorithms and deeply analyze the cognitive and non-cognitive perspectives and the contribution of personalized recommendations to personalized learning. Finally, we explore the challenges and future trajectories of personalized learning. This review provides a multidimensional analysis of personalized learning through a more comprehensive study, providing academics and practitioners with cutting-edge explorations to promote continuous progress in the field of personalized learning.Comment: 82 pages,5 figure

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

Machine Learning applications to e-learning courses

The Ph.D. thesis project is aimed at improving the quality and the effectiveness of on-line teaching in scientific degree courses at the University Level that required the use of E-learning platform, based on the Moodle Content Management System. The aim of this research project is to assist the teacher, through the development of new tools based on Artificial Intelligence, to design innovative successful e-learning courses to give to the students the opportunity to improve their learning outcomes. These originals tools overcome the limitations of the standard Moodle activities applying machine learning techniques by analysing large amount of students’ data extracted by Moodle log data. Recently many e-learning resources have been developed for university students, are available on the Web. The increase of LMS (Learning Management System) as Moodle and their ease of use led many teachers to realize e-learning paths for their students, often supporting them with some frontal activities, giving to them the advantages of on-line learning. The aim was to deepen the topics discussed in class through the consultation of additional materials, video recordings of lessons, and other activities to exploiting the potentials of on-line courses

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm

Multi-objective of wind-driven optimization as feature selection and clustering to enhance text clustering

Text Clustering consists of grouping objects of similar categories. The initial centroids influence operation of the system with the potential to become trapped in local optima. The second issue pertains to the impact of a huge number of features on the determination of optimal initial centroids. The problem of dimensionality may be reduced by feature selection. Therefore, Wind Driven Optimization (WDO) was employed as Feature Selection to reduce the unimportant words from the text. In addition, the current study has integrated a novel clustering optimization technique called the WDO (Wasp Swarm Optimization) to effectively determine the most suitable initial centroids. The result showed the new meta-heuristic which is WDO was employed as the multi-objective first time as unsupervised Feature Selection (WDOFS) and the second time as a Clustering algorithm (WDOC). For example, the WDOC outperformed Harmony Search and Particle Swarm in terms of F-measurement by 93.3%; in contrast, text clustering's performance improves 0.9% because of using suggested clustering on the proposed feature selection. With WDOFS more than 50 percent of features have been removed from the other examination of features. The best result got the multi-objectives with F-measurement 98.3%

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot