The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia.    The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results.   Indexed by DBLP, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago.  All books published in the series are submitted for consideration in Web of Science.

Advances in Intelligent Systems and Computing

Alongside the rise of educational technology, many teachers have been taking gradual but innovative steps to redesign their teaching methods. For example, in flipped learning or a flipped classroom, students watch instructional videos outside the classroom and do assignments or engage in activities inside the classroom. Language teachers are one group of educators exploring the flipped classroom. In foreign language classes, such an approach may offer great benefits for both the teachers and students since classroom time can be applied to more interactive tasks. By extending classroom hours in this way, language teachers can focus on successfully addressing all subjects in the curriculum. The aim of this study is (a) to gain insights into the perceptions of prospective English language teachers at a state university in Turkey on flipped classrooms and (b) to introduce the implementation of a flipped classroom into an English language class. A total of 47 prospective English teachers participated in the study. Qualitative research design was used and data were collected via an open-ended question. Findings of the study indicated that pre-service English teachers had positive perceptions towards the use of the flipped classroom as an integral part of face-to-face courses. It can be concluded that flipped classroom was beneficial in terms of 4 categories based on the content analysis of the responses: learning at one’s own pace, advance student preparation, overcoming the limitations of class time, increasing the participation in the classroom. The study also provides recommendations towards LMS integration into courses in other English language teaching departments and for implementing flipped classrooms in language teaching.

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The implementation of a flipped classroom in foreign language teaching

Change detection captures the spatial changes from multi temporal satellite images due to manmade or natural phenomenon. It is of great importance in remote sensing, monitoring environmental changes and land use –land cover change detection. Remote sensing satellites acquire satellite images at varying resolutions and use these for change detection. This paper briefly analyses various change detection methods and the challenges and issues faced as part of change detection. Over the years, a wide range of methods have been developed for analyzing remote sensing data and newer methods are still being developed. Timely and accurate change detection of Earth’s surface features provides the basis for evaluating the relationships and interactions between human and natural phenomena for the better management of resources. In general, change detection applies multi-temporal datasets to quantitatively analyse the temporal effects of the phenomenon. As such, this study attempts to provide a comprehensive review of the fundamental processes required for change detection. The study also gives a brief account of the main techniques of change detection and discusses the need for development of enhanced change detection methods.

Change detection techniques for remote sensing applications: a survey

Recent efforts in visible light communication over screen-camera links have exploited the display for data communication. Such practices, albeit convenient, have led to contention between space allocated for users and content reserved for devices, in addition to their visual anti-aesthetics and distractedness. In this paper, we propose INFRAME++, a system that enables concurrent, dual-mode, full-frame communication for both users and devices. INFRAME++ leverages the spatial-temporal flicker-fusion property of human vision system and the fast frame rate of modern display. It multiplexes data onto full-frame video contents through novel complementary frame composition, hierarchical frame structure, and CDMA-like modulation. It thus ensures opportunistic and unobtrusive screen-camera data communication without affecting the primary video-viewing experience for human users. Our prototype and experiments have confirmed its effectiveness of delivering data to devices in its visual communication with imperceptible video artifacts for viewers. INFRAME++ is able to achieve 150-240 kbps at 120FPS over a 24? LCD monitor with one data frame per 12 display frames. It supports up to 360kbps while data:video is 1:6.

InFrame++: Achieve Simultaneous Screen-Human Viewing and Hidden Screen-Camera Communication

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

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Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

A c hallenging topic in computer based education is to pr ovide personalization support for learners when e-learning t akes place in an open and dynamic envi ronment. By selecting and combining appropriate l earning ass ets into a l earning ob ject a l earner's needs and preferences may be ac counted for. Also by implementing web service in an adaptive learning environment the reusability of the learning content can be ensured. This paper addresses the problems of automatically selecting and integrating appropriate l earning materials for a learner using web services based o n the learners initial knowledge, goals, preferences etc. A system is described that provides l earning co ntent to multimedia i ndustries and institutions those who are working i n A dobe Flas h. Instead of providing a learner with static d ata, the approach is based on fulfilling learning objectives based on a d ynamic supply of services. Furthermore, the approach is based on reusable learning objects describing a learning process as a composition of learning goals. Based on the learning goals as well as web services, services appropriate t o ach ieve a specific learning goal ca n be selected, co mposed and invoked dynamically.

Adaptive Content Creation for Personalized e-Learning Using Web Services

https://cyberleninka.org/article/n/21503.pdf

A Machine Learning Technique for Semantic Search Engine

Multispectral remote sensing images are the primary source in the land use and land cover (LULC) monitoring. This is achieved by LULC classification and LULC change detection. The change detection in LULC includes the detection of water bodies, forest fire, forest degradation, agriculture areas monitoring, etc. Various change detection and LULC classification methods have their own advantages and disadvantages, and no single method is optimal and finds applicability for all cases. This paper summarizes and analyses the various soft computing and feature extraction techniques used for LULC classification and change detection. Based on the average error rate, performances of the different soft computing techniques are evaluated. The broad usage of multispectral remote sensing images, object-based change detection, neural networks and various levels of image fusion methods offer more potential in LULC monitoring.

Soft Computing Techniques for Land Use and Land Cover Monitoring with Multispectral Remote Sensing Images: A Review

Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analyzing image shapes, colors, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves.

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

Nowadays, digital content is widespread and simply redistributable, either lawfully or unlawfully. For example, after images are posted on the internet, other web users can modify them and then repost their versions, thereby generating near-duplicate images. The presence of near-duplicates affects the performance of the search engines critically. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from digital images. The main application of computer vision is image understanding. There are several tasks in image understanding such as feature extraction, object detection, object recognition, image cleaning, image transformation, etc. There is no proper survey in literature related to near duplicate detection of images. In this paper, we review the state-of-the-art computer vision based approaches and feature extraction methods for the detection of near duplicate images. We also discuss the main challenges in this field and how other researchers addressed those challenges. This review provides research directions to the fellow researchers who are interested to work in this field.

K. K. Thyagharajan

Papers

Adaptive Content Creation for Personalized e-Learning Using Web Services

A Machine Learning Technique for Semantic Search Engine

Soft Computing Techniques for Land Use and Land Cover Monitoring with Multispectral Remote Sensing Images: A Review

A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

A Review on Near-Duplicate Detection of Images using Computer Vision Techniques