Machine learning is an area of computer science in which the computer predicts the next task to perform by analyzing the data provided to it. The data accessed by the computer can be in the form of digitized training sets or via interaction with the environment. The algorithms of machine learning are constructed in such a way as to learn and make predictions from the data unlike the static programming algorithms that need explicit human instruction. There have been different supervised and unsupervised techniques proposed in order to solve problems, such as, Rule-based techniques, Logic-based techniques, Instance-based techniques, stochastic techniques. The primary objective of our paper is to provide a general comparison among various state-of-the-art supervised machine learning algorithms.

Comprehensive Review On Supervised Machine Learning Algorithms

Rapid growth and recent developments in education sector and information technologies have promoted E-learning and collaborative sessions among the learning communities and business incubator centers. Traditional practices are being replaced with webinars (live online classes) E-Quizes (online testing) and video lectures for effective learning and performance evaluation. These E-learning methods use sensors and multimedia tools to contribute in resource sharing, social networking, interactivity and corporate trainings. While, artificial intelligence tools are also being integrated into various industries and organizations for students’ engagement and adaptability towards the digital world. Predicting students’ behaviors and providing intelligent feedbacks is an important parameter in the E-learning domain. To optimize students’ behaviors in virtual environments, we have proposed an idea of embedding cognitive processes into information technologies. This paper presents hybrid spatio-temporal features for student behavior recognition (SBR) system that recognizes student-student behaviors from sequences of digital images. The proposed SBR system segments student silhouettes using neighboring data points observation and extracts co-occurring robust spatio-temporal features having full body and key body points techniques. Then, artificial neural network is used to measure student interactions taken from UT-Interaction and classroom behaviors datasets. Finally a survey is performed to evaluate the effectiveness of video based interactive learning using proposed SBR system.

Students’ behavior mining in e-learning environment using cognitive processes with information technologies

Human Interaction Recognition plays a key role in identification of usual and unusual human behaviors and facilitates public dealings, violence detection, robots perception, and virtual entertainments. This paper presents a novel human interaction recognition (HIR) system to recognize human interactions in continuous image sequences. The proposed technology segments full body silhouettes and identifies key body points to extract robust spatio-temporal features having distinct characteristics for each interaction. Our descriptors focus on local descriptions, capture intensity variations, point-to-point distances and time based relations. The system is trained through artificial neural network to recognize six basic interactions taken from UT-Interaction dataset.

Robust Spatio-Temporal Features for Human Interaction Recognition Via Artificial Neural Network

Automatic facial expression recognition (FER) is one of the most challenging tasks in computer vision. FER admits a wide range of applications in human–computer interaction, behavioral psychology, and human expression synthesis. Extensive works have been reported in this field, mainly, based on handcrafted features. However, it is a challenging task to accurately extract all the correlated handcrafted features due to the effect of variations caused by emotional state. Therefore, there is a quest for further research on accurately extracting relevant features that can capture changes in facial expressions (FEs) with high fidelity. In this study, we propose FER-net: a convolution neural network to distinguish FEs efficiently with the help of the softmax classifier. We implement our method FER-net along with twenty-one state-of-the-art methods and test them on five benchmarking datasets, namely FER2013, Japanese Female Facial Expressions, Extended CohnKanade, Karolinska Directed Emotional Faces, and Real-world Affective Faces. Seven FEs, namely neutral, anger, disgust, fear, happiness, sadness, and surprise, are considered in this work. The average accuracies on these datasets are 78.9%, 96.7%, 97.8%, 82.5% and 81.68%, respectively. The obtained results demonstrate that FER-net is preeminent in comparison with twenty-one state-of-the-art methods.

FER-net: facial expression recognition using deep neural net

The application of biometric recognition in personal authentication enables the growth of this technology to be employed in various domains. The implementation of biometric recognition systems can be based on physical or behavioral characteristics, such as the iris, voice, fingerprint, and face. Currently, the attendance tracking system based on biometric recognition for education sectors is still underutilized, thus providing a good opportunity to carry out interesting research in this area. As evidenced in a typical classroom, educators tend to take the attendance of their students by using conventional methods such as by calling out names or signing off an attendance sheet. Yet, these types of methods are proved to be time consuming and tedious, and sometimes, fraud occurs. As a result, significant progress had been made to mark attendance automatically by making use of biometric recognition. This progress enables a new and more advanced biometric-based attendance system being developed over the past ten years. The setting-up of biometric-based attendance systems requires both software and hardware components. Since the software and hardware sections are too broad to be discussed in one paper, this literature survey only provides an overview of the types of hardware used. Emphasis is then placed on the microcontroller platform, biometric sensor, communication channel, database storage, and other components in order to assist future researchers in designing the hardware part of biometric-based attendance systems.

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Biometric-Based Attendance Tracking System for Education Sectors: A Literature Survey on Hardware Requirements

Face Recognition begins with extracting the coordinates of features such as width of mouth, width of eyes, pupil, and compare the result with the measurements stored in the database and return the closest record (facial metrics). Nowadays, there are a lot of face recognition techniques and algorithms found and developed around the world. Facial recognition becomes an interesting research topic. It is proven by numerous number of published papers related with facial recognition including facial feature extraction, facial algorithm improvements, and facial recognition implementations. Main purposes of this research are to get the best facial recognition algorithm (Eigenface and Fisherface) provided by the Open CV 2.4.8 by comparing the ROC (Receiver Operating Characteristics) curve and implement it in the attendance system as the main case study. Based on the experiments, the ROC curve proves that using the current training set, Eigenface achieves better result than Fisherface. Eigenface implemented inside the Attendance System returns between 70% to 90% similarity for genuine face images.

Adrian Rhesa Septian Siswanto

Papers

Implementation of face recognition algorithm for biometrics based time attendance system