Performing Facial Recognition Using Ensemble Learning

Attending classes by students is associated with the assimilation of educational material by students and the ability to plan and organize activities. However, at present in educational institutions, as a rule, student attendance is recorded manually. Activities are performed frequently and repeatedly, thus wasting instructors' study time. Additionally, the face is one of the most widely used biometric characteristics for personal identification so an automated attendance system using face recognition has been proposed. In recent years, convolutional neural networks (CNN) have become the dominant deep le11arning method for face recognition. In this article, the features of building an automated student attendance system by biometric face recognition using the convolution neural network modelhas been discussed. Analyzed and solved the main tasks that arise when building an automated student attendance monitoring system: creating a dataset of students' face images; building and training a biometric face recognition model; face recognition from the camera and registration in the database; extension to the face image dataset. The use of the capabilities of the Python and OpenCV libraries is shown. The conducted testing of the accuracy of the developed CNN model of biometric face recognition showed good results – the overall accuracy score is not less than 0.75. The developed automated student attendance monitoring system in classrooms can be used to determine student attendance in different forms of the educational process. Its implementation will significantly reduce the monitoring time and reduce the number of errors in maintaining attendance logs. The introduction of an automated attendance monitoring system will significantly improve the organization of the educational process to ensure its quality.

Public relations, or PR, is the art of creating better understanding among the public so that it can increase public trust in certain individuals or institutions. In the context of educational institutions, public relations are a part of management related to interactions between educational institutions and the community. The purpose of the discussion is to find out the implementation of strategic management in educational institutions and public relations management in Islamic educational institutions. This discussion uses literature using the literature study method. data collection techniques, namely documentation such as books, journals, magazines, and others. Results of the discussion: 1) In implementing school public relations strategies, there needs to be an appropriate strategy to achieve educational goals, especially in increasing parent participation. One way to increase the participation of parents of students is to build a good relationship between the school and parents. 2) Humanitarian management in educational institutions itself is defined as a process of planning, organizing, directing, coordinating, mobilizing, communicating, and evaluating that involves all stakeholders in the school environment to achieve educational goals effectively and efficiently. The essence of implementing public relations management in Islamic boarding schools is almost the same as that of other Islamic public relations institutions, namely designing public relations activities based on the values of Al-Qur’an and Al-Hadith.

The advancement in the history of computer vision utilizing deep learning approaches especially convolutional neural networks have accomplished to solve difficult problems in face recognition field. Face recognition-based approach is one amongst the important identification methods which can be used as a possible substitution for conventional system of marking attendance manually, especially if a huge classroom of students is addressed for an hour session. In this proposed paper, an automated attendance system is implemented utilizing algorithms such as Histogram of Oriented Gradients (HOG) and Face landmark Estimation for face detection and finally, the deep convolutional neural network is trained for face recognition. In this system, a database containing the student’s images is created. A camera installed in the classroom captures the image of all the student in the classroom. This image is processed using machine learning algorithms to detect faces and to mark the attendance automatically in an excel sheet.KeywordsFace detectionFace recognitionDeep learningMachine learningAttendance systemPythonHOGFace landmark estimationSVMTriplet learning

In real world classroom settings, traditional classroom attendance methods are often time consuming, prone to human errors, and inefficient in terms of accuracy. Different solutions have been introduced from time to time, to make them easy, accurate and automatic. Use of computer vision and face recognition is gaining momentum as it provides automation and increases the precision and effectiveness of attendance system. The research aims to design automated attendance systems capable of working seamlessly in real classroom settings, focusing on increasing the accuracy as well as reliability for the development of automated attendance system. With the goal of providing effective fast automatic classroom attendance, this research focusses on evaluating performance of different Convolutional Neural Network architectures performance, such as VGGNet, ResNet, and DenseNet. For the evaluation of the performance of these Convolutional Neural Networks, we have used parameters like accuracy, precision, recall, and F1 score for a real world classroom scenario with different lighting conditions, poses, and occlusions. Our research shows that the ResNet performs better with respect to speed and accuracy in comparison to VGGNet and DenseNet. This makes ResNet an optimum choice for real time deployment.

Daily attendance marking is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is difficult to process, especially for a large group of students. Some automated systems developed to overcome these difficulties, have drawbacks like cost, fake attendance, accuracy, intrusiveness. To overcome these drawbacks, there is a need for a smart and automated attendance system. Traditional face recognition systems employ methods to identify a face from the given input but the results are not usually accurate and precise as desired. The system described in this we aim to deviate from such traditional systems and introduce a new approach to identify a student using a face recognition system, the generation of a facial Model. This describes the working of the face recognition system that will be deployed as an Automated Attendance System in a classroom environment. The proposed smart classroom system was tested for a classroom with 20 students at K L University Andhra Pradesh, Vijayawada, India and we got the experimental results to demonstrate the train and test accuracy of 97.67% and 96.66%, respectively. In this paper we selecting of the face recognition and detection giving result using Python language in PYCHARM tool. This requires high end specifications of a system in order to get better results. It won’t run on all the small specification systems. So, this can run only a small database and compare them with the face required.

Evaluation of the benchmark datasets for testing the efficacy of deep convolutional neural networks

Due to a large number of students in the schools and colleges most of the time teachers are not able to monitor on the attendance of the students. This may create the problems to both students and teachers in the end of the year or semester. The manual handling of all these can take much more effort in making the students to attend the classes regularly and also to intimate their parents on the valid interval of time. So to make this system more efficient and thereby to increase the productivity of the college or the school proposed system uses the automatic attendance system, where the proposed model takes the input from the staff regarding the attendance details and facial images of the students, then the system trains a Convolutional Neural Networks that have been deployed specifically for the facial recognition purpose. This trained model is then utilized for the purpose of achieving the identification of the student’s faces through the test image and the decision making approaches for the marking of the attendance. The methodology has been subjected to the evaluation for the accuracy of facial recognition which has resulted in highly accurate outcomes.

Objective: This study provides a bibliometric analysis of risk management research in educational institutions over the last two decades, examining global research trends, key contributors, and prominent thematic areas. Methods: Using the Scopus database, the study analyzed publications from 2003 to 2023 related to risk management in educational institutions. The analysis identified the top contributing countries, institutions, and authors, as well as the most frequently occurring research terms. Results: The United States was the leading contributor, accounting for 42% of the total research output, followed by Russia and the United Kingdom. The University of Florida and Kazan Federal University were among the top institutions contributing to this field. Manuel Alonso Castro emerged as the most influential author. Key research themes included disaster risk reduction, public health, climate change, and digital learning, reflecting the evolving challenges faced by educational institutions. Conclusion: The analysis highlights the global importance of risk management in educational institutions, with a growing emphasis on addressing emerging risks such as climate change and public health crises. Future research should expand to include regional studies and grey literature to provide a more comprehensive understanding of this critical area.

The article deals with the topical issue of creating the adaptive educational system applied within the process of professional training of Masters specializing in “Educational Institutions Management”. The paper reveals the historical aspect of training Master students specializing in management, defines some features of training Masters in educational institutions management and partially substantiates the adaptive education system with the aim of modernizing the system of training students who are taking their Master’s degree in management. On the basis of an analysis of the educational and vocational training program for Masters specializing in educational institutions management, the author singles out professionally relevant competences, which are integrated taking into account the market requirements and society demand and which must be acquired by students who are taking their Master’s degree. It is determined that the process of training Masters specializing in management of educational institutions requires significant changes based on the adaptive system, which will help provide educational establishments with educational institutions managers, who will be able to reach goals and to make high-quality new decisions in the ever-changing sphere of higher education. The article highlights the need for further development of the adaptive educational system and its implementation into the process of training Masters specializing in management of educational institutions.

Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries

Worm gearboxes are popular across various industrial applications since they offer significant gear ratios in small installation spaces. Despite having multiple advantages, worm gearboxes are subjected to higher friction due to sliding design and are prone to damage and increased transmission error over the period of operation. Delayed diagnosis of worm gearbox degradation can lead to low-quality products and/or unnecessary production line downtime. Using vibration characteristics of worm gearbox, it is possible to determine the fault and transmission error at a given period in time. In this paper, an ensemble machine learning model is trained and deployed to monitor the transmission error of worm gearbox and classify between new, operational and old conditions. 1D CNN (one dimensional convolutional neural network) model is used to automatically extract features in vibration signal of X, Y, and Z axes and predict the relevant state of worm gear. The proposed technique uses ensemble machine learning technique fusion of features extracted by multi-layer 1D CNN for three axes vibration data. The proposed method could achieve 96% accuracy and performs significantly better than traditional sequential and ensemble machine learning models on a dataset of with 7,870 samples with 800 samples labeled as new condition, 4,740 samples as operational and 2,330 as old.

This study broaches the matter of providing quality educational management in modern academic schools. The premise is advanced that with a growing significance of knowledge and technology in contemporary society and the intensification of economic and cultural globalization, education is increasingly viewed as an effective tool of shaping a personality capable of living in the conditions of dynamically developing knowledge. With this in mind, the article aim to analyze the concept of modernizing models and methods of educational management both in foreign countries and in Ukraine. The idea is maintained that there is an interrelation between the model of education management and the teacher’s style of instruction. A special emphasis in the article is placed on the world experience, which vividly illustrates that scientific reinforcement in training managers of education is accomplished simultaneously by research teams across the world, which prioritize both designing the processes of managing the development of pedagogical education and creating alternative frameworks of training managers. This study exposes the results of the conducted comparative analysis of modernizing models and methods of educational management both in foreign countries and in Ukraine. The indications are therefore that successful management of a modern educational institution is possible only when a collegial management model is applied. The evidence seems to be strong that under new socio-economic conditions it is required that the management of an educational institution should be able to organize the staff in order to enable them to participate in making and implementing joint strategic decisions. Given the evidence, such a managerial method will provide further support and consensus in execution and application of the adopted decisions. On this basis, it may be inferred that the strategic plan of actions is an important compulsory document in the activities of tertiary educational schools, a mechanism for managing the onset of functioning and monitoring the effectiveness of the entire management system.

    The IoT Based Automated Attendance System was designed and developed to make an efficient contactless attendance marking system with a mobile application feature. This study aimed to improve the traditional attendance log system and contact fingerprint attendance within an institution through the use of facial recognition. The study used the OpenCV and Python Programming in order to create the facial recognition program, while the mobile application was developed through the use of Visual Studio Code and Android Studio. The prototype was composed of mini pc which handled all the processes the device had to execute. The computer was used to interface other components of the device such as webcam, monitor, keyboard, and mouse. Since this is an automated attendance system, portability and efficiency were given importance. The prototype’s performance was tested and monitored through the use of 100 participants including the researchers themselves. The project was tested by the Department of Computer, Electronics, and Electrical Engineering (DCEE) at the Engineering Science Building, Cavite State University, Indang, Cavite with the Electronics Engineer Professors. Based on the results of the evaluation, the design project succeeded in achieving its objectives. The overall performance was considered satisfactory. The cost computation of the system amounted to PHP 14,150.00.  

Face recognition technology, defined as the process of detecting and verifying individuals’ identities via image processing techniques in computer vision, has become increasingly vital in today’s technologically and digitally evolved world. Automating the attendance system with face recognition software eliminates inefficiencies like tampered records, proxies, and security risks. This paper proposes incorporating facial recognition technology, Haar Cascade classifiers (Li et al. in ICEMI 2017-Proceedings IEEE 13th International conference on electronic measurement and instruments, vol 2018-Janua, pp 483–487), and the Local Binary Pattern Histogram (LBPH) (Ahmed et al. in 2018 International conference artificial intelligent big data, (ICAIBD 2018), pp 144–147) machine learning approach can be used for identifying faces to create a secure and reliable solution for automating attendance systems. The proposed method produces more accurate results than any other algorithms used earlier. Compared to existing algorithms, the suggested system showed better results, reliability, and robustness in accurately recognizing faces. Its facial recognition accuracy is 87%, and the false-positive rate is only 7%. The system can also reliably identify a person who has undergone unintentional changes in their appearance, such as growing a beard or putting on spectacles.

In this work we propose a Live Attendance Marking System for institutional purpose. This system will enable the department to mark the attendance of students automatically by recognizing their faces. The system is based on the face detection and recognition algorithms and automatically recognizes a student whenever he/she comes across the camera module. Further after the recognition, it automatically updates his/her attendance in the Cloud web server database. The basic architecture and all the algorithms used in the model are described in the paper elaborately. Moreover in order to evaluate and enhance the performance of the system many of the filter functions are used which provide the capability to capture and recognize the images even in dull and low light places. This project also suggests the technique to ensure that a student can mark the attendance only once in a day. The live attendance system is much more efficient to traditional attendance systems both in saving time and in maintaining the database.