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Challenges of Digital Transformation in Education in India

Challenges of Digital Transformation in Education in India

引用

advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Saurabh Shukla Lija Jacob Department of Data Science Christ Deemed to be University Pune India

Online learning has been present since the 1960s and has risen in popularity over time. World-class universities have been using online teaching-learning methodologies to fulfill the needs of students who reside far away from academic institutions for more than a decade. Many people predicted that online education would be the way of the future, but with the arrival of COVID-19, online education was imposed upon stakeholders far sooner and more suddenly than expected. When the COVID-19 pandemic broke out, educational institutions began to explore digital ways to keep students studying even when they couldn't be together in person as governments enacted legislation prohibiting large groups of people from gathering for any reason, including education. The future of such a transition looks promising. However, transitioning from one mode of education to another is not easy. Historically, when educators adopt new tools, learning still continues in the conventional manner. Based on the responses of 176 students, this paper studies the challenges of Digital transformation in the Education sector. The research is extremely beneficial in evaluating the scope of societal opposition to change.

关键词： COVID-19 Machine learning algorithms Pandemics Digital transformation Education Anxiety disorders Legislation

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Tracking scientific Authenticated computing Simulation

Tracking scientific Authenticated Computing Simulation

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international conference on Innovative Sustainable Computational technologies (CISCT)

作者： Harshit Sharma Awanish Kumar Singh Mudit Prakash Srivastava Devesh Katiyar Shivam Tiwari Prabhishek Singh JECRC University Jaipur Rajasthan India Government degree college punchmohani Siddharth Nagar India SRM Institute of Science & Technology India Dr. Shakuntala Misra National Rehabilitation University India G L Bajaj Institute of Technology and Management Greater Noida India Bennett University Greater Noida India

ISBN: (纸本)9781665474177

The motive for cloud-based affirmation is to defend agencies from programmers trying to take personal data. Cloud affirmation approves accredit-ed customers throughout businesses and mainland to safely get to records put away in the cloud with validation given via cloud-based administrations. Cloud computing verification is the approach worried in determining the personality of a client. The subtleties of verification fluctuate contingent upon how you are getting to Cloud Storage but fall into two standard sorts: A server-driven movement approves a software to straightforwardly keep the skills of an assist report to end confirmation. Most of the investigation frameworks proposed truly make them suppose in everyday that we supply a location as hostile to specific headings of the area of the client. Furthermore, the cloud resources are given to the end users with the help of IoT applications. The qualities are being maintained based on authentications of the cloud services to the end users.

关键词： Cloud computing Computational modeling Authentication Software Security Internet of Things Standards

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Multimodal Emotion Recognition Using Deep Learning Techniques

Multimodal Emotion Recognition Using Deep Learning Technique...

引用

advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： S Jerald James Lija Jacob Department of Data Science Christ University Pune Lavasa Pune India

Humans have the ability to perceive and depict a wide range of emotions. There are various models that can recognize seven primary emotions from facial expressions (joyful, gloomy, annoyed, dreadful, wonder, antipathy, and impartial). This can be accomplished by observing various activities such as facial muscle movements, speech, hand gestures, and so forth. Automatic emotion recognition is a significant issue that has been a hotly debated research topic in recent years. At the moment, several research people have taken a component in inheriting or extra multimodal for higher understanding. This paper indicates a method for emotion recognition that makes use of 3 modalities: facial images, audio indicators, and text detection from FER and CK+, RAVDESS, and Twitter tweets datasets, respectively. The CNN model achieved 66.67 percent on the FER-2013 dataset of labeled headshots while on the CK+ dataset, 98.4 percent accuracy was obtained. Finally, diverse fusion strategies had been approached, and each of those fusion techniques gave distinctive results. This project is a step towards the sense of interaction between human emotional aspects and the growing technology that is the future of development in today’s world.

关键词： Training Emotion recognition Adaptation models Schedules Social networking (online) Computer architecture Real-time systems

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A Smart System for Sign Language Recognition using Machine Learning Models

A Smart System for Sign Language Recognition using Machine L...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： B. Rajalingam R. Santhosh Kumar P. Deepan P. Santosh Kumar Patra S. Bavankumar CSE St. Martins Engineering College Secunderabad

When people exchange information, thoughts, or sentiments via the medium of communication, it is called communication. Both parties must be conversant in the same language in order for communication to occur. On the other hand, those who are deaf or dumb must use various communication methods. If you can't hear, you have deafness; if you can't speak, you have dumbness. As a group, they use sign language to communicate, but most people don't see it as a valuable skill. To communicate with someone who is deaf or mute is difficult since not everyone is familiar with or understands sign language. You may develop a model using machine learning to get around this roadblock. It is possible to teach a model to recognize different gestures used in sign language and to translate those gestures into English. Many people will get the ability to converse with the deaf and dumb as a result of this. By using a camera to capture Sign Language data, we may then use machine learning techniques like Convolutional Neural Networks to the datasets. Some other well-known models have been compared to a new strategy that has been presented for the same problem. Principal Component Analysis (PCA), Histogram of Gradients (HOG), and Local Binary Patterns (LBP) are some of the pre-processing approaches employed. ORB, Canny edge detection, and the state of word approach are all used to create the new model. This preprocessed data is then sent through a variety of classifiers in order to provide useful findings (including Random Forests and Support Vector Machines, as well as Nave Bayes and Logistic Regression). The new models are substantially more accurate than the previous ones. The method achieves good accuracy even with a little dataset.

关键词： Histograms Analytical models Computational modeling Gesture recognition Assistive technologies Feature extraction Sensors

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A Review of Big data Analytics on Post-COVID Health Issues

A Review of Big Data Analytics on Post-COVID Health Issues

引用

Recent advances and Innovations in Engineering (ICRAIE)

作者： Vinumon Jacob M. Prakash Department of Computer Science Vinayaka Mission’s Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation Deemed to be University Salem Tamil Nadu India

New technology developments and its processes generate large quantities of data every day. Organizations are faced a number of problems for managing these big data. IoT (Internet of Things) devices are now used for collecting data rapidly and safely in high capacity storage devices and internet clouds. Big data has many characteristics including unstructured which does not conform to a data model. In an organization about 85% data is in unstructured format. The semi-structured data does not conform to a data model but it has some structure. The structured data is in an organized form and can be easily used by a computer program. From the year 2019 a new big data formation happened with COVID-19 pandemic in the world society. Corona virus and COVID-19 pandemic have fully affected the people and this pandemic situation has frightened the people worldwide. It has to find excellent big data analytical methods to control and solve the future problems related to this pandemic. This paper presents big data quality dimensions, big data analytics in COVID-19 pandemic, and physical and mental post-COVID health issues.

关键词： COVID-19 Technological innovation Cloud computing Pandemics Scalability Organizations Mental health

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A Model to Automatically Evaluate Beginners’ Programs Based on Syntactic Knowledge Point and Deep Learning Technology

A Model to Automatically Evaluate Beginners’ Programs Based...

引用

international Symposium on Soft computing and Intelligent Systems (SCIS)

作者： Yu Yan Hirokuni Kurokawa College of Information Science and Engineering Ritsumeikan University Kusatsu Japan Personnel and Training Division Intelligent Vision & Image System. Co. Ltd Tokyo Japan

This report provided a CNN model to automatically evaluate beginners’ programs based on their understanding of program concepts. The model input is a beginner’s program and the output is a letter grade for each program. To train the model, each input program was encoded as a matrix, composed of positive integers by Syntactic Knowledge Point (SKP) technology. Labels for the training data were made by manually evaluating each program in the database, based on the beginner’s understanding of each of the 42 program concepts. In addition, several other technologies such as supervised learning, data standardization, data augmentation have also been used to train the model. Finally, a model with an accuracy of 0.93 has been produced, setting a new benchmark in this young field.

关键词： databases Computational modeling Education Supervised learning Training data Standardization Syntactics

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Increase the Range of Millimeter-Wave Breath Monitoring by Multi-Channel Signal Fusion

Increase the Range of Millimeter-Wave Breath Monitoring by M...

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international conference on Big data computing and Communications (BIGCOM)

作者： Shuhuiyu Yu Huanpu Yin Le Kang Anfu Zhou Department of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering Beijing Technology and Bussiness University Beijing China

With the continuous development of personalized medicines, many sensor technologies have begun to integrate into the medical industry, striving to raise the level of informatization of the medical care system. Particularly, breath, as an important sign of human health, has become an important research direction of smart vital sign monitoring. However, traditional wearable monitoring has not-comfort issues, and the previous millimeter-Wave based contactless solutions suffer from limited sensing range, due to signal attenuation and decrease in the directionality of beam. To solve this problem, in this paper, we propose a long-distance contactless respiration monitoring system based on multi-channel millimeter-wave signals. We use multi-channel antenna array to enhance the energy of the effective signal and adapt the minimum variance distortionless response (MVDR) algorithm to accurately locate the chest from multiple signal sources. Results show that the monitoring distance and accuracy can satisfy the demand for daily, long-distance, real-time monitoring.

关键词： Industries Precision medicine Millimeter wave technology Adaptive arrays Medical services Distortion Real-time systems

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Machine learning algorithms for big data analytics

Machine learning algorithms for big data analytics

引用

international conference on Computational Methods and data Engineering, ICMDE 2020

作者： Rahul, Kumar Banyal, Rohitash Kumar Goswami, Puneet Kumar, Vijay Department of CSE Rajasthan Technical University Kota India Department of Basic and Applied Science NIFTEM Sonepat India Department of CSE SRM University Sonepat India

ISBN: (纸本)9789811568756

A machine learning algorithm (MLA) is an approach or tool to help in big data analytics (BDA) of applications. This tool is suitable to analyze a large amount of amount generated by an application for effective and efficient utilization of the data. Machine learning algorithms considered to find out meaningful data and information for industrial applications. It is one of the services under big data analytics (BDA). Big data analytics (BDA) is suitable for identifying risk management, cause of failure, identifying the customer based on their procurement detail records, detection of fraud, etc. So, this paper deals with the work done in this field to analyze the importance of machine learning tools and techniques, identify the field where it is suitable to use including industries such as marketing, human resource, healthcare, insurance, banking, automobile, etc. This paper identifies different challenges of machine learning tools and technologies including the current status of adoption in industries. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Machine learning

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A Peer-to-Peer Approach for Extending Wireless Network Base for Managing IoT Edge Devices Off-Gateway Range 5th

A Peer-to-Peer Approach for Extending Wireless Network Base ...

引用

5th international conference on Smart computing and Informatics, SCI 2021

作者： Yellasiri, Ramadevi Tiruvayipati, Sujanavan Tumula, Sridevi Reddy, Khooturu Koutilya Department of Computer Science and Engineering Chaitanya Bharathi Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Osmania University Hyderabad India Maturi Venkata Subba Rao Engineering College Hyderabad India

ISBN: (纸本)9789811696688

Internet of Things (IoT) is a feature of the future Internet that has been portrayed as a worldview which principally coordinates and empowers advancements and correspondence arrangements with an outstanding interest to characterize how current standard conventions could uphold the acknowledgment of the far technological vision. Inside this specific situation, remote sensor organizations close to handle radio correspondences directing conventions as an intent to their appropriateness toward IoT. Apart from the standard infrastructure components especially power and network which are absolutely necessary for IoT gadgets, there is also a need to eliminate such dependencies to make the IoT future ready. One such move in building wireless-fidelity (Wi-Fi) peer-to-peer (P2P) communication strategy for IoT gadgets is portrayed in the research work. An uncomplicated approach is proposed through this research in order to establish a network backbone among IoT gadgets and also make them self-powered without the need to rely on an external infrastructure. The proposed methodology would minimize the overall capital investment in IoT network and power infrastructure including their maintenance but with the trade-off for lower data rates on further expansion. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Triggerability of Backdoor Attacks in Multi-Source Transfer Learning-based Intrusion Detection

Triggerability of Backdoor Attacks in Multi-Source Transfer ...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Nour Alhussien Ahmed Aleroud Reza Rahaeimehr Alexander Schwarzmann School of Computer and Cyber Sciences Augusta University Augusta Georgia USA

Network-based Intrusion Detection Systems (NIDSs) automate monitoring of events in networks and analyze them for signatures of cyberattacks. With the advancement of machine learning algorithms, more organizations started using machine learning based IDSs (ML-IDSs) to identify and mitigate cyberattacks. However, the lack of training datasets is a major challenge when implementing ML-IDSs. Therefore, using training data from external sources or transfer learning models are some solutions to overcome this challenge. However, using training data from external sources introduces the risk of backdoored datasets, specifically, when the adversaries also have background knowledge on data sources inside the target organization. This work investigates the role of backdoor attacks on intrusion detection techniques trained using multi-source data. The backdoor examples are injected into one or more training data sources. Transfer learning models are then created by projecting data from different sources into a new subspace containing all source data. The backdoor is then triggered in the target data. An anomaly-based intrusion detection classifier is applied to examine the effectiveness of the introduced backdoors. The results have shown that backdoor attacks on multis-source transfer learning models are feasible, although having less impact compared to backdoors on traditional machine learning models.

关键词： Training Computational modeling Soft sensors Transfer learning Intrusion detection Training data Organizations

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