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Data Analytics and Knowledge Management in a learning environment for Higher Education

Data Analytics and Knowledge Management in a learning enviro...

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international conference on signal processing and Information Security (ICSPIS)

作者： Seema Verma Jitendra Pandey Alavikunhu Panthakkan School of Physical Sciences Banasthali Vidyapith Rajasthan India Computer Science and Engineering Banasthali Vidyapith Rajasthan India College of Engineering and IT University of Dubai Dubai UAE

ISBN: (纸本)9781665492669

In the current work, the authors use the well-known Nonaka & Takeuchi and its extended model by Harsh, which includes knowledge reusability and knowledge sharing in a three-dimensional environment, to address the impact of communication and information technology on knowledge management issues in an e-learning environment. It has been discovered that incorporating knowledge sharing and reuse can greatly enhance e-learning. According to research, the use of Data Analytics (DA) can greatly increase the opportunity for qualitative learning, which not only helps us in discovering new areas of interest but also improves the quality of learning in higher academic settings. this is because Data Analytics (DA) has a wide range of applications for analysis of the existing knowledge. In order to include the idea of quality knowledge in our work, we took into consideration the well-known ADRI model of quality. Finally, it has also been highlighted how knowledge repositories might be used in a variety of educational settings.

关键词： Solid modeling Analytical models Data analysis Electronic learning Education Information security signal processing

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Deep learning Framework using CNN for Brain Tumor Classification

Deep Learning Framework using CNN for Brain Tumor Classifica...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Neha Bhardwaj Meenakshi Sood SS Gill ECE Department NITTTR Chandigarh India CDC Department NITTTR Chandigarh India

ISBN: (纸本)9781665476485

the brain tumor is one of the major concerns accounting to number of deaths caused by it worldwide. the available techniques to diagnose brain tumors requires human intervention, are time consuming and are subjective in nature. In the current scenario, automated methods for correct diagnosis of brain tumors is of utmost importance for timely detection and proper diagnosis. the existing frameworks are time consuming and intrusive, these limitations highlight the importance of using a fully automated deep learning system for brain tumour classification. the paper presents a deep learning framework for classification with minimal human intervention. Convolutional Neural Network is most preferred deep learning method for classification tasks owing to its flexible architecture and high accuracy. this paper aims to develop a deep learning based CNN framework for brain tumor classification. the proposed CNN framework has an edge over the other models by achieving classification accuracy of 99.4% and loss of 0.0030%. the Comparative analysis of the proposed framework with other state of art models shows a relative improvement in accuracy and test error.

关键词： Deep learning Image edge detection signal processing Brain modeling Communications technology Convolutional neural networks Multimedia communication

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A Study on Deep learning Approaches for Mars Weather Forecasting

A Study on Deep Learning Approaches for Mars Weather Forecas...

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international conference on signal processing and Information Security (ICSPIS)

作者： Mina Al-Saad Nour Aburaed Saeed Al Mansoori Wathiq Mansoor Hussain Al-Ahmad College of Engineering and IT University of Dubai UAE Mohammed Bin Rashid Space Centre Dubai UAE

ISBN: (纸本)9781665492669

Forecasting surface temperature over Mars is one of the most popular research areas in planetary science as it is essential to obtain information about a planet’s meteorological data without directly landing on its surface. this paper presents an in-depth exploratory analysis and prediction of Mars temperature as a time series problem. A Residual CNN-LSTM is devised to predict the temperature for the year 2018 based on the temperatures recorded in 2012–2017. the experimental results aim to explore four scenarios: predicting maximum temperature using terrestrial dates, predicting minimum temperature using terrestrial dates, and the remaining two aspects predict the same using Solar Days (SOL). the proposed model is compared with other commonly used approaches, and it shows superiority in terms of Root Mean Squared Error (RMSE).

关键词： Deep learning Mars Temperature distribution Time series analysis Weather forecasting Information security signal processing

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Reliable Implementation of Finite State machine for On-Board VLSI Designs 1

引用

5th international conference on VLSI, Communication and signal processing, VCAS 2022

作者： Jain, Sourabh Kumar Mehta, Usha Bhandari, Aarushi Poddar, Kamal Trivedi, S.M. Lal, A.K. Space Applications Centre Indian Space Research Organization Ahmedabad India Nirma University Ahmedabad Ahmedabad India

ISBN: (数字)9789819909735

ISBN: (纸本)9789819909728

Finite State machines (FSMs) are considered the "brain", which drives and controls the integrated circuit or system functionalities. therefore, radiation or glitch-induced failures in FSMs can have severe consequences on the circuit or system functionalities, where these FSMs are performing control operations. Generally, every FSM has some illegal states, and transition to these states may result in deadlock, interrupting the desired operations. Hence, necessary precautions are required to be taken in FSM design to ensure reliable FSM implementation that is also tolerant to radiation-induced issues. Field Programmable Gate Arrays (FPGAs) are extensively used to perform digital functions in on-board designs for space missions. these FPGAs are prone to space radiation if the device is not radiation hardened. For such devices, reliable implementation of FSM requires various good practices, starting from RTL designing to synthesis by EDA tools. Synthesis tools build reliable FSM by enabling certain switches, attributes, or constraints by creating recovery states or preventing the FSM from entering into invalid states. However, in all cases, EDA tools may not create a reliable FSM even after enabling desired switches. this paper presents an assessment for reliable FSM creation, based on RTL coding styles and EDA tools with their different available switches. the techniques for reliable implementation and verification of FSM are discussed. In addition, this paper also presents benchmarking of resource utilization and performance of FSM under various scenarios. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Integrated circuit design

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Classification and analysis of Alzheimer’s Disease using Deep learning methods on MRI and PET

Classification and analysis of Alzheimer’s Disease using De...

引用

international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Febin Antony Anita H B Department of Computer Science Christ Deemed to be University Bangalore India

ISBN: (纸本)9781665476485

Alzheimer’s disease (AD) falls in the category of neurodegenerative illness in which an individual loses his or her power to remember things and behaviors. It affects memory in younger patients and as it progresses causes diffuse cortical functions. However, a major issue with the diagnosis and treatment of AD symptoms is that it has complex pathogenesis because of which there is no clinical intervention for its treatment. there is no disease-modifying treatment to cure AD symptoms that increases co-morbidities among the patients. the present research identified this gap and focuses on using Deep learning methods on MRI and PET data so that there is early diagnosis of AD by healthcare experts and they could propose a better treatment process for reducing AD symptoms. the present research identified that by using deep learning-based approaches particularly ResNet50 architecture, there is the execution of quantitative assessment of brain MRI and PET to acquire insights about the internal abnormalities through self-learning features. It will help in initiating proper treatment and avoiding damage to the brain further.

关键词： Deep learning Neuroimaging Magnetic resonance imaging Medical services signal processing Predictive models Data models

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Data Analytics in Farming: Rice price prediction in Andhra Pradesh

Data Analytics in Farming: Rice price prediction in Andhra P...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Pundru Chandra Shaker Reddy G. Suryanarayana LNC Prakash K Sucharitha Yadala School of Computing and Information Technology REVA University Bengalure India Department of Computer Science … Engineering Vardhaman College of Engineering Hyderabad India Department of Computer Science … Engineering CVR College of Engineering Hyderabad India Department of Computer Science … Engineering CMR Institute of Technology Hyderabad India

ISBN: (纸本)9781665476485

the variances regarding prices of horticultural commodities adversely affect the nations GDP. the ranchers are genuinely and monetarily influenced as their long stretches of difficult work go to no end. Forecast of the prices might help the farming production network in settling on vital choices in limiting and dealing with the danger in value variances. By the way of outcome of the diminution of crop yield making due to uneven atmosphere circumstances and global warming. Rice is a significant yield developed in India, with Andhra Pradesh (AP) state being second as far as creation. At present ranchers in AP are moving from rice development to different harvests on account of worth variances and environmental variation. In this research, the costs of rice in AP remain anticipated utilizing time-series using data analytics like machine learning (ML) models. the prototypes SARIMA, Holt-Winter's Periodic technique, and Long Short-Term Memory (LSTM) Neural Networks (NN)be situated and utilized, and their recital was assessed dependent on the RMSE esteem on the rice database whose costs from 2001 to 2020. the conclusions demonstrate that the recommended process, LSTM neural network method is actual expectant and finest method which is suitable for the data.

关键词： Costs Data analysis Limiting Time series analysis Prototypes Production machine learning

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A Method of machine learning for Social Bot Detection Combined with Sentiment Analysis 22

A Method of Machine Learning for Social Bot Detection Combin...

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Proceedings of the 2022 5th international conference on machine learning and Natural Language processing

作者： Guanghua Long Deyu Lin Jie Lei Zhiyong Guo Yangyang Hu Linglin Xia School of Software Nanchang University China

ISBN: (纸本)9781450399067

Social Bot exists widely in major social networks. Some maliciously use a social bot to guide public opinion, steal user privacy, and create rumors, which seriously affects the security of social networks. Past approaches mainly extracted large amounts of contents but ignored bots’ text sentiment features, and it is hard to detect social bot just based on contents. this paper proposes a malicious social bot detection method that combines sentiment features in response to this problem. It trains a Bidirectional Long Short-Term Memory model(Bi-LSTM) with an Attention Mechanism to perform sentiment calculation on the online text information of social accounts and analyze the sentiment fluctuations of accounts to get the new sentiment features; then, it inputs the new features combined with metadata features into different machine learning models for analysis and comparison. through this method, different machine learning detection models have improved the detection accuracy after combining sentiment features.

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Fine-tuned LSTM-Based Model for Efficient Honeypot-Based Network Intrusion Detection System in Smart Grid Networks

Fine-tuned LSTM-Based Model for Efficient Honeypot-Based Net...

引用

international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Abdullatif Albaseer Mohamed Abdallah Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665482387

Honeypot is considered a powerful complement to the Network Intrusion Detection System (NIDS) in smart grid (SG) systems, which minimizes the workload of NIDSs while providing access to information about the attacker's actions. this assists in further tracing the attack surface and, in return, enables the NIDSs to prevent such behaviors. machine learning (ML) has recently attracted considerable attention in the SG security domain as a stringent technique for designing and implementing algorithms to predict security threats. However, large data sets collected by honeypots require more effort for faster response, real-time processing, and decision-making, especially for limited resources SG's devices. thus, this paper proposes an approach to address this challenge, including feature extraction, oversampling and weak label combinations. We demonstrate that all classic ML algorithms cannot maintain the desired performance level when reducing the number of selected features (i.e., using only 25% of the features). As a result, we resort to the Deep learning approach and propose an LSTM-based model that outperforms the state-of-the-art in terms of accuracy, precision, recall, and f1-score. We conduct extensive simulations using a realistic dataset that includes large log files. the proposed approach can employ just 25% of the features from each collected network packet while attaining 99.8% testing accuracy with a 13% improvement compared to the benchmarks.

关键词： Performance evaluation Training Decision making Network intrusion detection signal processing algorithms Prediction algorithms Feature extraction

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Ensemble Regression for 1-Bit Channel Estimation

Ensemble Regression for 1-Bit Channel Estimation

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Ahmed Elsheikh Ahmed S. Ibrahim Mahmoud H. Ismail Department of Eng. Mathematics and Physics Faculty of Engineering Cairo University Giza Egypt Electrical and Computer Eng. Dept. Florida International University Miami FL USA Department of Electrical Engineering American University of Sharjah Sharjah UAE

ISBN: (纸本)9781665482387

Employing 1-bit analog-to-digital converters (ADCs) is necessary for large-bandwidth massive multiple-antenna systems to maintain reasonable power consumption. However, conducting channel estimation with such 1-bit ADCs and with low complexity is a challenging task. In this paper, we propose to employ an Ensemble Regression (ER) model to conduct low-complexity and high-quality channel estimation. the amount of proposed computations are less than 3% of that proposed by similar deep learning (DL) methods, and in turn requires approximately 4% of the power consumed in computations while maintaining the same level of performance.

关键词： Training Adaptation models Power demand Computational modeling Channel estimation signal processing algorithms signal processing

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Acoustic Bandwidth Extension by Audio Deep Residual U-Net

Acoustic Bandwidth Extension by Audio Deep Residual U-Net

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Linlin Ou Yuanping Chen Computer Network Information Center Chinese Academy of Sciences Beijing China School of computer science and technology University of Chinese Academy of Science Beijing China

Mobile communication systems often rely on traditional channels with narrow bandwidth bottlenecks, reducing the audio quality. Due to the size of the network and the heterogeneity of devices, using high-quality audio codecs in real-world scenarios to transmit higher sample rate audio is difficult to practice. this paper proposes an approach by which communication nodes can extend the bandwidth of the band-limited input signal of a low sample rate codec, an audio synthesis neural network: ARUNet (Audio deep Residual U-Net), that integrates the advantage of residual learning, and U-Net is introduced to compensate for the high-frequency part of the live input audio clips. In addition, ARUNet has made outstanding achievements in various instrument solo data sets, with SNR close to 40dB and VGG distance close to 32. We explore the impact of our architectural design and demonstrate considerable improvements in terms of both perceptual and objective metrics.

关键词： Deep learning Codecs Neural networks Superresolution Noise reduction signal processing algorithms Bandwidth

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