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Improving projection of Deep learning-based Precipitation in India using Dimensionality Reduction Technique

Improving projection of Deep learning-based Precipitation in...

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2022 international conference on Sustainable Computing and data Communication Systems, ICSCDS 2022

作者： Mehta, Nishtha Thakkar, Amit Shashtri, Hiteshri Bhatt, Nikita Chandubhai S. Patel Institute of Technology U P. U. Patel Deptartment of Computer Engineering Charusat Changa India Chandubhai S. Patel Institute of Technology M. S. Patel Deptartment of Civil Engineering Charusat Changa India

ISBN: (纸本)9781665478847

Climate change is surely one of the major concerns in the 21st century. Various research efforts to understand the future of the world's climate are being made. The climate projections based on standard rational-based physics and global system models are well known for this. In this study, we examine the effectiveness of entire phenomenological model which uses top-down approach to obtain 21 century projections of precipitation in the country of India using dimensionality reduction technique and LstM, GRU and big data of global general circulation models (GCMs). As part of research work, I referred different literatures related to same. To train this neural network faster technique named UMAP (Uniform Manifold Approximation Projection) is applied here for getting better result. © 2022 IEEE.

关键词： Climate models

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Fake News Detection Using Machine learning Techniques

Fake News Detection Using Machine Learning Techniques

引用

international conference on Software engineering Research, Management and Applications (SERA)

作者： Achhiya Sultana Mahmudul Islam Mahady Hasan Farruk Ahmed Department of Computer Science and Engineering Independent University Bangladesh

A lot of information is spread by people in the social media to update their status and share crucial news with others. But the majority of these platforms don’t promptly validate the individuals or their posts and people aren’t able to identify the fake news manually. Therefore, there is a need for an automated system capable of detecting fake news. This research has proposed to build a model using four machine learning algorithms. The dataset employed in the experiment is a composite of two datasets containing almost equal amounts of true and fake news articles on politics. The preprocessing stages begin with cleaning the data by removing punctuation, tokenization, special characters, white spaces, redundant word elimination, numerals, and English letters followed by stemming and stop with data discretization. Then, we analyzed the collected data and 80% of the data has been used to train each model initially. After that, the four manifested classification algorithms are applied. For identifying fake news from news articles, meth-ods like Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting Classifier were used. The trained classifiers’ accuracy has been evaluated using the remaining 20% of the data. The results show that the decision tree model produces the best accuracy of 99.60% and gradient boosting of 99.55%. Besides, the random forest shows 99.10% along with the logistic regression 98.99%. Moreover, we have explored the best model to achieve the highest precision, recall, F1-score based on the confusion matrix’s outcome.

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Analyzing EEG data with Machine and Deep learning: A Benchmark 21st

Analyzing EEG Data with Machine and Deep Learning: A Benchma...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Avola, Danilo Cascio, Marco Cinque, Luigi Fagioli, Alessio Foresti, Gian Luca Marini, Marco Raoul Pannone, Daniele Sapienza Univ Rome Rome Italy Univ Udine Udine Italy

ISBN: (纸本)9783031064272;9783031064265

Nowadays, machine and deep learning techniques are widely used in different areas, ranging from economics to biology. In general, these techniques can be used in two ways: trying to adapt well-known models and architectures to the available data, or designing custom architectures. In both cases, to speed up the research process, it is useful to know which type of models work best for a specific problem and/or data type. By focusing on EEG signal analysis, and for the first time in literature, in this paper a benchmark of machine and deep learning for EEG signal classification is proposed. For our experiments we used the four most widespread models, i.e., multilayer perceptron, convolutional neural network, long short-term memory, and gated recurrent unit, highlighting which one can be a good starting point for developing EEG classification models.

关键词： Brain computer interfaces (BCI) Electroencephalography (EEG) Deep learning Benchmark Classification

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Detection of Pulmonary Conditions Using the DeepHealth Framework 21st

Detection of Pulmonary Conditions Using the DeepHealth Frame...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Carrion, Salvador Lopez-Chilet, Alvaro Martinez-Bernia, Javier Coll-Alonso, Joan Chorro-Juan, Daniel Ander Gomez, Jon Univ Politecn Valencia Pattern Recognit & Human Language Technol Res Ctr Valencia 46022 Spain

ISBN: (纸本)9783031133213;9783031133206

Medical diagnosis assisted by intelligent systems is an effective strategy to increase the efficiency of healthcare systems while reducing their costs. This work is focused on detecting pulmonary conditions from X-ray images using the DeepHealth framework. Our results suggest that it is possible to discriminate pulmonary conditions compatible with the COVID-19 disease from other conditions and healthy individuals. Hence, it could be stated that the DeepHealth framework is a suitable deep-learning software with which to perform reliable medical research. However, more medical data and research are still necessary to train deep learning models that could be trusted by medical personnel.

关键词： Deep learning Computer vision Neural networks Medical imaging Covid19

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Enhancing learning Outcomes Through the Use of Conducive learning Spaces 1st

Enhancing Learning Outcomes Through the Use of Conducive Lea...

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1st international conference on Computational Technologies and Electronics, ICCTE 2023

作者： Modi, Shabina Sale, Deepali Borate, Vishal Mali, Yogesh Kisan Karmaveer Bhaurao Patil College of Engineering Satara India Dr D Y Patil College of Engineering and Innovation Talegoan Pune India G H Raisoni College of Engineering and Management Wagholi Pune India

ISBN: (纸本)9783031819346

The learning spaces or the built environment or the physical learning environment, in educational institutions impact the acquisition of 21st century skills and quality education. Proficiency in digital learning is an important competency skill required for 21st century youth. With the developing trend of e-learning, the importance or the role of learning spaces in the improvement of learning outcome is diluting. This study aims to determine the parameters for developing learning spaces to support learning and improve the learning outcomes of the learner. The research is based on the analysis of the existing literature data to find the parameters of learning spaces required for improving cognitive, affective and behavioral aspects of learning outcomes. The research findings put forward five parameters, namely wellness, stimulation, individualization, social interaction and spatial function, required for learning spaces to support learning and improve learning outcomes. The findings have been validated through a pilot survey, conducted in secondary schools in Punjab. This research will help the designers, architects and stakeholders to understand the parameters required to make learning spaces, conducive to learning. This study will further add awareness regarding the importance of learning spaces in improving the learning outcome of students. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Towards Better Coverage of dataset with Software Product Line engineering 21

Towards Better Coverage of Dataset with Software Product Lin...

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21st IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Shi, Lei Kondo, Masanari Ubayashi, Naoyasu Kamei, Yasutaka Kyushu Univ Grad Sch Fukuoka Japan Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka Japan

ISBN: (纸本)9781665478366

The coverage of the dataset is one of the important factors for training deep learning models. This coverage measures how well the dataset covers the cases, which can be defined by metrics, where deep learning models should learn. Hence, the lower coverage has negative impacts on the training of deep learning models. In this paper, we introduce a concept to achieve high coverage in terms of multiple metrics and achieve a better coverage of the dataset. In particular, we propose data assets inspired by software assets of software product line engineering. A data asset indicates a subset of a dataset extracted by various metrics. Optimizing the proportion of such data assets in the dataset improves the coverage in terms of the corresponding metrics. In other words, we achieve the better coverage of the dataset based on data assets. To propose this approach, we describe our future research plan.

关键词： software product line engineering deep learning

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Comprehensive Pipeline Leak Detection Using Induced-Leak Enhanced Scalogram Analysis and Deep learning 25

Comprehensive Pipeline Leak Detection Using Induced-Leak Enh...

引用

25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Siddique, Muhammad Farooq Ullah, Niamat Kim, Jong-Myon University of Ulsan Department of Electrical Electronics and Computer Engineering Ulsan Korea Republic of University of Ulsan Pd Techology Co. Ltd. Department of Electrical Electronics and Computer Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350330014

Pipelines hold a crucial role in the effective transportation of liquids and gases over extensive distances. Detecting leaks through Acoustic Emission (AE) events is intricate due to the interference of noise. The conventional strategy of utilizing AE scalogram images from continuous wavelet transforms (CWT) faces challenges in precisely identifying leaks amidst noise. To surmount these complexities, we introduce an inventive technique. Our Enhanced Leak-Induced Scalograms are generated by processing CWT images with Laplacian filters, Non-Local Means (NLM) noise reduction, and adaptive histogram equalization (AHE) to enhance contrast. This unique approach ensures scalogram image quality while reducing background noise. We incorporate the Grey Level Run Length Matrix (GLRLM) model for feature extraction and deploy K-Nearest Neighbors (KNN) and t-Distributed stochastic Neighbor Embedding (t-SNE) for accurate classification of leak and normal conditions. The proposed method excels across diverse metrics using real-world datasets. This methodology elevates image quality, bolsters classification precision, and marks a significant advancement in pipeline leak detection. Validation through experimentation on an industrial-scale testbed involving real pipelines affirms the efficacy of our proposed approach. © 2023 IEEE.

关键词： Wavelet transforms

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Phase Locked Loop with Ramp Generator for FMCW LiDAR TX in 28-nm CMOS 21

Phase Locked Loop with Ramp Generator for FMCW LiDAR TX in 2...

引用

21st international System-on-Chip Design conference, ISOCC 2024

作者： Shin, Dohyun Kim, Jonghyun Burm, Jinwook Sogang University Dept. of Semiconductor Engineering Seoul121-742 Korea Republic of Sogang University Dept. of Electronics Engineering Seoul121-742 Korea Republic of

ISBN: (纸本)9798350377088

Frequency-Modulated Continuous Wave Light Detection and Ranging (FMCW LiDAR) is a coherent detection system that can achieve high resolution even with a low clock frequency. The frequency of the laser must be modulated with a constant slope over time. For this purpose, we study the design of a circuit for inputting a ramp wave to a laser and a PLL to compensate for the nonlinearity of the laser. The CMOS chip, which outputs an 8kV/s slope ramp wave, consists of a phase-frequency detector, charge pump, loop filter, ramp generator, etc. Through negative feedback, the beating frequency is kept equal to the reference frequency. As a result of post-simulation, the modulation slope error was confirmed to be less than 0.1%, and the Electric-Optical PLL system will be able to have a higher Signal-to-Noise Ratio and resolution than an open loop system. © 2024 IEEE.

关键词： Ramp generators

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An Autoencoder-Driven FDIA Detection Method for Virtual Coupling Systems of Heavy-Haul Trains 21

An Autoencoder-Driven FDIA Detection Method for Virtual Coup...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Ren, Wanwan Peng, Jun Zhang, Rui Rong, Jieqi Shu, Boyu Liu, Yongting Yang, Yingze Central South University School of Computer Science and Engineering Changsha China Central South University School of Electronic Information Changsha China Changsha University School of Electronic Information and Electrical Engineering Changsha China Central South University School of Automation Changsha China

ISBN: (纸本)9798350329223

The virtual coupling system provides a solution for improving the transport capacity of heavy-haul trains, and its implementation relies on reliable train-to-train communication. However, communications are vulnerable to cyberattacks. Among them, false data injection attacks (FDIAs) are a common and highly concealed form of attack. They interfere with train information interaction by destroying the authenticity of data, disrupting the cooperative control objectives of the virtual coupling system, and thus affecting the stable operation of the train. In order to ensure the safe, reliable, and efficient operation of heavy-haul trains, an autoencoder-driven FDIA detection method for virtual coupling systems of heavy-haul trains is proposed. First, the virtual coupling system of heavy-haul trains is modeled. Next, the collaborative control law of the virtual coupling system is designed, and the potential impact of false data injection on the system is analyzed. Afterward, a stacked autoencoder model is developed. Using the unsupervised idea, the model is trained to learn to reconstruct normal operating data, and the reconstruction error of the trained model is used as an attack detection indicator. Finally, the effectiveness of the proposed scheme is evaluated against several attack scenarios. © 2023 IEEE.

关键词： learning systems

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VSNT: Adaptive Network Traffic Forecasting via VMD and Deep learning with SCI-Block and Attention Mechanism 21

VSNT: Adaptive Network Traffic Forecasting via VMD and Deep ...

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作者： Dong, Pingping Zuo, Yuning Chen, Liying Li, Shangyu Zhang, Lianming Hunan Normal University College of Information Science and Engineering Changsha China

ISBN: (纸本)9798350329223

As the Internet evolves towards a dense network paradigm, advancements in mobile services have resulted in a consistent have consistently amplified the volume of mobile data. Consequently, precise network traffic forecasting has become critical for effective network planning and resource allocation. The Informer model, based on the Transformer architecture, has been optimized for time-series forecasting recently. However, the Informer tends to accentuate noise within individual time series, which leads to overfitting or decreased robustness, ultimately affecting forecasting performance. To address these challenges, an adaptive network traffic forecasting model named VSNT is introduced. VSNT employs Variational Mode Decomposition (VMD) to adaptively decompose original network traffic into Intrinsic Mode Function (IMF) components. For the preservation of the interrelation among IMF components, comprehensive representation learning is undertaken prior to their input into the neural network. This network utilizes an encoder-decoder structure and integrates tools such as the SCI-Block and attention mechanisms. The SCI-Block is instrumental in extracting multiscale network attributes, while the attention mechanism emphasizes pronounced features. Compared to the intricate Informer model, VSNT reduces the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) by approximately 51% and 35% respectively. This positions VSNT as a robust solution for accurate network traffic forecasting, providing a fruitful path for research in network traffic forecasting and time-series analysis. © 2023 IEEE.

关键词： Variational mode decomposition

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