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Student data Analysis using Hadoop

Student Data Analysis using Hadoop

引用

international conference on ent computing and Control Systems (ICICCS)

作者： N. Shirisha G. Divyajyothi A. Prashanthi G. Sowmya Department Of Computer Science & Engineering MLR Institute Of Technology Hyderabad India Department Of Computer Science & Engineering Nalla Narasimha Reddy Education Society’s Group of Institutions Hyderabad India Department Of AI&ML MLR Institute Of Technology Hyderabad India

The amount of data produced at educational institutions today is growing quickly. It is challenging to analyze the student performance from the vast amount of data using conventional data processing methods. One can quickly analyze the vast amount of data by using big data. Previously, different databases are used for students. So that it takes more time to analyze the performance of a student. By using this application, it takes less time to analyze the data, as it is just like a centralized database where the entire profile of a student is found. In this study, data are analyzed using map-reduce techniques, which is a programming model suitable for processing large amounts of data, and a Hadoop cluster for ease of processing. This helps educational institutions analyze the students’ data better and focus on their weak spots.

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Low-Power LoRa Weather Station with Adaptive data Fusion for Agriculture

Low-Power LoRa Weather Station with Adaptive Data Fusion for...

引用

6th international conference on Intelligent computing and Signal processing (ICSP)

作者： Yujun Luo Yucong Zhang Yang Huang Shiyan Tong School of Automation Chongqing University of Posts and Telecommunications Chongqing China

Agriculture is the foundation and basis of a nation's prosperity, and food is a fundamental element of people's livelihood. The uncertainty of the environment and the variability of weather have a significant impact on crop planting and yields. To address the issues of high cost, high power consumption, and difficult management in current agricultural meteorological data collection, this paper presents a small-scale weather station system based on LoRa and an adaptive data fusion algorithm, consisting of outdoor data collection stations and indoor gateway nodes. The LEACH routing protocol is used for outdoor data collection, and the adaptive data fusion algorithm is used for data transmission between nodes and gateway nodes. Due to the limited computing power of nodes, a linear ARIMA model is used as the evaluation model for data reliability in the data fusion algorithm, balancing computational complexity and accuracy. The relay station uploads the collected data from outdoor nodes to the cloud server via Wi-Fi for remote monitoring. Tests show that the proposed data fusion algorithm significantly prolongs the lifespan of cluster head nodes, enabling the system to work stably outdoors.

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Detection and Classification of DDoS Attacks in Cloud data Using Hybrid LSTM and RNN for Feature Selection

Detection and Classification of DDoS Attacks in Cloud Data U...

引用

international conference on Circuit, Power and computing Technologies (ICCPCT)

作者： R.A. Karthika P. Sriramya A. Rohini Dept. of Computational Intelligence SRMIST Chennai Dept. of Artificial Intelligence and Data Science SIMATS Saveetha School of Engineering Chennai Dept. of CSE Presidency University Bengaluru

Cyber threats are the main source of concern in the sector it is deployed. DDoS attacks are at the very top of the list. The rapid growth of cloud migration also expands the attack surface. Different forms of DDoS attacks exist, such as DoS and DDoS etc., There are several existing machine learning algorithms to detect DDoS attacks. There are also many hybrids of machine learning algorithms which are introduced for detecting DDoS attacks to increase the accurateness of detection. Here we use hybrid Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) for feature selection and deliver an improved detection system. The proposed exploratory model of Multi-layer Perceptron (MLP) classifiers uses deep neural network algorithms. The final outcome shows higher accuracy and precision by dealing with a huge dataset that was gathered. The accuracy and precision of 98.85% and 92 % respectively for grouping in structure.

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Geometry-Aware DOA Estimation Using a Deep Neural Network with Mixed-data Input Features

Geometry-Aware DOA Estimation Using a Deep Neural Network wi...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ulrik Kowalk Simon Doclo Joerg Bitzer Institute for Hearing Technology and Audiology Jade University of Applied Sciences Oldenburg Germany Department of Medical Physics and Acoustics and Cluster of Excellence Hearing4all University of Oldenburg Oldenburg Germany

Unlike model-based direction of arrival (DoA) estimation algorithms, supervised learning-based DoA estimation algorithms based on deep neural networks (DNNs) are usually trained for one specific microphone array geometry, resulting in poor performance when applied to a different array geometry. In this paper we illustrate the fundamental difference between supervised learning-based and model-based algorithms leading to this sensitivity. Aiming at designing a supervised learning-based DoA estimation algorithm that generalizes well to different array geometries, in this paper we propose a geometry-aware DoA estimation algorithm. The algorithm uses a fully connected DNN and takes mixed data as input features, namely the time lags maximizing the generalized cross-correlation with phase transform and the microphone coordinates, which are assumed to be known. Experimental results in a reverberant scenario demonstrate the flexibility of the proposed algorithm towards different array geometries and show that the proposed algorithm outperforms model-based algorithms such as steered response power with phase transform.

关键词： Geometry Deep learning Direction-of-arrival estimation Sensitivity Neural networks Signal processing algorithms Estimation

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Information Fusion for Seveirty Detection using Machine Learning in data Mining Healthcare

Information Fusion for Seveirty Detection using Machine Lear...

引用

Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Vaishali Gupta Pawan Bhambu Department of Computer Science & Engineering Chandigarh Engineering College Jhanjeri India Department of Computer Science Enginnering Vivekananda Global University Jaipur India

Improved patient care and less mental strain on healthcare providers are two benefits of using algorithms for machine learning to healthcare data. These algorithms may be used to spot irregularities in vital signs, which might speed up medical assistance or provide light on a disease's progression. While there is a wealth of literature comparing the unsupervised and supervised performances of anomaly detection algorithms on popular public datasets, this same level of conceptual comparison is lacking when it comes to physiological data. Knowing one's heart rate may provide valuable insight on one's health and level of physical activity, making it an underutilised data source. Specifically, we used and compared five machine learning methods, two of which were unsupervised and the other three supervised, to identify outliers in heart rate data. The algorithms were tested using physiological data from human subjects' hearts. Results demonstrated that both outlier factor and regression trees algorithms were effective in detecting heart rate anomalies, with both models successfully generalising from their simulation heart rate data training to real-world heart rate data. In addition, the findings lend credence to the idea that, in the absence of real labelled data, simulated data can be used to configure methodologies to a certain degree of performance, indicating that this kind of training could be particularly useful in the initial rollout of a system with no preexisting data.

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FedSM: A Practical Federated Shared Mobility System 50th

FedSM: A Practical Federated Shared Mobility System

引用

50th international conference on Very Large data Bases, VLDB 2024

作者： Wei, Shuyue Zhang, Yuanyuan Zhou, Zimu Zhang, Tianlong Xu, Ke SKLCCSE Lab FBPC Beijing Advanced Innovation Center Beihang University China North China Institute of Computing Technology China School of Data Science City University of Hong Kong Hong Kong SKLCCSE Lab Beihang University China

Shared mobility leverages under-utilized vehicles to offer on-demand transport services by sharing vehicles among users. It strives to match supply with demand via a series of data-intensive opera- tions such as supply prediction and task assignment. However, its full potential is often compromised in practice as most shared mobility platforms operate in isolation, leading to sub-optimal re- source utilization. In this demonstration, we advocate a federated approach to shared mobility, which enhances its effectiveness by enabling optimizations across platforms while retaining their au- tonomy. We develop privacy-preserving operators and incentive mechanisms dedicated to supply prediction and task assignment in shared mobility and implement generic interfaces that support diverse prediction and assignment algorithms. We showcase the shared mobility system with real-world ride-hailing applications. © 2024, VLDB Endowment. All rights reserved.

关键词： Arctic vehicles

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The Effect of Heat Treatment on the Quality Characteristics of Metal Materials 15th

The Effect of Heat Treatment on the Quality Characteristics ...

引用

15th international conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Wang, Tien-Chin Wu, Shun-Tung Yu, Jiunn-Ming Department of International Business Sanmin Dist National Kaohsiung University of Science and Technology No. 415 Jiangong Road Sanmin District Kaohsiung City807618 Taiwan

ISBN: (纸本)9789819994113

This study collects data on four physical properties of four different products of the case company after heat treatment: hardness, length, inner diameter, and outer diameter. 25 samples were collected for each product, and the statistical method t-test was used to test whether the four physical properties were significantly different before and after heat treatment. There are significant differences in the four physical properties of four different products before and after heat treatment by T-test. The results are as follows: (1) Hardness: S45C: After heat treatment, the difference in physical durability becomes larger, and the hardness has a significant difference. (2) Length: S45C: After heat treatment, its length becomes significantly longer. (3) Inner diameter: S45C: After heat treatment of medium carbon steel, the size after treatment is smaller than that before treatment, and there is a significant difference. (4) Outer diameter: S45C: After heat treatment, the "outer diameter" is significantly larger than before treatment, and generally must be reserved for processing. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Iron

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Convolutional Neural Network and Recursive Feature Elimination Based Model for the Diagnosis of Mild Cognitive Impairments 1

引用

6th international conference on Soft computing and Signal processing, ICSCSP 2023

作者： Bhasin, Harsh Mehrotra, Abheer Ohri, Ansh Department of Computer Science and Engineering Center for Health Innovations Manav Rachna International Institute of Research and Studies and Computer Science Engineering MRIIRS Aravali Hills Sector 43 Haryana Faridabad India Manav Rachna University Aravali Hills Sector 43 Haryana Faridabad India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

Aging leads to reduced cognitive abilities. A person heading toward dementia will also show such signs but with more severity. This stage is referred to as Mild Cognitive Impairment. It has been observed that nearly one-fifth of the people suffering from MCI convert to dementia. The patients suffering from MCI who convert to dementia are called MCI-Converts and those who do not convert are called MCI Non-converts. This work proposes a model that extracts gray matter from the s-MRI brain volume and explores the applicability of combination of Convolutional Neural Network and Recursive Feature Elimination in the diagnosis of MCI. Features of the data obtained using the carefully crafted CNN followed by the selection of the appropriate features using Recursive Feature Elimination are then used to accomplish the task. Empirical analysis has been done to select the parameters of the CNN. The results are better than the state of the art and pave way for the exploitation of the Deep Learning models to classify the Converts and the Non-converts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Magnetic resonance imaging

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Boosting Bandwidth Convergence: Optimizing Resource Allocation in Satellite-Terrestrial Integrated Networks 23

Boosting Bandwidth Convergence: Optimizing Resource Allocati...

引用

23rd IEEE international conference on Communication Technology, ICCT 2023

作者： Li, Dongbo Zhang, Jiajun Tian, Puyang Yin, Zhisheng Cheng, Siyao Liu, Jie Harbin Institute of Technology IoT and Ambient Intelligence Research Center Faculty of Computing Harbin China International Research Institute for Ai Harbin Institute of Technology Shenzhen Cyber-Physical Systems Research Center Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China School of Communication Engineering Xidian University Xi'an China International Research Institute for Ai Harbin Institute of Technology Shenzhen Shenzhen China

ISBN: (纸本)9798350325959

Satellite-Terrestrial Integrated Network (STIN) is a multidimensional network that integrates satellites and terrestrial netwoks, providing information services globally. However, facing time-varying network topology and transmission resources, STIN is limited by the uneven network resources in each network segment. Therefore, it is difficult for STIN to achieve optimal network performance during data transmission. In response to the above challenges, this paper proposes a soft-defined STIN based multipath selection and resource allocation method. We build a soft-defined network architecture for STIN, establish satellite-terrestrial dynamic network topology, and deploy distributed controller nodes. The proposed method models multiple factors that affect throughput performance in the network as a link transmission cost matrix, defining bandwidth resources, computing resources, and latency parameters for modeling. Finally, construct the optimal resource objective function for soft-defined STIN. Meanwhile, we propose a Boosting Bandwidth Convergence (BBC) method. This method merges all transmission paths using a recursive method, and obtains the optimal objective function solution. STK, Mininet, and Ryu are adopted as a joint simulation platform to simulate the three-layer network architecture of soft-defined STIN. The simulation results show that the proposed method outperforms default Ryu switch algorithm and Shortest Path Bridging (SPB) algorithm in terms of throughput, even in the presence of network interference. It exhibits comparable average delay but maintains consistent delay throughout the transmission process, unlike the fluctuating delays observed in the other two algorithms due to buffer accumulation. © 2023 IEEE.

关键词： resource allocation Satellite-Terrestrial Integrated Network soft-defined network throughput

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Trusted and Spectrum-Efficient Crowd computing in Massive MIMO Cellular Networks 23

Trusted and Spectrum-Efficient Crowd Computing in Massive MI...

引用

23rd IEEE international conference on Trust, Security and Privacy in computing and Communications, TrustCom 2024

作者： Zhang, Pengfeng Li, Lei Liu, Xin Wang, Rui Liu, Donglan Su, Bing Wang, Yuntao Liu, Yiliang Su, Zhou State Grid Shandong Electric Power Company Zibo Power Supply Company Zibo China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China Shandong Key Laboratory of Energy Industry Internet Big Data Technology Jinan China Shandong Smart Grid Technology Innovation Center Jinan China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China

Crowd computing in large-scale cellular networks typically involves a significant number of participants, leading to high spectrum interference, reduced communication efficiency, and low user trustworthiness. To overcome these challenges, this paper proposes trustworthy and spectrum-efficient crowd computing scheme in massive multiple-input multiple-output (MIMO) networks based on deep neural networks (DNNs). Existing machine learning-aided multiple antenna technologies usually ignore the pilot contamination, which reduces spectrum efficiency. Here, we leverage the DNN to devise detection and precoding algorithms by inputting an imperfect channel state information (CSI) big data and provides detection and precoding matrices as outputs, where the online-to-offline learning framework offloads the training task to servers to reduce the overhead of base station (BS). With the well-trained DNNs, the BS can generate the detection and precoding matrices with low computation overheads. Especially, minimum-mean-square-error (MMSE) triggers between received signals and sources considering channel estimation error are seen as labels to improve spectrum efficiency. Besides, a multi-factor trust model is designed to enhance user authentication security. The simulations and numerical analysis show the proposed scheme can provide higher spectral efficiency, compared to conventional methods. © 2024 IEEE.

关键词： Mobile telecommunication systems

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