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检索条件"任意字段=3rd International Conference on Soft Computing in Data Science, SCDS 2017"

共 694 条记录，以下是31-40 订阅

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COVID-19 data Forecasting Using soft computing Technique 3rd

COVID-19 Data Forecasting Using Soft Computing Technique

引用

3rd international conference on Communication, Devices and computing, ICCDC 2021

作者： Roy, Arindam Singh, Dharmpal Sahana, Sudipta Department of Computer Science and Engineering Surendra Institute of Engineering and Management Darjeeling Siliguri734009 India Department of Computer Science and Engineering JIS College of Engineering Nadia Kalyani741235 India

ISBN: (纸本)9789811691539

Corona viruses are large family of viruses that may affect human and animals. Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS) are the respiratory infections caused by several corona viruses in human. COVID-19 is an infectious disease due to most recently discovered Corona virus. This Corona Virus Disease (COVID-19)’s outbreak was started from Wuhan, China in December 2019. The disease can spread from one person who has the virus to another person who is not infected. This is how the outbreak began and became a pandemic. This pandemic has become the biggest problem in the world. Number of infected and deaths are gradually increasing throughout the world. This effort is to analysis the graph and determine that when the graph will go down. Basically, we will make the prediction using some a time series forecasting techniques based on neural network. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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CT/MRI 3D Fusion for Cerebral System Analysis 3rd

CT/MRI 3D Fusion for Cerebral System Analysis

引用

3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

作者： Chlebiej, Michal Zurada, Anna Gielecki, Jerzy Faculty of Mathematics and Computer Science Nicolaus Copernicus University in Toruń Chopina 12/18 Torun87-100 Poland Department of Anatomy Collegium Medicum School of Medicine University of Warmia and Mazury Olsztyn Poland

ISBN: (纸本)9783031057663

The main idea of our system is to create a fusion of CT & MRI data of the same patient. We care about the accuracy of fused data correlation, emphasising vascular structures. We propose volumetric correlation method, vascular system reconstruction using tubular structures, error measurement and analysis using surface distance measurements. The resulting data from the fusion system combines bone structures from CT imaging and the reconstructed vascular system from MRI. We ensure to match correctly cerebral vessels with corresponding data from CT. Such data enables extended analysis of vascular segments relative to bones. In this work, we also propose a new semi-circular coordinate system to unify the orientation of our fusion models. Such a system allows for a more convenient and precise numerical analysis of anatomical correspondence to the principal axes of the coordinate system. It is also possible to define precisely the left and right sides of the human head. © 2022, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Radial Basis Function Network Based Intelligent Scheme for software Quality Prediction 3rd

Radial Basis Function Network Based Intelligent Scheme for ...

引用

3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

作者： Ritu Sangwan, O.P. Department of CSE Guru Jambheshwar University of Science and Technology Haryana Hisar125001 India

ISBN: (纸本)9783031057663

With the increasing interest in digital world, various software whether it is in e-commerce or in the entertainment industry, have gained much attention in recent time. It becomes important to access the quality of a software which is usually done with the expert supervision and results into more money and time. Neural network systems have been effectively incorporated by famous companies such as FlipKart, Snapdeal, Netflix, and others due to their capacity to expand the amount of quantifiable attributes for prediction. This paper presents a framework of a radial basis function network for software quality prediction and uses Thin-plate spline RBF as its activation function. This is a network with a single layer making it easier to be trained with effective prediction results. The paper presents the theory of its training using gradient descent based back propagation algorithm with the training data-set of 20 samples and MATLAB results are provided to support the theoretical claim. Furthermore, the proposed scheme has been validated for five unknown software samples which quality is required to be predicted and It is demonstrated that the predicted soft quality results by the proposed approach are very close to the actual software quality which shows the effectiveness of the proposed approach. © 2022, Springer Nature Switzerland AG.

关键词： Radial basis function networks

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KCEPS: Knowledge Centric Entity Population Scheme for Research Document Recommendation 3rd

KCEPS: Knowledge Centric Entity Population Scheme for Resear...

引用

3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

作者： Krishnan, N. Deepak, Gerard Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031057663

The data in the Web has increased exponentially, and retrieval from the Web is quite demanding and challenging. There is always a need to improve results in yielding and recommending scholarly articles. An enhanced recommendation system to recommend scholarly articles is best suited to ease the work of a researcher. It is also used to display relevant articles with respect to the user queries. There is a huge probability that the research article recommended might be of no use to the user if the recommendation algorithm does not perform well. This paper proposes a knowledge centric approach for research paper recommendation using semantic similarity and Gated Recurrent Unit along with Cuttlefish optimization algorithm. The Related-Article Recommendation dataset is used for experimentation. The recommendation is based on the user query and user clicks. The performance is evaluated and compared with the baseline approaches and it is clearly observed that the proposed Knowledge centric recommendation system is superior in terms of performance and attained an average Accuracy, and F-measure of 96.63%, and 96.69% respectively. © 2022, Springer Nature Switzerland AG.

关键词： Recommender systems

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Psychological Analysis using Social Media Tweets

Psychological Analysis using Social Media Tweets

引用

Applied Artificial Intelligence and computing (ICAAIC), international conference on

作者： Kartik Singh Utkarsh Gupta P. Renukadevi (C. Tech Department) SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This study utilizes social media platforms to extract and analyze pertinent data for gaining a complete understanding of the psychological dimensions of depression, employing Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) for enhancing the motor skills. With a focus on language and temporal context, tweets are scrutinized to unearth the underlying dynamics within the Twitter network, shedding light on the social processes influencing the concept of depression. This research study intends to enhance the understanding of mental health professionals and decision-makers in this domain. The proposed methodology encompasses tweet tokenization, followed by LSTM and CNN models to identify significant features. The primary objective is to develop a system capable of conducting psychological analysis and supporting psychotherapy, achieving a classification accuracy of up to 99.02%. This high accuracy enables the detection of changes in behavioral patterns, offering promising avenues for intervention and support in mental health care.

关键词： Accuracy Social networking (online) soft sensors Natural languages Mental health Depression Motors

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Road Extraction from High-Resolution Satellite Images using Deep Learning Model 3

Road Extraction from High-Resolution Satellite Images using ...

引用

3rd international conference on Advances in computing, Communication Control and Networking, ICAC3N 2021

作者： Singhal, Tanushi Sharma, Nishtha Galgotias College of Engineering and Technology Dept. Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665438117

data Extraction has always been one of the major interests of several companies and government agencies for obtaining information through satellite images. The data extraction in the form of road extraction from satellite images has played a very important part in navigation, military, national security, agriculture, and understanding the geology of the earth's surface. We are introducing ReLU as the classification function, with the soft-max function. In this paper, we present an automated model for road extraction from a high-resolution satellite image. Here, we are implementing Rectified Linear Unit through Segmentation for obtaining roads out of satellite images with pre-processing, training, and validation of input data. And re-define the quality aspect of satellite images. © 2021 IEEE.

关键词： Deep learning

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Federated Deep Learning for Cybersecurity and Intrusion Detection in Decentralized Networks

Federated Deep Learning for Cybersecurity and Intrusion Dete...

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Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： Naeem Mia Jubair Ahmed Nabin Suzad Mohammad Mahedi Hasan Fahim Shakil Tamim Dipta Mohon Das Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The advancements in modern technologies demonstrate a growing trend in cyber attacks with complex patterns. These attacks can take various complex forms due to the diverse nature of data. To tackle this growing threat, ensuring data diversity is essential. However, contemporary works utilizing centralized model training face the challenge of maintaining data privacy. In this study, a federated deep learning based privacy-preserving architecture for Network Intrusion Detection is presented. This approach combines CNN and LSTM architecture within a Federated Learning (FL) framework. The framework was evaluated on a benchmark dataset CIC-IDS2017, encompassing seven classes - DoS, Portscan, Infiltration, Brute-force, Bot, Web Attack, and Benign. This framework demonstrates robustness in accurately detecting all the attack types with an accuracy of over 99.36%. This work will contribute to the cyber security field by addressing critical gaps in existing research and providing a robust solution for intrusion detection in a privacy-preserving manner.

关键词： Deep learning data privacy Accuracy Federated learning Network intrusion detection Computer architecture Benchmark testing Chatbots Robustness Long short term memory

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Optimizing Mental Health Status Prediction Models Using Machine Learning Algorithms

Optimizing Mental Health Status Prediction Models Using Mach...

引用

data Analytics, computing and Artificial Intelligence (ICDACAI), international conference on

作者： Dong Liu Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In contemporary society, mental health concerns are gaining heightened attention, and accurately forecasting individuals’ mental well-being has become a pivotal research focus. Traditional predictive models for mental health often struggle with issues like the complexity of data processing and insufficient prediction precision. However, the application of machine learning algorithms introduces new approaches to address these challenges. In the age of artificial intelligence, creative works are high-quality data sources for machine learning, raising crucial questions about copyright regulations. Balancing these rules to foster innovation in both cultural and technological sectors is a pressing issue. This study employs various machine learning techniques, such as support vector machines, decision trees, and deep learning algorithms, to thoroughly examine and evaluate mental health data. As artificial intelligence technology evolves rapidly and reshapes its applications, new research themes and methodologies continuously emerge within machine learning, with advances in deep learning and reinforcement learning driving the field forward. This paper not only reviews the essential techniques in the optimization process but also explores future research avenues and potential applications, aiming to provide valuable insights for ongoing research and practical efforts in mental health prediction.

关键词： Training Machine learning algorithms soft sensors Mental health Predictive models Feature extraction data models Stability analysis Optimization Thermal stability

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Iterative Turbo Receiver for FTN Based on Semi-Definite Relaxation

Iterative Turbo Receiver for FTN Based on Semi-Definite Rela...

引用

Cloud computing, Big data Applications and software Engineering (CBASE), international conference on

作者： YuTao Jiang WenBin Gong Honghao Chen Yu Gu Tangju Yuan Yu Su School of Electronic and Information Engineering Chongqing Three Gorges University Wanzhou Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Faster-than-Nyquist (FTN) technology, as a transmission technique that can improve spectrum efficiency, has received widespread attention. However, it introduces severe Inter-Symbol Interference (ISI), leading to increased complexity in symbol detection at the receiver end. An FTN detector based on Semidefinite Relaxation (SDR) exhibits good performance and low complexity. In this paper, an iterative FTN detector based on soft-output SDR is proposed. In traditional turbo receivers, forward error correction codes are only used at the decoder. However, the SDR detector proposed in this paper outputs external information to the decoder with higher reliability. By simplifying the SDR Turbo receiver, this approach solves only one SDR problem for each codeword instead of multiple SDR problems in the iterative turbo processing, thus reducing the time complexity of the SDR turbo receiver while maintaining the original error performance.

关键词： Symbols Receivers Detectors Iterative algorithms Decoding Reliability Time complexity Detection algorithms Iterative decoding software engineering

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KTSVidRec: A Knowledge-Based Topic Centric Semantically Compliant Approach for Video Recommendation on the Web 3rd

KTSVidRec: A Knowledge-Based Topic Centric Semantically Comp...

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3rd international conference on soft computing and its Engineering Applications, icsoftComp 2021

作者： Krishnan, Akhil S. Deepak, Gerard SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031057663

In this age of an increasing userbase of the internet on a daily basis, there results in a surplus of video content being available for consumption of the users of the Web. There also exists a problem of the current recommendation systems not being well suited to the semantic structure of the World Wide Web. In this paper, KTSVidRec framework has been proposed which is a semantically compliant approach for video recommendation. The proposed approach is user query centric and incorporates entity population using the Structural Topic Modelling and further entity aggregation from various knowledge bases. The KTSVidRec is a semantically infused machine learning model as it uses algorithms like XGBoost to classify popular video categories and Linked Open data cloud for the enrichment of categories. Computation of the semantic similarity is carried forward primarily using Kullback-Leibler Divergence and other measures for measuring similarities like Cosine Similarity, Normalized Pointwise Mutual Information and Jaccard similarity. KTSVidRec exhibits higher performance, achieving an accuracy of 93.02% and a high precision percentage for a different number of recommendations, compared to other baseline and benchmark models. The proposed approach was found to be effective as well as semantically compliant for the recommendation of videos on the Web. © 2022, Springer Nature Switzerland AG.

关键词： Benchmarking

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