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Summarization tool for multimedia data 多媒体数据汇总工具
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.04.001
Swarna Kadagadkai, Malini Patil, Ashwini Nagathan, Abhinand Harish, Anoop MV

Text summarization is an important Natural Language Processing problem. Manual text summarization is a laborious and time-consuming task. Owing to the advancements in the field of Natural Language Processing, this task can be effectively moved from manual to automated text summarization. This paper proposes a model named Term Frequency-Inverse Document Frequency (TF-IDF) Summarization Tool which implements a text analytics approach called TF-IDF to generate a meaningful summary. TF-IDF is used to identify the topic or context of the text statistically. As data today is mostly unstructured in nature, this paper aims to explore a combination of NLP techniques such as Speech Recognition and Optical Character Recognition to summarize multimedia data as well. The TF-IDF Summarization Tool is seen to produce summaries with Jaccard's Similarity value of 67% and Rogue-1 of 64.9%, Rogue-2 of 48.2%, and Rogue-L of 56.4% based on a self-developed dataset.

文本摘要是一个重要的自然语言处理问题。手工文本摘要是一项费时费力的工作。由于自然语言处理领域的进步,这项任务可以有效地从人工文本摘要转移到自动文本摘要。本文提出了一个术语频率-逆文档频率(TF-IDF)摘要工具模型,该模型实现了TF-IDF文本分析方法来生成有意义的摘要。TF-IDF用于统计识别文本的主题或上下文。由于今天的数据本质上大多是非结构化的,因此本文旨在探索语音识别和光学字符识别等自然语言处理技术的结合,以总结多媒体数据。TF-IDF摘要工具可以根据自己开发的数据集生成Jaccard的相似值为67%,Rogue-1的相似值为64.9%,Rogue-2的相似值为48.2%,Rogue-L的相似值为56.4%的摘要。
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引用次数: 3
Robust video summarization algorithm using supervised machine learning 基于监督机器学习的鲁棒视频摘要算法
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.04.009
Sunil S Harakannanavar , Shaik Roshan Sameer , Vikash Kumar , Sunil Kumar Behera , Adithya V Amberkar , Veena I. Puranikmath

The proposed approach uses ResNet-18 for feature extraction and with the help of temporal interest proposals generated for the video sequences, generates a video summary. The ResNet-18 is a convolutional neural network with eighteen layers. The existing methods don't address the problem of the summary being temporally consistent. The proposed work aims to create a temporally consistent summary. The classification and regression module are implemented to get fixed length inputs of the combined features. After this, the non-maximum suppression algorithm is applied to reduce the redundancy and remove the video segments having poor quality and low confidence-scores. Video summaries are generated using the kernel temporal segmentation (KTS) algorithm which converts a given video segment into video shots. The two standard datasets TVSum and SumMe are used to evaluate the proposed model. It is seen that the F-score obtained on TVSum and SumMe dataset is 56.13 and 45.06 respectively.

该方法使用ResNet-18进行特征提取,并借助为视频序列生成的时间兴趣建议生成视频摘要。ResNet-18是一个有18层的卷积神经网络。现有的方法不能解决摘要暂时一致的问题。建议的工作旨在创建一个暂时一致的摘要。实现分类回归模块,得到组合特征的固定长度输入。在此之后,采用非最大抑制算法减少冗余,去除质量差、置信度低的视频片段。视频摘要的生成采用核时间分割算法(KTS),该算法将给定的视频片段转换为视频片段。使用两个标准数据集TVSum和SumMe来评估所提出的模型。可以看出,在TVSum和SumMe数据集上得到的f分数分别为56.13和45.06。
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引用次数: 2
Importance of statistics to data science 统计学对数据科学的重要性
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.03.019
Jalajakshi V, Myna A N

This paper is mainly discussed on importance and contribution of statistics to Data science and how it emerges as the most important factor to solve realistic problems which contains huge amount of data processing. There are various methods in statistics which help Analysis in data science which will be explained in detail. This work also emphasizes on importance of Data Science in this present technology. Statistics is proved to be an important discipline in regulating the work analyzed in the field of Data Science. This work compare various statistical approaches with This outlines the numerous potential data analysis approach processes which helps in examining the influence of quantitative statistical measures on data collection and optimization, data interpretation, data processing and modelling, testing and presenting and Various challenges faced in the process of data science using statistics is given in brief. Here there is a numerous way to enhance the data science techniques with the help of statistics methodologies.

本文主要讨论了统计学对数据科学的重要性和贡献,以及它如何成为解决包含大量数据处理的现实问题的最重要因素。统计学中有各种各样的方法可以帮助数据科学中的分析,我们将详细解释这些方法。这项工作还强调了数据科学在当前技术中的重要性。统计被证明是规范数据科学领域分析工作的一门重要学科。这概述了许多潜在的数据分析方法过程,这些过程有助于检查定量统计措施对数据收集和优化、数据解释、数据处理和建模、测试和呈现的影响,并简要地给出了使用统计数据的数据科学过程中面临的各种挑战。这里有许多方法可以在统计方法的帮助下增强数据科学技术。
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引用次数: 4
Explainable machine learning in identifying credit card defaulters 识别信用卡违约者的可解释机器学习
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.04.025
Tanmay Srinath, Gururaja H.S.

Machine learning is fast becoming one of the central solutions to various real-world problems. Thanks to powerful hardware and large datasets, training a machine learning model has become easier and more rewarding. However, an inherent problem in various machine learning models is a lack of understanding of what goes on ’under the hood’. A lack of explainability and interpretability leads to lower levels of trust in the model's predictions, which means it can't be used in sensitive applications like diagnosing medical ailments and detecting terrorism. This has led to various advances in making machine learning explainable. In this paper various black-box models are used to classify credit card defaulters. These models are compared using different performance metrics, and explanations of these models are provided using a model-agnostic explainer. Finally, the best model-explainer combo is proposed with potential areas of future exploration.

机器学习正迅速成为各种现实问题的核心解决方案之一。多亏了强大的硬件和大型数据集,训练机器学习模型变得更容易,更有价值。然而,各种机器学习模型的固有问题是缺乏对“引擎盖下”发生的事情的理解。缺乏可解释性和可解释性导致对模型预测的信任度较低,这意味着它不能用于诊断疾病和探测恐怖主义等敏感应用。这导致了各种各样的进步,使机器学习可以解释。本文使用各种黑盒模型对信用卡违约者进行分类。使用不同的性能指标对这些模型进行比较,并使用与模型无关的解释器提供这些模型的解释。最后,提出了最佳模型-解释器组合以及未来探索的潜在领域。
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引用次数: 5
Leaf and skin disease detection using image processing 基于图像处理的叶片和皮肤疾病检测
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.03.010
Manjunatha Badiger , Varuna Kumara , Sachin C N Shetty , Sudhir Poojary

Agricultural production is something on which the economy significantly relies. Leaf diseases in agriculture are the key issue for every nation, as the food demand is expanding at a rapid speed due to a rise in population. Skin disorders are usually seen in animals and humans, it is a particular sort of illness caused by germs or infection. Early and accurate identification and diagnosis of leaf and skin diseases are vital to keeping them from spreading. Image processing techniques can be used for disease detection which involves mathematical equations and mathematical transformations. For humans eyes image is a mixture of RGB colour, because of these colours we can extract some of the features from the image, but modern computer stores image in a mathematical format which means computer sees the image as numbers, hence after evaluating the image as a number arrays or matrix we will perform various transforms on them, these transforms will extract specific details from the picture, before transforming the image must go under various operation like feature adjustment which is also carried out mathematically. The project is implemented using K-Means Clustering and Support Vector Machine Algorithm in MATLAB through which we can detect and distinguish different types of leaf and skin diseases.

农业生产是经济的重要支柱。随着人口的增长,粮食需求正在迅速扩大,农业中的叶病是每个国家的关键问题。皮肤病常见于动物和人类,它是由细菌或感染引起的一种特殊疾病。早期和准确地识别和诊断叶片和皮肤疾病对防止它们蔓延至关重要。图像处理技术可用于涉及数学方程和数学变换的疾病检测。对于人眼来说,图像是RGB颜色的混合物,因为这些颜色我们可以从图像中提取一些特征,但是现代计算机以数学格式存储图像,这意味着计算机将图像视为数字,因此在将图像评估为数字数组或矩阵之后,我们将对它们进行各种变换,这些变换将从图像中提取特定的细节。在对图像进行变换之前,必须经过特征调整等各种操作,这些操作也是用数学方法进行的。本项目在MATLAB中使用K-Means聚类和支持向量机算法实现,通过该算法可以检测和区分不同类型的叶片和皮肤疾病。
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引用次数: 10
An optimal cluster & trusted path for routing formation and classification of intrusion using the machine learning classification approach in WSN 基于机器学习分类方法的WSN路由形成与入侵分类的最优聚类可信路径
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.03.018
Putty Srividya, Lavadya Nirmala Devi

Generally, wireless sensor networks (WSN) are being utilized in a wide range of fields like queue tracking, military applications, environmental applications, and so on. This approach is an attempt to focus on the detection of attack with the utilization of machine learning and optimization strategies. Primarily, the system model is initiated and the nodes are deployed randomly based on the size of the network. The cluster formation will be carried out with the use of energy competent Particle swarm optimization depending on the passive clustering mechanism (ECPSO-PCM) strategy. Using spatial correlation, groups correlation group will be formed. The probability of transmission is then estimated by taking into account the spatial correlation, quality of link among CH and cluster member nodes, and the node's residual energy of the network. The management of the trust is employed by the selection of cluster heads. If node consists of the criteria for trust coverage, then this node is chosen as the cluster head. If this condition is not satisfied, then it is chosen as a cluster member. The optimal range of cluster paths for effective transmission of data is carried using the Computation of optimal cluster path using Bio-inspired Hierarchical order chicken swarm optimization (BIHO-CSO) at which the distance and residual energy are major constraints. Once the optimum and trusted path is chosen, the classification and detection of attack are carried out using a Recursive Binary partitioning decision tree classifier (RBP-DT). The performance analysis is made and the attained outcomes are compared with traditional approaches to validate the supremacy of the presented scheme

一般来说,无线传感器网络(WSN)在队列跟踪、军事应用、环境应用等领域得到了广泛的应用。这种方法是利用机器学习和优化策略来关注攻击检测的一种尝试。首先,初始化系统模型,根据网络的大小随机部署节点。利用基于被动聚类机制(ECPSO-PCM)策略的能态粒子群优化算法进行聚类。利用空间关联,形成群体关联群。然后通过考虑空间相关性、CH和集群成员节点之间的链路质量以及节点的网络剩余能量来估计传输概率。信任的管理是通过簇头的选择来实现的。如果节点包含信任覆盖的标准,则选择该节点作为簇头。如果不满足此条件,则选择它作为集群成员。以距离和剩余能量为主要约束条件,采用仿生层次次序鸡群优化算法(BIHO-CSO)进行最优簇路径的计算,得到有效传输数据的最优簇路径范围。一旦选择了最优可信路径,使用递归二叉划分决策树分类器(RBP-DT)对攻击进行分类和检测。进行了性能分析,并将所得结果与传统方法进行了比较,以验证所提方案的优越性
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引用次数: 5
Plant leaf disease detection using computer vision and machine learning algorithms 利用计算机视觉和机器学习算法进行植物叶片病害检测
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.03.016
Sunil S. Harakannanavar , Jayashri M. Rudagi , Veena I Puranikmath , Ayesha Siddiqua , R Pramodhini

Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recommended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farmers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to 256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally, the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM), Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples.

即使在人口迅速增长的情况下,农业也为所有人提供食物。在农业生产中,对植物病害进行早期预测,是保证粮食面向全体人口的重要手段。但是在作物生长早期就预测病害是很不幸的。这篇论文背后的想法是让农民意识到减少植物叶片疾病的尖端技术。由于番茄是一种单纯可用的蔬菜,因此确定了机器学习和图像处理的方法,并结合准确的算法来检测番茄植株的叶片病害。在本调查中,考虑了番茄叶片有病害的样品。有了这些番茄叶片的病样,农民可以根据早期症状很容易地发现疾病。首先将番茄叶片样本调整为256 × 256像素,然后利用直方图均衡化技术提高番茄样本的质量。引入k均值聚类方法将数据空间划分为Voronoi单元。采用轮廓跟踪的方法提取叶片样本的边界。利用离散小波变换、主成分分析和灰度共生矩阵等多重描述符提取叶片样本的信息特征。最后,使用支持向量机(SVM)、卷积神经网络(CNN)和k -近邻(K-NN)等机器学习方法对提取的特征进行分类。采用支持向量机(88%)、K-NN(97%)和CNN(99.6%)对番茄无序样本进行准确率测试。
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引用次数: 62
Application of few-shot object detection in robotic perception 少镜头目标检测在机器人感知中的应用
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.04.024
T.K. Shashank , N. Hitesh , H.S. Gururaja

An object detection technique for robotic perception plays a vital role for robots to perform the task that it is functioned to do. In this paper, an efficient and accurate method for object detection for robots is proposed. The paper suggests implementing Few-shot object detection network for robotic vision using the Attention network and attention RPN module. The Multi-relation detector is used to compare two frames and eliminate negative objects from the frame which further enforces the suggested model. Using Contrastive training strategy, the robot is trained to exploit the resemblance between the few-shot support frame and query frame to detect the positive objects and eliminate the negative objects. This method is proposed to help robots perceive the object of interest to perform pick, place, and various other actions. This paper utilizes the COCO dataset to train the network which contains close to 1000 different categories. This method would help accelerate industry 4.0 and has potential in a wide range of applications.

机器人感知的目标检测技术对机器人执行其功能任务起着至关重要的作用。本文提出了一种高效、准确的机器人目标检测方法。本文提出利用注意力网络和注意力RPN模块实现机器人视觉的少镜头目标检测网络。多关系检测器用于比较两帧,并从帧中消除负面对象,从而进一步强化所建议的模型。采用对比训练策略,利用少镜头支撑框架和查询框架之间的相似性对机器人进行训练,以检测正面目标并消除负面目标。提出这种方法是为了帮助机器人感知感兴趣的对象来执行拾取,放置和各种其他动作。本文利用COCO数据集对包含近1000个不同类别的网络进行训练。这种方法将有助于加速工业4.0,并具有广泛的应用潜力。
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引用次数: 3
Apache Hadoop based effective sentiment analysis on demonetization and covid-19 tweets 基于Apache Hadoop的去货币化和Covid-19推文的有效情绪分析
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.03.021
S. Anitha , Mary Metilda

In Recent, Twitter is the well-known public Network acquires a huge number of tweets. Sentiment analysis in twitter data are tremendously valuable in social media observing as it allows getting an overview of extensive global opinion in certain issue. This data are utilized for industrial, government, social and economic approaches by analyzing the tweets as per the requirement of the user. Processing and storing these data are more complicated to analyze. Hadoop is a distributed environment which process with Big and Huge variety of dataset which supports processing components that collectively called Hadoop Ecosystem. In this paper, regular tweets are analyzed by sentiment analysis technique in Hadoop Eco system. Dataset are taken from Kaggle data repository. This research has done by Apache Pig in Demonetization and Covid 19 twitter dataset.

近年来,Twitter作为知名的公共网络获得了大量的推文。twitter数据中的情感分析在社交媒体观察中是非常有价值的,因为它可以让我们对某些问题有一个广泛的全球观点的概述。这些数据被用于工业、政府、社会和经济方法,通过分析推特根据用户的要求。处理和存储这些数据的分析更加复杂。Hadoop是一个分布式环境,它处理大量的数据集,这些数据集支持处理组件,统称为Hadoop生态系统。本文采用情感分析技术对Hadoop生态系统中的常规推文进行分析。数据集取自Kaggle数据存储库。这项研究是由Apache Pig在Demonetization和Covid - 19 twitter数据集中完成的。
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引用次数: 6
Higher Order Degree of Freedom Controller for Load Frequency Control of Multi Area Interconnected Power System with Time Delays 具有时滞的多区域互联电力系统负载频率高阶自由度控制器
Pub Date : 2022-06-01 DOI: 10.1016/j.gltp.2022.03.020
CH. Naga Sai Kalyan, Chintalapudi V Suresh

In this paper, a seagull optimization algorithm (SOA) based 3-Degree-of-freedom (DOF) proportional-integral-derivative (3DOFPID) controller is suggested for load frequency control of multi-area interconnected power system (MAIPS). The considered MAIPS comprises of two areas with Thermal-Hydro-Nuclear generation units in each area. Analysis has been carried out by subjugating area-1 of MAIPS with a step load disturbance (SLD) of 10%. The sovereignty of presented SOA tuned 3DOFPID in regulating the stability of MAIPS is revealed upon comparing with the performances of 2DOFPID and conventional PID controllers. MIPS is analyzed dynamically without and with considering the nonlinear realistic constraint of communication time delays (CTDs) to demonstrate its impact on load frequency control performance. Simulation results disclosed that, MAIPS dynamical behavior is slightly more deviated up on considering CTDs and is justified.

本文提出了一种基于海鸥优化算法(SOA)的3自由度比例-积分-导数(3DOFPID)控制器,用于多区域互联电力系统(MAIPS)的负荷频率控制。所考虑的MAIPS包括两个地区,每个地区都有热水核能发电机组。采用10%阶跃负载扰动(SLD)控制maps的1区进行分析。通过与2DOFPID和传统PID控制器的性能比较,揭示了SOA调优3DOFPID在调节maps稳定性方面的主权。对MIPS进行了动态分析,分析了在不考虑和考虑通信时延非线性现实约束的情况下MIPS对负载频率控制性能的影响。仿真结果表明,在考虑CTDs的情况下,MAIPS的动力学行为略有偏差,是合理的。
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引用次数: 19
期刊
Global Transitions Proceedings
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