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Summarizing Clustering Results using Sentence prototype based Language Models

Summarizing Clustering Results using Sentence prototype base...

引用

2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Khare, Arpit Kumar, Dheeraj National Institute of Technology Jamshedpur Dept. of Computer Science and Engineering Jamshedpur India Indian Institute of Technology Roorkee Dept. of Electronics and Communications Engineering Roorkee India

ISBN: (纸本)9798350322972

Amount of experiment-related data in almost every domain has increased multifold. Hence, Understanding these novel datasets' essential characteristics is vital to developing complex statistical models and testing various hypotheses. Exploratory data analysis using unsupervised techniques such as data clustering is becoming popular among researchers. Although several clustering algorithms exist, little research has been done to develop strategies to help understand the characteristics and physical implications of generated clusters. This paper proposes to use natural language to describe the results generated by a clustering algorithm linguistically for straightforward interpretation and understanding. We discuss salient characteristics of clusters that one looks for while interpreting clustering results and develop several linguistic prototype summaries for each of them. These summaries are then coherently combined to form linguistic summarization of clustering results. Numerical experiments on real-life IRIS dataset generate highly readable and easy-to-understand descriptions of the clusters generated through three popular clustering algorithms - k means, single linkage, and DBSCAN. © 2023 IEEE.

关键词： K-means clustering

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Machine Learning Techniques for Detecting DDOS Attacks 3

Machine Learning Techniques for Detecting DDOS Attacks

引用

3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Saeed, Mamoon M. Rashad Mohammed, Husam Nedhal Hazaea Gazem, Othman Abdulkafi Saeed, Rashid A. Ahmed Morei, Hossam Mohammed Thabet Eidah, Aimen Ebdulkreem Gaid, AbdulGuddoos S.A. Ameen Saif Al-Uosfi, Ameen Sami Qasem Al-Madhagi, Mohamed Galal Faculty of Engineering Department of Communications and Electronics Engineering Yemen College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen

ISBN: (纸本)9798350305333

The development witnessed by the world of science and technology and the emergence of the Internet, where cybersecurity has become one of the most important areas that are growing rapidly every day, and one of its goals is to maintain access to users at any time. Where it is noted that the biggest enemy for the availability is Distributed Denial of Service (DDoS) attack, which works to prevent access to the service and slow down the service. Through this paper, machine learning will be used to discover the DDoS attack and know its type to be aware of it and take the necessary measures for that. Where the CICDDoS2019 dataset will be used. After it is studied, analyzed, cleaned and the best algorithm was selected. Five classification algorithms (Random Forest, Decision Tree, SVM, Naive Bayes, and xgboost) were used to train and test the data from the datasets, and it can be said that of the five, Random Forest algorithms had the highest level of accuracy (99.95426%). © 2023 IEEE.

关键词： Denial-of-service attack

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Human Activity Recognition using ShuffleNetV2 Model

Human Activity Recognition using ShuffleNetV2 Model

引用

2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Shanufa Nazrin, P.S. Benifa, J.V. Bibal Anu, K.P. Vijayakumar, Aiswarya Dept. of Computer Science Kerala Thrissur India Indian Institute of Information Technology Dept.of Computer Science and Engineering Kerala kottayam India

ISBN: (纸本)9798350394580

HAR is specified as the crucial task within the field of computer vision and artificial intelligence. Main aim of HAR is to observing and labelling different human activity images of distinct data sources by machines such as computer. HAR has applications in various fields. CNN model is an appropriate model for HAR task, due to their ability to automaticaly acquire relevant spatial features, capture hierarchical representations, and handle input of various sizes and types. So it is utilized for developing HAR model. This model can detect objects and also can classify images. The model contains wide ranging catogories. ShuffleNetV2 is a particular architecture within it, mainly for efficient and lightweight deep learning task. This paper develops an intelligent HAR using ShuffleNetV2 model which acquired an accuracy about 69.52 percentage. © 2023 IEEE.

关键词： HAR Human activity ShuffleNetV2

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Enhanced Building Prediction for Smart City Surveillance 1

Enhanced Building Prediction for Smart City Surveillance

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Saravanan, Lekshmipriya Natarajan, Mathivathani KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore641407 India KPR Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore641407 India

ISBN: (纸本)9798350348057

Nearly three-quarters of the total power used in the world is consumed by the built environment sector. Therefore, looking for viable methods to reduce building energy needs and decrease negative environmental repercussions is vital. By incorporating intelligence into applications, everyone in the current situation has become intelligent, which lessens the load of constant interruption or human control. By using Machine to Machine interactions, intelligence enables the ability to connect more real-time parameters and make moral judgments in perilous circumstances. The current study examined the framework, approach, and performance of currently available AI-based techniques. According to the research, choosing the best machine and deep learning model for a given task can be difficult. Among the primary areas of study in relation to the vast amounts of data collected is the viability in the practice of the used algorithms, both standard machine learning and the more recent branch known as deep learning. Present a quantitative analysis contrasting the effectiveness of conventional deep learning and machine learning strategies using the direction of human movement in detecting the applications based on the analogue pyroelectric infrared sensor (PIR) outputs. Here, Random Forest is used as the machine learning method because of its great prediction accuracy. Over deeper datasets, deeper CNNs outperform shallow models as a deep learning method. The study demonstrates that traditional Machine Learning performs with detecting values within the first few seconds, improving real-time detection. Additionally, it might be used on more smart buildings without upgrades. © 2023 IEEE.

关键词： Intelligent buildings

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Genetic Algorithm-Based Path Planning for Autonomous Mobile Robots

Genetic Algorithm-Based Path Planning for Autonomous Mobile ...

引用

2023 IEEE Jordan International Joint Conference on Electrical engineering and Information technology, JEEIT 2023

作者： Alabbadi, Areej Kanan, Awos Princess Sumaya University for Technology Dept. of Electrical Engineering Amman11941 Jordan Princess Sumaya University for Technology Dept. of Computer Engineering Amman11941 Jordan

ISBN: (纸本)9798350324051

In this paper, a Genetic Algorithm is used to solve the path planning problem for autonomous mobile robots in static environments. The goal of the path planning problem is to find a valid and practical path between two points while avoiding obstacles and optimizing a number of criteria including path length, safety, and distance from obstacles. A quality function is proposed to evaluate the optimization approach for different scenarios. Experimental results show that enhanced solutions can be achieved in less time using optimal values of the search algorithm parameters. © 2023 IEEE.

关键词： Genetic algorithms

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Simplifying Structure and Pattern in Complex Social Networks Using Graph Isomorphism 14

Simplifying Structure and Pattern in Complex Social Networks...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Arunakumari, B.N. Kshama, S.B. Swasti, S. Sameeksha, B.H. Puri, Sonia Rubini, P.A. Bms Institute of Technology and Management Dept. of Computer Science and Engineering Bengaluru India Bms Institute of Technology and Management Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

In recent years, networks have grown in size and complexity, traditional network testing methods have become time-consuming and inefficient. As a result, there is a need for more efficient and reliable network testing methods. One important topic in the field of network testing is graph pattern matching. Network analysis relies on graph pattern matching. The paper discusses the use of graph isomorphism for network testing. This paper also gives an overview of recent advances in isomorphism testing, which determines if two networks are structurally identical despite potential labeling discrepancies between them. The combination of isomorphism and pattern matching in network analysis is used to identify equivalent structures in different networks. This approach is used to identify and compare common patterns in complex network structures, such as those found in social networks. © 2023 IEEE.

关键词： Pattern matching

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Boosting-Based Machine Learning Approaches for Diabetes Prediction using Indian Demographic and Health Survey Data 3

Boosting-Based Machine Learning Approaches for Diabetes Pred...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India

ISBN: (纸本)9798350364569

Traditional diagnostic techniques are time-consuming and prone to error. Performance is added however time and cost are reduced by using computer-aided diagnostic methods. This study aims to calculate how healthy boosting-based machine learning classifiers perform in automatically identifying diabetes in the Indian context. For this experiment, Indian demographic and health survey 2021 data on women aged 19 to 79 are considered. This data includes clinical, anthropometric, and biochemical components. 12,103 of the 706,504 responders who tested positive for diabetic testing were considered. The predictive exploration-based boosting machine learning models are adopted, such as adaptive boosting, categorical boosting, extreme gradient boosting, gradient boosting, and light gradient boosting models. Kernel Principal Component Analysis is employed for feature extraction. The Extreme Gradient Boosting model demonstrated strong performance on the diabetes dataset both before and after applying Kernel PCA. The metrics including accuracy, F1-score, precision, and recall for the XGBoost model showed improvements: accuracy increased from 81% to 83%, F1-score from 81% to 82%, precision from 81% to 88%, and recall from 81% to 76%.In contrast, Adaptive Boosting performed poorly, while Categorical Boosting, Gradient Boosting, and Light Gradient Boosting models achieved moderate performance levels. Overall, the model attained optimal performance across metrics such as accuracy, precision, Area Under the Curve (AUC), and recall. © 2024 IEEE.

关键词： Adaptive boosting

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Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Machine Learning. 2

Earthquake Damage Prediction and Rapid Assessment of Buildin...

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Ranganathan, Preethi Akshaya Natarajan, Karthika KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore641407 India KPR Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore641407 India

ISBN: (纸本)9781665475174

A quick forecast of the region's structural damage following an earthquake is crucial for community relief and rescue efforts. Following an earthquake, getting detailed information about buildings in seismic disaster zones is frequently impossible. The information available is essential to estimate seismic damage to urban structures accurately. To address the aforementioned issues, we propose a novel method for quickly assessing building damage through extensive use of field research and data collected from earth observation. A model is created and trained to predict building damage from an earthquake. The algorithm used to indicate the building damage from the quake is Elastic net regression. The elastic net is a regularized regression method used to predict building damage from an earthquake. The proposed Elastic net regression is validated with standard accuracy and effectiveness compared with the Random forest and decision tree methods. The proposed model's outcomes show that elastic net regression is the most effective predictive model for predicting building damage. © 2023 IEEE.

关键词： Earthquakes

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Adaptive Neighborhood Sampling and KAN-Based Edge Features for Enhanced Fraud Detection

Adaptive Neighborhood Sampling and KAN-Based Edge Features f...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Batreddy, Subbareddy Pamisetty, Giridhar Kothuri, Manish Verma, Priya Babu, Ch Sobhan Indian Institute of Technology Dept. of Electrical Engineering Hyderabad India Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India Administrative Staff College of India Centre for Management Studies Hyderabad India

ISBN: (纸本)9798350362480

Fraud detection models have shown immense improvement in performance with the incorporation of graph neural networks (GNNs), as they are naturally capable of modeling relational data and capturing complex relations between users. However, real-world data has a significant class imbalance with very few fraudulent labels, resulting in a bias toward predicting the majority class as the decision boundaries are skewed towards it. To alleviate this class imbalance, we propose adaptive neighborhood sampling, an effective and optimal neighborhood sampling approach that uses sampling probability comprising of class frequency and global importance of the node through the page rank to reduce the dilution of the fraud nodes. We also propose a novel Kolmogorov-Arnold Network (KAN) based edge feature extraction approach, which treats incoming and outgoing edges separately to effectively capture the directional patterns that are crucial in distinguishing the patterns of both labels. In addition to this, we employed topology-aware margin loss to further improve the class imbalanced node classification. Incorporating these proposed approaches in the GNN-based fraud detection model helped outperform the state-of-the-art fraud models on open-source and complex real-world datasets. © 2024 IEEE.

关键词： Graph neural networks

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Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Deep Learning 1

Earthquake Damage Prediction and Rapid Assessment of Buildin...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

ISBN: (纸本)9798350348057

One of the most expensive natural disasters that affect people, earthquakes occur suddenly. As a result, earthquake prediction has grown in importance and difficulty for humanity. Although numerous existing approaches attempt to handle this problem, the majority rely on seismic indicators created by geologists or feature vectors extracted by the deep learning techniques to describe an earthquake for the earthquake prediction. Combining these two categories of characteristics to enhance ultimate Earthquake prediction performance is still a challenge. In order to achieve this, we put forth a deep learning model that successfully fuses explicit and implicit information for earthquake prediction. Here, we use a Convolutional Neural Network to extract implicit features while using eight precursory pattern-based indicators as the explicit features. After that, an attention-based approach is suggested to effectively combine these two categories of traits. A dynamic loss function is the additionally created to address the imbalance of category in seismic data. On a test picture dataset, the performances of multiple CNN models are contrasted. In order to classify earthquake damage, the model is further developed as a web application. Damage assessment values, which are determined using the Convolutional Neural Network model and gradient-weighted class activation mappings, are used to determine the extent of the damage. The web-based program can efficiently and automatically categorize earthquake-related structural damage, making it appropriate for decision-making in emergency response, resource allocation, and policy formation. © 2023 IEEE.

关键词： Earthquakes

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