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GSKTM: efficient of query search for spatial keyword in text mining

International Journal of Information and Communication Technology 2024年第4期25卷 352-379页

作者： Reddy, Ramya Rayacherlu Sambasadasiva Manu, Darshan Naveen Raju, G. Nimbhorkar, Sejal Santosh Rajuk, Venugopal Kuppanna Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University MiamiFL United States INSA National Institute of Advanced Studies Indian Institute of Science Campus Bangalore India

In today’s world, geo-positioning technologies, location-based services have attracted many researcher’s due to the increasing amount of spatio textual objects in various applications like social networks, geo location services. Each spatial object consists of spatial locations and a set of query terms. In this paper, an efficient group of query search for spatial keyword in text mining is proposed that retrieves both spatial and textual keyword objects to effectively reduce the search space. The clusters and subclusters are constructed based on the calculated range of the objects location and categories in the dataset. Further, categorylist is constructed that identifies the category of interest (CoI) of users query. Experiments are conducted on two real dataset namely Euro and geographic names. It is observed that GSKTM outperforms inverted linear quad-tree (ILQ) with improved response time and provides groupwise top-k results. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： group keyword query search spatial text feature selection

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advanced Optimizing Network Security in Manets: A Comprehensive Multilevel-Optimization and Monitoring Mechanism (Multi-Omm)

Advanced Optimizing Network Security in Manets: A Comprehens...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Aravind, A. Poongodi, A. School of Computing Sciences Vels Institute of Science Technology & Advance Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

With the proliferation of the Internet of Things (IoT), mobile ad hoc networks (MANET) have become an essential component of contemporary wireless communications. These networks, which rely on multi-hop data transmission, offer significant flexibility and adaptability. However, their open and dynamic nature makes them vulnerable to various internal and external security threats. Among these, Black hole attacks by malicious nodes present serious challenges. Traditional security measures, such as firewalls and encryption, often fall short in addressing these threats. Therefore, there is an urgent need for advanced intrusion detection systems tailored for these environments. Additionally, node selfishness-where nodes conserve their resources at the expense of others-further disrupts network performance and violates protocols. To address these issues, we propose the Multilevel Optimization and Monitoring Mechanism (Multi-OMM), a novel algorithm designed for real-time detection and prevention of malicious activities. Multi-OMM employs a multi-phase approach to enhance network security through traffic pattern analysis, dynamic topology inspection, and targeted attack detection. By implementing and evaluating Multi-OMM through comprehensive experiments, we aim to demonstrate its effectiveness in identifying and mitigating both selfish and malicious behaviors, thereby improving overall network security and performance. © 2024 IEEE.

关键词： Internet of things

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A Novel Deep Belief Network with Butterfly Optimization Algorithm for the Classification of Paddy Leaf Disease Detection 3

A Novel Deep Belief Network with Butterfly Optimization Algo...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Lathamaheswari, U. Jebathangam, J. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India

ISBN: (纸本)9781665462167

The widespread presence of a wide variety of diseases during paddy farming is one of the most significant elements that annually contributes to enormous economic losses. These losses occur as a direct result of the widespread prevalence of these diseases. In this paper, a deep learning algorithm using Deep Belief Network (DBN) and a meta-heuristic optimization using Butterfly optimization algorithm (BOA) is used to classify the images to detect the diseases in a Plant Leaf. The steps of classification involve three different process that includes pre-processing, feature extraction and classification. The simulation is conducted in python to test the efficacy of the classifier. The result of simulation shows that the proposed method has obtained higher classification rate than the existing machine learning classifiers.. © 2023 IEEE.

关键词： Image classification

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Fuzzy Logic-based Man-in-the-Middle Attack Detection and Improving Routing Efficiency in the IoT Network 1

Fuzzy Logic-based Man-in-the-Middle Attack Detection and Imp...

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1st IEEE International Conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Sivasankari, N. Kamalakannan, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India

ISBN: (纸本)9798350323795

The Internet of Things (IoT) is a remarkable technology for industrial development, smart homes, intelligent transportation, and more. Typically, in an IoT network, a Man in the Middle attack creates an intrusion node that can, either arbitrarily discard data packets or steal the data, thereby disturbing network performance and reducing network efficiency. While there have been numerous approaches using machine learning techniques were proposed to detect the intrusion nodes, they have often failed to address issues related to throughput and packet loss ratios. Additionally, the conventional approach does not concentrate on routing efficiency. A Fuzzy Logic based Man-in-the-Middle attack detection and Cuckoo Search Algorithm (FLCSA) is proposed to solve this issue for improving routing efficiency in the IoT Network. In this proposed work, the fuzzy logic system is used to efficiently detect intrusion nodes using the inputs such as node degree, node energy, and node delay and outputs that isolate intrusion nodes in the network. The cuckoo search (CS) algorithm is used to form the optimal route from sender to receiver in the network, selecting efficient relays based on node energy, link lifetime, and node bandwidth. The simulation results show that the proposed method has better throughput, increased detection ratio, and minimized network delay. © 2023 IEEE.

关键词： Internet of things

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Privacy Preserving Q-Pragmatic-Sensitive Feature Analysis based Blockchain Security Using Provable Partition Fold Padding Encryption

Privacy Preserving Q-Pragmatic-Sensitive Feature Analysis ba...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Punithavathi, S. Jeyalaksshmi, S. Vels Institute of Science Technology & Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai India

ISBN: (纸本)9798350398465

Information security is important factor in decentralized cloud environment because of sharing personalized information among the Internet need privacy preserving to protect the data. In recent days, the Blockchain security is a developing technique to protect the healthcare sensitive information in outsourced cloud environment. But increasing malicious activity and cryptography ley leakages leads security failures. Most security considering the privacy information as common to protect information publicly. This creates a chance of sensitive information attacks to attain the data. To resolve this security problem, we propose a privacy preserving Q-Pragmatic-Sensitive Feature Analysis (PP-QPSFA) based Blockchain security using provable partition fold padding encryption (PP-FPE) to improve the healthcare transaction security. Initially the Term Frequency Behavior Analysis (TFBA) estimate the privacy concerns of user accessibility. The Q-pragmatic-sensitive feature analysis (PP-QPSFA) is attained to find the sensitive privacy feature terms. Then to make secure data using Provable Partition Fold Padding Encryption (PP-FPE) and verified through Role Based Access Verification (RBA V) to make authentication. The proposed system proves higher security concern in blockchain healthcare to improve the privacy preserving in cloud environment. © 2023 IEEE.

关键词： Blockchain

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Investigating the Applications and Importance of Artificial Intelligence in Orthodontic Treatment Planning: A Systematic Review 14

Investigating the Applications and Importance of Artificial ...

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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Ramyaalakshmi, A. Poonguzhali, S. Vels Institute of Science Technology & Advanced Studies Chennai India Department of Computer Applications Vels Institute of Science Technology & Advanced Studies Chennai India

In the current scenario, the evolution of orthodontics and dental based applications have been raised with a strong impact of Artificial Intelligence. The main objective of the proposed systematic review is to study the various applications and importance of AI in orthodontic treatment planning. Electronic databases like Scopus, PubMed, Google Scholar, Embase, Cochrane, Medline, and Web of science have been thoroughly searched. Included were studies that employed AI to plan orthodontic treatments. Data were retrieved and examined to determine the clinical significance of applying AI to orthodontic treatment planning. This PRISMA model-based systematic review includes a total of 73 papers. The investigations demonstrated that artificial intelligence (AI) may be applied in orthodontics for evaluation and importance of ML and AI in Orthodontic Treatment Planning, AI-based software can provide accurate and efficient analysis of cephalometric images, reducing the time and effort required for manual analysis. AI-Enabled cephalometric landmark detection for orthodontic analysis and treatment planning, investigating the application of artificial intelligence on the dentofacial orthopedics and the specialty of orthodontics, application of automated orthodontic extraction indication detection using AI, investigating the application of artificial intelligence on maxillofacial radiology and specialty of oral diagnostics, investigating the importance of artificial intelligence in therapeutic dentistry, AI-powered prediction of post-orthognathic surgery facial aesthetics, investigating the major application of artificial intelligence on the forensic odontology, investigating the major application of artificial intelligence on the maxillofacial surgery and specialty in oral. The systematic evaluation shows that AI provides promising potential for enhancing orthodontic treatment planning. The use of AI can result in more precise diagnosis, treatment planning, and individualized ort

关键词： Dentistry

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Predictive Modeling for Asset Bubble Detection in Financial Markets

Predictive Modeling for Asset Bubble Detection in Financial ...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Murugan, Suganiya Sivakumar, Pradeep Kumar Goyal, Arpit Goenka, Pranay Srm Institute of Science and Technology Department of Computing Technologies Chennai India Vels Institute of Science Technology and Advanced Studies Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350378177

In response to the volatile nature of financial markets, our project, titled 'A Comprehensive Approach to Stock Market Crash Prediction using Time Series Forecasting, Machine Learning, and Deep Learning Techniques,' endeavors to create a robust prediction model for anticipating stock market crashes. Leveraging a diverse dataset gathered from various open-source links, with a primary source being YahooFinance, we employ an extensive feature set including time stamps, economic indicators such as unemployment, consumer price index (CPI), price-to-earning ratio (P/E), and market yields. Our innovative approach involves labeling major crashes on the S&P 500 chart and correlating them with diverse economic data. By filtering and marking significant crash phases, we create a binary classifier. Forecasting S&P 500 values for 24 months using ARIMA and SARIMAX models allows for better evaluation and economic analysis. The project integrates various machine learning algorithms, with random forests and decision trees emerging as top performers. Deep learning techniques using Keras, such as Convolutional Neural Networks (CNNs) and Dense Neural Networks (DNNs), achieve notable accuracy. Additionally, real-time stock-related tweets obtained through the Twitter API and market sentiments from the Alpha Vantage API enhance the predictive capabilities. The model employs ARIMA and SARIMAX for forecasting, alongside a CNN architecture for crash classification. The UI/UX showcases data visualizations, sentiment analysis, and predictive models. The technology stack includes REACT, NODE JS, TENSORFLOW, A WS, and more. This work presents a holistic strategy to equip investors with valuable insights and aid in making informed decisions during potential market downturns. © 2024 IEEE.

关键词： Convolutional neural networks

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Prediction of Covid and Covid-19 and Omicron data with ESPRINT, ERF, CSDLM and HMC-19HRP Models 5

Prediction of Covid and Covid-19 and Omicron data with ESPRI...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Ramya, V. Thirunavakkarasu, K.S. Vel's Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

SARS-CoV-2, also known as the Covid-19 Corona Virus, has caused widespread damage globally, and the situation is only becoming worse. Officials from all across the world are using a number of COVID-19 outbreak prediction models to help them make choices and implement pertinent control measures. Simple epidemiological and statistical models are well-liked by the media and have garnered more attention from authorities among the conventional models for COVID-19 global pandemic prediction. It's a pandemic illness that spreads daily from person to person. As a result, it's critical to monitor the total patient population impacted. The way that the computerised data is now provided by the system makes it extremely difficult to analyse and forecast the spread of disease both locally and globally. An enhanced Sprint algorithm, Enhanced Random forest algorithm, Customized sequential Deep learning Model (CSDLM), and HMC-19HRP model is proposed in this study and analysed each with two dataset that is Covid dataset and OMICRON dataset. Finally results obtained from all the method are compared. On overall comparison HMC-19HRP outperforms other methods on both the dataset with precision of 94.92 and 98.26, Recall of 99.03 and 95.86, F1-score of 96.93 and 97.04, Specificity of 78.71 and 97.06 and Accuracy of 94.98 and 96.30 on covid data and Omicron dataset respectively. © 2025 IEEE.

关键词： COVID-19

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Optimized Hybrid Cryptographic Technique Using Symmetric and Asymmetric Encryption for Cloud computing 5

Optimized Hybrid Cryptographic Technique Using Symmetric and...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Suresh, R. Dayana, T.R. Nisha Vel's Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

Cloud computing (CC) is an expanding computing concept in which computing resources are given as Internetbased services. Security and confidentiality of cloud storage services are critical and pose a difficulty in cloud computing owing to loss of control over data and reliance on the cloud computing provider. While there is a big number of transmitting data in the cloud system, the risk of data access by attackers raises. CC requires encryption to maintain the privacy and security of data stored, processed, and transported on the cloud. Encryption ensures that confidential information saved in the cloud stays private and accessible only to authorized parties. The field of cryptography is continuously evolving to deal with current challenges in cybersecurity, confidentiality, and computing power. Optimizing encryption algorithms entails enhancing security, performance, and efficiency while reducing resource usage. This study suggests a optimized framework for encryption using RSA, an asymmetric key cryptography for key exchange and advanced / Encryption Standard (AES), a symmetric key cryptography for bulk message encryption to improve the efficiency of encryption. The suggested models's performance evaluated with RSA with Data Encrytpion Standard(DES) and RSA with BlowFish algorithms and the efficiency of RSA with AES is found to more in terms of Execution time, Throughput, memory usage and latency. © 2025 IEEE.

关键词： Sensitive data

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A Model for the Analytical Performance of Data Lake in Stock Market Analysis with Databricks Delta Lake

A Model for the Analytical Performance of Data Lake in Stock...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kamalakkannan, S. Yasmin, A. Arunkumar, R. Kavitha, P. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Information Technology Chennai Pallavaram India Jbas College for Women Department of Computer Science Chennai India University of South Wales Faculty of Computing Engineering and Science Treforest/Newport Campus United Kingdom School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai Pallavaram India

ISBN: (纸本)9798350300857

Stock market investments are highly rewarding but also high in risk. Modern investors use variety of tools to take informed investment decisions. In the current era of digital world, financial service industry has generated huge volume and immense verities of data with extreme speed. Due to the rapid growth in data collection and the heterogeneous nature and complexity of the data, there is a need for Big Data analytical solution that would be able to deal with the stock market data. Large volumes of unstructured, heterogeneous raw data can be stored in a massively scalable manner using data lakes, which are the ideal solution to the big data storage conundrum. The ability of a data lake to preserve data in its original format while processing it at runtime using a schema on-read technique is its key feature. The challenge faced in the data lake is performing analytics which is a significant tool to calculate and analyze the stock market. The proposed architecture of Azure Databricks DeltaLake (ADDL) with Azure DataLake Storage Generation 2 (ADLSG2) is used for analytical processes like Fibonacci retracement for better stock analysis, which aid in forecasting the market price for better investment. As a result, the research focus is to produce a storage having read as well as write capabilities by taking into consideration the Extract-Load-Transform (ELT) operation on the datasource. In this experimental databricks implementation, runtime is performed using open source of Apache Spark API and a highly improved execution engine, which results in a significant performance improvement when comparing to the standard source of Apache Spark available on the ADLS platform. Additionally, the Fibonacci retracement level calculation is achieved with the analytics and forecasting of test close price with various ML and DL techniques such as KNN, LSTM are compared with original price of the test data for better prediction of forecast close price. © 2023 IEEE.

关键词： Big data

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