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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Rathod, Dushyantsinh B. Patel, Yesha Jethava, Archana Gohel, Namrata Suthar, Dhruvi Prajapati, Ramesh Rathod, Ghanshyam Computer Engineering Department Ahmedabad Institute of Technology Gujarat Ahmedabad India Department of CSE Faculty of Engineering and Technology GLS University Gujarat Ahmedabad India Computer Engineering Department Shree Swaminarayan Institute of Technology Gujarat Gandhinagar India Department of Computer Application Parul Institute of Computer Application Baroda India

ISBN: (纸本)9789819766772

Due to increased network traffic and security concerns, intrusion detection system (IDS) research has received a lot of attention in computer science. Advanced intrusion detection systems have arbitrary incursion categories and demand a high processing power usage. Notwithstanding the fact that there is a wealth of literature on IDS issues, our objective is to provide a more thorough review. We offer the taxonomy to differentiate between modern IDSs based on the extensive survey and intricate organization. In addition, the architecture we provided in the content aids the reader in grasping the whole idea of IDSs with ease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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Performance Analysis of Document Similarity-Based DBSCAN and K-Means Clustering on Text Datasets 5th

Performance Analysis of Document Similarity-Based DBSCAN and...

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kathiria, Preeti Pandya, Vandan Arolkar, Harshal Patel, Usha Institute of Technology Nirma University Sarkhej—Gandhinagar Hwy Gota Ahmedabad India Faculty of Computer Technology GLS University Ahmedabad India

ISBN: (纸本)9789811998751

The clustering of documents based on their similarity is prolific for an application that wants to extract similar documents and disparate non-similar documents to reduce ambiguity in finding relevant documents. Therefore, we require a robust clustering algorithm that can cluster document efficiently and effectively. In this paper, the performance of two clustering algorithms K-Means and DBSCAN with optimal parameters are compared on various textual datasets with distance measures—cosine and hybrid similarity. The challenge of finding the optimal value of epsilon in DBSCAN and value of K in K-Means is fulfilled by the DMDBSCAN and within-cluster sum of square algorithms, respectively. Hybrid similarity has an impact of single words and phrases, so the shared phrases across the corpus are drawn and the phrase similarity is computed. Then, hybrid similarity is formed using cosine and Phrase. To catch the shared phrases, the document representation model—Document Index Graph—is implemented in the Neo4j graph database. We utilized silhouette score to evaluate the performance of clustering algorithms. Experimental results reflect that DBSCAN performs better than K-Means on both the similarity measures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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Sentiment Analysis using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency

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Annals of Data Science 2024年第6期11卷 2019-2034页

作者： Loomba, Sankalp Dave, Madhavi Arolkar, Harshal Sharma, Sachin School of Cyber Security and Digital Forensics National Forensic Sciences University Gujarat Gandhinagar India Adani Institute of Digital Technology Management Gujarat Ahmedabad India Faculty of Computer Application and Information Technology GLS University Gujarat Ahmedabad India Indrashil University Gujarat Mehsana India

Due to the ever-increasing computing power and easy availability, social-networking platforms like Facebook, Twitter, etc. have become a popular medium to express one’s views instantly, be it about political situations, commercial products, or social occurrences. Twitter is a powerful source of information, whose data can be utilized to investigate the opinions of users through a process called Opinion Mining or Sentiment Analysis. Using the principles of Natural Language Processing and data science, this paper presents a comparative evaluation of multiple lexicon-based sentiment analysis algorithms to extract public opinion from tweets. The study explores the nuances of sentiment analysis using data science methodology, assessing how various lexicon-based algorithms may successfully identify and classify sentiments expressed in tweets from the Indian community about cryptocurrency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Sentiment analysis

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Monitor the Effectiveness of Cardiovascular Disease Illness Diagnostics Utilizing AI and Supervised Machine Learning Classifiers 7th

Monitor the Effectiveness of Cardiovascular Disease Illness ...

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7th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2023

作者： Rathod, Dushyantsinh B. Patel, Yesha Jethava, Archana Gohel, Namrata Suthar, Dhruvi Varia, Dhaval Shah, Nirav Barot, Janki Computer Engineering Department Ahmedabad Institute of Technology Gujarat Ahmedabad India Department of CSE GLS Institute of Faculty of Engineering & Technology GLS University Gujarat Ahmedabad India Vishwakarma Government Engineering College Gujarat Ahmedabad India Lambton College Toronto Canada Silver Oak University Gujarat Ahmedabad India

ISBN: (纸本)9789819940394

The heart is a crucial component of all living organisms. Greater thoroughness and precision are required for heart disease diagnosis and prognosis since even a little error may result in serious consequences or even death;there are many heart-related fatalities and the number is growing daily (Kumar et al. in Cardiac disease prediction using data mining algorithm. Springer Nature Singapore, Singapore, 2020). An illness awareness prediction system is a crucial component needed to address this issue. Machine learning is an illustration of AI (artificial intelligence). It is quite helpful for predicting all kinds of occurrences related to natural catastrophes. In this work, utilizing benchmark data sets from Kaggle for training and testing, we assess the efficiency of machine learning algorithms for heart disease prediction (Gupta and Vinayaka in Cardiac arrest disease prediction system using classification algorithms. Springer Nature Singapore, Singapore, 2020), including k-proximal neighbors, decision trees, linear regression, and Random Forest Classifier. The Anaconda (Jupyter) notebook, which includes a range of libraries and header files that make the process quick and easy, is the ideal tool for utilizing Python programming. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heart

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A Study of the open source framework OSGP / GXF for implementing Smart Water Metering

A Study of the open source framework OSGP / GXF for implemen...

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World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4)

作者： Urja Mankad Harshal A. Arolkar L. J. Institute of computer Applications Ahmedabad Gujarat India GLS Institute of Computer Technology Ahmedabad Gujarat India

ISBN: (数字)9781728168234

ISBN: (纸本)9781728168241

Water scarcity is a major issue in any developing countries like India. To fight with such natural resource scarcity problem we can have some smart applications. Smart water metering system is one such solution. It can monitor and track water usage and generate usage report to take corrective actions. This type of IoT (Internet of Things) based application development is somewhat costly and complicated but if we use a framework which can provide good platform for device connectivity and communication with it then things become easy. In this paper we would like to discuss applicability of OSGP (Open SmartGrid Platform) now known as Grid eXchange Fabric (GXF) for developing smart metering. They have developed an open source Internet of Things platform that is generic and scalable. Using this platform, smart objects can be easily connected, controlled, monitored and managed. The platform can also help simulate development of smart solutions like Smart Water Metering. As it is open source, it can be used for integrating new applications and devices with reduced turnaround time and development cost.

关键词： Protocols Web services Smart grids Smart meters Security Meters Monitoring

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A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

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Journal of Transportation Technologies 2018年第1期8卷 75-90页

作者： Rajendra S. Parmar Bhushan Trivedi Aleksandar Stevanovic Gujarat Technological University Ahmedabad India GLS Institute of Computer Technology Ahmedabad India Florida Atlantic University Boca Raton FL USA

On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.

关键词： Dynamic Traffic Assignment Intelligent Transportation Systems Intelligent Vehicles Road Traffic Control Road Traffic Sensing Traffic Management Vehicle Detection Vehicle Routing Traffic Signals Vehicular Congestion Detection System Vehicular Traffic Vehicle Mobility Sensors

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Incremental updates using Data Warehouse versus Data Marts

Incremental updates using Data Warehouse versus Data Marts

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International Conference for Convergence of technology (I2CT)

作者： Sonali Chakraborty Jyotika Doshi K. S. School of Business Management Gujarat University Ahmedabad Gujarat India GLS Institute of Computer Technology GLS University Ahmedabad Gujarat India

ISBN: (数字)9781538652329

ISBN: (纸本)9781538652336

Organization performs analytical processing on warehouse data and generates results using OLAP queries. Query result retrieval time is high as it traverses through huge data. With periodic warehouse refresh, frequent OLAP queries fired next time may require incremental update. For processing incremental results, all records in data warehouse are scanned. To overcome this issue, executed OLAP queries are stored in a relational database, MQDB, along with its result and metadata information. Further, a copy of incremental records is stored in data marts. When a query is fired next time, incremental results are generated by accessing data marts. Final result is based on combining existing results from MQDB and incremental results from data marts. Hence, need for repeated data warehouse access reduces, resulting in faster query result retrieval. This paper evaluates query execution time of materialized queries with incremental updates when done using data marts and using data warehouse.

关键词： Data warehouses Metadata Organizations Time-frequency analysis Education Relational databases

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Modulating Traffic Signal Phases to Realize Real-Time Traffic Control System

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Journal of Transportation Technologies 2017年第1期7卷 26-35页

作者： Rajendra S. Parmar Bhushan H. Trivedi Gujarat Technology University (GTU) Ahmedabad India GLS Institute of Computer Technology Ahmedabad India

This paper proposes innovations to address challenges emanating from road traffic congestion. Improving economies create more car owners resulting in increased automobile manufacturing, increased vehicle population giving rise to higher emission of CO2 resulting in traffic congestion. Congested traffic has idling vehicles which emit higher CO2 and pollution. Besides, traffic congestion increases turnaround time, delivery time, commuting time and related logistical aspects. Commuting time negatively impacts working hours. Unless the traffic congestion is mitigated, the economy will take a beating creating a vicious ecology cycle. Building new roads, bridges or reconditioning of infrastructure is not always the best possible solutions. Efficient traffic management is a key to country’s economic growth. Various analytical models are employed to study, appreciate traffic congestion. The paper studies these models to infer that real time approach is the only solution. Several approaches are being worked on and few commercial systems too are available. These systems provide traffic information for course correction. However, it has latency and hence deviates from real time environment. Traffic congestion being highly dynamic in nature, it necessitates real time solution with real time inputs. It is proposed to integrate Real time traffic data with the traffic signal thus modulating the cycle timings at every junction. Deviation from static asymmetric cycle timing is implemented by assigning green phases based on density of vehicles. With minimalistic infrastructure and negligible incremental cost, the paper not only proposes to address traffic congestion but also paves the way for capturing traffic offenses, vehicle tracking and toll collection. The research is imminently realizable and makes a strong case for a PPP (Public Private Partnership) project.

关键词： Vehicular Traffic Vehicular Congestion Detection System Vehicular Congestion Detection System Vehicle Mobility Sensors Traffic Signals Intelligent Traffic Signals DynamicTraffic Assignment

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2017年 508卷 v-vi页

作者： Modi, Nilesh Verma, Pramode Trivedi, Bhushan Narsinhbhai Institute of Computer Studies and Management Kadi MehsanaGujarat India The University of Oklahoma NormanOK United States Faculty of Computer Technology GLS University AhmedabadGujarat India

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Hybrid Intelligent Access Control Framework to Protect Data Privacy and Theft

Hybrid Intelligent Access Control Framework to Protect Data ...

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International Conference on Advances in Computing, Communications and Informatics

作者： Jignesh C. Doshi Bhushan Trivedi LJ Institute of Management Studies GLS Institute of Computer Technology

ISBN: (纸本)9781479987931

Web application usage has grown in the past few years. As a result more and more transactions are taking place via the web. Attacks have grown in sophistication and number. A database is a favorite target of attackers. As a result key challenge is to protect data privacy and prevent data theft. In this paper, a Hybrid Intelligent Database Security framework is proposed. The focus of the framework is to protect data privacy and prevent data theft. The proof of concept is implemented on Oracle Database. The proposed framework provides a multiple layer security, which can be used to protect data privacy and prevent data theft. The test results show that the framework is able to protect data privacy and prevent data theft. The proposed framework can be used to protect data in emerging technology like distributed databases, cloud computing etc...

关键词： Data Theft Database Security Data Protection Data privacy DBMS RDBMS DAC

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