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Investigating the Benefits of Text Mining for information Analysis

Investigating the Benefits of Text Mining for Information An...

2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Singh, Vaishali Venkadeshwaran, K. Jha, Gaurav Arunadevi, R. Sharma, Yogesh K. Maheswaran, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Textual content mining is the manner of extracting significant information and insights from large volumes of unstructured textual information. Many businesses are beginning to recognize the capacity of text mining in gaining effective insights and presenting greater accurate statistics. This paper will talk the ability blessings of using text mining strategies in data analysis. It's going to present quite number possible applications, such as herbal language processing, sentiment evaluation, subject matter modeling, and predictive analytics. It also evaluation the numerous technical techniques and algorithms used for textual content mining, together with lateens semantic indexing, and will look at how text mining can be used to guide a range of various commercial enterprise targets. Sooner or later, the paper will address how text mining can help pick out useful styles and traits in unstructured information, and the way it may be utilized in tandem with conventional data processing methods to achieve higher effects. This paper will finish by way of analyzing the capacity challenges that may be encountered when using text mining and how ability dangers can be mitigated. © 2023 IEEE.

关键词： Semantics

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Hybrid UHF Quad Antenna for Indoor Use

Hybrid UHF Quad Antenna for Indoor Use

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International Conference on Smart Systems and Technologies (SST)

作者： Tomislav Barić Hrvoje Glavaš Vanja Mandrić Department of Electromechanical Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Communication Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Amateur ultra-high frequency (UHF) directional antennas intended for indoor use are typically used in confined spaces. Also, their position and orientation often have to be changed, therefore they are exposed to frequent mechanical stress. For this reason, it is preferable that this type of antenna is lightweight, compact, with a mechanically reinforced (strong) construction. Also, it is desirable that the antenna has the best possible electrical and electromagnetic characteristics. This paper presents one of the ways in which the mechanical, electrical and electromagnetic requirements for amateur radio UHF directional indoor antenna can be simultaneously met. In the article, a relatively simple construction of the antenna is proposed. The antenna consists of two vertically stacked hybrid antennas from the Quad antenna family. The paper also presents an interesting choice of materials which ensures a mechanically reinforced (strong) construction and lightweight of the antenna. Numerical calculations and optimizations were carried out using 4nec2 software. Bandwidth and voltage standing wave ratio were determined by measurement.

关键词： Antenna measurements Coaxial cables Voltage measurement Aluminum UHF antennas Soldering Electromagnetics Junctions Antennas Stress

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An Exploration of the Effectiveness of Machine Learning Algorithms for Text Classification

An Exploration of the Effectiveness of Machine Learning Algo...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Agarwal, Ankita Aravindan Shrivastava, Manish Sathya, S. Verma, Devika A. Sohal, Jagmeet Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Centre of Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350329773

Text type is an important project in natural language processing (NLP), with programs in facts retrieval, sentiment evaluation, and report categorization. With the exponential increase of virtual information, the call for automatic text category has multiplied. Conventional text category techniques along with rule-based totally structures and statistical methods have obstacles in managing complex and massive datasets. System studying (ML) has emerged as a promising technique to text type, leveraging algorithms which can automatically learn from information and make correct predictions. This paper gives an exploration of the effectiveness of different machine getting to know algorithms for text category responsibilities. We evaluate and compare the performance of famous algorithms consisting of decision bushes, aid vector machines (SVM), ok-nearest pals (KNN), and deep learning fashions which include convolutional neural networks (CNN) and recurrent neural networks (RNN). © 2023 IEEE.

关键词： Recurrent neural networks

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FAQNet: Frequency-Aware Quaternion Network for Endoscopic Highlight Removal

FAQNet: Frequency-Aware Quaternion Network for Endoscopic Hi...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Dingzhou Huang, Guoheng Yuan, Xiaochen Chen, Xuhang Zhong, Guo Pun, Chi-Man Deng, Jie Guangdong University of Technology School of Computer Science and Technology China Macao Polytechnic University Faculty of Applied Sciences China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Guangdong University of Foreign Studies School of Information Science and Technology China First Affiliated Hospital of Sun Yat-Sen University China

ISBN: (纸本)9798350386226

Due to the built-in light source within the endoscope, the illumination of bodily mucous can cause the formation of highlight regions due to reflection. This not only interferes with the diagnosis conducted by doctors but also poses a challenge to subsequent computer vision tasks. To tackle this issue, we introduce FAQNet, a network specifically designed for endoscopic image highlight removal. FAQNet seamlessly integrates multi-channel information leveraging quaternion convolution and spatial channel attention within our Quaternion Multi-Channel Fusion (QMCF) Module. This allows it to capture intricate details of color, texture, spatial information, and highlight characteristics within the imaged organ. Additionally, by employing frequency domain transformation and dilated convolution, the Contextual information Integration (CII) Module effectively enlarges the receptive field, organizing contextual information between highlight regions and their surrounding areas. Lastly, the PixelShuffle Upsampling (PSU) Module generates the restored image. We validate our model's performance on two benchmark datasets, demonstrating its superiority over existing highlight removal methodologies. © 2024 IEEE.

关键词： Endoscopy

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Hospital Out Patient Visit Forecasting using Gated Recurrent Unit 6

Hospital Out Patient Visit Forecasting using Gated Recurrent...

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6th International Conference on information Systems and computer Networks, ISCON 2023

作者： Thapa, Krishna Timalsina, Arun K. Pokhara University Faculty of Management Nepal College of Information Technology Kathmandu Nepal Pulchowk Campus Institute of Engineering Tribhuvan University Department of Electronics and Computer Engineering Nepal

ISBN: (纸本)9798350346961

Recently, people have become more interested in their healthcare and well-being, resulting in a significant increase in patient flow for healthcare providers. To provide quality care, hospitals must manage patient flow and resources effectively. Hospitals need to forecast daily patient flow in the Out Patient department to ensure they have enough resources to provide care to all patients, manage resources efficiently, reduce wait times, and improve patient satisfaction. This study is about optimizing patient visit forecasting using deep learning techniques on univariate time series data. The LSTM-based deep learning approach is a reliable module for forecasting such data, however, an enhanced version of LSTM called GRU produced even more effective results. The experimental data from a private hospital's daily outpatient visits over the last 5 years verified that the GRU model achieved a higher accuracy of 92.42% before 1 minute 29 seconds compared to the LSTM model whose accuracy of 87.15% on an average time of 13minute 8 seconds.. © 2023 IEEE.

关键词： Forecasting

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An Enhanced Product Recommendation System Using Decision Tree Algorithm 7th

An Enhanced Product Recommendation System Using Decision Tre...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Akinyoola, Oluwatimilehin Moses Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany

ISBN: (纸本)9789819764648

Product recommendation systems are a critical element of e-commerce platforms, as they enable customers to identify the items that best meet their needs. Product recommendation systems are important tools used by organizations to increase customer engagement, satisfaction, and loyalty. Existing product recommendation systems have several weaknesses, such as an inability to incorporate features outside of customer preferences. Therefore, this paper proposes an enhanced product recommendation system using a decision tree algorithm. The main objectives of this system are to improve the accuracy and efficiency of the existing product recommendation systems. To accomplish this, a framework includes a data preprocessing phase, a feature selection phase, and a model training phase. The data preprocessing phase is used to clean the data and eliminate any noise. The feature selection phase is used to identify the most informative features, which are then used to train the decision tree model. The model is trained using CART, a supervised learning algorithm, and is evaluated using various metrics such as accuracy, precision, recall, and F1-score. Finally, the model is tested on a test dataset to compare with existing solutions. The results show that the proposed system outperforms existing recommender systems in terms of all the evaluation metrics discussed. Furthermore, the improved system also provides useful insights into the product recommendation process for future studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Improving Bandwidth Utilization for Underwater Fiber Optic Networks Through Intelligent Optimization Techniques

Improving Bandwidth Utilization for Underwater Fiber Optic N...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sakhare, Nitin N. Yadav, Rakesh Kumar Rajesh, A. Hassan, Inzimam Ul Vinithra Banu, T. Maheswaran, S. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Underwater Fiber Optic Networks (UFONs) are getting increasingly more important because of the emergence of the marine economic system, the enlargement of ocean exploration sports, and the need for reliable net get entry to. However, their bandwidth utilization is negative due to insufficient useful resource allocation techniques, constrained community design facts, and restricted expertise about consumer traffic styles. This paper proposes clever optimization techniques to improve the bandwidth usage of UFONs. These strategies are primarily based on gadget studying strategies along with neural networks and particle-swarm optimization. Numerous performance metrics are used to assess the proposed techniques. Simulations and experiments are performed to compare the overall performance of the proposed techniques with conventional techniques. Effects show the proposed smart optimization strategies are able to enhancing the bandwidth utilization of UFONs by means of as much as sixteen%. The proposed shrewd optimization strategies can provide significant enhancements for actual-time aid allocation, dynamic network control, and monitoring of underwater traffic styles. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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Text Mining for Intelligent information Processing and Analysis 3

Text Mining for Intelligent Information Processing and Analy...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Srivastav, Adarsh Indira, G. Tiwaskar, Shweta A. Garg, Avni Singh, Shweta Das, Sunanda Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

textual content Mining is the process of extracting meaningful information from large volumes of unstructured text. it's far a form of synthetic intelligence used to investigate and interpret files along with consumer feedback, emails, transcripts, net logs, surveys, and on line documents. Its intention is to discover hidden shape, patterns, institutions, and correlations in textual facts in order to draw inferences and broaden new insights. textual content Mining allows a deeper level of intelligence and selection-making by means of uncovering which means from unstructured textual facts. by means of utilising present day statistical strategies and herbal Language Processing (NLP), text Mining can produce automatic fashions that interpret and translate textual content into intelligent information which can be used to make correct predictions and provide actionable insights. textual content Mining is also being used to find out new trends, categorize client sentiment, locate correlations in purchaser comments, and section document collections. eventually, text Mining is being used to assist automate selection-making by automating workflows, processing massive volumes of text in a fragment of time, and providing deep insights into patron conversations. © 2023 IEEE.

关键词： Automating Categorize Collections Conversations Insights

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Detection of Thyroid Disease Through Machine Learning Technique 25

Detection of Thyroid Disease Through Machine Learning Techni...

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25th International Arab Conference on information technology, ACIT 2024

作者： Almomani, Mutaz Al-Qerem, Ahmad Nashwan, Shadi Jebreen, Issam Rajab, Mohammed Alauthman, Mohammad Jeadi, Mohammad Elrashidi, Ali Zarqa University Faculty of Information Technology Computer Science Department Zarqa13110 Jordan Middle East University Faculty of Information Technology Cybersecurity Department Amman11831 Jordan University Headquarter University of Anbar Ramadi Iraq University of Petra Department of Information Security Amman11196 Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Thyroid disease, one of the most common endocrine disorders, requires prompt diagnosis to effectively treat it. To improve diagnostic precision and expedite medical procedures, this paper explores the use of Machine Learning (ML) approaches for the early identification and categorization of thyroid diseasees. Sophisticated machine learning methods, including support vector machines, neural networks, and decision trees, are used to leverage a variety of datasets. These models outperform traditional diagnostic techniques in their capacity to identify complex patterns and minute variations in thyroid function parameters. The application of machine learning to the diagnosis of thyroid disease not only increases diagnostic accuracy but also makes preventive healthcare approaches possible. Personalized risk assessments are made possible by ML models, which analyze a wide range of patient-specific characteristics and help to improve the targeting of therapies. Furthermore, decision-making procedures are accelerated by the real-time processing powers of machine learning systems, which may shorten the interval between diagnosis and the start of therapy. this paper emphasizes how machine learning has revolutionized the diagnosis of thyroid disease and how it has the potential to completely change clinical procedures. The integration of sophisticated algorithms and extensive datasets presents opportunities for enhanced precision, effectiveness, and customization of diagnostic methods, which might eventually lead to better patient outcomes and more efficient use of healthcare resources for thyroid condition treatment. In this paper, the results were as follows: Two classifiers performed the best: LMT and LogitBoost. The Trees and Meta-learning strategy performed the best learning strategy, while the CorrelationAttributeEval and InfoGainAttributeEval methods performed the best feature selection. © 2024 IEEE.

关键词： Support vector machines

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An efficient astronomical image processing technique using advance dynamic workflow scheduler in cloud environment

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International Journal of information technology (Singapore) 2022年第6期14卷 2779-2791页

作者： Ahmad, Faiyaz Ahmad, Wakar Faculty of Engineering and Technology Department of Computer Engineering Jamia Millia Islamia New Delhi 110025 India Department of Information Technology Meerut Institute of Engineering and Technology Meerut 250005 India

The Montage project of NASA introduces an efficient astronomical image mosaic processing service with the help of Montage workflow applications. These workflow applications contain a set of input astronomical images that are able to produce custom mosaics of the sky to understand the universe’s activities. In the last 2 decades, extensive research has been done for processing Montage application in the various distributed computing paradigms such as Grids and Clusters but fails to achieve the required performance in terms of makespan. Recently, Cloud computing is the latest computing paradigm that provides online computing resources for efficiently processing these applications. In this article, we introduce an efficient astronomical image processing technique (EAIPT) for Montage workflow application in the cloud environment that shows notable improvements in terms of minimization of makespan without increasing the time complexity with the help of an advance dynamic scheduler. The proposed dynamic scheduler tries to schedule the current workflow task on the virtual machine that already processed its last parent task based on proposed scheduling constraints. In this way, it bypasses the communication time among different VMs. The performance of the proposed algorithm is compared with the existing state of art algorithms. The experimental results prove that our proposed technique has significant performance over compared algorithms in terms of schedule length ratio (SLR), the percentage of best results, and average running time metrics. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cloud computing Image processing Montage application Workflow scheduling

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