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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze Scientific Society, 2024.

关键词： Tweets

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Near-Optimal Content Service Algorithm with Procurement and Placement in Edge Networks

Near-Optimal Content Service Algorithm with Procurement and ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yang, Chun-An Chen, Shih-Chieh Peng, Yi-Hsuan Chen, Yu-Wen Kuo, Jian-Jhih Tsai, Ming-Jer National Tsing Hua University Dept. of Computer Science Hsinchu Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan New York City College of Technology Computer Systems Technology BrooklynNY United States

ISBN: (纸本)9798350351255

Telecom carriers have announced new content services by making a partnership with content providers and offered popular video streaming to customers. Via the integration with edge networks, telecom carriers can procure various contents from content providers and place them on edge servers proximate to end users to serve real-time requests with high bandwidth and ultra-low latency. Nevertheless, it is challenging to consider the content procurement, placement, and services jointly due to the user preference, user distribution, storage capacity of edge server, economic costs, etc. Telecom carriers would like to balance the procuring, placing, and transfer costs. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. Finally, the simulation results manifest that our algorithm outperforms other baselines. © 2024 IEEE.

关键词： Video streaming

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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Development and Evaluation of a Speech-Recognized Voice Bot

Development and Evaluation of a Speech-Recognized Voice Bot

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Babu, K. Roobini, M.S. Gopirajan, P.V. Sadagopan, S. Anand, Rishi Rohit, B.S. SRM Institute of Science & Technology Dept of Computational Intelligence India Sathyabama Institute of Science & Technology Dept of Computer Science & Engineering India

ISBN: (纸本)9798350319200

In recent years, there have been significant advancements in speech recognition technology, which have opened up new possibilities for voice-controlled applications and devices. This report presents the design, development, and evaluation of a Speech-Recognized Voice Bot (SRVB) that aims to enhance user interactions with technology through natural language processing. The SRVB utilizes cutting-edge speech recognition algorithms and machine learning techniques to convert spoken language into actionable commands. The methodology involves training a deep learning model on a large dataset of speech samples and conducting user experiments to assess its performance. The results indicate that the SRVB demonstrates promising accuracy and usability, making it a valuable tool for a wide range of applications. © 2023 IEEE.

关键词： Machine learning techniques NLP Speech-Recognized Voice Bot

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Analysis of Neural Machine Translation for English to Hindi using Long Short-Term Memory Model and Transformer Model 4

Analysis of Neural Machine Translation for English to Hindi ...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Naveen, Rashmi Kumar, Archana Praveen Dept. of Information Science and Engineering India Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science & Engineering Manipal India

ISBN: (纸本)9798331540364

By providing enhanced accuracy and fluency compared to traditional approaches, Neural Machine Translation (NMT) has transformed the area of machine translation. In this research paper, the specific task of translating English to Hindi using NMT techniques is discussed. Creation and assessment of NMT systems developed for this language pair, considering the linguistic complexities and cultural aspects inherent in English and Hindi is examined. Leveraging large-scale parallel corpora and advanced neural network architectures, this work proposes novel approaches to improve translation quality and efficiency. Experimental results evaluated using standard metrics such as BLEU score, shows the efficacy of the proposed NMT models in accurately capturing the semantics and preserving the naturalness of the translated text. Additionally, this study discusses the implications of findings for cross-language communication and access to information, particularly in the context of the growing importance of Hindi in a globalized digital domain. Overall, this research contributes to develop neural machine translation for English to Hindi to a higher level and highlights the potential of NMT in facilitating multilingual communication and knowledge dissemination. © 2024 IEEE.

关键词： Neural machine translation

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Efficient Model for Multiview classification for diagnosis of Brain Tumors.

Efficient Model for Multiview classification for diagnosis o...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management, IC-RVITM 2023

作者： Anil Kumar, B. Chandrakala, B.M. Shruthi, B.V. Rv Institute of Tech & Management Dept of Computer Science & Engineering Bangalore India Dayananda Sagar College of Engineering Dept of Information Science & Engineering Bengaluru India Nitte Meenakashi Institute of Technology Dept of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350306088

Brain tumors are caused mainly by DNA abnormalities, which result in aberrant cell division, prolonged cell lifespans, and the creation of aggregative masses inside the brain. Such tumors prevent the brain from operating at its best, sometimes resulting in long-term problems. These tumors' genesis (primary or secondary) and malignancy (benign or malignant) are used to classify them. While some might not be immediately lethal, their growth or contact with healthy brain tissue might cause damage. Investigations into various approaches, such as conventional medical imaging and artificial intelligence (AI), mainly using deep learning (DL) and transfer learning (TL), is prompted by efforts to identify and categorize brain tumors. Implementing the EfficientNetB0 model, which is famous for its adaptability and efficacy, is an excellent demonstration of this strategy. This work uses a well-curated dataset of 3264 photos from various classes to closely analyze the use of the EfficientNetB0 TL model to identify and classify brain tumors from MRI scans. The model's adaptability and accuracy are meticulously examined using a dual-pronged strategy incorporating transfer learning and fine-tuning, yielding an astounding accuracy of 98.5%. Accuracy and loss charts illustrate the model's learning development in vivid detail, and a well-defined confusion matrix provides extensive insights into categorization effectiveness across various tumor classifications. The achievement of a 98.5% accuracy rate highlights the potential paradigm change made possible by AI and TL in the diagnosis and classification of brain tumors. This accomplishment confirms the efficacy of such sophisticated procedures and promises to speed up diagnosis, enable prompt therapies, and improve patient prognoses. The combination of AI and TL heralds a new era of precision medicine, ready to reshape the field of brain tumor care as research in this area grows. © 2023 IEEE.

关键词： Brain

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

引用

2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Mingze Ren, Weihang Fan, Lei Guan, Yongpei University of Houston Dept. of Electrical and Computer Engineering TX United States Engineering Technology University of Houston TX United States University of Florida Dept. of Industrial and Systems Engineering FL United States

ISBN: (纸本)9798331541125

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation. © 2025 IEEE.

关键词： AC power flow clustering local search Unit commitment

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Machine Learning Methods Analysis for Indian Currency Counterfeiting 14

Machine Learning Methods Analysis for Indian Currency Counte...

引用

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

作者： Prathibha, T. Prakash, H.N. Padmajadevi Rajeev Institute of Technology Department of Computer Science and Engineering Hassan India Dept. of CSE Rajeev Institute of Technology Department of Computer Science and Engineering Hassan India Malnad College of Engineering Department of Electronics and Communication and Engineering Hassan India

Imitation of currency made with the intention of misleading people is referred to as counterfeit money. One of the risks that harms a country's economy and so slows global growth is counterfeit money. Producing counterfeit money or creating false features for currency is illegal activity, which needs to be properly analysed. Demonetization resulted in an unexpected inflow of fake currency into banks, which in turn lead an increase in doubtful activities. The review focused on the methodology used to detect counterfeit currency, as well as the success rate of each method. Numerous fake currency detection strategies offered by different researchers are presented in the study. This paper discusses the challenge of determining whether or not a particular sample of cash is fake or real. There are various conventional strategies and methods to detect illegal currency based on the basic features like colors, width, and serial numbers specified on the currency. As per the comparative survey analysis KNN and regression methods will give good accuracy for identifying the fake currency. © Grenze Scientific Society, 2023.

关键词： Feature extraction

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Health Care Data Mining using Intelligent Classification Techniques 5

Health Care Data Mining using Intelligent Classification Tec...

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Antil, Jyoti Rishi, Rahul Sharma, Sonia Yadav, Rajkumar University Institute Of Engineering And Technology Mdu Dept. Of Computer Science Engineering Hrayana Rohtak India University Institute Of Engineering And Technology Mdu Dept. Of Electronic And Communication Engineering Hrayana Rohtak India

ISBN: (纸本)9798350330861

Classification models can be learned from complicated multivariate temporal data and this is a topic we investigate in electronic health record systems. The difficulty is to come up with a collection of attributes that accurately reflect the data's temporal nature. Using temporal abstractions and pattern mining, we extract the classification features from the data. There are frequently a lot of temporal patterns that aren't related to the classification job that are returned by temporal pattern mining. © 2023 IEEE.

关键词： Classification (of information)

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An Effective Analysis of Machine Learning Models for Prediction of Heart Disease 2

An Effective Analysis of Machine Learning Models for Predict...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Patil, Rudragoud Kamkar, Shambahvi C. Goudar, Shwetha Indudhar K.L.S.Gogte Institute of Technology Dept of Computer Science and Engineering Karnataka Belagavi India K.L.S Gogte Institute of Technology Dept.of Master of Computer Applications Karnataka Belagavi India

ISBN: (纸本)9798350372892

Heart failure is a chronic illness that impacts millions of individuals worldwide. In order to forecast heart failure health status early and take the necessary activities to address this global issue an effective machine learning-based technique is required. Globally, heart disease is one of the leading causes of death. Medical professionals find it challenging to forecast since it demands experience and a deeper level of predictive knowledge. The healthcare industry operates in a knowledge-deficient but information-rich environment. Healthcare systems have access to a vast amount of data via the internet, but there aren't many useful tools for analyzing the data to find hidden patterns. Medical efficiency will increase with an automated system, which will also save money and time. This software uses information acquired from Kaggle to forecast the likelihood of a disease developing. To create a prediction model, the following algorithms are used: Random Forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR). Phases for feature selection and data preprocessing were finished before the models were created. When the models were evaluated based on accuracy and the F1-score, the RF model performed the best. © 2024 IEEE.

关键词： Adversarial machine learning

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