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Extractive Summarization on Twi Language Using Text-Rank and Fuzzy Clustering Algorithm 1

Extractive Summarization on Twi Language Using Text-Rank and...

引用

1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Dhole, Sayak Sagnika, Santwana Dominic, Ampofo Mishra, Bhabani Shankar Prasad Fernando, Aloka Dash, Satya Ranjan School of Computer Engineering Kiit Deemed to Be University India School of Computer Applications Kiit Deemed to Be University India University of Moratuwa Department of Computer Science and Engineering Sri Lanka

ISBN: (纸本)9798350315769

Twi is a dialect of the Akan language spoken in southern and central Ghana by several million people. About 80% of the Ghanaian population speaks Twi as a first or second language. Summarization is a basic natural language processing task, that is a step to further more advance tasks as well as an aid in reviewing information. This paper tackles the problem of summarizing Twi texts. Three extractive summarization techniques - TF-IDF, Text-Rank and Fuzzy C-means Clustering are implemented. We find that Text Rank performs best, while TF-IDF despite being fairly simple performs comparably well to Text Rank. Our work suggests that embedding based approaches are potent and further work should employ their use. © 2023 IEEE.

关键词： Fuzzy clustering

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Video enabled crowd monitoring system and analysis 3

Video enabled crowd monitoring system and analysis

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mehra, Gunjan Joshi, Aditya Singh, Aakriti Shuaib, Mohd Diwakar, Manoj Singh, Prabhishek Graphic Era Deemed to be University Department of Computer Applications Dehradun India Graphic Era Deemed to be University Department of Cse Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India

ISBN: (纸本)9798331515911

One of the most victorious strategies for slowing the spread of the COVID-19 virus has been proposed: social isolation. A computer vision-based AI-assisted approach for aiding with social distance norms compliance is utilized. The solution consists of modules for person detection, tracking, and distance violations detection. You are given the option to select a tool-based mode for camera calibration. In this paper, the various measures for determining the likelihood of social distance violations and how to tell them apart from temporary violations from persistent ones are demonstrated. On countless real-world issues, AI/Deep Learning has in the past shown promising results. In this paper, how to observe crowds in public areas and workplaces using Python, image processing, and computer vision is discussed in depth. © 2025 IEEE.

关键词： Deep learning

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5G in Smart Cities: Enhancing Urban Futures for Sustainable Growth

5G in Smart Cities: Enhancing Urban Futures for Sustainable ...

引用

2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Rajvanshi, Anita Mahajan, Prerna Singh, Alka Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Noida Institute of Engineering and Technology Computer Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

This essay delves into the concept of 'Smart Cities,' outlining its numerous benefits. It elucidates the diverse applications and services essential for transforming a city into a Smart City, underscoring the pivotal role of Information and Communication Technology (ICT) in their realization. Furthermore, it scrutinizes the challenges posed by the deployment of Smart Cities for contemporary ICT systems. The study investigates the promised attributes of 5G technology and its potential as a linchpin for the efficient implementation of urban smart cities. Additionally, it provides a catalog of 5G-enabled Smart City use cases, emphasizing how 5G will serve as the bedrock for the Internet of Things (IoT), fostering the evolution of smart cities. © 2023 IEEE.

关键词： 5G technology ICT infrastructure Information and Communication Technology Internet of Things Quality of Life Smart Cities Smart ICT infrastructure Sustainable City Urban Transit and City Governance

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Improving Early Cardiomyopathy Diagnosis with CNNs: A Deep Learning-Based Approach 8

Improving Early Cardiomyopathy Diagnosis with CNNs: A Deep L...

引用

8th International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Abinaya, M. Duvvuri, Suneel Kumar Sathish Kumar, P.J. Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Chengalpattu Department of Computer Science and Engineering Tamil Nadu India Rajahmundry Department of Computer Science and Applications Andhra Pradesh India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Chengalpattu Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331520762

Cardiomyopathy is the type of heart disease that results from diabetes that influences the heart muscles, leading to structural and functional abnormalities and these situations can weaken the heart muscle, loss its ability to supply blood effectively. Cardiomyopathy affects millions worldwide every year, with an estimated 1 in 500 people impacted. In the United States, it leads to approximately 50,000 deaths are reported annually. The existing method with Logistic Regression for the detection of Cardiomyopathy obtained inaccurate results with a high false alarm rate. Introduced a deep leaning convolution neural networks algorithm for the preprocessing, extraction of data selection of features and diagnosis of disease with accurate results in the early stage. As a result, got the accurate performance for accuracy, precision, sensitivity, and recall with 97%, 96%, 95.6%, and 96% respectively. © 2025 IEEE.

关键词： Deep neural networks

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Bubble Prediction in Stock Markets: Leveraging SVM and Technical Indicators for High Accuracy 3

Bubble Prediction in Stock Markets: Leveraging SVM and Techn...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Manogar, E. Doss, P.Lourdu Mahimai Gunasekaran, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering TamilNadu Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Chennai India

ISBN: (纸本)9798331509675

Predicting stock market bubbles is indispensable for risk control and refinement of trading strategies. Therefore, this study presents a Machine-Learning Framework to detect price bubble formations based on Support Vector Machine (SVM) and through important technical indicators. Among these are momentum indicators, such as the Relative Strength Index (RSI), and the Moving Average Convergence Divergence (MACD) histogram, which are complemented with volatility measures from Bollinger Bands and with normalized volume indicators, all to improve predictive accuracy. Recent historical stock data were fed to the model during training and testing, yielding exceptional accuracy: 98.54% on the test set and 98.95% on the training set. The performance evaluation was carried out based on Classification Reports, Confusion Matrices, Receiver Operating Characteristic (ROC) curve, and Precision-Recall metrics, thus confirming the performance efficiency of the developed framework. With this approach, bubbles' early detection allows traders and analysts a powerful tool to make informed decisions in risk mitigation or strategic enhancement. © 2025 IEEE.

关键词： Support vector machines

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Analysis of Various Hyperparameters for Predicting Hypertension using Machine Learning Algorithms 8

Analysis of Various Hyperparameters for Predicting Hypertens...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Duggal, Shallu Sood, Shivani Singh, Amanpreet Singh, Harjeet Malarvel, Muthukumaran Lovely Professional University School of Computer Applications Phagwara144411 India Chitkara Institute of Engineering and Technology Chitkara University Punjab 140401 India Department of Computer Science and Engineering India

ISBN: (纸本)9798350367904

Predicting hypertension, or high blood pressure, is crucial because it's a risky condition that frequently doesn't exhibit any obvious symptoms or indicators until major problems arise. By using predictive models for early identification, the risk of uncontrolled hypertension-related cardiovascular events, strokes, and organ damage can be decreased to take quick action. The study aims to predict the diagnosis of hypertension using three machine learning techniques: Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine(SVM). With the focus on improving accuracy and reliability, the various hyperparameters of each individual algorithm are tuned using the cross validation and grid search approaches. After extensive testing and parameter modification, the SVM algorithm was found to be the most accurate predictor, which achieved a significant accuracy of 9 7. 0 0 %. The best hyper-parameters achieved for SVM are c value 4 and kernel as 'rbf'. The models' performance is evaluated using accuracy, specificity, sensitivity, F1-score, or AUC (Area Under the Curve). This study facilitates proactive healthcare measures by offering insightful information to determine the best algorithms for the early prediction of hypertension. © 2024 IEEE.

关键词： Prediction models

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Recognition of Data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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IoT-Aided Charity-Redistribution of Excess Food 2

IoT-Aided Charity-Redistribution of Excess Food

引用

2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Yogananth, V.S. Balasubramani, M. Vellore Institute of Technology Department of Computer Applications Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The utilization of the Internet of Things (IOT) has shown significant potential in various aspects of daily life, yet its application in addressing social issues remains underdeveloped. India, with a substantial number of orphanages, faces challenges in efficiently distributing crucial information, hindering donors from obtaining accurate data on food reserves. This project proposes an IOT-based system to tackle this issue, using Node MCU IOT devices connected to sensors that relay data through a network, accessible and monitorable via web applications. The proposed system architecture aims to bridge the gap between those who waste food and those in poverty who desperately need it. We intend to facilitate food donations by implementing smart containers equipped with IOT technology. These containers can detect excess food waste and share this information via a GSM module with trusted charitable organizations. This application is designed to serve three main stakeholders: service providers, donors, and food recipients in need. By creating smart containers, we ensure that donated food reaches the right individuals. We prioritize collaborating with trusted charities to oversee the distribution process. Only authorized charity members can access and distribute the food stored in these containers, ensuring that it reaches those who need it most. © 2024 IEEE.

关键词： Containers

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Blockchain-Powered Student Feedback for Enhanced Learning Experiences

Blockchain-Powered Student Feedback for Enhanced Learning Ex...

引用

2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Naik, Girish R. Naik, Poornima G. KIT's College of Engineering Department of Mechanical Engineering Kolhapur India Csiber School of Computer Science and Applications Kolhapur India

ISBN: (纸本)9798350319019

In the contemporary landscape of education, the invaluable input provided by students through feedback is widely acknowledged as pivotal in elevating the quality of teaching and learning experiences. Yet, conventional feedback systems encounter notable constraints, notably in the realms of data security and transparency. Current research is dedicated to exploring the integration of blockchain technology as a robust and secure framework for collecting student feedback. Blockchain's distinct features, including its assurance of data integrity, transparency mechanisms, and the utilization of smart contracts, hold the promise of significantly refining the feedback process. This research meticulously outlines the academic significance of student feedback systems, defines essential components for effective feedback mechanisms, and elucidates the role of blockchain in constructing such a system. Moreover, it presents a conceptual model design, details the system's architecture, and elucidates the functionalities of smart contracts developed using Solidity. By leveraging blockchain, this innovative approach ensures the security, transparency, and streamlined efficiency of the feedback process, proclaiming unprecedented potential for transformative advancements in educational methodologies. © 2024 IEEE.

关键词： Smart contract

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IIOT Fault Detection Using Multi Deep Convolutional Neural Networks

IIOT Fault Detection Using Multi Deep Convolutional Neural N...

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Nithya, K. Kousalya, R. KG College of Arts and Science Department of Computer Science Dr.NGP Arts and Science College Coimbatore India Department of Computer Applications Dr.NGP Arts and Science College Coimbatore India

ISBN: (纸本)9781839539848

Industrial Internet of Things (IIoT) has revolutionized industrial automation and data-driven decision-making across industries. IIoT system reliability and efficiency are crucial for minimizing downtime and maximizing production. This study uses Multi-Deep Convolutional Neural Networks to identify IIoT faults in a novel way. IIoT system produce massive sensor data making real-time defect and detection difficult. These systems are dynamic and complicated, making traditional defect detection approaches difficult. Multi-Deep Convolutional Neural Network (MDCNN) can automatically learn and extract complex patterns from high-dimensional sensor data. The proposed MDCNN architecture effectively processes multi-modal sensor input with spatial and temporal interdependence model may detect small flaws or abnormalities by using many convolutional layers to record hierarchies of data attributes. The MDCNN technique is tested utilizing real-world IIoT datasets from diverse industrial sectors. The model's accuracy, recall, and F1-score are used to measure its ability to find errors with few false alarms. The MDCNN-based fault detection system outperforms standard approaches in sensitivity and specificity, indicating its potential to improve IIoT system reliability and efficiency. Transfer learning approaches are being investigated to help the model adapt and generalize its defect detection skills across industrial contexts. © The Institution of Engineering & Technology 2023.

关键词： Internet of things

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