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2022 International Conference on smart Generation Computing, Communication and Networking, sMART GENCON 2022

作者： Lakshmanarao, A. Gupta, Chabi Kiran, T.srinivasa Ravi Aditya Engineering College Department of Information Technology Surampalem India Christ University School of Commerce Finance and Accountancy India P.B. S College of Arts & Science Department of Computer Science A.P Vijayawada India

ISBN: (纸本)9781665454995

since the advent of the Internet, the way people express their ideas and beliefs has undergone significant transformation. blogs, online forums, product review websites and social media are increasingly the primary means of distributing information about new products. Twitter, in particular, is giving people a platform to air their views and opinions about a variety of events and products. In order to continually enhance the quantity and quality of their products and services, entrepreneurs constantly need input from their customers. businesses are always looking for ways to increase the quality of their products and services. As a result, it's tough to understand the consumer's sentiments because of the large volume of data. In this research work, a Kaggle dataset of airline tweets for sentiment analysis was used. The dataset contains 11,540 reviews. We proposed an ensemble CNN, LsTM architecture for sentiment analysis. For comparison of the proposed system, LsTM alone also tested for similar dataset. LsTM was given an accuracy of 91% and the proposed ensemble framework with LsTM and CNN was given an accuracy of 93%. The experiments showed that the proposed model achieved better accuracy when compared to conventional techniques. © 2022 IEEE.

关键词： sentiment analysis

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A Novel IoT Framework and Device Architecture for Efficient smart city Implementation 7

A Novel IoT Framework and Device Architecture for Efficient ...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Kawale, sheetalrani R Prasad, K.D.v. Palanikkumar, D. Mary, P. Arockia begum, A.Yasmine Dankan Gowda, v. Karnataka State Akkamahadevi Women's University Department of Computer Science Karnataka Vijayapura India Telangana Hyderabad India Dr Ngp Institute of Technology Department of CSE Tamilnadu Coimbatore India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Department of Electronics and Instrumentation Engneering Andhra Pradesh India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350397284

Internet of Things (IoT) apps will provide fast and effortless management of smart cities. This networked service management is dependent on the Internet service Provider (IsP). This framework reduces physical human interaction in banking, railway counters, water and power counters, and so on. The IoT based applications makes the smart cities more sustainable. Recently, the Indian government is more focused and supportive towards building a reliable IoT based framework for developing innovative products, and businesses that rely mostly on cyberspace. This study discusses about the advantages, disadvantages and future possibilities of smart cities. The importance of the Internet of Things (IoT) in the development of India's smart cities, as well as how widespread adoption may swiftly disrupt people's everyday routines are also discussed. © 2023 IEEE.

关键词： Internet of things

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Enhanced IoT system for Real-Time Detection and Monitoring of Cardiac Events Using Machine Learning Algorithms

Enhanced IoT System for Real-Time Detection and Monitoring o...

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Delhi section Conference (DELCON), IEEE

作者： safia Naveed.s Nabeena Ameen Nulin Jeriba J. Department of Computer Science and Engineering KCG College of Technology Chennai Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Tech

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Heart attacks remain a primary cause of global mortality, highlighting the urgent need for systems that enable early detection and intervention. This study proposes a smart IoT-based system designed to detect and monitor heart attack symptoms in real-time, thus facilitating timely medical response. The system integrates wearable sensors to collect continuous data on heart rate, stress levels, and positional changes, which is then processed through Arduino IDE and transmitted to an IoT server. Machine learning algorithms, including support vector Regression (svR) and Artificial Neural Networks (ANN), are employed to analyze these signals for abnormal patterns indicative of a potential heart attack. Major findings indicate that this system accurately detects heart rate irregularities, and stress fluctuations and can alert healthcare providers, significantly improving remote monitoring capabilities. The implications of this study suggest that IoT-enabled monitoring systems can substantially enhance patient outcomes by allowing for early diagnosis and proactive care in remote settings, thereby reducing the risk of fatal heart events.

关键词： Wireless sensor networks Machine learning algorithms Cardiac arrest Artificial neural networks Medical services Real-time systems biomedical monitoring Heart rate variability Monitoring stress

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Weather and Climate Forecasting system for Cultivation using Naive's Algorithm 2

Weather and Climate Forecasting System for Cultivation using...

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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： sheela, M. sahaya banu, s. shahar Rajendran, T. Raj, s. saran sreeja, b.P. Pavai College of Technology Department of Electronics and Communication Engineering Pachal Namakkal India B S Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur Chennai India Tamilnadu Coimbatore India Vel Tech Rangarajan Dr. Sagunthala Rd Institute of Science and Technology Department of Computer Science and Engineering Chennai India Karpagam College of Engineering Department of Information Technology Coimbatore India

ISBN: (纸本)9781665436052

Agriculture based advisory services and climatic information are more considerable in providing assistance for pastoralists and small farmers to manage climatic changes and risks factors to be adopted during changes. Moreover, there is some gap in providing climatic information that significantly addresses the requirement of pastoralists and farmers. Huge farmers rely over indigenous knowledge where experiences and indicators are utilized to forecast and observe weather conditions. When this prediction is inbuilt and implemented by many farmers, documentations, coordinators and so on for predicting weather, it should be accurate and reliable manner. This work concentrates on building a weather predictor system for farmers to identify the velocity flow that is needed during farming. This model includes Naives equation for performing this velocity prediction. The outcomes demonstrate that pastoralists and farmers utilize combination of biological and meteorological indications to predict weather conditions. Henceforth, it seems to be more crucial in providing assistance to enhance access to climatic information in various regions of India in vulnerable and poor resource communities. © 2022 IEEE.

关键词： Cultivation

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self-diagnosis platform via IOT-based privacy preserving medical data

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Measurement: sensors 2023年 25卷

作者： Muneeswari, G. sajithra varun, s. Hegde, Ramakrishna sharon Priya, s. Josephin shermila, P. Prasanth, A. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Industrial Internet of Things MVJ College of Engineering Bangalore India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysore India Department of Computer Science Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Tamil Nadu Thiruvallur India Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur India

Healthcare is one of the application domains that use IoT technology, where sensors and IoT-enabled medical devices transmit data to healthcare specialists without the need for human intervention. The health records of previous patients are both very confidential and crucial to the identification of the disease for current patients. Therefore, it is important to fully utilize the records of previous patients without disclosing their personal data. This paper proposes an innovative self Diagnosis Platform (sDP) that securely retrieves patients' records from past records without disclosing either the privacy of the patient or the record database. Initially, the patient device receives data from the sensors placed in the patient's body, which is then encrypted by using Edwards' Digital signature Algorithm. The encrypted data is then sent to the secure Disease Archive (sDA), which contains past records. based on the patient's data, similar records will be retrieved from the database using the sDP. The suggested framework is compared with traditional methods in terms of encryption time, execution time, and end-to-end delay. Experimental results indicate that the suggested sDP technique achieves a better execution time of 30.63%, 27.48%, 22.07%, and 9.23% than LDQN, sE-AC, PMDA, and EPPDA methods. This system is more effectiveand secure for real-time applications. © 2022 The Authors

关键词： Internet of things

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A Novel Transfer Learning Approach for Eye Tumour Detection using Pre-Trained CNN Models

A Novel Transfer Learning Approach for Eye Tumour Detection ...

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smart Technologies, Communication and Robotics (sTCR)

作者： G Prabaharan vani Rajasekar velliangiri sarveshwaran Department of Computer Science and Engineering B V Raju Institute of Technology Narasapur Telangana India Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur TamilNadu India

According to a recent study, using retinal imaging to identify the tumour in the eye is difficult. When an image is analyzed, the affected region of the lesion impacted retinal picture is not discovered in the conventional system due to uneven illumination, low contrast, and blurring, which are symptoms of an eye disease. The proposed methodology was created to identify the eye tumour in the earlier stage with enhanced image quality and increased accuracy. The conventional system has a few drawbacks such as lower accuracy and more computational time to analyze the image. Convolutional neural networks (CNN) have recently demonstrated its result of their capacity to learn various category image denotions that aid in image classification in various sectors. These networks have a tremendous ability to extract the proper features from input images with training on millions of images, which produces accurate categorization. The deep learning-based transfer learning approach is suggested for earlier detection of eye tumours. The five pre-trained models such as AlexNet, ResNet, DensNet, vGG 19 and Inception model were used for efficient eye tumour classification. The performance analysis of the proposed approach has shown that higher accuracy of 98.63% and this model shows enhanced performance in better classification.

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Real-Time Multilingual Farming Assistance using NLP Integrated Web API

Real-Time Multilingual Farming Assistance using NLP Integrat...

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Automation, Computing and Renewable systems (ICACRs), International Conference on

作者： N. shirisha M. srivani K. Kowsalyadevi G bhaskar Phani Ram Pavan Kumar vadrevu v. bhaskara Murthy Computer Science and Engineering MLR Institutions of Technology Dundigal Hyderabad India Department of Computer Science & Engineering(DS) CVR College of Engineering Hyderabad India Department of CSE Sona College of Technology Salem TamilNadu India Department of ECE Vidya Jyothi Institute of Technology Hyderabad Department of Information Technology Shri Vishnu Engineering College for Women Department of MCA B V Raju College Vishnupur Bhimavaram

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

This research introduces a smart helper designed to assist farmers in remote locations. The proposed system provides timely and relevant information on various aspects of farming, including soil management, pest control, and crop selection. Key features include personalized advice, real-time weather updates, market insights, and multilingual support. by providing farmers with easy access to critical information and decision-making tools, this system aims to enhance agricultural productivity, improve livelihoods, and promote sustainable farming practices.

关键词： Productivity Renewable energy sources soil Programming Pest control Real-time systems Multilingual Monitoring Farming Meteorology

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Estimation of Deforestation Rate and Forest Land Use Land Cover Change Detection

Estimation of Deforestation Rate and Forest Land Use Land Co...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： M. Kalaiyarasi s. saravanan balaji. R s. Karthi D s Naga Malleswara Rao D. Nagineni venkata sireesha Department of Computational Intelligence SIMATS Saveetha School of Engineering Chennai B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering V.S.B. Engineering College Karur Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Information Technology Institute of Aeronautical Engineering Hyderabad Telangana India

Amazon area is important for global energy, hydrology, and carbon balance. Amazon covers 5.5 million km2. between 1976–2003, 7.3% of the Amazon was deforested, and 2.6% amid 2000–2010, PRODEs monitors Amazon deforestation using satellite imagery. The yearly deforestation rate is anticipated based on the growth of desertification in brazilian Amazon satellite images. According to INPE and FAO, 768,935 km2 of Amazon basin have been deforested. In this paper, the deforestation rate and the Forest Land Use Land Cover change has been detected using sAGA GIs. business As Usual (bAU) scenario is used to find the rate. As indicated by bAU scenario in the result, Amazon rainforest could be diminished to 43.26% and the overall timberland misfortune would be 38.45% towards the end of 2050.

关键词： Hydrology satellites Estimation Forestry Knowledge discovery Concurrent engineering Compounds

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breast Cancer segmentation by K-Means and Classification by Machine Learning 1

Breast Cancer Segmentation by K-Means and Classification by ...

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRs 2022

作者： Priya, K. senthilkumar, v. samson Isaac, J. Kottu, sreekanth Ramakrishna, v.s. Jogendra Kumar, M. Department of Computer Science P. B Siddhartha College of Arts and Science Andhra Pradesh Vijayawada520010 India Department of Electronics and Communication Engineering Er. Perumal Manimekalai College of Engineering koneripalli Tamil Nadu Hosur635117 India Department of Biomedical Engineering Surgical and Critical Care Equipments Laboratory Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Department of Information Technology St Peter's Engineering College Telangana Malkajgiri500100 India Department of Computer Science Engineering BVC Engineering College Andhra Pradesh Odalarevu533210 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9781665460842

breast Cancer (bC) progression is currently a common health problem among modern women. It is the cause of death for a significant number of women. bC is the growth of malignant cells in the breast tissue. Adipose or connective tissue can also develop bC. because of developments in medical technology, ultrasonography is one of many procedures utilised for the early identification of cancer. Ultrasound is a technique that uses high-frequency sound wave technology to create images of inside body structures such as organs and soft tissues. because of the poor quality of the information, there is a lot of possibility for interpretational mistake s when diagnosing cancer based on ultrasound images. As a result of these concerns, this paper uses the idea of Machine Learning (ML) is employed for the classification and segmentation of bC. The K-means clustering approach is used as part of the segmentation procedure to detect where the cancer is present. A recent study has demonstrated that machine learning produces reliable findings, allowing specialists to make better decisions. Using standard bC datasets, the performance of three different Machine Learning algorithms-Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbors (KNN)-is tested in this work. In terms of accuracy, RF outperformed the other algorithms, according to the finding. Future bC researchers will be able to utilise the findings of this study to guide their investigations and influence their efforts to improve the efficiency of specific algorithms. © 2022 IEEE

关键词： K-means clustering

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Comparative study and Analysis of Cloud Container Technology

Comparative Study and Analysis of Cloud Container Technology

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International Conference on Computing for sustainable Global Development (INDIACom)

作者： E. saravana Kumar Raghu Ramamoorthy selvaraj Kesavan T shobha seema Patil b vighneshwari Department of Computer Science and Engineering The Oxford College of Engineering Bengaluru India DXC Technology Bengaluru India Department of ISE B.M.S.C.E. Bengaluru India Department of CSE B.M.S.C.E. Bengaluru India Department of Electrical and Electronics Engineering The Oxford College Engineering Bengaluru India

The advent of digital transformation has revolutionized the way businesses operate. Applications have become the focal point of this transformation, shifting the focus from being organization-centric to user-centric. To realize the full potential of businesses, high-quality, secure, and agile applications are essential. Containers are a cutting-edge invention in the world of virtualization, gaining immense popularity in recent years. They have replaced traditional business continuity solutions and are now used to address highly demanding business needs. Multiple containers and orchestration frameworks are available, both as standalone and cloud-based services. However, developers and industry experts face challenges in identifying and evaluating the appropriate containers and orchestration frameworks for their application needs. The selected tools may not always be feasible due to a lack of available features, inability to provide agility, and lack of platform support. When applications are deployed across multiple containers, coordination and management among container clusters become critical. since containers play a pivotal role in edge deployment, cloud-native, continuous integration, and continuous deployment, it is vital to have centralized orchestration for proper re-source management and scheduling. This paper discusses and compares emerging container platforms and cloud-centric orchestration frameworks, highlighting the challenges involved.

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