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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Devananthan, G.S. Nair, Lekshmi S Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350335095

Sentiment analysis is a valuable method for analyzing texts and understanding the opinions, attitudes, and emotions expressed toward different subjects. Its application to large-scale data, particularly on social media platforms like Twitter or Facebook, offers valuable insights. However, Twitter data presents unique challenges due to its complex and noisy nature, as well as its intricate syntactic and semantic structures. Moreover, analyzing sentiment becomes even more difficult when dealing with multimodal Twitter data in Indian languages. In order to address these obstacles, it is essential to create a robust framework capable of effectively processing Twitter content across diverse languages and encodings. To ascertain the most optimal methodology, we carry out extensive assessments employing multiple variations of multilingual and single-language models, ultimately relying on the evaluation outcomes to guide our selection process. Our aim is to create a comprehensive model capable of effectively characterizing all aspects of Twitter data. Through a comprehensive examination of the data, we gain a deeper understanding of the sentiments expressed across various languages used on Twitter in India. These results offer valuable insights into the diverse range of emotions, opinions, and attitudes expressed by Indian Twitter users, enabling a more nuanced understanding of public sentiment in this multilingual context. © 2023 IEEE.

关键词： Sentiment analysis

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Design and Simulation of a Healthcare Unit 3

Design and Simulation of a Healthcare Unit

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3rd IEEE International Conference on Technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Madhav, G Sethu Kommana, Chakrika Chandana, Baddireddi Sree Khanna, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397819

The most important aspect of any country is its healthcare system, but many healthcare providers struggle to provide patients with practical and innovative services. The current emergency clinics are outfitted with cutting-edge technology to save as many lives as possible given the situation. Automation contributes to improved patient health outcomes by assisting technological breakthroughs. Automation can also notify medical staff of known drug interactions and contraindications by querying a database. In this study, we used IoT technologies and Cisco Packet Tracer software to construct a smart hospital system. The system incorporates IoT devices, guarded medical wards, automated electrical appliances, and fire alarm systems. The Cisco Packet Tracer's characteristics can be used to simulate a smart hospital. Results from the simulation demonstrate that smart objects may successfully connect to the hospital server, raising the possibility of application in the real world. © 2023 IEEE.

关键词： Automation

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Early Prediction of Dengue Cases Using Time Series Model 8

Early Prediction of Dengue Cases Using Time Series Model

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Tejaswi, M. Supritha, V. Thangam, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798350334012

Dengue, is a seasonal infectious disease has been spreading widely throughout the world. Because of this many of the people are losing their lives. So public Health organizations and many other government organizations have been conducting surveys of dengue cases and they got to know that because of weather conditions like rain, precipitation, humidity are some of the main causes for dengue prediction. Also they use some ML algorithms and some Time Series models to predict dengue cases. Time series models they have used as (ARIMA) model and SARIMA model and seasonality. But out of this ARIMA model looks best to predict dengue cases. So we have taken ARIMA model to predicting the forecast of dengue cases. In ARIMA we have done two methods to find out the order of the model where it has values as autoregressive, moving average and non-seasonal in seasonality autocorrelation, partial autocorrelation, and AUTO-ARIMA for find out the best model for the order then we have checked for stationary also. If its stationary then we have proceeded to prediction of forecasting dengue cases, if it's not then we have converted it into stationary and then we moved on to do ARIMA best fit which gives the forecasting cases. © 2023 IEEE.

关键词： Forecasting

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Virtual Handwriting based Smart Board using Deep Learning

Virtual Handwriting based Smart Board using Deep Learning

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Malathi, P. Sai Gayathri, B. Lakshmi Sreshta, G. Sahitya, Edarada Rupa, Jhade Sai Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350325188

Writing is a specific technique for communication that allows us to express our ideas. The two most common methods for recording information are typing and writing. The process involves using a pen or a finger to inscribe letters or words in a relaxed environment. With wearable devices capable of observing and comprehending our actions, gesture recognitionhas become a computational technique that strives to identify and comprehend human gestures using mathematical algorithms. Our project aims to exploit this technology to develop a motion- to-text converter, a software program for smart wearable devices designed for writing in the air. The application will utilize computer vision to track finger movement, with the resulting text having diverse applications, including text messaging and emailing. This technology will prove beneficial in facilitating communication for people with hearing impairments. © 2023 IEEE.

关键词： computer vision human gesture recognition motion-to-text converter

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Wheat Disease Detection And Growth Stage Monitoring Using Deep Learning Architectures 14

Wheat Disease Detection And Growth Stage Monitoring Using De...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Stephi, S. Nair, Jyothisha J. Amrita School of Computing Amrita VishwaVidyapeetham Department of Computer Science and Engineering Kerala Kollam India

ISBN: (纸本)9798350335095

Traditional methods for diagnosing illnesses in wheat plants rely on visual inspections by experts, which is a tedious task. To enhance disease detection in plants, we propose a novel approach called Enhanced Vision CNN, which combines the multi-head attention feature of a vision transformer with the feature extraction powers of a CNN. Combining these methods allows the model to properly describe image features, and effectively capture both global and local information. Furthermore, to overcome limitations in conventional growth monitoring methods, we employ an ensemble approach using DenseNet201, InceptionV3, and InceptionResNetV2 models to track the growth stages of wheat plants. Our enhanced vision CNN model performed admirably in experimental findings, detecting diseases with a 99.4% accuracy. The ensemble models also obtain a growth phase detection accuracy of 88.5%. These results demonstrate the revolutionary potential of our methods for disease diagnosis and growth tracking in wheat plants. © 2023 IEEE.

关键词： Deep learning

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An experimentation of objective functions used for multilevel thresholding based image segmentation using particle swarm optimization

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1717-1732页

作者： Ahmed, Saifuddin Biswas, Anupam Khairuzzaman, Abdul Kayom Md Department of Computer Science and Engineering National Institute of Technology Silchar Cachar 788010 India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati 522237 India

For image segmentation, multilevel thresholding is treated as one of the widely used approach. However, this approach has a major issue that is it suffers of high computational complexity problem with the increase threshold levels. This paper presented a comparative performance analysis of some objective functions used for image segmentation using the concept of multilevel thresholding and a modified version of adaptive inertia weight Particle Swarm Optimization (PSO) technique. The PSO algorithm is applied to multilevel thresholding based image segmentation using either Otsu’s inter class variance, Kapur’s entropy or Masi entropy as an objective function. Each method is tested over various standard image dataset like Berkeley image database, USC-SIPI image dataset etc. The evaluated result of each method has been compared and the overall experimentation is performed in three different ways such as, PSO and Otsu’s method, PSO and Kapur’s entropy and PSO with Masi entropy. The performance and quality of the segmented images are measured using the following parameters such as, average Mean Structural Similarity Index (MSSIM), average Peak Signal to Noise Ratio (PSNR) values, average mean objective functions values and average CPU rum time values. The experimental analysis shows that the performance of Otsu’s inter-class variance function shows comparatively a better result than Kapur’s and Masi’s entropic method. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Kapur’s entropy Masi entropy Meta-heuristic Multilevel thresholding Non-parametric Otsu’s method Parametric Particle swarm optimization

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An Empirical Analysis of CNN for American Sign Language Recognition 5

An Empirical Analysis of CNN for American Sign Language Reco...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Vb, Lakshmi Kb, Sivachandra Parthasaradhi, H. Abhishek, S. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350321425

American Sign Language recognition is a crucial technology that aims to improve accessibility and communication for the deaf and hard-of-hearing community. This research aims to convert ASL gestures into text or speech by analyzing and interpreting them using gesture recognition in sign language. This study compares different deep learning algorithms for sign language identification to overcome communication difficulties that deaf or hard-of-hearing people face. Three models - VGG16, ResNet, and AlexNet - were created and trained using a dataset of hand motions. The findings show that all three models have excellent accuracy, with AlexNet performing the best at 99.87%, ResNet coming in second at 98.9%, and VGG16 third at 98.2%. © 2023 IEEE.

关键词： Learning algorithms

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Automating the Statutory Warning Messages in the Movie using Object Detection Techniques 2

Automating the Statutory Warning Messages in the Movie using...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Dhanush, M. Abhinay Reddy, B. Jagannath, B. Reddy, R Rohan Bhuvaneswari, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397635

Alcohol and cigarette use are both rising annually. Alcohol and tobacco are portrayed in movies as symbols of toughness. Many people may be influenced by this and develop bad habits as a result. It's crucial to alert viewers whether tobacco or alcohol is being utilized in the scene. To warn viewers, the government issues statutory warnings. At the moment, humans manually add these warnings. The government established a number of programs, including commercials and awareness campaigns, to lessen the influence on individuals. This process will be automated in the planned work. In this project, deep learning models are used to classify photos of bottles into beer bottles or plastic bottles and identify cigarette smoking in the image using CNN and TensorFlow (scene). These tasks are completed using employing two different datasets-'Bottles Synthetic Images' and 'Cigarette Smoker Detection' and two machine learning models. So, in the suggested work, one model is created to identify alcohol bottles, and the other is created to identify smoking in the provided video. The warning message is added to the frame where the detection of the alcohol and smoking bottles has been completed once the detection has been completed. The proposed model performed best, with accuracy ratings of 98% (Alcohol bottle Detection) and 89% (Accuracy) (Smoking detection). The automated addition of statutory warnings under the proposed model is intended to lessen the burden on the Censor Board. © 2023 IEEE.

关键词： Tobacco

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A Twitter-based Software Vulnerability Alert Framework using Natural Language Processing 8

A Twitter-based Software Vulnerability Alert Framework using...

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8th International Conference on Communication and Electronics systems, ICCES 2023

作者： Yadhu Krishna, M. Sneha, C.K. Thejaswi, A.R. Kumar, Sayooj B. Sarath, Greeshma Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350396638

With the increasing usage of networks, the frequency of cyber attacks are expected to increase. To prepare against these attacks, modern organizations require effective ways to ensure cyber security. Twitter is one of the most well-known social media platforms in the world. Tweets by users on Twitter cover a wide range of topics, from daily life to politics and breaking news. This social media platform has a sizable and diverse user base, provides great accessibility, and timeliness, resulting in the production of a huge amount of publicly available data, which in turn can be used effectively for a variety of purposes. For detecting security vulnerability events via Twitter, we introduce a Natural Language Processing-based approach. The tool can be used to provide alerts and notifications to users when a security vulnerability is detected. © 2023 IEEE.

关键词： Cybersecurity

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Green Computing: Optimized Underutilized Host detection in IQR Vm Allocation Policy 4

Green Computing: Optimized Underutilized Host detection in I...

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4th International Conference on Intelligent engineering and Management, ICIEM 2023

作者： Nadaf, Anwar Beena, B.M. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798350341126

In a broader sense, the term 'green computing' refers to techniques and guidelines for creating, utilizing, and decommissioning computer resources in a way that is ecologically beneficial while still preserving overall computing performance. To do this, it is necessary to utilize less hazardous materials, use products as efficiently as possible while using the least amount of energy, and increase the reusability, recyclable content, and biodegradability of trash. Many businesses are making efforts to lessen the negative effects of their activities on the environment. An international environmental treaty known as the United Nations Framework Convention on Climate Change (UNFCC) aims to stabilize greenhouse gas emissions in the atmosphere at a level that would stop harmful anthropogenic interference with the eco system. Sustainable development entails growth that doesn't jeopardize the needs of coming generations. It is achieving human development goals while protecting the ecosystems and natural resources that are essential to society. The importance of green computing for sustainable development is emphasized in this paper's assessment of several significant recent studies in the subject. The use of computers and other computing devices in ways that are both energy-efficient and ecologically friendly is known as green computing also referred to as green technology. Energy-efficient servers, peripherals, power systems, and central processing units (CPUs) are frequently used in green computing firms. They underline the necessity of conserving resources and properly disposing of physical and electronic trash (e-waste). © 2023 IEEE.

关键词： Global warming

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