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Sea Surface Temperature Prediction using Sample Convolution and Interaction Network

Sea Surface Temperature Prediction using Sample Convolution ...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Gayathri P Rahul Yalavarthi Jayanth Santosh Varma G Gopakumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Predicting oceanic phenomena plays a very important role in ensuring maritime safety, managing coastal areas and preparing them for any disasters like cyclone or tsunami. There are several factors that contribute to these severe weather events among which Sea Surface Temperature(SST) is a crucial one. Warmer oceans can often lead to stronger storms or even frequent heavy downpours, highlighting the importance of accurately forecasting SST. This study explores the performance of a deep learning model called Sample Convolution and Interaction Network (SCINet) in forecasting SST. This model follows a decompose-downsample-convolve-interact architecture that helps in learning complex trends in the data. On comparing SCINet’s performance with well established time-series models like Long-Short Term Memory (LSTM) network, it was able to achieve almost 4% - 20% lower Mean Absolute Error (MAE) than LSTM on various testing points. This highlights SCINet’s potential effectiveness in time-series forecasting, contributing to the ongoing development and revolutionary advancements in deep learning models for time series prediction.

关键词： Deep learning Analytical models Convolution Sea measurements computer architecture Predictive models Data models Sea surface temperature Forecasting Long short term memory

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Machine Learning Algorithm for Maternal Health Risk Classification with SMOTE and Explainable AI

Machine Learning Algorithm for Maternal Health Risk Classifi...

引用

International Conference for Convergence of Technology (I2CT)

作者： B. Uma Maheswari Aniket Dixit Alok Kumar Karn Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Targeted obstetric care refers to being specific with the caretaking of a pregnant woman based on her symptoms and risk levels. It is a crucial task because pregnancy-related complications can be life-threatening. Pregnant women's body undergo a variety of changes, which may be mental, emotional, or physical. It is possible to analyze these changes to assess the health complications. These include blood pressure and blood sugar readings, which can be examined to determine a pregnant woman's health risk level. Machine Learning (ML) algorithms are employed to determine maternity health risks based on a woman’s health statistics. In this work, several ML algorithms, have been applied to the maternal health risk dataset to predict the mother’s risk level. Moreover, parameterized tuning has been applied to improve the performances of these conventional algorithms. The performance of these algorithms is studied before and after applying the Synthetic Minority Over-sampling Technique (SMOTE) technique to balance the data. The results exhibit that after SMOTE enhancement, the XGBoost upon hyperparameter tuning gives an accuracy of 88.89% in predicting mothers’ risk level. Hence, XGBoost can be deployed in the field of providing targeted obstetric care by accurately predicting health risk levels. This work also embeds the Explainable AI (XAI) using SHapley Additive exPlanations (SHAP) and LIME (Local Interpretable Model-agnostic Explanations) to provide trust in the ML model to gynecologists. Various plots are generated using LIME, SHAP, and ELI5 to showcase the ML model’s interpretability on global and local levels.

关键词： Pregnancy Machine learning algorithms Additives Explainable AI Prediction algorithms Blood pressure Obstetrics

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Audio Encryption Using AES and Cellular Automata

Audio Encryption Using AES and Cellular Automata

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Amireddy Shashank Reddy Dishan Narsimhachari Achar Madithilveedu Sheethal Mol Niharika Panda Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Securing digital audio data during any transmission or storage is essential for ensuring privacy and confidentiality. Traditional encryption techniques like Advanced Encryption Standard (AES) offer high security, but they might be vulnerable to attacks that exploit encrypted data patterns. Cellular automata, on the other hand, exhibit complex and challenging behavior, making them attractive for introducing additional layers of security in encryption schemes. However, integrating these techniques effectively remains a challenge. This paper addresses this gap by proposing a novel audio encryption system that combines the features of AES and cellular automata. A pseudo-random number sequence produced by Rule 30, a chaotic cellular automaton rule, is XOR-ed with the AES-encrypted audio data. This disrupts potential pattern recognition attempts, significantly enhancing the overall security of the encryption process. The system operates on WAV format audio files and ensures the confidentiality and integrity of the data by preventing unauthorized access and manipulation. We evaluate the effectiveness of our approach by comparing the characteristics of original and encrypted audio files. The results demonstrate significant alterations in the audio data, rendering it unintelligible without the decryption key. Additionally, the system utilizes the well-established AES algorithm, maintaining efficiency during encryption and decryption. This system defeats other methods in terms of security, complexity, and computational efficiency making this a reliable and efficient method for securing audio data transmission and storage where audio privacy is essential.

关键词： Data privacy Automata Transforms Rendering (computer graphics) Encryption Pattern recognition Computational efficiency Reliability Data communication Standards

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Prognostic Modeling of Diabetes Mellitus in Women: A comparitive analysis of Machine Learning algorithms integrating Clinical and Genetic Data

Prognostic Modeling of Diabetes Mellitus in Women: A compari...

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Emerging Technologies in computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： M. Venkat Prashanth Nikhil Tom Jose Konda Hemesh Verma B. Sreevidya M. Rajesh Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Diabetes is considered as one of the most commonly observed medical condition among human beings. Millions of people worldwide are affected by a chronic condition that goes by the name of diabetes. There are 3 sorts of diabetes and they comprise of Type 1, Type 2 and Gestational diabetes. Early prediction of diabetes helps to avoid complications like diabetic retinopathy which leads to blindness. There is a 13% higher risk of death among people with diabetes, especially in women, cardiovascular disease caused by diabetes attributes to 50% higher risk of death, coronary heart disease caused by diabetes contributes to a 58% higher risk of death. The proposed research work aims in predicting diabetes from the symptoms a patient exhibit. The proposed system is a healthcare assisting system which is accessible to patients as well as medical practitioners. The system is developed as a web application using the features of Django python framework along with suitable database management system. The system is powered with gradient boosting machine learning algorithm for predictions. By using the web application along with a blood test to find out the insulin levels and glucose levels in blood, any user can predict whether he/she would be affected by diabetes in future and can avoid the complications caused by diabetes.

关键词： Heart Machine learning algorithms Computational modeling Predictive models Boosting Diabetes Glucose

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Efficient Data Management through ML-Based Feature Extraction

Efficient Data Management through ML-Based Feature Extractio...

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International Conference on computing and Networking Technology (ICCNT)

作者： E R Adwaith Krishna M. Varundev Sidharth Surendran Lekha S Nair Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Multimedia data production, such as audio, video, and acoustic measurements in large volumes and speed, has approached the scale of big data in many sensor networks. Multimedia Sensor Networks (MSN) are popular because of their capability to manage and integrate the different domains of sensor outputs, their representations, and the encoding of these measurements. Studies on generic and specific models for detection and tracing events in a sensor network have been established by multiple models. At the same time, there is still the requirement to handle these measurements from the Complex Dataset perspective. In the current world scenario, sensor networks are not limited to producing high-quality and diverse sensor readings but also large volumes at high speed. The most intuitive methods of handling them focus only on the interested range of measurement or store-for-later use. The possibility of data analysis in such an information-rich big data environment can be improved. In this study, we propose a model, “Multilink” that helps to align the multimedia data generated by a sensor network to a standard easy-to-process feature space with the incorporation of Machine Learning centric architecture.

关键词： Current measurement Scalability Machine learning Streaming media Big Data Feature extraction Acoustic measurements Data models Time measurement Security

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Cutting-Edge Steel Damage Classification Powered by Cloud Technology

Cutting-Edge Steel Damage Classification Powered by Cloud Te...

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International Conference on computing and Networking Technology (ICCNT)

作者： Pennabadi Devendra Reddy K Satya Sampath Reddy P Gnaneswarachary P Hemanth Reddy Mrudula Prashanth Shinu M. Rajagopal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In this era of rapid technological advancement, the detection of metal damage has become increasingly crucial across various industries. The proposed work “Cutting-Edge Steel Damage Classification Powered by Cloud Technology” aims to build an advanced system for real-time steel damage classification utilizing AWS services includes Rekognition, Lambda, and API Gateway that enables users to upload multiple images simultaneously for damage detection and classification, which will be an innovative approach to industrial defects detection which leads to their prevention. Such benefits of fast, accurate and scalable defect detection make it a critical asset for businesses expecting for exact and real-time solutions in every aspect. This study uses F1 score, precision, recall, and assumed threshold are as evaluation parameters. The use of AWS services is not just helping with model training and deployment, but it also provides failover mechanism and scaling capabilities to accommodate the varying workload demands. The model has the advantage of having rapid training time: 1.005 hours this shows the cloud infrastructure scalability and computational efficiency in dealing with the tasks requiring machine learning of complex nature.

关键词： Training Computational modeling Scalability Machine learning Logic gates Real-time systems Robustness Computational efficiency Steel Defect detection

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Efficient Airlift and Enhanced Care: Unmanned Aerial Vehicles and Droid Solutions for Battlefield Casualty Evacuation

Efficient Airlift and Enhanced Care: Unmanned Aerial Vehicle...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Angelina George Ashik F Alphonsa Jose Niranjan D. K. Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

In modern military operations, timely and effective medical evacuation is crucial for reducing soldier casualties and enhancing combat effectiveness. This research presents an investigation into the use of Unmanned Aerial Vehicles (UAVs) and Droids as innovative solutions for battlefield casualty evacuation. The study explores the potential of UAVs and Droids in swiftly and accurately evacuating injured soldiers from the battlefield, providing enhanced medical care, thereby reducing casualties. By leveraging these unmanned systems, this research aims to optimize the response time and medical care quality in combat scenarios. This study has conducted a detailed assessment of the technology and potential challenges, considering various battlefield conditions and scenarios. The accuracy obtained for this study was 81.81 % , achieved through a Deep Learning model based on the MobileNetV2 architecture, a powerful Convolutional Neural Network (CNN) pre-trained on ImageNet. The results of this investigation promise to significantly impact the efficiency and safety of military medical evacuation, ultimately improving the survival rates and well-being of soldiers in the heat of battle.

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Hate Speech Detection in Marathi Tweets using Stacked Deep Learning models

Hate Speech Detection in Marathi Tweets using Stacked Deep L...

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International Conference on computing and Networking Technology (ICCNT)

作者： Prachi Shedge Siddhi Kamalkar Deepa Gupta Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Digital platforms have revolutionized communication, but they have also led to an increase in hate speech, making it challenging to create welcoming online groups. The purpose of this study is to close this gap by using deep learning models to detect hate speech in Marathi, a regional language of India. The researchers use advanced word embeddings and DL models like Long Short-Term Memory and Bidirectional LSTM to demonstrate the efficacy of these methods. The study aims to build strong hate speech detection systems that can be extended to other low-resource languages, creating safer online spaces. The study emphasizes the importance of contextually and culturally appropriate data in enhancing model accuracy. In order to strengthen their models’ training and validation and guarantee reliable performance in real-world applications, the researchers also intend to integrate real-world social media data. The study closes a significant gap in regional language processing by concentrating on Marathi and opens the door for more inclusive and thorough hate speech detection frameworks across linguistic landscapes.

关键词： Deep learning Accuracy Social networking (online) Computational modeling Hate speech Stacking Blogs Linguistics Transformers Data models

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Elevating Predictions: Sculpting Flight Delay Predictions with Recursive Feature Elimination

Elevating Predictions: Sculpting Flight Delay Predictions wi...

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computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Wei Wei Chenchen Yu Zhao Yang Fu Bo School of Computer Science and Engineering and the Shaanxi Key Laboratory for Network Computing and Security Xi'an University Of Technology Xi’an China School of Computer Science and Engineering Xi'an University Of Technology Xi’an China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

With the rapid development of the aviation industry and the significant growth of aviation data, how to use big data technology to accurately predict flight delays has become a problem that needs to be solved in the civil aviation transportation industry, and the application of big data models in the aviation field has also become crucial. To address this challenge, a new combinatorial forecasting method is proposed in this study. This method integrates random forest algorithm and recursive feature elimination technology to construct a comprehensive prediction model, aiming at improving the accuracy and efficiency of flight delay prediction. Experiments show that the proposed method performs better in the prediction accuracy and prediction error of flight delay data after the data is normalized to eliminate the influence of different dimensions and magnitudes on the model performance, which proves the effectiveness and practicability of the proposed method.

关键词： computers Accuracy Transportation industry Predictive models Big Data Prediction algorithms Data models Delays Forecasting Random forests

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Implementing Multinomial Naive Bayes for Employee Empowerment: A Transparent Platform for Task Management and Issue Resolution

Implementing Multinomial Naive Bayes for Employee Empowermen...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Sudeesh Kumar V Mahendira Kumar S S Praveen Kumar Akyf Thanish A M. Rithani Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Developing a positive motivational culture among employees in today's changing workplace is achieved through effective communication and problem-solving. This abstract introduces an innovative Employee Empowerment Platform designed to enhance transparency and collaboration within the work-force. Using a text classification algorithm, this work presents an employee authorization system that classifies and analyzes problem reports submitted by employees. Using a multi-step approach to data preprocessing, the system extracts HTML tags, square brackets, and special characters from text data collected from problem reports. Count Vectorization and TF-IDF are two mathematical techniques used for transforming preprocessed text into features. At its heart, the platform uses a polynomial naive base classification to identify positive and negative emotions in problem reports and assign them to the appropriate category. In addition, the system uses data visualization techniques to provide a comprehensive understanding of the model's performance. Through accuracy plots and confusion matrices, you can study the prediction skill of the classifier. By establishing a clear process for finding work and facilitating problem classification, our employee empowerment program helps companies identify common concerns about work and fosters an exchange of ideas between employees creating a workplace environment that nurtures individual growth and collective success. The main objective of the platform is to increase enthusiasm and empowerment in the workplace by addressing challenges quickly.

关键词： Technological innovation Accuracy Employment Text categorization Companies Machine learning Polynomials Bayes methods Problem-solving Cultural differences

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