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Streamlining Software Development: A Comprehensive Study on CI/CD Automation 4

Streamlining Software Development: A Comprehensive Study on ...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Baitha, Sanjay Soorya, Vijayakumar Kothari, Osho Rajagopal, Shinu M. Panda, Niharika Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidypeetham Bengaluru India

ISBN: (纸本)9798331540364

Continuous Integration and Continuous Deployment (CI/CD) automation have emerge as critical for modern-day software development. Despite its blessings, demanding situations including integration complexity, inconsistent trying out environments, and deployment troubles persist. Recent strategies like superior containerization, cloud-based scaling, and improved testing frameworks intention to address those issues. This take a look at implements an self sustaining CI/CD pipeline using gear such as Jenkins, GitHub, and SonarQube to automate processes which include coding, building, checking out, and deployment. By addressing those demanding situations, our proposed objective is to enhance efficiency and reliability in the software program development lifestyles cycle, allowing improvement teams to seamlessly add features and solve problems. © 2024 IEEE.

关键词： Software design

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Technology Enhanced Cradle by using Sensors and Internet of Things 5

Technology Enhanced Cradle by using Sensors and Internet of ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thangam, S. Greeshma, Mogili Varun, Mekala Sanjana, N.V.S. Kumari, J. Jesy Janet Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331504403

Conventional cradles lack the ability to monitor and support infants continuously or adapt to varying needs. This study proposes a novel smart cradle equipped with advanced sensors and actuators, enabling parents to control environmental factors in real time, thereby ensuring the highest levels of safety and comfort for infants. Unlike previous models, the smart cradle automatically responds to stimuli, such as an infant's cry, and allows for remote monitoring via the Blynk app. Comparative analysis demonstrates that the proposed smart cradle offers faster response times and greater accuracy in environmental control compared to recent models, establishing it as a new benchmark in infant care products. This innovation addresses the deficiencies of current cradle designs and represents a significant advancement in automated infant care systems, ultimately conserving caregivers' resources and improving overall infant care. © 2024 IEEE.

关键词： Remote control

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Drug Satisfaction Analytics: Investigating the Effectiveness of Various Embedding Techniques 9

Drug Satisfaction Analytics: Investigating the Effectiveness...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： John, Ardra K. Anushka, C.V. Gupta, Deepa Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the healthcare economy, medication satisfaction can be defined as a patient reported upshot. Gaining insights into side effects of medication from individuals who have previously encountered them is crucial. This study concentrates on predicting satisfaction rating of drugs by extracting reviews from social healthcare forums for five distinct medications, each addressing a different health condition. Experimental analysis is conducted to assess the performance of Random Forest classifier integrated with three unique embeddings namely BERT, BioBERT and S- BERT. The results demonstrates that the fusion of Random Forest with BERT surpasses other approaches in terms of evaluation metrics. This superior performance is particularly evident in the range of F1 scores achieved across five different datasets, where this model combination scored between 0.91 and 0.98. © 2024 IEEE.

关键词： Embeddings

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Cerebral Stroke Risk Assessment: A Comparative Study of Machine Learning Techniques 9

Cerebral Stroke Risk Assessment: A Comparative Study of Mach...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Akshara Sajeev, Adith Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350377972

This study uses a data-driven machine learning (ML) model that takes into account important health markers like age, blood pressure, history of heart disease, and lifestyle factors like physical activity and smoking in order to predict the risk of stroke. Through thorough model comparison and optimization, a variety of algorithms for machine learning are used, such as Random Forest classifiers, K-Nearest Neighbors, and Support Vector Machines, to improve predicted accuracy and efficiency. It has been proven that machine learning plays a key role in identifying minor risk patterns that conventional diagnostic techniques often neglect, empowering medical professionals to make quick, evidence-based judgments. According to the results, this non-invasive, data-driven method greatly increases the accuracy of stroke risk assessments, facilitates the customization of treatment plans, and eventually improves patient outcomes while reducing the strain on healthcare systems. The Random Forest classifier shows the best prediction ability among the models that were assessed. This solution highlights the potential of machine learning to improve stroke risk assessment and management while blending in smoothly with clinical practice as a useful tool for identifying stroke risk, improving patient care and quality of life. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Attack Detection on IoT Devices Using Hybrid Deep Learning Model 1

引用

8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Rachamsetty, Uday Kiran Gyanendhar Reddy, Reddymalla Saravanan, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9789819777105

ISBN: (纸本)9789819777099

At present, Internet of Things has become a widely used subject for research because of its applications in our modern world. With this growth, securing IoT devices against cyber threats becomes essential. It will be difficult to put security system for each and every IoT device in the network and update it to counter new threats. Machine learning (ML) can be utilized to detect the attack in the data generated from the IoT devices. However, existing security models limited in their ability to detect a wide range of attacks and often rely on outdated datasets for evaluation. This research introduces a deep learning (DL)-based security model for attack detection, combining CNN for local anomaly detection, and LSTM for global anomaly and temporal dependency analysis. The dataset comprises data from 20 malicious and three benign IoT devices (IoT-23 dataset). Results show that the CNN-LSTM model achieves 95.42% accuracy, and the LSTM-CNN model achieves 91.825% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Feature Enhanced Semi-Supervised Attention U-Net for Cell Segmentation 15

Feature Enhanced Semi-Supervised Attention U-Net for Cell Se...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Raj, Ardra S. Asha, S.B. Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350370249

Cell segmentation is crucial for accurately assessing disease severity. Despite advances in deep learning, there is still a gap in understanding how U-Net variants perform in addressing the issue of limited labeled data in cell segmentation. We compare various U-Net models to see how they fare with live cell images. We also examine how different feature extraction methods affect their performance. Using the Laplacian and Standard Deviation features improves performance by more than 3%, giving Attention U-Net mean Intersection over Union (IoU) scores of 0.7956 and mean Dice scores of 0.8844. In addition, we introduce a semi-supervised Mean Teacher Attention U-Net that leverages the same feature extraction techniques. Remarkably, it achieves an IoU score of 0.7908 and a Dice score of 0.8831, outperforming the existing approaches even with only 60% labeled data and utilizing limited computational resources. ©2024 IEEE.

关键词： Deep learning

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Comparative Analysis of Eminent Algorithms for Detecting Ham and Spam Contents in OSN 4

Comparative Analysis of Eminent Algorithms for Detecting Ham...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ponnuchamy, Kardeepa Subbulakshmi, N. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India

ISBN: (纸本)9798331540364

Social or Communal media platforms is a lay of exchanging the thoughts or ideas of online users through various data formats like text content, image content or through audio, video, Graphics Interchange Format (GIF) etc and user can dynamically share real-time interactive events in a cost effective manner. Nowadays, with emerging fast-growing tools and technology, online socio-communal networks have shattered popularity as the central thought is to offer a framework for virtual users to link with their associative friends, subordinates and family members that blowouts their messages instantly which in turn paves a way for attackers to gain information or hack online user's data by unauthorized access. Extraction of contents is not accurate due to inflexible rule sets and some contents which are furnished in short texts are more difficult to correlate with their semantics and thereby it is difficult to prevent the display of undesired data in the existing system. Hence, the trained model system needs architecture with Enhanced Adaptive Rule-sets to overcome the inflexibility. Adaptation rule is employed by incorporating the blacklist mechanism along with eminent Machine learning algorithms to predict and prevent the undesired data. Information filtering system and Content filter removes redundant, irrelevant or unwanted information using user feedbacks with multiclass data labels. Furthermore, self-protective approaches are emphasized to achieve fidelity and ensure the trustworthiness in any micro blogging services or in online social networks. © 2024 IEEE.

关键词： Tweets

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Improving Disaster Response with U-Net and Attention Mechanism in Earthquake Damage Assessment Using Remote Sensing Data 6th

Improving Disaster Response with U-Net and Attention Mechani...

引用

6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Anvitha, Vemireddy Maheswari, V. Uma Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819734412

The significance of assessing building damage for post-disaster rescue and reconstruction is acknowledged, and a novel approach is introduced to address the challenges associated with detecting and classifying damage levels. Unlike many existing studies that primarily focus on binary classification, the proposed neural network combines localization and classification of damaged buildings simultaneously. The key component of this network comprises a variety of attention U-Nets with different backbones, which leverage attention mechanisms to prioritize relevant features and channels while minimizing the impact of irrelevant ones. Training is conducted using the extensive xBD dataset, dedicated to advancing building damage assessment, and results are assessed based on balanced F1-scores. Notably, the U-Net with an attention mechanism exhibits the best individual performance, achieving an impressive F1-score of 0.752 and overall score of *** method’s effectiveness is further validated on the xView2 dataset, a comprehensive building damage assessment dataset, demonstrating precise damage scale classification and building segmentation simultaneously. This breakthrough promises to significantly enhance post-disaster rescue efforts, offering robust and transferable solutions for improved disaster response. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

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Code Optimization for Python using Large Pre-Trained Model 5

Code Optimization for Python using Large Pre-Trained Model

引用

5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Adithya Gokulan, S. Reddy, Mahati Varshith, M. Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Optimizing Python code is essential for enhancing performance and efficiency. This project investigates the use of large pre-trained language models, specifically GPT (Generative Pre-trained Transformer), for Python code optimization. By leveraging the advanced capabilities of these models, we aim to improve traditional optimization techniques. Our approach involves preprocessing Python code, feeding it into the pre-trained model, and generating optimized code sequences. Experimental results show significant improvements in code efficiency and execution time, validating the effectiveness of our method. Additionally, we explore the practical implications, challenges, and future directions of incorporating large pre-trained models into code optimization, aiming to bridge the gap between standard optimization techniques and Python programming for a more intelligent and efficient software development practice. © 2024 IEEE.

关键词： Python

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Crop Recommendation and Yield prediction Using Machine Learning based Approaches 5

Crop Recommendation and Yield prediction Using Machine Learn...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Padmavathi, A. Gupta, Arnab Prakash, Koppadi Bhanu Sai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa VidyaPeetham Chennai India

ISBN: (纸本)9798350351378

India is an agricultural country, supporting a significant portion of the population. Agriculture is a source of income for millions of Indians. But due to lack of knowledge and the state's diverse climate and soil conditions often leave farmers grappling with the critical decisions of which crops to cultivate and what yields to expect. This in turn makes the farmers suffering from losses as they cultivate a crop hoping they will get a good harvest but in reality, they don't get the expected amount of production. To address these challenges prior researchers have used soil and climatic conditions data to recommend crops for a particular soil and predict its expected yield using various types of machine learning and deep learning algorithms. This paper presents a survey of previous works where deep learning and machine learning techniques are used and performs a comparative examination of some bagging and boosting machine learning techniques including random forest, gradient boost, XG boost, Ada boost, light bgm and cat boost for effective recommendation of crop and prediction of its expected yield. © 2024 IEEE.

关键词： Crops

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