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PyLoomer - The Automated Weaving of Python Text 2

PyLoomer - The Automated Weaving of Python Text

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2nd IEEE International Conference on Device Intelligence, Computing and communication Technologies, DICCT 2024

作者： Sahani, Anuj Trivedi, Rishikesh Bhupendra Singh, Tushar Goyal, Somya R. Manipal University Jaipur Dept. of Mechnical Engineering Rajasthan Jaipur India Manipal University Jaipur Dept. of Computer and Communication Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350372847

The introduction of attention mechanisms has evolved NLP methodologies, giving rise to Large Language Models (LLMs) that excel in various sequence-to-sequence tasks like translation, question-answering, summarization, intent, and entity classification. This research focuses on text to code translation, converting human language into executable code. Code generating bots enhances the quality and efficiency of developers and facilitates to tackle the repetitive or simple tasks. A novel and clever fusion of Python Code and the creative ability of Large Language Models to generate text is proposed in this paper named PyLoomer. PyLoomer is an innovative text-to-python code chatbot that bridges the gap between narrative expression and technical execution which uses attention mechanism. PyLoomer seeks to improve the performance of existing models for python code generation by using in-context learning and instruction fine-tuning. Rigorous evaluation metrices, including Rougel, Rouge2, RougeL, RougeLsum and model loss, demonstrated significant improvements in results. Results indicate that CodGen, among the three models, achieves the highest Rouge scores, demonstrating superior performance, the Rouge score improvements for Codegen, StarCoder and CodeParrot are 0.0224, 0.0097 and 0.0223 respectively. © 2024 IEEE.

关键词： Python

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Prediction of Irregular Bangladesh-EU Migration Trends Using Machine Learning Techniques 3

Prediction of Irregular Bangladesh-EU Migration Trends Using...

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Md. Shariful Rahim, Md Abdur Podder, Nitun Kumar Hossain, Md. Najmul Hossain, Md Imran Pabna University of Scence and Technology Dept. of Computer Science and Engineering Pabna Bangladesh Pabna University of Scence and Technology Dept. of Electrical Electronic and Communication Engineering Pabna Bangladesh Pabna University of Scence and Technology Dept. of Inormation and Communication Engineering Pabna Bangladesh

ISBN: (纸本)9798350388282

Remittances are important economic contributors to a countrya's economy. It has increased national savings, lowered foreign exchange limits, and helped balance payments and development budgets. Migrant workers are the main source of these remittance revenues. Many workers worldwide leave their homes and move to different parts of the world for overseas employment. These workers are authorized and have documentation of their country of origin and final destination. Besides, many workers have migrated illegally globally, particularly to European Union (EU) countries via marine routes. However, illegal immigration leads to a loss of national revenue due to non-payment of taxes and social security contributions and problems with living circumstances and legal issues. Therefore, this paper aims to present a time series forecast of irregular migrants in EU countries from Bangladesh over the next five years. We used five methods to analyze this forecasting: ARIMA (an autoregressive integrated moving average), XGBoost Series, Decision Tree, Feed Forward Neural Network, and CatBoost Regressor. In addition, we used data from the European Border and Coast Guard Agency to perform the time series forecast. A field intervention is carried out to assess the gender, age, education, socioeconomic level, and skill profile of migrant workers. Moreover, a few case studies of migrant workers and a survey were conducted to investigate the underlying causes of irregular migration. Finally, this study explains the underlying cause of irregular migration and its repercussions. It can provide some ideas for decreasing the loss of human life and property by reducing irregular migration to help take essential actions in the home and destination countries. © 2024 IEEE.

关键词： Forecasting

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video Database (CamVid) Dataset 2

Implementation of Pruned and Quantized Semantic Segmentation...

引用

2nd IEEE International Conference on Device Intelligence, Computing and communication Technologies, DICCT 2024

作者： Upadhyay, Deepak Malhotra, Sonal Gupta, Mridul Mishra, Somesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372847

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts. © 2024 IEEE.

关键词： Semantic Segmentation

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An Approach of Fog Computing and Edge Computing for Computing Resources Optimization Strategies 1

An Approach of Fog Computing and Edge Computing for Computin...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Yadav, Devansh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350365016

This paper investigates optimization strategies for fog and edge computing systems, focusing on the key challenges of resource allocation, load balancing, latency minimization, and power efficiency. We analyze the results of the simulation study to determine the impact and potential of our strategies and find that the improvements achieved are small to moderate, varying between 1.75% and 2.95%. This knowledge demonstrates that system optimization requires small, gradual increments and that fog and edge computing are dynamic systems. Our results suggest that optimization based on integration and adaptability can produce a significant difference in the performance and reliability of distributed systems. © 2024 IEEE.

关键词： Fog computing

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

引用

2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Gupta, Mridul Yadav, Devansh Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350388534

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life. © 2024 IEEE.

关键词： Contrastive Learning

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Joint Power and Channel Allocation in Fading Channels Using Deep Reinforcement Learning 2

Joint Power and Channel Allocation in Fading Channels Using ...

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2nd International Conference on Advanced Computing and communication Technologies, ICACCTech 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798331519056

This paper investigates joint power and channel allocation under fading communication channels using Deep Reinforcement Learning (DRL). To this end, we design a DRL to allocate dynamic resources with different network condition properly and achieve the spectral efficiency improvement while reducing latency to meet lower bound in cost function as well keeping target fairness. Results show that our DRL-based approach can achieve better spectrum efficiency and latency than traditional algorithms while at the same time, sustaining a higher value of fairness index. In addition, it is observed that the DRL method also offers both adaptability (with fast adaptation to network changes) and robustness in performance but at a higher computational expense. Our results illustrate that the DRL-based technique may serve as a strong, scalable candidate in future wireless systems up to several orders of magnitude better than existing resource allocation schemes. © 2024 IEEE.

关键词： Deep reinforcement learning

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Big Data Analytics for Proactive Financial Fraud Detection and Prevention 2

Big Data Analytics for Proactive Financial Fraud Detection a...

引用

2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Hasudungan, Yehezkeil Oganda, Fitra Putri Daeli, Ora Plane Maria Raihan Santoso, Nesti Anggraini University of Raharja Dept. of Informatics Engineering Tangerang Indonesia University of Raharja Dept. of Information System Tangerang Indonesia University of Raharja Dept. of Digital Business Tangerang Indonesia University of Raharja Dept. of Computer System Tangerang Indonesia

ISBN: (纸本)9798350351613

Financial fraud poses a significant challenge to the financial industry, with increasing complexity and scale threatening global economic stability. This paper explores the use of Big Data Analytics for proactive detection and prevention of financial fraud. By analyzing large and diverse datasets, this approach aims to identify patterns and anomalies that may indicate fraudulent activities before they harm financial institutions. In this study, Smart PLS is employed as the primary method to test the structural model and the influence of various variables on the effectiveness of fraud prevention. The findings reveal that integrating Big Data Analytics into fraud prevention strategies can enhance early detection and response to fraud threats, positively impacting financial risk management. This research contributes significantly to the field of risk management by offering a framework that can be implemented to enhance financial security through advanced data analytics. © 2024 IEEE.

关键词： Data Analytics Financial Fraud Prevention Proactive Detection Risk Management SmartPLS

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An Ensemble Deep Learning Framework for Binary Classification of COVID-19 Cases Using the VGG16 (Visual Geometry Group 16) Architecture: A Comprehensive Analysis and Performance Evaluation 2

An Ensemble Deep Learning Framework for Binary Classificatio...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Upadhyay, Deepak Gupta, Mridul Mishra, Somesh Mittal, Saksham Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

This paper presents a novel approach to deep learning by putting forth a cooperative system that uses the VGG16 architecture to categorise COVID-19 examples into two groups. Our model is distinguished by its remarkable recall metrics and precision, which achieve a careful balance that is essential for accurate categorization. What's more impressive is how well the model performs in the non-COVID category, effectively differentiating between COVID and non-COVID cases. With a remarkable overall accuracy of 96%, the model successfully classifies cases from both groups, demonstrating the potential of our suggested framework as a useful diagnostic tool useful in various clinical contexts. This work clarifies the effectiveness of deep learning techniques, concentrating on the VGG16 architecture in the crucial job of binary classification for COVID-19 identification. Our results open up new avenues for investigation in the field of accurate medical diagnosis in addition to providing insights into the real-world applications of sophisticated machine learning. The study highlights the ensemble approach's encouraging benefits, showing how it may strengthen diagnostic precision and advance clinical decision-making. © 2024 IEEE.

关键词： AI Machine Learning Neural Network VGG16

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Performance Evaluation of Triple-Branch Diversity Receivers in Composite Gamma-Shadowed Rician Fading Channels with AMC and Asymmetric SNR Conditions 1

Performance Evaluation of Triple-Branch Diversity Receivers ...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

ISBN: (纸本)9798350365016

This paper provides an in-dept. analysis of the performance enhancement resulting from AMC in triple-branch diversity receivers under composite Gamma shadowed Rician fading channels with uneven SNR conditions. This paper was able to show the level of enhancement in average SNR resulting through simulations of AMC on signal quality in several receiver branches, which clearly shows AMC's ability in alleviating fading and shadowing based degradations. The analysis revealed that AMC adequately alleviates degradations by changing modulation schemes and coding rates based on the current quality of the channel. This reinforces AMC's ability to improve the link's reliability and likelihood to optimize the efficiency of the communication channel. This makes AMC a desirable design and optimization feature for wireless communication in highly fading and shadowed environments, which makes this innovation a potential fundamental technology in the realization of robust, resilient, and adaptive communication systems. © 2024 IEEE.

关键词： Signal to noise ratio

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Optimizing Customer Segmentation: A Comparative Analysis of Clustering Algorithms Using Evaluation Metrics 8

Optimizing Customer Segmentation: A Comparative Analysis of ...

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8th IEEE International Conference on Computational system and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Sakina, Noor Arun, Ajith P. Pavithra, R. Vishalakshi Prabhu, H. Gupta, Praveen Kumar Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India Rv College of Engineering Dept. of Chemical Engineering Bengaluru India Rv College of Engineering Dept. of Electronics and Communication Engineering Bengaluru India Rv College of Engineering Dept. of Biotechnology Bengaluru India

ISBN: (纸本)9798331505462

Customer segmentation is the route to data-driven decisions within organizations serving a wide variety of customer needs. This research explores customer segmentation with the utilization of K-means, hierarchical clustering, fuzzy C-means, along with DBSCAN to behavioural and demographic data. These algorithms are measured against each other using their silhouette score, Davies-Bouldin index, and cluster cohesion value. These results highlight that clustering can reveal meaningful patterns in customer data for improving personalized marketing strategies, customer retention, and profitability. © 2024 IEEE.

关键词： Sales

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