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Augmenting the Proficiencies of GANs Through Modernizing Agriculture and Healthcare

Augmenting the Proficiencies of GANs Through Modernizing Agr...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： M. Jothi T. Veeramakali Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Nowadays, generative models with deep learning have a focus and give enormous success in various real-time experience domains. Deep learning (DL) is a subdivision of machine learning that is inspired by the structure and function of human brain activity, known as artificial neural networks. It is a data-driven technique that achieves the best accuracy while using a large number of input samples. Generative Adversarial Networks (GANs) are a robust class of neural networks used for unsupervised learning under DL categories. GAN is one of the most influential architectures demonstrating remarkable success in generating high-quality data. The reusable feature learning process from a huge number of unlabelled data paves the way for research. This survey article discusses an overview of the GAN model, such as the original GAN, Conditional GANs, and resolution GANs, along with their specific adaptations for various tasks. Additionally, we examine the situations faced by Generative Adversarial Networks depending on various applications such as image and video generation, medical image analysis, text-to-image synthesis, agriculture, and creative industries such as music and art. This survey explores diverse applications of recently developed frameworks and benchmark datasets used for development, along with their architecture and metric evaluations.

关键词： Deep learning Surveys Measurement Image analysis Accuracy Generative adversarial networks Agriculture Real-time systems Medical diagnostic imaging Farming

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Alzheimer Disease Detection System using AlexNet a novel Eight Layered Convolutional Neural Network

Alzheimer Disease Detection System using AlexNet a novel Eig...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Jenifel, M. Geetha Shyamala Devi, M. Umanandhini, D. Srm Institute of Science and Technology Data Science and Business Systems School of Computing Tamil Nadu Chengalpattu603203 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science & Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350308167

Alzheimer's disease (AD), an incurable brain ailment that is irreversible, affects thinking and memory while also shrinking the size of the mind as a whole. Alzheimer's is a neurological illness that causes severe memory loss and makes it difficult to carry out regular chores. The development of more effective treatments for AD depends on an early diagnosis of the condition. Finding Alzheimer's disease is a difficult and time-consuming task that requires a brain imaging report and competent people. This time-consuming and frequently inaccurate method of diagnosing Alzheimer's is traditional. There has been discussion of a different strategy that is quick, inexpensive, and more dependable. Better medical care and solutions can be provided with the use of artificial intelligence technologies. Due to distractions, technological flaws, cognitive biases, and weariness, human diagnosis performance suffers. However, AI-based diagnosis tools support physicians' safe detection and decision-making and are less prone to error. This study offers a clever and trustworthy method for identifying Alzheimer's disease and its potential early stage, moderate mental impairment. The given system accurately diagnoses Alzheimer's disease and its early stages from structural MRI images and is based on deep learning. The ability to predict whether patients with moderate cognitive impairment (MCI) may develop Alzheimer's disease is essential in clinical practice and has the potential to significantly improve clinical studies. To classify Alzheimer's disease and its prodromal stages, this initiative suggests combining MRI data with the Mini-Mental State Examination (MMSE), a neuropsychological test. © 2023 IEEE.

关键词： AlexNet Alzheimer Disease Convolutional Neural Network Mild Cognitive Impairment Mini-Mental State Examination

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis 40

Boosting Meaningful Dependency Mining with Clustering and Co...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Wang, Xi Jin, Ruochun Huang, Wanrong Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology Department of Intelligent Data Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDM?. In order to scale with large datasets, FDM ? employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ? is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost. © 2024 IEEE.

关键词： Large datasets

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Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling 38

Structured Multi-Track Accompaniment Arrangement via Style P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Jingwei Xia, Gus Wang, Ziyu Wang, Ye Institute of Data Science NUS Singapore School of Computing NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Machine Learning Department MBZUAI United Arab Emirates Computer Science Department NYU Shanghai China

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles;subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines. © 2024 Neural information processing systems foundation. All rights reserved.

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE 5

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Liang, Yiyuan Jia, Hong Wang, Licai Huang, Yangchen Yang, Zhe Big Data Research and Development Center North China Institute of Computing Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350385557

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions. © 2024 IEEE.

关键词： document-level event extraction relation augment Universal Information Extraction

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Road network representation learning with the third law of geography 24

Road network representation learning with the third law of g...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haicang Zhou Weiming Huang Yile Chen Tiantian He Gao Cong Yew-Soon Ong College of Computing and Data Science Nanyang Technological University Singapore Institute of High-Performance Computing and Centre for Frontier Al Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore and Institute of High-Performance Computing and Centre for Frontier Al Research Agency for Science Technology and Research Singapore

ISBN: (纸本)9798331314385

Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemphasis on the distance effect as outlined in the First Law of Geography. In response, we propose to endow road network representation with the principles of the recent Third Law of Geography. To this end, we propose a novel graph contrastive learning framework that employs geographic configuration-aware graph augmentation and spectral negative sampling, ensuring that road segments with similar geographic configurations yield similar representations, and vice versa, aligning with the principles stated in the Third Law. The framework further fuses the Third Law with the First Law through a dual contrastive learning objective to effectively balance the implications of both laws. We evaluate our framework on two real-world datasets across three downstream tasks. The results show that the integration of the Third Law significantly improves the performance of road segment representations in downstream tasks. Our code is available at https://***/Haicang/Garner.

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New Era of IoT with Cloud computing: A review of Technology, Issues and Challenges 2

New Era of IoT with Cloud Computing: A review of Technology,...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Chahar, Suman Kaswan, Kuldeep Singh Aggarwal, Alankrita Dhatterwal, Jagjit Singh University Institute of Computing Chandigarh University Mohali India Galgotias University Department of Computer Science and Engineering Greater Noida India University Institute of Engineering Apex Institute of Engineering Chandigarh University Punjab India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798350390179

The convergence of Internet of Things (IoT) and cloud computing has unleashed unprecedented opportunities across various domains, revolutionizing industries, and redefining the way we interact with technology. This paper presents a comprehensive review of the symbiotic relationship between IoT and cloud computing, exploring the technological advancements, key issues, and challenges in this transformative landscape. The review begins by elucidating the foundational concepts of IoT and cloud computing, highlighting their synergistic potential. Subsequently, it examines the integration of IoT devices with cloud infrastructure. Furthermore, the paper delves into the technological enablers such as edge computing, fog computing, and serverless architectures that augment the capabilities of IoT-cloud ecosystems. However, alongside these advancements, critical issues and challenges emerge, encompassing security and privacy concerns, interoperability issues, data management complexities, and regulatory compliance. The review elucidates these challenges in detail, offering insights into potential mitigation strategies and future research directions. In addition, the paper discusses emerging trends such as AI-driven IoT applications. © 2024 IEEE.

关键词： Cloud platforms

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An Extractive Automatic Summarization Method for Chinese Long Text 9th

An Extractive Automatic Summarization Method for Chinese Lon...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Zhu, Jizhao Duan, Wenyu Yu, Naitong Pan, Xinlong Fan, Chunlong Shenyang Aerospace University Shenyang China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Beijing China Naval Aviation University Yantai China

ISBN: (纸本)9783031466632

The extractive automatic summarization method is capable of quickly and efficiently generating summaries through the steps of scoring, extracting and eliminating redundant sentences. Currently, most extractive methods utilize deep learning technology to treat automatic summarization as a binary classification task. However, the effectiveness of automatic summarization for Chinese long text is limited by the maximum input length of the model, and it requires a large amount of training data. This paper proposes an unsupervised extractive automatic summarization method which solves the long text encoding problem by incorporating contextual semantics into sentence-level encoding. Firstly, we obtain the semantic representation of sentences by using the RoBERTa model. Secondly, we propose an improved k-Means algorithm to cluster sentence representations. By defining sparse and dense clusters, we improve the accuracy of summary sentence selection while preserving maximum semantic information from the original text. Experimental results on the CAIL2020 dataset show that our method outperforms baselines by 6.64/7.68/7.14% respectively on ROUGE-1/2/L. Moreover, we further enhance the automatic summarization results by 4.5/5.36/3.24% by adding domain rules tailored to the dataset’s characteristics. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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Analyzing Prognosis Methods using Machine Learning Algorithms for Detecting COPD 1

Analyzing Prognosis Methods using Machine Learning Algorithm...

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1st International Conference on Applied Artificial Intelligence and computing, ICAAIC 2022

作者： Nagendra Kumar, V.V. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665497107

Chronic Obstructive Pulmonary Disorder (COPD) is a significant challenge encountered by healthcare professionals. With millions of people being affected by COPD every year, there is a need for better diagnosis conditions. Many of the earlier studies on the facets of the machine learning model's application to COPD have discussed the success in terms of accuracy, the optimal classifiers resulting in success, etc. This study, with the objective of a contemporary literature review, has focused on the earlier contributions of critical models of machine learning for COPD detection. However, based on the information collated from the models, while some classifiers are resourceful in training the system and improved accuracy is a need to address the feature selection issues. Some of the non-technical and purely medical-related studies reviewed in this study have impressed the facts about diagnosis state as false positives or false negatives (misdiagnosis) of COPD conditions. Both conditions are challenging for offering the right treatment to the patients. Thus, considering the existing patterns and the dynamics, this study has discussed three significant gaps in feature selection, the need for multiple layers of diagnosis, and the lifestyle conditions based on COPD detection models as a prospective domain for future research. © 2022 IEEE.

关键词： Computer aided diagnosis

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Predicting Employee Churn- data Driven Approach for Retention Management

Predicting Employee Churn- Data Driven Approach for Retentio...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R Jayaraj Safal Mehrotra Harshit Kumar Dept. of Data Science and Business Systems School of computing SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Employee churn is a critical challenge faced by organizations across industries, leading to disruptions in productivity and increased costs associated with recruitment and training. In this study, we propose an effective solution leveraging Random Forests to predict employee churn accurately and efficiently. Our approach addresses the inherent complexities of employee turnover prediction while considering resource constraints commonly encountered in practical applications. The proposed model incorporates feature engineering techniques and ensemble learning principles to harness the predictive power of Random Forests. Through a comprehensive evaluation on real-world employee datasets, we demonstrate the superior performance of our model in comparison to existing state-of-the-art methods. Our analysis encompasses various evaluation metrics, including accuracy, precision, recall, and F1-score, showcasing the robustness and reliability of our model across diverse organizational contexts.

关键词： Training Analytical models Accuracy Forestry Predictive models Robustness Complexity theory Churn Random forests Recruitment

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