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Origins to Innovations: A Comprehensive Journey Into Artificial Intelligence's Future Application

Origins to Innovations: A Comprehensive Journey Into Artific...

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Saraswat, Shivam Mehra, Ritika Gurpreet Yuvaraj, K. Karthigaikumar, P. Wankhede, D.S. Department of Civil Engineering School of Engineering & Technology K. R. Mangalam University Haryana Gurugram India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

In the contemporary era characterized by the proliferation of extensive data sets and the advent of Industry 4.0, artificial intelligence (AI) emerges as a prominent catalyst for economic growth. It assumes a pivotal role in facilitating the integration of advanced technologies like bitcoin, virtualization, the Internet of Things (IoT), and graphics processors, hence fostering innovation and development. In this scholarly essay, a comprehensive examination of artificial intelligence (AI) and learning methodologies spanning the years 1961 to 2018 is conducted. Through an extensive examination of AI, encompassing various aspects such as causal factors, practical systems, fundamental machine learning, commercial achievements, and current advancements, this investigation serves as a highly valuable resource for both academic scholars and industry experts. There is a substantial number of challenges associated with artificial intelligence (AI). However, it is undeniable that AI has seen significant advancements, emerging as an innovative and transformative tool across all domains and applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Industrial applications with LSTM-Based Predictive Analytics 15

Enhancing Industrial Applications with LSTM-Based Predictive...

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

作者： Ramachandran, Lalitha Girinath, S. Padmavathi, N. Subramaniam, G. Chitra, M. Siva Sathya, V. R.M.K. Engineering college Department of Science and Humanities Chennai India Department of Computer Applications Tirupati India R.M.D. Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu India M. Kumarasamy College of Engineering Department of EEE Tamilnadu Karur India Kongu Engineering College Department of EEE Tamilnadu Perundurai India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350370249

Predictive analytics in industrial applications has the potential to significantly boost productivity and enhance operational efficiency. The research intends to solve is the difficulty of traditional models to capture the detailed temporal correlations and complex non-linear patterns that are common in industrial data. Long Short-Term Memory (LSTM)-based predictive analytics are the primary focus of this research, with the primary objective being the enhancement of industrial processes. Predicting complex and ever-changing patterns within operational data is a challenge in the modern industrial environment. The allocation of resources and decision-making processes are not as ideal as they may be. The LSTM architecture enables to use of the temporal correlations of the data in order to improve ability to predict the future. LSTM-based predictive analytics are significantly more accurate than conventional models when it comes to predicting the events that will occur in industrial processes. The findings shows LSTM-based predictive analytics have the potential to revolutionize industrial decision-making, which in turn leads to improved resource management and operational efficiency. © 2024 IEEE.

关键词： Industrial applications LSTM Operational efficiency Predictive analytics Temporal dependencies

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Chronic Kidney Disease Detection Using Machine Learning Regression Models 1

Chronic Kidney Disease Detection Using Machine Learning Regr...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Singh, B. Arora, K. Iyer, Sailesh Department of Computer Applications Manav Rachna International Institute of Research & Studies Haryana Faridabad India Department of Computer Science & Engineering Rai School of Engineering Rai University Gujarat Ahmedabad India

ISBN: (纸本)9781607685395

The database of developments in the health sector is growing very rapidly. The data is very important to be processed and needs to be useful. Machine Learning and data mining are fields of research which can process databases into knowledge that can be used for diagnosing disease, such as Chronic Kidney Disease (CKD). This mining technique is used to predict CKD condition using two-fold regression algorithms namely Multivariate Linear Regression and Logistic regression. The aim of this study is to ascertain if somebody is a patient of Chronic Kidney Disease or not. The steps which have been followed in this research are to pre-process data, making prediction models with help of Multi-Variate Linear Regression, Logistic Regression method and prediction model testing using test data. © The Electrochemical Society

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Machine Learning based Landslide Detection System 7

Machine Learning based Landslide Detection System

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Anusuya, R. Anusha, N. Sujatha, V. Radhika, R. Iniyan, S. Department of Computer Science Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India Department of Electronics and Communication Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Applications S.A Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering S.A Engineering College Tamil Nadu Chennai India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Landslides are herbaceous disasters that negatively affect social existence every year. It can be described as a rock mass with particles moving along a slope. Avalanches are one of the most basic forms of catastrophic disasters, devastatingly affecting human life and the whole monetary framework. Avalanche forecasting has evolved into a severe endeavor, comprising analyzing avalanche-related data and forecasting projected avalanches to limit their harmful repercussions. Artificial Intelligence (AI) is a cutting-edge investigation technology widely employed in avalanche prevention. This is due to herbal or artificial action. Asia was the most affected continent, accounting for 75% of landslides. A landslide, also known as a landslide, is a geological phenomenon comprising a diverse range of floor movements. Monitoring is essential for predicting landslide behavior and which storms can cause various landslides. This could help save many lives and sustain livelihoods and asset shortages as people become aware of imminent risk mitigation and can take necessary steps to stay safe. The Internet uses the IOT network and system acquisition generation internally. Humidity sensors, accelerometers, and vibration sensors were employed to detect landslides. Raspberry Pi (SOC) uses MQTT protocol to collect and update statistics from websites. These telemetry missions can help inform citizens of an impending disaster and better understandthe situation. © 2023 IEEE.

关键词： Machine learning

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Real-Time Environmental Monitoring and Prediction in IoT Networks With Graph Neural Networks

Real-Time Environmental Monitoring and Prediction in IoT Net...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kansal, Vipashi Hussein, Layth Vargas-Chavarrea, Álvaro Varshney, Neeraj Sharma, Gazal Rao, A Kakoli Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq Instituto Superior Tecnológico Department Of Investigation Liceo Aduanero Ibarra Ecuador Gla University Department Of Computer Engineering And Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This research investigates real-time natural checking and forecasting in IoT systems utilizing Graph Neural Networks (GNNs). Leveraging a comprehensive dataset from IoT sensors capturing discuss quality, temperature, mugginess, wind speed, and precipitation, we examine the execution of four calculations: Graph Convolutional Network (GCN), Graph Attention Network (GAT), Long Short-Term Memory (LSTM), and Graph Recurrent Neural Network (GRNN). Comes about illustrates the predominance of GNN-based models, especially GAT and GRNN, in capturing spatial and transient conditions for exact forecasts. The models accomplish amazing execution measurements, counting Mean Absolute Error (MAE) values of 4.95 for GAT and 4.73 for GRNN, Root Mean Squared Error (RMSE) values of 7.12 for GAT and 6.85 for GRNN, and R2 scores of 0.78 for GAT and 0.80 for GRNN. These discoveries emphasize the potential of GNNs in revolutionizing natural observing practices, encouraging proactive decision-making, and cultivating maintainable and flexible communities. The study contributes to the broader talk on leveraging AI and IoT innovations for tending to natural challenges and lays the establishment for future research in this space. © 2024 IEEE.

关键词： Wind

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Predictive Maintenance in Smart Grids with Long Short-Term Memory Networks (LSTM)

Predictive Maintenance in Smart Grids with Long Short-Term M...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Goyal, Himanshu Rai Almusawi, Muntather Otero-Potosi, Santiago Varshney, Neeraj Sharma, Vishal Rao, A. Kakoli Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Instituto Superior Tecnológico Department Of Investigation Liceo Aduanero Ibarra Ecuador Gla University Department Of Computer Engineering And Applications Mathura India Lovely Professional University Phagwara India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This research explores the application of Long Short-Term Memory Systems (LSTMs) and conventional machine learning calculations for prescient support in shrewd grids. Leveraging a differing dataset, the study compares the execution of these models, uncovering that the LSTM demonstration outperforms conventional partners such as Random Forest, Support Vector Machine, as well as K-Nearest Neighbors. The LSTM's capacity to capture intricate worldly conditions in successive information contributes to its predominant precision, precision, recall, and F1 score in anticipating hardware disappointments and support needs. Comparative examinations highlight the transformative potential of LSTM systems in improving the unwavering quality and proficiency of prescient support in energetic keen framework situations. The study adjusts with current patterns in related writing, emphasizing the developing noticeable quality of profound learning procedures in tending to complex assignments inside the vitality segment. As savvy grids advance, joining progressed data-driven approaches gets to be basic, and this investigation underscores the urgent part of LSTMs in handling the challenges related to prescient maintenance. The discoveries not as it were contribute to the body of information within the field but also offer practical suggestions for the improvement of clever and flexible smart network frameworks. © 2024 IEEE.

关键词： Long short-term memory

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AI-Powered Real-Time Big Data Analytics for ICU Resource Management

AI-Powered Real-Time Big Data Analytics for ICU Resource Man...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Kalaichelvi, S. Mohankumar, N. Uthayakumar, C. Barkathulla, A. Manojee, K.S. Murugan, S. Department of Computer Science and Applications Tamil Nadu Tiruchengode India Nagpur Campus Pune India Vivekanandha College of Engineering for Women Department of Electronics and Communication Engineering Tamil Nadu Namakkal Tiruchengode India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Trichy India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Improving patient outcomes and operational efficiency in intensive care units (ICUs) relies heavily on efficient resource management. This work presents a novel LSTM-based methodology for real-time optimization of ICU resources, using big data to enhance decision-making. Patient vitals, equipment utilization, and staff activities are just a few examples of the real-time data gathered from the many ICU sensors and monitoring devices the system incorporates using LSTM networks. LSTM networks excel in processing time-series data, allowing the system to anticipate resource requirements, spot bottlenecks, and allocate resources optimally. The proposed system offers predicted insights together with practical suggestions by analyzing historical and real-time data streams. Simulations and case studies show that the system can increase operational efficiency and predict demand. Findings point to major gains in patient care, savings, and decision-making assistance in fast-paced ICUs. This method uses LSTM networks to tackle the intricacies of contemporary hospital environments, revolutionizing ICU management with innovative AI and big data analytics. LSTM networks improve real-time decision-making, resource management, and patient outcomes in ICUs. © 2025 IEEE.

关键词： Resource allocation

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Machine Learning-based Characterization of COPD Progression from Spirometry Data 10

Machine Learning-based Characterization of COPD Progression ...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Varshney, Neeraj Bordoloi, Dibyhash Michaelson, Jacob Laxminarayanamma, K. Yadav, Vijay Kumar Srivastava, Amit Gla University Department of Computer Engineering and Applications Mathura India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

This study represents a substantial advance in regard to the application of machine learning to the treatment of chronic obstructive pulmonary disease (COPD). Recurrent neural networks (RNNs) incorporating Long Short-Term Memory (LSTM) units were used in the study, which was centered on the interpretivism concept, to mimic the time-dependent aspect of COPD development. During training, the model's prediction of COPD development was optimized, significantly outperforming current diagnostic techniques. Although encouraging, the study recommends increased data gathering, and multidisciplinary cooperation, including a diversification of data sources, particularly unstructured data. To fully utilize machine learning in the management of COPD, future efforts should look into real-time patient data integration, and telemedicine applications, including user-friendly technologies. © 2023 IEEE.

关键词： Chronic Obstructive Pulmonary Disease (COPD) data integration Long Short-Term Memory (LSTM) machine learning recurrent neural networks telemedicine

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Toward a reference architecture based science gateway framework with embedded e-learning support

Toward a reference architecture based science gateway framew...

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作者： Pierantoni, Gabriele Kiss, Tamas Bolotov, Alexander Kagialis, Dimitrios DesLauriers, James Ullah, Amjad Chen, Huankai Fee, David Chan You Dang, Hai-Van Kovacs, Jozsef Belehaki, Anna Herekakis, Themistocles Tsagouri, Ioanna Gesing, Sandra School of Computer Science and Engineering University of Westminster London United Kingdom Parallel and Distributed Systems Laboratory SZTAKI Budapest Hungary Institute of Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens Athens Greece Department of Computer Science and Engineering University of Notre Dame Notre DameIN United States

science gateways have been widely utilized by a large number of user communities to simplify access to complex distributed computing infrastructures. While science gateways are still becoming increasingly popular and the number of user communities is growing, the fast and efficient creation of new science gateways and the flexibility to deploy these gateways on-demand on heterogeneous computational resources, remain a challenge. Additionally, the increase in the number of users, especially with very different backgrounds, requires intuitive embedded e-learning tools that support all stakeholders to find related learning material and to guide the learning process. This paper introduces a novel science gateway framework that addresses these challenges. The framework supports the creation, publication, selection, and deployment of cloud-based reference architectures that can be automatically instantiated and executed even by nontechnical users. The framework also incorporates a knowledge repository exchange and learning module that provides embedded e-learning support. To demonstrate the feasibility of the proposed solution, two scientific case studies are presented based on the requirements of the plasmasphere, ionosphere, and thermosphere research communities. © 2022 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Ionosphere

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Tabular Learning: Encoding for Entity and Context Embeddings

arXiv

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arXiv 2024年

作者： Reusser, Fredy Institute for Data Applications and Security School of Engineering and Computer Science Bern University of Applied Sciences Biel Switzerland

Examining the effect of different encoding techniques on entity and context embeddings, the goal of this work is to challenge commonly used Ordinal encoding for tabular learning. Applying different preprocessing methods and network architectures over several datasets resulted in a benchmark on how the encoders influence the learning outcome of the networks. By keeping the test, validation and training data consistent, results have shown that ordinal encoding is not the most suited encoder for categorical data in terms of preprocessing the data and thereafter, classifying the target variable correctly. A better outcome was achieved, encoding the features based on string similarities by computing a similarity matrix as input for the network. This is the case for both, entity and context embeddings, where the transformer architecture showed improved performance for Ordinal and Similarity encoding with regard to multi-label classification tasks. © 2024, CC BY-SA.

关键词： Embeddings

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