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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Ranganathan, Chitra Sabapathy Sumathi, V. Shanmugam, Kannan Kavididevi, Venkatesh Bhavani, R. Gopi, B. Mphasis Corporation Tamil Nadu Chennai India Sri Sairam Engineering College Department of Mathematics Tamil Nadu Chennai India School of Computing Science and Engineering Vit Bhopal University Department of Gaming Technology Madhapradesh Bhopal India Vardhaman College of Engineering Department of Information Technology Telangana Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Rasipuram India

ISBN: (纸本)9798350379716

Safety, environmental protection, and regulatory compliance depend on risk management in industrial operations, particularly those involving hazardous chemicals. This paper introduces a new risk management method that uses the Internet of Things (IoT) and Random Forest algorithm to predict hazardous waste generation rates. In industrial processes, many characteristics, such as temperature, pressure, flow rates, and chemical concentrations, are monitored in real time due to the IoT. These data streams are sent to the Random Forest algorithm, an advanced machine learning (ML) method that can handle complicated datasets and make accurate predictions. Training the Random Forest model on previous operating conditions and waste production rates helps it understand patterns and correlations. This trained algorithm can estimate hazardous waste production rates in real-time, enabling timely risk minimization. The suggested strategy has various benefits over industrial risk management strategies. It identifies possible dangers in time to avert mishaps and reduce environmental damage. IoT allows continuous monitoring and data collecting, which refines and optimizes the prediction model. It proposes a complete methodology for industrial risk management using IoT and ML to estimate hazardous waste production rates properly. Such a strategy might improve industrial safety, sustainability, and regulatory compliance. The advantage of quantifying improvements may be shown in specific metrics, such as a 15% decrease in hazardous waste and a 20% increase in regulatory compliance. Measuring indicators would emphasize the technique's practical benefits and efficacy. © 2024 IEEE.

关键词： Risk management

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Using Bayesian Networks For Predicting Hypertension Risk Factors 10

Using Bayesian Networks For Predicting Hypertension Risk Fac...

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

作者： Varshney, Neeraj Madan, Parul Michaelson, Jacob Laxminarayanamma, K. Yadav, Vijay Kumar Kumar, Navneet Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering 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

Hypertension, a primary cause of mortality as well as morbidity in the cardiovascular system, demands accurate prediction models to ensure effective treatment and preventative measures. This study predicts hypertension associated risk factors using Bayesian Systems, an interpretative mindset, a deductive approach, and an exploratory approach. Using secondary data including genetic, lifestyle, as well as environmental characteristics, the study provides a comparison to established statistical methodologies. The Bayesian neural network model performs admirably in terms of prediction, with an accuracy of more than 85%. Both sensitivity and specificity show values above 80% and 90%, respectively, suggesting proficiency in diagnosing hypertensive people. Furthermore, sensitivity analysis shows that the model is resistant to changes in input parameters. Key contributions to hypertension risk are found, with biological factors, lifestyle choices, as well as exposure to environmental stressors all playing a substantial role. These factors' complicated relationships are explored, revealing their combined impact on antihypertensive susceptibility. Bayesian networks were able to beat traditional statistical methods in terms of accuracy and selectivity in predicting the incidence of hypertension variables. This demonstrates how Bayesian techniques have a potential to transform risk assessment as well as prevention tactics in cardiovascular health. Finally, this study demonstrates the effectiveness of Bayesian models in predicting risk for hypertension using multiple variables, giving a solid framework for focused interventions and preventative measures. The findings hold great promise for furthering public health measures aimed at controlling hypertensive and its effects. © 2023 IEEE.

关键词： Bayesian Networks global health problem Hypertension lifestyle factors using probabilities

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The Role of 5G Networks in Energy-Efficient Smart Cities Development using IoT 2

The Role of 5G Networks in Energy-Efficient Smart Cities Dev...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Vasu, V. Tamilarasi, A. Prakash, R.B.R. Kalaivani, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science India St. Claret College Department of Commerce Jalahalli Karnataka Bengaluru560013 India Kongu Engineering College Department of Computer Applications Perundurai India Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation vaddeswaram522302 India AP/ECE Erode Sengunthar Engineering College India

ISBN: (纸本)9781665456302

In theory, residents in 'smart cities' would have access to a wide range of amenities designed to enhance their daily lives by making better use of public resources and providing a more satisfying experience overall. The efficiency of these services depends on the information sharing across disparate systems serving similar functions. Large amounts of data with a variety of sophisticated, application-specific needs will likely be sent throughout the information exchange process. By better management of public resources and a focus on resident comfort, infrastructure upkeep, and environmental sustainability, 'smart cities,' which rely on ICT, aim to raise the bar on service quality. Fifth-generation (5G) wireless mobile communication paves the way for a new kind of communication network that can connect everyone and everything. Impacting economies and communities, 5G will offer the connectivity infrastructure required by many smart city applications. A glimpse into the future of urban areas may be seen in the rise of 'smart cities' and the IoT. The integration of many systems aimed to monitor varied components of the smart city may be utilized to generate a more sustainable and securer city. This research study presents a construction that mixes the data from diverse systems. In order to lessen the quantity of produced traffic in the 5G network and subsequently the energy consumption, suggest the usage of data aggregation in each antenna. A summary of 5G communication networks and different 5G technologies utilized in smart cities to promote sustainability is provided. The sustainability metrics for 5G networks are then examined across the environmental, social policy, and economic aspects, as well as sub-dimensions such as energy efficiency, energy usage, carbon footprint, contamination, cost, health, safety, and security. The findings indicate that while in an effort to address sustainability in 5G technology and intelligent buildings, the bulk of research publicat

关键词： 5G mobile communication systems

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The biological activities of postbiotics in gastrointestinal disorders (vol 10, pg 1, 2021)

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CRITICAL REVIEWS IN FOOD science AND NUTRITION 2022年第29期62卷 8247-8247页

作者： Abbasi, Amin Rad, Aziz Homayouni Ghasempour, Zahra Sabahi, Sahar Kafil, Hossein Samadi Hasannezhad, Paniz Saadat, Yalda Rahbar Shahbazi, Nayyer Student Research Committee Tabriz University of Medical Sciences Tabriz Iran Department of Food Science and Technology Faculty of Nutrition & Food Sciences Tabriz University of Medical Sciences Tabriz Iran Department of Nutritional Sciences School of Paramedical Sciences Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Drug Applied Research Center Tabriz University of Medical Sciences Tabriz Iran Department of Medical Engineering Science University College of Rouzbahan Sari Iran Nutrition Research Center Tabriz University of Medical Sciences Tabriz Iran Faculty of Agriculture Engineering Department of Food Science Shahrood University of Technology Shahrood Iran

According to outcomes from clinical studies, an intricate relationship occurs between the beneficial microbiota, gut homeostasis, and the host’s health status. Numerous studies have confirmed the health-promoting effects of probiotics, particularly in gastrointestinal diseases. On the other hand, the safety issues regarding the consumption of some probiotics are still a matter of debate, thus to overcome the problems related to the application of live probiotic cells in terms of clinical, technological, and economic aspects, microbial-derived biomolecules (postbiotics) were introducing as a potential alternative agent. Presently scientific literature confirms that the postbiotic components can be used as promising tools for both prevention and treatment strategies in gastrointestinal disorders with less undesirable side-effects, particularly in infants and children. Future head-to-head trials are required to distinguish appropriate strains of parent cells, optimal dosages of postbiotics, and assessment of the cost-effectiveness of postbiotics compared to alternative drugs. This review provides an overview of the concept and safety issues regarding postbiotics, with emphasis on their biological role in the treatment of some important gastrointestinal disorders.

关键词： Postbiotic gut microbiota gastrointestinal disorders infantile colic necrotizing enterocolitis gastrointestinal cancer

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Exponential Stabilization of Linear Systems using Nearest-Action Control with Countable Input Set

arXiv

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arXiv 2024年

作者： Almuzakki, Muhammad Zaki Jayawardhana, Bayu Tanwani, Aneel Vakis, Antonis I. Department of Computer Science Faculty of Science and Computer Universitas Pertamina Jakarta Indonesia Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Netherlands CNRS Université de Toulouse France

This paper studies stabilization of linear time-invariant (LTI) systems when control actions can only be realized in finitely many directions where it is possible to actuate uniformly or logarithmically extended positive scaling factors in each direction. Furthermore, a nearest-action selection approach is used to map the continuous measurements to a realizable action where we show that the approach satisfies a weak sector condition for multiple-input multiple-output (MIMO) systems. Using the notion of input-to-state stability, under some assumptions imposed on the transfer function of the system, we show that the closed-loop system converges to the target ball exponentially fast. Moreover, when logarithmic extension for the scaling factors is realizable, the closed-loop system is able to achieve asymptotic stability instead of only practical stability. Finally, we present an example of the application that confirms our analysis. © 2024, CC BY-NC-ND.

关键词： MIMO systems

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Automated Machine Learning-Based Gestational Monitoring Framework in Wearable Internet of Things Environment 2nd

Automated Machine Learning-Based Gestational Monitoring Fram...

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2nd International Conference on Mechanical and Energy Technologies , ICMET 2021

作者： Nandini, K. Seetha, J. Jegajothi, B. Rajmohan, R. Nageswari, D. Department of Electronics and Instrumentation Engineering Easwari Engineering College Chennai India Department of Computer Science SRM Institute of Science and Technology SRMIST Bharathi Salai Ramapuram Campus Chennai600089 India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai India Department of Electronics and Communication Science Agurchand Manmull Jain College Meenambakkam Chennai India Department of Electrical and Electronics Engineering RMK College of Engineering and Technology Chennai India

ISBN: (纸本)9789811901072

Recently, Internet of Things (IoT) and wearable technologies have become popular in diverse areas, and smart health is an important application area. Specifically, IoT and wearable technologies find useful in linking different medicinal gadgets, sensors, and physicians in offering better healthcare services in remote areas. It results in enhanced patient safety, minimum healthcare cost, improved healthcare service accessibility, and high operational efficiency in the healthcare sector. At the same time, high-quality care during pregnancy is essential to determine the probable difficulties earlier and guarantee the healthiness of mother as well as fetus. Several research works have been existed in the literature to monitor the maternal lifetime. But they are developed for a particular health issue and based on short-term data collection approaches. Since maternal monitoring necessitates long-term services, this paper designs an automated machine learning-based gestational monitoring framework in wearable IoT environment. The goal of this paper is to derive a new improved salp swarm optimization (ISSO) with extreme learning machine (ELM), called ISSO-ELM model for continual maternal health monitoring. The ISSO-ELM model encompasses different sub-processes namely data acquisition, pre-processing, classification, and parameter optimization. The presented model enables IoT devices and wearables to gather healthcare data about pregnant women. Then, the gathered data are pre-processed to remove the unwanted noise. Besides, the ELM model is employed as a classifier to determine the occurrence of difficulties. Moreover, ISSO algorithm is applied as a parameter tuning technique to optimally adjust the parameters that exist in the ELM model. The extensive experimental analysis highlighted the enhanced performance of the ISSO-ELM model over the recent state-of-art methods with the precision, recall, and accuracy of 94.00, 88.00, and 90.07%. © 2023, The Author(s), under exclusiv

关键词： Internet of things

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Wire grounding electrode placed near cylindrical multi-layered ground non-homogeneity 22

Wire grounding electrode placed near cylindrical multi-layer...

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22nd International Symposium on Electrical Apparatus and Technologies, SIELA 2022

作者： Jovanovic, Dejan Cvetkovic, Nenad Jovanovic, Dragana Vuckovic, Dragan Hederic, Zeljko University of Nis Faculty of Electronic Engineering Nis Serbia Josip J. Strossmayer University Faculty of El. Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (数字)9781665411394

ISBN: (纸本)9781665411394

Analysis and realization of grounding system fulfilling technical requirements, very often require analysis of the influence of soil non-homogeneity on grounding characteristics. In some cases, non-homogeneity can be approximated as vertical homogeneous cylindrical domain. Such non-homogeneities are various tubes, wells or pillar foundation. In this paper, the procedure for modeling of soil non-homogeneity as multilayered cylindrical domain is presented. The approach is based on using the Method of Moments and Green's function, applicable for analysis of grounding electrodes placed in the surrounding soil. Within that, particular procedure for grounding electrode formed of finite number of wires is described. It has been applied on problem of vertical electrode in the vicinity of well having concrete cylindrical shell. © 2022 IEEE.

关键词： Method of moments

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Hybrid Artificial Ecosystem Optimization Algorithm based on Search Manager Framework for Big Data Environment 3

Hybrid Artificial Ecosystem Optimization Algorithm based on ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sarao, Pushpender Milind, Milind Babu, G.N.P.V. Rameshbhai Savaliya, RajeshKumar Devi, Mousmita Tiwari, Mohit Sharad Institute of Technology College of Engineering Department of Cse Maharashtra Ichalkaranji India Scriet Ccs University Department of Computer Science Engineering Uttar Pradesh Meerut India Faculty of Marketing Telangana Hyderabad India Mibm Dica Sabargam Ambaba Commerce College Bca Department Gujarat Surat India Handique Girls' College Department of Computer Science Assam Guwahati India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665462167

Big Data Optimization (Big-Opt) represent optimization problem, which needs to handle the property of big data analytics. In recent years, metaheuristic algorithm has effectively resolved several real-time problems. Based on natural phenomena, the algorithm with distinctive search mechanism could be good at addressing specific challenges. But they might be failed to resolve other challenges. Amongst the different techniques, hybridizing metaheuristic algorithm might assist in enriching the searching behaviour while promoting search flexibility. Therefore, this study develops a Hybrid Artificial Ecosystem Optimization Algorithm based on the Search Manager Framework (HAEOA-S MF) for big data environments. In the presented HAEOA-SMF technique, the population gets produced arbitrarily and the objective function gets determined. For every round, the HAEOA-SMF technique executes the AEO algorithm with Oppositional Based Learning (OBL) concept. In the presented HAEOA-SMF technique, the population is divided into a few groups (or islands) and the vector can be allotted to every group of subpopulations. The HAEOA-SMF technique generated a styled vector with the length of many evolution models elected to hybridize the models. The experimental result of the HAEOA-SMF technique is tested using a series of data and the results can be studied under various aspects. The experimental outcomes highlighted the enhanced performance of the HAEOA-SMF technique. © 2023 IEEE.

关键词： Big data

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Ecological Development of a Novel Machine Learning Technique for Electricity Consumption and Cost Prediction in Cloud Computing 3rd

Ecological Development of a Novel Machine Learning Technique...

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3rd International Conference on Electronic Governance with Emerging Technologies, EGETC 2024

作者： Gogineni, Neelima Saravanan, M.S. Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha Institute of Medical and Technical Sciences Chennai602105 India Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India

ISBN: (纸本)9783031770289

In the realm of cloud computing, optimizing electricity consumption and accurately predicting costs are pivotal for Ecological and cost-effective operations. This paper introduces a pioneering machine learning methodology designed to predict electricity usage and estimate associated expenses in cloud computing environments. Leveraging historical consumption data and diverse parameters, the proposed technique employs a sophisticated algorithmic model, such as ensemble learning or deep neural networks, to forecast electricity consumption patterns. Additionally, it incorporates factors like workload fluctuations, resource allocation strategies, and pricing models to enhance the precision of cost predictions. In this research, we suggest an Extreme Gradient Boosting (XGBoost) model to predict electricity consumption, offload or transfer storage, rates, reducing the costs associated with energy use in data hubs. Based on an actual world dataset provided by the Independent Energy Systems Provider (IESO) inside the efficacy of this in the Canadian province of Ontario approach is evaluated to alleviate data storage in data centers and successfully cut down on energy use. Seventy percent of data goes toward testing, while the remaining thirty percent data is used for training. The results emphasize the potential of this technique to change the management of electricity consumption and cost projections in cloud computing, offering a significant step towards a more efficient and Ecological development of cloud ecosystem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cloud Data Storage Cost Prediction Electricity Consumption Machine Learning XGBoost

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Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks

arXiv

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arXiv 2025年

作者： Orfanoudakis, Stavros Palensky, Peter Vergara, Pedro P. Section Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

Maintaining grid stability amid widespread electric vehicle (EV) adoption is vital for sustainable transportation. Traditional optimization methods and Reinforcement Learning (RL) approaches often struggle with the high dimensionality and dynamic nature of real-time EV charging, leading to sub-optimal solutions. To address these challenges, this study demonstrates that combining Large Language Models (LLMs), for sequence modeling, with Graph Neural Networks (GNNs), for relational information extraction, not only outperforms conventional EV smart charging methods, but also paves the way for entirely new research directions and innovative solutions. © 2025, CC BY.

关键词： Charging stations

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