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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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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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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Efficient Hybrid Technique for Devanagari Numerals Recognition

Efficient Hybrid Technique for Devanagari Numerals Recogniti...

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Bura, Deepa Singh, Meeta Nandal, Poonam Khurana, Pooja Faculty of Engineering & Technology Manav Rachna International Institute of Research and Studies Department of Computer Science & Engineering Faridabad India Manav Rachna International Institute of Research and Studies Department of Applied Sciences Faculty of Engineering & Technology Faridabad India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

As a subset of Pattern Recognition, Handwritten Devanagari Character Recognition (HDCR) is one of the exciting field of research from decades but Devanagari numerals don't receive proper attention from the researchers. In India, Government offices provide preferences for filling the application form in Hindi or English & use the scanner for making the record of those applications. But the drawback of the scanned form is that they cannot be edited without using HDCR. This paper proposes a Handwritten Devanagari Numerals Recognition (HDNR) technique which is used to identify Devanagari numerals from given digital images, without human intervention. In this paper a novel technique is proposed which perform pre-processing on the inputted image where features are extracted & based on those features Devanagari numerals are classified. © 2022 IEEE.

关键词： Character recognition

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Agile System Development Lifecycle for AI Systems: Decision Architecture

arXiv

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arXiv 2025年

作者： Gill, Asif Q. School of Computer Science Faculty of Engineering & IT University of Technology Sydney UltimoNSW2007 Australia

Agile system development life cycle (SDLC) focuses on typical functional and non-functional system requirements for developing traditional software systems. However, Artificial Intelligent (AI) systems are different in nature and have distinct attributes such as (1) autonomy, (2) adaptiveness, (3) content generation, (4) decision-making, (5) predictability and (6) recommendation. Agile SDLC needs to be enhanced to support the AI system development and ongoing post-deployment adaptation. The challenge is: how can agile SDLC be enhanced to support AI systems? The scope of this paper is limited to AI system enabled decision automation. Thus, this paper proposes the use of decision science to enhance the agile SDLC to support the AI system development. Decision science is the study of decision-making, which seems useful to identify, analyse and describe decisions and their architecture subject to automation via AI systems. Specifically, this paper discusses the decision architecture in detail within the overall context of agile SDLC for AI systems. The application of the proposed approach is demonstrated with the help of an example scenario of insurance claim processing. This initial work indicated the usability of a decision science to enhancing the agile SDLC for designing and implementing the AI systems for decision-automation. This work provides an initial foundation for further work in this new area of decision architecture and agile SDLC for AI systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision support systems

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A Constructive Role for Social science in the Development of Automated Vehicles Based on LFM-BiGRU Approach 3

A Constructive Role for Social Science in the Development of...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Srikanth, J. Nitheesha, N. Khetree, Surekha Josephson, P Joel Akila, D. Divya, V. Kommuri Pratap Reddy Institute of Technology Computer Science and Engineering Telangana Ghatkesar India Telangana Hyderabad India Bharati Vidyapeeth College of Engineering Department of Mechanical Engineering Navi Mumbai India Department of Ece Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Applications Tamilnadu Chennai India Department of Information Technology Chennai India

ISBN: (纸本)9798350381931

This proposed system delves into the transformative realm of autonomous vehicles, a technological marvel that has captivated society's anticipation for its profound societal implications. While existing social science research predominantly focuses on regulatory, safety, and efficiency considerations, this method contends that these discussions fall short in comprehensively appreciating the extensive social ramifications of autonomous vehicles. Addressing this gap, the system aims to provide a more nuanced understanding of the broader societal impacts that autonomous vehicles are poised to instigate. The proposed methodology comprises three integral modules: preprocessing, feature extraction, and model training. Preprocessing involves essential tasks such as punctuation removal, URL elimination, and batch normalization. The feature extraction stage leverages TF-IDF, contributing to the discernment of significant features. For model training, the paper employs the LFM-BiGRU architecture, a distinctive aspect of the proposed method. Comparative analysis against conventional approaches, namely BiGRU-LSTM and BiLSTM, underscores the superior performance exhibited by the proposed method. This suggests that the LFM-BiGRU approach holds promise for advancing the capabilities of autonomous vehicle research and, consequently, societal understanding of this groundbreaking technology. © 2024 IEEE.

关键词： Autonomous vehicles

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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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Silicon Solar Cell-Integrated Flexible Retinal Prosthesis for Artificial Vision 38

Silicon Solar Cell-Integrated Flexible Retinal Prosthesis fo...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Kim, Chaesung Chung, Seung-Han Kim, Yong-Jin Roh, Hyeonhee Lee, Seung-Ki Kim, Yong-Kweon Lee, Hyung-Min Park, Jae-Hyoung Kang, Min Gu Im, Maesoon Korea University School of Electrical Engineering Seoul Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Photovoltaics Research Department Daejeon Korea Republic of Dankook University Department of Electronics and Electrical Engineering Yongin Korea Republic of Brain Science Institute Seoul Korea Republic of Division of Bio-Medical Science and Technology Seoul Korea Republic of Kyung Hee University KHU-KIST Department of Converging Science and Technology Seoul Korea Republic of

ISBN: (纸本)9798331508890

To date, no flexible silicon solar cell capable of directly converting visible light into electrical current has been developed for use in retinal prosthetic devices. In this study, we successfully fabricated silicon solar cells into small, pixelated structures and demonstrated their potential for retinal prostheses. To minimize surgical incisions during implantation and enhance device stability on the retina, we incorporated a flexible polymer substrate. Our results underscore the promise of silicon solar cells in biomedical engineering, especially for retinal prosthesis applications © 2025 IEEE.

关键词： Cell engineering

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