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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Giri, Sourav Kumar Dash, Sujata Sahoo, Tapaswini Department of Computer Science and Engineering Srinix College of Engineering Odisha Balasore756001 India Department of Computer Application Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada757003 India Skill Development Institute Odisha Bhubaneswar India

ISBN: (纸本)9789819950140

Diabetes seems to be a prevalent disease nowadays across the whole globe and is found to be very common on every doorstep. The consequences of this disease are very severe if it remains unnoticed during its initial stage. It affects populations of every age group. The causes of this disease are attributed to genetic links, lack of physical exercise, unhealthy food habits, overstress, etc. Many recent machine learning models have been proposed to predict this disease by considering the historical data of patients who have diabetes. This work attempts to choose a robust and reliable machine learning model for diagnosing the disease from seven widely used classification algorithms such as;Logistic Regression, Decision Tree, Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, and AdaBoost. The model is validated with the PIMA diabetes dataset. The efficiency of the models is augmented by addressing the imbalanced characteristics of the dataset by applying SMOTE technique and a univariate feature selection technique, ‘chi-square’ for feature selection. The experimental findings confirm that the Random Forest model has outperformed other models in terms of accuracy, precision, recall, F-score, and AUC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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A Concise Overview of Cloud-based Platform for Intelligent Converters

A Concise Overview of Cloud-based Platform for Intelligent C...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Narmadha, T. Shree, Ritu Alam, Intekhab Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

This research presents the mechanical implementation and management of freely available modules for the construction of smart conversions systems. Converts that are scattered or unsophisticated are not clever converts. To transmit the maximum power, they have stabilizers that have comparable impedance online. In current energy environments, intelligent adaptations provide fascinating solutions;in fact, their uses have become more and more varied. The suggested method was developed and tested in a laboratory environment using both real photovoltaic units and a solar cell simulator. Innovative conversions are shown in research, and enhanced intelligent capabilities allow for on-the-fly configuration adjustments, real-time diagnostics, and the management of the system's programs. Furthermore, the project's capabilities allows for a wide range of combinations and groups of various energy sources, generators, and modulation techniques. To the best of our knowledge, this project remains the sole platform for smart conversions that is available for free and is currently being utilized by research, industry, and government. © 2023 IEEE.

关键词： Cloud-Connected Smart Converters DC/AC Transformer Electrical Architectural Experimental Results FPGA Technology Maximum Power Point Tracking MPPT Techniques Power Shield Design and Efficiency Analysis Renewable Energy Techniques Smart Conversion Devices Wi-Fi Networking Module

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Novel Based Controller Design to Control Grid Systems and to Maintain Stability

Novel Based Controller Design to Control Grid Systems and to...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Haripriya Ahmed, Zahid Sing, Navjot Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

This study presents a novel cooperative control method for an interconnected hybrid DC/AC microgrid that uses an infinite impulses (IIR) filter as its core element. The creation of a digital proportional-resonant (PR) electrical controller forms the basis of the control system. The controller's integrated adaptive neural fuzzy induction system (ANFIS) effectively monitors the system's maximum battery life point (MPP). A particle swarm optimizing (PSO)-programmed ANFIS controls the inverter current that is output, ensuring that solar PV systems using boost converters and inverters may extract maximum power quickly and without oscillation. The suggested PR controller has the ability to cancel harmonics and shows remarkable gain at the resonance frequency. Fast reference signal tracking, flexibility in response to grid rate drift, a simple system design, and the lack of steady-state error are important characteristics. A frequency-domain study of the PR limiter is included in the report. Without changing the figure description, the proposed strategy improves the transient as steady-state responses and helps to stabilize the voltage. © 2023 IEEE.

关键词： Cooperative control Distributed Generation (DG) hybrid AC/DC microgrid converters Microgrid (MG) Power Electronics and Renewable Energy Proportional-Resonant controller Smart Grids

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Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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Agile Modeling and Simulation Technology of Heterogeneous Systems Based on Meta-model Modeling Method 3

Agile Modeling and Simulation Technology of Heterogeneous Sy...

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3rd International Conference on Modeling, Simulation, Optimization and Algorithm, ICMSOA 2021

作者： Liu, Yunfei Ma, Tao China Academy of Engineering Physics Institute of Computer Application Sichuan Mianyang China

Modeling and simulation of heterogeneous systems are faced with the problems of long modeling and simulation cycle, complex and changeable modeling fields and complex domain knowledge. In order to support the requirements analysis and initial design of the system, build the system model as the only authoritative truth source throughout the whole life cycle, and quantitatively characterize the requirements, functions, structure and performance of the system, this paper proposes an agile modeling and simulation technology for heterogeneous systems based on the meta-model modeling method, and on this basis, constructs an agile modeling and simulation system framework for heterogeneous systems. © Published under licence by IOP Publishing Ltd.

关键词： Life cycle

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Investigating Domain Adaptation for Medical Image Classification 5th

Investigating Domain Adaptation for Medical Image Classifica...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Agarwal, Ankita Seth, Jyoti Sharma, Ambrish Kumar Karthikeyan, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Master of Computer Application Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Area version goals bridge the space between supply and target domain names by remodeling statistics from the source domain to the target area without providing categorized goal facts. Medical image domain version is a critical trouble in scientific imaging obligations, consisting of most cancers prognosis, because it lets in for the switch of understanding throughout particular medical imaging modalities, obligations, and affected person cohorts. It can help address record distribution shifts and make undertaking medical photo classes more dependable and accurate. In this examination, we look into several area model techniques and benchmark their performance in the venture of the medical photo category. We examine supervised and unsupervised methods, as well as deep studying-based methods. The results of our experiments offer insights into the performance of numerous area model techniques for clinical picture class that could assist in deciding the pleasant method to use in any given medical imaging task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Optimizing Real-Time Action Recognition: A Hybrid CNN-LSTM Approach with Advanced Activation and Optimization Techniques 5

Optimizing Real-Time Action Recognition: A Hybrid CNN-LSTM A...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： T.Nandhini Subramanian, R. Raja Kalasalingam Academy of Research and Education Department of Computer Application Tamil nadu India Kalasalingam Academy of Research and Education Department of ComputerScience and Engineering Tamil nadu India

ISBN: (纸本)9798331518097

Wearable technology has significantly increased the importance of human activity recognition (HAR), which tracks physical activities for healthcare, fitness, and computer interaction. A new model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks has been developed to classify different physical activities more accurately. The GELU activation function and Adamax optimizer achieved the best classification accuracy at 98.3%, outperforming other combinations. The combined CNN-LSTM model is about 98% accurate in distinguishing different activities, highlighting the importance of activation functions and optimizers in HAR applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Visual abstraction of dynamic network via improved multi-class blue noise sampling

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Frontiers of computer Science 2023年第1期17卷 171-185页

作者： Yanni PENG Xiaoping FAN Rong CHEN Ziyao YU Shi LIU Yunpeng CHEN Ying ZHAO Fangfang ZHOU School of Computer Science and Engineering Central South UniversityChangsha 410083China Institute of Big Data Hunan University of Finance and EconomicsChangsha 410205China Rail Data Research and Application Key Laboratory of Hunan Province Changsha 410083China

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.

关键词： dynamic network visualization massive sequence view multi-class blue noise sampling visual abstraction

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Hindi News Article's Headline Generation based on Abstractive Text Summarization

Hindi News Article's Headline Generation based on Abstractiv...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Kumar, Jeetendra Shekhar, Shashi Gupta, Rashmi Atal Bihari Vajpayee University Dept. of Computer Science and Application Bilaspur India Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350300826

Text summarization is a technique that converts large text into shorter ones by keeping its important information. Most of the available summarizers, summarize English language text. The availability of summarizers in other languages is rare. In the proposed work, abstractive text summarization (ATS) for the Hindi language has been used to generate headlines from news text. To build the model, a Hindi news dataset has been used that contains different news articles related to sports, politics, crime, etc. After pre-processing the text, Word2Vec method has been used for word embedding. ATS model has been built using the Seq2Seq model with attention. After that, we have calculated evaluation metrics like ROUGE and BLEU scores of the result summaries. We have also fine-tuned two pre-trained models, indicBART and multi-lingual variant of T5. After result comparison, it was found that the indicBART perform better than the other two models, considered for the experiment. © 2023 IEEE.

关键词： Text processing

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A Comprehensive Study on Intrusion Detection System for Distributed Blockchain Ledger Security with Using Advanced Smart Contracts 8th

A Comprehensive Study on Intrusion Detection System for Dist...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Vats, Shailender Kumar Peddi, Prasadu Vats, Prashant Department of Computer Application Institute of Computer Science and Engineering Shri Jagdishprasad Jhabarmal Tibrewala University Jhunjhunu Rajasthan Chudela India Uttar Pradesh Noida India Department of Computer Science Engineering and Information Technology Shri Jagdishprasad Jhabarmal Tibrewala University Rajasthan Jhunjhunu India Department of Computer Science and Engineering SCSE Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819766772

The potential of blockchain technology to transform several industries, including supply chains, health care, and banking, has drawn much interest in recent years. Blockchain is becoming widely used, but this has raised questions about its security and ability to withstand attacks and threats. An overview of blockchain security issues and risk-reduction strategies is given in this abstract. The decentralized and unchangeable character of blockchain, which is accomplished through the application of cryptographic algorithms and consensus processes, is its primary security feature. However, it remains susceptible to security flaws such as 51% assaults, smart contract glitches, and compromised private keys. This abstract investigates these flaws and how they might affect the security and integrity of blockchain data. Several strategies have been put out and put into practice to increase blockchain security. The abstract looks at how important it is to set best practices and standards to protect blockchain networks from malicious activity and unauthorized access. Security issues will arise as the blockchain ecosystem develops, requiring constant study and cooperation between government, business, and academia. The appeal for a coordinated effort to address blockchain security issues in their entirety and guarantee the technology’s safe and dependable use across a range of applications and sectors is made in the abstract’s conclusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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