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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Sajja, Rushvi Ahmed, S.K. Feroz Bodapati, Jyostna Devi Yenduri, Lakshmana Kumar Department of Advanced Computer Science and Engineering VFSTR Deemed to be University Andhra Pradesh India Department of Advanced Computer Science and Engineering Vignan University Andhra Pradesh India IEEE San Francisco Bay Area United States

ISBN: (纸本)9781032642987

Air pollution, exacerbated by industrialization and urbanization, poses a significant environmental and public health challenge worldwide. Particular concern is directed towards PM2.5, which poses greater health risks than other airborne impurities combined. Accurately predicting PM2.5 levels is crucial for effective pollution management. This study focuses on predicting the Air Quality Index (AQI) as a measure of air pollution, employing machine learning algorithms to enhance predictive accuracy. Specifically, we utilize the Random Forest Classifier and AdaBoost boosting algorithm to develop a predictive model. Our approach involves data categorization and sensitivity analysis to ensure robust air quality predictions. © 2024 The Author(s).

关键词： Air quality

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Ensemble of diverse pre-trained convolutional neural networks for enhanced diabetic retinopathy image classification 5th

Ensemble of diverse pre-trained convolutional neural network...

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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Ahmed, S. K. Feroz Chowdary, Y. Yaswanth Bodapati, Jyostna Devi Yenduri, Lakshmana Kumar Department of Advanced Computer Science and Engineering VFSTR Deemed to be University Andhra Pradesh India Department of Advanced Computer Science and Engineering Vignan University Andhra Pradesh India IEEE San Francisco Bay Area United States

ISBN: (纸本)9781032642987

Diabetic retinopathy (DR) presents a significant threat to individuals with diabetes, affecting the retinal blood vessels and potentially leading to vision impairment or loss. Early detection plays a pivotal role in mitigating vision-related complications, and recent advancements in deep learning offer promising avenues for enhancing diagnostic accuracy. In this study, we introduce an ensemble model comprising six distinct pre-trained Convolutional Neural Network (CNN) architectures for early detection of diabetic retinopathy. The proposed model encompasses various stages, including data collection, pre-processing, augmentation, and modeling. Leveraging the benchmark APTOS19 dataset, our ensemble approach achieves an impressive accuracy of 85.18%, surpassing the performance of individual models involved. © 2024 The Author(s).

关键词： Convolutional neural networks

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A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering Algorithms

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Journal of computer science & Technology 2024年第3期39卷 610-636页

作者： Jagat Sesh Challa Navneet Goyal Amogh Sharma Nikhil Sreekumar Sundar Balasubramaniam Poonam Goyal Advanced Data Analytics and Parallel Technologies Laboratory Birla Institute of Technology and Science Pilani 333031India Uber New York 11101U.S.A. Computer Science and Engineering Department University of MinnesotaMinneapolis 55455U.S.A.

The advent of Big Data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as MPI,MapReduce,and *** important step for any parallel clustering algorithm is the distribution of data amongst the cluster *** step governs the methodology and performance of the entire *** typically use random,or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning,as per the requirements of the ***,these strategies are generic and are not tailor-made for any specific parallel clustering *** this paper,we give a very comprehensive literature survey of MPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they *** also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS,Cell-Based Dimensional Split for dGridSLINK,which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution,and Projection-Based Split,which is a generic distribution *** of these preserve spatial locality,achieve disjoint partitioning,and ensure good data load *** experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for,based on which we give appropriate recommendations for their usage.

关键词： parallel data mining data distribution parallel clustering spatial locality preservation

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Optimized Rainfall Prediction Model Using Optune CatBoost: An Innovative Approach 3

Optimized Rainfall Prediction Model Using Optune CatBoost: A...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Babu, Tina Nair, Rekha R Pavithra, K. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354379

Rainfall forecasting is important for several uses, such as agriculture, water supply, and flooding emergencies. This is because rainfall, which is a type of precipitation, is not easily predictable owing to several factors that include atmospheric, oceanic, and geographical factors. Therefore, a new Optune CatBoost prediction model has been developed here that can be used to predict rainfall. Some of the research techniques used include;data preprocessing, outlier analysis, normalization, SMOTE, feature engineering, and correlation analysis. This Optune CatBoost prediction gives a maximum accuracy of 96% a precision of 97% F1 score of 96.7%, and recall of 95.5%. The discrimination between rainfall and non-RA events is evaluated to be strong with the area under the ROC curve equalling 0.96. The study implies that the developed model could be used to enhance rainfall prediction, which in turn aids in decision-making on appropriate resource utilization in weather-sensitive areas. © 2024 IEEE.

关键词： Prediction models

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Enhanced Heart Disease Prediction Using Grid Search Optimized XGBoost Classifier 3

Enhanced Heart Disease Prediction Using Grid Search Optimize...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Babu, Tina Nair, Rekha R Pavithra, K. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354379

Since heart disease is still one of the major causes of death worldwide, there has been a lot of study done on early diagnosis and prevention techniques. Thus, an XGBoost classifier tuned for Grid Search is used to carry out a novel prediction. Finding the best characteristics in the dataset is aided by Grid Search optimization. The preprocessed dataset is subjected to suitable feature selection procedures in order to extract the most pertinent predictors. The accuracy, precision, recall, and F1-score are among the performance indicators used to assess the suggested method. Comparing the suggested model to other techniques as the Random Forest model, Decision Tree model, and Multi-Layer Perceptron (MLP), it yielded an accuracy of 97.56% and a Fi_Score of 97.69%. By enabling early identification and preventive actions, this research adds to the ongoing efforts to develop accurate and dependable predictive models for cardiac disease, which in turn improves patient outcomes and lessens the strain on healthcare systems. © 2024 IEEE.

关键词： Cardiology

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Buffer and Splitter Insertion for Adiabatic Quantum-Flux-Parametron Circuits

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 975-988页

作者： Fu, Rongliang Wang, Mengmeng Kan, Yirong Chen, Olivia Yoshikawa, Nobuyuki Yu, Bei Ho, Tsung-Yi The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan Nara Institute of Science and Technology Division of Information Science Nara630-0192 Japan Kyushu University Department of Advanced Information Technology Fukuoka819-0395 Japan

The extremely low-bit energy characteristic of the adiabatic quantum-flux-parametron (AQFP) circuit makes it a promising candidate for highly energy-efficient computing systems. However, in contrast with conventional circuit design, general logic synthesis tools can not make sure that the circuit functionality of generated AQFP circuits is correct. AQFP circuits require buffer and splitter insertion for dataflow synchronization at all clock phases of the circuit and multifan-out driving. Notably, buffers and splitters inserted take up much area and delay in AQFP circuits, also causing a significant increase in energy dissipation. To address this problem, this article analyses in detail why buffer and splitter insertion is necessary for AQFP circuits and proposes a global optimization framework for this purpose. This framework consists of three parts: 1) logic level assignment;2) splitter tree generation;and 3) buffer insertion. An integer linear programming algorithm is proposed for the logic level assignment to estimate the globally optimal number of inserted buffers and splitters. Subsequently, a dynamic programming-based multiway search tree generation algorithm is proposed to construct an optimal splitter tree for each net of the input circuit. Moreover, three optimization strategies are proposed to further enhance the effectiveness and efficiency of our framework. Experimental results on ISCAS'85 and EPFL benchmarks demonstrate the effectiveness and efficiency of our proposed framework compared with the state-of-the-art, particularly with significant advantages on large circuits. © 2024 IEEE.

关键词： Global optimization

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration Technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Efficient Pseudo-Random Number Generator Using Number-Theoretic Transform 10th

Efficient Pseudo-Random Number Generator Using Number-Theore...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Pandit, Anupama Arjun Kumar, Atul Mishra, Arun Computer Science and Engineering Defence Institute of Advanced Technology Pune India

ISBN: (纸本)9789811974540

In the finite field of integers, the number theoretic transform (NTT) is a specific variant of the discrete fourier transform (DFT). NTT is the essential method that permits efficient computing. Therefore, NTT could be used in the construction of lattice-based pseudo-random number generator. To construct the lattice-based pseudo-random number generator (PRNG), higher degree of polynomial multiplication is required. Therefore, NTT could be used for fast multiplication as it performs point-wise multiplication. In this paper, we would examine the algorithmic properties and performance of NTT for fast multiplication by implementing pseudo-random number generator using NTT. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete Fourier transforms

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Speed Harmonic based Saturation Free Inductance Modeling and Estimation of Interior PMSM Using Measurements Under One Load Condition

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CES Transactions on Electrical Machines and Systems 2025年第1期9卷 91-99页

作者： Guodong Feng Yuting Lu Zhe Tong Beichen Ding Guishan Yan Chunyan Lai the School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhenChina the School of Advanced Manufacturing Sun Yat-sen University the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) China the Department of Electrical and Computer Engineering Concordia UniversityQCCanada

For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from multiple load/current conditions,and the estimation is relying on the accuracy of saturation model and other machine parameters in the *** harmonic produced by harmonic currents is inductance-dependent,and thus this paper explores the use of magnitude and phase angle of the speed harmonic for accurate inductance *** estimation models are built based on either the magnitude or phase angle,and the inductances can be from d-axis voltage and the magnitude or phase angle,in which the filter influence in harmonic extraction is considered to ensure the estimation *** inductances can be estimated from the measurements under one load condition,which is free of saturation ***,the inductance estimation is robust to the change of other machine *** proposed approach can effectively improve estimation accuracy especially under the condition with low current *** and comparisons are conducted on a test PMSM to validate the proposed approach.

关键词： PMSM Inductance estimation Speed harmonic High frequency signal injection

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Exploring emotion detection in Kashmiri audio reviews using the fusion model of CNN, LSTM, and RNN: gender-specific speech patterns and performance analysis

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International Journal of Information Technology (Singapore) 2024年 1-19页

作者： Dar, Gh. Mohmad Delhibabu, Radhakrishnan School of Advanced Sciences and School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India

The research examines the challenge of emotion detection in Kashmiri language utilizing audio reviews. It proposes a fusion model integrating convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN). The dataset is segmented into combined, male, and female subsets, and the fusion model’s efficacy is rigorously assessed. Results demonstrate promising performance, achieving an overall accuracy of 87% on the combined dataset, albeit facing difficulties in accurately categorizing certain emotions such as sadness and passive anger. Gender-specific speech characteristics significantly impact the model’s performance, with heightened accuracy in discerning emotional nuances in female speech (91%) compared to male speech (86%). The study highlights the necessity of incorporating gender-specific speech patterns into emotion detection frameworks and underscores the efficacy of fusion models in analyzing underrepresented linguistic data. These findings advance the development of emotion detection systems tailored for Kashmiri language applications and have implications for sentiment analysis and human–computer interaction domains. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： AI Convolutional neural network (CNN) Fusion model Kashmiri Kashmiri language Long short-term memory (LSTM) Mel-frequency cepstral coefficients (MFCC) RNN

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