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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Chowdhury, Safiul Haque Mamun, Mohammad Hossain, Muhammad Minoar Hossain, Mohammad Ibrahim Iqbal, Md. Sadiq Kashem, Mohammod Abul Bangladesh University Department of Computer Science and Engineering Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering and Technology Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350388282

Newborn weight refers to the weight of a baby at the time of birth. It is typically measured in units such as pounds and ounces or kilograms. The weight of a newborn is an essential metric used by healthcare professionals to assess the health of an infant and its development. This research emphasizes the prediction of newborn weight using Machine Learning (ML) and Explainable Artificial Intelligence (XAI). The proposed approach involves preprocessing a dataset encompassing various parameters of newborns and their mothers during pregnancy. The primary objective is to develop an automated system capable of accurately predicting the weight of a newborn. Employing a range of regression ensemble ML models, namely Bootstrap Aggregating regression (BAGGINGR), Random Forest Regression (RFR), Ensemble Voting Regression (VOTIINGR), Extreme Gradient Boosting Regression (XGBOOSTR), Stacked Generalization Regression (STACKINGR), and Gradient Boosting Decision Trees Regression (GBDTR). This research develops a cross-validation approach, and notably, the GBDT model emerges as the top performer, yielding impressive results with average metrics Mean Squared Error (MSE) of 247.26, Mean Absolute Error (MAE) of 12.29, R-squared (R2) of 0.23, Peak Signal-to-Noise Ratio (PSNR) at 20.99 dB, and Signal-to-Noise Ratio (SNR) at 48.65 dB. The outcomes of the research are interpreted through Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) in XAI, emphasizing the significance of interpreting these parameters. The proposed research has valuable insights to enhance the long-term health conditions of newborns, reduce mortality rates, and provide crucial support to healthcare professionals. © 2024 IEEE.

关键词： Machine learning

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supported by the National Natural science Foundation of China(Nos.62172051,61772085,and 61877005)and Jiangsu Agriculture science and Technology Innovation Fund(***(18)3054).

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Tsinghua science and Technology 2023年第1期28卷 82-92页

作者： Jinghui Zhang Yuchen Wang Tianyu Huang Fang Dong Wei Zhao Dian Shen School of Computer Science and Engineering Southeast UniversityNanjing 211189China Hefei Comprehensive National Science Center Hefei 231299China School of Computer Science and Technology Anhui University of TechnologyHefei 230026China Department of Computer Science&Engineering The Chinese University of Hong KongHong Kong 999077China

Numerous neural network(NN)applications are now being deployed to mobile *** applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to the computing ability of mobile ***,devices’life and performance depend on ***,in many scenarios,such as industrial production and automotive systems,where the environmental temperatures are usually high,it is important to control devices’temperatures to maintain steady *** this paper,we propose a thermal-aware channel-wise heterogeneous NN inference *** contains two parts,the thermal-aware dynamic frequency(TADF)algorithm and the heterogeneous-processor single-layer workload distribution(HSWD)*** on a mobile device’s architecture characteristics and environmental temperature,TADF can adjust the appropriate running speed of the central processing unit and graphics processing unit,and then the workload of each layer in the NN model is distributed by HSWD in line with each processor’s running speed and the characteristics of the layers as well as heterogeneous *** experimental results,where representative NNs and mobile devices were used,show that the proposed method can considerably improve the speed of the on-device inference by 21%–43%over the traditional inference method.

关键词： neural network inference mobile device temperature adjustment channel-wise parallelization

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A Real-Time computer Vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Triplet CNN-based word spotting of Gujarati Handwritten Document 3

Triplet CNN-based word spotting of Gujarati Handwritten Docu...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bodana, Yuvrajsinh Hingrajiya, Heli Thakkar, Amit Suthar, Sanket Department of Computer Science and Engineering India

ISBN: (纸本)9798331528201

Word spotting of Gujarati handwritten documents is a highly challenging task due to the complexity of the handwritten text in the Gujarati language. This paper presents a novel approach to word spotting, which includes a transfer learning-based triplet CNN model for generating word embeddings and calculating triplet loss. During training, the CNN processes a set of triplet samples consisting of anchor, positive, and negative images. Triplet loss is then calculated to create word embeddings by minimizing intra-class distances and maximizing inter-class distances, which are further evaluated using Euclidean distance for comparison. The proposed approach is tested on handwritten image documents, and the experimental results demonstrate its effectiveness. © 2024 IEEE.

关键词： Embeddings

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DD-SPP: Dynamic and Distributed Service Placement Policy for Optimal Scheduling in Fog-Edge Computing

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SN computer science 2024年第7期5卷 1-12页

作者： Kaur, Navjeet Bhardwaj, Vivek Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University School of Computer Science and Engineering Manipal University Jaipur

The existing cloud model unable to handle abundant amount of Internet of Things (IoT) services placed by the end users due to its far distant location from end user and centralized nature. The edge and fog computing are the latest technologies for developing smart cities that are becoming popular to efficiently support latency-sensitive IoT application services. But these advanced technologies suffer from limited size and computation capabilities;therefore, optimal placement of services among available resources is an open issue. Therefore, the proposed work target the stated issue and presents a Dynamic and Distributed Service Placement Policy (DD-SPP) considering the edge and fog devices. The model is divided into three main phases, that is, the Service Type Estimator (STE), the Service Dependency Estimator & Resolution (SDER), and Resource Demand Estimator & Scheduling (RDES). STE and RDES modules depend on each other to perform optimal service placement. The results of the implementation showed satisfactory improvement with respect to present state-of-the-art policy where delay is improved by around 41%, energy is improved by 27%, response time by 28% and overall cost by 28%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing DD-SPP Edge computing Fog computing IoT Scheduling Service placement policy

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MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2313-2325页

作者： Ramanujam, E. Perumal, Thinagaran Department of Computer Science and Engineering National Institute of Technology Silchar Assam 788010 India Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang India

Smart home automation is protective and preventive measures that are taken to monitor elderly people in a non-intrusive manner using simple and pervasive sensors termed Ambient Assistive Living. The smart home produces a large volume of sensor activations to predict an elder’s health status to improve the quality of life and independent living. Machine learning techniques are very familiar and popular in recognizing single resident activities using such a sensor reading. However, multi-resident activities are more complex, and no correlation exists between sensor readings and activities. Recently deep learning and graphical models have been proposed to solve this problem, but it consumes more time to train the model. Moreover, models are primarily executed in parallel, or multi-resident activity recognition has been converted to single for classifying events. This paper proposes a Multi-label Multi-output Hybrid Sequential Model (MLMO-HSM), a feature engineering approach with a hybrid sequential model to recognize the multi-resident activities concurrently in the shortest time. Experimentation has been performed with various machine learning, graphical and deep learning models at the different sizes of the ARAS dataset to validate the efficiency of the proposed model in terms of activity recognition and computation time. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Automation

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Optic-GAN: a generalized data augmentation model to enhance the diabetic retinopathy detection

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2251-2269页

作者： Kapoor, Preeti Arora, Shaveta Department of Computer Science and Engineering The NorthCap University Gurugram India

Diabetic retinopathy is a severe eye condition that can lead to vision loss at severe stages necessitating the early detection. Automating the detection reduces the labor and facilitates timely intervention. Deep learning models have advanced detection capabilities but often suffer from overfitting due to limited or imbalanced labeled fundus data. Most of the existing models still depend on conventional augmentation techniques which fail to provide a controlled realistic fundus image generation. To address these limitations, this paper presents Optic-GAN, a strategic, memory- and time-efficient generative model for dataset expansion. Optic-GAN balances the MESSIDOR I and II datasets, achieving a low Fréchet inception distance score and improving model accuracy from 89.56 to 96.06%. Optic-GAN stands out by being independent of the dataset structures, unlike existing models, enabling broader, more effective application across diverse contexts. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Augmentation Cycle GAN Diabetic retinopathy detection Fréchet inception distance Fundus images Generative A.I InceptionV3 Medical images Optic-GAN ResNet50V2 XceptionNet

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Clinical deep model to analyse medical multivariate time-series data for health diagnosis

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Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

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Comprehensive analysis of various imputation and forecasting models for predicting PM2.5 pollutant in Delhi

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Neural Computing and Applications 2025年第17期37卷 11441-11458页

作者： Karnati, Hemanth Soma, Anuraag Alam, Adnan Kalaavathi, B. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

Understanding and predicting air quality is pivotal for public health and environmental management, especially in urban areas like Delhi. This study utilizes a comprehensive dataset from the Central Pollution Control Board, detailing air pollutant concentrations at the ITO (Income Tax Office), Delhi station, from 2017 to 2023. Our focus on pollutants such as PM2.5, PM10, NO2, NH3, SO2, ozone, and CO highlights the challenges posed by missing data in environmental studies. Through the application of several imputation models, including KNN, linear regression, forward fill + backward fill, Fourier + KNN, linear interpolation, and statistical methods, we identified combination of forward fill and backward fill as the most effective method for addressing data gaps, specifically for ozone measurements. Building on this imputed dataset, we employ various forecasting models to predict PM2.5 levels, a critical pollutant. Our exploration includes time series and deep learning approaches, such as LSTM, Bi-LSTM, CNN-LSTM, GRU, Deep AR, WaveNet, TCN, ARIMA, SARIMA, and Prophet. The performance of these models is evaluated using daily data, with Bi-LSTM and LSTM with attention emerging as the top performers due to their accuracy in predicting PM2.5 concentrations. The implications of our findings extend beyond academic interest, offering practical insights for environmental policy and health advisories in Delhi. By demonstrating the efficacy of specific imputation and forecasting techniques, our research contributes to the broader effort of improving air quality monitoring and prediction. The success of Bi-LSTM and LSTM with attention models, in particular, suggests a promising avenue for future studies aiming to enhance the precision of environmental forecasts. © The Author(s) 2025.

关键词： Time series

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Enhanced Diabetes Detection with Deep Learning: An Iris Image Analysis Approach 5

Enhanced Diabetes Detection with Deep Learning: An Iris Imag...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajarajeswari, T.S. Snehanjali, Beeram Rizwana, Rapthadu Kasi Vardhan, Bethi Phanindra Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331530013

Diabetes is a chronic and progressive condition that, if not diagnosed early, can lead to serious health complications, often we will see that patients learn they have diabetes only years after its emergence this points out the significance of precise and early identification. Thus, diabetes is a global health issue that requires timely detection. Traditional methods for diagnosing diabetes often involve expensive procedures, which can lead to delays in diagnosis. Using iris images for diabetes detection through deep learning algorithms offers a more accurate and cost-effective alternative. In general, a person with diabetes may experience changes in the iris, such as alterations in pattern, texture, or pigmentation. To identify these altered features, high-resolution observation is required. For this purpose, we use advanced imaging techniques and deep learning algorithms to analyze and interpret the changes in iris characteristics. CNN's, known for their powerful feature extraction capabilities, effectively captured and analyzed complex details in iris patterns. This approach utilizes several types of algorithms available in deep learning like Convolutional Neural Networks (CNNs), MobileNetV2, InceptionV3, ResNet50 and CNNs along with transformers. All these algorithms are widely known for their efficiency in image analyzing applications. Thus, we have chosen to implement the same procedural approach but for different algorithms. Finally, we had found an efficient algorithm in such a way that it gives the highest accuracy for the same dataset as compared with the other algorithms. It proved that CNNs are effective in identifying features and patterns in images and it gave the accuracy of about 93.91 % showing its effectiveness. Thus, ultimate aim of this approach is to provide an accurate, non-invasive and cost-effective way of diabetes disease diagnosis. © 2024 IEEE.

关键词： Convolutional Neural Networks Disease diagnosis InceptionV3 Iris MobileNetV2 ResNet50 Transformer Networks

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