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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Kanchan, Pratik Singh, Avishkar Kumar Guruprasad, N. Global Academy of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350302820

The use of Internet of Things (IoT) technology in renewable energy is examined in this study. The efficiency and productivity of renewable power plants must be increased to meet the rising demand for renewable energy. By enabling real-time monitoring of numerous parameters like dust collection, temperature, weather, and so forth, IoT offers a solution to this issue. The production of energy can be improved using these statistics. The paper also addresses the many IoT sensors that can be used to construct such a system and the difficulties that must be overcome for its adoption. The study's findings show how the Internet of Things (IoT) has the power to revolutionise renewable energy sources and open the door to greater effectiveness, financial success, and environmental sustainability. © 2023 IEEE.

关键词： Renewable energy resources

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A Novel Approximation Algorithm for Max-Covering Circle Problem 16th

A Novel Approximation Algorithm for Max-Covering Circle Pro...

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Zhang, Kaiqi Zhang, Siyuan Gao, Jirun Wang, Hongzhi Gao, Hong Li, Jianzhong School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Computer Science and Technology Zhejiang Normal University Jinhua China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783031496103

We study the efficient approximation algorithm for max-covering circle problem. Given a set of weighted points in the plane and a circle with specified size, max-covering circle problem is to find the proper place where the center of the circle is located so that the total weight of the points covered by the circle is maximized. Our core approach is to approximate the circle with a symmetrical rectilinear polygon (SRP). We first present a method to construct the circumscribed SRP of a given circle and disclose their area relationship. Then, we convert max-covering SRP problem to SRP intersection problem, which can be efficiently solved with simple partition and modification based on the existing method. Finally, the optimal solution returned from max-covering SRP problem can be used to produce an approximate answer to max-covering circle problem. We prove that for most of the inputs, our algorithm can give a (1 - Ε) approximation to the optimal solution, which only needs O(nΕ-1logn+nΕ-1log(1Ε)) time for unit points and o(nΕ-2logn) time for weighted points. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP 1

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meena, S. Murthy, Monisha Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting;with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances. © 2024 IEEE.

关键词： Adaptive boosting

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Characteristics of secondary electron emission from few layer graphene on silicon(111) surface

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Chinese Physics B 2022年第10期31卷 549-562页

作者： Guo-Bao Feng Yun Li Xiao-Jun Li Gui-Bai Xie Lu Liu National Key Laboratory of Science and Technology on Space Microwave China Academy of Space TechnologyXi’an 710100China School of Computer Science and Engineering Xi’an University of TechnologyXi’an 710048China

As a typical two-dimensional(2D) coating material, graphene has been utilized to effectively reduce secondary electron emission from the surface. Nevertheless, the microscopic mechanism and the dominant factor of secondary electron emission suppression remain controversial. Since traditional models rely on the data of experimental bulk properties which are scarcely appropriate to the 2D coating situation, this paper presents the first-principles-based numerical calculations of the electron interaction and emission process for monolayer and multilayer graphene on silicon(111) substrate. By using the anisotropic energy loss for the coating graphene, the electron transport process can be described more realistically. The real physical electron interactions, including the elastic scattering of electron-nucleus, inelastic scattering of the electron-extranuclear electron, and electron-phonon effect, are considered and calculated by using the Monte Carlo method. The energy level transition theory-based first-principles method and the full Penn algorithm are used to calculate the energy loss function during the inelastic scattering. Variations of the energy loss function and interface electron density differences for 1 to 4 layer graphene coating Go Si are calculated, and their inner electron distributions and secondary electron emissions are analyzed. Simulation results demonstrate that the dominant factor of the inhibiting of secondary electron yield(SEY) of Go Si is to induce the deeper electrons in the internal scattering process. In contrast, a low surface potential barrier due to the positive deviation of electron density difference at monolayer Go Si interface in turn weakens the suppression of secondary electron emission of the graphene layer. Only when the graphene layer number is 3, does the contribution of surface work function to the secondary electron emission suppression appear to be slightly positive.

关键词： secondary electron emission graphene on silicon numerical simulation

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Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

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computer Modeling in engineering & sciences 2023年第11期137卷 2025-2047页

作者： Huabin Wang Ruichao Mo Yuping Chen Weiwei Lin Minxian Xu Bo Liu School of Computer Science and Engineering Huizhou UniversityHuizhou516007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science and Technology South China Normal UniversityGuangzhou510631China Department of New Network Technologies Peng Cheng LaboratoryShenzhen518066China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen518055China

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile ***,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy *** addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge ***,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model *** results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher ***,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

关键词： Pedestrian and vehicle detection YOLOv4 channel pruning cloud-edge collaboration

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Detection of PCOS Leveraging Machine Learning and Interpretation Using Explainable AI 1

Detection of PCOS Leveraging Machine Learning and Interpreta...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Madhavi, Bindu V. Firdouse, Asfiya Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder impacting women globally. This research aims to early predict and detect PCOS which is needed to reduce long-term complications. Since it is considered to be a hard-to-diagnose disorder, machine learning is utilized for this purpose. This study leveraged the power of computational algorithms trained on patient data for model prediction where Decision tree with criterion Gini index outperformed with 88.07% accuracy, 85.29% precision, 78.37% recall and 81.69% F1 Score. In addition to this, Bagging and boosting algorithms were used to monitor their performance metrics where Gradient Boost stood out with a remarkable accuracy of 91.74%, 91.00% precision, 97.00% recall and 94.00% F1 Score. By optimizing the chosen parameters through Hyperparameter tuning, a notable increase in the model's accuracy was observed. Besides, three ensemble models were proposed out of which the ensemble classifier ensemble model involving bagging, boosting and single classifiers brought a significant difference of 95.41% accuracy. Additionally, Explainable (XAI) methodologies like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were used for model interpretability. © 2024 IEEE.

关键词： Adversarial machine learning

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Symptoms-based Classification of Disease Using Various ML Algorithms and Interpretation using LIME and SHAP KERNELS 4

Symptoms-based Classification of Disease Using Various ML Al...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Soujanya, K.J. Kodipalli, Ashwini Gosai, Sanjeev Sneha, B.J. Rao, Trupthi Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354218

Using a variety of machine learning techniques, this research study suggests a unique method for classifying diseases using symptom-based analysis. To improve model transparency and comprehension, the study makes use of the interpretability tools, SHAP kernels and LIME. The research attempts to close the gap between interpretability and prediction accuracy in medical diagnosis by combining two approaches. A thorough assessment of symptom-based categorization is ensured by the use of many machine learning methods. The application of LIME and SHAP kernels aids in the clarification of model predictions and provides information on the primary symptoms that impact illness categorization. This study advances the creation of accurate and comprehensible illness categorization models, promoting confidence in the use of machine learning methods in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Detection of Breast Cancer Through a Novel Framework: A Machine Learning Approach with Explainable AI Interpretation 1

Enhancing Detection of Breast Cancer Through a Novel Framewo...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Vidyashree, R. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798331518943

Breast cancer, the most prevalent cancer among women, significantly contributes to increased mortality rates and ranks as the second leading cause of cancer-related deaths in women. Early detection is crucial for effective treatment, yet traditional diagnostic approaches often involve multiple laboratory tests supervised by medical experts. To mitigate human error and expedite breast cancer detection, there is a pressing need for automated systems capable of accurate and timely diagnosis. This study aims to investigate the application of machine learning (ML) techniques in classifying breast cancer using features from digitized images of fine-needle aspiration (FNA) breast mass samples. Machine learning methods are instrumental in addressing this imperative. The study conducts a comparative analysis of various ML classification methods, including Logistic Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Trees, Bagging-Boosting, and ensemble models, using a breast cancer dataset to establish a novel framework. The performance of each algorithm is assessed based on metrics such as accuracy, precision, recall, and F1 score. The findings underscore the effectiveness of ensemble classifier models, achieving a notable accuracy rate of 98.87%. Additionally, the integration of explainable Artificial Intelligence enhances the interpretability of the models by providing valuable insights into their decision-making processes. © 2024 IEEE.

关键词： Contrastive Learning

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Masked Siamese Prompt Tuning for Few-Shot Natural Language Understanding

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 624-633页

作者： Ni, Shiwen Kao, Hung-Yu Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518000 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan

Recently, prompt-based learning has shown excellent performance on few-shot scenarios. Using frozen language models to tune trainable continuous prompt embeddings has become a popular and powerful methodology. For few-shot natural language understanding, even if we freeze the parameters of the pretrained language model, the learned pseudo-prompts might still be overfitted. In this article, we propose a novel masked Siamese prompt tuning (MSP-tuning) to improve few-shot natural language understanding. Concretely, MSP-tuning masks randomly out part of the prompt tokens to get a pair of masked Siamese prompts for each sample. Each training sample is then fed to the model twice with the masked Siamese prompts. Finally, MSP-tuning minimizes the Jensen-Shannon-divergence (JSD) between the two output probability distributions of the pretrained language model to regularize the model further. Experiment results on the few-shot GLUE benchmark and SuperGLUE benchmark show that MSP-tuning outperforms previous approaches. Numerically, our MSP-tuning achieves an average improvement of 1.79% (BERT-base) and 1.39% (BERT-large) of the GLUE benchmark and 1.90% (RoBERTa-base) and 1.71% (RoBERTa-large) of the SuperGLUE benchmark compared to state-of-the-art method P-tuning. Our method facilitates applying large pretrained language models in natural language understanding. © 2023 IEEE.

关键词： Bit error rate

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