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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

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Securing Logistic Regression Model from Data Poisoning Attack using AES and RSA Encryption Techniques 2

Securing Logistic Regression Model from Data Poisoning Attac...

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2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Dakhare, Bhawana S. Ragha, Lata L. Navi Mumbai Mumbai University Information Technology Terna College of Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Fr. C. Rodrigues Institute of Technology Vashi Navi Mumbai Mumbai University Computer Engineering Mumbai India

ISBN: (纸本)9798350325799

In today's information-exchange-driven world, data privacy, and confidentiality are of the utmost significance. The purpose of the proposed research work titled "Securing Logistic Regression Model from Data Poisoning Attack Using AES and RSA Encryption Techniques", is to show the use of encryption algorithms to provide privacy to data. As machine learning models have turned out to be more prevalent and are used to process sensitive data, ensuring their security is of utmost importance. Machine learning models are susceptible to a variety of threats, according to research, which jeopardizes the models' security. The proposed framework runs on systems that involve client and server. The server which performs inference over data sent from the client and the machine learning model which has been trained on proprietary data. The machine learning model on the server is protected against data poisoning attacks. The proposed system gives a solution for a Server-Side Request Forgery attack against unauthorized access to the machine model. For the privacy of the predicting breast cancer machine learning model on the server, this system employs the encryption methods such as AES and RSA. © 2023 IEEE.

关键词： Cryptography

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A Homogeneous Meta-learning LSTM-RNN Ensemble Method for Electric Vehicle Battery State of Charge Estimation 9

A Homogeneous Meta-Learning LSTM-RNN Ensemble Method for Ele...

引用

9th international conference on computer and Communication engineering, ICCCE 2023

作者： Wong, Rae Hann Manoharan, Aaruththiran Sooriamoorthy, Denesh Sarif, Nohaidda Binti Taylors University School of Engineering Subang Jaya Malaysia University of Nottingham Malaysia Department of Electrical and Electronic Engineering Semenyih Malaysia

ISBN: (纸本)9798350325218

Several currently popular data-driven estimators such as Long Short-Term Memory-Recurrent Neural Networks (LSTM-RNN) rely on a single strong model which produces accurate predictions at the expense of lengthy training times. However, as lithium-ion batteries (LIBs) age, these models must be retrained with new data to learn the updated battery characteristics, either by transfer learning or by retraining from the ground up. This poses a problem for model retraining on low-power edge computing devices found in electric vehicle (EV) battery management systems (BMS), as model training is typically computationally intense and time consuming. This work proposes an ensemble meta-learning model consisting of several LSTM-RNNs that aims to significantly reduce model training time. To achieve this, multiple weak base models were trained using randomly sampled subsets of the training dataset, and the outputs were used as training inputs for a meta-learner. The proposed meta-learning method outperformed a simple average and an MAE-based weighted average ensemble fusion method - both of which were found to be not more accurate than the best-performing base model of the ensemble. Using similar training and testing datasets, the proposed model achieved a training time 2.6-3.5 times less than equally accurate (1.4% MAE) conventional shallow and deep LSTMs (DLSTM). Future research directions will be targeted at reducing the prediction times of the proposed method for increased practicality. © 2023 IEEE.

关键词： learning systems

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Metastatic Breast Cancer Detection Using Deep learning Algorithms: A Systematic Review

Metastatic Breast Cancer Detection Using Deep Learning Algor...

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2023 international conference on Science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Adedayo-Ajayi, Victoria Oluwaseyi Ogundokun, Roseline Oluwaseun Tunbosun, Aderemi Emmanuel Adebiyi, Marion O. Adebiyi, Ayodele Ariyo Landmark University Department of Computer Science Omu Aran Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Landmark University Sdg 11. Sustainable Cities and Communities Omu Aran Nigeria

ISBN: (纸本)9798350324785

Breast cancer (BC) is a pervasive issue that leads to countless fatalities among women worldwide, and metastatic breast cancer is responsible for most of these deaths. Early detection of metastatic BC is essential for improving patient outcomes and increasing survival rates. There have been a lot of earlier Machine learning (ML)-based investigations. Decision trees (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and other ML procedures achieve better in their corresponding fields. However, current methods for detecting metastatic BC can be time-consuming, invasive, and costly. Recently, deep learning (DL) algorithms have shown great potential in improving the accuracy and efficiency of BC detection. This paper delivers an inclusive systematic review (SR) of the existing research on using DL algorithms for metastatic BC detection. The article highlights the potential of DL algorithms in improving BC detection and the challenges associated with their use. Future research should address these challenges to improve the clinical utility of DL algorithms for metastatic BC detection. © 2023 IEEE.

关键词： learning algorithms

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Use of Machine learning on Satellite and Drone Images for Agriculture Application

Use of Machine Learning on Satellite and Drone Images for Ag...

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2023 IEEE international conference on Electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Tarique, Ausaf Hasan Khalifa, Othman Omran Islam, Md. Rafiqul Kumar, Harish Singh, A.K. University Islam Antarabangsa Malaysia Iium Department of Electrical and Computer Engineering Malaysia King Khalid University Ksa Department of Computer Science Engineering Saudi Arabia Galgotias College of Engineering and Technology Mechanlcal Engineering Department UP Greater Noida India

ISBN: (纸本)9798350305111

The proposed study's primary goal is to evaluate land use change and land cover (LULC) utilizing machine learning techniques and algorithms. This was done using Sentinel-2 satellite images, which is viewable and available through the Copernicus Earth observation system. These Sentinel-2 photos cover four spectral bands and fall into four categories (urban, water, tall vegetation, short vegetation, and barren ground), and they are identified as Google Earth image data. Different land use classifications represent how much simpler the proposed method makes the process of image classification. The findings and discussion show that Random Forest, ANN, SVM, and KNN were the most effective algorithms for attaining our main goal. For each dataset, cross validation and random sampling were used to determine the best models for achieving the best results with less over fitting and finally different band combinations were used for classification of SAR image. © 2023 IEEE.

关键词： Drones

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A novel approach for monitoring agricultural production process using wireless sensor networks and Machine learning

A novel approach for monitoring agricultural production proc...

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2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Rath, Subhashree Senthil Kumar, R. Nishanth, S.B. Arabale, Aditya Charanraj, K.R. New Horizon College of Engineering Bangalore India New Horizon College of Engineering CSE Department Bangalore India

ISBN: (纸本)9798350348217

The majority of nations rely heavily on agriculture. In India, agriculture directly supports more than half of the country's population. The yield of a given crop is influenced by a number of variables, including the climate, wind speed, soil quality, humidity, etc. The growth of a crop is impacted by these components' ongoing variability. The agricultural industry has benefited from technological advancement. In the agriculture industry, wireless sensor networks and crop yield prediction have had a significant impact. This study introduces a revolutionary Precision Farming method and demonstrates how sensor data can be effectively utilised. The Crop Yield Prediction model receives real-time input from the sensor-generated data. This strategy aids us in getting more accurate results. The research suggests a mobile application that, every split second, sends data from wireless sensors used in precision farming as an input to a crop production forecast model. Additionally, these sensed parameters from different users can be used as a training dataset. This method not only makes the most of the sensor data but also predicts crop yield with accuracy and promptness. © 2023 IEEE.

关键词： Machine learning

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QCon: A Q-learning Based Congestion Control Scheme in Named Data Networking 9

QCon: A Q-Learning Based Congestion Control Scheme in Named ...

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9th international conference on computer and Communications, ICCC 2023

作者： Qayyum, Rehan Meng, Qingkai Wang, Zhiyuan Zhang, Shan Luo, Hongbin Beihang University School of Computer Science and Engineering Beijing China

ISBN: (纸本)9798350317251

In Named Data Networking (NDN), congestion control is essential for ensuring efficiency of data delivery. The present TCP/IP based end-to-end congestion control algorithms are inapplicable in NDN due to its multi-source and multipath transmission characteristics. Hop-by-hop congestion control is a distinctive feature in NDN that utilizes intermediate routers to control the congestion. However, the current congestion control schemes utilized in NDN are not up to the mark in controlling the congestion because of the varying network conditions of NDN. To overcome the limitations present in the current congestion control schemes, this paper proposes a novel Q-learning based hop-by-hop congestion control scheme named QCon. The proposed scheme utilizes congestion information (i.e., queue occupancy) from upstream nodes as a new field value in Data packet. The receiving router update the Q-values based on local congestion information and information received from upstream router and selects the path with the best Q-value for forwarding the Interest packets. The simulation results show that the proposed scheme outperform the similar hop-by-hop congestion control schemes in terms of packet loss ratio and throughput when encountered with congestion. © 2023 IEEE.

关键词： Reinforcement learning

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Effect of Dimensionality Reduction on Uncertainty Quantification in Trustworthy Machine learning

Effect of Dimensionality Reduction on Uncertainty Quantifica...

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2023 international conference on Machine learning and Cybernetics, ICMLC 2023

作者： Li, Yen-Chen Zhan, Justin University of Cincinanti Department of Electrical and Computer Engineering CincinnatiOH United States University of Cincinanti Department of Computer Science CincinnatiOH United States

ISBN: (纸本)9798350303780

Machine learning (ML) is a commonly employed computer-assisted tool for ECG diagnosis with above 85 % correct. However, the interpretability of the prediction has become a barrier for the clinical use. To enhance the trustworthiness of ML systems, this paper integrates the trust score framework with the existing ECG classification. At present, the trust score framework is effective for low-dimensional data, but it does not offer significant enhancement over the model confidence for feature spaces with high dimensions. Moreover, the ECG data is a time series data with duration about one or half minute with sampling time about 3 ms. In other words, each signal usually contains several thousand or more sample points. As a result, this paper investigated effect of six popular dimensionality reduction (DR) techniques, including linear and kernel-based principal component analysis (KPCA), independent component analysis (ICA), Autoencoder, uniform manifold Approximation and projection (UMAP), and local linear embedded (LLE). The experimental results showed that UMAP with 2 dimensions can achieve result as well as ICA with 25 dimensions in tracking the model tendency and the other four DR methods has better performance with 25 dimensions. © 2023 IEEE.

关键词： Independent component analysis

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An Explainable Machine learning Framework for Multiple Medical Datasets Classification

An Explainable Machine Learning Framework for Multiple Medic...

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2023 international conference on Next-Generation Computing, IoT and Machine learning, NCIM 2023

作者： Mitu, Mostarina Hasan, S. M. Mahedy Efat, Anwar Hossain Taraque, Md Fakrul Jannat, Nahrin Oishe, Mahjabin Rajshahi University of Engineering & Technology Department of Computer Science and Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350316001

Machine learning (ML) has emerged as a ground-breaking approach for disease prognostication, garnering considerable attention from researchers in recent times. Although separate disease-specific models have been developed, but it is very challenging to analyze the predictions of black-box ML classifiers to identify the most prominent risk factors and build a generalized model that can effectively detect commonly occurring diseases (i.e., Diabetes, Breast Cancer, and Heart Disease) with a high recognition rate. In this paper, we use a ML based approach for detecting these diseases and the Shapley Additive Explanations Framework (SHAP) for analyzing the black-box ML classifiers. We use six traditional ML classifiers and develop a weighted averaging (WA) ensemble model for this purpose. An empirical evaluation based on three publicly available datasets demonstrates that our proposed approach detects these three diseases with high accuracy, precision, and recall. The results obtained from this study can be valuable in creating more effective methods for diagnosing patients at an early stage and preventing the progression of diseases, thereby improving treatment outcomes and patient well-being. © 2023 IEEE.

关键词： Classification (of information)

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An Efficient Approach of Pneumonia Detection using Transfer learning Models, RCNN and FAST RCNN

An Efficient Approach of Pneumonia Detection using Transfer ...

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2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Santhoshi, M. Jyostna, J. Cvr College of Engineering Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350344387

computer vision can be used in health care for identifying diseases. The bacterial, fungal, or viral inflammation and fluid in one or both lungs are called pneumonia. Therefore, building a model for pneumonia detection is required through chest X-ray such that lung opacities on chest radiographs should be automatically detected by the algorithm. Pneumonia is diagnosed with the help of chest X-rays by qualified radiotherapists. Rarely, pneumonia can be mistaken with other diseases due to similarity of the symptoms. Therefore, designing a deep learning algorithm to detect pneumonia is accomplished by predicting the bounding box co-ordinates on lung opacities to direct the clinicians. The basic CNN model is designed for achieving this and can be tuned with different hyper parameters to achieve the best AUC score. Implementing the model and improving the performance through hyper-parameter tuning was done with transfer learning techniques such as RESNET, DENSENET, InceptionV3, U-Net and Mask-RCNN, Faster RCNN. Thus, an automated pneumonia detection mechanism will help in augmenting the efficacy of clinical decision making while reducing human interventions at the same time. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Deep learning MASK-RCNN and FAST RCNN pneumonia

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Machine learning based Extractive Text Summarization Techniques

Machine Learning based Extractive Text Summarization Techniq...

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2023 international conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Sundar, G. Ram Nageswari, C. Shobana Antony Vijay, J. Devi, S. Renuga Basker, N. Pushpa, N. Sri Ramakrishna Engineering College Department of It Coimbatore India R. M. D. Engineering College Department of Ece Kavaraipettai India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Sona College of Technology Department of Cse Salem India Rp Sarathy Institute of Technology Department of Ai&ds Salem India

ISBN: (纸本)9798350315455

Automatic Text Summarization (ATS) is a developing area of Natural Language Processing (NLP). It is concerned with simplifying a summary from a given text while retaining key information. Because of the exponential increase of content on the internet, this is a current area that is used in summarizing a text available in books, newsletters, internal document analysis, patent research, e-learning, and so on. Manually summarizing large amounts of text is difficult for humans. As a result, the demand for more complicated and powerful summarizers has skyrocketed. To match the performance of human-generated summaries, various machine learning algorithms are applied. Extractive text summarization finds and connects fundamental segments of a record to make a more limited report that sums up the first archive's items. A machine learning approach for Extractive text summarization has been explored in this paper. © 2023 IEEE.

关键词： Extractive Text Summarization feature extraction Natural Language Processing preprocessing summary

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