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Localization in WSNs Based on machine learning Approach 2nd

Localization in WSNs Based on Machine Learning Approach

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2nd international conference on Innovations of Intelligent Informatics, Networking and Cybersecurity, 3INC 2024

作者： Mahdi, Muhammed A. Yousif, Ali Y. Salman, Mahdi Abed University of Babylon Babil Iraq

ISBN: (纸本)9783031810640

Several researchers have paid attention on locating coordinates of sensors due to its importance for many applications in Wireless Sensor Networks (WSNs). Usually WSNs consist of a number of small, limited energy and low processing capabilities sensors. These sensors can communicate with each other to carry out a specific task according to the application they have been designed for. Using GPS embedded with each sensor may be expensive in terms of cost and consumed energy. So the attention is focused toward designing localization algorithms without using more GPSs. In this paper, machine-learning approach has been proposed to estimate the position of each unknown node in WSNs. It is based on signal strength and positions of three anchors (sensors know their positions). K-Nearest Neighbours KNN and Neural Network (perceptron back propagation) are used in the proposed model to estimate the coordinates of unknown sensors based on data collected from simulation. The main idea of this paper is motivated by a hypothesis which assumes that the position of sensor has a strong correlation with the signal intensity of other nearby sensors. The results emphasise a good accuracy obtained using the model comparing with some related works. Indeed, the average of difference in distances between the actual locations and the estimated locations of the sensor nodes is about 0.84 m. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Global positioning system

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Optimizing Cloud IoT Applications with machine learning Driven Hybrid Key Management Protocols for WSN Performance

Optimizing Cloud IoT Applications with Machine Learning Driv...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Simhadri Mallikarjuna Rao B. Adinarayana Kasani Vaddikasulu Satya Prasad Dontamsetti Peesapati Surya Venkata Ranganadh Research Scholar Department of Computer Science and Engineering Aacharya Nagarjuna University Guntur Andhra Pradesh India R &D Department of EEE & CSE Ravi Technology Vishakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Eluru college of engineering and technology Eluru Andhra Pradesh India Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Bhimavaram Andhra Pradesh India Software Engineer USA

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

AI-IoT integration is significant, changing the future of networking, particularly in terms of performance and security enhancements. The collection and transmission of sensitive data across secure networks is crucial for the IoT, which combines embedded systems and Networks of Wireless Sensors (WSNs). Ensuring the efficacy and security of these networks is crucial. In this research, a novel hybrid key Hybrid management protocol for Internet of Things applications using WSNs is proposed. The maximizes network performance of efficiency and security by utilizing cloud services and machine learning, specifically XGBoost. The suggested protocol creates a secure communication framework for WSNs by combining symmetric and asymmetric key management strategies. Furthermore, the centralized functions and scalability of cloud-based services enable network-wide updates and effective key distribution. The protocol offers anomaly detection, intelligent network load balancing, dynamic key management, predictive key distribution, and intelligent network load balancing by employing XGBoost-based machine learning algorithms. By using network behavior modelling and historical data analysis, the system forecasts the optimal times and locations for important updates, reducing overhead and enhancing security. Additionally, anomaly detection based on XGBoost improves the protocol's ability to recognize anomalies and potential security risks. This hybrid architecture creates a highly flexible and effective protocol for IoT-enabled WSNs by combining cloud integration, centralized key management, and AI-driven intelligence. The proposed approach performs better on the network due to proven methods and algorithms.

关键词： Support vector machines Wireless sensor networks Cloud computing Protocols Accuracy machine learning algorithms Scalability Internet of Things Security Anomaly detection

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Prediction Model of Automatic Sleep Staging Based on Ensemble learning 21

Prediction Model of Automatic Sleep Staging Based on Ensembl...

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2nd international Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021

作者： Liu, Kai Zhang, Hongjun Xia, Chenxi Command and Control Engineering College The Army Engineering University of PLA Nanjing China Unit 31680 of PLA Chendu China

ISBN: (纸本)9781450395588

A kind of sleep staging prediction model based on signal data preprocessing and ensemble learning method is constructed to solve the low accuracy rate of traditional automatic sleep staging. First, the preprocessing of 3,000 samples aims to eliminate the non-characteristic signal data and cement the distribution difference of data at different sleep stages;Then the soft-voting classifier prediction model is constructed by applying the ensemble learning thought that integrating the Adaboost and KNN model. The average classification accuracy rate of the method reaches 87.6% after the comparison and analysis of experimental results. Compared with other machine learning models and signal processing methods, accuracy rate of classification prediction improves greatly. © 2021 ACM.

关键词： Adaptive boosting

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2nd international conference on Sensor Networks and signal processing, SNSP 2019

2nd International Conference on Sensor Networks and Signal P...

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2nd international conference on Sensor Networks and signal processing, SNSP 2019

ISBN: (纸本)9789811549168

The proceedings contain 42 papers. The special focus in this conference is on Sensor Networks and signal processing. The topics include: Graphic qr code with the second hidden qr code by codeword rearrangement;a novel position-shift method of double-phase fresnel hologram for synthesizing a complex fresnel hologram;an examination of obstacle avoidance by sound for visually impaired children;efficient technique of impulsive noise detection and replacement in color digital images;development of walking support system for visually impaired people;research on video mosaic technology based on fully mechanized face of coal mine;optimization scheme for traceability of distributed denial of service attacks based on dynamic probability packet marking;processing initial data for the agent-based model of the russian federation spatial development;design and implementation of total station wireless data transmission system;improvement of rpl routing strategy based on 6lowpan;the comprehensive evaluation of "five aspects" based on coefficient-of-variation-modified g1 combination weighting;an inertia weight variable particle swarm optimization algorithm with mutation;performance of probabilistic approach and artificial neural network on questionnaire data concerning taiwanese ecotourism;ac operation hardware learning neural circuit using v-f converter system;an inequality for linear canonical transform;a general approach to probabilistic data mining;a novel four-dimensional chaotic system with four cross terms;influence of the optical aspects of photographic composition on the user experience in the issues related to decision making, choices and level of visual comfort;compositional models: iterative structure learning from data;computer vision-based demersal fish length measurement technology.

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Stock price prediction based on ARIMA-PCA-BP hybrid model

Stock price prediction based on ARIMA-PCA-BP hybrid model

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Zilin Li Jianbin Zhao Internal College of Zhengzhou University Zhengzhou University Zhengzhou China Henan Academy of Big Data Zhengzhou University Zhengzhou China

Stock price prediction has important practical significance and has always been a hot topic. Stock prices are affected by a variety of different factors and are highly dimensional and nonlinear, which makes forecasting difficult. Common stock price prediction models include statistical prediction models and machine learning models. However, the statistical prediction models lack the consideration of nonlinear information. Moreover, the information redundancy of stock price factors will reduce the learning rate of neural network. These defects can reduce the accuracy of prediction. In order to improve the prediction accuracy of stock price, this paper proposes a hybrid model based on autoregressive integrated moving average (ARIMA) and back propagation (BP) neural network, and the BP neural network is optimized by principal component analysis (PCA) algorithm. The empirical analysis of 5 stocks in different industries is carried out. The results show that compared with the single ARIMA model or BP neural network model, the ARIMA-PCA-BP hybrid model improves the prediction accuracy of stock prices and has good learning ability and generalization ability.

关键词： Industries Heuristic algorithms Neural networks Redundancy signal processing algorithms machine learning Predictive models

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Research on RGB-D image recognition technology based on feature fusion and machine learning 2

Research on RGB-D image recognition technology based on feat...

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2021 2nd international conference on signal processing and Computer Science, SPCS 2021

作者： Sun, Qingbo Shandong University of Political Science and Law 250014 China

The three-dimensional RGB-D image contains not only the color and texture information of the two-dimensional image, but also contains the surface geometry information of the target. This article analyzes the RGB-D image recognition methods, including stereo vision technology, structured light technology, etc. By studying the application points of RGB-D image recognition technology under the background of feature fusion and machine learning, the purpose is to improve the richness of image recognition content and provide reference for the smooth development of the follow-up work. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Image recognition

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A Comparative Study on Heart Disease Prediction Using Data Mining Techniques and Feature Selection 2

A Comparative Study on Heart Disease Prediction Using Data M...

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2nd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2021

作者： Tasnim, Farzana Habiba, Sultana Umme International Islamic University Chittagong Kumira Dept. of Cse Chittagong4318 Bangladesh Khulna University of Engineering Technology Dept. of Cse Khulna9203 Bangladesh

ISBN: (纸本)9781665415767

The world health organization shows us that cardiovascular disease is one of the noteworthy reasons for death in the world. In this paper, data mining classification techniques i.e. Naive Bayes (NB), Support Vector machine (SVM), k-nearest neighbors' (k-NN), Decision Tree (DT), Neural Network (NN), Logistic Regression (LR), Random Forest (RF), Gradient Boosting are proposed to predict the probability of the coronary heart disease. In the present world, researchers are trying heart and soul to make advancements in the smart health care system. An automated system predicting the risk of heart disease may be added as a great achievement. This work of predicting heart disease is evaluated using the dataset from the UCI machine learning repository. The feature selection method enhances the performance of traditional machine learning algorithms. Among the classification algorithms, Random Forest (RF) algorithm with PCA has given the best accuracy of 92.85% for heart disease classification. © 2021 IEEE.

关键词： Feature Selection

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Federated learning with Sample-Level Class Balancing Aggregation 23

Federated Learning with Sample-Level Class Balancing Aggrega...

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proceedings of the 2nd international conference on signal processing, Computer Networks and Communications

作者： Tong Liu Wei Yu Jiasheng Chen Lun Xin Fei Yu Yinfei Xu Southeast University China China Mobile Research Institute China Nanchang University China

ISBN: (纸本)9798400716430

Performance degradation under heterogeneous data distribution is one of the biggest challenges of federated learning. Among the optimization methods for federated learning with heterogeneous data, client selection strategies based on client grouping have received much attention due to their effectiveness and generality. However, current strategies are crude and only achieve client-level class balancing. In this paper, we propose FedCBA, which enables sample-level class balancing. First, we propose a privacy-preserving sample grouping approach, which consists of feature extraction, locality-sensitive hashing, and unsupervised clustering, to obtain the class distribution of each client. Then in each communication round, based on the clients' class distribution, the good scaling aggregation parameters which can make the class balanced are learnt. Comprehensive experimental results on widely used and publicly accessible datasets show that compared to existing methods, FedCBA can effectively improve the prediction accuracy of federated learning models under heterogeneous data.

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State-of-the-Art Analysis of Stock Market Forecasting: Techniques, Challenges, and Future Scopes

State-of-the-Art Analysis of Stock Market Forecasting: Techn...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Subhra Prakash Panda Bidush Kumar Sahoo Bibudhendu Pati Tanmay Mukherjee Sujit Bebortta Dept. of Computer Science & Engineering GIET University Gunupur Odisha India Dept. of Computer Science Rama Devi Women's University Bhubaneswar India Dept. of Computer Science & Engineering Siksha ‘O’ Anusandhan (Deemed to be) University Bhubaneswar India Dept. of Computer Science Ravenshaw University Cuttack India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Predicting stock market movements accurately is crucial for investors to make well-informed financial choices that might provide substantial gains. However, financial data is inherently unpredictable, ambiguous, and unstructured, predicting stock values is a crucial challenge. Traditional techniques of forecasting are inadequate for investors due to the market's rapid and complex movement. To tackle these issues in stock market, several ML approaches have been investigated. In this work, we comprehensively examine 50 research publications that explore the application of ML-based techniques for stock price forecasting. By examining important factors such datasets, software tools, assessment criteria, forecasting approaches, and performance results, the study provides light on the benefits and drawbacks of existing methods. Through illuminating current research gaps and obstacles, the study provides direction for future advancements. Many obstacles still need to be overcome before ML approaches can fully realize their promise of improving prediction accuracy. Review results highlight the difficulty of stock market forecasting and the need of multifactorial methods for greater efficacy and accuracy.

关键词： Performance evaluation Accuracy Embedded systems Reviews machine learning signal processing Forecasting Stock markets Software tools

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IoT Enabled Early Detection of Lung Cancer Using CT Scan Images and machine learning Techniques

IoT Enabled Early Detection of Lung Cancer Using CT Scan Ima...

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international conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Satya Brata Rath A.V.S Pavan Kumar Vishal Kumar Swain Neelamadhab Padhy Rasmita Panigrahi Tirupati Sahu Department of CSE Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Context: The IoT sensors can play a crucial role in enhancing CT scan lung detection, which will help monitor patient vital signs and improve patient comfort. In this disease the lung grows out of control, in recent years, many systems have been developed for the diagnosis of various diseases, among which the early detection of lung cancer is critical for which early detection is crucial for improving treatment outcomes. Objective: The objective is to develop and evaluate deep learning models for lung cancer detection using CT scans (Computed tomography scans). Method and Materials: We trained and tested different architectures of convolutional neural network (CNN) models, including VGG16, ResNet50, Inception-v3, DenseN et121, and an ensemble model combining DenseNet121 + Inception-v3, using a dataset of CT scans. Dataset Descriptions: We have taken an existing dataset of CT scans from the Kaggle repository which contains the different types of chest cancer. The images are in the form of JPGs to fit in the model. The dataset includes 3 chest cancer types large cell carcinoma, Adenocarcinoma, Squamous cell carcinoma, and normal cell. Results: In this experiment research the ensemble (DenseNet121 + Inception - v3) model achieved the highest accuracy 98.25% training and 99.4% testing.

关键词： Deep learning Computed tomography Lungs Computational modeling Microprocessors Lung cancer Computer architecture Feature extraction Convolutional neural networks Monitoring

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