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intelligent Lighting Control: A Neuro-Fuzzy Approach for Optimal Energy Efficiency in Smart Homes

Intelligent Lighting Control: A Neuro-Fuzzy Approach for Opt...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Saadaoui Fatma Elhsou Miaicha Higher Institute of Computer Science of Medenine Gabes University National Engineering school of GabesMACS LR16ES22 Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) specifically designed for an in-telligent control system for domestic lighting in a smart home environment. The aim is to create an efficient model that integrates both fuzzy logic and a neural network to improve the adaptability of the control system. To obtain this type of control, two key inputs are used: the indoor lighting level and the presence of a person. These factors are essential to determine the behaviour of the lighting system in different situations. The ANFIS model is trained using two different learning methods: backpropagation and a hybrid technique. To improve accuracy, eight different membership functions (MFs) are used in three sets of epochs: 20, 50, and 100. The training results give us the best choice of ANFIS model parameters that guarantee the desired performance and minimize errors. The results of the study show that the application of the present approach to lighting systems demonstrates the effectiveness of autonomous control.

关键词： Fuzzy logic Backpropagation Adaptation models Visualization Neural networks Lighting Smart homes Light emitting diodes Control systems Lighting control

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AgriAI: Revolutionizing Agriculture with data Science

AgriAI: Revolutionizing Agriculture with Data Science

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Networking and Communications (ICNWC), international conference on

作者： Nitin B Raut Ashwin U Aswin S Bala Selva Vishnu N Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In this ambitious agricultural project, cutting-edge technologies such as Machine learning (ML) and Deep learning (DL) are harnessed to revolutionize farming practices in India. The primary focus lies in automating vital tasks like crop disease detection, crop recommendation, and fertilizer suggestion, which are traditionally laborious and time-consuming for farmers. Leveraging recent advancements in ML algorithms, a comprehensive solution is envisaged. This innovative approach involves the integration of sophisticated models into a user-friendly website, creating a seamless user experience. Through this platform, farmers gain access to automated tools that significantly enhance their crop management strategies. The platform provides a comprehensive solution by combining deep learning-based crop disease detection mechanisms, intelligent crop recommendation systems, and effective fertilizer recommendations derived from machine learning algorithms. This combination of technologies helps farmers maximize their overall crop yields and minimize losses caused by diseases in addition to streamlining farming procedures. The synergy of ML, and DL on a unified web interface stands as a testament to the transformative power of technology in agriculture, marking a pivotal step toward sustainable and efficient farming practices in India.

关键词： Deep learning Machine learning algorithms Agriculture Vectors User experience Task analysis Recommender systems

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Personalized Adaptive Cruise Control Based on Passenger's Subjective Risk Evaluation and Model Predictive Control

Personalized Adaptive Cruise Control Based on Passenger's Su...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Chenshuo Zhang Yongjun Yan Jinxiang Wu Tingyu Zhang Hongliang Wang Dawei Pi School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In this study, a personalized adaptive cruise control system founded on driving risk field (DRF) is proposed, which aims to make the control system more consistent with the driver's driving habits and reduce discomfort. Firstly, founded on the collected driver's operation data, the common characteristics of different drivers' operation behavior under different conditions are analyzed, and the spatial-temporal coupling DRF model under the event of car-following is constructed. Secondly, the data processing and clustering analysis of the High D natural driving data set are carried out to obtain the individual driving preferences of aggressive, normal and conservative drivers, and the DRF is calibrated based on the clustering results. Finally, based on the calibrated DRF, an intelligent vehicle adaptive cruise controller with minimum driving risk is designed, and compared with the traditional optimal velocity model (OVM), the advantages of the constructed DRF in personalized cruise following are verified.

关键词： Couplings Adaptation models Analytical models intelligent vehicles Predictive models data processing data models Informatics Cruise control Predictive control

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Optimal Strategy Selection for Cyber Deception via Deep Reinforcement learning 19

Optimal Strategy Selection for Cyber Deception via Deep Rein...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhang, Yuantian Liu, Feng Chen, Huashan State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350346558

Cyber deception has emerged as an effective approach to enhance the security of computer systems. One important factor that affects the security effectiveness of cyber deception is the deployment strategy. In this paper, we develop a honeypot-based defense mechanism to deploy honeypot intelligently. We propose a Stackelberg game model to simulate the interaction between network attackers and defenders. We use attack graph to generate the attacker's strategy and find the optimal honeypot deployment strategy in a constant confrontation. We develop a reinforcement learning (RL) based approach to train the attack strategy and the honeypot deployment strategy. To cope with the large strategy space problem, we modify the neural network structure for the attacker and the defender according to the deception game rules. We conduct experiments with real network data and various game settings to evaluate the performance of our approach. Experiment results show that our RL-based approach outperforms baselines, and the approach is robust to the uncertain security environment. © 2022 IEEE.

关键词： Reinforcement learning

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learning to Pay Attention by Paying Attention: Attention U-Net with Extra Supervision for MRI-based Pseudo-CT Synthesis 23rd

Learning to Pay Attention by Paying Attention: Attention U...

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22nd Scandinavian conference on Image Analysis, SCIA 2023

作者： Dovletov, Gurbandurdy Karadeniz, Utku Lörcks, Stefan Pauli, Josef Gratz, Marcel Quick, Harald H. Intelligent Systems Group Faculty of Engineering University of Duisburg-Essen Duisburg Germany High-Field and Hybrid MR Imaging University Hospital Essen University of Duisburg-Essen Essen Germany Erwin L. Hahn Institute for MR Imaging University of Duisburg-Essen Essen Germany

ISBN: (纸本)9783031314377

Deep learning approaches can learn the mapping function from the MRI to the CT domain and thus can be used to synthesize pseudo-CTs. However, such approaches often introduce artifacts and especially fail to produce bone structures accurately because of the missing signal from bone regions in the source domain. To handle this limitation, we propose a novel approach inspired by the Attention U-Net that includes an additional bone segmentation-guided Extra Supervision (ES) mechanism for superior synthesis of bone structures in pseudo-CTs. The proposed Attention U-Net ES exploits the self-focusing ability of attention gates and puts additional (extra) constraints on the learned attention maps. Thus, it learns to focus better on task-specific features by paying particular attention to the bone regions. The performance of the proposed method is evaluated on the publicly available RIRE data set, which, however, required a prior image registration procedure. The achieved quantitative and qualitative results are compared to the baseline U-Net and Attention U-Net architectures and demonstrate significant improvements regarding the synthesis quality, especially for bone regions. Additionally, we compare our results to U-Net-based networks that are extended by state-of-the-art attention mechanisms, such as Squeeze-and-Excitation (SE), Bottleneck Attention Modules (BAMs), and Convolutional Block Attention Modules (CBAMs). MRI-based pseudo-CT synthesis plays an essential role in MRI-only radiotherapy planning. Accurate pseudo-CTs can deliver precise calculations of the required therapy doses and simultaneously prevent patients from harmful and unnecessary radiation exposure. Additionally, they are crucial for attenuation correction of Positron Emission Tomography images in PET/MRI systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Recognition of Brain Tumors Using Deep learning with data Balancing Techniques and Its Explainability with AI

Recognition of Brain Tumors Using Deep Learning with Data Ba...

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international conference on Computing and Networking Technology (ICCNT)

作者： Abdullah Al Noman Abu Shamim Mohammad Arif Computer Science and Engineering Discipline Khulna University Khulna Bangladesh

Brain tumors are the 22nd most frequent disease globally, accounting for 1.8% of all new conditions. It is such a type of disease that requires early treatment and diagnosis. As a result, an automated tumor recognition model is necessary to assist radiologists in detecting brain tumors. Automatically recognizing these tumors necessitates a large dataset that includes all variants of tumors. However, medical photos are always rare for various reasons, including a shortage of patients for specific conditions, a lack of medical equipment, patients who do not want their photographs to be used, and difficulty in getting images that fit the needed requirements. Therefore, this paper proposes a brain tumor recognition system that is based on deep learning with two renowned data balancing techniques and recapitulates the performance of the model with explainable AI. Because of the shortage of specific brain tumor patients, the dataset utilized in this paper was imbalanced. While SMOTE was used to balance the dataset, and the ResNet50 architecture was utilized for training to 15-class recognition, then an accuracy of 98.97% was achieved. The experimental results indicate the feasibility of the presented strategy for brain tumor multiclass recognition.

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Beyond Sensor data Analysis: Unexpected Challenges in a Honeybee Monitoring Project 22

Beyond Sensor Data Analysis: Unexpected Challenges in a Hone...

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22nd conference Information Technologies - Applications and Theory, ITAT 2022

作者： Várkonyi, Dániel Tamás Alexy, Márta Horváth, Tomáš ELTE - Eötvös Loránd University in Budapest Faculty of Informatics Department of Data Science and Engineering Budapest Hungary

With shortage in human workforce and increasing ecological challenges application of Internet of Things and Artificial Intelligence technologies in the agricultural sector, known also as precision farming, has become very popular in recent years. Agriculture is, however, a domain with specific challenges which AI engineers and data scientists usually do not face in other domains, such that healthcare, industry or services, just to name a few. In this paper we collect and summarize the challenges and problems which arose in our honeybee monitoring project from which the most important are trust, seasonality, data labeling issues and sudden events. We believe that, despite not focusing on Machine learning models nor providing technical details, this paper provides an interesting reading for data scientists and will serve as an aid for planning and executing data science projects in the agricultural domain. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： data Analytics

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Deep learning for sleep disorder diagnosis: Enhancing EEG Analysis with Machine learning Techniques

Deep learning for sleep disorder diagnosis: Enhancing EEG An...

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learning and Technology conference (L&T)

作者： Baher Mohamed Enfel Barkat Dept. of Electrical & Computer Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The rising prevalence of sleep disorders highlights their disastrous effects on health, well-being, cognitive performance, memory, and learning outcomes. Sleep disorders such as insomnia and sleep apnea normally require extensive instrumentation and expertise for diagnosis, which is not always possible or even available in most cases. This work, therefore, proposes a state-of-the-art use of Machine learning (ML) to develop a model for the classification and detection of sleep patterns from electroencephalogram (EEG) signals. The model is trained on a synthetic dataset, developed using mathematical simulations of real-life EEG data. This is a novelty in this area, which allows this model to be precise yet robust without relying too much on real-world *** application bridges advanced AI techniques with real-world applications and smooths out the experience for healthcare professionals and researchers alike. This research highlights how ML might transform sleep medicine through efficient, extended solutions for EEG data analysis and sleep disorder detection. We provide a realistic EEG simulation combined with an intuitive application, a powerful platform that makes advanced sleep diagnostics more accessible and impactful, which supports the development of educational strategies or technologies.

关键词： Deep learning Training Sleep Transforms Brain modeling Electroencephalography Mathematical models data models Medical diagnostic imaging Synthetic data

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Developing a Deep learning Based Approach for Anomalies Detection from EEG data 22nd

Developing a Deep Learning Based Approach for Anomalies Dete...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Alvi, Ashik Mostafa Siuly, Siuly Wang, Hua Victoria Univ Melbourne Vic Australia

ISBN: (纸本)9783030908881;9783030908874

Electroencephalography (EEG) contribute a leading role in brain studies, mental and brain diseases and disorders diagnosis, and treatments. Traditional Machine learning (TML) approaches are employed in most of the recent efforts in identifying the anomalies from EEG data. But their shallowed architecture is one of the reasons why they fail to detect correctly and efficiently. Furthermore, these systems need to be fed the discriminant features manually. To overcome these issues, this study aims to develop an EEG data analysis system involving a multi-layer Gated Recurrent Unit (GRU) for anomalies detection. There are four steps to the suggested framework: (1) Collecting Raw EEG data, (2) data pre-processing (de-noising, segmenting, and down-sampling), (3) discover hidden significant characteristics of EEG data and classification using GRU based scheme, and (4) model's performance evaluation. Our proposed model is tested on a publicly available EEG dataset and achieved 96.91% of accuracy, 97.95% of sensitivity, 96.16% of specificity and 96.39% of F1 score. This study will guide the future bio-medical researchers and technology experts to have a deep learning based automated anomaly detection system from EEG data.

关键词： Gated Recurrent Unit EEG Deep learning data mining Mild cognitive impairment

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A Novel Hybrid Scheme for Time Series Prediction Using LMS Filter and ISSA-based LSTM 19

A Novel Hybrid Scheme for Time Series Prediction Using LMS F...

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作者： Li, Pei Heng Chen, Dong Xia Huang, Han Yao Wu, Xiao Tong Jiang, Hua Wei Ji, Gen Lin Nanjing Normal University School of Computer Science and Technology Nanjing China Nanyang Institute of Technology Department of Computer and Software Engineering Nanyang China Sungkyunkwan Univeristy Department of Computer Science Engineering Suwon Korea Republic of

ISBN: (纸本)9798350346558

In the era of Internet of Everything (IoE), there is an explosive growth in data volumes and the data usually with time series characteristics. Therefore, how to deal with time series data to improve prediction accuracy remains a critical issue. In this paper, an efficient hybrid scheme combining linear and nonlinear models is proposed to greatly improve the prediction performance and simplify the hyperparameter selection process. Gradient-based least mean square (GD-LMS) is introduced for adaptive linear section, while a hyperparameter-determined long short-term memory (LSTM) is used for nonlinear section. In particular, an improved sparrow search algorithm combining opposition-based learning and Cauchy variance (ISSA) is used to construct the optimal hyperparameters for the two-layer LSTM. Computer simulations with the UMass weather dataset reveal that the proposed scheme considerably outperforms three benchmarking methods in terms of data prediction accuracy and robustness. © 2022 IEEE.

关键词： Long short-term memory

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