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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities

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Multimedia Tools and Applications 2025年第5期84卷 2209-2260页

作者： Karthiga, R. Narasimhan, K. Thanikaiselvan, V. Hemalatha, M. Amirtharajan, Rengarajan Department of ECE SRM Institute of Science and Technology Ramapuram Chennai600089 India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613 401 India School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

Breast cancer is a significant health concern;early detection and treatment are critical to improving patient outcomes. Artificial Intelligence has the potential to assist healthcare professionals in the diagnosis and treatment of breast cancer, and clinical decision support systems using AI algorithms can help to improve the accuracy and speed of breast cancer detection and diagnosis. By analysing large amounts of data from various imaging modalities, AI algorithms can help identify breast cancer early, increasing the chances of successful treatment. This survey aims to briefly overview the most prevalent breast cancer types, their staging, and the methods and modalities for their diagnosis to address these problems. Our investigation showed that, when compared to ML, DL techniques achieved the highest accuracy, with an approximate 2% improvement in accuracy. This review article mainly focuses on four commonly used imaging modalities: Mammogram, Thermogram, Ultrasound imaging and histopathology images. This survey critically analyzes breast cancer detection approaches and modalities, as well as a cost-to-accuracy comparison. Explainable AI (XAI) techniques and their application for breast cancer diagnosis are critically examined, as well as their advantages and limitations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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Hybrid Quantum-Classical Machine Learning for Sentiment Analysis 22

Hybrid Quantum-Classical Machine Learning for Sentiment Anal...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Mohammad Masum, Abu Kaisar Maurya, Anshul Murthy, Dhruthi Sridhar Pratibha Mahmud, Naveed Electrical Engineering and Computer Science Florida Institute of Technology United States

ISBN: (纸本)9798350345346

The collaboration between quantum computing and classical machine learning offers potential advantages in natural language processing, particularly in the sentiment analysis of human emotions and opinions expressed in large-scale datasets. In this work, we propose a methodology for sentiment analysis using hybrid quantum-classical machine learning algorithms. We investigate quantum kernel approaches and variational quantum circuit-based classifiers and integrate them with classical dimension reduction techniques such as PCA and Haar wavelet transform. The proposed methodology is evaluated using two distinct datasets, based on English and Bengali languages. Experimental results show that after dimensionality reduction of the data, performance of the quantum-based hybrid algorithms were consistent and better than classical methods. © 2023 IEEE.

关键词： Haar Transform Quantum Machine Learning Sentiment Analysis SVM

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Adversarial Attacks Against Double RIS-Assisted MIMO Systems-based Autoencoder in Finite-Scattering Environments

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IEEE Wireless Communications Letters 2024年第6期14卷 1593-1597页

作者： Son, Bui Duc Khanh, Ngo Nam Van Chien, Trinh In Kim, Dong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The School of Information and Communication Technology Hanoi University of Science and Technology Hanoi100000 Viet Nam

Autoencoder permits the end-to-end optimization and design of wireless communication systems to be more beneficial than traditional signal processing. However, this emerging learning-based framework has weaknesses, especially sensitivity to physical attacks. This paper explores adversarial attacks against a double reconfigurable intelligent surface (RIS)-assisted multiple-input and multiple-output (MIMO)-based autoencoder, where an adversary employs encoded and decoded datasets to create adversarial perturbation and fool the system. Because of the complex and dynamic data structures, adversarial attacks are not unique, each having its own benefits. We, therefore, propose three algorithms generating adversarial examples and perturbations to attack the RIS-MIMO-based autoencoder, exploiting the gradient descent and allowing for flexibility via varying the input dimensions. Numerical results show that the proposed adversarial attack-based algorithm significantly degrades the system performance regarding the symbol error rate compared to the jamming attacks. © 2012 IEEE.

关键词： Decoding Vectors Reconfigurable intelligent surfaces Finite element analysis Fading channels Correlation MIMO communication Jamming Optimization Channel state information

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An Adaptive Online Few-shot Network Intrusion Detection System Based on Meta-Learning 12

An Adaptive Online Few-shot Network Intrusion Detection Syst...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Ding, Shiru Wang, Zhen Lu, Yifei School of Electrical and Automation Engineering Nanjing Normal University China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798331511074

Previous deep learning-based Network Intrusion Detection Systems (NIDS) require a sufficient number of labeled samples to train deep neural network models. However, in certain scenarios of the Internet of Things (IoT), such as zero-day attacks, abnormal data is scarce and cannot meet the training conditions for neural network models. Thus, there is a need for a NIDS capable of few-shot learning. Additionally, previous online network intrusion detection systems did not comprehensively consider the significant impact of data feature drift and catastrophic forgetting on the performance of online models. In order to solve these two problems, we designed an adaptive online few-shot network intrusion detection system based on meta-learning. We evaluated the proposed system using the public datasets CIC-IDS2017, and the results show that our system can effectively handle the challenge of small samples while continuously adapting to new data with minimal catastrophic forgetting. © 2024 IEEE.

关键词： Zero-shot learning

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Improving Skin Cancer Diagnosis: A Comprehensive Analysis of Transfer Learning and Ensemble Models 26

Improving Skin Cancer Diagnosis: A Comprehensive Analysis of...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Rahman, Saifur Siddique, Md. Abu Ismail Ashik, S. M. Abdullah Rajshahi University of Engineering & Technology Computer Science & Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Electrical & Computer Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Skin cancer has become a major global health issue, necessitating improved diagnostic methods for better clinical outcomes. This study thoroughly evaluates deep learning methods for classifying skin cancer using dermoscopic images. We compare the performance of popular pre-trained models, including VGG16, ResNet50, Xception, and MobileNet, against a natively trained SqueezeNet model. The results show that VGG16 and ResNet50 models have achieved state-of-the-art accuracy of 94%, with SqueezeNet following closely at 93%. This validates the effectiveness of transfer learning. We suggest using ensemble models that combine pre-trained networks (VGG16, MobileNet, ResNet, and Xception) to achieve a maximum accuracy of 97% using model averaging. Beyond accuracy, our ensemble models excel across Precision, Recall, and F1-score metrics, emphasizing their potential for real-world usage. Additionally, we present ROC curve analysis and confusion matrices to provide clinical insights. This study highlights the capabilities of transfer learning for skin cancer classification while introducing novel ensemble techniques that can profoundly impact computer-aided diagnosis through improved accuracy and robustness. © 2023 IEEE.

关键词： and computer Vision CNN MobileNet ResNet50 SqueezeNet Transfer Learning VGG16 Xception

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IRS-Assisted Hybrid Spatial Modulation for Full-Duplex MIMO Communications 18

IRS-Assisted Hybrid Spatial Modulation for Full-Duplex MIMO ...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sanila, Kunnathully Sadanandan Rajamohan, Neelakandan Luleå University of Technology Department of Computer Science Electrical and Space Engineering Sweden Indian Institute of Technology School of Electrical Sciences Goa India

ISBN: (纸本)9798350391725

In this paper, we propose an intelligent reflecting surface (IRS)-assisted hybrid transmit-receive spatial modulation (HSM) for full-duplex (FD) multi-input multi-output communication, referred to as FD-IRS-HSM. In the proposed FD-IRS-HSM, the FD communication between two users is accomplished with a dedicated IRS for each user. In addition to the conventional data transfer, transmit and receive spatial modulations are simultaneously employed in the transmission by tuning the reflecting elements in the IRS. Further, we characterize the self-interference power, present the theoretical analysis for ergodic capacity and outage probability for the proposed system, and substantiate it with simulation results. We also compare the proposed FD-IRSHSM with closely related works in the literature and demonstrate the outperformance of our proposed system in terms of higher transmission rate and greater flexibility. © 2024 IEEE.

关键词： Signal modulation

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Machine Learning Based Pattern Recognition with HOG Features for Detection of Islanding 3

Machine Learning Based Pattern Recognition with HOG Features...

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3rd IEEE International Conference on electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Bera, Bidisha Basu, Sarmistha National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Global Institute of Science and Technology Department of Computer Science and Technology Haldia India Calcutta Institute of Engineering and Management Department of Physics Kolkata India

ISBN: (纸本)9798350390728

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a Machine Learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. Images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds. © 2024 IEEE.

关键词： Spectrographs

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Analysis of energy consumption for SPI and I2C communications in ultra-low power embedded systems 46

Analysis of energy consumption for SPI and I2C communication...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Zidar, Josip Aleksi, Ivan Matic, Tomislav Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9789532331042

Developing ultra-low-power devices requires low-power components, algorithms, and communication protocols. For environmental monitoring along the supply chain, products may travel a long way from the distributor to the customer. In addition, extreme climatic circumstances, such as temperatures below 0°C, affect battery capacity. In order to monitor package's environment, it is crucial to ensure that all embedded system parts use as little energy as possible. This paper addresses how much power the SPI and I2C communication protocols use. These protocols are widely used in low-power embedded devices for sensor communication. Based on its results, this research article presented recommendations for developing ultra-low power embedded devices that use the SPI/I2C protocol. © 2023 MIPRO Croatian Society.

关键词： Supply chains

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Impact of UV content of illumination on acceptability of colour reproduction 12

Impact of UV content of illumination on acceptability of col...

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12th Colour and Visual Computing Symposium, CVCS 2024

作者： Nussbaum, Peter Gudzenchuk, Anastasiia Green, Phil Faculty of Information Technology and Electrical Engineering Department of Computer Science NTNU Norway Department of Science and Technology Linköping University Sweden

Paper and board for printing commonly contains optical brightening agents that fluoresce in the presence of UV radiation, making the prints appear brighter and bluer. If the relative amount of optical brightening agents (OBAs) is not consistent between proof and print, and the UV content of measurement and the viewing illumination differs, there will be a colorimetric difference between proof and print. Colour management workflows are normally media-relative, and visual adaptation to the media white can conflict with the goals of colorimetric matching. The impact of UV content in measurement and viewing illumination on the visual acceptability of colour reproductions was investigated in a series of psychophysical experiments, using substrates with varying OBA concentrations and a media-relative colour reproduction workflow. The results showed that visual acceptability was largely unaffected by UV content of measurement or viewing illumination, suggesting that visual adaptation to media white discounts the effect of fluorescence. © 2024 Copyright for this paper by its authors.

关键词： Colorimetry

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