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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Parameswaran, Sumesh Eratt Ramachandran, Vidhyalavanya Shukla, Swati School of Electronics Engineering VIT-AP University Vijayawada India Department of Computing and Electronics N Engineering Middle East College Muscat Oman

Crypto currency price prediction is one of the biggest challenges for any prediction system owing to its volatility and dependence on many factors. Artificial intelligence powered by the development of sophisticated computing technology and developments in research has opened up a wide variety of applications and new possibilities to solve complicated problems. Extraction of appropriate features and input with features affecting the output is equally important in time series prediction problems. Wavelet transform is considered an effective method to split a non-stationary signal into uncorrelated components. This work attempts to extract the advantage of wavelets to extract features from an available data set and to predict the trend of the crypto price for next 16 days using the Bi-LSTM network. Historic data of crypto currencies available in Yahoo finance through the yFinance API with the python code is used as the primary data for this work. Due to the gaining importance of SHIB crypto due to its multiplication of value from its start, we have taken SHIB trend analysis as a case study with the use of a data set provided by Yahoo Finance API. The novelty of the proposed system lies largely on the combination of wavelet and deep learning system to improve the performance measures. The proposed systems evaluates the wavelet transform coefficients of the historic data available from the Yahoo Finance API and feed the wavelet transform values to the input of Bi-LSTM network to predict the wavelet transform coefficients for the next 16 days. Finally an inverse wavelet transform is used to get the price of the crypto currency for next 16 days. The results obtained shows that the model was able to estimate the trend in crypto price. The trend analysis shows whether there is an increase or decrease of prices in upcoming days and this could be useful to invest or to withdraw the funds to improve the returns. The proposed scheme resulted in lower Mean Square Error (MSE), Me

关键词： Wavelet transforms

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Variable Speed Drives AC Ground Fault Location by Voltages Components Analysis 14

Variable Speed Drives AC Ground Fault Location by Voltages C...

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14th IEEE International Symposium on Diagnostics for Electrical Machines, Power electronics and Drives, SDEMPED 2023

作者： Guerrero, Jose M. Serrano-Jimenez, Daniel Valino, Vanesa Platero, Carlos A. Mondragón Unibertsitatea Electronics and Computing Department Mondragón Spain Universidad Politécnica de Madrid Energy and Fuels Department Madrid Spain Universidad Politécnica de Madrid Automatic Electric and Electronic Engineering and Industrial Computing Department Madrid Spain

ISBN: (纸本)9798350320770

Variable Speed Drives (VSD) are key pieces in most of the industrial process. Its protection and diagnosis should be crucial in order to ensure the secure operation of VSDs especially against ground faults, the most common type of electrical faults. However, the transitions between AC and DC sides provoked by power electronics as well as the induced noises can cause the malfunction of protection relays. This paper proposed a method to locate ground faults in the AC output of a variable speed drive. The method is mainly based on a grounding resistor installed between the midpoint of the DC bus and ground. Measuring the phase-to-neutral voltages and grounding resistor voltage, the method can discern the faulty phase. Moreover, it estimates the fault position and fault resistance applying phasors equations with the fundamental and the commutation frequencies, no matters the inverter conditions or DC bus voltage. The method has been corroborated through computer simulations obtaining promising results in VSDs with floating supplies, which is a typical configuration for electric vehicles. © 2023 IEEE.

关键词： Harmonic analysis

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Botanic Alchemy: Cultivating Insight through Extensive Plant Disease Dataset and Reweighted Visual Analysis 2

Botanic Alchemy: Cultivating Insight through Extensive Plant...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Mustafa nawaz, S.M. Maharajan, K. Anand, J. Vijay Malathi, G. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India J.N.N Institutes of Engineeriung Department of Electronics and Communication Engineering Tamil Nadu India Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Kanchipuram Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Diagnosing plant diseases research tackles the vital challenge of identifying plant diseases in agriculture by focusing on the intricate complexities found in images displaying diverse symptoms, scattered lesions, and complex backgrounds. To aid this endeavor, a substantial dataset of 220,592 images is assembled. The proposed method uses a reweighting strategy that targets both visual regions and loss. Initially, weights for segmented patches are calculated based on their discriminative potential obtained from cluster distributions. These weights are then seamlessly integrated into the training process, assigning them to individual patch-label pairs during weakly-supervised training, aiding in recognizing disease-specific regions. Features extracted from this trained network create a comprehensive feature representation. This integration incorporates the GRU algorithm to encode the sequence of weighted patch features. Thorough evaluations on the newly curated dataset and a public dataset demonstrate the effectiveness and superiority of this methodology. This research significantly advances the field of plant disease recognition in image processing, opening doors for further innovative exploration. © 2024 IEEE.

关键词： Learning algorithms

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A Ground Fault Location Method for Modular Multilevel Converters 14

A Ground Fault Location Method for Modular Multilevel Conver...

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14th IEEE International Symposium on Diagnostics for Electrical Machines, Power electronics and Drives, SDEMPED 2023

作者： Guerrero, Jose M. Carrizosa, Miguel Jimenez Mahtani, Kumar Platero, Carlos A. Mondragon Unibertsitatea Electronics and Computing Department Mondragón Spain Universidad Politécnica de Madrid Energy and Fuels Department Madrid Spain Universidad Politécnica de Madrid Automatic Electric and Electronic Engineering and Industrial Computing Department Madrid Spain

ISBN: (纸本)9798350320770

The need of efficient electric transmission systems, the rise of renewable energy production and battery storage systems has empowered the use of Modular Multilevel Converters (MMC) which allows a better performance than convenctional converters. However, the diagnosis of electrical faults in MMC are more challenging because more components are involved in the fault circuit. Particularly, ground faults are the most common type of faults in power systems and their diagnosis has not been widely studied for AC-DC converters, where most of the industrial relays fail. This paper proposes a ground fault location method for MMC converters based on a high value grounding resistor placed in a DC midpoint or AC neutral point of the MMC. Measuring the voltage between its terminals and performing its frequency analysis the fault can be located. The novelty of this method resides in the detection of the ground fault in AC or DC side or in the faulty-submodule location if the ground fault happens inside the MMC. To corroborate the method, numerous simulations have been performed in a half-bridge three-phase MMC converter obtaining promising results. © 2023 IEEE.

关键词： Frequency response

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IoT and Cloud Solutions Waste-to-Energy in Hospitals for Energy Recovery from Medical Waste 10

IoT and Cloud Solutions Waste-to-Energy in Hospitals for Ene...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Thanigaivelu, P.S. Lakshmi, D. Ramasamy, Jayabharathi Kumaresh, P.S. Sumathi, G. Mathivanan, K. Srm Institute of Science and Technology Computing Technologies Chenagalpattu Chennai Tamil Nadu Kattankulathur India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India K.L.N.College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Sivagangai India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Innovative approach to hospital waste management and energy recovery due to the Internet of Things (IoT)-powered waste-to-energy system. This system addresses the complex problems of medical waste management and renewable energy production with a single framework. The system integrates several advanced sensors and components made possible by IoT technology's power to optimize waste management and energy generation. Sensors connected to the IoT, intelligent waste containers, and radio frequency identification labels all contribute to an improved garbage collection, sorting, and processing system. While fill-level sensors and RFID technology improve garbage collection routes and save operating costs and environmental effects, real-time data collection and analysis provide exact waste monitoring. Temperature and gas sensors enable safe and effective waste processing, eliminating risks and maximizing energy recovery. Medical waste is converted into useful energy sources like syngas in the system's energy recovery phase using pyrolysis or gasification reactors. These renewable energy sources lessen the environmental impact of medical waste disposal in landfills while relieving pressure on conventional fossil fuel supplies. The environmental advantages are highlighted, with less landfill trash and reduced greenhouse gas emissions specifically highlighted. © 2024 IEEE.

关键词： Renewable energy

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Real-Time Hydraulic Fluid Leak Detection in Heavy Machinery Using Cloud-Integrated Wireless Sensor networks for Proactive Maintenance 10

Real-Time Hydraulic Fluid Leak Detection in Heavy Machinery ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kumaresh, P.S. Sivanantham, E. Shoba, L.K. Priya, W. Deva Mohankumar, n. Elango, B. K.L.N.College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Sivagangai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chenagalpattu Chennai Tamil Nadu Kattankulathur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research introduces unique techniques for improving preventative servicing of heavy equipment using a real-time leak-detecting system for hydraulic fluid. The technology employs a network of sensors across the gear to monitor the hydraulics in real time. These wireless sensors communicate data to a cloud-based platform, where cloud computing analytics are used for real-time analysis. The cloud-integrated sensor network quickly detects hydraulic fluid leakage to address the critical demand for quick maintenance interventions. Preventing possible breakdowns and avoiding downtime are two areas where real-time skills shine. The cloud-based design enables remote monitoring and administration, which is essential for a preventative maintenance approach. Multiple pieces of heavy equipment were used in virtual and real-world tests to evaluate the proposed solution. The results reveal that the system is useful for real-time detection of hydraulic fluid leaks, which can potentially increase heavy equipment's overall dependability and performance. The system is a viable option for a wide range of industrial uses due to the incorporation of cloud computing, which guarantees scalability, flexibility, and accessibility. © 2024 IEEE.

关键词： Wireless sensor networks

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A Ground Fault Detection Method for Double Fed Induction Machines 14

A Ground Fault Detection Method for Double Fed Induction Mac...

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14th IEEE International Symposium on Diagnostics for Electrical Machines, Power electronics and Drives, SDEMPED 2023

作者： Guerrero, Jose M. Santamaria, Itxaso Aranzabal Barrena, Julen Gomez-Comejo Valverde, Victor Platero, Carlos A. Mondragon UnibertsitateaMondragón Electronics and Computing Department Mondragón Spain Universidad Del País Vasco / Euskal Herriko Unibertsitatea Electric Engineering Department Bilbao Spain Universidad Politécnica Automatic Electric and Electronic Engineering and Industrial Informatics de Madrid Madrid Spain

ISBN: (纸本)9798350320770

The use of Double Fed Induction Machines (DFIM) is widely implemented in wind farms and pumping stations due to their regulation capacity. The rotor of DFIMs is one of the most expensive parts of these installation. They are usually connected to low power converters to vary the machine operation point changing the frequency of the rotor. However, the injection of pulsed voltages in the rotor causes high insulation stresses and the deterioration of these machines. If the insulation fails, a ground fault can be originated which can damage the machine. In this field, this paper proposes a new method to detect ground faults in power converter fed DFIMs. The method consists of a grounding resistor in the midpoint of the DC bus where the voltage is measured. With the frequency response of this measurement, ground faults can be detected not only in the DFIM's rotor but also in its stator or even in its own converter DC bus. To corroborate the method, numerous simulations carried out in Matlab Simulink are presented, achieving positive results. © 2023 IEEE.

关键词： Frequency response

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Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor

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Sn Applied Sciences 2023年第7期5卷 178页

作者： Jawarneh, Malik Arias-Gonzáles, José Luis Gandhmal, Dattatray P. Malik, Rami Qays Rane, Kantilal Pitambar Omarov, Batyrkhan Mahapatra, Cosmena Shabaz, Mohammad Faculty of Computing Sciences Gulf College Muscat Oman University of British Columbia Arequipa Peru Department of Computer Science and Engineering N. B N. B. Navale Sinhgad College of Engineering Solapur India Medical Instrumentation Techniques Engineering Department College of Engineering and Technologies Al-Mustaqbal University Babil Hillah51001 Iraq Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Al-Farabi Kazakh National University Almaty Kazakhstan Vivekananda Institute of Professional Studies New Delhi India Arba Minch University Arba Minch Ethiopia

Malignant growth in liver results in liver tumor. The most common types of liver cancer are primary liver disease and secondary liver disease. Most malignant growths are benign tumors, and the condition they cause, essential liver disease, is the end result. Cancer of the liver is a potentially fatal disease that can only be cured by combining a number of different treatments. Machine learning, feature selection and image processing have the capability to provide a framework for the accurate detection of liver diseases. The processing of images is one of the components that come together to form this group. When utilized for the purpose of reviewing previously recorded visual information, the instrument performs at its highest level of effectiveness. The importance of feature selection on machine learning algorithms for the early and accurate diagnosis of liver tumors is discussed in this article. The input consists of images from a CT scan of the liver. These images are preprocessed by discrete wavelet transform. Discrete wavelet transforms increase resolution by compressing the images. Images are segmented in parts to identify region of interest by K Means algorithm. Features are selected by grey wolf optimization technique. Classification is performed by Gradient boosting, support vector machine and random forest. GWO Gradient boosting is performing better in accurate classification and prediction of liver cancer. © 2023, The Author(s).

关键词： Discrete wavelet transforms

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UAV -Based Object Tracking Using Siamese networks and Kalman Filtering

UAV -Based Object Tracking Using Siamese Networks and Kalman...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramanan, S.V. Rajasekar, M. Shanmugapriya, G. Sujitha, R. Akshya, J. Choudhry, Mani Deepak P Pg Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Vsb college of Engineering Department of Computer Science and Engineering Technical Campus Coimbatore India Dr.N G.P Institute of Technology Department of Computer Science and Business Systems Coimbatore India Christ The King Engineering College Department of Information Technology Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

The ability to match features and keep track of objects in changing dynamic environments is still an important problem, particularly due to varying noise levels, diverse datasets, and high dimensional feature spaces. Euclidean Distance and Cosine Similarity based matching are existing methods that commonly do not generalize well, particularly in the case of cross domain scenarios, where noise and motion dynamics are powerful factors to limit the accuracy of tracking. However, Deep Metric Learning approaches can extract robust features at the cost of computational efficiency and are unsuitable for real-time applications. This research proposes a Siamese network framework to solve these problems by using robust feature matching and multi-object tracking. Based on that, the proposed framework uses a dual-branch network architecture in which feature embeddings extracted from paired inputs are mapped through the same shared latent space. Furthermore, the adaptive learning rate leads to efficient convergence during the training. Experimental results show that the Siamese network not only achieves better accuracy than the existing methods and the more such as Deep Metric Learning but also performs significantly more efficient computation. With an accuracy of 90.2 % at a precision of 88.5%, the framework's inference time is much faster than that of Deep Metric Learning at 2.0ms. Furthermore, the proposed method performs well on the generalization over datasets with a 15% improvement of cross-domain matching accuracy on Euclidean Distance. By striking this balance between accuracy, efficiency and adaptability, it shows its suitability for dynamic and noisy environments for real-time tracking. The proposed method fills existing gaps and achieves a new state of the art for feature matching and tracking performance. © 2025 IEEE.

关键词： Object tracking

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Advanced deep learning approach for enhancing crop disease detection in agriculture using hyperspectral imaging

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Journal of Intelligent and Fuzzy Systems 2024年第2期46卷 3281-3294页

作者： Zekrifa, Djabeur Mohamed Seifeddine Lamani, Dharmanna Chaitanya, Gogineni Krishna Kanimozhi, K.V. Saraswat, Akash Sugumar, D. Vetrithangam, D. Koshariya, Ashok Kumar Manjunath, Manthur Sreeramulu Rajaram, A. Department of Bioinformatics Higher School of Food Science and Agri-Food Industry Ahmed Hamidouche Av Oued Smar Algiers Algeria Department of Computer Science & Information Technology School of Computing and Information Technology REVA University Rukmini Knowledge Park Kattigenahalli Karnataka Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science Engineering Dr. K. N. Modi University Jaipur Rajasthan Newai India Department of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Deemed to be University Coimbatore India Department of Computer Science and Engineering University Institute of Engineering Chandigarh University Punjab Mohali India Department of Plant Pathology School of Agriculture Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial network (AE-GAn) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAn model combines the strengths of the Autoencoder for feature extraction and the Generative Adversarial network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAn model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAn model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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