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International Conference on Computing and Networking technology (ICCNT)

作者： Ankit Gupta Mamatha K. M. Kiran M. Dept. of Information Technology National Institute of Technology Karnataka Surathkal India Dept. of Computer Science and Engineering Viseveshvaraya Technological University Belgaum India

The issue of decreased coverage rate in Mobile Wireless Sensor Networks (MWSNs), caused by mobile sensor nodes being randomly placed inside a monitoring area. Additionally, it becomes extremely important to utilise a sensor node’s energy very effectively due to the finite energy of sensor nodes. Hence, to provide optimised positions for the sensor nodes while using the energy of sensor nodes adept.y authors propose an energy efficient coverage algorithm. Initially, article focus on optimal placement of the sensor nodes within a area to achieve the maximum coverage and later authors have focused on improvising the network lifetime. Article presents a combination of Grey Wolf Optimization and Virtual Force algorithm for optimization of coverage in MWSN. Further, to improve the network lifetime, a GWO-based clustering algorithm is presented using distance and energy as a parameter. The algorithms are implemented and simulated on Matlab. The efficiency of the presented algorithm is observed comparing with other Swarm Intelligence (SI) based optimization algorithms, like GWO, VFA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant-lion Optimization (ALO) and the results of the GWO-based clustering is compared with the traditional LEACH algorithm and energy-balanced clustering based on PSO. Simulation results demonstrate that the presented algorithms outperform the considered algorithms.

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A Novel Approach Towards Gas Turbine Emission Reduction by Using Neural Networks

A Novel Approach Towards Gas Turbine Emission Reduction by U...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Roy, Atanu Pramanik, Sabyasachi Mitra, Kalyan Chakraborty, Manashi Sarisa West Bengal 743368 India Haldia Institute of Technology Dept. of Computer Science and Engineering Hatiberia West Bengal Haldia721657 India Indian Maritime University Dept. of Maritime Studies P - 19 Taratalla Road Kolkata700088 India Sarisa West Bengal 743368 India

ISBN: (纸本)9798350313987

Optimizations of controlling parameters are the key factors to achieve effectual output and emission reduction from machinery running. This study relates prediction technology for gas turbine's (GT's) running optimization and emission reduction. The tool can identify upcoming disputes and alter the engine control setting to achieve highly efficient running operations. This research objects to get a machine learning approach to predict GT performance on emissions. The forecasting capabilities of this system are based on real-time data collected from a GT plant. A neural network is employed to analyze and predict the emissions of exhaust pollutants, primarily carbon monoxide and nitrogen oxides. The model performance is studied by considering the values of MSE, MAE, and residuals. The model's accuracy and precision are revealed by its low MSE (0.0035) and MAE (0.043). This approach is a cost-effective way to gage emissions accurately and can be used to confirm compliance with environmental regulations. It is also a helpful tool for monitoring the health of the turbine and identifying any potential issues with its operation. © 2023 IEEE.

关键词： Convolutional Neural Networks Emission Gas Turbine Predictive Emission Monitoring

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Probabilistic Dynamic Line Rating Forecasting with Line Graph Convolutional LSTM

arXiv

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arXiv 2024年

作者： Kim, Minsoo Dvorkin, Vladimir Kim, Jip Dept. of Energy Engineering Korea Institute of Energy Technology Korea Republic of Dept. of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at the scheduling stage is thus necessary for system operators to proactively optimize power flows, manage congestion, and reduce the cost of grid operations. However, the DLR forecast remains challenging due to weather uncertainty. To reliably predict DLRs, we propose a new probabilistic forecasting model based on line graph convolutional LSTM. Like standard LSTM networks, our model accounts for temporal correlations between DLRs across the planning horizon. The line graph-structured network additionally allows us to leverage the spatial correlations of DLR features across the grid to improve the quality of predictions. Simulation results on the synthetic Texas 123-bus system demonstrate that the proposed model significantly outperforms the baseline probabilistic DLR forecasting models regarding reliability and sharpness while using the fewest parameters. Copyright © 2024, The Authors. All rights reserved.

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Integrating NIR Hyperspectral Techniques with Neural Network for Barley Seeds Classification

Integrating NIR Hyperspectral Techniques with Neural Network...

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： Anuj Kumar Shveta Mahajan Dept. of Computer Science and Engineering Dr. B.R. Ambedkar National Institute of Technology Jalandhar India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Nondestructive identification of seed varieties from different origins is crucial in optimizing crop improvement, plant growth, and advancement in breeding methods. Evaluating varietal purity is essential for seed quality. Traditional barley seed classification relies on visual inspection, which is a subjective and error-prone process and is time-consuming. Moreover, chemical testing involves the breaking of seeds which highlights the necessity for exploring non-destructive methods. The integration of Near-infrared (NIR) spectroscopy with neural networks offers a promising alternative. In this study, we have used a hyperspectral imaging dataset consisting of 34 different Indian barley varieties (1008 per variety). Six spectral pre-processing techniques were used to extract and pre-treat the mean reflectance spectrum associated with each seed, namely Savitzky-Golay (SG) smoothing, SG first derivative, SG second derivative, detrending, standard normal variate (SNV), and multiplicative scatter correction (MSC). Further, we have used five different classifiers: K-nearest neighbors (KNN), partial least squares discrimination analysis (PLS-DA), artificial neural networks (ANN), support vector machine techniques (SVM), and convolutional neural networks (CNN). CNN demonstrated superior classification accuracy when implementing it on unprocessed data, whereas the ANN model outperformed it when combined based on the SG2 pre-processing method. Their respective classification accuracy rates were $98.44 \%$ and $98.88 \%$. The current study examined the use of an ANN model coupled with the NIR-HSI technology to accurately, quickly, and nondestructively classify different types of barley seeds.

关键词： Support vector machines Visualization Spectroscopy Accuracy Data models Seeds (agriculture) Convolutional neural networks

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An Effective Hybrid Model for Skin Cancer Detection Using Transfer Learning

An Effective Hybrid Model for Skin Cancer Detection Using Tr...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Hussam J. Mohammed Ahmed Adil Nafea Hussein K. Almulla Salah A. S. Aliesawi Mohammed M Al-Ani dept. Artificail inteligence College of Computer Science and Information Technology University of Anbar Ramadi Iraq Computer Center University of Anbar Ramadi Iraq dept. Computer Science College of Computer Science and Information Technology University of Anbar Ramadi Iraq dept.Center for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

Skin cancer is considered one of the most fatal illnesses in the human population. Within the current healthcare system, the procedure of identifying skin cancer is time-consuming and poses a potential risk to human life if not recognized promptly. Early identification of skin cancer is imperative to maximize the likelihood of achieving complete recovery. This research proposed using a hybrid model including DenseNet201 and auto-encoder for feature extraction, and a support vector machine (SVM) as the classifier. The proposed model was evaluated in the ISIC 2016 dataset, which consists of nine different classes. The hybrid model achieved a classification accuracy of 91.09% percent in accurately identifying nine different forms of skin cancer. The findings indicate that the proposed model is competitive compared to the baseline models. This outcome offers substantial assistance to dermatologists and health professionals in the field of skin cancer detection.

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DeepStock Forecast: Unveiling Market Movements through Advanced Deep Learning Models

DeepStock Forecast: Unveiling Market Movements through Advan...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Smruti Rekha Das Debahuti Mishra Arundhati Lenka Kailash Shaw Dept. Computer Science and Engineering GITAM Deemed to be University Visakhapatnam Andhra Pradesh India Dept. Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha India Odisha India dept. of AIML Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

This study uses deep neural network (DNN) methodologies, including auto encoder, deep belief network (DBN), and backpropagation neural network (BPNN), to predict stock prices over 15 and 30 days. Utilizing BSE Sensex and NSE Sensex datasets with diverse technical indicators, the research highlights DNN’s superior training accuracy, measured by minimal mean squared error (MSE). The study’s key contribution is applying DNN to predict stock market volatility. While acknowledging the limitation of relying solely on training error, the study incorporates additional measures like RMSE, MAPE, MAE, and ARV during testing. Notably, DNN’s MAPE results fall within a narrow range (0.0221 to 0.0255), indicating minimal error deviation. In a comprehensive evaluation across all datasets, DNN consistently outperforms DBN and BPNN in both training and testing performance.

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Optimizing Age-of-Information in Adversarial Environments with Channel State Information 14

Optimizing Age-of-Information in Adversarial Environments wi...

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14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

作者： Mandal, Avijit Bhattacharjee, Rajarshi Sinha, Abhishek Indian Institute of Technology Madras Dept. of Electrical Engineering Chennai600036 India University of Massachusetts at Amherst College of Information and Computer Sciences United States

ISBN: (纸本)9781665421041

This paper considers a multi-user downlink scheduling problem with access to the channel state information at the transmitter (CSIT) to minimize the Age-of-Information (AoI) in a non-stationary environment. The non-stationary environment is modelled using a novel adversarial framework. In this setting, we propose a greedy scheduling policy, called MA-CSIT, that takes into account the current channel state information. We establish a finite upper bound on the competitive ratio achieved by the MA-CSIT policy for a small number of users and show that the proposed policy has a better performance guarantee than a recently proposed greedy scheduler that operates without CSIT. In particular, we show that access to the additional channel state information improves the competitive ratio from 8 to 2 in the two-user case and from 18 to 8/3 in the three-user case. Finally, we carry out extensive numerical simulations to quantify the advantage of knowing CSIT in order to minimize the Age-of-Information for an arbitrary number of users. © 2022 IEEE.

关键词： Channel state information

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Face Recognition from Partial Face Data and Fraud Detection

Face Recognition from Partial Face Data and Fraud Detection

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Thavisala Veneela Sonti Vivek Sai Yeturu Vamshidhar Reddy Vishnu Karthik Varma Kothapally dept. of Assistant professor/computer science and engineering B V RAJU INSTITUTE OF TECHNOLOGY Narsapur Medak Telangana India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Face recognition is a critical era with applications starting from safety to personalization. However, traditional face Recognition systems frequently conflict with partial face information, which can occur because of occlusions or picture excellent g troubles. I t consists of a novel technique to stand reputation using partial face statistics, specializing in fraud detection scenarios. This approach leverages deep getting to know strategies, mainly convolutional neural networks (CNNs), to extract functions from partial face snap shots. These features are then used to create a compact illustration of the face, which is robust to occlusions and noise. A similarity measure is employed to evaluate those representations, enabling the identification of individuals even from partial face records. In the context of fraud detection, it can be used to verify the identity of people based totally on partial face data captured from surveillance cameras or different sources. Experimental consequences on benchmark datasets show the effectiveness of the proposed approach, outperforming conventional face recognition procedures in eventualities with partial face information. It gives a promising solution for improving face recognition accuracy in challenging conditions, with capability packages in security, regulation enforcement, and personal identification systems. It also analyzes facial images with masks using Multi-Task Cascaded Convolutional Neural Networks (MTCNN). FaceNet algorithm is also used which adds more embeddings and verifications to face recognition. Support vector Machine (SVM) algorithm labels the data sets to produce a reliable prediction probability and along with that it also detects the frauds.

关键词： Support vector machines Accuracy Face recognition Surveillance Prediction algorithms Vectors Robustness Fraud Safety Convolutional neural networks

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Towards Standardized Manufacturing as a Service through Asset Administration Shell and International Data Spaces Connectors 48

Towards Standardized Manufacturing as a Service through Asse...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Inigo, Miguel A. Legaristi, Jon Larrinaga, Felix Perez, Alain Cuenca, Javier Kremer, Blanca Montejo, Elena Porto, Alain Mondragon S.Coop. Innovation and Technology Dept. Spain Mondragon University Electronics and Computer Science Dept. Mondragón Arrasate Spain Ikerlan Research Centre Brta Ict Dept. Spain Ideko Research Centre Brta Ict and Automation Dept. Elgoibar Spain

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This paper presents an industrial scenario that simulates a Manufacturing as a Service system for the execution of remote production orders built upon the implementation of emerging Asset Administration Shell (AAS) capabilities and International Data Space connectors. Static and dynamic information from industrial assets (presses and laser cutting machines) are modelled with new AAS submodels and the result is stored in an AAS manager/registration system. A manufacturing orchestrator discovers assets through the registry and completes production orders. The AAS registry allows the selection of assets with capabilities to perform tasks and also shares the AAS catalogue available in the system. The catalogue is shared with external parties through Data Space Connectors. Third party companies can launch manufacturing orders remotely using the same connectors. The paper validates the implementation of AAS components and IDS connectors in a manufacturing context where remote production orders can be securely activated. © 2022 IEEE.

关键词： Standardization

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Machine Learning Approaches To Predict The Stability of Smart Grid

Research Square

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Research Square 2024年

作者： Satu, Shahriare Khan, Imran Dept. of Management Information Systems Noakhali Science and Technology University Noakhali3814 Bangladesh Dept. of Computer Science and Engineering Gono Bishwabidyalay Dhaka1344 Bangladesh

The smart grid is a modern solution to generate, distribute, and use energy effectively and efficiently. Ensuring the stability of the smart grid is critical to guarantee safe and consistent operation. This study proposes a machine learning model to identify smart grid stability more efficiently. To accomplish this, we collected the electrical grid stability dataset from a public machine learning repository and analyzed the correlation of individual features. Different feature selection and transformation techniques were employed to generate subsequent feature sets. Then, various classification techniques were applied to the primary and its subsets to identify the best machine-learning model for predicting smart grid stability more efficiently. The results indicated that MLP showed the highest accuracy (97.06%) for the Z-score transformed data set using Grid Search Optimization. This study demonstrates the potential of using machine learning to predict smart grid stability accurately and efficiently, providing a critical tool for improving the reliability, efficiency, and sustainability of the energy industry. © 2024, CC BY.

关键词： Smart power grids

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