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An Optimized Hybrid Deep Learning Model for Appliance Energy Prediction in Smart Homes

Journal of engineering Science and Technology Review 2024年第3期17卷 151-158页

作者： Vidhya, S. Balaji, M. Kamaraj, V. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

The increased energy consumption due to industrial machines and household appliances affectenergy sustainability as well as economic stability. To meet the energy demand various practices are followed worldwide. Research on sustainable energy production is at peak meanwhile research towards reducing the cost of energy generation is gaining more interest in recent times. one of the key points considered to reduce the power generation cost is energy consumption prediction. Future demands can be predicted, and necessary power can be generated or delivered to meet the demand. Specifically, prediction model is essentially required for smart homes as they utilize multiple devices for smart connectivity. Machine learning algorithms are widely used for energy prediction applications. However, the performance of machine learning models needs manual selection of features and is still complex in data analysis and decision-making process. Recently deep learning (DL) techniques are used in various classification and prediction applications. DL superior performance is incorporated for energy prediction for smart home appliances. This research work presents a hybrid optimized DL model using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network. Additionally, to improve the prediction performances, the parameters of the hybrid DL model is fine-tuned using canonical particle swarm optimization algorithm. Experiments of the proposed model utilizes UCI Household power consumption dataset to evaluate the performances in terms of RMSE, MAPE, MAE and R2-score. The proposed model attained RMSE of 26.2154W, MAPE of 2.965%, MAE of 18.65W and R2-Score of 0.9989 which is much better than existing linear regression, extreme learning machine and diverse LSTM models. © 2024 School of Science, DUTH. All rights reserved

关键词： Domestic appliances

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Transfer learning based epileptic seizure classification using scalogram images of EEG signals

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Multimedia Tools and Applications 2024年第36期83卷 84179-84193页

作者： Pattnaik, Sasweta Rao, B. Nageswara Rout, Nirmal Kumar Sabut, Sukanta Kumar Sensing & amp Computing Lab School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

Epilepsy is a common neurological disorder that occurs due to an abnormality of the nerve cells in the brain. Electroencephalogram (EEG) analysis is one of the most vital tools used to detect seizure events. Normally, EEG analysis is done manually by expert neurologists, which is time-consuming, tedious and error-prone, hence an automatic detection approach based on deep learning (DL) is essential in the dignosis process. In this study, we focused on automated epileptic seizure classification using a transfer learning. A pre-trained network ResNet50 is used for the feature extraction and classification using a deep neural network (dense layer) on 2D scalogram images of EEG signal. The time-frequency scalogram images were generated using continuous wavelet transform (CWT) from EEG signals collected from the CHBMIT scalp EEG database. The method was evaluated in terms of accuracy, sensitivity, specificity and computational time. The results are compared with the tested results of standard pre-trained models such as VGG16 and InceptionV3 which are also implemented in this study to classify the EEG events. The proposed approach achieved a best classification with an accuracy of 95.23%, sensitivity of 99.54%, and specificity of 90.28% respectively, which is better than the result obtained from the VGG16 and InceptionV3 networks. The training time for ResNet50 + DNN, VGG16, InceptionV3 are 2340, 2520, and 1440 s respectively, which indicates the computational complexity is more in transfer learning but classification accuracy is better than the standard models. Therefore, ResNet50 based transfer learning using 2D-scalogram images of EEG signals has evidently proved to be a better choice to a neurologist for fast anticipation of epileptic seizure. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neurons

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An Enhanced Co-Prime Sampler for Effective Coarray-Domain Processing of Underdetermined Direction-of-Arrival Estimation

IEEE Canadian Journal of Electrical and Computer Engineering

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IEEE Canadian Journal of Electrical and Computer engineering 2025年第2期48卷 124-130页

作者： Chandrasekaran, Ashok Venkateswaran, N. Sai University School of Computing and Data Science Chennai603104 India Sri Sivasubramaniya Nadar College of Engineering Department of Electronics and Communication Engineering Chennai603110 India

The design of a sparse sampler for effective coarray-domain processing of underdetermined direction-of-arrival (DOA) estimation is of prime research interest. Toward this, several sparse samplers have been proposed, among which co-prime samplers have garnered significant attention due to their closed-form expression for sampler element positioning and minimal susceptibility to mutual coupling effects. In this article, an enhanced co-prime sampler (ECPS) is proposed by concatenating the two operations on subsamplers, involving the scaling of one subsampler and shifting of another subsampler within the hold of the co-prime property. The proposed ECPS offers several advantages over existing samplers, including increased consecutive lags, reduced holes in the difference coarray, larger physical aperture, enhanced sparsity, and reduced susceptibility to mutual coupling effects. These improvements lead to high consecutive degrees-of-freedom (cDOF) in detecting more targets than the number of physical elements in the sampler with high estimation accuracy. The superiority and efficacy of the proposed ECPS are analyzed and validated with several simulation studies. © 2021 IEEE.

关键词： Estimation Direction-of-arrival estimation Mutual coupling Apertures Multiple signal classification Interpolation Accuracy Vectors Distance measurement Closed-form solutions

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DIFFERENTIAL EVOLUTION ALGORITHM SUPPORTED RANDOM FOREST CLASSIFIER FOR EFFECTIVE FEATURE SELECTION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and Computer Science 2023年第1期85卷 131-142页

作者： Vidhya, S. Balaji, M. Fantin Irudaya Raj, E. Kamaraj, V. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electrical and Electronics Engineering Dr. Sivanthi Aditanar College of Engineering Chennai India

The rise in the usage of power electronic equipment, which induces a deviation in the voltage and current waveforms, has raised the relevance of power quality (PQ) issues. It is important to classify them accurately in order to address the PQ problems. One of the most effective methods to classify PQ problems is to combine Machine Learning (ML) algorithms with efficient feature extraction and feature selection techniques. In the present work, the S-transform-based feature extraction technique is adopted and extracts around 20 features from the PQ disturbance signal. And then, Differential Evolution (DE) based feature selection technique is utilized for feature selection and five optimized features are selected and given as input to the ML classifiers. It is a method that is essential in data mining in order to reduce dimensionality and enhance the performance of ML classifiers. Eight different ML classifiers namely the K-Means, Adaptive Boosting (AdaBoost), K-nearest neighbor (KNN), Naive Bayes, Recursive Support Vector Machine (R-SVM), Linear SVM, Decision Tree, and Random Forest (RF) algorithms have been employed in this work. The effectiveness of the ML Classifiers is measured in terms of various performance evaluation measures namely precision, accuracy, F1-score, recall, and specificity. The results revealed that the DE-supported RF classifier is more efficient when compared with other classifiers in classifying the PQ disturbances even in environments with noise inference. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Support vector machines

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PalmDiseaseDetect: A Web App To Detect Palm Leaf Diseases 1

PalmDiseaseDetect: A Web App To Detect Palm Leaf Diseases

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Al-Farsi, Yusra Rashid Mohammad Nair, Smitha Sunil Kumaran Venugopal, Dhanalakshmi Middle East College Department of Computing and Electronics Engineering Muscat Oman

ISBN: (纸本)9798350348637

The agricultural sector, particularly date palm cultivation, is a cornerstone of economic revenue in Oman and other Gulf nations, accounting for approximately 50% of Oman's agricultural land. Given the significant local and international demand for dates, their production plays a vital role in the nation's economy. However, the threat of date palm leaf diseases poses a significant risk to production, potentially impacting the economic stability of the country. Thus, the early detection of palm leaf diseases is essential to prevent further damage to these crucial crops. This paper proposes the development of a web application aimed at detecting diseases in palm trees through an intelligent system, empowering farmers to ascertain the health of palm leaves efficiently. The system employs a Convolutional Neural Network (CNN) model, guided by the Dynamic System Development Method (DSDM) for its implementation. The CNN model is trained using a dataset that includes images of both healthy and diseased date palm leaves. A web application has been implemented to facilitate the prediction and detection of diseases in date palm leaves, aiming to minimize reliance on expert diagnosis. This innovation enables farmers to independently detect diseases early, fostering a proactive approach to disease management in date palm cultivation. © 2024 IEEE.

关键词： Agricultural economics

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MEC SIS APP GUIDE: An application for college students 1

MEC SIS APP GUIDE: An application for college students

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Subair, Shehaz Kumaran Nair, Smitha Sunil Middle East College Department of Computing and Electronics Engineering Muscat Oman

ISBN: (纸本)9798350348637

MEC SIS APP GUIDE is designed to enhance students' experience at Middle East College (MEC) in the Sultanate of Oman by providing a user-friendly mobile application for managing daily college activities with the help of the Student Information System (SIS) web service. Recognizing the preference for mobile devices among students, the app aims to offer services traditionally accessed through the website in a more convenient format. A survey conducted among MEC students showed strong support for an application that includes features such as attendance tracking, reminders, and campus navigation. The app, developed using Visual Studio Community (Xamarin framework), is available for both iOS and Android devices. Initial testing of the prototype yielded positive results, with plans for future improvements based on students' feedback and evolving needs. © 2024 IEEE.

关键词： Students

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Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2339-2356页

作者： R.Saravanan R.Muthaiah A.Rajesh School of Computing SASTRA Deemed UniversityThanjavur613401India School of Electrical&Electronics Engineering SASTRA Deemed UniversityThanjavur613401India

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio *** threshold identification method is implemented in the received signal at the secondary user based on the square *** proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division ***,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio *** the dynamic threshold,the signal ratio-based threshold is *** threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and *** general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user *** limitations undermine the sensing accuracy of the energy identification ***,the ETBED technique is developed to enhance the energy efficiency of cognitive radio *** projected approach is executed and analyzed with performance and comparison *** proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

关键词： Cognitive radio network spectrum sensing noise uncertainty modified black widow optimization algorithm energy detection technique

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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Timer Entrenched Baited Scheme to Locate and Remove Attacks in MANET

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Intelligent Automation & Soft computing 2023年第1期35卷 491-505页

作者： S.Padmapriya R.Shankar R.Thiagarajan N.Partheeban A.Daniel S.Arun Department of Computing Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Electronics and Communication Engineering Teegala Krishna Reddy Engg.College500097TelanganaIndia School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Department of Electronics and Communication Engineering Prathyusha Engineering CollegeThiruvallur602025India

The Mobile Ad-hoc Network(MANET)is a dynamic topology that provides a variety of executions in various *** most sticky topic in organizationalfields was MANET *** is helpless against various threats that affect its usability and *** dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organi-zation's execution and reliability by dropping all approaching packages via the noxious *** Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target ***-hoc On-demand Distance Vector(AODV)is a responsive steering convention with no corporate techniques to locate and destroy the dark opening *** improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and *** recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network *** implement the suggested method's performance with the help of Network Simulator(NS)-3 simulation ***,the proposed approach comes exceptionally near to the original AODV,absent black holes in terms of bandwidth,end-to-end latency,error rate,and delivery ratio.

关键词： Mobile ad-hoc network(MANET) wireless ad hoc network ADOV attacks denial of service

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Noise-tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 167-177页

作者： Xiuchun Xiao Chengze Jiang Qixiang Mei Yudong Zhang School of Electronics and Information Engineering Guangdong Ocean UniversityZhanjiangChina School of Cyber Science and Engineering Southeast UniversityNanjingChina School of Computing and Mathematical Sciences University of LeicesterLeicesterUK

The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering *** the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence ***,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed ***,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed ***,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem *** with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.

关键词： adaptive intelligent systems neural network real-time systems

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