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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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NSGAN:a non-dominant sorting optimisation-based generative adversarial design framework for alloy discovery

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npj Computational Materials 2024年第1期10卷 2087-2101页

作者： Z.Li N.Birbilis College of Engineering Computing and CyberneticsThe Australian National UniversityActonACTAustralia Faculty of Science Engineeringand Built EnvironmentDeakin UniversityWaurn PondsVICAustralia

The design and discovery of new materials is fundamental to advancing scientific and technological *** recent emergence of the materials genome concept holds great promise in revolutionising materials science by enabling the systematic utilisation of data for efficient prediction and optimisation of‘superior’***,the materials genome approach can be stymied by the vast complexity of design spaces,which often demand substantial computational resources and sophisticated data processing *** address these challenges,this work introduces a generative design framework called the non-dominant sorting optimisation-based generative adversarial networks(NSGAN).Capitalising on the synergies of genetic algorithms(GA)and generative adversarial networks(GANs),NSGAN provides a robust and efficient approach for tackling high-dimensional multi-objective optimisation design *** validate the efficacy of the proposed framework,we applied the model to a comprehensive dataset of aluminium ***,an online tool was created as a supplementary resource,offering a brief introduction to this innovative method for the wider scientific *** study explores the potential of a predictive and data-driven approach in material design,indicating a promising pathway for widespread applications in the field of materials science.

关键词： alloy offering holds

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of Computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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A hybrid memory architecture supporting fine-grained data migration

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Frontiers of Computer science 2024年第2期18卷 31-41页

作者： Ye CHI Jianhui YUE Xiaofei LIAO Haikun LIU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Department of Computer Science Michigan Technological UniversityMichigan 49931USA

Hybrid memory systems composed of dynamic random access memory(DRAM)and Non-volatile memory(NVM)often exploit page migration technologies to fully take the advantages of different memory *** previous proposals usually migrate data at a granularity of 4 KB pages,and thus waste memory bandwidth and DRAM *** this paper,we propose Mocha,a non-hierarchical architecture that organizes DRAM and NVM in a flat address space physically,but manages them in a cache/memory *** the commercial NVM device-Intel Optane DC Persistent Memory Modules(DCPMM)actually access the physical media at a granularity of 256 bytes(an Optane block),we manage the DRAM cache at the 256-byte size to adapt to this feature of *** design not only enables fine-grained data migration and management for the DRAM cache,but also avoids write amplification for Intel Optane *** also create an Indirect Address Cache(IAC)in Hybrid Memory Controller(HMC)and propose a reverse address mapping table in the DRAM to speed up address translation and cache ***,we exploit a utility-based caching mechanism to filter cold blocks in the NVM,and further improve the efficiency of the DRAM *** implement Mocha in an architectural *** results show that Mocha can improve application performance by 8.2%on average(up to 24.6%),reduce 6.9%energy consumption and 25.9%data migration traffic on average,compared with a typical hybrid memory architecture-HSCC.

关键词： non-volatile memory hybrid memory system data migration fine-grained caching

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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Computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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Enhanced electrode-level diagnostics for lithium-ion battery degradation using physics-informed neural networks

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Journal of Energy Chemistry 2025年第5期104卷 618-627页

作者： Rui Xiong Yinghao He Yue Sun Yanbo Jia Weixiang Shen National Engineering Research Center of Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation *** models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing ***-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational *** study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity,electrode capacities,and lithium inventory *** proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public *** results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 *** demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.

关键词： Lithium-ion batteries Electrode level Ageing diagnosis Physics-informed neural network Convolutional neural networks

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Hybrid Seagull and Whale Optimization Algorithm-Based Dynamic Clustering Protocol for Improving Network Longevity in Wireless Sensor Networks

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China Communications 2024年第10期21卷 113-131页

作者： P.Vinoth Kumar K.Venkatesh Department of Networking and Communications School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur 603203India

Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach essential for minimizing unnecessary transmission energy consumption with sustained network lifetime. This clustering process is identified as the Non-deterministic Polynomial(NP)-hard optimization problems which has the maximized probability of being solved through metaheuristic *** adoption of hybrid metaheuristic algorithm concentrates on the identification of the optimal or nearoptimal solutions which aids in better energy stability during Cluster Head(CH) selection. In this paper,Hybrid Seagull and Whale Optimization Algorithmbased Dynamic Clustering Protocol(HSWOA-DCP)is proposed with the exploitation benefits of WOA and exploration merits of SEOA to optimal CH selection for maintaining energy stability with prolonged network lifetime. This HSWOA-DCP adopted the modified version of SEagull Optimization Algorithm(SEOA) to handle the problem of premature convergence and computational accuracy which is maximally possible during CH selection. The inclusion of SEOA into WOA improved the global searching capability during the selection of CH and prevents worst fitness nodes from being selected as CH, since the spiral attacking behavior of SEOA is similar to the bubble-net characteristics of WOA. This CH selection integrates the spiral attacking principles of SEOA and contraction surrounding mechanism of WOA for improving computation accuracy to prevent frequent election process. It also included the strategy of levy flight strategy into SEOA for potentially avoiding premature convergence to attain better trade-off between the rate of exploration and exploitation in a more effective manner. The simulation results of the proposed HSWOADCP confirmed better network survivability rate, network residual energy and network overall throughput on par wi

关键词： clustering energy stability network lifetime seagull optimization algorithm(SEOA) whale optimization algorithm(WOA) wireless sensor networks(WSNs)

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A new perspective exploration of machine learning algorithms for defending Side-Channel attacks

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Multimedia Tools and Applications 2025年第16期84卷 17021-17040页

作者： Ramakrishnan, Vedhavathy Thoguluva Seethapathy, Murugaanandam Sundar, Ramesh Dhandapani, Saveetha Munusamy, Sundarrajan Balasubrahmaniam, Lakshmi Dhevi Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

Machine learning algorithms are used in various real-time applications, where security is one of the major problems. Security is applied in various aspects of the application in cloud computing. One of the security issues is a side-channel attack, which is not a common problem that can be solved by a point solution. The emerging computing industries need a better solution for side-channel attack detection since the amount of data used in communication and sharing on the Internet is enormous. Several old research works have concluded that machine learning algorithms are highly suitable for data analytics. Hence, this paper has aimed to propose a better machine-learning model for side-channel attack detection. This paper explains some machine learning algorithms from a technical point of view. The explanation identifies that the Convolution Neural Network algorithm is selected as a better algorithm for side-channel attack detection. The proposed CNN model has experimented with Python software to verify the results. Since CNN is more efficient in data analytics, the analysis result produces more accurate predictions than earlier algorithms. The performance of the proposed CNN is compared with the earlier algorithms for evaluating it. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data Analytics

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Revealing the effect of external pressure on solid-electrolyte interphase and lithium plating in lithium-ion batteries

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Journal of Energy Chemistry 2025年第3期102卷 734-744页

作者： Rui Xiong Haocheng Li Bing-Ang Mei Hongwen He Weixiang Shen National Engineering Research Center of Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 00081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthornVictoria 3122Australia

Lithium plating is a detrimental phenomenon in lithium-ion cells that compromises both functionality and *** study investigates electro-chemo-mechanical behaviors of lithium plating in lithium iron phosphate pouch cells under different external *** force microscopy nanoindentation is performed on the graphite electrode to analyze the influence of external pressure on solid-electrolyte interphase(SEI),revealing that the mechanical strength of SEI,indicated by Young's modulus,increases with the presence of external ***,an improved phase field model for lithium plating is developed by incorporating electrochemical parameterization based on nonequilibrium *** results demonstrate that higher pressure promotes lateral lithium deposition,covering a larger area of ***,electrochemical impedance spectroscopy and thickness measurements of the pouch cells are conducted during overcharge,showing that external pressure suppresses gas generation and thus increases the proportion of lithium deposition among galvanostatic overcharge *** integrating experimental results with numerical simulations,it is demonstrated that moderate pressure mitigates SEI damage during lithium plating,while both insufficient and excessive pressure may exacerbate *** study offers new insights into optimizing the design and operation of lithium iron phosphate pouch cells under external pressures.

关键词： Lithium-ion batteries Overcharge Lithium plating Electrochemical mechanics Synergistic effects

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