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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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Computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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A decision tree based quasi-identifier perturbation technique for preserving privacy in data mining

A decision tree based quasi-identifier perturbation techniqu...

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2009 3rd International Conference on Research Challenges in information science, RCIS 2009

作者： Dai, Bi-Ru Lin, Yang-Tze Department ofComputer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781424428656

Classification is an important issue in data mining, and decision tree is one of the most popular techniques for classification analysis. Some data sources contain private personal information that people are unwilling to reveal. The disclosure of person-specific data is possible to endanger thousands of people, and therefore the dataset should be protected before it is released for mining. However, techniques to hide private information usually modify the original dataset without considering influences on the prediction accuracy of a classification model. In this paper, we propose an algorithm to protect personal privacy for classification model based on decision tree. Our goal is to hide all person-specific information with minimized data perturbation. Furthermore, the prediction capability of the decision tree classifier can be maintained. As demonstrated in the experiments, the proposed algorithm can successfully hide private information with fewer disturbances of the classifier. © 2009 IEEE.

关键词： Decision trees

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Assessing and improving authentication confidence management

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information Management and Computer Security 2010年第2期18卷 124-139页

作者： Pearce, Michael Zeadally, Sherali Hunt, Ray Department of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand Department of Computer Science and Information Technology University of the District of Columbia Washington DC United States Department OfComputer Science and Software Engineering University of Canterbury Christchurch New Zealand

Purpose-The purpose of this paper is to address some weaknesses in the handling of current multi-factor authentication, suggests some criteria for overcoming these weaknesses and presents a simple proof of concept authentication system. Design/methodology/approach-First, this paper evaluates some of the underlying practices and assumptions in multi-factor authentication systems. Next, the paper assesses the implications of these when compared to a quantitative authentication risk management approach. Based upon these implications this paper next note the requirements for an improved system and detail some related research areas that meet these requirements. Finally, this paper discussed how a system that meets these requirements through the application of that research could provide benefits and outlined a simple points-based authentication system. Findings-The paper proposes that many of the weaknesses in authentication confidence management could be effectively mitigated through the deployment of a factor independent multi-modal fusion quantitative authentication-based system. This paper details a simple point-based approach that does this and discuss how addressing the problems in handling authentication confidence could further optimise risk management in multi-factor authentication systems. Practical implications-This paper's suggestions for optimising multi-factor authentication have many implications within medium to high-security commercial and government applications. Correct authentication risk handling enables decisions regarding risk and authentication to be made more accurately. Originality/value-This implications of the issues discussed in this paper have relevance to anyone who deploys or uses any medium to high-security authentication system. As the bottom end of the medium to high-security range includes online banking, there are implications for a wide range of stakeholders. © Emerald Group Publishing Limited.

关键词： Authentication

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A scenario-based model to improve the quality of software inspection process

A scenario-based model to improve the quality of software in...

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4th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2012

作者： Taba, Navid Hashemi Ow, Siew Hock Department of Software Engineering Faculty OfComputer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9781467331135

Independent researches of expert groups and scientific papers show that , although some testing models and automated and manual tools are mostly used, but still some software and software projects deal with important defects that may lead to project failure. Most of software inspection models are independent of software developing process and concentrating on inspection artifacts. In this article, the proposed model is based on software developing phases adapted predefined scenarios. This inspection approach removes some possible defects in each of software development phase and avoids amplifying these defects in each of the next phases. Defect learning is the interesting point of scenario-based proposed model. Learning is a basic factor for a model to be intelligent. In this model a learning plan is created and executed through which, according to founded results, inspection process is modified and updated. The efficiency of model is evaluated through a case study. © 2012 IEEE.

关键词： Software testing

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An integration platform for developing context-aware applications based on context reasoning and service automation techniques

An integration platform for developing context-aware applica...

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11th International Conference on Advanced Communication Technology, ICACT 2009

作者： Huang, Po-Cheng Chien, Cheng-Vi Lee, Kuan-Rong Kuo, Yau-Hwang Wu, Ming-Zoo Department ofComputer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Information Engineering Kun Shan University Yung-Kang Tainan Taiwan Institute for Information Industry Taiwan

ISBN: (纸本)9788955191387

This paper proposes an integration platform composed of Agent platform and Open Services Gateway Initiative (OSGi) framework to assist in developing context-aware applications. For developing context-aware applications, we firstly have to gather data through sensor networks and reason the meaningful context information. Secondly, context-aware applications must make the decision according to the context information, and provide the user-centric service through selecting the suitable device and deciding the service flow. In the integration platform, the agent platform providing personalization and learning capabilities to assist in reasoning context information. Furthermore, OSGi providing the modularization bundles of applications which aid to management the devices and services. Finally, the elder mental care scenario is implemented to verify the performance of the integration platform.

关键词： Sensor networks

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Cardiovascular Disease Prediction with Convolutional Neural Networks and Hyperparameter Tuning 8th

Cardiovascular Disease Prediction with Convolutional Neural ...

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8th International Conference on information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Metwally, Abdelnasser Abdelwakil Khan, Asif Ali Amin, Sherif Tawfik Hamdan, Alfadil Ahmed Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department ofComputer and Network Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence & Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

CVD continues to pose a substantial global health burden, demanding the need for the creation of precise predictive models that can aid in the timely identification and evaluation of risks. This research paper aims to provide a thorough examination of the prediction of CVD by utilizing CNN in aggregation with hyperparameter tuning techniques. This paper examines the effectiveness of three widely recognized CNN architectures, namely ResNet, VGGNet, and Xception CVD. The Tree-structured Parzen Estimator (TPE) is utilized as a method for tuning hyperparameters in order to optimize the CNN models. The study commences with a comprehensive examination of the dataset, encompassing a wide array of cardiovascular CT image data. The chosen CNN architectures serve as the fundamental framework for our predictive models. The selection of these architectures is based on their established track record in effectively addressing image analysis tasks and their capacity to accurately capture complex patterns and features present in medical images. The models undergo training using a dataset in order to acquire knowledge about distinctive features that are linked to various cardiovascular conditions. The experimental findings presented in the study indicate that the utilization of the Xception architecture, in aggregation with TPE for hyperparameter optimization, yields the most favorable predictive accuracy. Specifically, the achieved accuracy rate of 98.26% is notably remarkable. This result highlights the significance of not only choosing advanced CNN architectures but also optimizing their hyperparameters to fully exploit their potential in predicting cardiovascular disease. This establishes it as a promising tool for the early diagnosis and risk assessment of CVD. This study establishes a fundamental basis for future investigations in the advancement of precise and dependable predictive models applicable to various medical imaging contexts. © The Author(s), under exclusive license

关键词： Diseases

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Attribute Assailability and Sensitive Attribute Frequency based Data Generalization Algorithm for Privacy Preservation

Attribute Assailability and Sensitive Attribute Frequency ba...

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Basapur, Satish B. Shylaja, B.S. Venkatesh Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Bengaluru560056 India Dr. Ambedkar Institute of Technology Department OfComputer Science and Engineering Bengaluru560056 India University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru560 001 India

ISBN: (纸本)9781665420051

The data publishing and analysis expose users' and users group's sensitive data and their identities. As a result, the adversary initiate inference attack using exposed users' identifies and associated sensitive information. Some of the users' attribute and users' group attributes values are highly vulnerable and their contiguity increase privacy breach. The traditional k-anonymity based privacy models prevent identity disclosure but fails to prevent sensitive attribute values disclosure and ignore repercussion of assailable attributes on users' group privacy. This research propose a novel data generalization algorithm that enhances users' group privacy and data utility. The proposed method calculate the susceptibility of quasi-identifier attributes, frequency of sensitive attributes and their association. These values are used for form equivalence class that satisfies k-anonymity and β-likeness property. The proposed generalization algorithm considers both susceptibility, frequency of sensitive attributes and their association while anonymizing data. The extensive experiments are carried out on open source Apache Spark environment. The simulation results demonstrate that efficiency and effectiveness of proposed algorithm. This research work outperform other state-art data privacy model in terms of thwarting inference attack, information loss and accuracy of model. © 2021 IEEE.

关键词： Sensitive data

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Solving quadratic assignment problems by a tabu based simulated annealing algorithm

Solving quadratic assignment problems by a tabu based simula...

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International Conference on Intelligent and Advanced Systems, ICIAS

作者： Jiunn-Chin Wang Department ofComputer Science and Information Engineering Jinwen University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781424413553

The quadratic assignment problem (QAP) is a hard and classical combinatorial optimization problem. In this paper a hybrid algorithm that combines the simulated annealing (SA) and tabu search (TS) is proposed to solve the QAP. The search of simulated annealing may stuck at a local optimum due to the low acceptable moves, particularly as the barrier is high and the temperature is low. A guided restart strategy is incorporated into SA to escape from a local optimum and re-annealing from a promising point more efficiently. The tabu list, as a short-term memory, is used in the move generation procedure to prohibit the highly frequent moves and diversify the search. Performance evaluation has been carried out on several problem instances in the QAPLIB. Experimental results indicate that these two strategies significantly improve the performance of simulated annealing algorithm.

关键词： Simulated annealing Annealing Optimization Search problems Schedules Biological system modeling Distance measurement

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Design of Portable ECG Recorder with USB Storage

Design of Portable ECG Recorder with USB Storage

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IEEE Conference on Electron Devices and Solid-State Circuits

作者： C. S. Ho T. K. Chiang C. H. Lin P. Y. Lin J. L. Cheng S. H. Ho Department ofComputer Science Information Engineering Southern Taiwan University of Technology Tainan Taiwan Department ofElectronic Engineering Southern Taiwan University of Technology Tainan Taiwan Department ofComputer Science Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781424406364

The goal of the study is to integrate several techniques, which includes biomedical signal amplifier/filter, protection circuit, analog to digital converter, USB flash disk (storage) and computer's interface, to design a portable ECG recorder. The subjects could measure their ECG signal any time and anywhere. After transferring their ECG signal to the computer, the subject and clinician can observe the ECG data on the computer window. Besides, to test the validity of the system, a computer program is designed to locate R-wave peak of the recorded ECG data. Then, R-R interval can be calculated by location of R-wave peak and sampling rate. Heart rate can be evaluated by the following equation: heart rate (per minute) = 60/R-R interval (unit: second).

关键词： Electrocardiography Universal Serial Bus Heart rate Digital filters Protection Circuits Analog-digital conversion Computer interfaces Signal design Biomedical measurements

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Adaptive Parallel Model Combination for reduced environmental mismatch in noisy speech recognition

Adaptive Parallel Model Combination for reduced environmenta...

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International Conference on Electronic Design (ICED)

作者： S. S. Tan Abdul Manan Ahmad Software Engineering Department Faculty ofComputer Science and Information System University of Technology Malaysia Speech Laboratory Faculty ofComputer Science and Information System University of Technology Malaysia

Due to environmental mismatch, speech recognition systems often exhibit drastic performance degradation in noisy conditions. This paper presents a model based technique termed adaptive parallel model combination (APMC) which compensates the initial acoustic models to reduce the discrepancy. APMC used the well-known PMC technique to composite a set of corrupted speech models, while fine tuning the mean parameter of the models using a transformation-based adaptation technique called Maximum Likelihood Spectral Transformation (MLST). Evaluated on a context-independent phone recognition task, APMC was found to be superior to both PMC and MLST, especially in non-stationary noisy conditions. On average, APMC has achieved 48.81% improvement over the initial models, whereas PMC and MLST have improved the accuracy by 34.12% and 35.23% respectively.

关键词： Noise reduction Working environment noise Speech recognition Hidden Markov models Noise robustness information systems Degradation Speech enhancement Software engineering Acoustic noise

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