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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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Trustworthy DNN partition for blockchain-enabled digital twin in wireless IIoT networks

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Science China(Information Sciences) 2024年第11期67卷 373-374页

作者： Xiumei DENG Jun LI Long SHI Kang WEI Ming DING Yumeng SHAO Wen CHEN Shi JIN Pillar of Information Systems Technology and Design Singapore University of Technology and Design National Mobile Communications Research Laboratory Southeast University School of Electronic and Optical Engineering Nanjing University of Science and Technology Department of Computing Hong Kong Polytechnic University Data61 CSIRO Department of Electronics Engineering Shanghai Jiao Tong University

The integration of artificial intelligence(AI) and digital twin(DT) technology has revolutionized the industrial Internet of Things(IIoT), enabling advanced automation and intelligent manufacturing [1]. Through sophisticated interactions between physical entities and their virtual counterparts,AI-driven DTs facilitate performance monitoring, analysis,simulation, and optimization of physical assets, enabling predictive maintenance and informed decision-making [2].

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Perception Gaps Between Hashtag Stakeholders on X (Twitter) - Application on the Australian National Disability Insurance Scheme (#NDIS) 20

Perception Gaps Between Hashtag Stakeholders on X (Twitter) ...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Zhang, Yu Bui, Hang Thanh Zipperle, Michael Buick, Fiona Chand, Satish Hussain, Omar Wang, Min Mo, Huadong Zhang, Wenjie School of Business Unsw Canberra Canberra Australia University of Canberra Faculty of Science and Technology Canberra Australia School of Systems & Computing Unsw Canberra Canberra Australia School of Computer Science and Engineering Unsw Sydney Sydney Australia

ISBN: (纸本)9798350365856

A hashtag on X (Twitter) can attract a range of stakeholders with diverse perceptions on the hashtag. Under-standing and considering the differences between these stake-holders' perceptions on specific hashtags can be highly valuable for policy making in both public and private sectors. This paper proposes a framework to analyse the depth and width of the perception gap between stakeholders of a hashtag using sentiment analysis and zero-shot learning on large scale Twitter data. The framework was applied to the hashtag '#NDIS', the National Disability Insurance Scheme, Australia's pilot personalised disability service which provides funding to support people with disability. Over 155,000 tweets were collected and analysed to identify and assess the perception gap between the two most significant stakeholders (NDIS participants and service providers) on six main topics during the implementation of the NDIS. The study verified the effectiveness of the proposed framework, revealing a widespread perception gap between the two NDIS stakeholders, as well as differences in their online behaviours using Twitter. The results suggest that the NDIS policymakers and service providers should pay attention to the utilisation of social media, including hearing voice from the broader public and creating channels for transparent engagement with other NDIS stakeholders. © 2024 IEEE.

关键词： Tweets

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Optimal Tracking Performance of Networked Control systems Under DoS and Its Applications

Optimal Tracking Performance of Networked Control Systems Un...

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2024 China Automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

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A Priced Oblivious Transfer Scheme with Time Constraints for Mobile Edge computing 10

A Priced Oblivious Transfer Scheme with Time Constraints for...

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10th IEEE Smart World Congress, SWC 2024

作者： Yang, Huijie Zhang, Tao Zhou, Tianqi Sun, Jianfei Shen, Jian Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou China Zhejiang Sci-Tech University School of Info Sci and Eng Hangzhou China Singapore Management University School of Computing and Informaion Systems Singapore

ISBN: (纸本)9798331520861

The development of mobile edge computing has significantly influenced the popularity and adoption of products, including smart phones, smart appliances, smart wearable devices, etc. As consumers interact with websites or APPs, they provide their consumption data to service providers. Subsequently, service providers analyze consumer behavior with the help of smart data driven modeling (SDDM) so that they can offer more personalized and suitable products to consumers. However, this process raises concerns about user or group privacy and the potential leakage of sensitive information to the SDDM. Consequently, the primary objective of this paper is to ensure that the SDDM can analyze the users' preferences effectively without knowing the privacy of the group members. A membership privacy group signature protocol with time constraints is proposed to guarantee the member has the authority to access APPs with valid signature periods. In addition, The proposed protocol protects the privacy of the user who joins in or leaves the group and time privacy. In addition, a priced oblivious transfer protocol is designed to protect the selection of members without exposing their privacy. The security and performance indicate that the proposed protocol can be instantiated in mobile edge computing with high security and efficiency. © 2024 IEEE.

关键词： Authentication Protocol

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Sustainable Energy Planning for Community Microgrids Considering Economic, Environmental, and Resilience Factors

Sustainable Energy Planning for Community Microgrids Conside...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Uddin, Moslem Mo, Huadong Dong, Daoyi School of Engineering and Technology The University of New South Wales CanberraACT2610 Australia School of Systems and Computing The University of New South Wales CanberraACT2610 Australia School of Engineering Australian National University Ciicada Lab CanberraACT2601 Australia

ISBN: (纸本)9781665410205

This study presents a framework for sustainable energy planning of community microgrids (MGs), integrating optimal design and decision-support tools. A rural community in New South Wales, Australia, is considered as a case study for this investigation. The proposed microgrid framework is evaluated based on economic viability, environmental sustainability, and community resilience. The economic analysis reveals an attractive net present cost of 3.26 million over the MG's 25-year lifetime, with a competitive levelized cost of energy of 0.196 per kWh. The environmental impact assessment quantifies a significant reduction of 394.429 tonnes of CO2- equivalent greenhouse gas emissions annually through the integration of 200 kW of solar photovoltaic and 258 kW of wind turbines. The resilience assessment demonstrates a high energy reliability with zero unmet loads facilitated by backup systems and decision-making tools. The findings contribute to the field of sustainable energy planning by providing a comprehensive and integrated approach that addresses the complex interplay of economic, environmental, and resilience factors in the context of community MGs. © 2024 IEEE.

关键词： Clean energy

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Normalized Least Dependent Difference: A Method in Solving Incorrectly Identified Monotonic Linearity Relationship in Linear Regression

Normalized Least Dependent Difference: A Method in Solving I...

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2025 International Workshop on Advanced Imaging Technology, IWAIT 2025

作者： Yeap, Herrick Han Lin Lim, Le Ying Lim, Michelle Chee Ern Chow, Brandon Chen Hong Ong, Hui Yan Kimberly Eu, Kok Seng Yap, Kian Meng Research Centre for Human-Machine Collaboration Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Petaling Jaya47500 Malaysia School of Engineering Faculty of Innovation & Technology Taylor's University Subang Jaya47500 Malaysia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

In machine learning, the reliability of an analytical method is crucial in validating the assumption of linearity for any linear regression *** existing analytical method of proving the assumption linearity, such as Pearson's Correlation Coefficient (PCC), Spearman's Rank Correlation Coefficient (SRCC) and Kendall's Tau Correlation Coefficient (KTCC), has its limitation as it does not work in monotonic relationship *** this paper, we propose a Normalized Least Dependent Difference (NLDD) method to improve the limitation of existing linearity method in identifying monotonic relationship *** calculating the difference between each data point and its predicted value on the regression line, we can determine how much the predicted value deviates from the observed value.A consistent difference between each data point and its predicted value, represented by a relative standard deviation in the y-axis that is near to its mean, suggests that the model accurately reflects the relationship between the dependent variable and the independent *** findings show that our NLDD is effective in identifying linearity in linear relationship graph and non-linearity in monotonic relationship graph. © 2024 SPIE.

关键词： Multiple linear regression

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Of Rodents and Primates: Time-Variant Gain in Drift–Diffusion Decision Models

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Computational Brain and Behavior 2024年第2期7卷 195-206页

作者： Asadpour, Abdoreza Tan, Hui Lenfesty, Brendan Wong-Lin, KongFatt Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Magee Campus Northern Ireland Derry~Londonderry United Kingdom Département Electronique et Technologies Numériques Polytech Nantes Nantes Université Nantes France

Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift–diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM’s signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to a different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys, and humans, we systematically showed that time-variant gain only on the DDM’s noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM’s different components can lead to different choice behaviours, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting. © 2024, Crown.

关键词： Cognitive computational modelling Drift–diffusion model Perceptual decision-making Time-variant gain Urgency signal

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Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

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Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd Alghamdi Abdullah S.A.L-Malaise AL-Ghamdi Mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information Systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long Short-Term Memory(LSTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(AI) Deep Learning(DL) snapshot ensemble

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Autism Spectrum Disorder Detection from EEG Through Hjorth Parameters and Classification Using Neural Network 1st

Autism Spectrum Disorder Detection from EEG Through Hjorth P...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Peya, Zahrul Jannat Zaman, Bipasha Akhand, M.A.H. Siddique, Nazmul Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna9203 Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland Londonderry United Kingdom

ISBN: (纸本)9783031346217

Autism spectrum disorders (ASD) are a collection of neurodevelopmental disorders. Even though ASD has no cure, early detection and intervention can help developing language, behavior, and communication skills. Research shows that ASD can be diagnosed from brain signals, particularly electroencephalography (EEG) where the brain activity is recorded over time as an EEG signal from humans’ scalp and then used to study neuropsychiatric disorders. This study investigates the classification performance of the Neural Networks (NN) model with Hjorth features namely activity, mobility, and complexity extracted from EEG signals through selected channels using the XGBoost algorithm. The classification accuracy is 80% using all 19 channels of EEG data whereas the accuracy of the model using activity, mobility and complexity reached 100% with selected channels. Hjorth parameter-based NN model with channel selection seems to be promising that improves the computation complexity significantly. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Electroencephalography

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