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Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

computers, Materials & Continua 2023年第1期74卷 2261-2278页

作者： Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad Faculty of Engineering and IT The British University in DubaiUnited Arab Emirates Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Faculty of Computer Science NCBA&ELahore54660Pakistan

Energy is essential to practically all exercises and is imperative for the development of personal ***,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current ***,there is a shortage of energy,as the energy required is higher than that *** new plans are being designed to meet the consumer’s energy *** many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future *** overcome the challenges of energy consumption optimization,in this study,we apply an energy management *** fusion has recently attracted much energy efficiency in buildings,where numerous types of information are *** proposed research developed a data fusion model to predict energy consumption for accuracy and miss *** results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

关键词： Energy consumption intelligent machine learning technique smart homes prediction

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A Novel Machine Learning Unmanned Swarm Intelligence Based Data Security in Defence system 2

A Novel Machine Learning Unmanned Swarm Intelligence Based D...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Chouhan, Nitesh Jakka, Geethamanikanta Navale, Geeta S. Verma, Garima Patil, Prerna Siddharth Dhotay, Megha Ashok Mlv Textile and Engineering College Department of Information Technology Rajasthan Bhilwara India University of the Cumberlands Department of Information Technology FremountCA United States Sinhgad Institute of Technologyand Science Department of Computer Engineering Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India Dr. VishwanathKarad Mit World Peace University Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350315905

In addition to conducting research on applications in the field of civilian life, the article provides an overview of existing and anticipated possibilities for the development of artificial intelligence algorithms, particularly in military applications. The use of artificial intelligence calculations in advanced mechanics, object distinguishing proof, military coordinated factors, and cybersecurity got most of the consideration. It features the issues with the ongoing fixes and how man-made reasoning might help. Large media communications firms are at this point not only ready to offer 3G/4G administrations for remote organizations;all things considered, another time for "drones"has advanced to empower "Zero Touch Innovation,"especially in pandemic circumstances like those brought about by the Covid. Robots may now be utilized for an assortment of crisis circumstances, including food conveyance, clinical circulation, tower imaging, calamity help, and then some. These robots' data may be utilized as a hotspot for data examination. These a large number with swarm intelligence give customary frameworks - which utilize a few units to execute an errand - an upper hand. © 2023 IEEE.

关键词： Machine learning

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Crafting The Chance: Linear Congruential Generator in Randomization system in RPGs

Crafting The Chance: Linear Congruential Generator in Random...

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International Conference on information technology systems and Innovation (ICITSI)

作者： Jos Timanta Tarigan Avin Chaili Salim Anandhini Medianty Nababan Brian Wijaya Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

In role-playing games (RPGs), randomization plays an important role in designing the loot system. The unpredictability of rewards enhances player engagement and replayability. Thus, it is crucial for developers to build an algorithm to simulate an engaging randomization method. This research proposed the use of Linear Congruential Generator (LCG) based pseudo-random numbers generator in an RPG. To test the performance of the proposed method, we developed our own game titled "Rudantara", a third person roguelike game. The game employed LCG-based random generators to randomized loot drops, statistics, and upgrades. The objective of this research is to measure the performance of the proposed randomization method in our game and analyze its impact on gameplay, fairness and player satisfaction.

关键词： Human computer interaction Technological innovation Role playing games Games Generators information technology

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Modelling and Optimal Allocation of CCHP-Integrated-Energy-system Considering Low-Carbon and Economic Benefits: Methods and Vision 6

Modelling and Optimal Allocation of CCHP-Integrated-Energy-S...

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6th International Conference on Energy systems and Electrical Power, ICESEP 2024

作者： Li, Maodong Lao, Kengweng Lu, Wentao University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China School of Information Science and Technology Fudan University Shanghai China

ISBN: (纸本)9798350353563

Along with the development of smart energy, we take typical integrated energy systems (IES) into research considering various coupling modes and constraints, building a model of the IES for combined cooling, heating and power system, and propose the using of gas micro-turbine as a power supply. On this basis, the three dimension particle swarm optimization algorithm is applied to conduct operational and management research in one typical day, which provides a reference for the economic and low-carbon planning and operation of the IES, moreover demonstrates the superiority and effectiveness of the integrated energy systems compared to the traditional discrete energy systems. Furthermore, the paper gives the effective steps of allocation and optimization methods and vision of integrated energy systems. © 2024 IEEE.

关键词： Energy development

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Real-Time Deepfake Detection for AI-Generated Arabic Speech

Real-Time Deepfake Detection for AI-Generated Arabic Speech

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Rewaa Awaad Abdelrahman Hedya Mariam Fadel Ahmed B. Zaky Department of Computer Science and Information Technology Egypt-Japan University of Science and Technology Alexandria Egypt Department of Computer Science and Information Systems Minia University Minya Egypt Faculty of Engineering at Shoubra Benha University Benha Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

This study investigates the effectiveness of Retrieval-based Voice Conversion (RVC) in detecting AI-generated Arabic speech across diverse linguistic contexts. The primary research questions address whether the RVC model can accurately differentiate between real and synthetic speech samples in various languages and its generalization capability across different linguistic contexts. Experimental evaluations show that the model achieves accuracies of 92% using Ensemble Learning (XGBoost and LightGBM), 93% with Meta Learning, and 90% with Neural Networks. Assessments encompass scenarios with real Arabic and English speech not included in training datasets, as well as different speakers across languages. The study contributes insights into the robustness and practical application of RVC for speech authentication.

关键词： Training Metalearning Deepfakes Neural networks Speech recognition Linguistics Robustness Real-time systems Speech synthesis Ensemble learning

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SVIFNN: Robust Inpainting Fourier Neural Network for SST Scientific Visualization Image: Leveraging Significant Stability and Non-Significant Anomalies

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Zuo, Zijie Nie, Jie Wang, Xin Dong, Junyu Ocean University of China Faculty of Information Science and Engineering Qingdao266100 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

The Scientific Visualization Images (SVI) of Sea Surface Temperature (SST) play a pivotal role as visual resources for investigating oceanographic processes. However, they are often plagued by extensive data gaps due to objective factors like cloud cover. Additionally, their content deviates significantly from ordinary images, posing challenges for conventional completion techniques. Given the intricate nature of marine systems, completing the visualization of sea surface temperature presents several challenges. Firstly, predicting missing segments relies not only on prominent patterns but also on subtle anomalies, which are often overlooked by methods focused on extracting prominent features. Secondly, these images exhibit chaos and lack clear semantics, making it difficult for methods primarily focused on semantic extraction to effectively complete them. To address these challenges, this study presents a novel method named the Inpainting Fourier Neural Network for SVI (SVIFNN). This approach employs a twin-stream architecture to highlight both significant stability and non-significant anomalies. Notably, it incorporates a "reverse attention mechanism" in the non-significant anomalies extraction stream to preserve unconventional information. Furthermore, by cascading Fourier neural operator (FNO), it leverages frequency domain characteristics to mitigate spatial chaos. Through a frequency domain feature extraction module, it achieves an adaptive fusion of significant stability and nonsignificant anomalies. Experiment results demonstrate SVIFNN’s superiority over State-Of-The-Art (SOTA) methods, particularly under a 68% missing rate condition. Significant improvements are observed in R2 (18.1%, 19.3%, and 21.8%) and reductions in RMSE (22.6%, 28.8%, and 23.8%) across different Noise-to-Signal (N/S) ratios of 0.1, 0.2, and 0.3, respectively, underscoring SVIFNN’s robustness in handling SVIs extensive data gaps. Adequate ablation experiments further validate the effec

关键词： Image enhancement

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Hybrid approach for next basket recommendation system

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International Journal of information technology (Singapore) 2023年第3期15卷 1733-1740页

作者： Yannam, V Ramanjaneyulu Kumar, Jitendra Vankayala, Tejaswini Babu, Korra Sathya Department of Computer Science and Engineering National Institute of Technology Rourkela Sector-1 Odisha Rourkela 769008 India Department Indian Institute of Information Technology Design and Manufacturing Kurnool Jagannathagattu Andhra pradesh Kurnool 518007 India

The recommendation system’s task is to predict what a user would like to buy in his/her next shopping period given in shopping history. Many state-of-the-art recommendation systems have relied mainly on user explicit feedback to deliver recommendations to the end-users since implicit feedback does not directly reflect user feedback. A set of items (multiple) that can be recommended for an user is called next basket recommendation. Most of the next basket recommendation systems treat user sequential behavior and common interests differently. These types of systems are only capable to capture local sequential behavior between adjacent baskets. In this study, an effort was made to close the gap between the hybrid approach’s locally and globally collected item sequences. This paper proposes a hybrid recommendation system that deals with both issues, achieving better results. The model consists of an autoencoder to extract rich contextual information from the user implicit feedback and a recurrent neural network to learn a dynamic representation of the user and capture global sequential features between baskets. The proposed approach has been tested on two bench mark datasets and the results indicate that it is more effective than the state-of-the-art recommendation systems. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Autoencoders Deep learning Hybrid Recurrent neural networks

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TSAC-DRM: A Drug Recommendation Model Based on Two-Stage Attention Mechanism and Constraint Learning

TSAC-DRM: A Drug Recommendation Model Based on Two-Stage Att...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Shaofu Lin Ziqian Qiao Jianhui Chen Faculty of Information Technology Beijing University of Technology Beijing China School of Computer Science Beijing University of Technology Beijing China Department of Informatics School of Computer Science Beijing University of Technology Beijing China School of Information Science and Technology Beijing University of Technology Beijing China

ISBN: (数字)9798331504946

ISBN: (纸本)9798331504953

Current drug recommendation systems primarily rely on data-driven approaches, which often overlook the importance of incorporating prior knowledge and capturing temporal dependencies in patient histories. To address these limitations, we propose TSAC-DRM, an innovative drug recommendation model that integrates a two-stage attention mechanism and constraint learning. The two-stage attention mechanism, adapted from the Crossformer, focuses on learning patient historical records and exploring implicit temporal dependencies. This allows the model to better understand the patient's condition and provide more accurate recommendations. Furthermore, TSAC-DRM formalizes drug contraindication relationships as constraints in the loss function, balancing the benefits of drugs for diseases and the risks of contraindications among comorbidities. By integrating prior knowledge through constraint learning, the model ensures the safety and rationality of the recommendations. Experiments conducted on the MIMIC-III dataset demonstrate the superiority of TSAC-DRM compared to traditional recommendation models. The results highlight the advantages of combining the two-stage attention mechanism and constraint learning in drug recommendation tasks, offering new perspectives for developing intelligent decision support systems in healthcare.

关键词： Drugs Adaptation models Technological innovation Attention mechanisms MIMICs Medical services Natural language processing Safety Recommender systems Diseases

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Managing aging bridges under seismic hazards through deep reinforcement learning 12th

Managing aging bridges under seismic hazards through deep re...

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12th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2024

作者： Metwally, Z. Andriotis, C.P. Molaioni, F. Faculty of Architecture and the Build Environment Delft University of Technology Delft Netherlands Department of Civil Engineering and Computer Science Engineering University of Rome Tor Vergata Rome Italy

ISBN: (纸本)9781032770406

Structural systems must satisfy multiple performance and functionality requirements during their life cycle, withstanding safety-reducing degradation mechanisms and hazards. Intervention strategies must be planned accordingly to maintain structural integrity and minimize total life-cycle costs and risks, posing a complex optimization problem. Recent advances in multi-agent deep reinforcement learning (DRL) in conjunction with partially observable Markov Decision Processes (POMDPs) have shown great potential for determining optimal structural integrity management policies for systems with large state and action spaces compared to traditional decision practices. This paper tackles the maintenance optimization problem of aging bridges in seismic-prone areas, creating an updatable environment that embeds chloride-induced corrosion and state-dependent seismic fragility throughout the bridge life-cycle. The evolution of the environment is captured by a dynamic Bayesian network, and it is further integrated with decentralized multi-agent DRL algorithms to identify near-optimal lifecycle decisions under risk constraints. Results on a multi-component bridge system show the suitability of the developed framework for minimizing expected life-cycle costs, and for providing detailed and adaptive policies that significantly outperform traditional condition- and time-based maintenance plans. © 2024 The Author(s).

关键词： Life cycle

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Implementation of Intelligent Elderly Care system Based on Cloud Platform and NNB Algorithm 2

Implementation of Intelligent Elderly Care System Based on C...

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2nd IEEE International Conference on Control, Electronics and computer technology, ICCECT 2024

作者： Huang, Xiaoping Palaoag, Thelma Domingo Nanfang College Department of Art Design & Creative Industries Guangdong Guangzhou510970 China College of Information Technology and Computer Science University of the Cordilleras Baguio City2600 Philippines

ISBN: (纸本)9798350380958

This paper discusses how to apply advanced machine learning technology and cloud platform to the smart elderly care service system, aiming to provide more accurate and personalized service recommendations for the elderly. With the acceleration of the aging process of society, smart elderly care has become one of the key ways to solve the aging problem, and efficient service matching for the diversified and personalized needs of the elderly is particularly critical. In this study, a set of service demand prediction model based on artificial neural network (ANN) was first constructed. Through in-depth mining of multidimensional data of elderly users, a detailed portrait of users was formed, so as to accurately predict their potential service demand. However, a single ANN model may be limited by overfitting or underfitting when dealing with complex and nonlinear relationships. Therefore, AdaBoost algorithm is introduced to improve the overall model performance and is deployed on the cloud platform. A powerful and efficient Neural Network Boost Model (NNB) for intelligent decision system is constructed. As an ensemble learning method, AdaBoost can combine multiple weak learners into a strong learner, strengthen the learning ability of hard-to-learn samples by dynamically adjusting sample weights, and effectively reduce overfitting. Through seamless docking with cloud computing platform, NNB model not only makes full use of elastic computing resources of cloud services to conduct large-scale parallel training, greatly shortening model training time, but also realizes real-time processing and analysis of massive data. In the design process of personalized recommendation system for intelligent elderly care service, AdaBoost algorithm not only optimizes the training process of neural network model, but also improves the accuracy and stability of recommendation results. In summary, the implementation of the intelligent elderly care system based on cloud platform and NNB prop

关键词： Neural networks

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