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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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Machine Learning and Deep Learning Approaches for Guava Disease Detection

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SN computer science 2025年第4期6卷 1-28页

作者： Paramesha, K. Jalapur, Shruti Hanok, Shalini Puttegowda, Kiran Manjunatha, G. Kumara, Bharath Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Department of Computer Science and Engineering Christ University Karnataka Bengaluru India Department of Electronics and Communication Engineering ATME College of Engineering Karnataka Mysuru India Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Department of CSD PES Institute of Technology and Management Karnataka Shivamogga India Department of ECE MS Ramaiah University of Applied Science (MSRUAS) Karnataka Bangalore India

A larger proportion of crops face disease outbreaks, making agricultural output difficult. Detecting and predicting diseases at an early stage can enhance productivity. Guava, a tropical and subtropical fruit, is cultivated in various countries. In regions such as Bangladesh, Pakistan, India, and South America, guava cultivation faces significant challenges due to diseases like Canker, Dot, Mummification, Phytophthora, Scab, and Styler and Root. Traditional diagnosis methods based on visual observation are often unreliable and time-consuming. To address this, we developed an automated system leveraging deep learning techniques. Our study utilized a dataset comprising 4046 guava leaf images categorized into these seven disease classes. We compared the performance of traditional methods with deep learning approaches using vision transformers and transfer learning. The results demonstrate the superiority of deep learning methods over traditional approaches, where traditional machine learning model SVM gave accuracy near 78% and deep learning methods gave over 90%. The transfer learning method gave an accuracy of nearly 97% and on the other hand, the vision transformer gave accuracy of 98%. This offers a promising solution for early disease detection in guava crops. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy Deep learning Guava disease Leaf images Machine learning

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

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computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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FedHZone: Federated Learning Based Hazardous Zone Detection Using Internet of Drone Things

FedHZone: Federated Learning Based Hazardous Zone Detection ...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Biswas, Payel De, Debashis Obaidat, Mohammad S. Hazra, Arnab Sadoun, Balqies Jogesh Chandra Chaudhuri College Department of Computer Science Kolkata India Maulana Abul Kalam Azad University of Technology Centre of Mobile Cloud Computing Department of Computer Science and Engineering West Bengal WB Nadia India School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing SRM University Department of Computational Intelligence SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Dr. Sudhir Chandra sur Institute of Technology & Sports Complex Department of Computer Science and Engineering Kolkata India College of Engineering Al Balqa ' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Air pollution can affect human health, so it is necessary to predict the air quality index (AQI) in advance. In this work, air quality data collected by the Internet of Drone Things (IoDT) is predicted and analyzed to detect highly polluted zones using a distributed privacy-preserving federated learning (FL) approach. Air quality data for a particular zone is collected and then fed into a Long short-Term memory (LSTM) based model to predict the pollution level in local training. Global aggregation is done by an IoDT-mounted central server to aggregate the received models from the local IoDT level. Privacy-preserving FedHZone predicts and analyses the AQI values from the past time series dataset in a multi-drone environment. This work detects the most hazardous zone in a larger region of Jaipur, Rajasthan using real-Time data. The result shows FedHZone with stacked LSTM having ''2%-7% better accuracy than the vanilla LSTM-based model. © 2024 IEEE.

关键词： Federated learning

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LSTM Deep Learning Method for Radiation Short and Long-Term Prediction 7th

LSTM Deep Learning Method for Radiation Short and Long-Term ...

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7th International Conference on Smart City Applications, SCA 2022

作者： Khala, Mohamed Abouzid, Houda Teidj, Sara Eloutassi, Omar Messaoudi, Choukri Optoelectronics and Applied Energy Techniques Faculty of Science and Technology Moulay Ismail University of Meknes Meknes Morocco Computer Science and New Technology Trends Abdelmalek Essaadi University Tetouan Morocco Department of Physics Faculty of Science and Technology Moulay Ismail University of Meknes Meknes Morocco

ISBN: (纸本)9783031268519

The renewable energy market, like conventional energy, is often unstable. Solar energy in particular presents disturbances linked to the spatiotemporal fluctuations that solar radiation undergoes. To remedy this problem, it is essential to predict its fluctuations that have exhausting consequences for integration into the electricity network and reduce the dependence on fossil fuels essential for socio-economic development. The main objective of this study is to propose a prognostication model based on artificial intelligence to predict with great accuracy the fluctuations of global solar radiation (GSR). Indeed, we adopted a deep dynamic recurrent memory artificial neural network (ANN) model which can manipulate the dynamics of atmospheric factors and is capable to save the history of these characteristics. For this purpose, we developed two long-short term memory (LSTM) models for short and long term forecasting of global solar radiation time series. The first model is a monovariate LSTM, the second one is a multivariate LSTM, after selecting the input variables (predictors) by correlation study, the models are trained by dividing the data set into two subsections, the first subsection is used for model training and the second subsection is reserved for testing. The indices and results established in this work demonstrate the robustness attributed to our method which can provide power grid managers at Er-Rachidia, MOROCCO (Latitude: 31°55′53″N;Longitude: 4°25 ′35″ W;Altitude: 1039 m) credible predictions to ensure better supervision of the energy system and to adapt solar energy to the electricity production system, especially multivariate LSTM model which proved more accuracy compared to monovariate LSTM. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Inventory Model for Imperfect Deteriorating Items Under Fuzzy Environment 11

Inventory Model for Imperfect Deteriorating Items Under Fuzz...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Mandeep Pandey, Jayanti Tripathi Jain, Muskan School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Amity Institute of Applied Sciences Amity University Uttar Pradesh Department of Mathematics Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The aim of this study is to introduce the application of fuzzy numbers in an Economic Order Quantity (EOQ) model for defective items under deterioration. In practice, the fuzziness of parameters tends to reduce over time compared to the theoretical assumptions, which justifies the use of fuzzy numbers. The proposed model starts with crisp approach which later involves the fuzzification of inventory parameters, such as demand rate, holding cost, deterioration rate, purchase cost, screening cost, and ordering cost, to account for uncertainty and imprecision. The triangular fuzzy numbers are utilized to represent these parameters, and a fuzzy model is developed accordingly. To defuzzify the model, Yager's ranking index is adopted, and a detailed comparison is made between crisp and general fuzzy solutions. The model is validated through numerical examples and graphical illustrations, followed by a thorough sensitivity analysis to gain a deeper comprehension of how modifications in parameters affect the optimal solution. © 2024 IEEE.

关键词： Deterioration

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Machine Learning and Network Traffic to Distinguish Between Malware and Benign Applications 1st

Machine Learning and Network Traffic to Distinguish Between ...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Abualigah, Laith Abualigah, Sayel Almahmoud, Mothanna Forestiero, Agostino Sachdeva, Gagan Hanandeh, Essam S. Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Department of Computer Information Systems Jordan University of Science and Technology Ar-Ramtha Jordan Performance Computing and Networking National Research Council of Italy Rende87036 Italy First Abu Dhabi Bank Abu Dhabi United Arab Emirates Department of Computer Information System Zarqa University Zarqa Jordan

ISBN: (纸本)9783031314681

Virus detection software is widely used for servers, systems, and devices that seek to maintain security and reliability. Although these programs provide an excellent safety level, the traditional defense methods fail to detect new Malware. The more advanced approach relies on predicting malicious behavior with dynamic analysis of the process executed. This paper presents a new method for detecting malware using machine learning algorithms applied to data obtained from the Cuckoo sandbox. The Cuckoo sandbox isolates the file being analyzed, providing detailed dynamic analysis reports. The machine learning algorithms were compared and the most important features were identified. The results were obtained using six popular classifiers, including SVM, Random Forest, and LightGBM, and the XGBOOST algorithm had the highest accuracy, at an average of 97%. However, the research on machine learning-based malware analysis is limited in terms of computational complexity and detection accuracy. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Software reliability

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Enhancing Microgrid Stability: Optimal BESS Sizing via Modified PSO for Frequency Control and Cost Efficiency 7

Enhancing Microgrid Stability: Optimal BESS Sizing via Modif...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Singh, S. Vikram Revathi, V. Babu, B Sankara Bhavana, Munugapati Singh, Navdeep Nagamani, G. Amity University Amity School of Engineeringh and Technology Uttar Pradesgh Greater Noida India New Horizon College of Engineering Department of Applied Sciences Bangalore India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

When constructing a Battery Energy Storage System (BESS) to be incorporated in a microgrid, the main objective is to minimise the risk of microgrid stability and system failure. An improper or careless sizing of the BESS may lead to system losses, excessive capacity, and high installation costs. Consequently, this study suggest a novel technique for managing the frequency of the independent microgrid using an improved particle swarm optimisation (PSO), which aims to find the optimum size of the BESS while minimising its total cost. Our research focuses on finding the most cost-effective way to deploy BESS in microgrids for a period of 15 years. We will also recommend the optimal measurements of BESS to minimise microgrid volatility and system failure in the case of utility grid outage. We will use the PSO approach for frequency regulation. The next step is to examine a standard microgrid and evaluate BESS in comparison to other existing storage systems in terms of their economic performance. By comparing the optimum size of BESS- modified PSO method to both the conventional size of BESS and the ideal size of BESS-based empirical approach, the findings show that the latter may improve the dynamic system effectiveness. In a typical microgrid, the polysulfide-chromium BESS is predicted to be cheaper throughout the 15 years of deployment compared to the chrome redox BESS, when evaluating the financial health of the two storage techniques. Findings indicate that microgrid planning flexibility, grid security, and power system stability may all be greatly enhanced using the suggested PSO method-based frequency management. Simultaneously, it is able to meet the needs for frequency control economically. © 2024 IEEE.

关键词： Cost effectiveness

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Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

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computers, Materials & Continua 2022年第7期72卷 1465-1481页

作者： Ali E.Takieldeen El-Sayed M.El-kenawy Mohammed Hadwan Rokaia M.Zaki Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia Higher Institute of Engineering and Technology KafrelsheikhEgypt Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated *** has its unique hunting technique by performing rapid bowing *** show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark ***,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization ***,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection *** strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)*** findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.

关键词： Metaheuristic optimization swarmoptimization feature selection function optimization

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IoT-Enabled Health Monitoring System for Safeguarding Vital Organs with Cloud-Based Diagnosis and Advanced Algorithms 4

IoT-Enabled Health Monitoring System for Safeguarding Vital ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Ahmad, Salman Khursheed Ikra, K.M. Sharma, Preeti Kaushik, Yogita Choudhary, Amar Tripathi, Pradeep Kumar Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science -DS Ghaziabad India Sunder Deep Group of Institutions Department Mca Ghaziabad India Alliance University Department of Electronics and Communication Engineering Bengaluru India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350360165

This study presents a novel IoT-enabled fitness tracking device intended to improve patient care through the capture of real-time physiological records and predictive analytics. The system uses a variety of sensors, including an Arduino UNO, an LM35, a MAX30100 pulse charge and SPo2 sensor, and a stress sensor, to continuously gather health data from ten different patients. Proactive health assessments and early intervention are made possible by the combination of cloud-based analysis and advanced system learning models, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbours (KNN), and Recurrent Neural Networks (RNN). Tested on real-world datasets, the experimental results show how effective the device is at making accurate and timely predictions. Notably, SVM is the most accurate variant, closely followed by ANN, KNN, and RNN. The confusion matrices ensure accurate and thorough examination of each version's performance, aiding in selecting the most relevant set of rules for real-time health monitoring to be used by clinicians. The present study, as groundbreaking work, demonstrates a major leap in merging IoT with device expertise in the healthcare sector that usages new possibilities in proactive and personalized treatment strategies. The recommended tool's innovative and reliable predictive analytics can potentially revolutionize patient transportation in the healthcare system. © 2024 IEEE.

关键词： Electronic health record

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