Machine learning-driven load forecasting for urban energy optimization in morocco

OPSEARCH, Год журнала: 2025, Номер unknown

Опубликована: Апрель 18, 2025

Язык: Английский

---

Application of Smart Computing Systems for Smart Cities and Urban Infrastructure: Framework, Data Management, and Smart Monitoring Attributes

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 212 - 234

Опубликована: Янв. 1, 2025

Язык: Английский

---

Evolutionary Multi-Objective Feature Selection Algorithms on Multiple Smart Sustainable Community Indicator Datasets

Sustainability, Год журнала: 2024, Номер 16(4), С. 1511 - 1511

Опубликована: Фев. 10, 2024

The conceptual fusion of smart city and sustainability indicators has inspired the emergence sustainable (SSC). Given early stage development in this field, most SSC studies have been primarily theoretical. Notably, existing empirical overlooked crucial aspect feature engineering context SSC, despite its significance advancing initiatives. This paper introduces an approach advocating for subset selection to maximize prediction accuracy minimize computational time across diverse encompassing socio-cultural, economic, environmental, governance categories. study systematically collected multiple datasets on indicators, covering various themes within framework. Employing six carefully chosen multiple-objective evolutionary algorithms, research selected subsets. These subsets were then utilized modeling algorithms predict indicators. proposal enhanced life expectancy, online shopping intentions, energy consumption, air quality, water traffic flow a by minimizing features. findings underscore efficacy generating minimal features, thereby enhancing both efficiency realm For researchers aiming develop systems real-time data monitoring identified features offer valuable resource, negating necessity extensive dataset collection. provided are anticipated serve as catalyst, inspiring embark that explore from perspectives, ultimately contributing more profound understanding dynamics.

Язык: Английский

---

Blockchain and differential privacy-based data processing system for data security and privacy in urban computing

Computer Communications, Год журнала: 2024, Номер 222, С. 161 - 176

Опубликована: Апрель 24, 2024

Recently, big data related to human movement, air quality, and meteorology have been generated in urban computing through sensing technology the infrastructure. However, security problems arise as utilization increases. If from internet of things devices are constantly exposed, users' private information can be determined, a critical risk that could result privacy breaches. This paper proposes secure processing system using blockchain differential for protection computing. When service provider requests information, generates it machine learning. We apply these protect privacy. if query repeats, may provide insufficient protection. Therefore, we reduce total cost by reusing noise same parameters blockchain. Machine learning accuracy decrease when noisy used training. Thus, increase storing appropriately model design, simulate, analyze results an experimental environment parameter The proposed approach reduces costs compared existing mechanism while protecting demonstrate that, utilization, improves conventional mechanisms.

Язык: Английский

---

Digital and Culture: Towards More Resilient Urban Community Governance

Land, Год журнала: 2024, Номер 13(6), С. 758 - 758

Опубликована: Май 28, 2024

Urban communities are characterized by significant population size, high density, and strong mobility. While we might enjoy the dividends of rapid modernization, there nonetheless variable frequent public crises that occur. Modernization’s problems gradually emerging, traditional risk prevention logic relies on administrative “rigidity” has begun to be widely challenged. Traditional urban depend institutional, structural, spatial aspects improve community resilience. Because big data become popular, attention paid digital empowerment However, emergence such as “digital paradox” ethics” in realm itself prompted calls for cultural resilience continue rise. Therefore, urgently needed resolutions required questions regarding communities, construction resilience, relationship between manner which is coordinated solve problem A quantitative analysis 350 questionnaires from five found these communities’ spatial, structural dimensions driving factors improving In contrast, constraints. question how coordinate represented modern societies order compensate shortcomings future must consider. The authors this study believe open up “first mile” resilient break down “blocks middle” coupling link “last communities. One use culture pay failures before one can move towards more governance.

Язык: Английский

---

5G enabled smart cities: A real-world evaluation and analysis of 5G using a pilot smart city application

Internet of Things, Год журнала: 2024, Номер 28, С. 101326 - 101326

Опубликована: Авг. 14, 2024

Язык: Английский

---

Integrating machine learning for the sustainable development of smart cities

Frontiers in Sustainable Cities, Год журнала: 2024, Номер 6

Опубликована: Дек. 19, 2024

The purpose of this study is to assess the potential machine learning in advancing Sustainable Development Goals, particularly Goal 11, which focuses on sustainable urban and community development. To reduce impacts increasing urbanization environment, it necessary prioritize development smart cities. Smart cities use information communication technology techniques enhance sustainability by improving resource management reducing environmental impact. In context, artificial intelligence enhances overall quality life, a critical component Machine learning, subset intelligence, crucial promoting This application cities, ranging from energy management, transportation efficiency, waste public safety. It highlights role algorithms improve operational minimize expenses, practical ML across several countries demonstrates its ability handle challenges increase sustainability. paper discusses variety real-world initiatives that have successfully employed develop as well in-depth studies used obtained results. also covers implementing into city projects, such data quality, model interpretability, scalability, ethical considerations. emphasizes importance high-quality data, clear models, right tools.

Язык: Английский

---

Connecting Internet of Drones and Urban Computing: Methods, protocols and applications

Computer Networks, Год журнала: 2023, Номер 239, С. 110136 - 110136

Опубликована: Дек. 6, 2023

Язык: Английский

---

An Efficient Attribute-Based Participant Selecting Scheme with Blockchain for Federated Learning in Smart Cities

Computers, Год журнала: 2024, Номер 13(5), С. 118 - 118

Опубликована: Май 9, 2024

In smart cities, large amounts of multi-source data are generated all the time. A model established via machine learning can mine information from these and enable many valuable applications. With concerns about privacy, it is becoming increasingly difficult for publishers applications to obtain users’ data, which hinders previous paradigm centralized training through collecting on a scale. Federated expected prevent leakage private by allowing users train models locally. The existing works generally ignore architectures designed in real scenarios. Thus, there still exist some challenges that have not yet been explored federated applied such as avoiding sharing with improper parties under privacy requirements designing satisfactory incentive mechanisms. Therefore, we propose an efficient attribute-based participant selecting scheme ensure only someone who meets task publisher participate premise high requirements, so improve efficiency avoid attacks. We further extend our encourage clients take part provide audit mechanism using consortium blockchain. Finally, present in-depth discussion proposed comparing different methods. results show enabling reliable selection promote extensive use cities.

Язык: Английский

---

A Novel Deep Learning Approach for Real-Time Critical Assessment in Smart Urban Infrastructure Systems

Electronics, Год журнала: 2024, Номер 13(16), С. 3286 - 3286

Опубликована: Авг. 19, 2024

The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies temporal dynamics inherent these systems, risking resilience. This study aims to enhance criticality geographic zones within cities by introducing a novel deep learning architecture. Utilizing Convolutional Neural Networks (CNNs) for spatial feature extraction Long Short-Term Memory (LSTM) networks dependency modeling, proposed framework processes inputs such as total electricity use, flooding levels, population, poverty rates, energy consumption. CNN component constructs hierarchical maps through successive convolution pooling operations, while LSTM captures sequence-based patterns. Fully connected layers integrate features generate final predictions. Implemented Python using TensorFlow Keras on an Intel Core i7 system with 32 GB RAM NVIDIA GTX 1080 Ti GPU, model demonstrated superior performance. It achieved mean absolute error 0.042, root square 0.067, R-squared value 0.935, outperforming existing methodologies real-time adaptability resource efficiency.

Язык: Английский

---