Опубликована: Янв. 1, 2025
Язык: Английский
Review of Business and Economics Studies, Год журнала: 2025, Номер 12(4), С. 6 - 28
Опубликована: Фев. 11, 2025
The purpose of the study is to explore Artificial intelligence (AI) integration into sustainable marketing techniques highlights a transformational potential, combining modern technology with urgent needs sustainability. This article thoroughly examines how AI plays crucial role in improving by enabling more efficient and socially responsible tactics that support sustainability goals. Method: AI-driven insights analytics enhance decision-making processes, improve customer engagement, increase impact campaigns on environmental social outcomes reviewing existing literature practices. conversation delves difficulties moral aspects involved using marketing, such as issues related data privacy, algorithmic bias, importance strategic framework focuses development Results: investigation shows promising yet intricate environment, where seen tool for balancing economic goals need responsibility. research stresses continuous research, multidisciplinary teamwork, policy creation maximize shaping practices intelligence. provides valuable contributions scholarly discussion around artificial intelligence, while also offering practical guidance professionals operating this dynamic commercial sector.
Язык: Английский
Процитировано
1
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Journal of Open Innovation Technology Market and Complexity, Год журнала: 2025, Номер unknown, С. 100516 - 100516
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
Future Internet, Год журнала: 2025, Номер 17(4), С. 169 - 169
Опубликована: Апрель 11, 2025
The Internet of Things (IoT) has revolutionized modern communication systems by enabling seamless connectivity among low-power devices. However, the increasing demand for high-performance wireless networks necessitates advanced frameworks that optimize both energy efficiency (EE) and security. Cell-free massive multiple-input multiple-output (CF m-MIMO) emerged as a promising solution IoT networks, offering enhanced spectral efficiency, low-latency communication, robust connectivity. Nevertheless, balancing EE security in such remains significant challenge due to stringent power computational constraints This study employs secrecy (SEE) key performance metric evaluate trade-off between consumption secure efficiency. By jointly considering rate, our analysis provides comprehensive assessment security-aware CF m-MIMO-based networks. To enhance SEE, we introduce hybrid deep-learning (DL) framework integrates convolutional neural (CNN) long short-term memory (LSTM) joint optimization. CNN extracts spatial features, while LSTM captures temporal dependencies, more adaptive modeling dynamic patterns. Additionally, multi-objective improved biogeography-based optimization (MOIBBO) algorithm is utilized hyperparameters, ensuring an balance convergence speed model performance. Extensive simulation results demonstrate proposed MOIBBO-CNN–LSTM achieves superior SEE compared benchmark schemes. Specifically, attains gain up 38% 22% over converging significantly faster at early training epochs. Furthermore, reveal improves with AP transmit saturation point (approximately 9.5 Mb/J PAPmax=500 mW), beyond which excessive limits gains. decreases number APs increases, underscoring need selection strategies mitigate static backhaul links. These findings confirm offers effective optimizing paving way energy-efficient communications.
Язык: Английский
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0
Опубликована: Янв. 1, 2025
Язык: Английский
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0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0