Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Commencing with the transformative fusion of Smart Home and Internet of Things (IoT) technologies, this study scrutinizes the efficacy of predictive modeling approaches, specifically the autoregressive integrated moving average (ARIMA) and persistence algorithms. The primary focus lies in their potential for forecasting and optimizing energy consumption dynamics within the intricate framework of smart homes. The investigation reveals a nuanced comparison between the proposed ARIMA and conventional Persistence models. Smart Home, emblematic of innovative living, integrates seamlessly with IoT, promising an intelligent and interconnected domestic ecosystem. To enhance energy efficiency, this study explores the ARIMA model's capabilities alongside the persistence algorithm. Notably, the proposed ARIMA model showcases exceptional prowess in forecasting, substantiated by a significantly lower compared to the Persistence model. The ARIMA model, with an Root Mean Square Error value of 0.03378, outshines the Persistence model with a higher Root Mean Square Error value of 0.158 when evaluated on the test dataset. This substantial reduction in emphasizes the superior performance of the ARIMA model, making it a compelling choice for time series forecasting tasks. Beyond quantitative metrics, the precision of the ARIMA model holds transformative potential, promising cost-effective energy consumption, proactive maintenance, and an elevated quality of life within smart homes. This research establishes a robust foundation for integrating advanced predictive modeling, particularly the ARIMA model, to enhance the efficiency, sustainability, and inhabitant satisfaction of smart homes in the era of IoT.
Read MoreDoi: https://doi.org/10.54216/JAIM.060201
Vol. 6 Issue. 2 PP. 08-15, (2023)
This research endeavors to advance the realm of parking space surveillance through a meticulously designed methodology situated within the critical context of urban planning and the dynamic landscape of smart city development. Focused on addressing the challenges posed by escalating urbanization and burgeoning vehicular density, our study introduces a carefully curated dataset comprising images of parking spaces annotated with bounding box masks and occupancy labels. The methodology unfolds across distinct phases, commencing with a comprehensive dataset description that unveils its diversity and intricacies. Feature extraction techniques, harnessing the capabilities of cutting-edge architectures such as AlexNet and ResNet-50, play a pivotal role in enhancing pattern discernment, which is essential for accurate detection. The crux of our approach lies in the integration of Neural Networks with optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and the innovative Dipper Throated Optimization (DTO). Results are presented without explicit mention of tables and figures, strategically emphasizing the methodology's effectiveness in enhancing parking space detection accuracy. Notably, Dipper Throated Optimization (DTO) emerges as a key contributor to optimized Neural Network performance, achieving an impressive accuracy of 0.9908. This research contributes significantly to the ongoing discourse on intelligent urban planning and sets a promising trajectory for the future of efficient parking space utilization in modern cities.
Read MoreDoi: https://doi.org/10.54216/JAIM.060202
Vol. 6 Issue. 2 PP. 16-25, (2023)
Smart city development necessitates the implementation of effective traffic management strategies. In this vein, various deep learning architectures, including VGG16Net, VGG19Net, GoogLeNet, ResNet-50, and AlexNet, are employed to predict diverse traffic patterns extracted from a comprehensive dataset. Evaluating performance metrics such as accuracy, sensitivity, and specificity reveals discernible variations among models, with ResNet-50 and AlexNet demonstrating superior predictive capabilities. Descriptive statistics and statistical analyses, including ANOVA and the Wilcoxon Signed Rank Test, provide nuanced insights into model differences and significance. The findings bear significant implications for urban planners and policymakers transforming cities into intelligent ecosystems, offering valuable insights for informed decision-making in innovative city development. Improved traffic predictions enhance daily commuting experiences and contribute to the informed development of sustainable urban infrastructure, aligning seamlessly with the ongoing evolution of smart cities toward a more connected and efficient future. Notably, AlexNet exhibits a significant accuracy of 0.931780366 in the context of traffic pattern prediction.
Read MoreDoi: https://doi.org/10.54216/JAIM.060203
Vol. 6 Issue. 2 PP. 26-35, (2023)
Traffic detection is critical in ensuring road safety and efficient traffic management, demanding deploying accurate and practical algorithms. This research explores the fusion of Convolutional Neural Networks (CNNs) and the Waterwheel Plant Algorithm to augment global traffic detection capabilities, utilizing a diverse dataset primarily collected from Turkey. A comprehensive evaluation of prominent CNN architectures, such as VGG19Net, AlexNet, ResNet-50, GoogLeNet, and a generic CNN, underscores substantial efficacy, with the CNN achieving an accuracy of 92.14%. Introducing the Waterwheel Plant Algorithm (WWPA) further enhances performance, as exemplified by the hybrid WWPA-CNN model, exhibiting an impressive accuracy of 97.28%. These findings highlight the promising synergies between traditional optimization algorithms and advanced neural networks, showcasing the potential for innovative developments in traffic monitoring systems and broader applications within computer vision. The statistical analyses, encompassing ANOVA and the Wilcoxon Signed Rank Test, robustly underscore the significance of this integrated approach. As the research contributes to the evolution of traffic monitoring systems, these insights provide a solid foundation for advancements in the field, fostering innovation and shaping the future landscape of computer vision applications.
Read MoreDoi: https://doi.org/10.54216/JAIM.060204
Vol. 6 Issue. 2 PP. 36-45, (2023)
Within the realm of intelligent transportation systems, the imperative challenge of pothole detection assumes a pivotal role in ensuring road safety and upholding infrastructure integrity. This research undertaking meticulously navigates the intricacies of automated pothole detection, employing a nuanced and multifaceted approach. The dataset, comprising over 300 meticulously labeled images of roads with and without potholes, constitutes the cornerstone of our investigation. By leveraging the robust GoogLeNet for feature extraction and orchestrating the optimization of XGBoost through the Al-Biruni Earth Radius Metaheuristic Algorithm, our proposed methodology exhibits a commendable efficacy in discerning road anomalies. The outcomes elucidate the efficacy of the implemented strategies, with BER-XGBoost emerging as a preeminent performer, achieving an accuracy rate of 96.01%. This model not only attains superior accuracy but also manifests a comprehensive array of metrics, including sensitivity, specificity, positive predictive value, negative predictive value, and F-score. Rigorous statistical analyses, encompassing ANOVA and the Wilcoxon Signed Rank Test, furnish empirical substantiation of the consequential nature of our methodologies. In conclusion, this study not only contributes practical insights to the pertinent field but also stimulates pivotal inquiries regarding the ramifications of optimization strategies and the intricate role played by feature extraction in the domain of automated pothole detection. This research propels the ceaseless evolution of intelligent systems, effectively bridging the chasm between technological progressions and real-world applications, thereby augmenting road safety and fortifying infrastructure management.
Read MoreDoi: https://doi.org/10.54216/JAIM.060205
Vol. 6 Issue. 2 PP. 46-55, (2023)