A proposed hybrid clustering algorithm using K-means and BIRCH for cluster based cab recommender system (CBCRS)

Mann, Supreet Kaur; Chawla, Sonal

doi:10.1007/s41870-022-01113-6

Cited by 13 publications

(2 citation statements)

References 18 publications

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“…The aim is to assess the effectiveness, accuracy, and efficiency of our proposed approach by using the same benchmark datasets and metrics commonly employed in the field. Specifically, the GSCSO-IHNN method is compared with ensemble classifier (EC) [35], random forest (RF) [31], and K-means + BIRCH clustering [36] methods. These methods have been selected because they represent different approaches to intrusion detection, ranging from ensemble learning to clustering techniques, and have been widely used in the literature.…”

Section: Reduction In False Alarmsmentioning

confidence: 99%

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

Ramachandran,

Albathan,

Hussain

et al. 2023

Systems

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Developing a simple and efficient attack detection system for ensuring the security of cloud systems against cyberthreats is a crucial and demanding process in the present time. In traditional work, various machine-learning-based detection methodologies have been developed for securing the cloud network. However, those methodologies face the complications of overfitting, complex system design, difficulty understanding, and higher time consumption. Hence, the proposed work contributes to the design and development of an effective security model for detecting cyberthreats from cloud systems. The proposed framework encompasses the modules of preprocessing and normalization, feature extraction, optimization, and prediction. An improved principal component analysis (IPCA) model is used to extract the relevant features from the normalized dataset. Then, a hybrid grasshopper–crow search optimization (GSCSO) is employed to choose the relevant features for training and testing operations. Finally, an isolated heuristic neural network (IHNN) algorithm is used to predict whether the data flow is normal or intrusive. Popular and publicly available datasets such as NSL-KDD, BoT-IoT, KDD Cup’99, and CICIDS 2017 are used for implementing the detection system. For validation, the different performance indicators, such as detection accuracy (AC) and F1-score, are measured and compared with the proposed GSCSO-IHNN system. On average, the GSCO-IHNN system achieved 99.5% ACC and 0.999 F1 scores on these datasets. The results of the performance study show that the GSCSO-IHNN method outperforms the other security models. Ultimately, this research strives to contribute to the ongoing efforts to fortify the security of cloud systems, making them resilient against cyber threats more simply and efficiently.

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Section: Reduction In False Alarmsmentioning

confidence: 99%

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

Ramachandran,

Albathan,

Hussain

et al. 2023

Systems

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show abstract

“…The contour coefficients and Calinski-Harabaz index coefficients of each algorithm in clusters ranging from 2 to 10 are calculated, and the clustering performance of each algorithm in each cluster is compared. The corresponding expressions are shown in equations ( 9) to (11) [32][33][34].…”

Section: B Selection and Correction Of Urban Building Energy Consumpt...mentioning

confidence: 99%

Construction and Research of a Data-Driven Energy Consumption Evaluation Model for Urban Building Operation

Gao,

Yang

2023

IEEE Access

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The bottom-up simulation method of building energy consumption, which mainly focuses on individual buildings, is difficult to apply to urban and regional level building energy consumption planning. Therefore, based on data-driven research, a simulation evaluation model for extreme gradient boosting at the mesoscale and K-means energy consumption at the macro scale is proposed and validated. The experimental results show that in the mesoscale model simulation prediction, in terms of training time comparison, the support vector method and sequential model have a time of 138.69 seconds and 90.00 seconds, respectively, which is much higher than other algorithms. The random forest algorithm has the highest accuracy comparison, at 83%; In the comparison of accuracy recall rate, the gradient improvement decision tree algorithm has the highest accuracy, at 83%. The extreme gradient boosting model proposed based on it has a mean square error value of 11.45 in residential sample prediction under refrigeration load and 9.16 under heating load, both of which are better than the comparative model. Applying it to practice, it is found that the correlation between building heating and cooling load and the 8 variables of the building is clearly demonstrated. In the macro scale model simulation prediction, the overall effect of K-means model I is the best under the comparison of contour coefficients, with the highest value maintained at around 0.1 when the number of clusters is 10. By applying it in practice, the spatial distribution of load can be clearly demonstrated, and the predicted value of total energy supply in the constructed regression equation is highly consistent with the actual value. Overall, the simulation and evaluation model for urban building energy consumption proposed in the study at two scales is practical and can effectively accelerate the development and improvement of urban energy systems.In addition, the research method considers the relevant operational energy consumption throughout the entire life cycle and the dynamic energy consumption of urban transportation systems, and provides reference value for existing urban renovation, thus possessing innovation.INDEX TERMS Data-driven, Urban architecture, Energy consumption evaluation model, Simulation methods, ML

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An Algorithmic Framework for Clustering Cab Pickup Geo-points for Cab Recommender System (CRS)

Mann,

Chawla

2024

Smart Innovation, Systems and Technologies

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A proposed hybrid clustering algorithm using K-means and BIRCH for cluster based cab recommender system (CBCRS)

Cited by 13 publications

References 18 publications

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

Enhancing Cloud-Based Security: A Novel Approach for Efficient Cyber-Threat Detection Using GSCSO-IHNN Model

Construction and Research of a Data-Driven Energy Consumption Evaluation Model for Urban Building Operation

An Algorithmic Framework for Clustering Cab Pickup Geo-points for Cab Recommender System (CRS)

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