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Diabetes is a major worldwide health issue, stressing the importance of early diagnosis and care. Machine learning algorithms offer promising prospects for developing precise models to classify diabetes. By leveraging vast healthcare datasets, machine learning can uncover hidden insights and patterns, enabling healthcare professionals to make informed predictions about patient outcomes. Despite advancements, current methods for diabetes classification suffer from accuracy limitations. In this research, we provide a novel hybrid machine learning approach that combines support vector machine, decision tree, and random forest classifiers. To improve forecast accuracy, we extend our technique by using new parameters like as glucose levels, BMI, age, and insulin levels. We trained and validated the algorithm using the Pima Indian Diabetes dataset using holdout and k-fold cross-validation approaches. On the holdout set, the hybrid method produced an accuracy of 88.5%, while k-fold cross-validation yielded 90.1%. While decision tree and random forest classifiers yielded individual accuracies of 76.8% and 75.3%, respectively, we further evaluated the algorithm's performance using recall, precision, and F1 score metrics. These indicators are critical in the field of diabetes prediction as they provide insights into the algorithm's capacity to correctly detect true positive cases and reduce false positives. They highlight the algorithm's effectiveness in diabetes prediction, making it a significant tool for early detection and intervention. [ABSTRACT FROM AUTHOR]

The number of tourism and hospitality academic journals has witnessed a considerable increase in the last decades, along with a remarkable evolution in their scientific production. This raises the call to systematically review and analyze the publications of such journals. To this end, the current work seeks to conduct a systematic review via exhaustive bibliometric analysis of the content of the International Journal of Hospitality and Tourism Systems (IJHTS) for the period from 2008 to 2022. Different bibliometric metrics were employed to analyze the publications of the IJHTS obtained from the journal's website and Scopus database. As the journal's stature and varied contributions to the field of hospitality and tourism management and systems have expanded, the quantity of publications, contributors, and citations of the IJHTS-related papers have significantly risen over the given period. Theoretical and practical implications have been illustrated. Additionally, the current research limitations and avenues for upcoming academic work have been highlighted. [ABSTRACT FROM AUTHOR]

The capacitated vehicle routing problem (CVRP) is regarded as an NP-hard problem. Moreover, the CVRP is described as a model that can be used in many applications such as transport, logistics, and distribution. The exact algorithms can find exact optimal solutions on the small-sized problem instances; however, for large-sized instances it is difficult to find the exact optimal solutions in polynomial time. This reason motivated the researchers to present heuristic/metaheuristic algorithms to solve large-sized problem instances within a reasonable computational time. One of the good algorithms that deal with the CVRP is the ant colony optimization (ACO) algorithm. Several ACO algorithms have been suggested in the literature, such as the ant system (AS) algorithm, ant colony system (ACS) algorithm, and so on. On the other hand, ACO is designed to solve the path problem that finds the best way. However, this algorithm still lacks exploratory mechanisms, which results in premature convergence and stagnation issues. Therefore, we propose to develop an enhanced ACS (EACS) algorithm for solving the CVRP based on subpaths. In our proposed algorithm, we propose to utilize the K-nearest neighbour (KNN) algorithm for finding the best initial solution and then enhance the diversity mechanism of the proposed algorithm by avoiding the generation of the same solution using subpaths. This uses the diversity of the generated solution to find a better solution with a shorter route in a reasonable amount of computational time. Furthermore, we propose to apply the three-opt algorithm to the completed subtour and the k-opt algorithm to the subpath gained from the experience of the subpath. Finally, to verify the effectiveness of the proposed EACS algorithm, the algorithm is tested on some CVRP instances and is compared with one of the state-of-the-art methods, namely, the enhanced simulated annealing algorithm. The comparative study showed a better performance of our EACS compared to the enhanced simulated annealing algorithm. [ABSTRACT FROM AUTHOR]

The decision tree algorithm-based load identification (DTA-LI) system’s fusion data method is crucial in the monitoring of appliance loads for the purposes of improving energy efficiency and management. Common home electrical devices are identified and classified from smart meter data through the analysis of voltage and current variations, allowing for the measurement of energy usage in residential buildings. A load identification system based on a decision tree algorithm may infer information about the residents of a building based on their energy usage habits. Better power savings rates, load shedding management, and overall electrical system performance are the results of the clusters’ ability to capture families’ purchasing patterns and geodemographic segmentation. The DTA-LI system’s fusion data method presents a promising avenue for improving residential buildings’ energy performance and lowering their carbon footprint, especially in light of the widespread use of smart meters in recent years. [ABSTRACT FROM AUTHOR]

Problems in autonomous systems may be tackled with the help of the attention-based siamese fully convolutional deep learning (AS-FC-DL) approach, which integrates autonomous fuzzy clustering and deep learning methods. The system can anticipate human behavior on crowded roadways by employing these techniques to recognize patterns and extract features from complicated unsupervised data. Each image point’s membership value is associated with the cluster’s epicenter using the fuzzy clustering methodology in the AS-FC-DL approach. Using least-squares methods, this approach finds the optimal position for each data point within a probability space, which may be anywhere among multiple clusters. Data points from an unlabeled dataset may be organized into distinct groups using a deep learning technique called cluster analysis. Data fusion from many sources, including sensor data and video data, can improve the AS-FC-DL method’s precision and performance. The algorithm is able to deliver an all-encompassing and precise evaluation of human behavior on crowded roadways by fusing data from many sources. The AS-FC-DL approach may also be employed in autonomous vehicles (AV) to help them learn from their experiences and improve their performance. Using reinforcement learning, a model for AV driving may be constructed. The AS-FC-DL approach helps the self-driving car traverse the area with increased precision and efficiency by allowing it to recognize structures and extract features from complicated unsupervised data. [ABSTRACT FROM AUTHOR]