Data Mining Practical Machine Learning Tools and Techniques

Drones in agriculture: A review and bibliometric analysis

An approach towards missing data management using improved GRNN-SGTM ensemble method

Digitization within Human Resource Management (HRM) has resulted in Artificial Intelligence (AI) becoming increasingly prevalent in Human Resource Management Systems (HRMS) and HR Information Systems (HRIS). The tactical procedures of recruitment, employee performance evaluation and satisfaction, compensation and benefit analysis, best practice analysis, discipline management, and employee training and development systems have seen a growth in the incorporation of AI. To better understand this evolution, we seek to explore publication sources and literature that feature the application of AI within HRM. By utilizing a systematic literature review methodology, this paper identifies which tactical HRIS (T-HRIS) components are featured in literature and how each T-HRIS component is represented. This paper gives insight to which component of tactical HRM/HRIS receives attention and identifies gaps in research to give direction to future research agendas.

Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review

The adoption of Unmanned Aerial Vehicles (UAVs) in numerous sectors is projected to grow exponentially in the future as technology advances and regulation evolves. One of the promising applications of UAVs is in transportation systems. As the current transportation system is moving towards Intelligent Transportation Systems (ITS), UAVs will play a significant role in the functioning of ITS. This paper presents a survey on the recent advances of UAVs and their roles in current and future transportation systems. Moreover, the emerging technologies of UAVs in the transportation section and the current research areas are summarized. From the discussion, the challenges and opportunities of integrating UAVs towards future ITS are highlighted. In addition, some of the potential research areas involving UAVs in future ITS are also identified. This study aims to lay a foundation for the development of future intelligent and resilient transportation systems.

Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities

Companies are adopting innovative methods for responsiveness and efficiency in the logistics sector The implementation of drones in logistics sector is a move in this direction Potential obstacle

Analysis of barriers to implement drone logistics

This research introduces the Recursive General Regression Neural Network Oracle (R-GRNN Oracle) and is demonstrated on several binary classification datasets. The traditional GRNN Oracle classifier (Masters et al., 1998) combines the predictive powers of several machine learning classifiers by weighing the amount of error each classifier has on the final predictions. Each classifier is assigned a weight based on the percentage of errors it contributes to the final predictions as the classifiers evaluate the dataset. The proposed R-GRNN Oracle is an enhancement to the GRNN Oracle in which the proposed algorithm consists of an oracle within an oracle – where the inner oracle acts as a classifier with its own predictions and error contribution. By combining the inner oracle with other classifiers, the R-GRNN Oracle produces superior results. The classifiers considered in this study are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian Naive Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). To demonstrate the effectiveness of the proposed approach, several datasets were used, with the primary one being the publicly available Spambase dataset. The predictions of SVM, MLP, KNN, and RF were used to create the first GRNN Oracle, which was then enhanced with the high performances of SVM and RF to create the second oracle, the R-GRNN Oracle. The combined recursive model was 93.24% accurate using 10-fold cross validation, higher than the 91.94% of the inner GRNN Oracle and the 91.29% achieved by RF, the highest performance by a stand-alone classifier. The R-GRNN Oracle was not only the most accurate, but it also had the highest AUC, sensitivity, specificity, precision, and F1-score (97.99%, 91.86%, 94.40%, 93.28%, and 92.57%, respectively). The research contribution of this paper is introducing the concept of recursion (a concept not fully explored in machine learning models and applications) and testing this structure's ability on further enhancing the performance of the traditional oracle. The recursive model has also been applied to several other datasets: The Human Resources, Bank Marketing, and Monoclonal Gammopathy of Undetermined Significance (MGUS) datasets. The results of these implementations are summarized in this paper.

A Recursive General Regression Neural Network (R-GRNN) Oracle for classification problems

Machine learning assisted development of IT equipment compact models for data centers energy planning

• Developed a fast and accurate hybrid approach for leukocyte classification. • Achieved fast convergence of hyperparameters with a random search approach. • Demonstrated superior performance of an optimized convolutional neural network. Hematologic diseases and blood disorders can be studied through the microscopic or chemical examination of blood smear images. Many researchers work on identifying, counting, and classifying different types of blood cells as a theoretical and practical problem that is crucial for disease diagnosis and treatment planning. There are various approaches to classify blood cells such as thresholding, morphological operators, segmentation, edge-based techniques, region-based techniques, and hybrid approaches. Each of these techniques has several limitations in effectively classifying different types of cells; however, methods based on deep learning (DL) have remarkably contributed to the progress of blood cell classification by combining feature extraction, feature selection, and classification into one interconnected step. This study develops a hybrid approach of DL and optimization for accurate and efficient classification of four types of leukocytes: neutrophils, eosinophils, lymphocytes, and monocytes. Model hyperparameters are optimized using grid search (GS) and random search (RS), in which a convolutional neural network (CNN) is used to classify leukocytes. CNNs work through pattern recognition to detect significant features that help distinguish different classes. The blood cell count and detection (BCCD) dataset provides basic information, but the data is insufficient and highly unbalanced for CNNs to accurately classify the images, so the data is augmented to improve model performance. This segmentation-free optimized CNN achieved a classification accuracy of 97% for the validation set, which includes 2,487 cell images, and 99% for the training set, which includes 9,966 cell images. The model reached a sensitivity and specificity of 94% and 98%, respectively. RS accelerates the process of hyperparameter optimization while achieving the same accuracy as GS. The results are compared with the results accomplished by recent CNN models on the BCCD database using seven performance measures and demonstrate the superior performance and competence of the proposed method. This research study develops a fast and accurate approach for leukocyte classification and can be beneficial for other image classification tasks and help clinicians in diagnosing blood diseases. 

Dana Bani-Hani

Papers

Analysis of barriers to implement drone logistics

A Recursive General Regression Neural Network (R-GRNN) Oracle for classification problems

Machine learning assisted development of IT equipment compact models for data centers energy planning

Leukocytes Image Classification Using Optimized Convolutional Neural Networks