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Bayesian optimization (BO) is a powerful and sample‐efficient method for optimizing black‐box functions. In practical applications, however, it faces two major challenges: (a) scaling to high‐dimensional parameter spaces and (b) optimizing in mixed spaces that encompass continuous, binary and categorical variables. This paper presents a unified approach, termed ComplexBO, to address these challenges by leveraging complex‐valued representations. Our approach utilizes complex numbers to represent the variables in both high‐dimensional input spaces and low‐dimensional target spaces, modelling the mapping between the two spaces as a rotation operation of complex numbers. Unlike the subspace embedding methods, our ComplexBO provides an elegant solution to handle diverse types of input variables while preserving non‐linear properties. Empirical evaluations show that our ComplexBO achieves competitive results compared to the state‐of‐the‐art methods across a wide range of tasks, including machine learning benchmarks and context length extension in large language models. We introduce a novel Bayesian optimization (BO) approach, termed ComplexBO, that utilizes complex‐valued variables in both input and target spaces, and thus is able to effectively tackle the challenges due to high‐dimensionality and mixed types of spaces. To the best of our knowledge, it is the first attempt to address the embedding of BO from a complex field perspective, allowing a unified treatment of different variable types while preserving the non‐linear relationships that are crucial for effective optimization.

Metaheuristic optimization techniques have successfully been used to solve the Optimal Power Flow (OPF) problem, addressing the shortcomings of mathematical optimization techniques. Two of the most popular metaheuristics are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The literature surrounding GA and PSO OPF is vast and not adequately organized. This work filled this gap by reviewing the most prominent works and analyzing the different traits of GA OPF works along seven axes, and of PSO OPF along four axes. Subsequently, cross-comparison between GA and PSO OPF works was undertaken, using the reported results of the reviewed works that use the IEEE 30-bus network to assess the performance and accuracy of each method. Where possible, the practices used in GA and PSO OPF were compared with literature suggestions from other domains. The cross-comparison aimed to act as a first step towards the standardization of GA and PSO OPF, as it can be used to draw preliminary conclusions regarding the tuning of hyper-parameters of GA and PSO OPF. The analysis of the cross-comparison results indicated that works using both GA and PSO OPF offer remarkable accuracy (with GA OPF having a slight edge) and that PSO OPF involves less computational burden.

The Artificial Intelligence has evolved and is now associated with Deep Learning, driven by availability of vast amount of data and computing power. Traditionally, researchers have adopted a Model-Centric Approach, focusing on developing new algorithms and models to enhance performance without altering the underlying data. However, Andrew Ng, a prominent figure in the AI community, has recently emphasized on better (quality) data rather than better models, which has given birth to Data Centric Approach, also known as Data Oriented technique. The transition from model oriented to data oriented approach has rapidly gained momentum within the realm of deep learning. Despite its promise, the Data-Centric Approach faces several challenges, including (a) generating high-quality data, (b) ensuring data privacy, and (c) addressing biases to achieve fairness in datasets. Currently, there has been limited effort in preparing quality data. Our work aims to address this gap by focusing on the generation of high-quality data through methods such as data augmentation, multi-stage hashing to eliminate duplicate instances, to detect and correct noisy labels, using confident learning. The experiments on popular datasets, namely MNIST, Fashion MNIST, and CIFAR-10 were performed by utilizing ResNet-18 as the common framework followed by both Model Centric and Data Centric Approach. Comparative performance analysis revealed that the Data Centric Approach consistently outperformed the Model Centric Approach by a relative margin of at least 3%. This finding highlights the potential for further exploration and adoption of the Data-Centric Approach in various domains such as healthcare, finance, education, and entertainment, where the quality of data could significantly enhance the performance.

High penetration of Android applications along with their malicious variants requires efficient and effective malware detection methods to build mobile platform security. API call sequence derived from API call graph structure can be used to model application behavior accurately. Behaviors are extracted by following the API call graph, its branching, and order of calls. But identification of similarities in graphs and graph matching algorithms for classification is slow, complicated to be adopted to a new domain, and their results may be inaccurate. In this study, the authors use the API call graph as a graph representation of all possible execution paths that a malware can track during its runtime. The embedding of API call graphs transformed into a low dimension numeric vector feature set is introduced to the deep neural network. Then, similarity detection for each binary function is trained and tested effectively. This study is also focused on maximizing the performance of the network by evaluating different embedding algorithms and tuning various network configuration parameters to assure the best combination of the hyper-parameters and to reach at the highest statistical metric value. Experimental results show that the presented malware classification is reached at 98.86% level in accuracy, 98.65% in F -measure, 98.47% in recall and 98.84% in precision, respectively.

Location-based indoor applications with high quality of services require a reliable, accurate, and low-cost position prediction for target device(s). The widespread availability of WiFi received signal strength indicator (RSSI) makes it a suitable candidate for indoor localization. However, traditional WiFi RSSI fingerprinting schemes perform poorly due to dynamic indoor mobile channel conditions including multipath fading, non-line-of-sight path loss, and so forth. Recently, machine learning (ML) or deep learning (DL)-based fingerprinting schemes are often used as an alternative, overcoming such issues. This paper presents an extreme gradient boosting-based ML indoor localization scheme, simply termed as XGBLoc, that accurately classifies (or detects) the positions of mobile devices in multi-floor multi-building indoor environments. XGBLoc not only effectively reduces the RSSI dataset dimensionality but trains itself using structured synthetic labels (also termed as relational labels), rather than conventional independent labels, that classify such complex and hierarchical indoor environments well. We numerically evaluate the proposed scheme on the publicly available datasets and prove its superiority over existing ML or DL-based schemes in terms of classification and regression performance.

Background Supervised machine learning is increasingly being used to estimate clinical predictive models. Several supervised machine learning models involve hyper-parameters, whose values must be judiciously specified to ensure adequate predictive performance. Objective To compare several (nine) hyper-parameter optimization (HPO) methods, for tuning the hyper-parameters of an extreme gradient boosting model, with application to predicting high-need high-cost health care users. Methods Extreme gradient boosting models were estimated using a randomly sampled training dataset. Models were separately trained using nine different HPO methods: 1) random sampling, 2) simulated annealing, 3) quasi-Monte Carlo sampling, 4-5) two variations of Bayesian hyper-parameter optimization via tree-Parzen estimation, 6-7) two implementations of Bayesian hyper-parameter optimization via Gaussian processes, 8) Bayesian hyper-parameter optimization via random forests, and 9) the covariance matrix adaptation evolutionary strategy. For each HPO method, we estimated 100 extreme gradient boosting models at different hyper-parameter configurations; and evaluated model performance using an AUC metric on a randomly sampled validation dataset. Using the best model identified by each HPO method, we evaluated generalization performance in terms of discrimination and calibration metrics on a randomly sampled held-out test dataset (internal validation) and a temporally independent dataset (external validation). Results The extreme gradient boosting model estimated using default hyper-parameter settings had reasonable discrimination (AUC=0.82) but was not well calibrated. Hyper-parameter tuning using any HPO algorithm/sampler improved model discrimination (AUC=0.84), resulted in models with near perfect calibration, and consistently identified features predictive of high-need high-cost health care users. Conclusions In our study, all HPO algorithms resulted in similar gains in model performance relative to baseline models. This finding likely relates to our study dataset having a large sample size, a relatively small number of features, and a strong signal to noise ratio; and would likely apply to other datasets with similar characteristics.

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating Computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation.

In the field of supervised machine learning, accurate evaluation of classification models is a critical factor for assessing their performance and guiding model selection. This paper delves into the domain of ordinal classification and raises the question of adapting ordinal metrics to the interval scale. In scenarios where measurements are recorded at intervals, not only the order but also their length assume significance, and this promotes the adoption of novel performance metrics. Initially, we revisit two existing confusion matrix-based ordinal metrics and introduce a normalization technique to render them comparable and enhance their practical utility. We extend our focus to classification by intervals, proposing a robust framework for adapting ordinal metrics to the interval scale, and applying it to the aforementioned ordinal metrics. We address the challenge of unbounded rightmost intervals, a common issue in practical applications, from both theoretical and simulation perspectives, by providing a solution that enhances the applicability of the proposed metrics. To further explore practical implications, we conducted experiments on real-world datasets. The results reveal a promising trend in the use of interval-scale metrics to guide hyper-parameter tuning for improving model performance.

Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide more accurate, authentic, and standardized results.In this study, we aimed to create a larger and balanced dataset by image patching and applied different variations of CNN models, including training from scratch, fine-tuning, and weight adjustment, and data augmentation through random rotation, reflection, and translation. The results showed that the best results were achieved through augmentation and fine-tuning of deep models. We also modified existing architectures, such as InceptionV3 and MobileNetV2, to better capture complex features. The robustness of the proposed ensemble model was evaluated using two data splits (7:2:1 and 6:2:2) to see how performance changed as the training data was increased from 10 to 20%. In both cases, the model exhibited exceptional performance. For the 7:2:1 split, the model achieved an accuracy of 99.91%, F-Score of 98.95%, precision of 98.98%, recall of 98.96%, and MCC of 98.92%. For the 6:2:2 split, the model yielded an accuracy of 99.94%, F-Score of 99.28%, precision of 99.31%, recall of 98.96%, and MCC of 99.26%. This demonstrates that automatic classification using the ensemble model can be a valuable tool for diagnostic staff and microbiologists in accurately identifying pathogenic bacteria, which in turn can help control epidemics and minimize their social and economic impact.

Online transactions, medical services, financial transactions, and banking all have their share of fraudulent activity. The annual revenue generated by fraud exceeds $1 trillion. Even while fraud is dangerous for organizations, it may be uncovered with the help of intelligent solutions such as rules engines and machine learning. In this research, we introduce a unique hybrid technique for identifying financial payment fraud by combining nature-inspired-based Hyperparameter tuning with several supervised classifier models, as implemented in a modified version of the XGBoost Algorithm. At the outset, we split out a sample of the full financial payment dataset to use as a test set. We use 70% of the data for training and 30% for testing. Records that are known to be illegitimate or fraudulent are predicted, while those that raise suspicion are further investigated using a number of machine learning algorithms. The models are trained and validated using the 10-fold cross-validation technique. Several tests using a dataset of actual financial transactions are used to demonstrate the effectiveness of the proposed approach.