Existing Benchmark Techniques Research Articles

A groundwater level-based filtering to improve the accuracy of locating agricultural tile drain and ditch networks

Remote sensing (RS) using satellites circling around the Earth has great potential for monitoring surface processes with reduced cost and greater access. This study uses three approaches to identify possible drainage unit locations: existing benchmark techniques and a novel complementary approach based on groundwater table depth. The study area comprises a site in Ontario, Canada, and the Kleine Nete catchment, Belgium. First, a change detection method based on the interpretation of RS imagery is used to retrieve soil moisture differences. Based on the retrieved soil moisture differences, it is possible to distinguish between drained and undrained fields. Secondly, the decision tree classification (DTC) method based on filtering pixels corresponding to agricultural fields with a slowly draining soil class along with a gentle slope was applied to identify drainage units. Finally, a novel filtering technique based on groundwater table depth is applied as a complementary identification tool to the former approach.The remote sensing method resulted in 87.8% accuracy in the first study area, while the decision tree classification achieved 96.7% accuracy. Although the RS approach was not successful in following the ditch network, the DTC was able to indicate ditch networks with up to 58% accuracy. However, the additional filtering using groundwater level measurements increased the drainage unit identification accuracy in the first study area (corresponds to finding an additional 19.4 km2 area of drains). A final quantitative assessment for the second study area revealed a close follow-up of the ditch network to the shallow groundwater table maps. In general, it can be concluded that both the remote sensing and the DTC method have tremendous potential to identify drainage units, although with limitations in particular cases, such as low accuracy. Moreover, it can be advised that a local visit to the study area is required to investigate what type of drainage system is used. Next, the novel use of groundwater level-based filtering further improves the drainage identification procedure. Finally, combining several data and techniques allows for accurately identifying drainage units, which is ultimately useful for the sustainable management of drained water from agricultural fields.

A novel step detection technique for pedestrian dead reckoning based navigation

Precise step detection is an essential task for pedestrian dead reckoning based navigation. This study presents a novel step detection method using a Dynamic Weight Integrated Fuzzy C-Means (DWIFCM) algorithm. It utilizes the statistical criteria extracted from the readings of accelerometer sensors and improves the accuracy of step detection. The performance of the proposed step detection technique is evaluated by considering the variations in walking patterns of the pedestrian in different landscapes of the path. The experimental results yield that the proposed method outperforms among the existing benchmark techniques.

DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long Text

Topic modelling is important for tackling several data mining tasks in information retrieval. While seminal topic modelling techniques such as Latent Dirichlet Allocation (LDA) have been proposed, the ubiquity of social media and the brevity of its texts pose unique challenges for such traditional topic modelling techniques. Several extensions including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">auxiliary aggregation</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">self aggregation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">direct learning</i> have been proposed to mitigate these challenges, however some still remain. These include a lack of consistency in the topics generated and the decline in model performance in applications involving disparate document lengths. There is a recent paradigm shift towards neural topic models, which are not suited for resource-constrained environments. This paper revisits LDA-style techniques, taking a theoretical approach to analyse the relationship between word co-occurrence and topic models. Our analysis shows that by altering the word co-occurrences within the corpus, topic discovery can be enhanced. Thus we propose a novel data transformation approach dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DATM</i> to improve the topic discovery within a corpus. A rigorous empirical evaluation shows that DATM is not only powerful, but it can also be used in conjunction with existing benchmark techniques to significantly improve their effectiveness and their consistency by up to 2 fold.

Neural Synchronization-Guided Concatenation of Header and Secret Shares for Secure Transmission of Patients' Electronic Medical Record: Enhancing Telehealth Security for COVID-19.

This paper deals with one of the key problems of e-healthcare which is the security. Patients are worried about the confidentiality of their electronic medical record (EMR) which could be used to expose their identities. It is high time to revisit the confidentiality and security issues of the existing telehealth system. Intruders can perform sniffing, spoofing, or phishing operations effortlessly during the online exchange of the EMR using a digital platform. The EMR must be transmitted anonymously with a high degree of hardness of encryption by protecting the authentication, confidentiality, and integrity criteria of the patient. These requirements recommend the security of the current system to be improved. In this paper, a neural synchronization-guided concatenation of header and secret shares with the ability to transmit the EMR with an end-to-end security protocol has been proposed. This proposed methodology breaks down the EMR into the n number of secret shares and transmits to the n number of recipients. The original EMR can be reconstructed after the amalgamation of a minimum k (threshold) number of secret shares. The novelty of the technique is that one share should come from a specific recipient to whom a special privilege is given to recreate the EMR among such a predefined number of shares. In the absence of this privileged share, the original EMR cannot be reconstructed. This proposed technique has passed various parametric tests. The results are compared with existing benchmark techniques. The results of the proposed technique have shown robust and effective potential.

Aspect Based Multi-Labeling Using SVM Based Ensembler

Sentiment analysis is one of the most prominent sub-areas of research in Natural Language Processing (NLP), where it is important to consider implicit or explicit emotions conveyed by review material. Researchers also recognized that the generic feelings derived from the textual material are insufficient, so the sentiment analysis aspect based was coined to extract the emotions from textual data. Multi-labeling based on aspects data can resolve the issue of extracting emotion aspect based. In this work, a novel approach namely: Evolutionary Ensembler (EEn) is proposed to effectively boost the accuracy and diversity of multi-label learners. Unlike traditional multi-label training methods, EEn emphasizes the accuracy and diversity of multi-label-based models. We have used seven datasets (medical, hotel, movies, automobiles, proteins, birds, emotions, news). At first, we applied a pre-processing step to gain the refined and clean data. Second, we have applied the Vader tool with Bag of Words (BoW) for the feature extraction. Third, the word2vec method is applied to draw an association between words. Moreover, the SVM model (tuned with GA) is trained and tested on the refined data. The accuracy of the aspect-based multi-labeling using the SVM-GA on medical, hotel, movies, automobiles, proteins, birds, emotions, news are 93.13%, 94.32%, 94.0%, 95.10%, 90.20%, 93.22%, 90.0%, and 94.0%, respectively. Our proposed model with different dimensions of multi-label datasets shows that EEn is vastly superior to other popular techniques. Experimental outcomes validate the success of the implemented approach among existing benchmark techniques.

Multistage Ensemble Learning Model With Weighted Voting and Genetic Algorithm Optimization Strategy for Detecting Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung ailment and a significant cause of morbidity and fatality globally. The early detection of COPD can provide timely proper medication and reduce the mortality rate. To obtain proper treatment and lessen the death rate, this study proposes a novel ensemble model: the Multistage Ensemble model (MSEN) with an optimized weighted voting technique to detect COPD early and help clinicians provide proper and timely medication. In this study, there are two pools of classifiers created in which four classifiers are placed in each pool. These two pools of classifiers are employed to form two weighted ensemble models based on a weighted voting strategy. This study combines those generated ensemble models using a weighted voting technique to form an MSEN model. The genetic algorithm is utilized to optimize the hyperparameters of each classifier in each pool. The weights of two generated ensemble models and each classifier are optimized using the grid search technique. This study employs the K-Nearest Neighbors approach to fill in the missing values, isolation forest to remove the outliers, and the LightGBM with Recursive Feature Elimination for feature selection. An evaluation of the suggested MSEN model is conducted on a real-world Exasens dataset to validate the suggested model's effectiveness which exhibits that the proposed model obtains better performance to detect COPD and provides superior performance than other machine learning models and existing benchmark techniques.

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID‐19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real‐time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.

Joint Bit Allocation and Hybrid Beamforming Optimization for Energy Efficient Millimeter Wave MIMO Systems

In this article, we aim to design highly energy efficient end-to-end communication for millimeter wave multiple-input multiple-output systems. This is done by jointly optimizing the digital-to-analog converter (DAC)/analog-to-digital converter (ADC) bit resolutions and hybrid beamforming matrices. The novel decomposition of the hybrid precoder and the hybrid combiner to three parts is introduced at the transmitter (TX) and the receiver (RX), respectively, representing the analog precoder/combiner matrix, the DAC/ADC bit resolution matrix and the baseband precoder/combiner matrix. The unknown matrices are computed as a solution to the matrix factorization problem where the optimal fully digital precoder or combiner is approximated by the product of these matrices. A novel and efficient solution based on the alternating direction method of multipliers is proposed to solve these problems at both the TX and the RX. The simulation results show that the proposed solution, where the DAC/ADC bit allocation is dynamic during operation, achieves higher energy efficiency when compared with existing benchmark techniques that use fixed DAC/ADC bit resolutions.

A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting

This paper proposes an effective computing framework for Short-Term Load Forecasting (STLF). The proposed technique copes with the stochastic variations of the load demand using a stacked generalization approach. This approach combines three models, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Multi-Layer Perceptron (MLP). The inner mechanism of Stacked XGB-LGBM-MLP model consists of generating a meta-data from XGB and LGBM models to compute the final predictions using MLP network. The performance of the proposed Stacked XGB-LGBM-MLP model is validated using two datasets from different locations: Malaysia and New England. The main contributions of this paper are: 1) A novel stacking ensemble-based algorithm is proposed; 2) An effective STLF technique is introduced; 3) A critical multi-study analysis for hyperparameter optimization with five techniques is comprehensively performed; 4) A performance comparative study using two datasets and reference models is conducted. Several case studies have been carried out to prove the performance superiority of the proposed model compared to both existing benchmark techniques and hybrid models.

A bootstrap-based differential split-sample test to assess the transferability of conceptual rainfall-runoff models under past and future climate variability

This study proposes a general differential split-sample test (GDSST) based on an oriented bootstrap to assess the transferability of conceptual rainfall-runoff models to climatically contrasting periods. Compared to existing benchmark techniques, the GDSST allows a larger number of climatically contrasted discontinuous periods to be sampled, and is computationally more effective than the basic bootstrap to identify the most contrasted periods. When applied to three hydrological models (GR4J, HBV and IHACRES) in five catchments in northern Tunisia, the GDSST provided clear limits of the transferability of the models under changing precipitation (P) and temperature (T) conditions towards drier and hotter conditions. According to the criteria and thresholds retained, approximate limits of model transferability are drawn. The models are roughly transferable for relative changes in precipitation ΔP < (0.08 ΔT − 0.18) with ΔP ∈ [−30%, +80%], and changes in temperature ΔT ∈ [−2 °C, +2 °C]. These transferability limits suggest selecting a past sub-period as close as possible to the future climate to identify calibration parameters, which can be used for hydrological projections. The limits of transferability were then compared to climate projections by eight high-resolution Regional Climate Model (RCM) simulations resulting from the EURO-CORDEX initiative. The RCMs’ precipitation and temperature simulations of the historical period 1970–2000 were first assessed to select the most realistic ones for future projections. A delta-change monthly correction was used to perturb the observed climate series according to climate simulations under two Radiative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5) for one medium-term horizon (2040–2070) and one long-term horizon (2070–2100). The effects of the selected past calibration period on the hydrological projections were then analysed. The RCP 8.5 climate projections fall outside the limits of transferability of all rainfall-runoff models tested. Models calibrated on the whole observed period were found to underestimate the impacts of climate change on runoff by 5%–20% in comparison with models calibrated on sub-periods with mean annual P and T closer to projected climate conditions.

Semantic fuzzy mining: Enhancement of process models and event logs analysis from syntactic to conceptual level

Semantic-based process mining is a useful technique towards improving information values of process models and analysis by means of conceptualization. The conceptual system of analysis allows the meaning of process elements to be enhanced through the use of property characteristics and classificati on of discoverable entities, to generate inference knowledge that can be used to determine useful patterns and predict future outcomes. The work in this paper presents a Semantic-Fuzzy mining approach that makes use of labels within event log about real-time process to provide a method which allows for mining and improved process analysis of the resulting process models through semantic – annotation, representation and reasoning. Qualitatively, the study shows by using a case study of Learning Process – how data from various process domains can be extracted, semantically prepared, and transformed into mining executable formats to support the discovery, monitoring and enhancement of real-time domain processes through further semantic analysis of the discovered models. Also, the paper quantitatively assess the level of accuracy of the classification results to predict behaviours of unobserved instances within the process knowledge-base by determing which traces are fitting or not fitting the discovered model by using a training set and test log for the cross-validation experiment. Accordingly, the work looks at the sophistication of the proposed semantic-based approach and the discovered models, validation of the classification results and their influence compared to other existing benchmark techniques and algorithms for process mining. The experimental results and data validation ends with the supposition that a system which is formally encoded with semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner) has the capability to lift process mining analysis and outcomes from the syntactic level to a much more conceptual level, resulting in a mining approach that is able to induce new knowledge based on previously unobserved behaviours and a more intuitive and easy way to envisage the relationships between the process instances found within the available event data logs and the discovered process models.

Recognition of Chinese artists via windowed and entropy balanced fusion in classification of their authored ink and wash paintings (IWPs)

As one of the most important cultural heritages, ink and wash paintings (IWPs) play an important role in the world of traditional Chinese arts. In comparison with western arts, the Chinese IWPs have the unique feature that the art form is primarily populated with limited number of content elements, such as stones, mountains, flowers, and animals etc. and hence most likely different art pieces share similar content, making it difficult to differentiate in terms of content alone. In this paper, we propose to extract histogram-based local feature and global feature to characterize different aspects of art styles, and such features are applied to drive neural networks to complete the classification of IWPs in terms of individual artistic descriptors. We then propose a windowed and entropy balanced fusion scheme to make integrated decisions to optimize the final classification and recognition results. Extensive evaluation via experiments is also reported, which supports that the proposed algorithm achieves good performances, outperforming the existing benchmark techniques and hence providing an excellent potential for computerized analysis and management of traditional Chinese IWPs.