Representation Techniques Research Articles

Validation of Wind Turbine Models Based on Test Bench Measurements: A System for Theoretical Representation of the Grid Replica

In recent years, nacelle test benches for wind turbines have been developed internationally. New standards are currently being developed that explicitly refer to the measurement of the electrical properties of wind turbines on these test benches. Thus, they are suitable for measuring the electrical properties required for certification. Another part of the certification is the creation and validation of suitable models of the wind turbine, which are used for stability analyses of the utility grid. Validation requires a suitable model of grid replication on the test benches, which is not yet covered by any applicable standard. Such models should be as simplified a representation of the artificial grid replication as possible to ensure that they are accessible to certification bodies. A model of the grid emulator installed at the CWD of RWTH Aachen University, which was validated with real measurement data, serves as a reference. A step-by-step reduction of the model’s depth up to the system’s technical representation is followed by a model evaluation with respect to the level of detail and an analysis of time and frequency. The evaluation shows that even a highly simplified model consisting of a reference voltage and an impedance replica meets the requirements for the validation of wind turbine models according to IEC 61400-27-2.

MATC-Net: Learning compact sequence representation for hierarchical loop closure detection

Loop closure detection (LCD) is a challenging task to judge whether the current position of an intelligent robot returns to the previously visited position. Mainstream appearance-based approaches apply robust image representation techniques to describe the scene. However, most of these methods are designed for single images, and the sequence representation method incorporating temporal sequence information is still in the preliminary exploration. In this paper, we propose a compact sequence representation method for hierarchical LCD, ensuring conspicuous performance for the LCD task. Deriving from a group of global features of image sequences, we propose a multi-scale asymmetric temporal convolution network (MATC-Net), which generates sequential features and transformed global features through its aggregation branch and transformation branch, respectively. Based on these two types of features, a MATC-Net-based hierarchical LCD framework including two similarity measurement processes is constructed, through which the best place match is identified. The experimental results show that our method outperforms other counterparts on three datasets, exhibiting the promising potential of leveraging sequential features to LCD task.

Cocoa beans classification using enhanced image feature extraction techniques and a regularized Artificial Neural Network model

Cut-Test technique employs visual inspection of interior coloration, compartmentalization, and defects of beans for effective classification of cocoa beans. However, due to its subjective nature and natural variations in visual perception, it is intrinsically limited, resulting in disparities in verdicts, imprecision, discordance, and time-consuming and labor-intensive classification procedures. Machine Learning (ML) techniques have been proposed to address these challenges with significant results, but there is still a need for improvement. In this paper, we propose a color and texture extraction technique for image representation, as well as a generalized, less complex Neural Network model, to help improve the performance of machine classification of Cut-Test cocoa beans. A total of 1400 beans were classified into 14 grades. Experimental results on the equal cocoa cut-test dataset, which is the standard publicly available cut-test dataset, show that the novel extraction method combined with the developed Artificial Neural Networks provides a more homogeneous classification rate for all grades, obtaining 85.36%, 85%, 83%, and 83% for accuracy, precision, recall, and F1 measure, respectively. The proposed model outperforms other ML models, such as Support Vector Machines, Decision Trees, Random Forests, and Naïve Bayes, on the same dataset. Additionally, the proposed ANN model demonstrates relatively better generalization when compared with earlier work by Santos on the same dataset. The proposed techniques in this work are robust on the cut-test dataset and can serve as an accurate Computer-Aided Diagnostic tool for cocoa bean classification.

A Word Embeddings based Approach for Author Profiling: Gender and Age Prediction

Author Profiling (AP) is a method of identifying the demographic profiles such as age, gender, location, native language and personality traits of an author by processing their written texts. The AP techniques are used in multiple applications such as literary research, marketing, forensics and security. The researchers identified various differences in the authors writing styles by analysing various datasets. The differences in writing styles are represented as stylistic features. The researchers extracted several style based features like structural, content, word, character, syntactic, readability and semantic features to recognize the profiles of the authors. Traditionally, the researchers extracted various feature combinations for differentiating the profiles of authors. Several existing works are used Machine Learning (ML) methods for predicting the author characteristics of a new author. The existing works achieved good accuracies for predicting the author characteristics by considering the both stylistic features and ML algorithms combination. Recently, in advent of Deep Learning (DL) techniques the researchers are proposed approaches to author profiling by using these techniques. Few researchers identified that the deep learning techniques performance is good for author profiles prediction than the results of style based features. In this work, a word embeddings based approach is proposed for gender and age prediction. In this approach, the experiment conducted with different word embedding models such as Word2Vec, GloVe, FastText and BERT for generating word vectors for words. The documents are converted as vectors by using the document representation technique which uses the word embeddings of words. The document vectors are transferred to three different ML algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Logistic Regression (LR) for generating the trained model. This model is used for predicating the accuracy of age and gender prediction. The XGBoost classifier with word embeddings of BERT achieved good accuracies for age and gender prediction than other word embeddings and ML algorithms. The experiment implemented on PAN 2014 competition Reviews dataset for age and gender prediction. The proposed approach attained best accuracies for predicting age and gender than the performances of various existing approaches proposed for AP.

Cancer disease multinomial classification using transfer learning and SVM on the genes’ sequences

INTRODUCTION: Early disease detection plays an important role in medical field especially for cancer disease, which helps doctors in diagnosing and identifying the therapeutic process. Aiming to provide assistance, many biological techniques other than machine and deep learning models were proposed. They were applied on a different type of data such as medical images and clinical data. Despite the efficiency of those techniques, they remain costly and need a lot of execution and preparation time, and resources.OBJECTIVES: In this paper, we present a novel method of disease detection analyzing the genes sequences composition.METHODS: We start by extracting k-mer nucleotides as features from gene sequences with the Frequency Chaos Game Representation (FCGR) technique. Since extracted data are huge, we use a DeepInsight model to extract the most representative k-mers.A combination of a transfer learning model, which is Residual neural Network (ResNet), and a support vector machine (SVM) algorithm is then used then to classify samples into 18 cancer disease types.RESULTS: We achieved an accuracy of 0.98 while choosing FCGR6 in feature extraction, and a combination of ResNet50 and SVM in the multinomial classification step, against an accuracy of 0.97 while using ResNet50 with a fully connected layer and FCGR5.CONCLUSION: Defining the gene sequence alterations helps in the disease detection at early stage. Here, we adopt the FCGR method (that gives the frequency of each k-mer) in defining features of the gene sequences. Then, we use deep learning models to deal with the big number of characteristics and predicting different cancer diseases.

Coefficient Extraction of SAC305 Solder Constitutive Equations Using Equation-Informed Neural Networks.

Equation-Informed Neural Networks (EINNs) are developed as an efficient method for extracting the coefficients of constitutive equations. Subsequently, numerical Bayesian Inference (BI) iterations were applied to estimate the distribution of these coefficients, thereby further refining them. We could generate coefficients optimally aligned with the targeted application scenario by carefully adjusting pre-processing mapping parameters and identifying dataset preferences. Leveraging graphical representation techniques, the EINNs formulation is implemented in temperature- and strain-rate-dependent hyperbolic Garofalo, Anand, and Chaboche constitutive models to extract the corresponding coefficients for lead-free SAC305 solder material. The performance of the EINNs-based extracted coefficients, obtained from experimental results of SAC305 solder material, is comparable to existing studies. The methodology offers the dual advantage of providing the coefficients' value and distribution against the training dataset.

Hybrid Defect Prediction Model Based on Counterfactual Feature Optimization

Software defect prediction is critical to ensuring software quality. Researchers have worked on building various defect prediction models to improve the performance of defect prediction. Existing defect prediction models are mainly divided into two categories: models constructed based on artificial statistical features and models constructed based on semantic features. DP-CNN [Li J, He P, Zhu J, et al. Software defect prediction via convolutional neural network. In: 2017 IEEE international conference on software quality, reliability and security (QRS). IEEE, 2017; 318–328.] is one of the best defect prediction models, because it combines both artificial statistical features and semantic features, so its performance is greatly improved compared to traditional defect prediction models. This paper is based on the DP-CNN model and makes the following two improvements: first, using a new Struc2vec network representation technique to mine existing information between software modules, which specializes in learning node representations from structural identity and can further extract structural features associated with defects. Let the DP-CNN model once again incorporate the newly mined structural features. Then, this paper proposes a feature selection method based on counterfactual explanations, which can determine the importance score of each feature by the feature change rate of counterfactual samples. The origin of these feature importance scores is interpretable. Under the guidance of these interpretable feature importance scores, better feature subsets can be obtained and used to optimize artificial statistical features within the DP-CNN model. Based on the above methods, this paper proposes a new hybrid defect prediction model DPS-CNN-STR. Evaluating our model on six open source projects in terms of F1 score in defect prediction. Experimental results show that DPS-CNN-STR improves the state-of-the-art method by an average of 3.3%.

"The babbitry of professors' humble abodes": Representation of Everyday Life of the "Positivist Father" in Memoirs of Russian Symbolism

The formation of symbolism as an artistic and worldview phenomenon took place in intellectual and cultural opposition to the tradition of Russian academic positivism. In the article positivism and symbolism are viewed as two discourses competing for influence in the Russian intellectual environment, the competition of which often took on the features of a conflict between "fathers and children". One of the ways of " battle for the authority" was the construction of the image of "father-positivist" in memoirs and diary notes by symbolists. The object of the study is the representation of the everyday culture of the conventional "father" in the memoirs of A. Beliy, G. I. Chulkov, V. Y. Brusov, N. Petrovskaya, B. M. Runt and Z. N. Gippius. The subject is those discursive mechanics, through which the representation of otherness and "othering" of such everyday life are achieved. The methodology of the work is built around the optics of the translation turn, which allows us to look at the scenes from the memoirs as "third space", where the battle of discourses unfolds at the expense of representational practices. The novelty of the research is manifested both in drawing attention to this problem and in the development of an actual methodological base. Based on the results of the analysis, the following mechanics of "othering" were identified: unification and generalization, binarity and asymmetry, giving the Other the status of timelessness and non-historicity, the use of pejorative categories, the narrative technique of double representation and concealment. The aim of the fathers "othering" was the construction of personal identity and selfhood, as well as the attempt to define oneself through the constitutive Other.

Accurate Coiflet wavelet solution of extended [formula omitted]-dimensional Kadomtsev–Petviashvili equation using the novel wavelet-homotopy analysis approach

We established a novel wavelet-homotopy analysis technique to give accurate solutions to nonlinear wave problems with unsteady state, which is the originality of the paper. The extended (2+1)-dimensional Kadomtsev–Petviashvili equation is taken as the research object owing to its complexity and importance. The convergence of the proposed technique is proved. The modulation instability is employed to determine the range of the normalized wave numbers of the transformed traveling wave equation. The validity and reliability of our results are checked via a rigid comparison with analytical solutions. Results and the comparison with the numerical solutions reveal that the proposed technique maintains capabilities of the homotopy analysis method for strong nonlinear processing and the wavelet technique for excellent local detail representation. It is expected that the proposed method can be used as an alternative tool to analyze more complex wave problems such as rogue wave or shock wave problems.

Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions

Hospital readmissions rate is reportedly high and has caused huge financial burden on health care systems in many countries. It is viewed as an important indicator of health care providers’ quality of care. We examine the use of machine learning-based survival analysis to assess quality of care risk in hospital readmissions. This study applies various survival models to explore the risk of hospital readmissions given patient demographics and their respective hospital discharges extracted from a health care claims dataset. We explore advanced feature representation techniques such as BioBERT and Node2Vec to encode high-dimensional diagnosis code features. To our knowledge, this study is the first to apply deep-learning based survival-analysis models for predicting hospital readmission risk agnostic of specific medical conditions and a fixed window for readmission. We found that modeling the time from discharge date to readmission date as a Weibull distribution as in the SparseDeepWeiSurv model yields the best discriminative power and calibration. In addition, embedding representations of the diagnosis codes do not contribute to improvement in model performance. We find dependency of each model’s performance on the time point at which it is evaluated. This time dependency of the models’ performance on the health care claims data may necessitate a different choice of model in quality of care issue detection at different points in time. We show the effectiveness of deep-learning based survival-analysis models in estimating the quality of care risk in hospital readmissions.

DIGITAL HERITAGE&DESIGN THINKING: THE RAILTOLAND WORKSHOP AS AN INNOVATIVE PRACTICE IN THE HIGHER EDUCATION SCENARIO

Abstract. The higher education scenario is constantly evolving and COVID-19 restrictions traced a demarcation line with previous behaviours and practices. Even though the European Commission spends many efforts to monitor the progresses, indicate the challenges as well as to give the perspectives, innovating the way in which we teach, especially about heritage & design, is rarely a priority.The article presents an interdisciplinary learning activity carried out within the RAILtoLAND (RtL) project, a co-funded Erasmus+ project under the Key Action 2 “Cooperation for innovation”. RtL is a collective ideation platform to develop innovative tools to communicate the European Cultural Landscapes by train and it has the goal to explore the social and educational value of the railway landscape as a common heritage and as a catalyst for European identity.The workshop was carried out in the Sub-Apennine area dealing with two complementary and fundamental topics “Digital Heritage & Design Thinking”, experiencing virtual events and innovative practices for the knowledge, narrative and design of heritage and landscape. The workshop stemmed from an interdisciplinary approach: experts in digital cultural heritage, survey and representation techniques, geomatics, and urban and landscape designers brought together their expertise and performed a ground-breaking study activity. The learning activities involved areas in the Marche Region (Italy) through on field activities, and the Porto-Vigo railway line (Portugal-Spain) through the experience of a virtual trip.

AIO-P: Expanding Neural Performance Predictors beyond Image Classification

Evaluating neural network performance is critical to deep neural network design but a costly procedure. Neural predictors provide an efficient solution by treating architectures as samples and learning to estimate their performance on a given task. However, existing predictors are task-dependent, predominantly estimating neural network performance on image classification benchmarks. They are also search-space dependent; each predictor is designed to make predictions for a specific architecture search space with predefined topologies and set of operations. In this paper, we propose a novel All-in-One Predictor (AIO-P), which aims to pretrain neural predictors on architecture examples from multiple, separate computer vision (CV) task domains and multiple architecture spaces, and then transfer to unseen downstream CV tasks or neural architectures. We describe our proposed techniques for general graph representation, efficient predictor pretraining and knowledge infusion techniques, as well as methods to transfer to downstream tasks/spaces. Extensive experimental results show that AIO-P can achieve Mean Absolute Error (MAE) and Spearman’s Rank Correlation (SRCC) below 1p% and above 0.5, respectively, on a breadth of target downstream CV tasks with or without fine-tuning, outperforming a number of baselines. Moreover, AIO-P can directly transfer to new architectures not seen during training, accurately rank them and serve as an effective performance estimator when paired with an algorithm designed to preserve performance while reducing FLOPs.

FINE-GRAINED SENTIMENT ANALYSIS IN SOCIAL MEDIA USING GATED RECURRENT UNIT WITH SUPPORT VECTOR MACHINE

Social media platforms are widely used to share opinions, leading to a large growth of text data on the internet. This data can be a key source of up-to-date and inclusive information by conducting sentiment analysis. Typically, sentiment analysis research classifies binary based on the polar values generated. However, this has its limitations, such as classifying sentences containing positive and negative expressions, leading to incorrect predictions. Fine-grained sentiment analysis provides more precise results by associating values with more than two classification targets. The objective of this study is to carry out sentiment analysis at a fine-grained level related to public policy in Indonesia using the GRU-SVM model with feature extraction and expansion techniques. However, sentiment analysis research still faces challenges in NLP. Deep learning have successfully overcome the challenges of traditional machine learning models in terms of efficiency and performance. This study proposes GRU-SVM model. GRU is used because it can adaptively control dependencies, making it more efficient in memory usage, while SVM is used as it is state-of-the-art in sentiment analysis. Result of the study show that the selection of word representation techniques, the addition of feature extraction techniques, datasets, data ratios, and feature expansion are crucial in the model testing process. The GRU-SVM model achieved the best performance with an accuracy of 96.02%. Overall, the results of this study demonstrate that the GRU-SVM method is effective in analyzing sentiments in Indonesian tweets.

Hypergraph-Regularized Lp Smooth Nonnegative Matrix Factorization for Data Representation

Nonnegative matrix factorization (NMF) has been shown to be a strong data representation technique, with applications in text mining, pattern recognition, image processing, clustering and other fields. In this paper, we propose a hypergraph-regularized Lp smooth nonnegative matrix factorization (HGSNMF) by incorporating the hypergraph regularization term and the Lp smoothing constraint term into the standard NMF model. The hypergraph regularization term can capture the intrinsic geometry structure of high dimension space data more comprehensively than simple graphs, and the Lp smoothing constraint term may yield a smooth and more accurate solution to the optimization problem. The updating rules are given using multiplicative update techniques, and the convergence of the proposed method is theoretically investigated. The experimental results on five different data sets show that the proposed method has a better clustering effect than the related state-of-the-art methods in the vast majority of cases.

Fairness and feasibility in deep mitigation pathways with novel carbon dioxide removal considering institutional capacity to mitigate

Questions around the technical and political feasibility of deep mitigation scenarios assessed by the Intergovernmental Panel on Climate Change have increasingly been raised as have calls for more directly analyzing and incorporating aspects of justice and fairness. Simultaneously, models are increasing the technical representation of novel carbon-dioxide removal (CDR) approaches to provide policy-relevant analyses of mitigation portfolios in the context of the rising number of net-zero CO2 and GHG targets made by parties to the Paris Agreement. Still, in most cost-effective mitigation scenarios developed by integrated assessment models, a significant portion of mitigation is assumed to take place in developing regions. We address these intersecting questions through analyzing scenarios that include direct air capture of CO2 with storage (DACCS), a novel CDR technology that is not dependent on land potential and can be deployed widely, as well as regional variations in institutional capacity for mitigation based on country-level governance indicators. We find that including novel CDR and representations of institutional capacity can enhance both the feasibility and fairness of 2 °C and 1.5 °C high-overshoot scenarios, especially in the near term, with institutional capacity playing a stronger role than the presence of additional carbon removal methods. However, our results indicate that new CDR methods being studied by models are not likely to change regional mitigation outcomes of scenarios which achieve the 1.5 °C goal of the Paris Agreement. Thus, while engineered carbon removals like DACCS may play a significant role by midcentury, gross emissions reductions in mitigation pathways arriving at net-zero CO2 emissions in line with 1.5 °C do not substantially change. Our results highlight that further investment and development of novel CDR is critical for post-net-zero CO2 mitigation, but that equitable achievement of this milestone will need to arrive through technical and financial transfers, rather than by substantial carbon removals in developed countries before mid-century.

Representation of Ophelia's Painting in the Scene of Melancholia Movie 2011

Sir John Everett's Ophelia is a very famous painting. The object of the painting is the death of Ophelia, a character taken from Shakespeare's Hamlet. This painting has inspired many artists and filmmakers to use its visualization as inspiration. Research on the visualization of Ophelia's death has never been done, especially with the use of the tableau vivant technique in the object of research of Lars Von Trier's 2011 film Melancholia. based on this, the purpose of this study is to determine the role of the tableau vivant technique in the field of cinema. Especially its role in the representation of the painting "Ophelia" by Sir John Everett Millais by director Lars Von Trier in the scene of the 2011 film "Melancholia". Trier with his creativity uses the technique with his specialty. It is important to discuss this as a reference in making cinema works by utilizing paintings that were present before. The analysis is done with the main theory of tableau vivant so that its role as a representation technique is visible. The theory of film cinematic elements (mise en scène and cinematography) to support the main theory, describing how the elements in the film represent the painting. Data collection techniques are carried out by means of observation and literature study related to the object of the movie scene that shows the representation of painting, and the painting it represents. The results of the analysis are how the creative use of tableau vivant technique by Lars Von Trier plays a role in representing Ophelia's painting in the scene of the movie Melancholia in 2011 through its cinematic elements. With this research, filmmakers are able to make Trier's creativity using the tableau vivant technique as a reference in making movie scenes inspired by works of art.
 
 Keywords: Film; Lars Von Trier; Melancholia; Ophelia; Tableau Vivant

Hyperspectral image-aided LiDAR point cloud labeling via spatio-spectral feature representation learning

Urban scene-level 3D point cloud labeling is a very laborious and expensive task compared to images. Conversely however, image processing techniques, deep learning or otherwise are more established and mature. Thus, in a multi-source data environment, the labeling of a point cloud scene via an automated image process as an initial step, followed by a manual human verification process is an effective way to save man hours and cost. With the above as the goal, this study presents a simple but robust spatio-spectral feature representation approach. In this approach, a class-aware band selection and reduction (CBSR) technique is developed for optimal hyperspectral feature representation. A double-branched convolutional Gaussian Bernoulli deep belief network (CGBDBN) is then used for hierarchical spatial feature extraction from LiDAR-derived data and the CBSR data. Using stacked ensemble learning, spatio-spectral features are generated from the two feature streams via a fusion rule and then classified — the results of which are used in labeling a raw 3D LiDAR point cloud through projection. To evaluate this study, extensive experiments were conducted on the IEEE 2018 Houston dataset — the only publicly available dataset with both hyperspectral image (HSI) and 3D point cloud covering the same area — for urban scene classification. The results indicate that the developed CBSR attained comparatively competitive results with state-of-the-art approaches, thus making it a robust spectral feature representation technique. Also, the weight-sharing property, probabilistic modeling, and hierarchical nature of CGBDBN gives our approach the ability to capture high-level contextual features. Furthermore, compared to the spatial- or spectral-only features, the generated spatio-spectral features are more discriminative and significantly aided in improving the proposed model’s efficacy. Overall, the proposed approach, based on the evaluation metrics, is a robust and effective approach for both coarse- and fine-grained raw LiDAR point cloud labeling tasks.

GIS-based hydrodynamic modeling for urban flood mitigation in fast-growing regions: a case study of Erbil, Kurdistan Region of Iraq

Floods threaten urban infrastructure, especially in residential neighborhoods and fast-growing regions. Flood hydrodynamic modeling helps identify flood-prone locations and improve mitigation plans' resilience. Urban floods pose special issues due to changing land cover and a lack of raw data. Using a GIS-based modeling interface, input files for the hydrodynamic model were developed. The physical basin's properties were identified using soil map data, Land Use Land Cover (LULC) maps, and a Digital Elevation Model (DEM). So, the HEC-RAS 2-D hydrodynamic model was developed to estimate flood susceptibility and vulnerability in Erbil, Iraq. The case study examines the quality of flood modeling results using different DEM precisions. Faced with the difficulty, this study examines two building representation techniques: Building Block (BB) and Building Resistance (BR). The work presented here reveals that it is possible to apply the BR technique within the HEC-RAS 2-D to create urban flood models for regions that have a lack of data or poor data quality. Indeed, the findings confirmed that the inundated areas or areas where water accumulated in past rainfall events in Erbil are the same as those identified in the numerical simulations. The study's results indicate that the Erbil city is susceptible to flood hazards, especially in areas with low-lying topography and substantial precipitation. The study's conclusions can be utilized to plan and develop flood control structures, since it identified flood-prone areas of the city.

DeepCNN: Spectro‐temporal feature representation for speech emotion recognition

AbstractSpeech emotion recognition (SER) is an important research problem in human‐computer interaction systems. The representation and extraction of features are significant challenges in SER systems. Despite the promising results of recent studies, they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive fields. To mitigate this problem, this article proposes DeepCNN, which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks (CNNs) and a convolution layer‐based transformer. Two parallel CNNs are applied to extract the spectral features (2D‐CNN) and temporal features (1D‐CNN) representations. A 2D‐convolution layer‐based transformer module extracts spectro‐temporal features and concatenates them with features from parallel CNNs. The learnt low‐level concatenated features are then applied to a deep framework of convolutional blocks, which retrieves high‐level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification layer. This fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature map. The Berlin Database of Emotional Speech (EMO‐BD) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) datasets are used in experiments to recognise distinct speech emotions. With efficient spectral and temporal feature representation, the proposed SER model achieves 94.2% accuracy for different emotions on the EMO‐BD and 81.1% accuracy on the IEMOCAP dataset respectively. The proposed SER system, DeepCNN, outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO‐BD and IEMOCAP datasets.

Design of a machine learning-aided screening framework for antibiofilm peptides

Biofilms are formed by multicellular colonies of microorganisms that are protected by hard extracellular matrices. Eradication of biofilms is a challenging task due to their recalcitrant nature and thus biofilm formation poses a global threat to public health. In this regard, antibiofilm peptides are a promising class of therapeutics that are active against biofilms. However, large-scale experimental screening and testing of peptides for antibiofilm activity is a resource-intensive task. In this study, a machine learning-aided design framework is proposed to aid in screening of antibiofilm peptides. An SVM-based binary classification model is developed using amino acid compositions, sequence, and physicochemical properties of peptides as independent features. The physicochemical property-based model developed in this study achieved the highest accuracy of 97.9%, which is found to be substantially higher than the other feature representation techniques. The explainability of this model is performed using SHAP analysis. Results obtained show that amphiphilicity, aliphaticity and cationicity have positive correlation whereas steric parameter, length, and volume have negative correlation with antibiofilm activity of peptides. The developed model can be accessed freely via web tool: AntiBFP.