Traditional Approaches Research Articles

Reevaluating the Constitutionality of Abortion: Can U.S. Jurisprudence Truly Protect Womens Interests in a Post-Roe America?

This Article examines the U.S. Supreme Court's evolving constitutional frameworks for abortion rights. It traces the shift from Roe v. Wade's privacy-based rationale to Planned Parenthood v. Casey's undue burden standard. While Roe and Casey attempted to balance state interests with women's reproductive autonomy, they ultimately fell short of fully addressing the unique social, economic, and psychological burdens women face. Dobbs v. Jackson Women's Health Organization introduced a historical tradition approach, rejecting this balancing test and returning regulatory power to states. This decision undermines women's autonomy and complicates democratic access, particularly for marginalized groups. Recognizing the limitations of Roe and Casey in fully protecting women's equality and critiquing Dobbs' unbalanced historical focus, this Article proposes a new framework that prioritizes women's needs. Drawing on international perspectives, such as Japan's emphasis on family and societal welfare, it argues that centering women's lived experiences, including the often-overlooked burdens of motherhood, is essential for crafting a reproductive rights model that balances individual autonomy with social equity

Improving hierarchical models of individual differences: An extension of Goldberg's bass-ackward method.

Goldberg's (2006) bass-ackward approach to elucidating the hierarchical structure of individual differences data has been used widely to improve our understanding of the relationships among constructs of varying levels of granularity. The traditional approach has been to extract a single component or factor on the first level of the hierarchy, two on the second level, and so on, treating the correlations between adjoining levels akin to path coefficients in a hierarchical structure. This article proposes three modifications to the traditional approach with a particular focus on examining associations among all levels of the hierarchy: (a) identify and remove redundant elements that perpetuate through multiple levels of the hierarchy; (b) (optionally) identify and remove artefactual elements; and (c) plot the strongest correlations among the remaining elements to identify their hierarchical associations. Together these steps can offer a simpler and more complete picture of the underlying hierarchical structure among a set of observed variables. The rationale for each step is described, illustrated in a hypothetical example and three basic simulations, and then applied in real data. The results are compared with the traditional bass-ackward approach together with agglomerative hierarchical cluster analysis, and a basic tutorial with code is provided to apply the extended bass-ackward approach in other data. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Review of Modern and Traditional Approaches for the Management of Pulmonary Hypertension

Research Problem: Hypertension, either high blood pressure or pulmonary hypertension, both affect the life of patients in many ways. Research Questions: Does Pulmonary hypertension similar to high blood pressure? : Does it affect old age people or the neonatal age groups also? : What were the reasons for pulmonary hypertension? : Does pulmonary hypertension manageable with modern and traditional drugs? Literature Review: The purpose of the literature review is to identify the targeted therapy for pulmonary hypertension and also include the plants that may be effective for the management of pulmonary hypertension. Conclusion: The author has reviewed many targeted drugs that are presently used, and many are in research that was found effective in making the life of patients as normal as possible. : They also reviewed about 15 herbal plants which were found effective in the management of the disease. : As pulmonary hypertension is not treatable, it can only be managed by changing lifestyle and food habits; therefore, the authors emphasized reviewing the traditional approach for the management of pulmonary hypertension.

Low-Light Image Enhancement by Retinex-Based Algorithm Unrolling and Adjustment.

Low-light image enhancement (LIE) has attracted tremendous research interests in recent years. Retinex theory-based deep learning methods, following a decomposition-adjustment pipeline, have achieved promising performance due to their physical interpretability. However, existing Retinex-based deep learning methods are still suboptimal, failing to leverage useful insights from traditional approaches. Meanwhile, the adjustment step is either oversimplified or overcomplicated, resulting in unsatisfactory performance in practice. To address these issues, we propose a novel deep-learning framework for LIE. The framework consists of a decomposition network (DecNet) inspired by algorithm unrolling and adjustment networks considering both global and local brightness. The algorithm unrolling allows the integration of both implicit priors learned from data and explicit priors inherited from traditional methods, facilitating better decomposition. Meanwhile, considering global and local brightness guides the design of effective yet lightweight adjustment networks. Moreover, we introduce a self-supervised fine-tuning strategy that achieves promising performance without manual hyperparameter tuning. Extensive experiments on benchmark LIE datasets demonstrate the superiority of our approach over existing state-of-the-art methods both quantitatively and qualitatively. Code is available at https://github.com/Xinyil256/RAUNA2023.

Evolutionary Stress Detection Framework through Machine Learning and IoT (MLIoT-ESD)

Background: Life nowadays is full of stress due to lifestyle changes and the modernera race. Almost everyone around us is suffering from stress and anxiety. Mostly, stress identification is done by medical practitioners in a very late stage in which suitable help measures cannot be provided and hence result in suicides or early age deaths due to cardiac arrest, etc. One major reason behind the delay is the time required in stress identification by traditional approaches, and above that, the amount of time and financial support expected is always not feasible to be available. Hence, in this paper, we proposed an evolutionary research framework for stress identification by the usage of both machine learning and IoT. Here, we also conducted a pilot study on 83 records available over the decade since 2014 using PRISMA guidelines, and a bibliographic network visualization was also performed using VOS viewer. Objectives: This study aimed to develop a stress detection framework using Machine Learning and the Internet of Things (IoT) as technology advanced over a decade. Methods: More than 80 research papers from honorable repositories like Scopus and Web of Science were gathered according to the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020, and the VOSviewer tool was further applied to construct the bibliographic depictions. Various datasets and methods used over ten years with their performance were also discussed. Results: This research was conducted to gather various types of stressors, the impact of various Machine Learning and IoT algorithms and concepts on various datasets and their respective results. Conclusion: Various available datasets and results with multiple algorithms were discussed in a crisp tabular form for better understanding. A methodology based on an amalgamation of Machine Learning and IoT was also proposed due to various research gaps available so that stress detection could be done in a cost-effective way.

Categorical and latent profile approaches to temperamental infant reactivity and early trajectories of socioemotional adjustment.

This article examines the patterns, and consequences, of infant temperamental reactivity to novel sensory input in a large (N = 357; 271 in current analysis) and diverse longitudinal sample through two approaches. First, we examined profiles of reactivity in 4-month-old infants using the traditional theory-driven analytic approach laid out by Jerome Kagan and colleagues, and derived groups characterized by extreme patterns of negative reactivity and positive reactivity. We then used a theory-neutral, data-driven approach to create latent profiles of reactivity from the same infants. Despite differences in sample characteristics and recruitment strategy, we noted similar reactivity groups relative to prior cohorts. The current data-driven approach found four profiles: high positive, high negative, high motor, and low reactive. Follow-up analyses found differential predictions of internalizing, externalizing, dysregulation, and competence trajectories across 12, 18, and 24 months of life based on 4-month reactivity profiles. Findings are discussed in light of the initial formulation of early reactivity by Kagan and the four decades of research that has followed to refine, bolster, and expand on this approach to child-centered individual differences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Valid RBFNN Adaptive Control for Nonlinear Systems With Unmatched Uncertainties.

In this article, an adaptive tracking controller based on radial basis function neural networks (RBFNNs) is proposed for nonlinear plants with unmatched uncertainties and smooth reference signals. The concept of valid RBFNN adaptive control is introduced where all closed-loop arguments of the involved RBFNNs should always remain inside their corresponding compact sets. Considering the local approximation capacity of RBFNNs, validity requirements are necessary for ensuring reliable closed-loop approximation accuracy and stability. To obtain valid RBFNN adaptive controllers, a novel iterative design method is proposed and embedded into the traditional backstepping approach. In the initial iteration, an ideal RBFNN and its online estimated version are introduced in each step where the initial compact set guarantees the validity requirement for only a finite time interval. Then, by carefully investigating the dependence among different signals and introducing some auxiliary variables, the compact sets are redesigned for prolonging the time interval satisfying validity requirements to infinity as the iteration goes on. Consequently, a closed-loop system model can be formulated during the entire control process, which underlies a rigorous proof on closed-loop stability and some guidelines on practical implementation. Meanwhile, rigorous analysis from validity requirements reveals, for the first time, a new feature of RBFNN adaptive controllers in the presence of unmatched uncertainties: excessively large scales of RBFNNs in intermediate steps may impair the closed-loop performance. Finally, simulation results are provided to illustrate the efficiency and feasibility of the obtained results.

Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification.

Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.

A Review on Nano/Microfluidic Devices for Cell Isolation Techniques: Recent Progress and Advances

Abstract: Micro/nanofluidic devices and systems have gained increasing interest in healthcare applications over the last few decades because of their low cost and ease of customization, with only a small volume of sample fluid required. Many biological queries are now being addressed using various types of single-molecule research. With this rapid rise, the disadvantages of these methods are also becoming obvious. Micro/nanofluidics-based biochemical analysis outperforms traditional approaches in terms of sample volume, turnaround time, ease of operation, and processing efficiency. A complex and multifunctional micro/nanofluidic platform may be used for single-cell manipulation, treatment, detection, and sequencing. We present an overview of the current advances in micro/nanofluidic technology for single-cell research, focusing on cell capture, treatment, and biochemical analyses. The promise of single-cell analysis using micro/nanofluidics is also highlighted.

HMS-TENet: A hierarchical multi-scale topological enhanced network based on EEG and EOG for driver vigilance estimation

Driving vigilance estimation is an important task for traffic safety. Nowadays, electroencephalography (EEG) and electrooculography (EOG) have made some achievements in vigilance estimation, but there are still some challenges: 1) The traditional approachs with direct multimodal fusion may face the problems of information redundancy and data dimensionality mismatch; 2) Capture key discriminative features during multimodal fusion without losing specific patterns to each modality. In order to solve the above problems, this paper proposes a approach with fusion of EEG and EOG features in split bands, which not only preserves the information about brain activities in different bands of EEG, but also effectively integrates the relevant information of EOG. On this basis, we further propose a hierarchical multi-scale topological enhanced network (HMS-TENet). This network first introduces a pyramid pooling structure (PPS) to capture contextual relationships from different discriminative perspectives. And then we design a selective convolutional structure (SCS) for adaptive sense-field selection, which enables us to mine the desired discriminative information in small-size features. In addition, we design a topology self-aware attention to enhance the learning of representations of complex topological relationships among EEG channels. Finally, the output of the model can be selected for both regression and classification tasks, providing higher flexibility and adaptability. We demonstrate the robustness, generalizability, and utility of the proposed method based on intra-subject and cross-subject experiments on the SEED-VIG public dataset. Codes are available at https://github.com/tangmeng28/HMS-TENet.

Interpretable Model-Driven Deep Network for Hyperspectral, Multispectral, and Panchromatic Image Fusion.

Simultaneously fusing hyperspectral (HS), multispectral (MS), and panchromatic (PAN) images brings a new paradigm to generate a high-resolution HS (HRHS) image. In this study, we propose an interpretable model-driven deep network for HS, MS, and PAN image fusion, called HMPNet. We first propose a new fusion model that utilizes a deep before describing the complicated relationship between the HRHS and PAN images owing to their large resolution difference. Consequently, the difficulty of traditional model-based approaches in designing suitable hand-crafted priors can be alleviated because this deep prior is learned from data. We further solve the optimization problem of this fusion model based on the proximal gradient descent (PGD) algorithm, achieved by a series of iterative steps. By unrolling these iterative steps into several network modules, we finally obtain the HMPNet. Therefore, all parameters besides the deep prior are learned in the deep network, simplifying the selection of optimal parameters in the fusion and achieving a favorable equilibrium between the spatial and spectral qualities. Meanwhile, all modules contained in the HMPNet have explainable physical meanings, which can improve its generalization capability. In the experiment, we exhibit the advantages of the HMPNet over other state-of-the-art methods from the aspects of visual comparison and quantitative analysis, where a series of simulated as well as real datasets are utilized for validation.

Effects of Situated Game Teaching Through Set Plays on Badminton Tactical Knowledge, Technical Ability, and Game Performance Among Turkish Secondary School Students

Background: The Situated Game Teaching Through Set Plays (SGTSP) model, a newly proposed curricular model, extends the previous game-based approaches by adopting the Theory of Situated Learning as a framework to focus on the relational and situational nature of the changing relations of all game parameters in a specific momentary game scenario. However, as a new game-based model, SGTSP warrants further empirical research investigations to establish its effectiveness at advancing student learning such as skill acquisition and game competence. Purpose: To examine the effects of the SGTSP curricular model on secondary school students’ tactical knowledge, technical ability, and game performance in badminton when compared with a technique-focused approach during a 10-lesson unit. Methods: A completely randomized block design with a repeated measure was used to address the research purpose. A sample of 158 sixth graders from eight classes at two Turkish middle schools (67 girls and 89 boys; Mage = 12.62 ± 0.47) were assigned to either the SGTSP (n = 79 from four classes) or the technique-focused approach conditions (n = 79 from four classes) for the experiment. Assessments were conducted before and after the badminton instructional unit. The statistical models consisted of one of the four technical ability performances, components of game performance, or tactical knowledge as a dependent variable and eight independent variables: teacher, treatment, class nested within teacher and treatment, gender, skill levels, and three 2-way interactions between treatment, gender, and skill levels. A series of repeated-measure multivariate analyses of variance were conducted to analyze the data. Results: Participants in both groups improved tactical knowledge, forehand long serve, forehand clear, smash, and drop skills, and game performances from pre to post. Participants in the SGTSP condition showed significantly greater improvements (). Discussion and Conclusion: The findings of this study substantiated the positive effects of the SGTSP model on developing secondary school students’ badminton tactical knowledge, technical ability, and game performances in badminton compared with the traditional technique-focused approach.

Hybrid deep model for predicting anti-cancer drug efficacy in colorectal cancer patients

Cancers are genetically diversified, so anticancer treatments have different levels of efficacy on people due to genetic differences. The main objective of this work is to predict the anticancer drug efficiency for colorectal cancer patients to reduce the mortality rates and provides immune energy for the patients. This paper proposes a novel anti-cancer drug efficacy system in colorectal cancer patients. The input data gene is normalized with the Min–Max normalization technique that normalizes the data in distinct scales. Subsequently, proposes an improved entropy-based feature to evaluate the uncertainty distribution of data, in which it induces weight to overcome the issue of computational complexity. Along with this feature, a correlation-based feature and statistical features are also retrieved. Subsequently, proposes a Recursive Feature Elimination with Hybrid Machine Learning (RFEHML) mechanism for selecting the appropriate feature set by eliminating the recursive features with the aid of hybrid Machine Learning strategies that combine decision tree and logistic regression. Also, the Gini impurity is employed for ranking the feature and selecting the maximum importance score by eliminating the least acquired importance score. Further, proposes a hybrid model for predicting the drug efficiency with the trained feature set. The hybrid model comprises of Long Short-Term Memory (LSTM) and Updated Rectified Linear Unit-Deep Convolutional Neural Network (UReLU-DCNN) model, in which DCNN is modified by updating the activation function at the fully connected layer. Consequently, the learned feature predicts the drug efficacy of anti-cancer in colorectal cancer patients by determining whether the patient is a responder or non-responder of the drug. Finally, the performance of the proposed RFEHML model is compared with other traditional approaches. It is found that the developed method has higher accuracy for each learning percentage, with values of 60LP = 92.48%, 70LP = 94.28%, 80LP = 95.24%, and 90LP = 96.86%, respectively.

STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer

Introduction: Microbes are intimately involved in the physiological and pathological processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced diseases in clinical settings. Predicting potential microbe-drug associations is, therefore, essential for both disease treatment and novel drug discovery. However, it is costly and time-consuming to verify these relationships through traditional wet lab approaches. Methods: We proposed an efficient computational model, STNMDA, that integrated a StructureAware Transformer (SAT) with a Deep Neural Network (DNN) classifier to infer latent microbedrug associations. The STNMDA began with a “random walk with a restart” approach to construct a heterogeneous network using Gaussian kernel similarity and functional similarity measures for microorganisms and drugs. This heterogeneous network was then fed into the SAT to extract attribute features and graph structures for each drug and microbe node. Finally, the DNN classifier calculated the probability of associations between microbes and drugs. Results: Extensive experimental results showed that STNMDA surpassed existing state-of-the-art models in performance on the MDAD and aBiofilm databases. In addition, the feasibility of STNMDA in confirming associations between microbes and drugs was demonstrated through case validations. Conclusion: Hence, STNMDA showed promise as a valuable tool for future prediction of microbedrug associations.

Toward Explainable Multiparty Learning: A Contrastive Knowledge Sharing Framework.

Multiparty learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multiparty learning approaches are confronted with obstacles, such as system heterogeneity, statistical heterogeneity, and incentive design. Determining how to deal with these challenges and further improve the efficiency and performance of multiparty learning has become an urgent problem to be solved. In this article, we propose a novel contrastive multiparty learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication and analogize multiparty learning as a many-to-one knowledge-sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets.

Continuity and Evolution in Vermeule on Legal Interpretation

Abstract This article is a constructive interpretation of Adrian Vermeule’s thought on legal interpretation, putting his previous work on these issues in conversation with his explicit embrace of the classical legal tradition so prominently on display in Common Good Constitutionalism. What emerges is a picture of Vermeule’s contemporary thinking on interpretation that has an interesting core of continuity, one running concurrently with several significant conceptual and normative evolutions brought about by his more explicit embrace of a classical legal framework. Part I offers a synthesis of Vermeule’s treatment of core questions of legal interpretation pre-Common Good Constitutionalism. Part II begins by outlining Vermeule’s express turn to the classical legal tradition and how this tradition approaches important public law issues linked to interpretation. Part II then proceeds to pick out and elaborate on themes of continuity and evolution. I argue here that while Vermeule’s embrace of the classical legal tradition has involved several significant conceptual and normative shifts in his thinking, this has not involved repudiation of his prior work on legal interpretation. Rather, the thrust and insights of much of his previous public law thought on interpretation retain vibrancy within the explicitly classical framework he has now adopted. Overall, I suggest Common Good Constitutionalism, and the works accompanying and building upon it, can be profitably read as providing a form of retroactive coherence to Vermeule’s long-standing arguments concerning legal interpretation, offering them a unifying conceptual framework. From the perspective of a classical natural law jurist at least, Vermeule’s embrace of the classical tradition provides his extensive body of scholarship in this area a more explicit and robust conceptual and normative anchor.

Human-in-the-Loop Behavior Modeling via an Integral Concurrent Adaptive Inverse Reinforcement Learning.

One goal of artificial intelligence (AI) research is to teach machines how to learn from humans, such that they can perform a certain task in a natural human-like way. In this article, an online adaptive inverse reinforcement learning (IRL) approach to human behavior modeling is proposed to enhance machine intelligence for a class of linear human-in-the-loop (HiTL) systems using the state data only, where the human behavior is described by a linear quadratic optimal control model with an unknown weighting matrix for the quadratic cost function. First, an integral concurrent adaptive law is developed to learn the human feedback gain matrix online using the demonstrated state data only, which removes the persistent excitation (PE) conditions required by traditional adaptive estimation approaches and thus is more in line with real applications. Then, with the learned feedback gain matrix, the IRL problem is formulated as a linear matrix inequality (LMI) optimization problem, which can be efficiently solved to retrieve the weighting matrix of the human cost function. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.

Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide.

Since December 2019, the emergence of severe acute respiratory syndrome coronavirus 2, which gave rise to coronavirus disease 2019 (COVID-19), has considerably impacted global health. The identification of effective anticoronavirus peptides (ACVPs) and the establishment of robust data storage methods are critical in the fight against COVID-19. Traditional wet-lab peptide discovery approaches are timeconsuming and labor-intensive. With advancements in computer technology and bioinformatics, machine learning has gained prominence in the extraction of functional peptides from extensive datasets. In this study, we comprehensively review data resources and predictors related to ACVPs published over the past two decades. In addition, we analyze the influence of various factors on model performance. We have reviewed nine ACVP-containing databases, which integrate detailed information on protein fragments effective against coronaviruses, providing crucial references for the development of antiviral drugs and vaccines. Additionally, we have assessed 15 peptide predictors for antiviral or specifically anticoronavirus activity. These predictors employ computational models to swiftly screen potential antiviral candidates, offering an efficient pathway for drug development. Our study provides conclusive results and insights into the performance of different computational methods, and sheds light on the future trajectory of bioinformatics tools for ACVPs. This work offers a representative overview of contributions to the field, with an emphasis on the crucial role of ACVPs in combating COVID-19.

A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-Time Systems.

Deep learning inference that needs to largely take place on the "edge" is a highly computational and memory intensive workload, making it intractable for low-power, embedded platforms such as mobile nodes and remote security applications. To address this challenge, this article proposes a real-time, hybrid neuromorphic framework for object tracking and classification using event-based cameras that possess desirable properties such as low-power consumption (5-14 mW) and high dynamic range (120 dB). Nonetheless, unlike traditional approaches of using event-by-event processing, this work uses a mixed frame and event approach to get energy savings with high performance. Using a frame-based region proposal method based on the density of foreground events, a hardware-friendly object tracking scheme is implemented using the apparent object velocity while tackling occlusion scenarios. The frame-based object track input is converted back to spikes for TrueNorth (TN) classification via the energy-efficient deep network (EEDN) pipeline. Using originally collected datasets, we train the TN model on the hardware track outputs, instead of using ground truth object locations as commonly done, and demonstrate the ability of our system to handle practical surveillance scenarios. As an alternative tracker paradigm, we also propose a continuous-time tracker with C++ implementation where each event is processed individually, which better exploits the low latency and asynchronous nature of neuromorphic vision sensors. Subsequently, we extensively compare the proposed methodologies to state-of-the-art event-based and frame-based methods for object tracking and classification, and demonstrate the use case of our neuromorphic approach for real-time and embedded applications without sacrificing performance. Finally, we also showcase the efficacy of the proposed neuromorphic system to a standard RGB camera setup when simultaneously evaluated over several hours of traffic recordings.

Deciphering the Feature Representation of Deep Neural Networks for High-Performance AI.

AI driven by deep learning is transforming many aspects of science and technology. The enormous success of deep learning stems from its unique capability of extracting essential features from Big Data for decision-making. However, the feature extraction and hidden representations in deep neural networks (DNNs) remain inexplicable, primarily because of lack of technical tools to comprehend and interrogate the feature space data. The main hurdle here is that the feature data are often noisy in nature, complex in structure, and huge in size and dimensionality, making it intractable for existing techniques to analyze the data reliably. In this work, we develop a computational framework named contrastive feature analysis (CFA) to facilitate the exploration of the DNN feature space and improve the performance of AI. By utilizing the interaction relations among the features and incorporating a novel data-driven kernel formation strategy into the feature analysis pipeline, CFA mitigates the limitations of traditional approaches and provides an urgently needed solution for the analysis of feature space data. The technique allows feature data exploration in unsupervised, semi-supervised and supervised formats to address different needs of downstream applications. The potential of CFA and its applications for pruning of neural network architectures are demonstrated using several state-of-the-art networks and well-annotated datasets across different disciplines.