Task Characteristics Research Articles

On-time power mobility and physical activity in toddlers with motor delays: A randomized controlled trial using body-worn sensors

ABSTRACT This study conducted a secondary analysis to objectively measure the effects of modified ride-on cars (MROCs) training in different postures on physical activity (PA) levels in children with motor delays. Data from 39 participants were analyzed in a randomized controlled trial. The participants included those using MROC in standing postures (n = 11, mean age = 22.87 months), MROC in sitting postures (n = 16, mean age = 19.53 months), and a control group that received conventional therapy (n = 12, mean age = 23.30 months). PA intensity was measured using an ActiGraph GT3X on the participants’ wrists and right hip at weeks 1, 6, and 12 of the 12-week intervention period. This trial was registered at ClinicalTrials.gov (NCT02527356). The demographic data were similar across the groups. No significant differences were found in the interaction, group, or time effects for any of the positions (p > 0.05). The PA levels were consistent in at least light PA intensity range. MROC training may yield PA levels comparable to those of conventional therapy. Variations in activity and motor impairment severity could affect outcomes. Future studies should investigate factors such as task characteristics, motor delay severity, PA cutoff points, and training dosage that may influence PA intensity.

Trust it or not: Understanding users’ motivations and strategies for assessing the credibility of AI-generated information

The evolution of artificial intelligence (AI) facilitates the creation of multimodal information of mixed quality, intensifying the challenges individuals face when assessing information credibility. Through in-depth interviews with users of generative AI platforms, this study investigates the underlying motivations and multidimensional approaches people use to assess the credibility of AI-generated information. Four major motivations driving users to authenticate information are identified: expectancy violation, task features, personal involvement, and pre-existing attitudes. Users evaluate AI-generated information’s credibility using both internal (e.g. relying on AI affordances, content integrity, and subjective expertise) and external approaches (e.g. iterative interaction, cross-validation, and practical testing). Theoretical and practical implications are discussed in the context of AI-generated content assessment.

Beyond risk preferences in sequential decision-making: How probability representation, sequential structure and choice perseverance bias optimal search

Sequential decision-making, where choices are made one after the other, is an important aspect of our daily lives. For example, when searching for a job, an apartment, or deciding when to buy or sell a stock, people often have to make decisions without knowing what future opportunities might arise. These situations, which are known as optimal stopping problems, involve a risk associated with the decision to either stop or continue searching. However, previous research has not consistently found a clear connection between individuals’ search behavior in these tasks and their risk preferences as measured in controlled experimental settings. In this paper, we explore how particular characteristics of optimal stopping tasks affect people’s choices, extending beyond their stable risk preferences. We find that (1) the way the underlying sampling distribution is presented (whether it is based on experience or description), (2) the sequential presentation of options, and (3) the unequal frequencies of choices to reject versus to accept significantly bias people choices. These results shed light on the complex nature of decisions that unfold sequentially and emphasize the importance of incorporating context factors when studying human decision behavior.

Geometric neural operators (gnps) for data-driven deep learning in non-euclidean settings

Abstract We introduce Geometric Neural Operators (GNPs) for data-driven deep learning of geometric features for tasks in non-euclidean settings. We present a formulation for accounting for geometric contributions along with practical neural network architectures and factorizations for training. We then demonstrate how GNPs can be used (i) to estimate geometric properties, such as the metric and curvatures of surfaces, (ii) to approximate solutions of geometric partial differential equations on manifolds, and (iii) to solve Bayesian inverse problems for identifying manifold shapes. These results show a few ways GNPs can be used for incorporating the roles of geometry in the data-driven learning of operators.

An efficient deep reinforcement learning based task scheduler in cloud-fog environment

Efficient task scheduling in cloud and fog computing environments remains a significant challenge due to the diverse nature and critical processing requirements of tasks originating from heterogeneous devices. Traditional scheduling methods often struggle with high latency and inadequate processing times, especially in applications demanding strict computational efficiency. To address these challenges, this paper proposes an advanced fog-cloud integration approach utilizing a deep reinforcement learning-based task scheduler, DRLMOTS (Deep Reinforcement Learning based Multi Objective Task Scheduler in Cloud Fog Environment). This novel scheduler intelligently evaluates task characteristics, such as length and processing capacity, to dynamically allocate computation to either fog nodes or cloud resources. The methodology leverages a Deep Q-Learning Network model and includes extensive simulations using both randomized workloads and real-world Google Jobs Workloads. Comparative analysis demonstrates that DRLMOTS significantly outperforms existing baseline algorithms such as CNN, LSTM, and GGCN, achieving a substantial reduction in makespan by up to 26.80%, 18.84, and 13.83% and decreasing energy consumption by up to 39.60%, 30.29%, and 27.11%. Additionally, the proposed scheduler enhances fault tolerance, showcasing improvements of up to 221.89%, 17.05%, and 11.05% over conventional methods. These results validate the efficiency and robustness of DRLMOTS in optimizing task scheduling in fog-cloud environments.

Perception-Action dissociations depend on factors that affect multisensory processing.

Behavioral perception-action dissociations are widely used to test models of high-level vision, but debates concerning their interpretation have underestimated the role of multisensory mechanisms in such tests. Sensorimotor tasks engage multisensory processing in fundamentally different ways than perceptual tasks, and these differences can modulate dissociations in different ways based on task features. To test this idea, we compared perception and action using a well-understood size-contrast effect, the Uznadze illusion, and manipulated both unimodal and crossmodal stimulation as well as conditions that are known to favor or hinder multisensory integration. Results demonstrated that similar or dissociable effects on perception and action can be observed depending on factors that are known to affect multisensory processing. Specifically, such factors can cause a visual task to be affected by the illusion or remain fully unaffected, whereas a visuomotor task can be affected by the illusion, remain immune from the illusion, or, unexpectedly, even show a clear reverse effect. These findings provide a novel perspective on a long standing debate in behavioral cognitive neuroscience.

Assessing variable importance in survival analysis using machine learning

SUMMARY Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction task at hand. For example, in HIV vaccine trials, participant baseline characteristics are used to predict the probability of HIV acquisition over the intended follow-up period, and investigators may wish to understand how much certain types of predictors, such as behavioral factors, contribute toward overall predictiveness. Time-to-event outcomes such as time to HIV acquisition are often subject to right censoring, and existing methods for assessing variable importance are typically not intended to be used in this setting. We describe a broad class of algorithm-agnostic variable importance measures for prediction in the context of survival data. We propose a nonparametric efficient estimation procedure that incorporates flexible learning of nuisance parameters, yields asymptotically valid inference, and enjoys double-robustness. We assess the performance of our proposed procedure via numerical simulations and analyze data from the HVTN 702 vaccine trial to inform enrollment strategies for future HIV vaccine trials.

A robust multi-label feature selection based on label significance and fuzzy entropy

Multi-label feature selection is one of the key steps in dealing with multi-label classification problems in high-dimensional data. In this step, label enhancement techniques play an important role. However, it is worth noting that many current methods tend to ignore the intrinsic connection between inter-sample similarity and inter-label correlation when implementing label enhancement learning. The neglect may prevent the process of label enhancement from accurately revealing the complex structure and underlying patterns within data. For this reason, a fuzzy multi-label feature selection method based on label significance and fuzzy entropy is proposed. An innovative label enhancement technique that considers not only the intrinsic connection between features and labels, but also the correlation between labels was first devised. Based on this enhanced label representation, the concept of fuzzy entropy is further defined to quantify the uncertainty of features for multi-label classification tasks. Subsequently, a feature selection algorithm based on feature importance and label importance was developed. The algorithm guides the feature selection process by evaluating how much each feature contributes to multi-label classification and how important each label is to the overall classification task. Finally, through a series of experimental validation, the proposed algorithm is proved to have better performance for multi-label classification tasks.

Mitigating the Impact of Lognormal Atmospheric Turbulence Channel Estimation on FSO Communication Systems Using Advanced Deep Learning Modules

ABSTRACTCurrently, researchers and commercial entities worldwide are highly interested in optical wireless communication links operating in terrestrial environments. These links offer the distinct advantage of enabling high‐speed data transmission while keeping operational and installation costs low, all without requiring licensing. Free‐space optical (FSO) communication has established itself as a reliable method for delivering ultra‐high data‐rate services. However, the presence of atmospheric turbulence‐induced fading poses a significant challenge for FSO communication systems, particularly when operating over channels affected by time‐varying turbulence. The effectiveness of FSO systems is greatly influenced by atmospheric conditions in a specific area because the laser beam propagates through the atmosphere. As a consequence, this can lead to a significant degradation such as high complexity, high estimation error, and high bit error rate (BER) in the performance of FSO systems. This paper presents a stacked dual attention LSTM network with layered neural Turing machine (SDALSTM‐LaNTM) for FSO channel estimation over lognormal turbulence‐induced fading channels. In this approach, the stacked structure ensures a robust feature extraction capacity of SDALSTM and enhances its ability to capture intricate relationships among multiple atmospheric variables. After employing the stacked LSTM networks, the extracted features are combined with the augmented features obtained through the dual attention (DAttn) mechanism by concatenation. Then the LaNTM architecture used external memory to solve the channel parameter estimation problem and update new set of hidden features for estimation task. The ability to access and update memory enables the model to capture long‐term dependencies and context, making it beneficial for channel prediction in FSO systems. Presently, we are in the process of verifying the channel estimation model using measured FSO channel data. Simulation results clearly demonstrate the outstanding estimation performance of the SDALSTM‐LaNTM model in FSO systems, specifically when dealing with lognormal turbulence‐induced fading channels. Moreover, we highlight the effectiveness of the SDALSTM‐LaNTM model by conducting a comprehensive comparison with existing models, considering factors such as average capacity performance, outage probability estimation, and BER.

MSSA: Multi-Representation Semantics-Augmented Set Abstraction for 3D Object Detection

Accurate recognition and localization of 3D objects is a fundamental research problem in 3D computer vision. Benefiting from transformation-free point cloud processing and flexible receptive fields, point-based methods have become accurate in 3D point cloud modeling, but still fall behind voxel-based competitors in 3D detection. We observe that the set abstraction module, commonly utilized by point-based methods for downsampling points, tends to retain excessive irrelevant background information, thus hindering the effective learning of features for object detection tasks. To address this issue, we propose MSSA, a Multi-representation Semantics-augmented Set Abstraction for 3D object detection. Specifically, we first design a backbone network to encode different representation features of point clouds, which extracts point-wise features through PointNet to preserve fine-grained geometric structure features, and adopts VoxelNet to extract voxel features and BEV features to enhance the semantic features of key points. Second, to efficiently fuse different representation features of keypoints, we propose a Point feature-guided Voxel feature and BEV feature fusion (PVB-Fusion) module to adaptively fuse multi-representation features and remove noise. At last, a novel Multi-representation Semantic-guided Farthest Point Sampling (MS-FPS) algorithm is designed to help set abstraction modules progressively downsample point clouds, thereby improving instance recall and detection performance with more important foreground points. We evaluate MSSA on the widely used KITTI dataset and the more challenging nuScenes dataset. Experimental results show that compared to PointRCNN, our method improves the AP of “moderate” level for three classes of objects by 7.02%, 6.76%, and 5.44%, respectively. Compared to the advanced point-voxel-based method PV-RCNN, our method improves the AP of “moderate” level by 1.23%, 2.84%, and 0.55% for the three classes, respectively.

ViCo: Human visual perception-based contour extraction of Chinese Tang dynasty textile patterns

The study of ancient textile patterns not only enriches historical material but also inspires modern costume art design. Recent developments in computer vision technology provide novel analytical methods for the study of ancient textile patterns. As contours are one of the most significant features in computer vision tasks, contour extraction serves as a prerequisite to accomplishing intelligent understanding and automatic design of textile patterns. In this paper, a perception-consistent contour extraction method (ViCo) that simulates the human visual system from the retina, the lateral geniculate nucleus (LGN), to the V1 area in the cortex is proposed, along with an ancient textile pattern dataset represented by TuanKe in the Chinese Tang dynasty. Experiments were conducted both qualitatively and quantitatively to verify the effectiveness of the proposed method on the TuanKe dataset. Analysis of the results implies the effectiveness of ViCo because the retinal and LGN filter enhances edge information and restrains tiny pixel changes, and the cortex simulation is direction selective. This study is among the initial efforts to employ computer vision technology for ancient textile pattern studies. The study presents a TuanKe dataset and a novel bio-inspired contour extraction method, which has significant historical research importance, fabric art design inspiration, and computer application value.

Identification of Relationship between Meta Cognition and Misconceptions

This study investigates the complex interplay between meta cognition and misconceptions within the educational context, focusing on implications for effective teaching and learning strategies. Aligning with India's National Education Policy (NEP) 2020, which advocates for a transition from rote memorization to in- depth understanding, this research explores the concurrent presence of misconceptions and meta cognition in elementary science education. Meta cognition, the process of reflecting on one's cognitive processes, and misconceptions, or predefined notions that impede authentic learning, are analyzed through influential frameworks, including those developed by Flavell and Nelson and Narens. The MASRL model by Efklides is also incorporated to understand the interplay of meta cognitive knowledge, control strategies, experiences, task characteristics, and learning outcomes. The study highlights the impact of misconceptions as cognitive barriers and their influence on meta cognition, emphasizing the need for effective educational strategies. The literature review explores existing research on meta cognition and misconceptions, showcasing how meta cognition acts as a cognitive mirror, enabling learners to navigate and rectify misconceptions. This research has significant educational implications, highlighting the importance of cultivating self-directed learning, critical thinking, and evidence- based study strategies to enhance academic outcomes. The study concludes by emphasizing the dynamic interplay between meta cognition and misconceptions, offering insights for educators and researchers to design interventions fostering accurate conceptual understanding in learners.

Human factors considerations of Interaction between wearers and intelligent lower-limb prostheses: a prospective discussion.

Compared to traditional lower-limb prostheses (LLPs), intelligent LLPs are more versatile devices with emerging technologies, such as microcontrollers and user-controlled interfaces (UCIs). As emerging technologies allow a higher level of automation and more involvement from wearers in the LLP setting adjustments, the previous framework established to study human factors elements that affect wearer-LLP interaction may not be sufficient to understand the new elements (e.g., transparency) and dynamics in this interaction. In addition, the increased complexity of interaction amplifies the limitations of the traditional evaluation approaches of wearer-LLP interaction. Therefore, to ensure wearer acceptance and adoption, from a human factors perspective, we propose a new framework to introduce elements and usability requirements for the wearer-LLP interaction. This paper organizes human factors elements that appear with the development of intelligent LLP technologies into three aspects: wearer, device, and task by using a classic model of the human-machine systems. By adopting Nielsen's five usability requirements, we introduce learnability, efficiency, memorability, use error, and satisfaction into the evaluation of wearer-LLP interaction. We identify two types of wearer-LLP interaction. The first type, direct interaction, occurs when the wearer continuously interacts with the intelligent LLP (primarily when the LLP is in action); the second type, indirect interaction, occurs when the wearer initiates communication with the LLP usually through a UCI to address the current or foreseeable challenges. For each type of interaction, we highlight new elements, such as device transparency and prior knowledge of the wearer with the UCI. In addition, we redefine the usability goals of two types of wearer-LLP interaction with Nelson's five usability requirements and review methods to evaluate the interaction. Researchers and designers for intelligent LLPs should consider the new device elements that may additionally influence wearers' acceptance and the need to interpret findings within the constraints of the specific wearer and task characteristics. The proposed framework can also be used to organize literature and identify gaps for future directions. By adopting the holistic usability requirements, findings across empirical studies can be more comparable. At the end of this paper, we discuss research trends and future directions in the human factors design of intelligent LLPs.

“Which Proof for Which Class?” Factors Influencing the Selection of Mathematical Proofs for Teaching

Selecting a proof for teaching is a frequent task for teachers. However, it is so far unclear, which factors are considered by teachers when selecting proofs. Is the selection based on task and proof characteristics such as the didactical type of proof? Or based on class characteristics such as students’ algebraic skills? Or do teachers’ characteristics such as their proof skills govern their decision? Or is the selection too non-generic for these characteristics to show a meaningful impact?To address these questions, a quasi-experimental study with N = 183 pre-service teachers was conducted to evaluate the influence of each of these factors on their selection of proofs for teaching. Results highlight several significant effects of the abovementioned characteristics and underline that—even at the pre-service level—the selection of proofs is more nuanced than often assumed in prior research and that teachers deliberately and adaptively select proofs for their teaching based on these factors.

Shared leadership strategies for languages education. A case of project-based learning at CLC in Poznan University of Technology

Abstract This article explains how shared leadership can be recognised and established in project-based learning as a situated English practice. It specifically explores the task characteristics of Cox et al.’s (2003) leadership model, which illustrates the functional relations between the procedural features of project work and its outcomes. The result of the analysis is that project work underlies the collective and highly integrative potential of the group of students, transforming them into a community of practice. The findings indicate that factors contributing to facilitating shared leadership are the interdependence of tasks between individuals driven by intrinsic motivation to act and the low complexity of tasks. The analysis also suggests that, although low complexity tasks may weaken shared leadership, they maximise relevance, thereby minimising cognitive effort. Consequently, this response strategy can help boost collaboration.

ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.

Multi-UAVs task allocation method based on MPSO-SA-DQN

Multi-UAVs play an important role in the battlefield. Although many methods are proposed to solve the Multi-UAV task allocation, there still existing the problems of complex time constraints and uncertain solution space. The reason is that multi-UAVs usually face changing environmental factors. Aiming at solving such problem, this paper proposes a multi-UAV task assignment method based on Deep Q-based evolutionary reinforcement learning algorithms (MPSO-SA-DQN). Specifically, this method builds a multi-agent training framework based on the deep evolutionary reinforcement learning mechanism and SA-DQN. Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. Based on task priority mapping, the MPSO-SA-DQN algorithm framework is proposed. As a result, multi-agents can optimize the execution state in real time in the environment interaction. Besides, it also has the ability to reach optimal state and maximum reward. According to the characteristics of multi-UAV global task assignment, this paper designs a priority state space autoencoder strategy and global task feature. A multi-UAVs tasks allocation and iterative optimization method based on MPSO-SA-DQN algorithm is proposed, so as to continuously optimize the task allocation scheme. The simulation results show that the multi-UAV task allocation method based on MPSO-SA-DQN can effectively solve the problem of uncertainty in the optimal solution space of task allocation. At the same time, the algorithm achieves faster convergence result, and a good prospect of promotion in the field of UAV swarm cooperative task planning.

Estimation of Speech Features Using a Wearable Inertial Sensor

Speech features have been investigated as novel digital biomarkers for many psychiatric and neurocognitive diseases. Microphones are the most used devices for speech recording but inevitably suffering from several disadvantages such as privacy leakage and environmental noises, limiting their clinical applications particularly for long-term ambulatory monitoring. The aim of the present study is therefore to explore the feasibility of extracting speech features from the acceleration recorded on the sternum. Ten healthy subjects volunteered in our study. Two speech tasks, i.e., repeating one sentence 20 times and reading 20 different sentences, were performed by each subject, with each task repeated 8 times under different speech rate and loudness. Voice signals and speech-caused chest vibrations were simultaneously recorded by a microphone and an accelerometer placed on the sternum. 42 acoustic features and 6 time-related prosodic features were extracted from both signals using a standard toolbox, and then compared by a linear fit and correlation analysis. Good agreement between the acceleration features and microphone features is observed in all 6 time-related prosodic features for both tasks, but only in 19 and 17 acoustic features for task 1 and 2, respectively, with most of them loudness- or pitch-related. Our results suggest the sternum acceleration to track time-related speech prosody, loudness, and pitch very well, demonstrating the feasibility of deriving digital biomarkers from the acceleration signal for diseases strongly related to time-related prosodic and loudness features.

Lock Operations Through the Operator’s Eyes: A Qualitative Exploration of Gaze Strategies

This paper reports a qualitative exploration of gaze strategies during closed-circuit television (CCTV) tasks in remote nautical object control of a lock. Previous research has not examined gaze strategies in scenarios where systems, such as nautical objects, are operated remotely using CCTV. As contextual factors matter in nautical object control, a qualitative approach was necessary to uncover domain-specific terminology and insights into gaze strategies. We recorded eye gaze from professional lock operators and then conducted semi-structured interviews with domain experts to assess these recordings. Thematic analysis revealed that experts were able to identify (features of) gaze strategies but did not share the same terminology. Based on this analysis we defined four strategies: anticipating, verifying, overview, and movement-directed gazes (RQ1). All strategies, except movement-directed gaze, were observed consistently across operators (RQ2), with verifying gaze aligning with task steps in a predefined protocol (RQ3). More generally, our classification framework from thematic analysis could help to systematically define and verify gaze strategies based on domain and task features across various CCTV working contexts. For nautical object control, the framework can be instrumental in interpreting and verifying future (quantitative) eye tracking results and informing instructional procedures.

A multi-task graph deep learning model to predict drugs combination of synergy and sensitivity scores

BackgroundDrug combination treatments have proven to be a realistic technique for treating challenging diseases such as cancer by enhancing efficacy and mitigating side effects. To achieve the therapeutic goals of these combinations, it is essential to employ multi-targeted drug combinations, which maximize effectiveness and synergistic effects.ResultsThis paper proposes ‘MultiComb’, a multi-task deep learning (MTDL) model designed to simultaneously predict the synergy and sensitivity of drug combinations. The model utilizes a graph convolution network to represent the Simplified Molecular-Input Line-Entry (SMILES) of two drugs, generating their respective features. Also, three fully connected subnetworks extract features of the cancer cell line. These drug and cell line features are then concatenated and processed through an attention mechanism, which outputs two optimized feature representations for the target tasks. The cross-stitch model learns the relationship between these tasks. At last, each learned task feature is fed into fully connected subnetworks to predict the synergy and sensitivity scores.The proposed model is validated using the O’Neil benchmark dataset, which includes 38 unique drugs combined to form 17,901 drug combination pairs and tested across 37 unique cancer cells. The model’s performance is tested using some metrics like mean square error (MSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$MSE$$\\end{document}), mean absolute error (MAE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$MAE$$\\end{document}), coefficient of determination (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document}), Spearman, and Pearson scores. The mean synergy scores of the proposed model are 232.37, 9.59, 0.57, 0.76, and 0.73 for the previous metrics, respectively. Also, the values for mean sensitivity scores are 15.59, 2.74, 0.90, 0.95, and 0.95, respectively.ConclusionThis paper proposes an MTDL model to predict synergy and sensitivity scores for drug combinations targeting specific cancer cell lines. The MTDL model demonstrates superior performance compared to existing approaches, providing better results.