Problems In Other Domains Research Articles

A Secure Auditable Remote Registry Pattern for IoT Systems

In software engineering, pattern papers serve the purpose of providing a description of a generalized, reusable solution to recurring design problems, based on practical experience and established best practices. This paper presents an architectural pattern for a Secure Auditable Registry service based on Message-Oriented Middleware to be used in large-scale IoT systems that must provide auditing capabilities to external entities. To prepare the pattern, the direct experience in applying the pattern solution in an industry-funded R&D project has been a key aspect because it has allowed us to gain a deep understanding of the problem and the solution, and it has contributed to the correctness and real-world applicability of the pattern as described. To further improve the quality of the paper, we have followed the commonly accepted practices in pattern development (including peer reviews) to ensure that the core aspects of the solution are correctly represented and that the description allows it to be applicable to similar problems in other domains, such as healthcare, autonomous devices, banking, food tracing or manufacturing to name a few. The work done in applying this pattern confirms that it solves a recurring problem for IoT systems, but also that it can be adopted in other domains, providing an effective solution in order to achieve enhancement of the auditability capabilities of the target systems. This pattern will be part of a pattern language (i.e., a family of related patterns) that we are developing for transitioning from legacy systems to IoT with an emphasis on security.

An ensemble learning framework for snail trail fault detection and diagnosis in photovoltaic modules

This research proposes a method for detecting subtle faults named snail trails for their visual similarity with the trail of a snail in photovoltaic modules. Snail trails do not significantly reduce panel performance but they are the main cause of serious panel deterioration such as microcracks and delamination and can go so far as to set the panel on fire. To detect these faults, this research uses an ensemble learning framework, named ensemble learning for diagnosis, which combines several complementary learning algorithms, namely Support Vector Machines, K-Nearest Neighbors, and Decision Trees. A set of features is obtained by extracting the time–frequency characteristics and statistics from the photovoltaic current signal of the photovoltaic panel. This is followed by a feature selection and dimensionality reduction step that delivers the input to the learning algorithms. The approach presented in this study is experimentally validated, independently for the 4 seasons of the year, with data from a real photovoltaic string of 16 panels. The results demonstrate that the proposed approach can efficiently classify healthy panels and panels with snail trails efficiently. Interestingly, the method only requires the electrical current signal, measured on panels with data acquisition systems that are standard in the photovoltaic industry. The genericity of the approach makes it a good candidate for detecting other photovoltaic faults and for solving diagnosis problems in other domains.

Comparing Hierarchical Approaches to Enhance Supervised Emotive Text Classification

The performance of emotive text classification using affective hierarchical schemes (e.g., WordNet-Affect) is often evaluated using the same traditional measures used to evaluate the performance of when a finite set of isolated classes are used. However, applying such measures means the full characteristics and structure of the emotive hierarchical scheme are not considered. Thus, the overall performance of emotive text classification using emotion hierarchical schemes is often inaccurately reported and may lead to ineffective information retrieval and decision making. This paper provides a comparative investigation into how methods used in hierarchical classification problems in other domains, which extend traditional evaluation metrics to consider the characteristics of the hierarchical classification scheme, can be applied and subsequently improve the classification of emotive texts. This study investigates the classification performance of three widely used classifiers, Naive Bayes, J48 Decision Tree, and SVM, following the application of the aforementioned methods. The results demonstrated that all the methods improved the emotion classification. However, the most notable improvement was recorded when a depth-based method was applied to both the testing and validation data, where the precision, recall, and F1-score were significantly improved by around 70 percentage points for each classifier.

KAPE: k NN-based Performance Testing for Deep Code Search

Code search is a common yet important activity of software developers. An efficient code search model can largely facilitate the development process and improve the programming quality. Given the superb performance of learning the contextual representations, deep learning models, especially pre-trained language models, have been widely explored for the code search task. However, studies mainly focus on proposing new architectures for ever-better performance on designed test sets but ignore the performance on unseen test data where only natural language queries are available. The same problem in other domains, e.g., CV and NLP, is usually solved by test input selection that uses a subset of the unseen set to reduce the labeling effort. However, approaches from other domains are not directly applicable and still require labeling effort. In this article, we propose the k NN-b a sed p erformance t e sting ( KAPE ) to efficiently solve the problem without manually matching code snippets to test queries. The main idea is to use semantically similar training data to perform the evaluation. Extensive experiments on six programming language datasets, three state-of-the-art pre-trained models, and seven baseline methods demonstrate that KAPE can effectively assess the model performance (e.g., CodeBERT achieves MRR 0.5795 on JavaScript) with a slight difference (e.g., 0.0261).

A Computationally Efficient Benders Decomposition for Energy Systems Planning Problems with Detailed Operations and Time-Coupling Constraints

Energy systems planning models identify least-cost strategies for expansion and operation of energy systems and provide decision support for investment, planning, regulation, and policy. Most are formulated as linear programming (LP) or mixed integer linear programming (MILP) problems. Despite the relative efficiency and maturity of LP and MILP solvers, large scale problems are often intractable without abstractions that impact quality of results and generalizability of findings. We consider a macro-energy systems planning problem with detailed operations and policy constraints and formulate a computationally efficient Benders decomposition separating investments from operations and decoupling operational timesteps using budgeting variables in the master model. This novel approach enables parallelization of operational subproblems and permits modeling of relevant constraints coupling decisions across time periods (e.g., policy constraints) within a decomposed framework. Runtime scales linearly with temporal resolution; tests demonstrate substantial runtime improvement for all MILP formulations and for some LP formulations depending on problem size relative to analogous monolithic models solved with state-of-the-art commercial solvers. Our algorithm is applicable to planning problems in other domains (e.g., water, transportation networks, production processes) and can solve large-scale problems otherwise intractable. We show that the increased resolution enabled by this algorithm mitigates structural uncertainty, improving recommendation accuracy. Funding: Funding for this work was provided by the Princeton Carbon Mitigation Initiative (funded by a gift from BP) and the Princeton Zero-carbon Technology Consortium (funded by gifts from GE, Google, ClearPath, and Breakthrough Energy). Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoo.2023.0005 .

Pentingnya Berpikir Komputasional dalam Pembelajaran Matematika

Computational thinking is a way of thinking that refers to the ability to solve problems using the concepts used in computing. These concepts include systematic problem solving, data analysis, algorithms, abstraction, and information representation. The method used in this research is library research. Computational thinking not only helps us acquire computer-related skills, but can also enhance our ability to solve problems in other domains. Several studies have found that computational thinking is beneficial for improving mathematical abilities, reasoning abilities, and creative thinking.

How Can a Bundled Payment Model Incentivize the Transition from Single-Disease Management to Person-Centred and Integrated Care for Chronic Diseases in the Netherlands?

To stimulate the integration of chronic care across disciplines, the Netherlands has implemented single-disease management programmes (SDMPs) in primary care since 2010; for example, for COPD, type 2 diabetes mellitus, and cardiovascular diseases. These disease-specific chronic care programmes are funded by bundled payments. For chronically ill patients with multimorbidity or with problems in other domains of health, this approach was shown to be less fit for purpose. As a result, we are currently witnessing several initiatives to broaden the scope of these programmes, aiming to provide truly person-centred integrated care (PC-IC). This raises the question if it is possible to design a payment model that would support this transition. We present an alternative payment model that combines a person-centred bundled payment with a shared savings model and pay-for-performance elements. Based on theoretical reasoning and results of previous evaluation studies, we expect the proposed payment model to stimulate integration of person-centred care between primary healthcare providers, secondary healthcare providers, and the social care domain. We also expect it to incentivise cost-conscious provider-behaviour, while safeguarding the quality of care, provided that adequate risk-mitigating actions, such as case-mix adjustment and cost-capping, are taken.

Feature extraction and health status prediction in PV systems

Diagnosis aims at predicting the health status of components and systems. In photovoltaic systems, it is vital to guarantee energy production and extend the useful life of photovoltaic power plants. Multiple prediction and classification algorithms have been proposed for this purpose in the literature. The accuracy of these algorithms depends directly on the quality of the data and the features with which they are tuned or trained. In this paper, an innovative approach for predicting the health status of photovoltaic systems is proposed, which includes a feature selection stage. This approach first discriminates severely affected photovoltaic panels using basic electrical features. In a second step, it discriminates the other faulty panels using more elaborated time–frequency features and selecting the most relevant features through correlation and variance analysis. Finally, the approach predicts the health status of photovoltaic panels using a nonlinear regression method named partial least squares. This later is then combined with linear discriminant analysis and compared. The approach is validated with real current data from a photovoltaic plant composed of twelve photovoltaic panels with power between 205 and 240 Wp in three health states, namely broken glass, healthy, and big snail trails. The results obtained show that the proposed approach efficiently predicts the three health states. It determines the level of degradation of the panels, which indicates priorities to corrective and predictive maintenance actions. Furthermore, it is cost-effective since it uses only electrical measurements that are already available in standard photovoltaic data acquisition systems. Above all, the approach is generic and it can be easily extrapolated to other diagnosis problems in other domains.

An Evidential Software Risk Evaluation Model

Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.

Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing

Automatic teeth segmentation in panoramic x-ray images is an important research subject of the image analysis in dentistry. In this study, we propose a post-processing stage to obtain a segmentation map in which the objects in the image are separated, and apply this technique to tooth instance segmentation with U-Net network. The post-processing consists of grayscale morphological and filtering operations, which are applied to the sigmoid output of the network before binarization. A dice overlap score of 95.4±0.3% is obtained in overall teeth segmentation. The proposed post-processing stages reduce the mean error of tooth count to 6.15%, whereas the error without post-processing is 26.81%. The performances of both segmentation and tooth counting are the highest in the literature, to our knowledge. Moreover, this is achieved by using a relatively small training dataset, which consists of 105 images. Although the aim in this study is to segment tooth instances, the presented method is applicable to similar problems in other domains, such as separating the cell instances.

Sensory features in autism: Findings from a large population-based surveillance system.

Sensory features (i.e., atypical responses to sensory stimuli) are included in the current diagnostic criteria for autism spectrum disorder. Yet, large population‐based studies have not examined these features. This study aimed to determine the prevalence of sensory features among autistic children, and examine associations between sensory features, demographics, and co‐occurring problems in other areas. Analysis for this study included a sample comprised of 25,627 four‐ or eight‐year‐old autistic children identified through the multistate Autism and Developmental Disabilities Monitoring Network (2006–2014). We calculated the prevalence of sensory features and applied multilevel logistic regression modeling. The majority (74%; 95% confidence interval: 73.5%–74.5%) of the children studied had documented sensory features. In a multivariable model, children who were male and those whose mothers had more years of education had higher odds of documented sensory features. Children from several racial and ethnic minority groups had lower odds of documented sensory features than White, non‐Hispanic children. Cognitive problems were not significantly related to sensory features. Problems related to adaptive behavior, emotional states, aggression, attention, fear, motor development, eating, and sleeping were associated with higher odds of having documented sensory features. Results from a large, population‐based sample indicate a high prevalence of sensory features in autistic children, as well as relationships between sensory features and co‐occurring problems. This study also pointed to potential disparities in the identification of sensory features, which should be examined in future research. Disparities should also be considered clinically to avoid reduced access to supports for sensory features and related functional problems.Lay SummaryIn a large, population‐based sample of 25,627 autistic children, 74% had documented differences in how they respond to sensation. We also identified significant associations of sensory features with adaptive behavior and problems in other domains. Sensory features were less common among girls, children of color, and children of mothers with fewer years of education, suggesting potential disparities in identification.

Neuropsychological and Psychosocial Functioning of Children with Perinatal HIV-Infection in The Netherlands.

Advances in antiretroviral treatment improved the life expectancy of perinatally HIV-infected children. However, growing up with HIV provides challenges in daily functioning. This cross-sectional cohort study investigated the neuropsychological and psychosocial functioning of a group of perinatally HIV-infected children in the Netherlands and compared their outcomes with Dutch normative data and outcomes of a control group of uninfected siblings. The children’s functioning was assessed with internationally well-known and standardized questionnaires, using a multi-informant approach, including the perspectives of caregivers, teachers, and school-aged children. In addition, we explored the associations of socio-demographic and medical characteristics of the HIV-infected children with their neuropsychological and psychosocial functioning. Caregivers reported compromised functioning when compared to Dutch normative data for HIV-infected children in the areas of attention, sensory processing, social-emotional functioning, and health-related quality of life. Teachers reported in addition compromised executive functioning for HIV-infected children. A comparison with siblings revealed differences in executive functioning, problems with peers, and general health. The concurrent resemblance between HIV-infected children and siblings regarding problems in other domains implies that social and contextual factors may be of influence. A family-focused approach with special attention to the child’s socio-environmental context and additional attention for siblings is recommended.

Special issue on future hybrid artificial intelligence and machine learning for smart expert systems

Artificial Intelligence (AI) has grown extensively in recent times and already encroaches into our lives, for example, with smart cars and smart homes. AI current and future trends in areas such as smart cars, face recognition, robotic surgeries, or automatic disease classification, focus on hybridization to improve the performance of smart expert systems. Despite the huge growth of various AI and machine learning techniques, such as decision trees, data mining, or deep convergence learning, there are still many challenges and threats which limit the performance of these smart expert systems. This special issue focuses on new directions in hybrid AI and machine learning methodologies to address current research problems. It focuses on bringing together leading-edge articles on hybrid AI techniques for smart expert systems for various applications. From about 25 articles submitted, 4 papers were selected based on the review process. Each paper was reviewed by at least two independent reviewers and undertook at least one round of review. Their contributions are summarized below. Raghavendra et al. (2019) has proposed a research work on accurate and early detection of breast cancer using effective imaging modalities with medical image analysis. The research results have shown that two-dimensional synthesized mammogram imaging and conventional full-field digital mammography (FFDM) are two important imaging modalities which can be used for screening breast cancer. The developed method achieved an average performance of 92.9% accuracy using a probabilistic neural network classifier for FFDM images with tenfold cross validation. The obtained results can be used as a distinct system in rural hospitals. Gupta et al. (2020) has done their research studies on to detect infected leaves using machine learning techniques. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private data set of 236 images. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for the classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. Hammad et al. (2020) has found a new authentication approach based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals. This work shows two novel deep neural network (DNN) models (convolutional neural network (CNN) and ResNet-Attention) using ECG signals for human authentication and the proposed CNN algorithm achieved an accuracy of 98.59% and 99.72% using PTB and CYBHi, respectively. The results obtained by the developed model proved that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data. Teles et al. (2020) has devised a decision support system on credit operations which can provide different estimations of expected recovery based on the same data sets. This research work classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. Results reveal that while linear regression can be used for the prediction of a continuous variable in the credit process using collateral, it is not suitable for categorical data. Out of linear and logistic regressions, linear regression is more applicable in determining groups of variables that can significantly predict an outcome. We would like to take this opportunity to thank Dr. Jon G. Hall, Editor-in-Chief of Expert Systems, The Journal of Knowledge Engineering, for the privilege to edit this special issue. Gustavo Ramírez González is a professor at department of telematics engineering in Universidad del Cauca, Colombia. His research interests include image processing, secure communication, and machine learning. He has published several research papers in reputed journals and served as a Guest Editor for several Special Issues at many journals.

A New Model Averaging Approach in Predicting Credit Risk Default

The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.

Analysis of threat based algorithm using different performance measures

Since its introduction in 1985, competitive analysis is a widely used tool for the performance measurement of online algorithms. Despite its simplicity and popularity, competitive analysis has its own set of drawbacks which lead to the development of other performance measures. However, these measures were seldom applied to problems in other domains. Recently Boyar et al. (Theor. Comput. Sci. 532 (2014) 2–13) studied the online search problem using various performance analysis measures for non-preemptive algorithms. We extend the work by considering preemptive threat-based algorithms and evaluate it using competitive analysis, bijective analysis, average case and relative interval analysis. For competitive analysis, and average case analysis, our findings are in contrast with that of Boyar et al., whereas for bijective and relative interval analysis our findings complement that of Boyar et al.

A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance

Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student performance. Although the available sets of common prediction approaches, such as Artificial Neural Networks (ANN) and regression, work well with large datasets, they face challenges dealing with small sample sizes, limiting their practical applications in real practices. Methodology: Six distinct categories of performance antecedents are adopted here as course characteristics, LMS characteristics, student characteristics, student engagement, student support, and institutional factors, along with measurement items within each category. Furthermore, we assessed the student’s overall performance using three items of student satisfaction score, knowledge construction level, and student GPA. We have collected longitudinal data from 30 postgraduates in four subsequent semesters and analyzed data using the Learning Fuzzy Cognitive Map (LFCM) technique. Contribution: This research proposes a brand new approach, Learning Fuzzy Cognitive Map (LFCM), to predict student performance. Using this approach, we identified the most influential determinants of student performance, such as student engagement. Besides, this research depicts a model of interrelations among the student performance determinants. Findings: The results suggest that the model reasonably predicts the incoming sequence when there is a limited sample size. The results also reveal that students’ total online time and the regularity of learning interval in LMS have the largest effect on overall performance. The student engagement category also has the highest direct effect on student’s overall performance. Recommendations for Practitioners: Academic institutions can use the results and approach developed in this paper to identify students’ performance antecedents, predict the performance, and establish action plans to resolve the shortcomings in the long term. Instructors can adjust their learning methods based on the feedback from students in the short run on the operational level. Recommendation for Researchers: Researchers can use the proposed approach in this research to deal with the problems in other domains, such as using LMS for organizational/institutional education. Besides, they can focus on specific dimensions of the proposed model, such as exploring ways to boost student engagement in the learning process. Impact on Society: Our results revealed that students are at the center of the learning process. The degree to which they are dedicated to learning is the most crucial determinant of the learning outcome. Therefore, learners should consider this finding in order the gain value from the learning process. Future Research: As a potential for future works, the proposed approach could be used in other contexts to test its applicability. Future studies could also improve the performance level of the proposed LFMC model by tuning the model’s elements.

Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets.

BackgroundEmerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, or X-ray microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods.ResultsWe developed an ecosystem of neuroimaging data analysis pipelines that use open-source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, which connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines.ConclusionsOur accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets but may be applied to similar problems in other domains.

Ensembles of Hydrophobicity Scales as Potent Classifiers for Chimeric Virus-Like Particle Solubility - An Amino Acid Sequence-Based Machine Learning Approach.

Virus-like particles (VLPs) are protein-based nanoscale structures that show high potential as immunotherapeutics or cargo delivery vehicles. Chimeric VLPs are decorated with foreign peptides resulting in structures that confer immune responses against the displayed epitope. However, insertion of foreign sequences often results in insoluble proteins, calling for methods capable of assessing a VLP candidate’s solubility in silico. The prediction of VLP solubility requires a model that can identify critical hydrophobicity-related parameters, distinguishing between VLP-forming aggregation and aggregation leading to insoluble virus protein clusters. Therefore, we developed and implemented a soft ensemble vote classifier (sEVC) framework based on chimeric hepatitis B core antigen (HBcAg) amino acid sequences and 91 publicly available hydrophobicity scales. Based on each hydrophobicity scale, an individual decision tree was induced as classifier in the sEVC. An embedded feature selection algorithm and stratified sampling proved beneficial for model construction. With a learning experiment, model performance in the space of model training set size and number of included classifiers in the sEVC was explored. Additionally, seven models were created from training data of 24–384 chimeric HBcAg constructs, which were validated by 100-fold Monte Carlo cross-validation. The models predicted external test sets of 184–544 chimeric HBcAg constructs. Best models showed a Matthew’s correlation coefficient of >0.6 on the validation and the external test set. Feature selection was evaluated for classifiers with best and worst performance in the chimeric HBcAg VLP solubility scenario. Analysis of the associated hydrophobicity scales allowed for retrieval of biological information related to the mechanistic backgrounds of VLP solubility, suggesting a special role of arginine for VLP assembly and solubility. In the future, the developed sEVC could further be applied to hydrophobicity-related problems in other domains, such as monoclonal antibodies.

Artificial plant optimization algorithm to detect infected leaves using machine learning

AbstractPlant leaves play an important role in the diagnosis of plant diseases. Losses from such diseases can have a significant economic as well as environmental impact. Thus, examination of leaves into a healthy or infected carries substantial importance. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private dataset of 236 images. Features are extracted from the images using histogram of oriented gradients (descriptor). The concepts of artificial plant optimization are then applied to study the features of healthy leaves using IAPO. A machine learning algorithm has been created to make the model adaptive with varied datasets. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. The proposed IAPO is also compared with other classification algorithms including k‐nearest neighbours, support vector machine, random forest and convolution neural network that show accuracies of 78.24%, 83.48%, 87.83%, and 91.26%, respectively, whereas IAPO shows quite accurate results in classification of leaves with an accuracy of 97.45% on training set and 95.0% accuracy on test set.

The need for improved cognitive, hearing and vision assessments for older people with cognitive impairment: a qualitative study.

BackgroundHearing and vision (sensory) impairments are highly prevalent in people with dementia (PwD) and exacerbate the impact of living with dementia. Assessment of sensory or cognitive function may be difficult if people have concurrent dual or triple impairments. Most standard cognitive assessment tests are heavily dependent on having intact hearing and vision, and impairments in these domains may render the assessments unreliable or even invalid. Likewise, dementia may impede on the accurate reporting of symptoms that is required for most hearing and vision assessments. Thus, there is an urgent need for hearing, vision and cognitive assessment strategies to be adapted to ensure that appropriate management and support can be provided.ObjectiveTo explore the perspectives of PwD and the care partners regarding the need for accurate hearing, vision and cognitive assessments.MethodsWe conducted focus groups and semi-structured interviews regarding the clinical assessment for cognitive, hearing and visual impairment. Participants (n = 18) were older adults with mild to moderate dementia and a sensory impairment as well as their care partners (e.g. a family member) (n = 15) at three European sites. The qualitative material was analysed according to Mayring’s summative content analysis approach.ResultsParticipants reported that hearing, vision and cognitive assessments were not appropriate to the complex needs of PwD and sensory comorbidity and that challenges in communication with professionals and conveying unmet needs and concerns by PwD were common in all three types of clinical assessments. They felt that information about and guidance regarding support for the condition was not adequate in the assessments and that information sharing among the professionals regarding the concurrent problems was limited. Professionals were reported as being concerned only with problems related to their own discipline and had limited regard for problems in other domains which might impact on their own assessments.ConclusionsThe optimal assessment and support for PwD with multiple impairments, more comprehensive, yet easy to understand, information regarding these linked to conditions and corrective device use is needed. Communication among health care professionals relevant to hearing, vision and cognition needs to be improved.