Classification of COVID-19 Cases: The Customized Deep Convolutional Neural Network and Transfer Learning Approach

The recent advancements under the umbrella of artificial intelligence (AI) open opportunities to tackle complex problems related to image analysis. Recently, the proliferation of COVID-19 brought multiple challenges to medical practitioners, such as precise analysis and classification of COVID-19 cases. Deep learning (DL) and transfer learning (TL) techniques appear to be attractive solutions. To provide the precise classification of COVID-19 cases, this article presents a customized Deep Convolutional Neural Network (DCNN) and pre-trained TL model approach. Our pipeline accommodated several popular pre-trained TL models, namely DenseNet121, ResNet50, InceptionV3, EfficientNetB0, and VGG16, to classify COVID-19 positive and negative cases. We evaluated and compared the performance of these models with a wide range of measures, including accuracy, precision, recall, and F1 score for classifying COVID-19 cases based on chest X-rays. The results demonstrate that our customized DCNN model performed well with randomly assigned weights, achieving 98.5% recall and 97.0% accuracy

Calibrating mini-mental state examination scores to predict misdiagnosed dementia patients

Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records

Background: Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients’ Electronic Medical Records, they represent promising options to identify the presence and severity of dementia more precisely. Methods: The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests and decision tree, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation strategy in which a dementia expert is involved to verify that curation decisions are meaningful. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the classification of dementia. Results: Our experiment results prove that baseline arithmetic or cognitive tests, along with demographic features, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.81 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. Conclusions: This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various electronic medical record features and cognitive tests from the episodes of the elderly population. Moreover, a set of decision rules may comprise the building blocks for an efficient patient classification. Relevant clinical and screening test features (e.g. simple arithmetic or animal fluency tasks) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. Such predictive model can identify not only meaningful features, but also justifications of classification. As a result, the predictive power of machine learning models over curated clinical data is proved, paving the path for a more accurate diagnosis of dementia

Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking

The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG, in general, is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows approx 8% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking.Comment: 15 page

Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking

The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG in general is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows ≈8% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking

Development of SSR markers and construction of a linkage map in jute

Jute is an important natural fibre crop, which is only second to cotton in its importance at the global level. It is mostly grown in Indian subcontinent and has been recently used for the development of genomics resources. We recently initiated a programme to develop simple sequence repeat markers and reported a set of 2469 SSR that were developed using four SSR-enriched libraries (Mir et al. 2009). In this communication, we report an additional set of 607 novel SSR in 393 SSR containing sequences. However, primers could be designed for only 417 potentially useful SSR. Polymorphism survey was carried out for 374 primer pairs using two parental genotypes (JRO 524 and PPO4) of a mapping population developed for fibre fineness; only 66 SSR were polymorphic. Owing to a low level of polymorphism between the parental genotypes and a high degree of segregation distortion in recombinant inbred lines, genotypic data of only 53 polymorphic SSR on the mapping population consisting of 120 RIL could be used for the construction of a linkage map; 36 SSR loci were mapped on six linkage groups that covered a total genetic distance of 784.3 cM. Hopefully, this map will be enriched with more SSR loci in future and will prove useful for identification of quantitative trait loci/genes for molecular breeding involving improvement of fibre fineness and other related traits in jute

An LSTM Based Approach for the Classification of Customer Reviews: An Exploratory Study

Significant research has been conducted to address the problem of identification and elimination of malicious content. The credibility of such information is always in question, especially in the E-commerce domain. This research proposes a classification model that automatically classifies customer reviews as credible or non-credible. This model encompasses a Long Short-Term Memory (LSTM) as a classification technique. The preliminary results have shown the potential of our model to classify customer reviews as credible / non-credible based on textual features

Classification of COVID-19 Cases: An Exploratory Study by Incorporating Transfer Learning with Cloud

The coronavirus pandemic raised several challenges globally due to heavy demand for patient care and led researchers to find various methods to detect coronavirus. This work aims to provide a transfer learning (TL) based approach for detecting the COVID-19 cases by employing cloud computing thereby minimizing the processing time and costs. In contrast to the previous studies, we have used real-time COVID-19 positive cases chest X-ray images for the training of adopted pre-trained models such as VGG16, InceptionV3, and DenseNet121. The obtained results showed that VGG16 outperforms those models by 98.21% to precisely classify the COVID-19 positive and negative cases

Domestic artefacts: sustainability in the context of indian middle class

Sustainability has become one of the important research topics in the field of Human Computer Interaction (HCI). However, the majority of work has focused on the Western culture. In this paper, we explore sustainable household practices in the developing world. Our research draws on the results from an ethnographic field study of household women belonging to the so-called middle class in India. We analyze our results in the context of Blevis' [4] principles of sustainable interaction design (established within the Western culture), to extract the intercultural aspects that need to be considered for designing technologies. We present examples from the field that we term "domestic artefacts". Domestic artefacts represent creative and sustainable ways household women appropriate and adapt used objects to create more useful and enriching objects that support household members' everyday activities. Our results show that the rationale behind creating domestic artefacts is not limited to the practicality and usefulness, but it shows how religious beliefs, traditions, family intimacy, personal interests and health issues are incorporated into them