A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

Infection of leukemia in humans causes many complications in its later stages.It impairs bone marrow’s ability to produce blood.Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case.The binary classification is employed to distinguish between normal and leukemiainfected cells.In addition,various subtypes of leukemia require different treatments.These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia.This entails using multi-class classification to determine the leukemia subtype.This is usually done using a microscopic examination of these blood cells.Due to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for diagnosis.Researchers utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of classification.More advanced deep-learning methods are also utilized.Due to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding performances.This study introduces a deep learning-machine learning combined approach for leukemia diagnosis.It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers.The transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature extractors.The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells.For the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the system.This dataset achieves an impressive multi-class classification accuracy of 97.08%.The proposed approach is robust and generalized by a standardized dataset and the real image dataset with a limited sample size(520 images).Hence,this method can be explored further for leukemia diagnosis having a limited number of dataset samples.

THERMAL ENVIRONMENTS OF AN OFFICE BUILDING WITH DOUBLE SKIN FACADE

As a symbol of green architecture,double skin facade(DSF)represents a design which possesses many energy saving features,but due to the complexity of the system,the real performances and benefits have been difficult to predict.The objective of this study was to inform the applicability of DSFs,and contribute to the positive impacts of DSF designs.This study compared and contrasted energy savings in a temperate climate,where heating was the dominant energy strategy,and in a subtropical climate,where cooling spaces was the dominant issue.This paper focused on a university office building with a west facing shaft box window facade.The research method was a paired analysis of simulation studies which compared the energy performance of a set of buildings in two different climates.Simulation results showed a good agreement with measurements undertaken in the exiting building during a two-week period.The results specified that DSFs are capable of almost 50%energy savings in temperate and 16%in subtropical climates.Although these indicated DSFs are more suitable for temperate climates than warmer regions,the amount of energy savings in subtropical climates were also considerable.However,due to the costs of DSFs and potential loss of leasable floor area,investigations into other feasible ventilation options are necessary before final building design decisions are made.

Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent years.Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing power.Deep learning models are at the forefront of this advancement.However,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their conclusions.Such a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep learningmodels.The situation is substantially different in safety-critical applications.The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case.Significantly,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an option.This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment industries.Explainability is the term introduced in the preceding years.The AImodel’s black box nature will become explainable with these frameworks.Especially in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial use.These models’explainable natures will help them commercially in diagnosis decisions in the medical field.This paper explores the different frameworks for the explainability of AI models in the medical field.The available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

Spatial flood susceptibility mapping using an explainable artificial intelligence(XAI)model

Floods are natural hazards that lead to devastating financial losses and large displacements of people.Flood susceptibility maps can improve mitigation measures according to the specific conditions of a study area.The design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning models.Although these models have achieved better accuracy than traditional models,they are not widely used by stakeholders due to their black-box nature.In this study,we propose the application of an explainable artificial intelligence(XAI)model that incorporates the Shapley additive explanation(SHAP)model to interpret the outcomes of convolutional neural network(CNN)deep learning models,and analyze the impact of variables on flood susceptibility mapping.This study was conducted in Jinju Province,South Korea,which has a long history of flood events.Model performance was evaluated using the area under the receiver operating characteristic curve(AUROC),which showed a prediction accuracy of 88.4%.SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study area.In light of these findings,we recommend the use of XAIbased models in future flood susceptibility mapping studies to improve interpretations of model outcomes,and build trust among stakeholders during the flood-related decision-making process.

Evaluation on interactive visualization data with scatterplots

Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and brushing.This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix.Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the Simultaneous-Scatterplots.While the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more accurate.Moreover,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study.Overall,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.

Impact of occupant autonomy on satisfaction and building energy efficiency

The philosophy of building energy management is going through a paradigm change from traditional,often inefficient,user-controlled systems to one that is centrally automated with the aid of IoT-enabled technologies.In this context,occupants’perceived control and building automation may seem to be in conflict.The inquiry of this study is rooted in a proposition that while building automation and centralized control systems are assumed to provide indoor comfort and conserve energy use,limiting occupants’control over their work environment may result in dissatisfaction,and in turn decrease productivity.For assessing this hypothesis,data from the post-occupancy evaluation survey of a smart building in a university in Australia was used to analyze the relationships between perceived control,satisfaction,and perceived productivity.Using structural equation modeling,we have found a positive direct effect of occupants’perceived control on overall satisfaction with their working area.Meanwhile,perceived control exerts an influence on perceived productivity through satisfaction.Furthermore,a field experiment conducted in the same building revealed the potential impact that occupant controllability can have on energy saving.We changed the default light settings from automatic on-and-offto manual-on and automatic-off,letting occupants choose themselves whether to switch the light on or not.Interestingly,about half of the participants usually kept the lights off,preferring daylight in their rooms.This also resulted in a reduction in lighting electricity use by 17.8%without any upfront investment and major technical modification.These findings emphasize the important role of perceived control on occupant satisfaction and productivity,as well as on the energy-saving potential of the user-in-the-loop automation of buildings.