Hierarchical Ensemble Research Articles

SmartScanPCOS: A feature-driven approach to cutting-edge prediction of Polycystic Ovary Syndrome using Machine Learning and Explainable Artificial Intelligence

PolyCystic Ovarian Syndrome (PCOS) poses significant challenges to women's reproductive health due to its diagnostic complexity arising from a variety of symptoms, including hirsutism, anovulation, pain, obesity, hyperandrogenism, and oligomenorrhea, necessitating multiple clinical tests. Leveraging Artificial Intelligence (AI) in healthcare offers several benefits that can significantly impact patient care, streamline operations, and improve medical outcomes overall. This study presents an Explainable Artificial Intelligence (XAI)-driven PCOS smart predictor, structured as a hierarchical ensemble consisting of two tiers of Random Forest classifiers following extensive analysis of seven conventional classifiers and two additional stacking ensemble classifiers. An open-source data set comprising numerical parametric features linked to PCOS for classifier training was used. Moreover, to identify essential features for PCOS prediction three feature selection methods: Threshold-driven Optimized Principal Component Analysis (TOPCA), Optimized Salp Swarm (OSSM), and Threshold-driven Optimized Mutual Information Method (TOMIM) were fine-tuned through thresholding and improvisation to detect diverse attribute sets with varying numbers and combinations. Notably, the two-level Random Forest classifier model outperformed others with a remarkable 99.31 % accuracy by employing the top 17 features selected through the Threshold-driven Optimized Mutual Information Method (TOMIM) along with anoverallaccuracy of 99.32 % with 8 fold cross validation for 25 runs. The Smart predictor, constructed using Shapash - a Python library for Explainable Artificial Intelligence - was utilized to deploy the two-level Random Forest classifier model. Ensuring transparency and result reliability, visualizations from robust Explainable AI libraries were employed at different prediction stages for all considered classifiers in this study.

AHEAD: A Novel Technique Combining Anti-Adversarial Hierarchical Ensemble Learning with Multi-Layer Multi-Anomaly Detection for Blockchain Systems

Blockchain technology has impacted various sectors and is transforming them through its decentralized, immutable, transparent, smart contracts (automatically executing digital agreements) and traceable attributes. Due to the adoption of blockchain technology in versatile applications, millions of transactions take place globally. These transactions are no exception to adversarial attacks which include data tampering, double spending, data corruption, Sybil attacks, eclipse attacks, DDoS attacks, P2P network partitioning, delay attacks, selfish mining, bribery, fake transactions, fake wallets or phishing, false advertising, malicious smart contracts, and initial coin offering scams. These adversarial attacks result in operational, financial, and reputational losses. Although numerous studies have proposed different blockchain anomaly detection mechanisms, challenges persist. These include detecting anomalies in just a single layer instead of multiple layers, targeting a single anomaly instead of multiple, not encountering adversarial machine learning attacks (for example, poisoning, evasion, and model extraction attacks), and inadequate handling of complex transactional data. The proposed AHEAD model solves the above problems by providing the following: (i) data aggregation transformation to detect transactional and user anomalies at the data and network layers of the blockchain, respectively, (ii) a Three-Layer Hierarchical Ensemble Learning Model (HELM) incorporating stratified random sampling to add resilience against adversarial attacks, and (iii) an advanced preprocessing technique with hybrid feature selection to handle complex transactional data. The performance analysis of the proposed AHEAD model shows that it achieves higher anti-adversarial resistance and detects multiple anomalies at the data and network layers. A comparison of the proposed AHEAD model with other state-of-the-art models shows that it achieves 98.85% accuracy against anomaly detection on data and network layers targeting transaction and user anomalies, along with 95.97% accuracy against adversarial machine learning attacks, which surpassed other models.

Detecting adolescent depression in social media: A hierarchical ensemble learning approach

As digital landscapes evolve, adolescents increasingly rely on social media platforms for self-expression, leading to the vast dissemination of their mental and emotional states. Amidst the physiological and psychological transitions characteristic of adolescence, there is a heightened risk of depressive disorders. These shifts, coupled with the dynamic nature of their online expressions, create a compelling case for detecting latent signs of depression within their digital footprints. This thesis delves into the intersection of Natural Language Processing (NLP) and adolescent depression detection, introducing an innovative hierarchical ensemble model tailored for the intricate task of identifying depressive markers in adolescent social media content. This model amalgamates traditional word embeddings with state-of-the-art pretrained models, encapsulating both sentence-level and word-level representations. Empirical validation, conducted on a unique dataset centered on adolescent depression detection, indicates the models superior efficacy over existing baselines. By offering an ensemble approach that captures the nuances of adolescent linguistic expressions, this research illuminates the potential for timely and non-intrusive interventions in adolescent mental health.

Visual working memory models of delayed estimation do not generalize to whole-report tasks.

Whole-report working memory tasks provide a measure of recall for all stimuli in a trial and afford single-trial analyses that are not possible with single-report delayed estimation tasks. However, most whole-report studies assume that trial stimuli are encoded and reported independently, and they do not consider the relationships between stimuli presented and reported within the same trial. Here, we present the results of two independently conducted whole-report experiments. The first dataset was recorded by Adam, Vogel, and Awh (2017) and required participants to report color and orientation stimuli using a continuous response wheel. We recorded the second dataset, which required participants to report color stimuli using a set of discrete buttons. We found that participants often group their reports by color similarity, contradicting the assumption of independence implicit in most encoding models of working memory. Next, we showed that this behavior was consistent across participants and experiments when reporting color but not orientation, two circular variables often assumed to be equivalent.Finally, we implemented an alternative to independent encoding where stimuli are encoded as a hierarchical Bayesian ensemble and found that this model predicts biases that are not present in either dataset. Our results suggest that assumptions made by both independent and hierarchical ensemble encoding models-which were developed in the context of single-report delayed estimation tasks-do not hold for the whole-report task. This failure to generalize highlights the need to consider variations in task structure when inferring fundamental principles of visual working memory.

Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux

AbstractIn this work, we address the computational challenge of large‐scale physics‐based simulation models for the ring current. Reduced computational cost allows for significantly faster than real‐time forecasting, enhancing our ability to predict and respond to dynamic changes in the ring current, valuable for space weather monitoring and mitigation efforts. Additionally, it can also be used for a comprehensive investigation of the system. Thus, we aim to create an emulator for the Ring current‐Atmosphere interactions Model with Self‐Consistent magnetic field (RAM‐SCB) particle flux that not only improves efficiency but also facilitates forecasting with reliable estimates of prediction uncertainties. The probabilistic emulator is built upon the methodology developed by Licata and Mehta (2023), https://doi.org/10.1029/2022sw003345. A novel discrete sampling is used to identify 30 simulation periods over 20 years of solar and geomagnetic activity. Focusing on a subset of particle flux, we use Principal Component Analysis for dimensionality reduction and Long Short‐Term Memory (LSTM) neural networks to perform dynamic modeling. Hyperparameter space was explored extensively resulting in about 5% median symmetric accuracy across all data sets for one‐step dynamic prediction. Using a hierarchical ensemble of LSTMs, we have developed a reduced‐order probabilistic emulator (ROPE) tailored for time‐series forecasting of particle flux in the ring current. This ROPE offers accurate predictions of omnidirectional flux at a single energy with no pitch angle information, providing robust predictions on the test set with an error score below 11% and calibration scores under 8% with bias under 2% providing a significant speed up as compared to the full RAM‐SCB run.

FTG-Net-E: A hierarchical ensemble graph neural network for DDoS attack detection

Distributed Denial-of-Service (DDoS) attacks are a major threat to computer networks. These attacks can be carried out by flooding a network with malicious traffic, overwhelming its resources, and/or making it unavailable to legitimate users. Existing machine learning methods for DDoS attack detection typically use statistical features of network traffic, such as packet sizes and inter-arrival times. However, these methods often fail to capture the complex relationships between different traffic flows. This paper proposes a new DDoS attack detection approach that uses Graph Neural Networks (GNN) ensemble learning. GNN ensemble learning is a type of machine learning that combines multiple GNN models to improve the detection accuracy. We evaluated our approach on the Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset (CICIDS2018) and CICIDS2017 datasets, a benchmark dataset for DDoS attack detection. Our work provides two main contributions. First, we extend our DDoS attack detection approach using GNN ensemble learning. Second, we explore the evaluation and fine-tuning of hyperparameter metrics through ensemble learning, significantly enhancing accuracy compared to a single GNN model and achieving an average 3.2% higher F1-score. Additionally, our approach effectively reduces overfitting by incorporating regularization techniques, such as dropout and early stopping. Specifically, we use a hierarchical ensemble of GNN, where each GNN learns the relationships between traffic flows at a different granularity level. We then use bagging and boosting to combine the predictions of the individual GNN, further improving detection accuracy. Results show that our system can achieve 99.67% accuracy, with a F1-score of 99.29%, which is better than state-of-the-art methods, even using single traffic architecture.

A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future.

Hierarchical Pruning of Deep Ensembles with Focal Diversity

Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study and apply deep ensemble techniques in the deep learning community. Some mission-critical applications utilize a large number of deep neural networks to form deep ensembles to achieve desired accuracy and resilience, which introduces high time and space costs for ensemble execution. However, it still remains a critical challenge whether a small subset of the entire deep ensemble can achieve the same or better generalizability and how to effectively identify these small deep ensembles for improving the space and time efficiency of ensemble execution. This article presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks. Our hierarchical ensemble pruning approach (HQ) leverages three novel ensemble pruning techniques. First, we show that the focal ensemble diversity metrics can accurately capture the complementary capacity of the member networks of an ensemble team, which can guide ensemble pruning. Second, we design a focal ensemble diversity based hierarchical pruning approach, which will iteratively find high quality deep ensembles with low cost and high accuracy. Third, we develop a focal diversity consensus method to integrate multiple focal diversity metrics to refine ensemble pruning results, where smaller deep ensembles can be effectively identified to offer high accuracy, high robustness and high ensemble execution efficiency. Evaluated using popular benchmark datasets, we demonstrate that the proposed hierarchical ensemble pruning approach can effectively identify high quality deep ensembles with better classification generalizability while being more time and space efficient in ensemble decision making. We have released the source codes on GitHub at https://github.com/git-disl/HQ-Ensemble .

HiTaxon: a hierarchical ensemble framework for taxonomic classification of short reads.

Whole microbiome DNA and RNA sequencing (metagenomics and metatranscriptomics) are pivotal to determining the functional roles of microbial communities. A key challenge in analyzing these complex datasets, typically composed of tens of millions of short reads, is accurately classifying reads to their taxa of origin. While still performing worse relative to reference-based short-read tools in species classification, ML algorithms have shown promising results in taxonomic classification at higher ranks. A recent approach exploited to enhance the performance of ML tools, which can be translated to reference-dependent classifiers, has been to integrate the hierarchical structure of taxonomy within the tool's predictive algorithm. Here, we introduce HiTaxon, an end-to-end hierarchical ensemble framework for taxonomic classification. HiTaxon facilitates data collection and processing, reference database construction and optional training of ML models to streamline ensemble creation. We show that databases created by HiTaxon improve the species-level performance of reference-dependent classifiers, while reducing their computational overhead. In addition, through exploring hierarchical methods for HiTaxon, we highlight that our custom approach to hierarchical ensembling improves species-level classification relative to traditional strategies. Finally, we demonstrate the improved performance of our hierarchical ensembles over current state-of-the-art classifiers in species classification using datasets comprised of either simulated or experimentally derived reads. HiTaxon is available at: https://github.com/ParkinsonLab/HiTaxon.

Long Island University’s Library Learning Center: Noel Phyllis Birkby’s Anti-Ableist Activism in the 1970s

ABSTRACT: In 1968, the architect and filmmaker Noel Phyllis Birkby (1932–1994) spearheaded the redevelopment of Long Island University’s Library Learning Center in Brooklyn, New York. The university’s mission was to retrofit a collection of buildings to create an accessible and equitable campus layout. The original design for the library complex featured a hierarchical ensemble of disconnected buildings. To serve the university’s mission, Birkby steered the revisions of the master plan in 1970 in ways that represented her understanding of disability as a social construct. Influenced by environmental design, Birkby transformed the built environment so as to address the compounding impacts of exclusionary practices in architecture. Environmental design was an emerging discipline that examined the human-built environment relationship at the intersection of architecture, social and behavioral sciences, and political action. Birkby’s spatial strategies of accessibility, interconnectedness, and transparency uncovered the socio-political power imbalances embedded within the ideological program of environmental design as a field of knowledge. Her work situates a critique of socio-spatial hierarchies within broader feminist conceptualizations of discrimination in design. The analysis of Birkby’s project—its original design and redesign—foregrounds her subsequent political activism and feminist design experimentations with fantasy drawings.

Diagnosis of anemia using non-invasive anemia detector through parametrical analysis

Anemia, characterized by low hemoglobin levels, affects a significant portion of the global population, particularly children and women in low- and lower-middle-income countries. Traditional hematological tests for diagnosing anemia can be invasive, costly, and resource-intensive. This study presents the development of a wearable, non-invasive anemia detector utilizing infrared (IR) technology. The device integrates a NodeMCU microcontroller, an OLED display, a buck converter, an IR transmitter, and a receiver in a compact, wearable package. The IR system measures hemoglobin levels by analyzing the absorption of infrared light emitted through the skin, typically on a fingertip or earlobe. The NodeMCU processes the light absorption data using sophisticated algorithms to determine hemoglobin concentration and facilitates wireless data transfer to smartphones or cloud servers for further analysis. The device is designed with energy efficiency in mind, utilizing a buck converter to ensure optimal power distribution to sensitive components. The OLED display provides users with clear, real-time feedback on their hemoglobin levels, offering a user-friendly interface. Initial testing and calibration with known hemoglobin concentrations demonstrated strong correlations between device readings and traditional lab tests. The device employs a hierarchical ensemble classifier to categorize individuals as healthy, mildly anemic, or severely anemic, aligning with clinical standards. The non-invasive nature of this device, combined with its portability and cost-effectiveness, makes it an ideal solution for point-of-care anemia detection, particularly in resource-limited settings. The integration of IR technology, microcontroller processing, and wireless communication presents a practical tool for healthcare providers, facilitating rapid anemia screening and ongoing monitoring. This innovation holds the potential to improve patient outcomes by enabling early detection and intervention in anemia management.

Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers

Abstract Statistical postprocessing of global ensemble weather forecasts is revisited by leveraging recent developments in machine learning. Verification of past forecasts is exploited to learn systematic deficiencies of numerical weather predictions in order to boost postprocessed forecast performance. Here, we introduce postprocessing of ensembles with transformers (PoET), a postprocessing approach based on hierarchical transformers. PoET has two major characteristics: 1) the postprocessing is applied directly to the ensemble members rather than to a predictive distribution or a functional of it, and 2) the method is ensemble-size agnostic in the sense that the number of ensemble members in training and inference mode can differ. The PoET output is a set of calibrated members that has the same size as the original ensemble but with improved reliability. Performance assessments show that PoET can bring up to 20% improvement in skill globally for 2-m temperature and 2% for precipitation forecasts and outperforms the simpler statistical member-by-member method, used here as a competitive benchmark. PoET is also applied to the ENS-10 benchmark dataset for ensemble postprocessing and provides better results when compared to other deep learning solutions that are evaluated for most parameters. Furthermore, because each ensemble member is calibrated separately, downstream applications should directly benefit from the improvement made on the ensemble forecast with postprocessing.

A Hierarchical Ensemble Learning Approach for Cable Network Impairment Diagnosis

A Hierarchical Ensemble Learning Approach for Cable Network Impairment Diagnosis

Hierarchical ensemble deep learning for data-driven lead time prediction

This paper focuses on data-driven prediction of lead times for product orders based on the real-time production state captured at the arrival instants of orders in make-to-order production environments. In particular, we consider a sophisticated manufacturing system where a large number of measurements about the production state are available (e.g. sensor data). In response to this complex prediction challenge, we present a novel ensemble hierarchical deep learning algorithm comprised of three deep neural networks. One of these networks acts as a generalist, while the other two function as specialists for different products. Hierarchical ensemble methods have previously been successfully utilised in addressing various multi-class classification problems. In this paper, we extend this approach to encompass the regression task of lead time prediction. We demonstrate the suitability of our algorithm in two separate case studies. The first case study uses one of the largest manufacturing datasets available, the Bosch production line dataset. The second case study uses synthetic datasets generated from a reliability-based model of a multi-product, make-to-order production system, inspired by the Bosch production line. In both case studies, we demonstrate that our algorithm provides high-accuracy predictions and significantly outperforms selected benchmarks including the single deep neural network. Moreover, we find that prediction accuracy is significantly higher in the synthetic dataset, which suggests that there is complexity (i.e. subtle interactions) in industrial manufacturing processes that are not easily reproduced in artificial models

A hierarchical ensemble causal structure learning approach for wafer manufacturing

A hierarchical ensemble causal structure learning approach for wafer manufacturing

A semi-supervised hierarchical ensemble clustering framework based on a novel similarity metric and stratified feature sampling

Recently, both ensemble clustering and semi-supervised clustering have emerged as important paradigms of traditional clustering. Ensemble clustering seeks to integrate multiple clustering results from different methods or the same methods with different parameters. Semi-supervised clustering involves using a small amount of class membership information in some samples for the learning process. Meanwhile, Semi-Supervised Ensemble Clustering (SSEC) has attracted increasing attention due to its high performance. However, most SSEC algorithms are configured based on partitional clustering techniques, and there are few attempts on hierarchical clustering techniques. Even in existing hierarchy-based SSEC algorithms, prior knowledge is not sufficiently used and is often applied to create primary partitions. To address these problems, we propose a Semi-supervised Hierarchical Ensemble Clustering framework based on a novel Similarity metric and stratified feature Sampling, which we call SHECSS. SHECSS uses the information of all primary partitions according to their strength to calculate the similarity between samples. Also, SHECSS is equipped with a stratified feature sampling mechanism that can improve the diversity of primary partitions and deal with high-dimensional data. Here, the primary partitions are created based on multiple hierarchical clustering techniques, and the target partition is configured by a consensus function based on the clusters clustering policy. Experimental results show the effectiveness and efficiency of SHECSS compared to representative clustering methods.

: A Semantics-Guided Safety Enhancement Framework for AI-Enabled Cyber-Physical Systems

Cyber-Physical Systems (CPSs) have been widely adopted in various industry domains to support many important tasks that impact our daily lives, such as automotive vehicles, robotics manufacturing, and energy systems. As Artificial Intelligence (AI) has demonstrated its promising abilities in diverse tasks like decision-making, prediction, and optimization, a growing number of CPSs adopt AI components in the loop to further extend their efficiency and performance. However, these modern AI-enabled CPSs have to tackle pivotal problems that the AI-enabled control systems might need to compensate the balance across <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple operation requirements</i> and avoid possible defections in advance to safeguard human lives and properties. Modular redundancy and ensemble method are two widely adopted solutions in the traditional CPSs and AI communities to enhance the functionality and flexibility of a system. Nevertheless, there is a lack of deep understanding of the effectiveness of such ensemble design on AI-CPSs across diverse industrial applications. Considering the complexity of AI-CPSs, existing ensemble methods fall short of handling such huge state space and sophisticated system dynamics. Furthermore, an ideal control solution should consider the multiple system specifications in real-time and avoid erroneous behaviors beforehand. Such that, a new specification-oriented ensemble control system is of urgent need for AI-CPSs. In this paper, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathtt {SIEGE}$</tex-math></inline-formula> , a semantics-guided ensemble control framework to initiate an early exploratory study of ensemble methods on AI-CPSs and aim to construct an efficient, robust, and reliable control solution for multi-tasks AI-CPSs. We first utilize a semantic-based abstraction to decompose the large state space, capture the ongoing system status and predict future conditions in terms of the satisfaction of specifications. We propose a series of new semantics-aware ensemble strategies and an end-to-end Deep Reinforcement Learning (DRL) hierarchical ensemble method to improve the flexibility and reliability of the control systems. Our large-scale, comprehensive evaluations over five subject CPSs show that 1) the semantics abstraction can efficiently narrow the large state space and predict the semantics of incoming states, 2) our semantics-guided methods outperform state-of-the-art individual controllers and traditional ensemble methods, and 3) the DRL hierarchical ensemble approach shows promising capabilities to deliver a more robust, efficient, and safety-assured control system. To enable further research along this direction to build better AI-enabled CPS, we made all of the code and experimental results data publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/ai-cps-siege/home</uri> .

Predicting the Cognitive Ability of Young Women Using a New Feature Selection Algorithm.

Cognitive abilities are the capabilities to perform mental processes that include executive function, comprehension, decision-making, work performance, and educational attainment. This study aimed to investigate the relationship between several biomarkers and individuals' cognitive ability using various machine learning methods. A total of 144 young women aged between 18 and 24years old were recruited into the study. Cognitive performance was assessed using a standard questionnaire. A panel of biochemical, hematological, inflammatory, and oxidative stress biomarkers in serum and urine was measured for all participants. A novel combination of feature selection and feature scoring techniques within a hierarchical ensemble structure has been proposed to identify the most effective features in recognizing the importance of various biomarker signatures in cognitive abilities classification. Multiple feature selection methods were employed in conjunction with different classifiers to construct this model. In this manner, using three filter methods, the scores of each feature were considered. The combination of high-scoring features for each filter method was stored as the primary feature subset. A high-accuracy feature subset was selected by using a wrapper method. The collection of highly scored features from each filter method formed the primary feature subset. A wrapper method was also employed to select a feature subset with high accuracy. To ensure robustness and minimize random variations in the feature subset search process, a repeative tenfold cross-validation was conducted. The most frequently recurring features were determined. This iterative step facilitated the identification of an optimal feature subset, effectively reducing the dimensionality of features while maintaining accuracy. Among the 47 extracted factors, serum level of NOx (nitrite ± nitrate), alkaline phosphatase (ALP), and phosphate as well as blood platelet count (PLT) was entered into the model of cognitive abilities with the highest accuracy of approximately 70.9% using a decision tree classifier. Therefore, the serum levels of NOx, ALP, phosphate, and blood PLT count may be important markers of the cognitive abilities in apparently healthy young women. These factors my provide a simple procedure to identify mental abilities and earlier cognitive decline in healthy adults.

Human-Behavior-Based Personalized Meal Recommendation and Menu Planning Social System

The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. It takes a lot of effort to learn people’s food preferences and make recommendations based on their affects and nutrition. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social affect has some significant benefits over the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">locked-in syndrome</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">amyotrophic lateral sclerosis</i> (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person’s affect is detected using electroencephalography (EEG) signals. EEG allows to capture the brain signals and analyze them to anticipate affective state toward a food. In this study, we have used a 14-channel wireless Emotiv Epoc <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person’s energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment parameters.

Trustworthy Privacy-Preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 With Blockchain

The advancement of Internet and Communication Technologies (ICTs) has led to the era of Industry 4.0. This shift is followed by healthcare industries creating the term Healthcare 4.0. In Healthcare 4.0, the use of IoT-enabled medical imaging devices for early disease detection has enabled medical practitioners to increase healthcare institutions' quality of service. However, Healthcare 4.0 is still lagging in Artificial Intelligence and big data compared to other Industry 4.0 due to data privacy concerns. In addition, institutions' diverse storage and computing capabilities restrict institutions from incorporating the same training model structure. This paper presents a secure multi-party computation-based ensemble federated learning with blockchain that enables heterogeneous models to collaboratively learn from healthcare institutions' data without violating users' privacy. Blockchain properties also allow the party to enjoy data integrity without trust in a centralized server while also providing each healthcare institution with auditability and version control capability.