Reviewerly/exHarmony huggingface dataset:
Application of digital pathology and machine learning in the liver, kidney and lung diseases

The development of rapid and accurate Whole Slide Imaging (WSI) has paved the way for the application of Artificial Intelligence (AI) to digital pathology. The availability of WSI in the recent years allowed the rapid development of various AI technologies to blossom. WSI-based digital pathology combined with neural networks can automate arduous and time-consuming tasks of slide evaluation. Machine Learning (ML)-based AI has been demonstrated to outperform pathologists by eliminating inter- and intra-observer subjectivity, obtaining quantitative data from slide images, and extracting hidden image patterns that are relevant to disease subtype and progression. In this review, we outline the functionality of different AI technologies such as neural networks and deep learning and discover how aspects of different diseases make them benefit from the implementation of AI. AI has proven to be valuable in many different organs, with this review focusing on the liver, kidney, and lungs. We also discuss how AI and image analysis not only can grade diseases objectively but also discover aspects of diseases that have prognostic value. In the end, we review the current status of the integration of AI in pathology and share our vision on the future of digital pathology.
A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

Digitization in healthcare systems, with the wid adoption of Electronic Health Records, connected medical devices, software and systems providing efficient healthcare service delivery and management. On the other hand, the use of these systems has significantly increased cyber threats in the healthcare sector. Vulnerabilities in the existing and legacy systems are one of the key causes for the threats and related risks. Understanding and addressing the threats from the connected medical devices and other parts of the ICT health infrastructure are of paramount importance for ensuring security within the overall healthcare ecosystem. Threat and vulnerability analysis provides an effective way to lower the impact of risks relating to the existing vulnerabilities. However, this is a challenging task due to the availability of massive data which makes it difficult to identify potential patterns of security issues. This paper contributes towards an effective threats and vulnerabilities analysis by adopting Machine Learning models, such as the BERT neural language model and XGBoost, to extract updated information from the Natural Language documents largely available on the web, evaluating at the same time the level of the identified threats and vulnerabilities that can impact on the healthcare system, providing the required information for the most appropriate management of the risk. Experiments were performed based on CS news extracted from the Hacker News website and on Common Vulnerabilities and Exposures (CVE) vulnerability reports. The results demonstrate the effectiveness of the proposed approach, which provides a realistic manner to assess the threats and vulnerabilities from Natural Language texts, allowing adopting it in real-world Healthcare ecosystems.
A Customized Efficient Deep Learning Model for the Diagnosis of Acute Leukemia Cells Based on Lymphocyte and Monocyte Images

The production of blood cells is affected by leukemia, a type of bone marrow cancer or blood cancer. Deoxyribonucleic acid (DNA) is related to immature cells, particularly white cells, and is damaged in various ways in this disease. When a radiologist is involved in diagnosing acute leukemia cells, the diagnosis is time consuming and needs to provide better accuracy. For this purpose, many types of research have been conducted for the automatic diagnosis of acute leukemia. However, these studies have low detection speed and accuracy. Machine learning and artificial intelligence techniques are now playing an essential role in medical sciences, particularly in detecting and classifying leukemic cells. These methods assist doctors in detecting diseases earlier, reducing their workload and the possibility of errors. This research aims to design a deep learning model with a customized architecture for detecting acute leukemia using images of lymphocytes and monocytes. This study presents a novel dataset containing images of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). The new dataset has been created with the assistance of various experts to help the scientific community in its efforts to incorporate machine learning techniques into medical research. Increasing the scale of the dataset is achieved with a Generative Adversarial Network (GAN). The proposed CNN model based on the Tversky loss function includes six convolution layers, four dense layers, and a Softmax activation function for the classification of acute leukemia images. The proposed model achieved a 99% accuracy rate in diagnosing acute leukemia types, including ALL and AML. Compared to previous research, the proposed network provides a promising performance in terms of speed and accuracy; and based on the results, the proposed model can be used to assist doctors and specialists in practical applications.
DKTNet: Dual-Key Transformer Network for small object detection

Object detection is a fundamental computer vision task that plays a crucial role in a wide range of real-world applications. However, it is still a challenging task to detect the small size objects in the complex scene, due to the low resolution and noisy representation appearance caused by occlusion, distant depth view, etc. To tackle this issue, a novel transformer architecture, Dual-Key Transformer Network (DKTNet), is proposed in this paper. To improve the feature attention ability, the coherence of linear layer outputs Q and V are enhanced by a dual-K integrated from K1 and K2, which are computed along Q and V, respectively. Instead of spatial-wise attention, channel-wise self-attention mechanism is adopted to promote the important feature channels and suppress the confusing ones. Moreover, 2D and 1D convolution computations for Q, K and V are proposed. Compared with the fully-connected computation in conventional transformer architectures, the 2D convolution can better capture local details and global contextual information, and the 1D convolution can reduce network complexity significantly. Experimental evaluation is conducted on both general and small object detection datasets. The superiority of the aforementioned features in our proposed approach is demonstrated with the comparison against the state-of-the-art approaches.
Exploring blockchain-based Traceability for Food Supply Chain Sustainability: Towards a Better Way of Sustainability Communication with Consumers

While there is consensus that blockchain technology can improve supply chain transparency and operational efficiency, scholars and practitioners have only recently considered how blockchain could improve the sustainability of supply chains. This article contributes to this emerging body of work by considering how to use blockchain to enable sustainable food supply chains. To advance the use of blockchain for sustainability in the food supply chain context, this study proposes a blockchain-enabled architectural framework for trustworthy communication about the sustainability attributes of food products. The blockchain-enabled architectural framework outlines a multi-layered architecture for utilising blockchain-based traceability as a way to signal to consumers the trustworthy sustainability attributes of a particular food product. This kind of sustainability communication enables the accessibility of traceable and verifiable evidence and therefore can significantly enhance the reliability and trustworthiness of sustainable claims/credentials compared to printed food labels or other digital means of communication. The study contributes to sustainability communication empowered by blockchain-based traceability and offers invaluable insights to both researchers and practitioners in the industry 4.0 landscape.
Logistics outsourcing: Effects of greenwashing and blockchain technology

In this paper, we study the logistics outsourcing strategy of a manufacturer with regard to green logistics, greenwashing, and blockchain. The manufacturer sells its products through an e-commerce platform, which has self-built logistics service. To transport the products to the customers, the manufacturer can choose to outsource this logistics service to the e-commerce platform or a third-party logistics (3PL) firm. The e-commerce platform and 3PL firm are called logistics firms in this paper. Both logistics firms consider investing in green logistics activities and may involve in greenwashing behavior. We show that outsourcing to the e-commerce platform is more beneficial for the manufacturer, no matter whether the logistics firms pursue greenwashing. Moreover, whether the logistics firms pursue greenwashing depends on both the probability that the greenwashing behavior is exposed and the corresponding penalty for greenwashing. Although adopting blockchain has the potential to prevent greenwashing, the logistics firms may still take risks to pursue greenwashing when the cost of blockchain adoption is too large. Interestingly, we find that the logistics firms will set the same green levels in logistics activities, independent of the outsourcing strategy of the manufacturer, and greenwashing decisions and blockchain adoption decisions of the logistics firms.
EGA-Net: Edge feature enhancement and global information attention network for RGB-D salient object detection

With the supplement of texture and geometry cues in depth maps, salient object detection (SOD) shifts from 2D to 3D, aiming to detect the most attractive object in a pair of color and depth images. Previous work primarily focused on regional integrity. Few methods are used to improve the edge quality of prediction results, resulting in a final prediction with a complete structure but blurred edges. Moreover, due to the complexity of real-life scenarios, the problem of effectively separating the salient object from complex background has become a hot potato. Aiming to address these issues, we propose a novel network, EGA-Net, to improve the edge quality and highlight the main features of the salient object. Specifically, in the EGA-Net, we propose a feature interaction (FI) module and an edge feature enhancement (EFE) module, respectively. Among them, the FI module is used to remove unimodal feature redundancy, capture multi-modal feature complementarity, and reduce the contamination of low-quality depth maps. The EFE is used to improve the edge quality of the final salient object prediction results. Furthermore, a Global Information Guide Integration (GIGI) module has been proposed to suppress the background noise and effectively highlight the salient objects' main features. It uses interleaving and fusion methods to automatically select and enhance the vital information in the original input features under the guidance of global features. We put the training of EGA-Net under the supervision of a new hybrid loss function that can simultaneously take global pixel point, foreground, and depth map quality into account. Quantitative and qualitative experiment results demonstrate that our method outperforms the 19 advanced methods on eight publicly available RGB-D salient object detection datasets with five evaluation metrics. You can find the code and results of our method athttps://github.com/guanyuzong/EGA-Net.
Framework for Detecting Breast Cancer Risk Presence Using Deep Learning

Cancer is a complicated global health concern with a significant fatality rate. Breast cancer is among the leading causes of mortality each year. Advancements in prognoses have been progressively based primarily on the expression of genes, offering insight into robust and appropriate healthcare decisions, owing to the fast growth of advanced throughput sequencing techniques and the use of various deep learning approaches that have arisen in the past few years. Diagnostic-imaging disease indicators such as breast density and tissue texture are widely used by physicians and automated technology. The effective and specific identification of cancer risk presence can be used to inform tailored screening and preventive decisions. For several classifications and prediction applications, such as breast imaging, deep learning has increasingly emerged as an effective method. We present a deep learning model approach for predicting breast cancer risk primarily on this foundation. The proposed methodology is based on transfer learning using the InceptionResNetV2 deep learning model. Our experimental work on a breast cancer dataset demonstrates high model performance, with 91% accuracy. The proposed model includes risk markers that are used to improve breast cancer risk assessment scores and presents promising results compared to existing approaches. Deep learning models include risk markers that are used to improve accuracy scores. This article depicts breast cancer risk indicators, defines the proper usage, features, and limits of each risk forecasting model, and examines the increasing role of deep learning (DL) in risk detection. The proposed model could potentially be used to automate various types of medical imaging techniques.
Evolutionary Learning of Interpretable Decision Trees

Reinforcement learning techniques achieved human-level performance in several tasks in the last decade. However, in recent years, the need for interpretability emerged: we want to be able to understand how a system works and the reasons behind its decisions. Not only we need interpretability to assess the safety of the produced systems, we also need it to extract knowledge about unknown problems. While some techniques that optimize decision trees for reinforcement learning do exist, they usually employ greedy algorithms or they do not exploit the rewards given by the environment. This means that these techniques may easily get stuck in local optima. In this work, we propose a novel approach to interpretable reinforcement learning that uses decision trees. We present a two-level optimization scheme that combines the advantages of evolutionary algorithms with the advantages of Q-learning. This way we decompose the problem into two sub-problems: the problem of finding a meaningful and useful decomposition of the state space, and the problem of associating an action to each state. We test the proposed method on three well-known reinforcement learning benchmarks, on which it results competitive with respect to the state-of-the-art in both performance and interpretability. Finally, we perform an ablation study that confirms that using the two-level optimization scheme gives a boost in performance in non-trivial environments with respect to a one-layer optimization technique.
A Parallel Compact Gannet Optimization Algorithm for Solving Engineering Optimization Problems

The Gannet Optimization Algorithm (GOA) has good performance, but there is still room for improvement in memory consumption and convergence. In this paper, an improved Gannet Optimization Algorithm is proposed to solve five engineering optimization problems. The compact strategy enables the GOA to save a large amount of memory, and the parallel communication strategy allows the algorithm to avoid falling into local optimal solutions. We improve the GOA through the combination of parallel strategy and compact strategy, and we name the improved algorithm Parallel Compact Gannet Optimization Algorithm (PCGOA). The performance study of the PCGOA on the CEC2013 benchmark demonstrates the advantages of our new method in various aspects. Finally, the results of the PCGOA on solving five engineering optimization problems show that the improved algorithm can find the global optimal solution more accurately.
Incomprehensible but Intelligible-in-time logics: Theory and optimization algorithm

The human mind is a complex biological member with unique abilities. Today, even the most advanced computers cannot do all the brain’s calculations in one second. The mind is teachable, and its logic will change based on what it has learned over time. This behavior will be the base of a theory presented in this article, named Incomprehensible but Intelligible-in-time (IbI) Logics. From a mental point of view and according to human knowledge, what it has not learned is a non-logic. However, this may change through time. Meanwhile, the IbI logic is a non-logic that may become an obvious logic in the future. This article has been formed to introduce a side of science to identify IbI logic and organize the mind’s scientific idioms. Based on the introduced theory, a new optimization algorithm called IbI Logics Algorithm (ILA) was also presented and compared with some other algorithms. The performance of the proposed algorithm in several constrained and unconstrained examples were evaluated. The results showed the acceptable performance and potential of the ILA for optimization goals.
Optical multi-task learning using multi-wavelength diffractive deep neural networks

Abstract Photonic neural networks are brain-inspired information processing technology using photons instead of electrons to perform artificial intelligence (AI) tasks. However, existing architectures are designed for a single task but fail to multiplex different tasks in parallel within a single monolithic system due to the task competition that deteriorates the model performance. This paper proposes a novel optical multitask learning system by designing multiwavelength diffractive deep neural networks (D 2 NNs) with the joint optimization method. By encoding multitask inputs into multiwavelength channels, the system can increase the computing throughput and significantly alleviate the competition to perform multiple tasks in parallel with high accuracy. We design the two-task and four-task D 2 NNs with two and four spectral channels, respectively, for classifying different inputs from MNIST, FMNIST, KMNIST, and EMNIST databases. The numerical evaluations demonstrate that, under the same network size, multiwavelength D 2 NNs achieve significantly higher classification accuracies for multitask learning than single-wavelength D 2 NNs. Furthermore, by increasing the network size, the multiwavelength D 2 NNs for simultaneously performing multiple tasks achieve comparable classification accuracies with respect to the individual training of multiple single-wavelength D 2 NNs to perform tasks separately. Our work paves the way for developing the wavelength-division multiplexing technology to achieve high-throughput neuromorphic photonic computing and more general AI systems to perform multiple tasks in parallel.
Data-driven review of blockchain applications in supply chain management: key research themes and future directions

Blockchain (BC) applications in supply chain management (SCM) have recently received extensive attention. It is important to synthesise the extant literature on the field to identify key research themes and navigate potential future directions. This study thus develops an efficient, scalable data-driven review approach that uses text mining and Latent Dirichlet Allocation (LDA)-based topic modelling for automatic content analysis of full-text documents. Our method overcomes the drawbacks of traditional systematic literature reviews using either manual coding or bibliographic analysis for article classifications, which are highly time-consuming and biased when dealing with large amounts of text. 108 papers published between 2017 and 2022 were analysed which identified 10 key research themes, including revenue management, sustainability, traceability, manufacturing system, scheduling in cloud manufacturing, healthcare SCM, anti-counterfeit system, logistics and transportation, system architecture development, and food & agriculture SC. Five future directions are then suggested, including (1) integration of BC and other emerging technologies for global and scalable SCM, (2) crypto-X applications in SCM, (3) BC-enabled closed-loop SCM, (4) the environmental and social impacts of BC-based SCM and (5) decentralised autonomous organisations in SCM.
A two-stage CNN method for MRI image segmentation of prostate with lesion

Prostate magnetic resonance imaging (MRI) is widely used in the diagnosis of prostate cancer and other prostate diseases. The automatic segmentation of images from prostate MRI plays an important role in the auxiliary diagnosis of prostate diseases. Currently, there are two commonly used methods for automatic segmentation of prostate MRI, which are 2D image segmentation and 3D image segmentation. In this paper, a two-stage CNN method for MRI image segmentation of prostate with lesion is proposed. At the first stage, we used a CNN model incorporating the Squeeze-Excitation module to discriminate whether the image contains prostate or not. At the second stage, we proposed a Residual-Attention U-Net for segmentation of images containing prostate. Eventually, the 3D prostate MRI segmentation results are obtained and fully automated segmentation is accomplished. We evaluated our proposed method and other common 2D and 3D segmentation methods on the test dataset and compared their results based on Dice Similarity Coefficient (DSC) value. Our method performed the best and achieved the DSC metric value of 0.860.
Application of artificial neural network and multi-linear regression techniques in groundwater quality and health risk assessment around Egbema, Southeastern Nigeria

This paper examined the efficiency of artificial neural network (ANN) and multivariate linear regression (MLR) models in the prediction of groundwater quality parameters such as ecological risk index (ERI), pollution load index (PLI), metal pollution index (MPI), Nemerow pollution index (NPI), and geoaccumulation index (Igeo). 40 groundwater samples were collected systematically and analyzed for mainly heavy metals. Results revealed that adopting measured parameters is effective in modeling the parameters with high level of accuracy. Contamination factor results reveal that Ni, Zn, Pb, Cd, and Cu have relatively low values < 1 within the region while the Iron values ranged from low contamination to very high contamination (> 6). PLI, MPI, and ERI results indicated low pollution. NPI results indicated that the majority of the samples were heavily polluted. Quantification of Contamination results revealed that most of the sample's quality was geogenically influenced. Igeo results revealed that most of the samples had extreme pollution. The health risk assessment results revealed that children are substantially prone to more health risk more than adults. The ANN and MLR models showed a high effective tendency in the prediction of ERI, PLI, MPI, NPI and Igeo. Principal component analysis results showed appreciable variable loadings while the correlation matrix results reveal that there exists weak and positive correlation amongst elements. Based on the outcome of this study, this research recommends the use of ANN and MLR models in the prediction of groundwater quality parameters as they yielded positive, reliable, acceptable, and appropriate accuracy performances.