다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic–orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users’ service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed.

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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

The potential of Big Data allows us to hope to slow the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine. Yet, as with the Internet, social media, and cloud computing, early enthusiasts are creating hyperbolic expectations about how and how quickly Big Data will transform healthcare. A number of issues challenge the adoption and success of healthcare Big Data, including privacy and security, who owns the data, and the regulatory labyrinth. Furthermore, real advances depend on better ways to exploit the disconnected puddles and lakes of existing data as well as better ways to generate, capture, analyze and make use of the streams of new kinds of data that are about to flood healthcare. This paper will introduce to Big Data and explore how it is becoming a growing force in the changing healthcare landscape.

Big Data in Healthcare Hype and Hope

Early diagnosis of breast cancer

BACKGROUND
The differential diagnosis of hyponatremia is often challenging because of its association with multiple underlying pathophysiological mechanisms, diseases, and treatment options. Several algorithms are available to guide the diagnostic approach to hyponatremia, but their diagnostic and clinical utility has never been evaluated. We aimed to assess in detail the diagnostic utility as well as the limitations of the existing approaches to hyponatremia.


METHODS
Each of the 121 consecutive subjects presenting with hyponatremia (serum sodium <130 mmoL/L) underwent 3 different and independent diagnostic and therapeutic approaches: inexperienced doctor applying an established Algorithm, intensive care senior physicians acting as Senior Physician, and senior endocrinologist serving as Reference Standard.


RESULTS
The overall diagnostic agreement between Algorithm and Reference Standard was 71% (respective Cohen's kappa and delta values were 0.64 and 0.70), the overall diagnostic agreement between Senior Physician and Reference Standard was 32% (0.20 and 0.19, respectively). Regarding the therapeutic consequences, the diagnostic accuracy of the Algorithm was 86% (0.70 and 0.72, respectively) and of the Senior Physician was 48% (0.01 and 0.04, respectively). In retrospect, by disregarding the patient's extracellular fluid volume and assessing the effective arterial blood volume by determination of the fractional urate excretion, the Algorithm improved its diagnostic accuracy to 95%.


CONCLUSION
Although the Algorithm performed reasonably well, several shortcomings became apparent, rendering it difficult to apply the Algorithm without reservation. Whether some modifications may enhance its diagnostic accuracy and simplify the management of hyponatremia needs to be determined.

Utility and limitations of the traditional diagnostic approach to hyponatremia: a diagnostic study

Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming

Big Data is a term which denotes data that is beyond storage capacity and processing capabilities of classical computer and getting some insight from large amount of data is a very big challenge at hand. Quantum Computing comes to rescue by offering a lot of promises in information processing systems, particularly in Big Data Analytics. In this paper, we have reviewed the available literature on Big Data Analytics using Quantum Computing for Machine Learning and its current state of the art. We categorized the Quantum Machine learning in different subfields depending upon the logic of their learning followed by a review in each technique. Quantum Walks used to construct Quantum Artificial Neural Networks, which exponentially speed-up the quantum machine learning algorithm is discussed. Quantum Supervised and Unsupervised machine learning and its benefits are compared with that of Classical counterpart. The limitations of some of the existing Machine learning techniques and tools are enunciated, and the significance of Quantum computing in Big Data Analytics is incorporated. Being in its infancy as a totally new field, Quantum computing comes up with a lot of open challenges as well. The challenges, promises, future directions and techniques of the Quantum Computing in Machine Learning are also highlighted.

Quantum Computing in Big Data Analytics: A Survey

Automated atrophy assessment for Alzheimer's disease diagnosis from brain MRI images.

The efficiency in breast cancer from imaging-based computer-aided diagnosis (CAD) has been revealed in recent years. As a fact, the methods grounded on a single modality constantly lack behind multimodal CAD imaging. However, owing to the restrictions of imaging devices, expressly in rural hospitals, single-modal imaging becomes a favorite in clinical practice for diagnosis. A fresh learning model trending nowadays known as learning using privileged information (LUPI) adopts additional privileged information (PI) modality to help during the training stage, but PI does not contribute in the testing stage. Meanwhile, the link exists between PI and training samples; the same is then reassigned to the learned model. We propose a LUPI-based CAD framework for breast cancer using privileged information in this work. The work offers both a classifier- or feature-level LUPI, in which the information is shifted from the additional PI modality to the diagnosis modality. A thorough comparison has been made among six classifier-level algorithms and six feature-level LUPI algorithms. The experimental results on both the acquired primary datasets show that all classifier-level and deep learning-based feature-level LUPI algorithms can enhance the performance of a single-modal imaging-based CAD for breast cancer by relocating PI.

Transfer learning privileged information fuels CAD diagnosis of breast cancer

With the advancement in the technological age, the deadly diseases threatening human survival also increase at the same pace. Breast cancer being at number two in causing the deaths among women is equally among the most curable type of cancer if diagnosed prior to time. There is an utmost thirst for diagnosis of breast cancer through an automation system in everyday health applications. This paper uses dimensionality reduction technique offered by Weka tool called WrapperSubsetEval on two benchmark cancer datasets of Wisconsin and Portuguese “Breast Cancer Digital Repository” (BCDR), on top four data mining algorithms available in literature. The final experiments carried in MATLAB and Weka demonstrated that Naive Bayes, J48, k-NN and SVM got an improvement in accuracy from 92.6186, 92.9701, 96.1336, 97.891 to 97.0123, 96.8366, 97.3638, 97.9123% in case of Wisconsin dataset and an improvement from 87.4126, 80.4196, 93.7063, 91.6084 to 89.5105, 90.9091, 97.9021, 95.1049% in case of BCDR-D01_Dataset.

Tawseef Ayoub Shaikh

Papers

Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming

Quantum Computing in Big Data Analytics: A Survey

Automated atrophy assessment for Alzheimer's disease diagnosis from brain MRI images.

Transfer learning privileged information fuels CAD diagnosis of breast cancer

Applying Machine Learning Algorithms for Early Diagnosis and Prediction of Breast Cancer Risk