This paper presents a neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD). The model is trained with the demographic data and medical care information of two population groups: on the one hand, people diagnosed with CKD in Colombia during 2018, and on the other, a sample of people without a diagnosis of this disease. Once the model is trained and evaluation metrics for classification algorithms are applied, the model achieves 95% accuracy in the test data set, making its application for disease prognosis feasible. However, despite the demonstrated efficiency of the neural networks to predict CKD, this machine-learning paradigm is opaque to the expert regarding the explanation of the outcome. Current research on eXplainable AI proposes the use of twin systems, where a black-box machine-learning method is complemented by another white-box method that provides explanations about the predicted values. Case-Based Reasoning (CBR) has proved to be an ideal complement as this paradigm is able to find explanatory cases for an explanation-by-example justification of a neural network's prediction. In this paper, we apply and validate a NN-CBR twin system for the explanation of CKD predictions. As a result of this research, 3,494,516 people were identified as being at risk of developing CKD in Colombia, or 7% of the total population.

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Explainable Prediction of Chronic Renal Disease in the Colombian Population Using Neural Networks and Case-Based Reasoning

A mosquito borne pathogen called Dengue virus (DENV) has been emerged as one of the most fatal threats in the recent time. Infections can be in two main forms, namely the DF (Dengue Fever), and DHF (Dengue Hemorrhagic Fever). An efficient detection method for both fever types turns out to be a significant task. Thus, in the present work, a novel application of Particle Swarm Optimization (PSO) trained Artificial Neural Network (ANN) has been employed to separate the patients having Dengue fevers from those who are recovering from it or do not have DF. The ANN’s input weight vector are optimized using PSO to achieve the expected accuracy and to avoid premature convergence toward the local optima. Therefore, a gene expression data (GDS5093 dataset) available publicly is used. The dataset contains gene expression data for DF, DHF, convalescent and healthy control patients of total 56 subjects. Greedy forward selection method has been applied to select most promising genes to identify the DF, DHF and normal (either convalescent or healthy controlled) patients. The proposed system performance was compared to the multilayer perceptron feed-forward neural network (MLP-FFN) classifier. Results proved the dominance of the proposed method with achieved accuracy of 90.91 %.

Dengue Fever Classification Using Gene Expression Data: A PSO Based Artificial Neural Network Approach

Intelligent Computing in Medical Imaging: A Study

Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm — II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features.

Detection of skin disease using metaheuristic supported artificial neural networks

The current work proposes a neural based detection method of two different skin diseases using skin imaging. Skin images of two diseases namely Basel Cell Carcinoma and Skin Angioma are utilized. SIFT feature extractor has been employed followed by a clustering phase on feature space in order to reduce the number of features suitable for neural based models. The extracted bag-of-features modified dataset is used to train metaheuristic supported hybrid Artificial Neural Networks to classify the skin images in order to detect the diseases under study. A well-known multi objective optimization technique called Non-dominated Sorting Genetic Algorithm — II is used to train the ANN (NN-NSGA-II). The proposed model is further compared with two other well-known metaheuristic based classifier namely NN-PSO (ANN trained with PSO) and NN-CS (ANN trained with Cuckoo Search) in terms of testing phase confusion matrix based performance measuring metrics such as accuracy, precision, recall and F-measure. Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model.

Image based skin disease detection using hybrid neural network coupled bag-of-features

Object recognition can be done based on local feature description algorithm or through global feature description algorithm. Both types of these descriptors have the efficiency in recognizing an object quickly and accurately. The proposed work judges their performance in different circumstances such as rotational effect scaling effect, illumination effect and blurring effect. Authors also investigate the speed of each algorithm in different situations. The experimental result shows that each one has some advantages as well as some drawbacks. SIFT (Scale Invariant Feature Transformation) and SURF (Speeded Up Robust Features) performs relatively better under scale and rotation change. MSER (Maximally stable extremal regions) performs better under scale change, MinEigen in affine change and illumination change while FAST (Feature from Accelerated segment test) and SURF consume less time.

Analysis of Different Feature Description Algorithm in object Recognition

In the present work, a genetic algorithm (GA) trained neural network (NN)-based model has been proposed to detect chronic kidney disease (CKD) which has become one of the newest threats to the developing and undeveloped countries. Studies and surveys in different parts of India have suggested that CKD is becoming a major concern day by day. The financial burden of the treatment and future consequences of CKD could be unaffordable to many, if not detected at an earlier stage. Motivated by this, the NN-GA model has been proposed which significantly overcomes the problem of using local search-based learning algorithms to train NNs. The input weight vector of the NN is gradually optimized by using GA to train the NN. The model has been compared with well-known classifiers like Random Forest, Multilayer Perception Feedforward Network (MLP-FFN), and also with NN. The performance of the classifiers has been measured in terms of accuracy, precision, recall, and F-Measure. The experimental results suggest that NN-GA-based model is capable of detecting CKD more efficiently than any other existing model.

Detection of Chronic Kidney Disease: A NN-GA-Based Approach

AbstractCryptocurrency has become a buzzword globally. The number of stakeholders in the cryptocurrency takeover has increased at an unprecedented level and what was once mocked and speculated upon only as a theoretical construct may now very well be implemented globally. The ethos guiding the cryptocurrency revolution is in stark contrast with the existing economic structure and thus appears to be in direct competition with the existing fiat currencies. The authors ponder upon the regulatory challenges related to cryptocurrencies specifically with the lens of competition law in this article and suggest some probable remedies which might help tame this volatile resource.KeywordsCryptocurrencyDigital economyCompetitionDigital market 

Competition Between Cryptocurrency and Fiat Currency: Control Over the Future of Global Economy

Since its introduction, blockchain technology has been revered, ridiculed, dismissed, embraced, and presently has become too large to ignore, witnessing exponential growth. The obvious indicator of this growth is that research revolving around blockchain technology has already raised competition in the form of directed acyclic graphs and hashgraph, all of which fall under the umbrella of distributed ledger technology (DLT). Segueing on the back of visibly positive effects of competition, we arrive at the essence of our paper. We show that the current competition regimes around the world are inefficient at promoting and maintaining competition around the world, dominated by the behemoth technology enterprises that have successfully monopolized and monetized data, which is indubitably, one of the most important assets in today’s digital age. Data gathered from users fuels the algorithms, machine learning, and artificial intelligence programs employed by these tech giants, which further entrenches their monopolistic hold over cyberspace. Blockchain and DLTs, just like any other technology, pose new threats to the competition law regimes, while also allowing the authorities to utilize the technology themselves to explore new horizons involving smart contracts, Decentralized Autonomous Organizations (DAOs), Web 3.0, and enforce competition more effectively. In our paper, we briefly illustrate the challenges presented before the competition authorities by the assimilation of blockchain in the existing establishments, and how the competition authorities can themselves collude with blockchain stakeholders to take a holistic approach and establish a symbiotic relationship, which ensures that both, survive, prosper, and enhance consumer welfare.

Rahul Kumar Shaw

Papers

Analysis of Different Feature Description Algorithm in object Recognition

Detection of Chronic Kidney Disease: A NN-GA-Based Approach

Competition Between Cryptocurrency and Fiat Currency: Control Over the Future of Global Economy

Augmenting Blockchain With Competition Law for a Sustainable Economic Evolution