This note derives the Golden Rule of capital accumulation in a Chakraborty-type economy, i.e. a two-period OLG economy where longevity is endogenous. It is shown that the capital per worker maximizing steady-state consumption per head is inferior to the Golden Rule capital level prevailing under exogenous longevity. We characterize also the Lifetime Golden Rule, that is, the capital per worker maximizing steady-state expected lifetime consumption per head, and show that this tends to exceed the standard Golden Rule capital level.

On the Golden Rule of capital accumulation under endogenous longevity

This﻿article﻿describes﻿how﻿sequential﻿data﻿modeling﻿is﻿a﻿relevant﻿task﻿in﻿Cybersecurity.﻿Sequences﻿are﻿ attributed﻿temporal﻿characteristics﻿either﻿explicitly﻿or﻿implicitly.﻿Recurrent﻿neural﻿networks﻿(RNNs)﻿are﻿ a﻿subset﻿of﻿artificial﻿neural﻿networks﻿(ANNs)﻿which﻿have﻿appeared﻿as﻿a﻿powerful,﻿principle﻿approach﻿ to﻿learn﻿dynamic﻿temporal﻿behaviors﻿in﻿an﻿arbitrary﻿length﻿of﻿large-scale﻿sequence﻿data.﻿Furthermore,﻿ stacked﻿recurrent﻿neural﻿networks﻿(S-RNNs)﻿have﻿the﻿potential﻿to﻿learn﻿complex﻿temporal﻿behaviors﻿ quickly,﻿including﻿sparse﻿representations.﻿To﻿leverage﻿this,﻿the﻿authors﻿model﻿network﻿traffic﻿as﻿a﻿time﻿ series,﻿particularly﻿transmission﻿control﻿protocol﻿/﻿internet﻿protocol﻿(TCP/IP)﻿packets﻿in﻿a﻿predefined﻿ time﻿ range﻿ with﻿ a﻿ supervised﻿ learning﻿ method,﻿ using﻿ millions﻿ of﻿ known﻿ good﻿ and﻿ bad﻿ network﻿ connections.﻿To﻿find﻿out﻿the﻿best﻿architecture,﻿the﻿authors﻿complete﻿a﻿comprehensive﻿review﻿of﻿various﻿ RNN﻿architectures﻿with﻿its﻿network﻿parameters﻿and﻿network﻿structures.﻿Ideally,﻿as﻿a﻿test﻿bed,﻿they﻿ use﻿the﻿existing﻿benchmark﻿Defense﻿Advanced﻿Research﻿Projects﻿Agency﻿/﻿Knowledge﻿Discovery﻿ and﻿Data﻿Mining﻿(DARPA)﻿/﻿(KDD)﻿Cup﻿‘99’﻿intrusion﻿detection﻿(ID)﻿contest﻿data﻿set﻿to﻿show﻿the﻿ efficacy﻿of﻿these﻿various﻿RNN﻿architectures.﻿All﻿the﻿experiments﻿of﻿deep﻿learning﻿architectures﻿are﻿ run﻿up﻿to﻿1000﻿epochs﻿with﻿a﻿learning﻿rate﻿in﻿the﻿range﻿[0.01-0.5]﻿on﻿a﻿GPU-enabled﻿TensorFlow﻿and﻿ experiments﻿of﻿traditional﻿machine﻿learning﻿algorithms﻿are﻿done﻿using﻿Scikit-learn.﻿Experiments﻿of﻿ families﻿of﻿RNN﻿architecture﻿achieved﻿a﻿low﻿false﻿positive﻿rate﻿in﻿comparison﻿to﻿the﻿traditional﻿machine﻿ learning﻿classifiers.﻿The﻿primary﻿reason﻿is﻿that﻿RNN﻿architectures﻿are﻿able﻿to﻿store﻿information﻿for﻿ long-term﻿dependencies﻿over﻿time-lags﻿and﻿to﻿adjust﻿with﻿successive﻿connection﻿sequence﻿information.﻿ In﻿addition,﻿the﻿effectiveness﻿of﻿RNN﻿architectures﻿are﻿shown﻿for﻿the﻿UNSW-NB15﻿data﻿set. KEywoRDS Deep Learning (DL) Approaches, Gated Recurrent Unit (GRU), Intrusion Detection (ID) Data Sets, KDDCup ’99’, Long Short-Term Memory (LSTM), Machine Learning (ML), Recurrent Neural Network (RNN), UNSW-NB15

Evaluation of Recurrent Neural Network and its Variants for Intrusion Detection System (IDS)

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

Economic profit is the main governing power of any industrial and socio-economic growth. It is imperative to maximize profit related with economic systems to retain economic stability. Traditional methods involving Lewis model has been found to be unsuitable in terms of computational complexity. Motivated by the recent developments and successful application of meta-heuristic algorithms in achieving potent solutions, the present work proposed efficient meta-heuristic algorithm to support the profit maximization formalism. The genetic algorithm (GA) is employed to maximize the profit in terms of the total revenue (TR) and total cost (TC). The real parameter objective function depicting profit has been gradually optimized by GA. Experimental results suggest that the GA based profit maximization method is extremely fast, accurate and robust.

Efficient Economic Profit Maximization: Genetic Algorithm Based Approach

The current study deals with maximizing consumption per worker in connection with the economic growth of society The traditional Solow model based approach is well-studied and computationally complex The present work proposes a Genetic Algorithm (GA) based consumption maximization in attaining the Golden rule An objective function derived from traditional Solow model based on depreciation rate and amount of accumulated capital is utilized The current study considered a constant output per worker to incorporate a constant efficiency level of labor Different ranges of Depreciation rate and accumulated capital are tested to check the stability of the proposed GA based optimization process The mean error and standard deviation in optimization process is utilized as a performance metric The experimental results suggested that GA is very fast and is able to produce economically significant result with an average mean error 0142% and standard deviation 0021%

/pdf/towards-golden-rule-of-capital-accumulation-a-genetic-ywn746jy4g.pdf

Rhitaban Nag

Papers

Efficient Economic Profit Maximization: Genetic Algorithm Based Approach

Towards Golden Rule of Capital Accumulation: A Genetic Algorithm Approach