The Novel Approach of Adaptive Twin Probability for Genetic Algorithm

The performance of GA is measured and analyzed in terms of its performance parameters against variations in its genetic operators and associated parameters. Since last four decades huge numbers of researchers have been working on the performance of GA and its enhancement. This earlier research work on analyzing the performance of GA enforces the need to further investigate the exploration and exploitation characteristics and observe its impact on the behavior and overall performance of GA. This paper introduces the novel approach of adaptive twin probability associated with the advanced twin operator that enhances the performance of GA. The design of the advanced twin operator is extrapolated from the twin offspring birth due to single ovulation in natural genetic systems as mentioned in the earlier works. The twin probability of this operator is adaptively varied based on the fitness of best individual thereby relieving the GA user from statically defining its value. This novel approach of adaptive twin probability is experimented and tested on the standard benchmark optimization test functions. The experimental results show the increased accuracy in terms of the best individual and reduced convergence time.Comment: 7 pages, International Journal of Advanced Studies in Computer Science and Engineering (IJASCSE), Volume 2, Special Issue 2, 201

Learning and tuning fuzzy logic controllers through reinforcements

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing

System design for health information management in rural India

The problem of child mortality in India is one of the biggest that today\u27s developing nation is dealing with. A complex matrix of different societal and behavioral forces is present at the root of this problem. Not only are absolute numbers of child deaths high in India, but the Child Mortality Rate (CMR) is also substantially higher among developing nations. Different sources of information at national as well as at state levels give feedback to the Indian Government on this nationwide issue. Looking closely at the west coast of India, the State of Maharashtra is having systematic problems in recording each child death. The way of operation of information sources and their accountability raises some serious issues about their efficiency, and the state governments are completely reliant on these sources to make decisions that save a child\u27s life. This thesis study works in the domain of the health management information system in the context of the rural region of the State of Maharashtra, India. This study documents practical experience gained in relation with user centered product design while working with Auxiliary Nurse Midwife (ANM) workers. Research was carried out to see different problems that health workers face during field work and their ways of operations to collect data on child and maternal health. Existing infrastructure for communication in the rural region of Maharashtra and the efficiency of health workers to adapt a new system are primary concerns about design decisions made in response to this issue. The intention behind this study is to create a strong network of health information and communication transfer among sufferers and helpers. A sound information system can create a foundation for correct decision making and formulation of public policies to save one complete life not lived

Power Profile Obfuscation using RRAMs to Counter DPA Attacks

Side channel attacks, such as Differential Power Analysis (DPA), denote a special class of attacks in which sensitive key information is unveiled through information extracted from the physical device executing a cryptographic algorithm. This information leakage, known as side channel information, occurs from computations in a non-ideal system composed of electronic devices such as transistors. Power dissipation is one classic side channel source, which relays information of the data being processed. DPA uses statistical analysis to identify data-dependent correlations in sets of power measurements. Countermeasures against DPA focus on hiding or masking techniques at different levels of design abstraction and are typically associated with high power and area cost. Emerging technologies such as Resistive Random Access Memory (RRAM), offer unique opportunities to mitigate DPAs with their inherent memristor device characteristics such as variability in write time, ultra low power (0.1-3 pJ/bit), and high density (4F2). In this research, an RRAM based architecture is proposed to mitigate the DPA attacks by obfuscating the power profile. Specifically, a dual RRAM based memory module masks the power dissipation of the actual transaction by accessing both the data and its complement from the memory in tandem. DPA attack resiliency for a 128-bit AES cryptoprocessor using RRAM and CMOS memory modules is compared against baseline CMOS only technology. In the proposed AES architecture, four single port RRAM memory units store the intermediate state of the encryption. The correlation between the state data and sets of power measurement is masked due to power dissipated from inverse data access on dual RRAM memory. A customized simulation framework is developed to design the attack scenarios using Synopsys and Cadence tool suites, along with a Hamming weight DPA attack module. The attack mounted on a baseline CMOS architecture is successful and the full key is recovered. However, DPA attacks mounted on the dual CMOS and RRAM based AES cryptoprocessor yielded unsuccessful results with no keys recovered, demonstrating the resiliency of the proposed architecture against DPA attacks

Unusual Large Sporadic Angiomyolipoma Co-existing with Huge Simple Renal Cyst

Renal Angiomyolipoma (AML) is an unusual benign mesenchymal tumor with no malignant potential. It is composed of adipose tissue, smooth muscle and abnormal thick walled blood vessels. It can occur sporadically or may be associated with tuberous sclerosis. Sporadic angiomyolipoma (AML) coexisting with simple renal cyst is extremely rare and only one case report is available in the literature. In our case, unique combination of sporadic AML along with simple renal cyst with huge size and weight was noted. To the best of our knowledge, ours is the second such case and first case from India. Due to its large size, complete nephrectomy was performed to avoid chances of rupture and retroperitoneal hemorrhage. Post-operative period was uneventful and the patient ahs been on regular follow-up

An Automated System for Depression Detection Based on Facial and Vocal Features

Diagnosing depression is a challenge due to the subjective nature of traditional tools like questionnaires and interviews. Researchers are exploring alternative methods for detecting depression, such as using facial and vocal features. This study investigated the potential of facial and vocal features for depression detection using two datasets: images of facial expressions with emotion labels, and a vocal expression dataset with positive and negative words. Four deep-learning models were evaluated for depression detection from facial expressions, and two traditional machine-learning models were trained for sentiment analysis on the vocal expression dataset. The CNN model performed best for facial expression analysis, while the Naive Bayes model performed best for vocal expression analysis. The models were integrated into a web application for depression analysis, allowing users to upload a video and receive an analysis of their facial and vocal expressions for signs of depression. This study demonstrates the potential of using facial and vocal features for depression detection and provides insight into the performance of different machine learning algorithms for this task. The web application has the potential to be a useful tool for individuals monitoring their mental health and may support mental health professionals in their clinical assessments of depression

Effect of nanomaterial properties on thermal conductivity of heat transfer fluids and nanomaterial suspension

This paper was presented at the 4th Micro and Nano Flows Conference (MNF2014), which was held at University College, London, UK. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute, ASME Press, LCN London Centre for Nanotechnology, UCL University College London, UCL Engineering, the International NanoScience Community, www.nanopaprika.eu.Energy has been rated as the single most important issue facing humanity in the current as well as next 50 years. Securing clean energy has become the top priority of most developed countries. Considering the rapid increase in energy demand worldwide, intensifying the heat transfer process and reducing energy loss due to ineffective use have become an increasingly important task. Fundamentally, energy conversion and transportation occur at atomic or molecular levels, Nanoscience and nanotechnology are expected to play a significant role in revitalizing the traditional energy industries and stimulating the emerging renewable energy industries. Nanofluid is a modern engineering heat transfer fluid with superior potential for enhancing the heat transfer performance of conventional fluids such as water, ethylene glycol and oils. It is consisting of solid nanoparticles with sizes typically of 1–100 nm suspended in base fluids. Many attempts have been made to investigate its important thermal properties, i.e. thermal conductivity; however, no definitive agreements and idea have emerged about this property. This article reports the effect of different nanomaterial on the thermal conductivity enhancement of nanofluids experimentally. TiO2, Fe3O4 and Al2O3 nanoparticles dispersed in water and ethylene glycol with volume concentration of 1 – 7.5 vol. % is used in the present study. A transient hot-wire apparatus (KD2 pro) is used for measuring the thermal conductivity of nanofluids. The results show that all the heat transfer fluids show an increase in thermal conductivity with the addition of nanoparticles in it. The measured thermal conductivity of nanofluids increased as the particle concentrations increased and are higher than the values of the base liquids. This confirms the effect of volume concentration of nanoparticles on the thermal conductivity enhancement