Detection And Prevention Of Types Of Attacks Using Machine Learning Techniques In Cognitive Radio Networks

A number of studies have been done on several types of data link and network layer attacks and defenses for CSS in CRNs, but there are still a number of challenges unsolved and open issues waiting for solutions. Specifically, from the perspective of attackers, when launching the attack, users have to take into account of the factors of attack gain, attack cost and attack risk, together.  From the perspective of defenders, there are also three aspects deserving consideration: defense reliability, defense efficiency and defense universality. The attacks and defenses are mutually coupled from each other. Attackers need to adjust their strategies to keep their negative effects on final decisions and avoid defenders’ detection, while defenders have to learn and analyze attack behaviors and designs effective defense rules. Indeed, attack and defense ought to be considered together. the proposed methodology overcomes the problems of several data link and network layer attacks and it effects in CSS(Co-operative Spectrum Sensing) of CNRs using Machine Learning based Defense, Cross layers optimization techniques and Defence based Prevention mechanisms

A Study on Techniques/Algorithms used for Detection and Prevention of Security Attacks in Cognitive Radio Networks

In this paper a detailed survey is carried out on the taxonomy of Security Issues, Advances on Security Threats and Countermeasures ,A Cross-Layer Attack, Security Status and Challenges for Cognitive Radio Networks, also a detailed survey on several Algorithms/Techniques used to detect and prevent SSDF(Spectrum Sensing Data Falsification) attack a type of DOS (Denial of Service) attack and several other  Network layer attacks in Cognitive Radio Network or Cognitive Radio Wireless Sensor Node Networks(WSNN’s) to analyze the advantages and disadvantages of those existing algorithms/techniques

PLASMA TRACE ELEMENT STATUS IN BETA-THALASSEMIA TRAIT PATIENTS

Objective: The study aims to evaluate the plasma trace element status in beta-thalassemia trait (BTT) patients and demonstrate the correlation between trace elements and hemoglobin (Hb)-A1, HbA2, and HbF.Methods: The study population consisted of 20 normal individuals and 40 patients with BTT aged between 25 and 55 years of both the sex. Hemoglobin variant analysis was performed in blood samples using cation exchange high-performance liquid chromatography. Patients were labeled as beta-thalassemia trait only if they had HbA2 >3.5% or HbF >2.0%. Plasma zinc was estimated by atomic absorption spectrometry, copper by bathocuproine disulfonate method, and iron by ferrozine method spectrophotometrically. Data were analyzed statistically by Student's t -test and Pearson's correlation using software SPSS version 20. p<0.05 was considered statistically significant.Results: Plasma zinc and copper decreased significantly in BTT patients compared to healthy controls. There was an apparent decrease in plasma iron in these patients. Degree of decline in zinc (p<0.001) was much greater than that of copper (p<0.04). Moreover, there was significant elevation of copper-zinc ratio and iron-zinc ratio (p<0.001) in BTT patients compared to controls. Both HbA1 and HbA2 correlated positively with both copper and iron. Interestingly, HbF showed negative correlation with all the three trace elements in BTT patients.Conclusion: The study highlights marked deficiency of plasma trace elements, hence, the need for periodic assessment and prompt administration of these micronutrients to reduce the complications in BTT patients. Further, ratios are more valuable markers in the determination of trace element status than individual elements

Selection of traits for seed yield improvement through variability parameters in sesame (Sesamum indicum L.) genotypes.

Sesame (Sesamum indicum L.) was renowned in the ancient ages for the stability of its healthy oil, accessibility of extraction, and drought resilience. Consequently, to produce novel varieties, the present work entails the utilization of basic metrics of variability and genetic variation. The investigation included 60 sesame genotypes and 13 quantitative characteristics. The analysis of variance revealed that genotypes were significant for all of the parameters assessed (P>0.05 and P>0.01). The average seed yield per plant was 6.93g, ranging from 4.84 to 9.17g. In all of the traits studied, the phenotypic coefficient of variation (PCV) was greater than the genotypic coefficient of variance (GCV). The tense relationship between PCV and GCV values indicated that the environment had minimal impact on the expression of the traits under investigation. The no. of branches, no. of capsules/axil, capsules per plant, no. of seeds per capsule, and oil content suggested higher estimations of PCV and GCV. The heritability varied from 75.13% for seed width to 99.03% for no. of capsules/axil. The genetic advance (GA) percent means varied from 7.71 (seed length) to 110.93 (no. of capsules/axil) at 5% selection intensity. plant height, no. of branches, 1000 seed weight, capsule length, capsules per plant, no. of seeds per capsule, seed thickness, oil content, and seed yield per plant all showed substantial heredity and a high GA% mean. Because of this, the traits of additive genetic control and direct selection in sesame have a high potential for increasing its yield

Fetus in fetu in the scrotal sac of newborn

Fetus in fetu (FIF) in the scrotal sac is often overlooked in the differential diagnosis of other scrotal swellings in infants and children. Unlike  teratoma, FIF is a benign disorder. Scrotal FIF is extraordinarily rare and has been reported only twice in the international literature. We describe the case of a 15-day-old neonate who presented with scrotal swelling since birth. Radiography and ultrasound revealed a FIF. On surgical removal and pathological evaluation, the anencephalic fetus had limb buds, male external genitals, and vertebral column, supporting the diagnosis of FIF. FIF in the scrotum is a very rare entity. It should be  differentiated from other scrotal swellings such as teratoma because of the malignant potential of the latter. Preoperative diagnosis is based on radiologic findings. The treatment is complete excision.Keywords: fetus in fetu, pluripotent cells, scrotal swelling, scrotal teratom

QUANTITATION OF METFORMIN IN URINE BY RP-HPLC METHOD AND ITS APPLICATION IN PHARMACOKINETICS

Objective: To develop and validate an easy and sensitive HPLC method for quantitation of metformin in urine. Methods: The technique involved deproteinisation of urine sample with methanol and analysis of the supernatant the usage of Zorbax 300–SCX, 4.6 X 150 mm ID, 5 µm particle size and UV detection at a wavelength of 233 nm. Results: The assay was specific for metformin and linear from 1.25 to 50.0μg/ml. The relative standard deviation of intra-and inter-day assays was lesser than 7%. The recovery of metformin from urine ranged from 97-103%. Conclusion: An easy and sensitive HPLC approach for quantitation of metformin in urine had been developed. Due to its simplicity in sample preparation and instrumentation, this technique can be used for pharmacokinetic studies of metformin in urine samples

CHARACTERIZATION OF Pr6O11 DOPED ZINC FLUOROBORATE GLASS

Pr3+ doped zinc fluoroborate glasses with the chemical composition [(mol %) 30ZnF2 – 20 TeO2 – (50-x) B2O3 – xPr6O11] (where x = 0.0, 0.1, 0.5, 1.0 and 1.5 mol %) of different concentration of rare earth element praseodymium (Pr3+) have been prepared by conventional melt quench technique. The physical parameters like density, refractive index, number density, molar refraction, molar electronic polarizability, electronic polarizability, dielectric constant, polaron radius, inter–ionic separation, molar volume and oscillator strength of these glasses were calculated as a function of dopant concentration. The densities and refractive indices of these glasses were found to be in the range 2.0 g/cm3 - 2.67 g/cm3 and 1.644 – 1.73 respectively. Stability of the glass doped with 1.0 mol % Pr3+ was found to be 120

The Study of Electron Beam Irradiation on Eu2O3 Doped Pbf2 - Teo2 - B2O3 Glasses

We report here the combined structural, optical andthermal studies carried out for the prepared Eu2O3 doped PbF2-TeO2-B2O3 glass [built using melting molding technique] samplesbefore and after being subjected to electron beam of energy 7.5MeV [Dose: 150 kGy], containing 0-2.5 mol% of Eu2O3. Theincrease in densities of the samples both before and afterirradiation has been understood, mainly due to the UV-VIS-NIRdata, energy band gap (Eg) values and the characteristictemperatures obtained from Differential Scanning Calorimetry(DSC) study. The decrement in the values of energy band gapand the shifting of the absorption cut-off wavelength of UV-VISNIRspectra towards red edge, proved the formation of colorcenters in the glass network after irradiation. The changes in thephysical appearance of the glasses from light yellow to deepyellowish color after irradiation were studied through colormeasurement. The change in Hunter L values after irradiation,through color measurement was a proof for the color centers orabsorption centers created in the glass sites. Glass transformationtemperatures [glass transition (Tg), onset of crystallization (Tx),crystallization (Tc) and melting temperature (Tm)] throughDifferential Scanning Calorimetry (DSC), were analyzed to studythe thermal effects on amorphized borate glasses and check itsreaction on stability and vitreous network of the glass samplesboth before and after electron beam irradiation

Prediction of stroke using deep learning model

© Springer International Publishing AG 2017. Many predictive techniques have been widely applied in clinical decision making such as predicting occurrence of a disease or diagnosis, evaluating prognosis or outcome of diseases and assisting clinicians to recommend treatment of diseases. However, the conventional predictive models or techniques are still not effective enough in capturing the underlying knowledge because it is incapable of simulating the complexity on feature representation of the medical problem domains. This research reports predictive analytical techniques for stroke using deep learning model applied on heart disease dataset. The atrial fibrillation symptoms in heart patients are a major risk factor of stroke and share common variables to predict stroke. The outcomes of this research are more accurate than medical scoring systems currently in use for warning heart patients if they are likely to develop stroke

Density Matrix Renormalization Group Study of the Spin 1/2 Heisenberg Ladder with Antiferromagnetic Legs and Ferromagnetic Rungs

The ground state and low lying excitation of the spin 1/2 Heisenberg ladder with antiferromagnetic leg ($J$) and ferromagnetic rung ($-\lambda J, \lambda >0$) interaction is studied by means of the density matrix renormalization group method. It is found that the state remains in the Haldane phase even for small $\lambda \sim 0.02$ suggesting the continuous transition to the gapless phase at $\lambda = 0$. The critical behavior for small $\lambda$ is studied by the finite size scaling analysis. The result is consistent with the recent field theoretical prediction.Comment: 11 pages, revtex, figures upon reques