Almost Oscillation Criteria for Second Order Neutral Difference Equations

In this paper, we consider the second order neutral difference equation of the form ∆ (an(∆zn) α ) + qnx β n−σ = en, n ≥ n0, where zn = xn + pnxn−τ and α > 0, β > 0 are the ratios of odd positive integers. Examples are provided to illustrate the results

A Sociological Perspective about Marutham Regional People

When we closely approach the lifestyle of the people of the Sangam period, the people of that time have their own lifestyles and occupations according to the backgrounds of five landforms Kurinji, Mullai, Marutham, Neithal and Paalai. The lifestyle and culture of the people of these five lands have the common elements of the people of the Sangam period, but when we approach these subtly, it is possible to know that each land's people operate with their own unique lifestyle norms, social structure, and cultural elements. The people of Marutham land, who know the occupation of plowing, do not know occupations like robbery and murder that was followed by Paalai land people.  The people of the Nellai land who caught fish in the sea do not know the professions like rice paddy and weeding. Each land people have their own unique land identity, occupation, lifestyle, worship, ritual, customs, food, dress, clothing, etc. The hypothesis of this article is to examine the life style of Marutham land people as described in Ettuthogai anthology take the way of life of the people of Marudham lands and describes about the high cultural thoughts of Tamil people

Oscillatory Behavior of Second Order Neutral Dierence Equations with Mixed Neutral Term

In this paper, we study the oscillatory behavior of solution of second order neutral dierence equation with mixed neutral term of the form(an(zn)) + qnx(n) = 0; n 2 N0; where zn = xn + bnxnô€€€l + cnxn+k and 1Ps=n01as= 1. We obtain some new oscilla-tion criteria for second order neutral dierence equation. Examples are presented to illustrate the main results

Auto Deep Learning-based Automated Surveillance Technique to Recognize the Activities in the Cyber-Physical System

In recent days, the Internet of Things (IoT) plays a significant role and increasing in rapid usage in various applications. As IoT is being developed for cyber-physical systems in the specific domain of e-health care, military, etc. Based on real-time applications, security plays a vital role in certain activities in educational institutions. In the institutions, there are multiple videos are collected and stored in the data repositories. Those datasets are developed specifically for certain activities and no other datasets are developed for academic activities. As there is a large number of videos and images are collected and considered, advanced technologies like, deep learning and IoT are used to perform certain tasks. In this paper, a Auto Deep learning-based Automated Identification Framework (DLAIF) is proposed to consider and reconsider the activities based on image pre-processing, model can be trained through the proposed GMM model and then predication to make an effective surveillance process based on HMM. This proposed process makes to recognize the activities through EM and log Likelihood for cyber-physical systems. In the performance analysis, the proposed model efficiency can be determined through Accuracy detection, False Positive rate and F1 Score requirement. Then calculating the accuracy is more effective for the proposed model compared to other existing models such as BWMP and LATTE

Integral solutions of the heptic equation with five unknowns

The non-homogeneous Diophantine equation of degree seven with five variables represented by is analyzed for its non-zero distinct integer solutions. A few interesting relation between the solutions and special numbers namely Polygonal numbers, Pyramidal numbers, centered Polygonal numbers are exhibited

EODM: On Developing Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN)

In present scenario, in machine learning technology, computer vision technology and image processing have attained a massive growth. Amongst many branches of image processing and classification, Object Detection (OD) is the major research domain. In several domains such as face detection, self-driving cars, pedestrian detection, and security surveillance systems, object detection (OD) and classification have experienced a significant surge in popularity in recent years. The conventional techniques for object detection, such as background removal, Gaussian Mixture Model (GMM), and Support Vector Machine (SVM), exhibit limitations such as object overlap, distortion caused by environmental factors including smoke, fog, and varying lighting conditions.Though there are several methods developed for OD, the respective field still stumbles upon many confrontations at the real-time implementations. Detecting objects from the undefined background is the major problem to be considered. Hence, machine learning techniques are incorporated for detecting the objects accurately, when the Neural Networks are effectively trained. With that note, this paper develops a new model, called Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN). For producing appropriate results, sensitivity Measurement is carried out based on brightness, saturation, contrast, Gaussian blur, Gaussian Noise and sharpness. Following this, FRCNN is trained for OD and the results are obtained. The model evaluations are carried out based on some evaluation factors with the acquired dataset images. The obtained results are compared with CNN, YOLO. The result shows that the model exemplifies the other compared works in terms of efficiency and accuracy

A comparative clinical study on efficacy of terbinafine and griseofulvin in patients with tinea corporis

Background: The objective of the present study was to compare the efficacy of terbinafine and griseofulvin in patients with tinea corporis in a tertiary care hospital, Madurai.Methods: About 60 patients are selected from the outpatient department of Dermatology according to inclusion and exclusion criteria. They were divided into 2 groups of 30 patients each. Group 1 received tab. terbinafine 250 mg OD and group 2 received 250 mg BD for 4 weeks. All patients were investigated at baseline, end of 2nd week and at end of 4 weeks. Effectiveness of both the drugs were determined by achieving clinical as well as mycological cure. The results were recorded, tabulated and analysed using student’s t test.Results: Patients in group 1 showed higher clinical and mycological cure rate when compared with group 2.Conclusions: Oral terbinafine is the effective antifungal agent in the treatment of extensive tinea corporis infection

EODM: On Developing Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN)

In present scenario, in machine learning technology, computer vision technology and image processing have attained a massive growth. Amongst many branches of image processing and classification, Object Detection (OD) is the major research domain. In several domains such as face detection, self-driving cars, pedestrian detection, and security surveillance systems, object detection (OD) and classification have experienced a significant surge in popularity in recent years. The conventional techniques for object detection, such as background removal, Gaussian Mixture Model (GMM), and Support Vector Machine (SVM), exhibit limitations such as object overlap, distortion caused by environmental factors including smoke, fog, and varying lighting conditions.Though there are several methods developed for OD, the respective field still stumbles upon many confrontations at the real-time implementations. Detecting objects from the undefined background is the major problem to be considered. Hence, machine learning techniques are incorporated for detecting the objects accurately, when the Neural Networks are effectively trained. With that note, this paper develops a new model, called Enhanced Object Detection Model using Fast Region-based Convolution Neural Networks (FRCNN). For producing appropriate results, sensitivity Measurement is carried out based on brightness, saturation, contrast, Gaussian blur, Gaussian Noise and sharpness. Following this, FRCNN is trained for OD and the results are obtained. The model evaluations are carried out based on some evaluation factors with the acquired dataset images. The obtained results are compared with CNN, YOLO. The result shows that the model exemplifies the other compared works in terms of efficiency and accuracy

Marine biotoxins and its detection

The incidences of intoxication due to the consumption of marine foods have been increasing in recent years. This is due to the presence of biotoxins in foods of marine origin. The biotoxins will be accumulated in the marine foods due to the consumption of toxic biota of marine origin. When this contaminated food is taken by the humans or animals, those toxins will be transferred to them causing intoxication and lethality. Among these intoxications, most of them are caused by the harmful algal blooms (HAB). In order to avoid the harmful effects from marine biotoxins, it is necessary to have the proper knowledge. In this manuscript, the different types of biotoxins, source of intoxication, characteristics of toxins, detection and control measures are discussed in detail. Key words: Harmful algal blooms, harmful algal blooms (HAB), ciguatara fish poisoning (CFP), paralytic shellfish poisoning (PSP), diarrhetic shellfish poisoning (DSP) blooming, detection

Microbial Metabolism and Inhibition Studies of Phenobarbital

Purpose: Screening scale studies were performed with eight cultures for their ability to metabolize phenobarbital, an antiepileptic, sedative, hypnotic and substrate for CYP 2C9 and 2C19.Methods: The transformation of phenobarbital was confirmed and characterized by fermentation techniques, high performance liquid chromatography (HPLC), mass spectrometry (MS) and metabolism inhibition studies.Results: Among the different cultures screened, a fungus, Rhizopus stolonifer NCIM 880, transformed phenobarbital to its metabolite, the N-glucuronide of p- hydroxy phenobarbital. HPLC data show a solvent peak at 2.4 min, culture components peaks at 4.0 and 5.4 min, respectively, and phenobarbital peak at 10.3 min, for both controls and test samples, but only the sample of Rhizopus stolonifer showed an additional peak at 3.1 min, indicating formation of a metabolite.Conclusion: Microbial metabolism of phenobarbital was similar to the metabolism of the drug in mammals. Therefore, Rhizopus stolonifer can be used as a suitable in vitro model to mimic CYP 2C9 metabolism and to synthesize metabolites required for further pharmacological and toxicological studies.Keywords: Microbial metabolism, Phenobarbital, Inhibition studies, Rhizopus stolonifer, CYP 2C9, Fenofibrat