Detection of K+ mesons in segmented electromagnetic calorimeters

The combination of the CrystalBall and TAPS electromagnetic calorimeters were installed in the MAMI A2 hall in 2003. Here they are able to detect the reaction products from photo-induced reactions in combination with the Glasgow photon tagger. In the last two years the MAMI facility was upgraded from 885 MeV to 1.5 GeV, the A2 photon tagger underwent a similar upgrade crossing the threshold for strangeness photoproduction. For the CrystalBall this created a new challenge, to identify K+ mesons above the large background from other charged hadrons, in a situation where the detector setup does not benefit from a magnetic field to help separate particle species. These proceedings outline a novel technique which uses the decay products of the K+ as a strangeness tag

Assessment of periodontal health status among Koraga tribes residing in Mangalore taluk: a cross sectional study

 Background: To assess the periodontal health status among Koraga tribal community residing in Mangalore Taluk.Methods: The study subjects comprised of 400 Koraga tribal’s in the age range of 20-55 years living in Mangalore Taluk. The data regarding oral hygiene practices prevalent in the tribal population was collected by interviewing. Intra oral examination was carried out by using mouth mirror and CPI probe and included simplified oral hygiene index (OHI-S), community periodontal index (CPI), loss of attachment and dental aesthetic index (DAI).Results: Of the total population examined, 81% brushed once daily with 34% of the subjects using tooth paste and brush as oral hygiene aid while, the rest of them used a combination, with other indigenous methods. Majority of them used tobacco in the smokeless form (36%). The oral hygiene status was poor in 56% of the subjects. The present study showed that majority of the Koragas suffered from various gingival and periodontal diseases as assessed by community periodontal index. The dental aesthetic index indicated that 37.5 % of study subjects had very severe malocclusion.Conclusions: This group of people has a poor oral hygiene and periodontal status because they are deprived of the awareness and availability of treatment facilities. Their inappropriate oral hygiene practices, inadequate dental health resources and low socio-economic status are the major factors in this population to cause increased prevalence of periodontal disease

Image Steganography using Hybrid Edge Detector and Ridgelet Transform

Steganography is the art of hiding high sensitive information in digital image, text, video, and audio. In this paper, authors have proposed a frequency domain steganography method operating in the Ridgelet transform. Authors engage the advantage of ridgelet transform, which represents the digital image with straight edges. In the embedding phase, the proposed hybrid edge detector acts as a preprocessing step to obtain the edge image from the cover image, then the edge image is partitioned into several blocks to operate with straight edges and Ridgelet transform is applied to each block. Then, the most significant gradient vectors (or significant edges) are selected to embed the secret data. The proposed method has shown the advantages of imperceptibility of the stego image is increased because the secret data is hidden in the significant gradient vector. Authors employed the hybrid edge detector to obtain the edge image, which increases the embedding capacity. Experimental results demonstrates that peak signal-to-noise (PSNR) ratio of stego image generated by this method versus the cover image is guaranteed to be above 49 dB. PSNR is much higher than that of all data hiding techniques reported in the literature.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.214-219, DOI: http://dx.doi.org/10.14429/dsj.65.787

Training feedforward neural network using genetic algorithm to diagnose left ventricular hypertrophy

In this research work, a new technique was proposed for the diagnosis of left ventricular hypertrophy (LVH) from the ECG signal. The advanced imaging techniques can be used to diagnose left ventricular hypertrophy, but it leads to time-consuming and more expensive. This proposed technique overcomes thesef issues and may serve as an efficient tool to diagnose the LVH disease. The LVH causes changes in the patterns of ECG signal which includes R wave, QRS and T wave. This proposed approach identifies the changes in the pattern and extracts the temporal, spatial and statistical features of the ECG signal using windowed filtering technique. These features were applied to the conventional classifier and also to the neural network classifier with the modified weights using a genetic algorithm. The weights were modified by combining the crossover operators such as crossover arithmetic and crossover two-point operator. The results were compared with the various classifiers and the performance of the neural network with the modified weights using a genetic algorithm is outperformed. The accuracy of the weights modified feedforward neural network is 97.5%

Iris Image Recognition using Optimized Kohonen Self Organizing Neural Network

The pursuit to develop an effective people management system has widened over the years to manage the enormous increase in population. Any management system includes identification, verification and recognition stages. Iris recognition has become notable biometrics to support the management system due to its versatility and non-invasive approach. These systems help to identify the individual with the texture information distributed around the iris region. Many classification algorithms are available to help in iris recognition. But those are very sophisticated and require heavy computation. In this paper, an improved Kohonen self-organizing neural network (KSONN) is used to boost the performance of existing KSONN. This improvement is brought by the introduction of optimization technique into the learning phase of the KSONN. The proposed method shows improved accuracy of the recognition. Moreover, it also reduces the iterations required to train the network. From the experimental results, it is observed that the proposed method achieves a maximum accuracy of 98% in 85 iterations

A systematic review on machine learning models for online learning and examination systems

Examinations or assessments play a vital role in every student’s life; they determine their future and career paths. The COVID pandemic has left adverse impacts in all areas, including the academic field. The regularized classroom learning and face-to-face real-time examinations were not feasible to avoid widespread infection and ensure safety. During these desperate times, technological advancements stepped in to aid students in continuing their education without any academic breaks. Machine learning is a key to this digital transformation of schools or colleges from real-time to online mode. Online learning and examination during lockdown were made possible by Machine learning methods. In this article, a systematic review of the role of Machine learning in Lockdown Exam Management Systems was conducted by evaluating 135 studies over the last five years. The significance of Machine learning in the entire exam cycle from pre-exam preparation, conduction of examination, and evaluation were studied and discussed. The unsupervised or supervised Machine learning algorithms were identified and categorized in each process. The primary aspects of examinations, such as authentication, scheduling, proctoring, and cheat or fraud detection, are investigated in detail with Machine learning perspectives. The main attributes, such as prediction of at-risk students, adaptive learning, and monitoring of students, are integrated for more understanding of the role of machine learning in exam preparation, followed by its management of the post-examination process. Finally, this review concludes with issues and challenges that machine learning imposes on the examination system, and these issues are discussed with solutions