2,955 research outputs found
Popular matchings with two-sided preferences and one-sided ties
We are given a bipartite graph where each vertex has a
preference list ranking its neighbors: in particular, every ranks its
neighbors in a strict order of preference, whereas the preference lists of may contain ties. A matching is popular if there is no matching
such that the number of vertices that prefer to exceeds the number of
vertices that prefer to~. We show that the problem of deciding whether
admits a popular matching or not is NP-hard. This is the case even when
every either has a strict preference list or puts all its neighbors
into a single tie. In contrast, we show that the problem becomes polynomially
solvable in the case when each puts all its neighbors into a single
tie. That is, all neighbors of are tied in 's list and desires to be
matched to any of them. Our main result is an algorithm (where ) for the popular matching problem in this model. Note that this model
is quite different from the model where vertices in have no preferences and
do not care whether they are matched or not.Comment: A shortened version of this paper has appeared at ICALP 201
LIVE ENERGY METER READING - CONTINUOUS MONITORING AND ANALYSIS USING THINGSPEAK
The current energy meters do not provide any provision to monitor continuous reading about our electricity consumption. Even the provision for automatic billing system is inefficient. Recently developed automatic meter systems uses the GSM modem for transferring the data from the user to provider over particular time through SMS. It has several disadvantages that the cost of the SMS is comparatively high and does not provide continuous monitoring. However, the designed energy meter uses GPRS to transfer the data continuously. When compared to SMS facility, the cost of GPRS is low at continuous monitoring. There is no need for a person to visit every home at the end of the month for checking the unit consumption. In recent years, the numbers of smartphones and computer users have increased drastically. The users can view their current energy meter reading through the web link thingspeak.com and also pay their Electric bills through the internet. The users and providers can also monitor their energy meters online, and they are also given an option of paying the bill online. Using this system, the users will be aware of the electricity usage in his/her home. Even the provider can remotely cut the supply of the user who is not paying electricity bill in time by visiting their host page. Once the bill is paid the provider can resume the power supply. With some modifications, we can track the energy consumption of any industrial plants, offices, educational institutions, and corporates companies and hence this system may help them to save the power consumption to a greater extent
An Imperceptible Method to Monitor Human Activity by Using Sensor Data with CNN and Bi-directional LSTM
Deep learning (DL) algorithms have substantially increased research in recognizing day-to-day human activities All methods for recognizing human activities that are found through DL methods will only be useful if they work better in real-time applications. Activities of elderly people need to be monitored to detect any abnormalities in their health and to suggest healthy life style based on their day to day activities. Most of the existing approaches used videos, static photographs for recognizing the activities. Those methods make the individual to feel anxious that they are being monitored. To address this limitation we utilized the cognitive outcomes of DL algorithms and used sensor data as input to the proposed model which is collected from smart home dataset for recognizing elderly people activity, without any interference in their privacy. At early stages human activities the input for human activity recognition by DL models are done using single sensor data which are static and lack in recognizing dynamic and multi sensor data. We propose a DL architecture based on the blend of deep Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) in this research which replaces human intervention by automatically extracting features from multifunctional sensing devices to reliably recognize the activities. During the entire investigation process we utilized Tulum, a benchmark dataset that contains the logs of sensor data. We exhibit that our methodology outperforms by marking its accuracy as 98.76% and F1 score as 0.98
Generalization of Picture Fuzzy Matrix
In this paper, we introduce the concept of k-regular Picture Fuzzy Matrix (PiFuM) as a generalization of regular matrix and investigate some basic properties of a k-Regular Picture Fuzzy Matrix (k-RPiFuM). Moreover we analyze the characterization of a matrix for which the regularity index and the index are identical. Furthermore the relation between regular, k-regular and regularity of powers of picture fuzzy matrices are discussed.Publisher's Versio
Decent Love and Intuition Scenes of Kambaramayanam
Valmiki's epic has a special pride in the unparalleled epics that have ever appeared in the world. It is perfectly appropriate to say that Kambar gave life to all the characters created by Valmiki. Kambar has made the characters go for a walk in the atmosphere without lowering them with his own individuality and changing their character.The real meaning of the epic is "the sin of morality and defeat". It is very special that Kambar did not just emphasize that point in the epic, but also handled the news. When Rama and Sita devi see each other in the mithila scene in Kambaramayana, kambar's poetic beauty is able to understand the joy and suffering that occurred by the mind. When Raman sees Sita, he faints and all that he sees appears to be the form he has seen, and the sight of mayangal, monday and kaman being blamed, and wailing in his eyes, is seen in front of his mind. This article explains such events
An Exploration of Stree Shakti Programme in Karnataka through Self-Help Groups: With Special Reference to Chitradurga District
For the Empowerment of women, government has come up with many schemes. Among those, Self Help Group (SHG) is found to be the strongest route. The purpose of the Programme is to endow women economically and socially by bringing them in self-help groups, and it spent corers of rupees at a time in the project taken up for the development of women and organizing women empowerment in such huge number
Meconium stained liquor and fetal outcome: a hospital based follow up study
Background: Meconium staining of the amniotic fluid is a common problem occurring in 10-22% of all deliveries. Meconium aspiration syndrome complicates approximately 1.5% of these deliveries with a reported mortality rate as high as 40%. Objectives of the study were to determine whether meconium stained liquor during labour is associated with fetal distress and to check if there is any correlation in between the degree of meconium staining liquor and fetal outcome.Methods: A clinical study of 200 cases of meconium stained amniotic fluid in cephalic presentation was undertaken among patients who were admitted in labour room were examined in detail and investigations were done. Fetal heart rate and its pattern were noted every fifteen min. Total APGAR score at one minute and five minute was noted down. Odds ratio with 95% confidence interval was calculated. Proportions were analyzed using chi square value.Results: Majority of the study subjects belonged to the age group of 21-30 years. Majority i.e. 54.5% were multigravida. Majority had toxemia in 24% of the cases. Fetal distress and the incidence of forceps delivery or LSCS was significantly more in those with thick meconium compared to those with thin meconium (p<0.05). But fetal asphyxia was not associated with type of stained meconium. It is seen that as the duration of the meconium staining increased, the proportion of babies with low APGAR score, perinatal mortality and morbidity increased.Conclusions: Thick meconium stained amniotic fluid was associated with increased rate of interventions, neonatal morbidity and mortality compared to thin meconium stained meconium fluid
Predictive Modelling for Medical Image Analysis Using Deep Learning Techniques
Recent advancements in healthcare for the prediction of autosomal diseases have led to the usage of deep learning algorithms in analysing medical images. Autosomal diseases are an extensive group of illnesses that range from cardiovascular diseases to specific types of tumours. Leveraging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can forecast models accurately and then very efficiently, but it has limitations in finding autosomal chromosomes. The autosomal chromosome uses advanced deep-learning algorithms to analyse a database of medical images, including MRI and CT scans, to predict the onset and progression of inherited disorders. Predictive accuracy is maximized by the usage of data preparation, model training, and learning strategies. The prognosis and early finding of autosomal diseases can be greatly enhanced by algorithms for timely intervention and customized treatment for patients. Further integration of analysing medical images can give more patient care and improve disease prediction results, particularly in the case of autosomal disorders and diagnosis
Assessment of knowledge and attitude towards alternative to animal experimentation in research and education among interns and post graduate medical students in a teaching hospital, Tamilnadu, India
Background: Use of animals for various purposes like food, transportation, pets, sports, recreation and companionship is as old as the human beings itself. Animals also serve as a tool for education, research, medical procedures, toxicological screening, for several decades. Since pain, distress and death of animals occur commonly during scientific experiments, various guidelines have been proposed and posed many restrictions over the experimental use of animals.To assess the knowledge and attitude towards alternative to animal experimentation in research and education among interns and postgraduate medical students in a teaching hospital.Methods: A cross sectional study was carried out by self-administered questionnaire among interns (92) and postgraduate medical students (53) in a teaching hospital in July 2018. The data was analysed by descriptive statistics and expressed in percentage.Results: Among 145 participants, interns 92 (63.4%), postgraduates 53 (36.6%). 70.2% interns and 68.8% postgraduates had adequate knowledge about alternative animal experimentation and 67% and 67.1% of interns and postgraduates had knowledge about animal experiments. 69.4% interns and 68.8% postgraduates had positive attitude towards alternative animal experimentation.83.8% interns and 70.9% postgraduates were agreed to have various barriers to alternative animal experimentation.Conclusions: Majority of interns and postgraduates have appreciable knowledge of alternative to animal experimentation, but their attitude is scarce. It is imperative to incorporate continuous training through workshops for budding medical professionals to provide innovative scientific knowledge in research and education towards alternative to animal experimentation
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