Computer ScienceEngineering

A tsunami can be referred to series of water waves initiated by the dislocation of a large quantity of water, usually in ocean or a large lake. Tsunamis obliterate not only human population but all other species. There are many factors that have potential to cause a tsunami like Earthquakes, volcanic eruptions and other underwater explosions (including explosions of underwater nuclear devices), landslides, meteorite impacts and other disturbances above or below water. With the preface of modern science and computer technology, the field of Artificial Intelligence is screening a definite efficacy in all continuum of life. There are certain ways to predict such disasters and design different kinds of early warning systems. These can be predicted on the basis of climatic conditions and several other parameters. One such concept that is working as a boom in the fields of environmental science and policy is fuzzy logic. As fuzzy logic can be widely stated as working with the vague, imprecise data and the linguistic terms. So, this methodology can be successfully applied in the field of tsunami warning as the influence of different parameters that cause tsunami is different and can be said as vague. In this work, a fuzzy system has been developed to declare the occurrence of tsunami in terms of linguistic parameters rare_tsunami, advisory_tsunami and warning by taking the tsunami characteristics earthquake magnitude, volcanic eruption index, landslide and height of waves in deep ocean. Fuzzy rule base is generated and the results are compared with an IEEE research paper.

Water is a pivotal resource not only for human community, but for every single species existing on this earth. Water is used for different purposes, but primarily, it is used for drinking. But the rapid industrialization and urbanization is continuously increasing the pollution level in the water. This makes the drinking water quality an issue of concern. As it has been reported that one of the every five deaths of the children is due to a water-related disease. This is why continuous monitoring and control of drinking water quality is an essential factor for any human community and for this purpose, great efforts are being made with a view to develop efficient, modern and complete systems in order to ensure the water quality. In this research, we are proposing a Fuzzy based drinking water quality estimator, which will be able to tell the quality of drinking water, i.e. fully_accepted, partially_accepted or not_accepted for drinking. The WHO and US EPA databases are used in this wor...

Tsunamis are one of the most petrifying natural hazards known to man and have been accountable for enormous loss of life and property throughout history. Because of their destructiveness, tsunamis have prominent effect on the human, social and economic sectors of our societies. Several methods are used to detect tsunami and warn people before hand so that destruction can be minimized. One such method has been proposed in this study. Based on the different parameters, an alert system will be generated that will notify whether tsunami is rare, advisory or definite. Fuzzy logic has been used for the current work as we find fuzzy logic to be best for the problem as it impersonates human-style analysis of the situation and supplies varying warnings, accordingly. The system is designed using Matlab fuzzy logic toolbox. Along with the alert type of the tsunami, we analyze the behavior of five different kinds of defuzzification techniques that are available in the Matlab fuzzy logic toolbox...

Macrophage responses to infection with Mycobacterium tuberculosis (MTB) and treatment with soluble isoniazid (INH) plus rifabutin (RFB) versus microparticles containing equivalent amounts of drugs were compared. It was investigated whether macrophages driven to alternative activation upon infection with MTB could be rescued to display the classical activation phenotype. It was established that microparticles sustain high levels of drugs in cytosol of macrophages for longer period as compared to soluble drugs. Microparticles co-localized with intracellular bacteria, and induced a variety of innate bactericidal responses, including induction of free radicals, alteration of mitochondrial membrane potential and apoptosis. The data strongly suggest that additional benefit may be derived from the nature of the drug delivery system, which fulfils Koch's dictum 'stimulate the phagocyte' for curing tuberculosis.

Factors that are inimical to the success of inhaled therapies for tuberculosis (TB) include: (i) lack of access of inhaled therapies to poorly-aerated areas of the tubercular lung; (ii) limited ability to penetrate biofilms formed by extracellular bacilli; (iii) selection for resistant bacilli on account of administration of low doses of anti-TB agents; (iv) induction of inflammation and/or immunopathology in the airways and lungs; and (v) anomalies in antigen processing and presentation of vaccines delivered to the lungs. Further, the claim that inhaled therapies rescue alternatively-activated macrophages may not be applicable to all individuals. Fortunately, there are ways and means to address each of the above factors.

A hand-held apparatus designed for the purpose of administering dry powder inhalations to rodents by "Nose-only" exposure was standardized. An aerosol of microparticles containing the anti-tuberculosis drugs isoniazid and rifabutin was generated and the dose available for inhalation by rodents was determined by collecting microparticles emitted at the delivery port. Separate groups of mice received inhalations of microparticles, free rifabutin orally, or free rifabutin intravenously. Rifabutin was estimated in serum and tissues by HPLC. When ~20 mg of microparticles were loaded in the apparatus, ~2.5 mg were collected at the delivery port in 30 seconds of operation. Mice inhaled ~300 μg of the 2.5 mg emitted at the delivery port. Alveolar macrophages of mice receiving inhalations for 30 sec accumulated 0.38 μg of rifabutin, while the amount in blood serum of these mice was 0.62 μg. In mice receiving 83 μg rifabutin intravenously or orally, the intracellular amounts were 0.06 and 0.07 μg respectively, while the amounts in serum were 1.02 and 0.80 μg. These observations confirmed that inhalation of microparticles targeted lung macrophages. The dose available for inhalation was consistent with the duration of exposure (10.5-13.5 CV%) and its dependence on the amount of microparticles loaded (r 2 =0.982), and on duration of exposure (r 2 =0.992), was satisfactory.

Epidemiology is the study of spread of diseases among the group of population. If not controlled properly, the epidemic would cause
an enormous number of problems and lead to pandemic situation. Here in this paper we consider the situation of populated areas
where people live in patches. A dynamic cellular automata model for population in patches is being proposed in this paper. This
work not only explores the computing power of cellular automata in modeling the epidemic spread but also provides the pathway
in reduction of computing time when using the dynamic cellular automata model for the patchy population when compared to
the static cellular automata which is used for a nonpatchy homogeneous population.The variation of the model with movement of
population among the patches is also explored which provides an efficient way for evacuation planning and vaccination of infected
areas.

Epidemics is very important area of concern for
most our living being family in the world. Any
epidemic situation when properly not controlled
could lead to a disaster when large amount of
human population is involved. Here we propose
a fundamental model of computation in terms of
non-deterministic finite automata (NFA) for the
Susceptible-Infectives-Recovered (SIR) model.
Through this model we could prove there could
be certain languages which are epidemic regular
since it could be compared with the normal
regular languages for which we can have NFA or
regular grammar. If we could classify how the
epidemic model could behave then we could
better develop strategies that could tackle a
similar epidemic situation in future. This model
has been tested with the data of H1N1 obtained
from CDC USA

The world without a disease is a dream of any human being. The disease spread if not controlled could cause an epidemic situation to spread and lead to pandemic. To control an epidemic we need to understand the nature of its spread and the epidemic spread
model helps us in achieving this. Here we propose an epidemic spread model which considers not only the current infective
population around the population but also the infective population which remain from the previous generations for computing
the next generation infected individuals. A pushdown cellular automata model which is an enhanced version of cellular automata by adding a stack component is being used to model the epidemic spread and the model is validated by the real time data of H1N1 epidemic in Abu Dhabi.

This paper is focused on a cellular automata based computational model for the spread of disease named Leptospirosis using voting rules. Leptospirosis is most commonly found in bovine rats and the humans get infected when they come into contact
with them. The disease spread is modelled in terms of Susceptible-Infective-Recovered-Susceptible (SIRS) model through one of the efficient computational modeling tool–Cellular Automata (CA); and requires strategic change in the rule set of the traditional CA. An idea of voting based rule on the neighborhood environment is studied for modeling the Leptospirosis and compared with real data of such infection in Thailand during the year 2000 and 2001. The simulation of the model is done with real data of Leptospirosis infection in Thailand during 2000 and 2001 and the results yielded from this model were closely in match with the real time data.

Parsing is a technique for processing of sentence. Clinical parser is used for clinical narratives by using medical lexicons. These clinical narratives is in unstructured format so parser is used for converting in to structured format so that it can be readable by anyone. Clinical Parser depends on type of reports as different medical reports is having different format as well as these reports differ from hospital to hospital also. In this paper we are describing about clinical parser for different types of reports by using phrase structured grammar rules. PCFG (probabilistic context free grammar) is used for approximation of tree in which each rule is having probability.

Agriculture plays a very important role in the development of Indian economy. Farmers if aware about their surroundings can contribute widely in the agricultural development. Many improvements and enhancement have been inculcated from past to help the farmers so that optimum utilisation of resources, maintaining the fertility of soil, temperature, humidity , efficient usage of power and irrigation methods, etc can be achieved. This may not only beneficial to the famers but also to the larger section of the rural poor who are directly , indirectly involved in agricultural activities as consumers. This will provide a excellent crop productive environment which facilitates the over all rural development. Beside them, this sector is also facing the crises of lack of man power and lack of water, which adversely affects the production of crops. Use of IoT devices is one of the solution one could find in this type of environment. This paper highlights the comparative study of all the available IoT tools to monitor production of crops. As IoT has already entered the agricultural domain, itshowcased efficient ways to increase and maintain thecropproduction, balanced prices of agricultural tools, herbicides, fertilizers etc. The use of IoT devices for agriculture especially for developing and monitoring crop production hasled to the collaborated environment necessary for the development of the economy as a whole.

The issue of leakage in the irrigation pipeline of an agricultural setup not only contributes to the wastage of water but if the water becomes contaminated then its spread due to leakage may harm the crop and also add to the spread of the disease, if any, in the crop. There are many signal based as well as model based techniques for detection of leakage in the water pipeline. From the study carried out, it is evident that model-based techniques are more suitable for agriculture's irrigation setup. This research paper presents cellular automata-based leakage detection techniques in smart drip irrigation setup for preventing disease to spread in the crop by detecting the leakage, cutting the water supply to that drip pipe and sending SMS to the farmer so that timely action could be taken to treat the leakage area thereby preventing contamination and spread of water to the field preventing the spread of disease to the crop and aiding in water conservation. Moreover, the paper also demonstrates experimental results and discussion of the real time data collected from the soil moisture sensors, flow sensors, rain sensors for three different dates and different time validates the proposed work. Water is a necessary resource for life, health, economic growth and the environment globally. Since water is important to all, it is necessary to ensure its availability and quality [1]. Some of the global factors like climate change [2], population overgrowth and imbalance [3], drought [4] are playing a major role in reducing the existing fresh water resources. India's economy is an agro-based economy [5]. Around 70% of rural population of India depends on agriculture for their livelihood [6]. Income from agriculture contributes to around 17% in GDP [7]. According to researchers around 70% of the available fresh water, mainly is being consumed in agricultural activities [8]. Leaky pipes can cause flooding and water infiltration, which can lead to cause more severe problems in an agricultural setup. Most commonly, water leaks are from cracked or broken water pipes. A number of factors, including erosion and the shifting of the landscape or soil, can result in cracked pipes. Other factors which can contribute to the leakage in pipe can be due to the result of weather exposure, aging of pipelines, physical damage to the pipeline, tree roots, or other sharp instruments. Leaks can be big or small and require some techniques to detect it. The leakage problems should be immediately addressed by the replacement or repair of damaged valves and pipes so that the after effects which may occur due to water pipeline leakage can be reduced to a greater extent [9]. Some of the issues erupting as a result of contamination of water in agriculture due to leaky pipes are water stress on crop [10], pesticide runoff [11], increased erosion [12], increased nutrient levels and eutrophication resulting in algal blooms [13], waterlogging and salinization of soils [14]. The damage to the pipe has a major effect on the water quality and hydraulics of the water delivery system [15]. Broadly, two classifications exist for leakage detection and localization of water pipeline structure in irrigation setup i.e., signal based techniques and model-based techniques. In signal based techniques, observation is used as a manual process which is a time consuming process and required manual intervention and inspection by a person, which is not ideal for a large field [16], acoustic which is a traditional way of identifying leakage and its accuracy is dependent only on the placement of sensor and it is not ideal from smaller leakage [17], infrared which can be used for shorter distance with continuous monitoring [18], fibre optics which gives precise leak detection but it is suitable for shorter distance [19], tracer gas which uses combination of insoluble gases to detect the leakage but they are not suitable for old pipelines as the gas may evade through the gaps of the joints [20], and ground penetrating radar which employs electromagnetic waves to detect and locate leak in the pipeline but they are more suitable for underground pipelines and also the disturbance if any may make this technique inefficient to detect and locate leakage [21]. In model based techniques, deep learning model is employed which uses logic based learning models like CNN and ANN with limitation that higher number of hidden layers are needed for more accurate detection [22], negative pressure wave model with limitation of more false alarm [23], mass

The research paper explains the cellular automata based model that uses pressure variation, soil moisture variation, rain sensor, and GPS sensor to detect and locate plausible leak in a prototype smart drip irrigation pipeline structure. In order to discover that the possible leak is not a False Positive leak and also to obtain accurate location of leakage in the water pipeline structure, cellular automata based algorithm is used along with the Euclidean distance an also the rain sensor and GPS sensors are used for comprehensive and near accurate localization. The whole network comprises mainly of four blocks i.e., hardware and middleware block, real time data collection, storing and analysis block, pipeline leakage localization decision block, and action & communication block. If deployed in IoT based drip irrigation system in India, the farmers with the help of the proposed model will be able to remotely monitor the water pipeline structure for any plausible leaks and reduce the wastage of water and economic loss resulting from such situation.

The world without a disease is a dream of any human being. The disease spread if not controlled could cause an epidemic situation to spread and lead to pandemic. To control an epidemic we need to understand the nature of its spread and the epidemic spread model helps us in achieving this. Here we propose an epidemic spread model which considers not only the current infective population around the population but also the infective population which remain from the previous generations for computing the next generation infected individuals. A pushdown cellular automata model which is an enhanced version of cellular automata by adding a stack component is being used to model the epidemic spread and the model is validated by the real time data of H1N1 epidemic in Abu Dhabi.

Epidemiology is the study of spread of diseases among the group of population. If not controlled properly, the epidemic would cause an enormous number of problems and lead to pandemic situation. Here in this paper we consider the situation of populated areas where people live in patches. A dynamic cellular automata model for population in patches is being proposed in this paper. This work not only explores the computing power of cellular automata in modeling the epidemic spread but also provides the pathway in reduction of computing time when using the dynamic cellular automata model for the patchy population when compared to the static cellular automata which is used for a nonpatchy homogeneous population. The variation of the model with movement of population among the patches is also explored which provides an efficient way for evacuation planning and vaccination of infected areas.