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In countries like India where the density of population is more, it is very difficult to reach the hospital in case of emergency due to heavy traffic. Sometimes the communication to the hospitals for ambulance also may get delayed from the patient's end. Timely treatment is very important for heart attack patients where the delay in the treatment leads to the death of the patient. Advancement in the IoT (Internet of Things) technology motivates to present the proposed work. Here using sensors and microcontrollers, a smart health monitoring system is proposed which can alert the hospitals in case of emergency and further schedules ambulance facilities. Devices are connected to the patient for the purpose of regular health monitoring and alerting the hospitals during emergency.

Antibiotics known as the drugs of wonder seem to have long run since olden times as significant in its application towards agriculture. Antibiotics have its application to control bacterial, fungal, viral and phytoplasmal diseases of high valued tree crops and plants of ornamental in nature. The laws of drug in various countries differ distress over use of antibiotics as crop protectants. The main concern related to use of antibiotics is appropriate and hence more information is needed over the effectiveness and safety use of antibiotics in controlling plant diseases. Development of antibiotic resistance in plant-pathogenic bacteria alarmed problem in the agro pathosystems where it have been used for many years in disease control programs. The efficacy of antibiotics to control plant diseases has been diminished due to the emergence of antibiotic-resistant strains for the particular pathogens. Inspite of negative aspect, the antibiotics seems to continue important tools for the management of the important devastating plant diseases.

Data mining techniques are used to extract useful patterns from a large data set. k-mean algorithm is one of the most famous partitioning clustering algorithm. But, Euclidean distance is sensitive to outliers and is suitable to only numeric values. Real time datasets have mixed attribute values, missing values and measurements are not in the standard format.The proposed algorithm extends the ability of the kmean algorithm to use a mixed simil arity measure to find the similarity between data objects for clustering mixed datasets.For imp uting missing values, correlation based data imputation is used.In addition, k-mean output depends on the initial cluster centre and local optima suffers from the number of clusters(k). In order to improve the efficiency of the k-mean algorithm, Artificial Bee Colony Optimization (ABC) based clustering algorithm is suggested. ABC is successful at exploring the search space, but endures in leveraging the search space. Collaborative search is used to amplify the search quality of bees to amplify the search quality of bees employees. To determine the number of clusters for the given data set, the Elbow method is used. In order to evaluate the outcome of the proposed algorithm, real time datasets are used. The results showed that the proposed method performs well compared to comparative algorithms.

The usage of fungicides to manage plant diseases in many crops is very significant. Reduced disease control with fungicides can result from several causes including application of insufficient quantity, low effectiveness of the fungicide on the target pathogen, improper timing or method of application and excessive rainfall. Resistance is the evolutionary response of a fungus to threat its survival by the fungicides. Fungicides with single site of action are more prone to develop resistance. The quicker development of resistance and the way to control its loss solely depends both on fungicide and the target fungus. The development of fungicide resistance is complex interactions of factors involving nature of fungicide and target pathogen. Thorough knowledge about the ecology of fungicide resistance pertaining to its development and its management is crucial for ensuring sustainable disease control with fungicides.

Mammography aims to detect, characterize and evaluate the findings of breast cancer and related breast diseases. An X-ray of a patient's breast is a diagnostic mammogram which can show anomalies being detected through screening mammography or earlier mammography findings requiring follow-up. In automated mammogram classification, features are extracted and classified. The number of features is high, and the feature set contains irrelevant and redundant features leading to degradation of classifiers. In this work, the features from the mammograms are extracted using Multi-scale filter bank and Genetic Algorithm (GA) based feature selection is evaluated. This study proposes to classify features using Multilayer Perceptron Neural Network (MLPNN). Results show improvement of the proposed method.