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Background: Soil nutrients are the major source of plant nutrition that helps for the plant growth. Soil resource is currently facing threats due to various soil nutrients deficiency. Assessing the spatial variability of soil nutrients quality is necessary for agriculture. Quickly and efficiently monitoring soil nutritional status using remote sensing technology is of great significance for farmland soil productivity, food security and sustainable agricultural development. Methods: The present study focuses on spatial variability of soil quality. Current research has been conducted to estimate and map soil nutrient contents in large areas using GIS technology and other related maps were prepared from remote sensing data in ArcGIS 10.1. 288 soil samples were collected for the study from different Blocks of the Coimbatore District of Tamil Nadu. The Organic carbon, N, P, K, pH and EC status of the soil were analysed and reported. Result: The organic carbon status of the soil is very low in most of the parts of Coimbatore and the pH status is slightly alkaline. The primary nutrients N, P and K were found to be low and found to have micronutrient imbalance which is highly influenced by the changes in pH.

The Nilgiri mountain range in south India is an unique ecosystem and of considerable importance to anthropologists, geologists, climatologists, botanists as well as tourists. After the 19 th century the Nilgiris and its popular hill stations emerged as favourite places for rest and recuperation, game and for raising commercial plantations. Land cover refers to the natural vegetative cover types of particular region. Land use is the term that is used to describe human uses of the land or converting natural land cover for agriculture or settlement. The assessment of changes of land use pattern is necessary for estimating the land degradation rate and planning for sustainable land use. In this study the remote sensing data for land cover and land use of Nilgiris district is utilized for assessing the dynamics of land use and classification as use, misuse and abuse of land use pattern for the past decade and it was observed that there is a considerable increase in the built up area and decrease in forest cover in the recent years. The fallow land cover also increased concluding that the abuse (built up) and misuse of the land (fallow land) is in an increasing rate compared to proper usage of land (forest and agriculture).

Due to deforestation, urbanization and land use changes, the Nilgiris district of Tamil Nadu has been impacted by climate variation. This study was carried out to explore the impact of the land use changes in Nilgiris District from 2011 to 2017using remote sensing images acquired from Landsat 7. Temperature and rainfall data of the study area were also obtained from IMD Pune. The results show that the current trend of decreasing forest area and increasing agricultural land, and the area also experienced a temperature increase of 0.4 ◦C between 2011 and 2017. This study is crucial for land planners and environmentalists to understand the impacts of land use change on the climate in Nilgiris District.

Background: The solid wastes and the wastewater (effluent) generated in the pulp and paper industry can be used to improve the soil fertility. However, the cation concentration in these residues can influence the bioavailability of some nutrients and soil properties. The objective of this study is to experiment the availability of the cations namely Ca, Mg, Na and K in laboratory (column) and the field. Methods: An experiment with PVC column was carried out in the laboratory to study the availability of cations as influenced by 13 irrigations with papermill effluent vis-à-vis in a field experiment in the Department of Environmental Sciences, Tamil Nadu Agricultural University, Coimbatore during 2019-2020. Result: The pH, EC and the concentration of the cations were higher in the field soil compared to the column soil barring magnesium. This may be due to the clogging of pores resulting in decreased infiltration in field soil.

Water is the basic requirement for the survival of life on earth. The water is mainly used for cooking, cleaning and drinking purpose. In which more solid ingredients along with water also reaches. The disposals of sewages are still a challenging task in this modern world. The main purpose of smart drainage system is collecting the solid waste which includes bottles, polythene and transport to the disposal area. Otherwise the solid waste will block the drainage system. To avoid such situation, the solid waste are need to be collected and disposed it into collecting bin. The proposed system will automatically cleans the water in the drainage system with the help of drive system controlled by Arduino and the mechanical setup which are driven by servo motor collect the solid waste and dispose it in waste bucket to avoid drainage block. It reduces the cost of manual labour as well as reduces the threat to human life.

The World Health Organization (WHO) Director-General, Dr. Tedros Adhanom Ghebreyesus on March 11, 2020 declared the novel coronavirus (COVID-19) outbreak a global pandemic [4] the reason being the number of cases outside China increased 13-fold and the number of countries with cases increased threefold. In this paper a time series model to predict short-term prediction of the transmission of the exponentially growing COVID-19 time series is modelled and studied. Auto Regressive Integrated Moving Average (ARIMA) model prediction is performed on the number of cumulative cases over a time period and is validated over Akaike information criterion (AIC) statistics.

The evolving automotive industry with the rapid growth of using fuel resources causes a shortfall of fuel availability. The engine maintenance has great importance and it is essential to develop a fault detection system and condition monitoring is done to reduce the damage-causing circumstances to improve engine safety. The engine tested at the rated speed of 1800 rpm by varying the loads. Captured vibration data is analyzed by using feature extraction and classification algorithms to obtain the best performance and minimum vibrations of blends. The best classification accuracy blend results are selected and used to develop a neural network model and to predict the vibration signatures. The neural network model is developed with a feedforward backpropagation algorithm and using Levenberg–Marquardt as training function. The developed ANN model is trained and vibration signatures are predicted for the selected blend B25 with the classification accuracy of 97%.

Machine learning involves artificial intelligence, and it is used in solving many problems in data science. One common application of machine learning is the prediction of an outcome based upon existing data. The machine learns patterns from the existing dataset, and then applies them to an unknown dataset in order to predict the outcome. Classification is a powerful machine learning technique that is commonly used for prediction. Some classification algorithms predict with satisfactory accuracy, whereas others exhibit a limited accuracy. This paper investigates a method termed ensemble classification, which is used for improving the accuracy of weak algorithms by combining multiple classifiers. Experiments with this tool were performed using a heart disease dataset. A comparative analytical approach was done to determine how the ensemble technique can be applied for improving prediction accuracy in heart disease. The focus of this paper is not only on increasing the accuracy of weak classification algorithms, but also on the implementation of the algorithm with a medical dataset, to show its utility to predict disease at an early stage. The results of the study indicate that ensemble techniques, such as bagging and boosting, are effective in improving the prediction accuracy of weak classifiers, and exhibit satisfactory performance in identifying risk of heart disease. A maximum increase of 7% accuracy for weak classifiers was achieved with the help of ensemble classification. The performance of the process was further enhanced with a feature selection implementation, and the results showed significant improvement in prediction accuracy. Keywords: Heart disease, Machine learning, Ensemble classifier, Prediction model

Mobile Ad hoc Networks (MANETs) is a continuously self-configuring, infrastructure-less network of mobile devices connected without wires and also it is an open network environment where nodes can join and leave the network freely. Hence, the dynamic natures of MANETs is not secure than the wired networks. To overcome this issue, the Cluster-based Certificate Revocation with Vindication Capability (CCRVC) scheme is proposed. In the meanwhile nodes forms a cluster consists of a Cluster Head with some Cluster Members (CMs) situated within the transmission range of their cluster Head. Each node should have to acquire valid certificates from the Certification Authority (CA) before it can join the network that is responsible for distribution and management of certificates to all nodes and also CA is responsible for updating two lists, Warning list and Black list. Both the lists are used to hold the accusing and accused nodes information, respectively. Experimental results show that the proposed CCRVC scheme is effective and efficient to provide efficient and secure communication.

Phishing is an online scandalous act that occurs when a malevolent web page impersonates as legitimate web page in the intension of exploiting the confidential information from the user. Phishing attack continues to pose serious risk for web users and annoying threat in the field of electronic commerce. Feature selection is the process of removing unrelated features and thus reduces the dimensionality of the feature. This paper focuses on identifying the foremost features that categorise legitimate websites from phishing websites based on feature selection. In real world identifying phishing URL with low computational time and accuracy is very important and thus feature selection is considered in this work. A comparative study is carried out on different data mining classifiers before and after feature selection and the performance are evaluated in terms of accuracy and computational rate. The results indicate that the proposed approach detects phishing websites with considerable accuracy.

In this study, the authors' goal was to understand the groundwater quality of Nedumkandam panchayat by an integrated approach of traditional water quality analysis and Geographical Information Systems (GIS). Fourteen wells were identified from the study area and samples were collected and analyzed using standard protocols (American Public Health Association, 1998). Parameters analyzed include pH, hardness, nitrate, chloride, sulfate, phosphate, trace metals (cadmium, zinc, copper, and lead), and fecal coliforms. All parameters except pH, cadmium, and fecal coliforms were within the limit of drinking water quality standards prescribed by the Bureau of Indian Standards (BIS) (BIS, 1983). The spatial distribution of physico-chemical and biological parameters was analyzed using the Inverse Distance Weighted (IDW) approach and the maps thus obtained were integrated using the raster calculator option of spatial analyst in ArcGIS 8.3 software, and a water quality index (WQI) was calculated. Based on the WQI values, the study area was divided into poor, moderate, and good water quality zones.

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Phishing is an online criminal act that occurs when a malicious webpage impersonates as legitimate webpage so as to acquire sensitive information from the user. Phishing attack continues to pose a serious risk for web users and annoying threat within the field of electronic commerce. This paper focuses on discerning the significant features that discriminate between legitimate and phishing URLs. These features are then subjected to associative rule mining—apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. Analyzing the knowledge accessible on phishing URL and considering confidence as an indicator, the features like transport layer security, unavailability of the top level domain in the URL and keyword within the path portion of the URL were found to be sensible indicators for phishing URL. In addition to this number of slashes in the URL, dot in the host portion of the URL and length of the URL are also the key factors for phishing URL.