Water Pollution Management and detection techniques: a Review

Now a day’s water pollution became a national and global issue not only in India but over the whole world. Like India other countries are also facing the same problem of water pollution due to agricultural waste, industrial waste, sewage waste and so on. In this paper it has been explained clearly with the help of considerable number of references. It gives the information about the pollutants which pollutes the water. Sensors have a ability to Control and monitor the quality of water as well as able to detect the contaminants added due to human generating activities. Sensors can be used for the selection and identification of the techniques which will be suitable for performing the given task that is identification of pollutants present in water. Various pollutants found in water are pesticides, harmful chemicals, heavy metals, nutrients, etc. We will also going to discuss about the different techniques used for water pollution detections. Tethered.

An Efficient Mining Approach for Handling Web Access Sequences

The World Wide Web (WWW) becomes an important source for collecting, storing, and sharing the information. Based on the users query the traditional web page search approximately retrieves the related link and some of the search engines are Alta, Vista, Google, etc. The process of web mining defines to determine the unknown and useful information from web data. Web mining contains the two approaches such as data-based approach and process-based approach. Now a day the data-based approach is the widely used approach. It is used to extract the knowledge from web data in the form of hyper link, and web log data. In this study, the modern technique is presented for mining web access utility-based tree construction under Modified Genetic Algorithm (MGA). MGA tree are newly created to deploy the tree construction. In the web access sequences tree construction for the most part relies upon internal and external utility values. The performance of the proposed technique provides an efficient Web access sequences for both static and incremental data. Furthermore, this research work is helpful for both forward references and backward references of web access sequences.

Quad-band FSS for Electromagnetic Shielding

In this paper, a novel method to design Quad-band stop frequency selective surface (FSS) is proposed to shield medical/non-medical devices from electromagnetic radiation of wifi/Bluetooth, GPS,1G, 2G, 3G, 4G and 5G frequencies. The proposed device can also be used in security and surveillance devices to protect them from electromagnetic attack. The FSS unit cell consists of convoluted structures on FR4 substrate. The study was performed by designing individual frequency selective surfaces for 1G, 2G, Wi-Fi/Bluetooth, GPS, 4G and 5G, then combining it to form a single Quad-band FSS unit.

Drivers' Real-Time Drowsiness Identification Using Facial Features and Automatic Vehicle Speed Control

The road crash is one of the significant problems that is of great concern in today's world. Road accidents are often caused by drivers' carelessness and negligence. The drowsy condition of the drivers, which occurs due to overwork, fatigue, and many other factors, is one of those causes. It is therefore most critical to establish systems that can detect the driver's drowsy state and provide the drivers with the appropriate warning system. In addition to the automatic speed control of the car, this system thus supports drivers in incidents by providing warnings in advance. This means that road collisions that are harmful to living lives are minimised. This is achieved by using the technique of image recognition, where driver drowsiness is observed, and using this method, simultaneous warning and speed monitoring of the vehicle is carried out.

Sensor Based Industrial Kitchen Foodstuffs Monitoring System

Artificial Intelligence based foodstuff monitoring system is used for inventory management in industrial kitchens, restaurants, canteens, vegetable stores and so on. If the store keeper is not available to monitor the grocery and orders, the process will be risky. The proposed method considers the level estimation detection using ultrasonic sensor and if the container is empty then the information is sent to the store keeper. By this method, the intimation about availability of specific food item can be found and items not available can be ordered for purchasing. The DHT11 sensor is used to monitor the humidity and temperature inside the container, if any of these two is high then the notification will be sent. Decomposed organic items are identified by MQ3 sensor based on detection of alcoholic gas produced by organic items. The sensors data such as grocery level, humidity, temperature and decomposition range of organic items are collected from the corresponding smart container. This data transaction happens via micro controller known as Node MCU. The level of the groceries present, and spoiled organic items can be identified and mail notification can be triggered in the early stage with the help of mobile application called IFTTT.

Image based Plant leaf disease detection using Deep learning

Agriculture is important for India. Every year growing variety of crops is at loss due to inefficiency in shipping, cultivation, pest infestation in crop and storage of government-subsidized crops. There is reduction in production of good crops in both quality and quantity due to Plants being affected by diseases. Hence it is important for early detection and identification of diseases in plants. The proposed methodology consists of collection of Plant leaf dataset, Image preprocessing, Image Augmentation and Neural network training. The dataset is collected from ImageNet for training phase. The CNN technique is used to differentiate the healthy leaf from disease affected leaf. In image preprocessing resizing the image is carried out to reduce the training phase time. Image augmentation is performed in training phase by applying various transformation function on Plant images. The Network is trained by Caffenet deep learning framework. CNN is trained with ReLu (Rectified Linear Unit). The convolution base of CNN generates features from image through the multiple convolution layers and pooling layers. The classifier part of CNN classifies the image based on the features extracted from the convolution base. The classification is performed through the fully connected layers. The performance is measured using 10-fold cross validation function. The final layer uses activation function like softmax to categorize the outputs.

Search Engine Optimization – Study and Analysis

Search Engine Optimization is about understanding what people are searching for online, the answers they are seeking, the words they are using, and the type of content they wish to consume. Knowing the answers to these questions will allow you to connect to the people who are searching online for the solutions you offer. Building the onsite and offsite page tactics will help us to build a better site that ranks higher in search and also with better click through rates. Understanding what the users or consumers need and producing the same to them is one of the major factor in the Digital Marketing. Using Analytics tool we can analyze how our site is doing well or not with our intended action will help us to understand our audience better.

Comparison of Fracture Delineation Methods in Anteroposterior Pelvic Radiographs

Pelvic fractures are very difficult to detect due to the visual complexity of the pelvic bone. Pelvic fracture occurs less frequently, only when there is a high energy event such as fall from a height or vehicle collision. In elder people and in osteoporosis patients even a low energy incident may cause fracture. The paper includes the comparison of three different fracture detection methods – GLCM and ANN based, Statistical curve fitting and classifier based and finally statistical curve fitting and ANN based method.

Comparative analysis of various Image compression techniques for Quasi Fractal lossless compression

The most important Entity to be considered in Image Compression methods are Paek to signal noise ratio and Compression ratio. These two parameters are considered to judge the quality of any Image.and they a play vital role in any Image processing applications. Biomedical domain is one of the critical areas where more image datasets are involved for analysis and biomedical image compression is very, much essential. Basically, compression techniques are classified into lossless and lossy. As the name indicates, in the lossless technique the image is compressed without any loss of data. But in the lossy, some information may loss. Here both lossy & lossless techniques for an image compression are used. In this research different compression approaches of these two categories are discussed and brain images for compression techniques are highlighted. Both lossy and lossless techniques are implemented by studying it’s advantages and disadvantages. For this research two important quality parameters i.e. CR & PSNR are calculated. Here existing techniques DCT, DFT, DWT & Fractal are implemented and introduced new techniques i.e Oscillation Concept method, BTC-SPIHT & Hybrid technique using adaptive threshold & Quasi Fractal Algorithm.

Performance Analysis of Regular and Irregular LDPC Codes on SPIHT Coded Image Data

The LDPC (Low Density Parity Check Code) has Shown interesting results for transmitting embedded bit streams over noisy communication channels. Performance comparison of regular and irregular LDPC codes with SPIHT coded image is done here. Different Error Sensitive classes of image data are obtained by using SPIHT algorithm as an image coder. Irregular LDPC codes map the more important class of data into a higher degree protection class to provide more protection. Different degree protection classes of an LDPC code improves the overall performance of data transmission against channel errors. Simulation results show the superiority of irregular LDPC over regular LDPC codes.