GIS-MAP based Spatial Analysis of Rainfall Data of Andhra Pradesh and Telangana States Using R

The rainfall conditions across wide geographical location and varied topographic conditions of India throw challenge to researchers and scientists in predicting rainfall effectively. India is Agriculture based country and it mainly depends on rainfall. Seasons in India are divided into four, which is winter in January and February, summer is from March to May, monsoon is from June to September and post monsoon is from October to December. India is Agriculture based country and it mainly depends on rainfall. It is very difficult to develop suitable rainfall patterns from the highly volatile weather conditions. In this Paper, it is proposed that Map based Spatial Analysis of rainfall data of Andhra Pradesh and Telangana states is made using R software apart from Hybrid Machine learning techniques. A Study will be made on rainfall patterns based on spatial locations. The Visual analytics were also made for effective study using statistical methods and Data Mining Techniques. This paper also introduced Spatial mining for effective retrieval of Remote sensed Data to deal with retrieval of information from the database and presents them in the form of map using R software

Identifying Road Accidents Severity Problems Using Data Mining Approaches

Roadway traffic safety is a major concern for transportation governing agencies as well as ordinarycitizens. In order to give safe driving suggestions, carefulanalysis of roadway traffic data is critical to find outvariables that are closely related to fatal accidents. Inthis paper we apply statistics analysis and data miningalgorithms on the FARS Fatal Accident dataset as an attempt to address this problem. The relationship betweenfatal rate and other attributes including collision manner,weather, surface condition, light condition, and drunkdriver were investigated. Association rules were discoveredby Apriori algorithm, classification model was built byNaive Bayes classifier, and clusters were formed by simple K-means clustering algorithm. Here we are also using one more classification technique for comparing with Naïve bayes classifier. Certain safety driving suggestions were made based on statistics, association rules, classification model, and clusters obtained

Data science: Identifying influencers in social networks

Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. The common use of Online Social Networks (OSN)[2] for networking communication which authorizes real-time multimedia capturing and sharing, have led to enormous amounts of user-generated content in online, and made publicly available for analysis and mining. The efforts have been made for more privacy awareness to protect personal data against privacy threats. The principal idea in designing different marketing strategies is to identify the influencers in the network communication. The individuals influential induce “word-of-mouth” that effects in the network are responsible for causing particular action of influence that convinces their peers (followers) to perform a similar action in buying a product. Targeting these influencers usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network, we use centrality measures to identify assign an influence score to each user. The user with higher score is considered as a better influencer

Medical disease prediction using Grey Wolf optimization and auto encoder based recurrent neural network

Big data development in biomedical and medical service networks provides a research on medical data benefits, early ailment detection, patient care and network administrations.e-Health applications are particularly important for the patients who are unfit to see a specialist or any health expert. The objective is to encourage clinicians and families to predict disease using Machine Learning (ML) procedures. In addition, diverse regions show important qualities of certain provincial ailments, which may hinder the forecast of disease outbreaks. The objective of this work is to predict the different kinds of diseases using Grey Wolf optimization and auto encoder based Recurrent Neural Network (GWO+RNN). The features are selected using GWO and the diseases are predicted by using RNN method. Initially the GWO algorithm avoids the irrelevant and redundant attributes significantly, after the features are forwarded to the RNN classifier. The experimental result proved that the performance of GWO+RNN algorithm achieved better than existing method like Group Search Optimizer and Fuzzy Min-Max Neural Network (GFMMNN) approach. The GWO-RNN method used the medical UCI database based on various datasets such as Hungarian, Cleveland, PID, mammographic masses, Switzerland and performance was measured with the help of efficient metrics like accuracy, sensitivity and specificity. The proposed GWO+RNN method achieved 16.82% of improved prediction accuracy for Cleveland dataset

Face Recognition and Raspberry Pi Powered Smart Door Unlocking System

Security plays a major role in the well-being of people. It is not possible to hire a security guard in person and always ensure his presence on our premises. It is an inefficient investment. The main role of security personnel is to stay on patrol, monitor, inspect and defend against any breach of security. A smart lock is an electromechanical lock that allows entry based on the authorization device that gives it instructions to lock and unlock. This system is proved to be inefficient as well These locks are either based on Pin, Bluetooth, WIFI. We propose a smart locking system that unlocks based upon face recognition that pic up specific, distinctive details about a person’s face. This is a powerful library that can run even by taking up a single picture of the person provided that the facial features are distinctly identified. The model is proven to be reaching accuracy levels of 99.7% according to the Centre for Strategic and International Studies (CSIS). The database is stored on the cloud consisting of all the authorized personnel that can pass through the door. The client holds the power of customizing the entry and exit of an individual through an application on his mobile

Ecosense: An IoT System for Detecting Suitable and Sustainable Living Conditions

The EcoSense project intends to create a sustainable IoT system to detect and track whether living circumstances are suitable for individuals. The system gathers sensor data and measures important aspects including temperature, air quality, and noise level. The degree of sustainable air pollution is determined by air pollution sensors, which monitor several dangerous chemicals including CO and CO2 in the atmosphere. The noise sensor measures the level of noise and pinpoints the source of excessive noise. Environmental temperature and humidity are measured using sensors. These sensors sustainably and continuously track all of these parameters, sending out notifications when specified thresholds are reached or exceeded. In order to assess the habitat’s general compatibility, the sensor data is processed, saved in the cloud, and then examined. Following that, this information is used to give locals immediate feedback so they may decide for themselves how best to enhance their living arrangements as well as the environment and public health. their general wellbeing. This initiative helps to spread the word about living sustainably. The EcoSense initiative has the potential to have a big impact on the environmental sustainability industry. The initiative can assist people and communities in making educated decisions about their environment and taking constructive action to enhance it by offering real-time data on quality of life. The project has the potential to increase public awareness of environmental problems and inspire individuals to take action to safeguard the environment

Sustainable Hand Gesture Recognition for Speech Conversion, Empowering the Speech-Impaired

A sustainable language disorder affects an individual’s ability to reach out to others through speaking and listening. So utilizing sustainable hand gestures is among the most widespread means of non-verbal and visual communication used by people with speech disabilities worldwide. However, even though sustainable sign language is used everywhere by speech-impaired and hearing-impaired people, most of the populace who don't have any knowledge about sign language face difficulties in sustainably communicating with them. This sustainable problem requires better solutions that can successfully support communication for people with speech disabilities. This sustainable approach will reduce the communication gap for the speech-impaired population. There are many sustainable solutions in the market such as using sensors to make a sustainable device that gives a helpful output. But these sustainable solutions are expensive and not everyone can afford them. We are employing Convolutional Neural Networks to create a sustainable model that is trained on different gestures. This sustainable model enables speech-impaired individuals to convey their information using signs which get converted to human-understandable language, and sustainable voice is given as output. The sustainable hand gestures made are captured as a series of sustainable images which are processed using Python code. This sustainable endeavor introduces a solution that not only automates the identification of sustainable hand gestures but also transforms them into sustainable speech. By interpreting these recognized sustainable gestures, the corresponding recorded audio will be played sustainably. The focus of this sustainable paper is to offer accessibility, convenience, and safety to individuals with speech impairments in a sustainable manner. These sustainable individuals often experience societal discrimination solely due to their disabilities. This sustainable paper is aimed at innovating a sustainable device to help those without the knowledge of sign language sustainably communicate with the people who face difficulty in speech

Enhancing Impulsive Hatred Detection with Ensemble Techniques and Active Learning

The increasing propagation in recent years of hatred on social media and the dire requirement for counter measures have drawn critical speculation from state run administrations, organizations, and analysts. Despite the fact that specialists have observed that disdain is an issue across different Social media stages, there is an absence of models for online disdain location utilizing this multi-stage information. Different techniques have been produced for robotizing disdain discovery on the web. Here we will begin by giving the current issue that comes the right to speak freely of discourse on the Internet and the abuse of virtual entertainment stages like Twitter, as well as distinguishing the holes present in the current works. At long last, figured out how to tackle these issues. It is a considerably more testing task, as examination of the language in the common datasets shows that disdain needs one of a kind, discriminative highlights and in this manner making it challenging to find. Removing a few exceptional and significant elements and joining them in various sets to look at and dissect the presentation of different machine learning classification calculations as to each list of capabilities. At long last, subsequent to leading a top to bottom investigation, results show that it is feasible to fundamentally expand the classification score acquired

DFR-TSD: A Sustainable Deep Learning Based Framework for Sustainable Robust Traffic Sign Detection under Challenging Weather Conditions

The development of reliable and sustainable traffic sign detection under difficult weather conditions, or DFR-TSD, is a key step in creating effective, safe, and sustainable autonomous driving systems. The suggested sustainable framework makes use of deep learning techniques to overcome the drawbacks of the current traffic sign detection systems, especially in difficult weather circumstances like haze and snow. The system uses a sustainable CNN pre-processing step to make traffic signs more visible in photos that have been impacted by the weather, followed by a sustainable pre-trained ResNet-50 model to recognize traffic signs. On the CURE-TSD dataset, which includes difficult weather circumstances such as haze, snow, and fog, the suggested sustainable framework was assessed. The testing findings showed how sustainably well the suggested framework performed in identifying traffic signs in adverse weather. The suggested sustainable framework outperforms previous approaches with a sustainable accuracy rating of 98.83%. The outcomes show that sustainable deep learning methods have the potential to enhance traffic sign identification models' functionality. The proposed sustainable framework’s front end offers a user-friendly interface that enables users to upload test photographs and view the results of the detection. There are four sustainable buttons on the UI for loading the model, uploading test photographs, spotting signs, and seeing the training graph. The Tkinter framework, which offers a user-friendly GUI toolkit that enables developers to quickly design and customize sustainable GUI programs, is used to develop the front end. The suggested sustainable DFR-TSD framework is a potential sustainable option for reliable traffic sign detection in adverse weather due to the sustainable pre-processing step, the sustainable pre-trained ResNet-50 model, and the sustainable user-friendly interface

Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Breast cancer is a major public health issue that may be remedied with early identification and efficient organ therapy. The diagnosis and prognosis of severe and serious illnesses are likely to be followed and examined by a biopsy of the affected organ in order to identify and classify the malignin cells or tissues. The histopathology of tissue is one of the major advancements in modern medicine for the identification of breast cancer. Haematoxylin and eosin staining slides are used by pathologists to identify benign or malignant tissue in clinical instances of invasive breast cancer. A digital whole slide imaging (WSI) is a high-resolution digital file that is permanently stored in memory for flexible use. This article will look at and compare how breast cancer cells are categorised manually and automatically. lobular carcinoma in situ and ductal carcinoma in situ are the two types of breast cancer. Here, detailed explanations of numerous techniques utilised in histopathology pictures for nucleus recognition, segmentation, feature extraction, and classification are given. The pre-processed image is utilised to extract the nucleus patch using several feature extraction approaches. Thanks to the great computational capability of the general processing unit (GPU), algorithms may be implemented effectively and efficiently. Deep Convolution Neural Network (DCNN), Support Vector Machines (SVM), and other machine learning methods are the most popular and effective computer algorithms