Ashish Patel 🇮🇳

Abstract

Image Segmentation with Remote Sensing (RS) dataset is highly challenging in many applications, such as agriculture, land classification, and disaster management, due to high interclass similarities in features, noise in the data, and the need for appropriate algorithms for segmentation. Traditional approaches utilize machine learning (ML) and deep learning (DL) algorithms, and they have performed well for this in the availability of many image datasets. Because of the… Show more

Abstract

Image Segmentation with Remote Sensing (RS) dataset is highly challenging in many applications, such as agriculture, land classification, and disaster management, due to high interclass similarities in features, noise in the data, and the need for appropriate algorithms for segmentation. Traditional approaches utilize machine learning (ML) and deep learning (DL) algorithms, and they have performed well for this in the availability of many image datasets. Because of the advantages and applications of U-Net architecture for semantic segmentation, even with the scarcity of training image samples, this paper aims to apply pixel-wise classification of aerial images and image segmentation with U-Net and U-Net image blocks. The paper's novelty is to apply segmentation for a single-day varying altitude image dataset collected from a village in Gujarat using the Google Earth’s sentinel satellite image views. The classification has been carried out for two major crops, wheat, and Ricinus. The assessment of various architectural frameworks for the segmentation and classification, including ML, DL, and U-Net, by fine-tuning the models with monotonic learning rate (LR) and Cyclic LR, are included in this paper. The U-Net architecture for a dataset with 500m altitudes with a Validation Accuracy of 94.6%, Loss of 3.48%, Area Under Cover (AUC) of 95.76%, Sensitivity of 99.5%, and Specificity of 99.07% and another U-Net image block ResUNet architecture with Cyclic LR for 1000 m altitude outperform the traditional ML, DL algorithms with Validation Accuracy 98.5%, Loss 0.93%, AUC 88.91%, Sensitivity 92.86%, and Specificity 95.42%. Significantly, the U-Net and its image block architectures with Cyclic LR outperform the ML and DL variants. Show less

3D object detection is vital as it would enable us to capture objects' sizes, orientation, and position in the world. As a result, we would be able to use this 3D detection in real-world applications such as Augmented Reality (AR), self-driving cars, and robotics which perceive the world the same way we do as humans. Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or… Show more

3D object detection is vital as it would enable us to capture objects' sizes, orientation, and position in the world. As a result, we would be able to use this 3D detection in real-world applications such as Augmented Reality (AR), self-driving cars, and robotics which perceive the world the same way we do as humans. Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or other sensors or multiple images. Monocular 3D object detection is an important yet challenging task. Beyond the significant progress in image-based 2D object detection, 3D understanding of real-world objects is an open challenge that has not been explored extensively thus far. In addition to the most closely related studies. Show less

The coronavirus disease (COVID) erupted at the end of 2019 and quickly spread throughout the globe. The virus is capable of spreading at an exponential speed within a few days. As COVID-19 is transmissible among humans, it is required to detect infectious humans as soon as possible. One of the rapid techniques to identify COVID-19 infection in human beings is an X-ray of various body parts. As COVID-19 cases rise, computerized diagnostic methods are needed for initial recognition and prognosis… Show more

The coronavirus disease (COVID) erupted at the end of 2019 and quickly spread throughout the globe. The virus is capable of spreading at an exponential speed within a few days. As COVID-19 is transmissible among humans, it is required to detect infectious humans as soon as possible. One of the rapid techniques to identify COVID-19 infection in human beings is an X-ray of various body parts. As COVID-19 cases rise, computerized diagnostic methods are needed for initial recognition and prognosis of infection. In this study, the convolution neural network deep net architecture is developed for classifying among binary classes – COVID-19 and normal – and for multi-classes – COVID-19, pneumonia, and normal chest X-ray images. The novelty is added in terms of larger features for map extraction. When a 3 × 3 size max-pooling layer is applied, it extracts more knowledge than the 2 × 2 size max-pooling layer, which helps increase the model knowledge and performance. The proposed model is estimated to deliver accurate indicatory results of classification accuracy. Show less

To cope with the increasing healthcare costs and nursing shortages in the Aging Society the care system is transferred, as much as possible, to the home environment, making use of ambient assisted living (AAL) monitoring and communication possibilities and to actively involve informal cares to fill in large part of the care that is needed. The proposed system is the AAL based, acoustics sensing system ready to dissect, recognize, and distinguish specific acoustic events occurring in day-by-day… Show more

To cope with the increasing healthcare costs and nursing shortages in the Aging Society the care system is transferred, as much as possible, to the home environment, making use of ambient assisted living (AAL) monitoring and communication possibilities and to actively involve informal cares to fill in large part of the care that is needed. The proposed system is the AAL based, acoustics sensing system ready to dissect, recognize, and distinguish specific acoustic events occurring in day-by-day life situations, which empowers not only the individual subjects but also the healthcare professionals to remotely follow the status of each individual continuously. This system only processes the background acoustics related to the activity of daily living (ADL) for preventive healthcare. The novel contribution of the research is based on prototype development, audio signal processing algorithms and deep learning algorithms to satisfy the research gap. Show less

Introduction

​This book explains the concepts of time series from traditional to bleeding-edge techniques with full-fledged examples.

The book begins by covering time series fundamentals and its characteristics, the structure of time series data, pre-processing, and ways of crafting the features through data wrangling. Next, it covers the traditional time series techniques like Smoothing methods, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA using trending framework like StatsModels… Show more

Introduction

​This book explains the concepts of time series from traditional to bleeding-edge techniques with full-fledged examples.

The book begins by covering time series fundamentals and its characteristics, the structure of time series data, pre-processing, and ways of crafting the features through data wrangling. Next, it covers the traditional time series techniques like Smoothing methods, ARMA, ARIMA, SARIMA, SARIMAX, VAR, VARMA using trending framework like StatsModels, pmdarima. Further, Book explains the building classification models using sktime, and covers how to leverage advance deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It finally concludes by explaining the popular framework fbprophet for modeling time series analysis.

After completion of the book, the reader will have a good understanding of working with different techniques of time series methods. All the codes presented in this notebook are available in Jupyter notebooks, which allows readers to do hands-on and enhance them in exciting ways.

What You'll Learn

Explains basics to advanced concepts of time series

How to design, develop, train, and validate time-series methodologies

What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results

Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder  to solve both Univariate and multivariate problems by using two types of data preparation methods for time series.

Univariate and multivariate problem solving using fbprophet.

Show less

Falls are a noteworthy reason for grievances and deaths in elderlies. Notwithstanding when no damage happens, about majority of elderlies are identity unfit to get up without help. The expanded time of lying on the floor frequently prompts restorative complications, including muscle impairment, lack of hydration, unease, and trepidation of falling. Here, a fall sensing unit is accounted that is affixed to a subjects' midsection and incorporates a 3-axis accelerometer, 3-axis gyroscope, a… Show more

Falls are a noteworthy reason for grievances and deaths in elderlies. Notwithstanding when no damage happens, about majority of elderlies are identity unfit to get up without help. The expanded time of lying on the floor frequently prompts restorative complications, including muscle impairment, lack of hydration, unease, and trepidation of falling. Here, a fall sensing unit is accounted that is affixed to a subjects' midsection and incorporates a 3-axis accelerometer, 3-axis gyroscope, a multiplexer, a filter, and a microcontroller. Moreover, the fall detection system also used IMU data on the mobile phone. Change in angular velocity, noise cancelation, and the ADC transformation was achieved by the hardware. The handled flag is conveyed to a PC through ZigBee and processed through the dedicated programming. Fall sensing approach comprised feature selection, mining and a machine learning calculation for characterizing the parameters. In this paper, we propose a fall discovery calculation which is shaped by feature selection, discovery, mining and handling. An aggregate of six highlights was ascertained in feature selection. Four of them are identified with the gravity vector which is extricated from accelerometer information by utilizing the low-pass filter. As falling generally happens in a vertical course, the gravity-related characteristics are helpful. The system also uses one of the ambient sensing units, which is a movement sensing unit. The PIR sensor-based movement sensing unit is used to enhance the accuracy of fall detection activity. The feature from the movement sensing unit substantially reduced the false alarms. Show less

n the course of the most recent couple of years, profound neural systems have gotten the most consideration insoftware engineering, particularly in design acknowledgment, machine vision and machine learning. One of its superb applications is in the feeling acknowledgment by means of Facial expression recognition region. Facial expression recognition investigation is valuable for some undertakings and the utilization of profound learning here is… Show more

n the course of the most recent couple of years, profound neural systems have gotten the most consideration insoftware engineering, particularly in design acknowledgment, machine vision and machine learning. One of its superb applications is in the feeling acknowledgment by means of Facial expression recognition region. Facial expression recognition investigation is valuable for some undertakings and the utilization of profound learning here is additionally growing quick. We survey some current research works in this space, present some new applications and demonstrate the general strides to actualizing every one of them Show less

The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal… Show more

The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach. Show less

In recent years there has been a trend towards online purchase so stealing of credit data is high like identity of the credit card owner, password or etc. the attacker may use this data for to take loan from financial domain and they make credit default. Credit scoring is the give the creditworthiness of person. Anomaly Detection is the process of classifies unusual behavior. It is important data analysis task used for classify interesting and emerging patterns, trends and anomalies from data… Show more

In recent years there has been a trend towards online purchase so stealing of credit data is high like identity of the credit card owner, password or etc. the attacker may use this data for to take loan from financial domain and they make credit default. Credit scoring is the give the creditworthiness of person. Anomaly Detection is the process of classifies unusual behavior. It is important data analysis task used for classify interesting and emerging patterns, trends and anomalies from data. Anomaly detection is an important tool to detect irregularity in many different domains including financial fraud detection, computer network intrusion, human behavioral analysis and many more. In today’s era the credit and Loan Default is become high because of many fraudulent activity or increase online purchases. To perform anomaly detection in this paper linear regression with rule based classification and logistic regression is used. The preprocessing is used for to perform explore, analyze and determine the factor that play crucial role to find credit default. Show less

The Field of Natural Language Processing (NLP) is getting to be one of the dynamic regions in Human-PC connection which has seen a study moved in both research and philosophy bearing in the previous couple of years. Normal Language is advantageous technique for getting to information, particularly for easy going clients who don't comprehend the specialized method for seeking. To translate the client inquiry which is communicated in type of inquiries, sentence and irregular words utilizing… Show more

The Field of Natural Language Processing (NLP) is getting to be one of the dynamic regions in Human-PC connection which has seen a study moved in both research and philosophy bearing in the previous couple of years. Normal Language is advantageous technique for getting to information, particularly for easy going clients who don't comprehend the specialized method for seeking. To translate the client inquiry which is communicated in type of inquiries, sentence and irregular words utilizing characteristic dialect handling are present pattern. This idea of common dialect inquiry handling assists the web crawlers with retrieving more semantic pictures identified with the client question. Flickr is one of the well-known informal communication tag based picture seek benefit which recover the pictures in type of labels, which is not generally splendidly identifying with information gave by individuals who hunt to them. Show less

The Field of Natural Language Processing (NLP) is getting to be one of the dynamic regions in Human-PC connection which has seen a study moved in both research and philosophy bearing in the previous couple of years. Normal Language is advantageous technique for getting to information, particularly for easy going clients who don't comprehend the specialized method for seeking. To translate the client inquiry which is communicated in type of inquiries, sentence and irregular words utilizing… Show more

The Field of Natural Language Processing (NLP) is getting to be one of the dynamic regions in Human-PC connection which has seen a study moved in both research and philosophy bearing in the previous couple of years. Normal Language is advantageous technique for getting to information, particularly for easy going clients who don't comprehend the specialized method for seeking. To translate the client inquiry which is communicated in type of inquiries, sentence and irregular words utilizing characteristic dialect handling are present pattern. This idea of common dialect inquiry handling assists the web crawlers with retrieving more semantic pictures identified with the client question. Flickr is one of the well-known informal communication tag based picture seek benefit which recover the pictures in type of labels, which is not generally splendidly identifying with information gave by individuals who hunt to them. Show less

Anomaly Detection is the process of classify unusual behavior. It is important data analysis task used for classify interesting and emerging patterns, trends and anomalies from data. Anomaly detection is an important tool to detect irregularity in many different domains including financial fraud detection, computer network intrusion, human behavioral analysis and many more. In today’s era the credit and Loan Default is become high because of many fraudulent activity or increase online… Show more

Anomaly Detection is the process of classify unusual behavior. It is important data analysis task used for classify interesting and emerging patterns, trends and anomalies from data. Anomaly detection is an important tool to detect irregularity in many different domains including financial fraud detection, computer network intrusion, human behavioral analysis and many more. In today’s era the credit and Loan Default is become high because of many fraudulent activity or increase online purchases. This paper give survey about many technique for anomaly detection in credit or loan default. Show less

An ad hoc network is anaccumulation of wireless mobile nodes dynamically forming a tentative network without the use of any existing network infrastructure or medication administration. Due to the limited transmission range of wireless network interfaces, multiple network "hops" may be needed for one node to transpositionof data with another node across the network. In recent years, wireless ad hoc networks (WANETs) have become very in-vogue due to their wide range of petition and their ability… Show more

An ad hoc network is anaccumulation of wireless mobile nodes dynamically forming a tentative network without the use of any existing network infrastructure or medication administration. Due to the limited transmission range of wireless network interfaces, multiple network "hops" may be needed for one node to transpositionof data with another node across the network. In recent years, wireless ad hoc networks (WANETs) have become very in-vogue due to their wide range of petition and their ability to be deployed under normal and rugged conditions while supporting high data rates. Although many intrusion detection and trust-based systems have been developed to protect ad hoc networks against misconduct such as rushing attacks, query-flood attacks, and selfishness of nodes, these aegis mechanisms are still not able to detect protocol compliant attacks called Jellyfish (JF) attacks. They target closed-loop flows such as TCP that are responsive to network conditions like delay and packet losses and can easily partition the network. In this paper, we introduce a technique which can be used to detect and detain Jellyfish delay variance attacks in ad hoc networks Show less

Dynamic Source Routing protocol (DSR) has been accepted itself as one of the distinguished and dominant routing protocols for Mobile Ad Hoc Networks (MANETs). From various performance analysis and results, it is shown that DSR has been an outstanding routing protocol that outperforms consistently than any other routing protocols. But it could not pervade the same place when the performance was considered in term of energy consumption at each node, energy consumption of the networks, energy… Show more

Dynamic Source Routing protocol (DSR) has been accepted itself as one of the distinguished and dominant routing protocols for Mobile Ad Hoc Networks (MANETs). From various performance analysis and results, it is shown that DSR has been an outstanding routing protocol that outperforms consistently than any other routing protocols. But it could not pervade the same place when the performance was considered in term of energy consumption at each node, energy consumption of the networks, energy consumption per successful packet transmission, and energy consumption of node due to different overhead. Because, DSR protocol does not take energy as a parameter into account at all. And as MANET is highly sensible towards the power related issues and energy consumption as it is operated by the battery with the limited sources, needed to be used efficiently, so that the life time of the network can be prolonged and performance can be enhanced. we have proposed a novel energy efficient DSR (Dynamic Source Routing) routing protocol which modified to improve the networks lifetime in MANET in terms of energy. We also provided a solution, based on considering the energy of each node because each node's energy state has a huge influence on the entire network lifetime. All simulation is performed in NS2 simulator. Show less

Data mining is that the extraction of attention-grabbing patterns or data from large quantity of information. In recent years, with the explosive development in web, knowledge storage and processing technologies, privacy preservation has been one in every of the larger issues in data processing. variety of ways and techniques are developed for privacy conserving data processing. This paper provides a good survey of various privacy conserving data processing algorithms and analyses the… Show more

Data mining is that the extraction of attention-grabbing patterns or data from large quantity of information. In recent years, with the explosive development in web, knowledge storage and processing technologies, privacy preservation has been one in every of the larger issues in data processing. variety of ways and techniques are developed for privacy conserving data processing. This paper provides a good survey of various privacy conserving data processing algorithms and analyses the representative techniques for privacy conserving data processing, and points out their deserves and demerits. Finally the current issues and directions for future analysis area unit mentioned. Show less