Importance of Tides for Periastron Precession in Eccentric Neutron Star - White Dwarf Binaries

Although not nearly as numerous as binaries with two white dwarfs, eccentric neutron star-white dwarf (NS-WD) binaries are important gravitational-wave (GW) sources for the next generation of space-based detectors sensitive to low frequency waves. Here we investigate periastron precession in these sources as a result of general relativistic, tidal, and rotational effects; such precession is expected to be detectable for at least some of the detected binaries of this type. Currently, two eccentric NS-WD binaries are known in the galactic field, PSR J1141-6545 and PSR B2303+46, both of which have orbits too wide to be relevant in their current state to GW observations. However, population synthesis studies predict the existence of a significant Galactic population of such systems. Though small in most of these systems, we find that tidally induced periastron precession becomes important when tides contribute to more than 3% of the total precession rate. For these systems, accounting for tides when analyzing periastron precession rate measurements can improve estimates of the WD component mass inferred and, in some cases, will prevent us from misclassifying the object. However, such systems are rare due to rapid orbital decay. To aid the inclusion of tidal effects when using periastron precession as a mass measurement tool, we derive a function that relates the WD radius and periastron precession constant to the WD mass.Comment: Published in The Astrophysical Journa

An Empirical Performance Analysis of Multi-Classification of Diseases of Tomato Leaf using CNN Models in the Deep Learning

Tomato farming in India, producing tomatoes is one of the leading productions and stands second-largest producer of tomatoes in the world. Tomato farming has been facing challenges as the crop is susceptible to tomato diseases that include Bacterial_Spot, Early_Blight, Septoria_Leaf Spot, Spider_Mites and Late_Blight, that accounts to massive decline in the crop production. The significant drop in the production raises alarm in the analysis of the leaf of tomato with adoption of state of art technologies into the farming. The analysis of tomato leaf with the intent of early prediction of particular disease, includes employment of Convolutional Neural Network (CNN) Models include LetNet5, ResNet50 and AlexNet of the Deep Learning. The proposed work employed the kaggle database tomato leaf diseases dataset that contains 10,000 images that consist of healthy leaves and disease affected leaves. Deep Learning includes Convolutional Neural Networks models: LetNet5, ResNet50, AlexNet are applied on the  disease affected and healthy leaves of tomato dataset and it is performed empirical analysis of the CNN models in the prediction of diseases of leaf of tomato through metrics related to performance such as F1-Score, Accuracy, Precision, Recall. The proposed work which highlights empirical performance analysis of the CNN models: LetNet5, ResNet50, AlexNet, provided the noteworthy result that ResNet50 model is able to perform multi-classification the tomato leaf diseases with better accuracy 0.98701 and F1-score 0.98932

The Investigative Study on the Performance Analysis of SMOTE employed Machine Learning Classifier Models to DDoS Attack Detection

Distributed Denial of Service (DDoS) attack, a severe attack on the network services during the contemporary era, is categorized under active attacks in security attacks. The impact of this attack on the organization or individual resources leads to massive loss in terms of finance, reputation. Therefore, detecting Distributed DDoS attacks is vital in ensuring the availability and integrity of online services of an organization. The work in this paper employed machine learning techniques, complemented by Synthetic Minority Over-sampling Technique (SMOTE), to tackle the inherent challenge of imbalanced DDoS attack dataset: CSE-CIC-2018 and to enhance computational efficiency while maintaining accuracy with a fraction of the original dataset. The emphasis of the this works is to comprehensively assess the performance of five prominent algorithms of machine learning - Naive Bayes, Random Forest, Logistic Regression, Decision Tree, and XGBoost - in the context of detection of DDoS attack. The overhead of oversampling is handled with the application of SMOTE oversampling and it has been addressed data imbalance issues, improving the algorithms' capability to identify attacks of DDoS effectively. The work of this paper finds and reveals distinct comparative advantages among the algorithms employed in the DDoS attack detection and provides actionable insights in choosing the most suitable algorithms of Machine learning for the detection of DDoS attack, provided emphasizing the significance of SMOTE to enhance the algorithms' performance in the presence of imbalanced data. Eventually, this paper offers invaluable guidance for organizations seeking to make safe their network against DDoS attacks while considering the crucial tradeoffs between accuracy and computational efficiency. The proposed work in this paper presented the results that Random Forest classifier ensured the better performance with F1-Score value 0.99, Mathews Correlation Coefficient (MCC) value 0.98 and accuracy value 0.99 relative to other classifiers employed

A Review on Software Performance Analysis for Early Detection of Latent Faults in Design Models

Organizations and society could face major breakdown if IT strategies do not comply with performance requirements. This is more so in the era of globalization and emergence of technologies caused more issues. Software design models might have latent and potential issues that affect performance of software. Often performance is the neglected area in the industry. Identifying performance issues in the design phase can save time, money and effort. Software engineers need to know the performance requirements so as to ensure quality software to be developed. Software performance engineering a quantitative approach for building software systems that can meet performance requirements. There are many design models based on UML, Petri Nets and Product-Forms. These models can be used to derive performance models that make use of LQN, MSC, QNM and so on. The design models are to be mapped to performance models in order to predict performance of system early and render valuable feedback for improving quality of the system. Due to emerging distributed technologies such as EJB, CORBA, DCOM and SOA applications became very complex with collaboration with other software. The component based software systems, software systems that are embedded, distributed likely need more systematic performance models that can leverage the quality of such systems. Towards this end many techniques came into existence. This paper throws light into software performance analysis and its present state-of-the-art. It reviews different design models and performance models that provide valuable insights to make well informed decisions

Ascertaining Along With Taxonomy of Vegetation Folio Ailment Employing CNN besides LVQ Algorithm

In agriculture, early disease detection is crucial for increasing crop yield. The diseases Microbial Blotch, Late Blight, Septoria leaf spot, and yellow twisted leaves all have an impact on tomato crop productivity. Automatic plant illness classification systems can assist in taking action after ascertaining leaf disease symptoms. This paper emphasis on multi-classification of tomato crop illnesses employs Convolution Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm-based methodology. The dataset includes 500 photographs of Tomato foliage with four clinical manifestations. CNN paradigm performs feature extraction and categorization in which color information is extensively used in plant leaf disease investigations. The model's filters have been applied to triple conduit similar tendency on RGB hues. The LVQ was fed during training by a yield countenance vector of the convolution component. The experimental results reveal that the proposed method accurately detects four types of solanaceous leaf diseases

Empowering Visually Impaired through the Assistance of SAHAYAK – A Walking Aid for the Blind

To help blind people overcoming difficulty in their movement in the physical environment and even in their home, a study on an engineering concept is very much necessary. So, our research comes out with an aid that will help blind people in their surroundings. It can detect any obstacle that will block the path of the blind. And The motion of the user can be sensed by the bot. Thus, Blind people can comfortably receive the help of our bot in assisting their movement from one place to another. This paper describes about an automated vehicle which can be controlled by an ultrasonic sensor to avoid obstacles when they move in their environment. Our automated robotic system is made up of an ultrasonic sensor and Arduino micro controller controls our automated bot. It is located in the front part of the bot. The ultrasonic sensor retrieves the data from the environment through the sensors attached to the bot. When any obstacle is detected then immediately that path is changed and an obstacle free path is chosen. The bot wheel is moved based on the data received by the controller from the sensor. The direction and wheel movement of the bot and will be decided from the ultrasonic sensor sensing and also using wheel encoder. It is used for detection and avoidance of interference. The controller is also programmed to be used with an android application

IoT based Driver Drowsiness and Pothole Detection Alert System

One of the common in progressing countries is the maintenance of roads. Well maintained roads contribute a major portion to the country’s economy. Identification of pavement distress such as potholes and humps not only help drivers to avoid accidents or vehicle damages, but also helps authorities to maintain roads. This paper discusses various pothole detection methods that have been developed and proposes a simple and cost-effective solution to identify the potholes and humps on roads and provide timely alerts to drivers to avoid accidents or vehicle damages. Not only Potholes and humps are the main cause of accidents other than over speeding and drowsiness of driver includes the issue of accidents. Drowsy state may be caused by lack of sleep, medication, tiredness, drugs or driving continuously for long period of time. So, here is the solution for detecting the potholes and humps and to alert the driver from drowsiness while driving. In this paper, the system is structured to detect potholes and to alert the drowsy driver by using the ultrasonic sensor, eyeblink sensor and IR sensor and microcontroller. Ultrasonic sensor senses the humps, IR sensor senses the potholes and eye blink sensor the blinking of eye and this sensing signals fed into the Arduino to alert the driver by buzzer sound

Weighted contrast enhancement based enhancement for remote sensing images

This paper discuss a novel approach based on dominant brightness level analysis and adaptive intensity transformation to enhance the contrast for remote sensing images. In this approach  we first perform discrete wavelet (DWT) on the input images and then decompose the bLL sub band into low-, middle-, and high-intensity layers using the log-average luminance. After estimating the intensity transformation, the resulting enhanced image is obtained by using the inverse DWT. The proposed algorithm overcomes this problem using the adaptive intensity transfer function. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than existing techniques