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

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

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

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

Basic science232. Certolizumab pegol prevents pro-inflammatory alterations in endothelial cell function

Background: Cardiovascular disease is a major comorbidity of rheumatoid arthritis (RA) and a leading cause of death. Chronic systemic inflammation involving tumour necrosis factor alpha (TNF) could contribute to endothelial activation and atherogenesis. A number of anti-TNF therapies are in current use for the treatment of RA, including certolizumab pegol (CZP), (Cimzia ®; UCB, Belgium). Anti-TNF therapy has been associated with reduced clinical cardiovascular disease risk and ameliorated vascular function in RA patients. However, the specific effects of TNF inhibitors on endothelial cell function are largely unknown. Our aim was to investigate the mechanisms underpinning CZP effects on TNF-activated human endothelial cells. Methods: Human aortic endothelial cells (HAoECs) were cultured in vitro and exposed to a) TNF alone, b) TNF plus CZP, or c) neither agent. Microarray analysis was used to examine the transcriptional profile of cells treated for 6 hrs and quantitative polymerase chain reaction (qPCR) analysed gene expression at 1, 3, 6 and 24 hrs. NF-κB localization and IκB degradation were investigated using immunocytochemistry, high content analysis and western blotting. Flow cytometry was conducted to detect microparticle release from HAoECs. Results: Transcriptional profiling revealed that while TNF alone had strong effects on endothelial gene expression, TNF and CZP in combination produced a global gene expression pattern similar to untreated control. The two most highly up-regulated genes in response to TNF treatment were adhesion molecules E-selectin and VCAM-1 (q 0.2 compared to control; p > 0.05 compared to TNF alone). The NF-κB pathway was confirmed as a downstream target of TNF-induced HAoEC activation, via nuclear translocation of NF-κB and degradation of IκB, effects which were abolished by treatment with CZP. In addition, flow cytometry detected an increased production of endothelial microparticles in TNF-activated HAoECs, which was prevented by treatment with CZP. Conclusions: We have found at a cellular level that a clinically available TNF inhibitor, CZP reduces the expression of adhesion molecule expression, and prevents TNF-induced activation of the NF-κB pathway. Furthermore, CZP prevents the production of microparticles by activated endothelial cells. This could be central to the prevention of inflammatory environments underlying these conditions and measurement of microparticles has potential as a novel prognostic marker for future cardiovascular events in this patient group. Disclosure statement: Y.A. received a research grant from UCB. I.B. received a research grant from UCB. S.H. received a research grant from UCB. All other authors have declared no conflicts of interes

Research Journal of Pharmaceutical, Biological and Chemical Sciences An Analytical Method Development and Validation for Simultaneous Estimation of Atazanavir and Ritonavir in Tablet Dosage Forms by Using UPLC

ABSTRACT The present work was undertaken with the aim to develop and validate a rapid and consistent UPLC method in which the peaks will be appear with short period of time as per ICH guidelines. The UPLC separation was achieved on a Symmetry C 18 (2.1 x 50 mm, 1.7 m, Make: BEH) or equivalent in an isocratic mode. The mobile phase was composed of phosphate buffer (40%) [pH 2.5] and acetonitrile (60%). The flow rate was monitored at 0.25 ml per min. The wavelength selected for the detection was 249 nm. The run time was 4 min. The retention time found for Atazanavir and Ritonavir were 0.819 and 1.236 min. respectively. The % recovery was found 98.75 -101.01 % for Atazanavir and 99.05 -100.39 % for Ritonavir. The linearity was established in the range of 30 to 90 g/ml for Atazanavir and 10 to 30 g/ml for Ritonavir. The LOD found for Atazanavir and Ritonavir were 0.026 and 0.048 µg/ml respectively. The LOQ found for Atazanavir and Ritonavir were 0.096 and 0.15 µg/ml respectively. Overall the proposed method was found to be suitable, sensitive, reproducible, specific and accurate for the quantitative determination of the drug in tablet dosage form

STABILITY INDICATING RP-HPLC METHOD DEVELOPMENT & VALIDATION FOR SIMULTANEOUS DETERMINATION OF DUTASTERIDE AND TAMSULOSIN IN BULK AS WELL AS IN PHARMACEUTICAL DOSAGE FORM BY USING PDA DETECTOR

ABSTRACT Objective: The present work was undertaken with the aim to develop and validate a rapid and consistent stability indicating RP-HPLC in which the peaks will be appear with short period of time as per ICH Guidelines. The proposed method was simple, fast, accurate and precise method for the Quantification of drug in the dosage form, bulk drug as well as for routine analysis in Quality control. Method: Reversed-phase high-performance liquid chromatography (RP-HPLC) methods was developed and validated for simultaneous estimation of Tamsulosin hydrochloride and Dutasteride in bulk drug and in combined dosage forms. RP-HPLC separation was achieved on a Symmetry C18 (4.6 x 150mm, 5mm, Make: XTerra) under an Isocratic Mode. The mobile phase was composed of Phosphate Buffer (20%) whose pH was adjusted to 2.5 by using Orthophosporic Acid & Acetonitrile (80%) [HPLC Grade]. The flow rate was monitored at 0.8 ml per min. The wavelength was selected for the detection was 274 nm. Result: The run time was 7min. The retention time found for the drugs Dutasteride & Tamsulosin were 2.003 min. & 5.067 min. respectively. The linearity was established in the range of 25 to 125µg/ml. The proposed method was adequate sensitive, reproducible, and specific for the determination of Dutasteride and Tamsulosin hydrochloride in bulk as well as in Pharmaceutical dosage form. The validation of method was carried out utilizing ICH-guidelines. Conclusion: The described RP-HPLC method was successfully employed for the analysis of pharmaceutical formulations containing combined dosage form. The drug was exposed to Thermal, Hydrolytic and Oxidative stress conditions and the stressed samples were analyzed by the proposed method. The peak homogeneity data for the drugs Dutasteride and Tamsulosin hydrochloride were obtained by using Photodiode Array Detector in the stressed sample chromatograms which demonstrated the specificity of the method for the estimation in the presence of degradants. Overall the proposed method was found to be suitable and Accurate for the Quantitative determination and stability study of the drug in Pharmaceutical dosage form. . The method was effectively separated the drug from its degradation product and it was employed as a stability- indicating one. The method was simple, precise, accurate and sensitive and applicable for the simultaneous determination of Dutasteride and Tamsulosin hydrochloride in bulk drug and in combined dosage forms. Keywords: Tamsulosin, Dutasteride, ICH Guideline, RP-HPLC, LOD, LOQ