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This research is focussed on detection of cracks in concrete surfaces. Existence of cracks shows that the surface is degrading and cause severe damage when it is continuously exposed to all climatic conditions. Generally an expert examines the crack but it is not surely known about the depth of the crack to be repaired. This research work can be the replacement and finds the spreading level of the concrete cracks accurately[2]. Image preprocessing techniques are used to extract the significant features of the input images. Classification algorithms such as Support Vector Machine and Convolutional Neural Network (CNN) are experimenting with the cracks features into different stages as initial level, medium level and advanced level[14]. CNN gives better accuracy, reduced training and testing time. By detecting the level-of crack, the necessary steps can be taken before any accident happens. [ABSTRACT FROM AUTHOR]

The primary audience for this project is the younger generation, which frequently enters the stock market and makes investments based solely on advice from others, despite the fact that the stock price genuinely depends on the stock's back graph, or previous ups and downs. The goal of this project is to build a web application that will allow users to view all stocks by stock code. After creating an account, the user can sign into the website. Each member will be taken to the stock prediction page after logging into their accounts, where they can view the most recent trends and enter the stock code. Prior to use, the indicators are converted to binary data after being initially derived using stock trading values. Three metrics are used to evaluate each prediction model in accordance with the input techniques. According to the evaluation results, RNN and LSTM greatly outperform other prediction models for continuous data. Also, the findings show that those deep learning methods are the best for analyzing binary data, albeit the gap between them is gradually narrowing due to the second method's obviously enhanced model performance. [ABSTRACT FROM AUTHOR]

Educational institutions frequently struggle to develop a workable lecture/tutorial schedule in a major universitydepartment. In this study, an evolutionary algorithm (EA)-based method is used to resolve the university scheduling problem. Throughout the process, a chromosomal representation tailored to the task is used. In order to generate workable timetables in a fair amount of computation time, heuristics and context-based reasoning have been applied. The strategy used to accelerate convergence is clever adaptive mutation. Real data from a large university has been used to validate, test, and debate the whole course scheduling system mentioned in this paper. Higher education institutions are currently concerned about students' attendance patterns. For the faculty to construct schedules automatically, a computer-based automatic timetable generator system is required. Our ideas for the "Automatic Time Table System," which consists of many applications, have been laid out. The application's timetable generation feature helps you save time. The system's automated Excel spreadsheet is utilized to maintain a record of the teachers' availability and scheduling. Our students will find this system helpful in the twenty-first century. It takes a lot of time and labor for educational staff in universities with huge student numbers to manually create a schedule. Due to this, it commonly happens that the same professor will give numerous lectures or competing classes. Automatically generating test and class schedules will be made simpler by the availability of a timetable generator. It will be produced by the system automatically, which will also help you save time. It prevents having to manually set up and manage a schedule. The purpose of this project is to develop a simple, functional, and user-friendly application that will facilitate the construction and distribution of schedules. The genetic algorithm is themain technique used to make schedules. It helps in the development of the ideal schedule by regulating all the rules. Conflicts in scheduling are not an issue for the faculty. With inputs such teacher name, data for the rooms, labs, and subject, this system will have a user-friendly, interactive, and less complicated interface. To store all the data entered as input, the system will have a carefully constructed database. The system will have an algorithm that manages all of the database's data while taking into account both hard and soft constraints. a timetable generation technique that seeks out the optimal solution using genetic algorithms. Instead of requiring time-consuming documents, the staff and students can easily view the timetable. [ABSTRACT FROM AUTHOR]

In this era, technology has revolutionized the health industry to a great extent. The proposed model aims to design a diagnostic system for various diseases based on their symptoms. The Disease Prediction System has implemented different ML prediction models for the prediction of the user's disease based on various symptoms inputted by the user. Machine learning classification algorithms analyse the inputs given by the user and then predict the disease and probability of occurrence of the disease as output. The proposed system predicts the diseases such as i) Diabetes, ii) Kidney, iii) Cancer, iv) Heart and v) Liver. Four prediction models, Naive Bayes, Decision Tree, Random Forest and Logistic Regression, has been implemented in the proposed system for various disease. The dataset "Disease Prediction Using Machine Learning," with a count of 132 symptoms, has been used in the proposed model. The main goal of the proposed model is to predict the disease; however, the user doesn't need a medical report to use this system as the prediction is based on the symptoms, which will save time and money. The system also has an easy-to-use user interface, and all the users can use it to predict genetic diseases. [ABSTRACT FROM AUTHOR]

Tumours are currently the most common cancer cause. Many patients are at risk as a result of cancer. To detect tumours like brain tumours, the medical sector needs a quick, automated, effective, and trustworthy technique. A crucial part of treatment is detection. Doctors keep a patient out of danger if accurate tumour identification is possible. In this application, many image processing methods are applied. Doctors treat many tumour patients properly and save their lives using this application. Currently, medical professionals manually inspect the patient's Magnetic imaging scans of the brain to identify the position and magnitude of the brain neoplasm. This process is time-consuming and results in imprecise detection of the neoplasm. To identify brain tumours, we can apply a Deep Learning framework integrating Convolutional Neural Network (CNN), also called Neural Network, with Visual Geometry Group (VGG) 16 Transfer learning. The advantage of the model is to anticipate the existence of a tumour in an image by returning a positive result if present and a negative effect if not. [ABSTRACT FROM AUTHOR]

Twitter-based sentiment analysis (TSA) is a method for automatically processing digital data to extract opinions. This study can offer a plethora of data on consumer perceptions of different products. Yet, a machine would have trouble comprehending the subtleties that individuals can take away from the content on social networks because it is meant for people to read rather than machines. Because of this, the majority of research in this field has historically concentrated on categorizing opinions into one of three primary groups: positive, negative, or neutral. In this research, we examine alternative techniques and emotion models that might assist in teaching computers to recognize the emotions elicited by such confusing utterances. The use of different cutting-edge classifiers, including Naive Bayes and Logistic Regression algorithms that predict outcomes with high accuracy, is suggested in this study. A front end is also created utilizing the Django server. [ABSTRACT FROM AUTHOR]

Plethora of people are victims of a wide variety of abuses. These abuses range from Domestic Violence to newly married woman, Child Abuse to young children by their parents, Cyber Bullying and many more. Some of them also increaseddue to Covid 19. Domestic violence is a problem faced a million of people worldwide. This project deals with Domestic Violence. The problem we are solving is preventable. With advanced technology, social media has become very common forpeople to share their thoughts, ideas, and stories. Many victims of online abuse report these cases and experiences on Social Media Platforms, including Twitter. Through advanced machine learning algorithms, we can automatically detect these abusesand thus help those in need. The tweets involving domestic violence can be scraped online and used to detect instances of domestic violence. This project aims to help individuals in need. Here, a bunch of tweets from Twitter were taken using key words related to abuse. The Tweets were analyzed and categorized. A Natural Language Processing Model is then is developedusing the training and testing dataset using SVC (Support Vector Classifier). The accuracy of our model is determined in the end. [ABSTRACT FROM AUTHOR]

To travel securely on roads, traffic signs are very important. The main cause of road accidents is laxity in viewing the traffic signboards and analysing them wrongly. To avoid all these problems, the new system is implemented. This system helps in identifying the traffic sign and sends a voice command to the driver, so that the driver will take necessary actions to travel safely. The system is trained by using Convolutional Neural Network (CNN/ConvNet), helps in classification and recognisation of traffic sign images. To make the system more accurate, a particular dataset is taken and trained by defining a set of classes. The German Traffic Sign Recognition Benchmark dataset was used for classification of sign boards,it contains more than 40 classes and more than 50000 images of traffic signs. After detecting the sign, a voice command is sent to notify the driver and aware him/her to follow the necessary rules on the road. The main aim of this system is safe and secure travelling and the lives of the people. [ABSTRACT FROM AUTHOR]

Security is the major concern in the wireless networks. These wireless networks are not connected through any type of cables but instead of using these cables this wireless networks use radio waves to connect different devices. Wi-Fi is a technology for this wireless networks to establish the communication between various devices which are based on 802.11 standards. These devices include personal computers, laptops, phones, tablets, video-game consoles etc. These wireless networking is not totally secure because variety of attack methods like jamming, eavesdropping, man-in-the middle attack, key reinstallation attack called as KRACK against the wireless networks. Among these all the attacks, key reinstallation attack(KRACK) is the most powerful attack against the wireless network users to steal the sensitive information of the users like bank account details, email and password. This key reinstallation attack alter the normal communication flaws in cryptographic protocols during 4-way handshake with wireless access point for again installing the key which is already generated by authenticated user. The main purpose of this attack on wireless network is for resetting some parameters related to key such as replay counter, receive counters, and transmit nonces for generating the trap for the victim. This paper includes detail analysis of key reinstallation attack on wireless network and security protocols for wireless network and then proposed a new handshake mechanism for securing wireless networks. [ABSTRACT FROM AUTHOR]

Interpreting certain story content and producing its corresponding set of images in a manner that is logically coherent is the sole purpose of story visualization. Methods and tools for the generation of an image from texts are in high demand because there are always new challenges for educators and students in presenting and understanding any tale material. On the other hand, finding images to picture uncommon characters and events in a story often requires a great deal of manual labor and is occasionally neglected. The proposed work tackles automatic scene sequence generation, a new multimedia task that makes it easier to do this and encourages teachers and students to effectively visualize stories. In order to achieve this, it is necessary to transform the story's words into a series of visuals. The series of images is an unbroken line of reliable images that all relate to the same narrative or incident. Hence, a novel phenomenon called Story Visualization is proposed. From a collective passage, a narrative story can be conceived by creating an image sequence, one for every sentence or phrase. Less emphasis is placed on the narrative's flow in story visualization, but more emphasis is placed on overall coherence among the story's dynamic scenes and characters. This is still a challenge that no conventional image or video generation methods have addressed. Henceforth, we presented an approach namely Logically Consistent Story Visualization (LoCoSV) to confront the issues. LoCoSV efficiently discovers how to represent the story visually through the use of three key modules: figure-ground segmentation (auxiliary task to provide information for maintaining character and story consistency), story and context encoder (learning of story and sentence representation), and figure-ground aware generation (using figure-ground information to generate the sequence of image). Various Evaluation Metrics and human evaluation studies were used to evaluate the performance and they demonstrated that LoCoSV improves generation over baseline models, especially in improving the Logical coherency and background consistency of the stories [ABSTRACT FROM AUTHOR]

Recommendation system provides product recommendation or assigning a rating to product. It recognizes person's likes and dislikes so as to find new, desirable content for them automatically based on the ratings of a product. It is also applied in E-commerce domain to recommend best consumer products or web-services to the users. Traditionally, most of the existing recommendation system framework applies collaborative filtering or content-based filtering approaches for product recommendation. However, the cold-start and sparsity issues are not taken care off by the existing systems. To overcome these issues, the proposed system integrates content-based filtering, collaborative filtering, and parallel hybrid methods for providing dynamic recommendation. Moreover, this system uses predicted fuzzy ratings and product description for providing domain specific product recommendation. It also provides a user friendly interface for sellers and customers for handling the products. The proposed system results in better performance and accuracy that leads to suitable Product recommendation. [ABSTRACT FROM AUTHOR]

The aim of this model is effect of heat source/radiation and motile micro-organisms nanoliquid stream among two equivalent channel. We analysed a stream in a parallel channel considering the model offered by Kuznestov and Nield (2013). Scaling bunch conversions are utilized to find similarity circumstances of such nano bio-convection streams. Similarity circumstances are mathematically resolved with a shooting performance with R-K-F 4th order. In calculation, the impact of distant parameters is analyze comprehensively on the temperature portrays, nanoparticle volume fraction depicts and thickness of gyro-tactic microorganisms. Results are associated with current study and our discoveries are found in great concurrence with these studies. [ABSTRACT FROM AUTHOR]

The characteristics of chemicals is more important for finding the solubility nature of various molecules in the compound. The chemicals can be used for various applications after validating the aqueous solubility nature of the compound. With these aspects, machine learning can be used for finding the nature of the compound and this paper uses the aqueous solubility dataset with 21 features ad 9982 compound information to predict the aqueous solubility nature of the compound. The aqueous Solubility dataset is applied to Anova test Analysis. The P-value of the F-statistic value of the feature Group is found to have "0.935708" which is greater than 0.05. So the feature "Group' is removed from the dataset which do not influence the target "Aqueous Solubility". The anova-reduced dataset is applied to ensemble regressors like Ada boost, Random Forest, Gradient Boost and Extra Trees Regressor to extract the feature importance of the Aqueous Solubility dataset. Then the feature importance ensemble regressor reduced dataset is applied to the regressors to analyze the performance indices. The performance of algorithm is analyzed with intercept, EVS, MAE, MSE and RScore. Experimental results shows that the ElasticNet regressor with extra tree regressor reduced dataset found to have the RScore of 0.9715, which is approximately close to 1. [ABSTRACT FROM AUTHOR]

The science of reliability is concerned with the problems of evaluating risks and their consequences. They are always related to some uncertainties based on which certain conclusions drawn. Reliability has been modelled for 4-component system by using Markov technique and also Mean Time to Failure (MTTF) has been computed in this paper. [ABSTRACT FROM AUTHOR]

Rainfall monitoring is one of the most essential and time-consuming services provided by the meteorological department. Rainfall, rather than other meteorological occurrences, has a critical role in human life. Although various statistical models for predicting have been presented, fuzzy logic models produce better outcomes. Fuzzification and defuzzification are the two main procedures that fuzzy logic models do. In this study, we look at how fuzzy rules/logic can be used to model rainfall in the Chennai district. We also compare the anticipated outcomes to actual rainfall data. [ABSTRACT FROM AUTHOR]

Precise and early diagnosis is crucial for the cancer patients to get optimal care. Tools available to predict cancer in advance remains deficient and also there are considerable false positive rates. In order to avoid this, the proposed work focuses on Clinical Data analysis and Image analysis of Ultra sound images to predict cancer at initial stages. In this analysis, Adaptive Randomized Tree (ART) uses clinical data such as percentage of unbound Prostate Specific Antigen (PSA), bound PSA, age, PSA density and PSA velocity and intensity extracted from Ultra sound images as metrics to predict carcinoma. Hence ART has a better prognostic performance than other diagnostic indicators for prostate cancer. This gives the preliminary evidence that ART can be used as an effective diagnosis tool for other type of cancers with appropriate metrics. [ABSTRACT FROM AUTHOR]

The rate of death in heart failure patients keeps increasing, but the causes of death are always uncertain. Even though heart failure is a diverse syndrome classified by ejection fraction, the relationship among both ejection fraction and death rates is significant but elusive. According to health cardiology report, Patients with heart failure and reduced ejection fraction have greater risk of dying, compared with heart failure patients and preserved ejection fraction. However, the factors influencing the death rate of heart failure patients remains a challenging task for the doctors and researchers. With this motivation, this paper attempts to analyze the important features that influences the mortality rate of the patients. The Heart Failure dataset from UCI data warehouse repository is subjected for analyzing the heart failure patients. The dataset is preprocessed with data cleaning process and missing values. The preprocessed dataset is applied to all the classifiers and the performance of the mortality grading is analyzed before and after feature scaling. The raw dataset with entire 12 features are applied to Ada Boost, Random Forest, Extra Tree and Gradient Boost classifier to extract the top six important features to increase the accuracy performance. The feature importance dataset is then applied to all the classifier and the performance is analyzed in terms of accuracy, precision, recall, FScore and Run Time. The implementation results shows that random forest classifier is showing the accuracy of 89% after feature scaling and the random forest classifier with the feature importance of Gradient Boost classifier shows the accuracy of 97%. [ABSTRACT FROM AUTHOR]

In this research, we will look at interstitial lung disease (ILD), which is an inflammation and fibrotic respiratory illness that mostly damages the cells and area around the lungs' air sacs known as the interstitium. There are around group of 200 diseases belonging to this category. This disease affects the other compartments of lungs, the airways known as alveoli, the blood vessels and the outside lining of lung known as pleura. This disease is characterized by respiratory symptoms, chest radiographic abnormalities, pulmonary fibrosis and fibrosis results from chronic inflammation. The lung biopsies with predominance of fibrosis results in poor prognosis comparatively than inflammation. Many studies reported that 80.9 per 1, 00,000 men and 62.7 per1, 00,000 women was diagnosed with this disease every year. There is no established treatment that prolongs life for patients with these diseases. It is critical to do accurate diagnosis in order tohave significantly dissimilar treatment choices and a improved prognosis. [ABSTRACT FROM AUTHOR]

The system reliability is derived by considering a multi component Series system, Parallel system and standby system Reliability is derived when (i) stress and strength follow Frechet distribution. (ii)Strength follow frechet distribution and stress follows mixture of two Frechet distributions. Also reliability is computed for different values of stress, strength parameters numerically. [ABSTRACT FROM AUTHOR]

The reliability is derived for n-cascade system is considered for two cases. Where Strength-stress follows Pareto and Weibull distributions. The general expression for the reliability of an n-cascade system is obtained and the system reliability is computed numerically for different values and parameters. [ABSTRACT FROM AUTHOR]

Dark green colour Tetrakis (4-aminopyridine-κN1) di chloride copper(II)monohydrate serine crystals were extracted. The powder X-ray diffraction reveals that the as grown crystals were crystalized in orthorhombic crystal system with cell parameters a=8.08692Å, b= 7.71558 Å and c= 5.72423 Å. Using the Scherrer formula the particle size were calculated. For the sample the field dependency of magnetization at room temperature upto two teslawas studied. By increasing the field the negative magnetization increase conforming that it has diamagnetic property. We have estimated the current density Jc for the sample in antiferromagnetic/paramagnetic region. [ABSTRACT FROM AUTHOR]