bluuebunny/crossref_metadata_2025 huggingface dataset:
Stock Market Ontology-Based Knowledge Management for Forecasting Stock Trading
Authors: Devi M.Uma , Akilandeswari P. , Eliazer M.
Published: February 2023

Today’s markets are rather matured and arbitrage opportunities remain for a very short time. The main objective of the paper is to devise a stock market ontology-based novel trading strategy employing machine learning to obtain maximum stock return with the highest stock ratio. The paper aims to create a dynamic portfolio to obtain high returns. In this work, the impact of the applied machine learning techniques on the Chinese market was studied. The problem of investing a particular total amount in a large universe of stocks is considered. The Chinese stocks traded on Shanghai Stock Exchange and Shenzhen Stock Exchange are chosen to be the entire universe. The inputs that are considered are fundamental data and company-specific technical indicators unlike the macroscopic factors considered in the existing systems. In the stock market document repository, ontological constructs with Word Sense Disambiguation (WSD) algorithm improve the conceptual relationships and reduce the ambiguities in Ontological construction. The machine learning techniques Kernel Regression and Recurrent Neural Networks are used to start the analysis. The predicted values of stock prices from the Artificial Neural Network provided quite accurate results with an accuracy level of 97.55%. In this study, the number of nodes will be selected based on Variance-Bias plots by tracking the error on the in-sample data set and the validation data set.
Network Intrusion Detection System Based Security System for Cloud Services Using Novel Recurrent Neural Network - Autoencoder (NRNN-AE) and Genetic
Authors: Priya S. , Ponmagal R.S.
Published: February 2023

Cloud Computing (CC) is a platform where resources and services are huge such as platforms, infrastructure, software and much more. Cloud computing builds its entire environment on the framework based on the user's requirement. Although many interventions are implemented for the problems that are identified in cloud security systems, intrusion and security issues on various services are rising day by day. This research focuses on cloud security systems where trusted access can be guaranteed for various resources and services using deep learning techniques. Deep Learning techniques can detect the anomaly variation based on selected features to find the intruder in the service provider's environment. A Novel Recurrent Neural Network (NRNN) - Auto Encoder (AE) model with a dataset is used to identify the abnormal and behavioral variation in the network. The proposed algorithm NRNN-AE is basically identifying the uncertainty of different types of malicious theft where the auto-encoder predicts the attacks against the unexpected network security challenges along with a genetic algorithm for optimization. Attacks based on the service are identified on each hidden layer based on classification that is processed in the cloud system. The results are obtained from the comparison of NSL-KDD dataset and KDD Cup 99 dataset for monitoring the behavioral and frequent changes in patterns. The system can improve the detection rate and achieve accuracy of 96% compared to the existing RC-NN model. Also the detection rate is reduced to 0.0008 which has a precision value in both positive and negative rate as a gradual increase in performance.
Recognition of Plant Leaf Diseases Using CNN
Authors: Gurumurthi Vishnupriya , Manohar S.
Published: February 2023

Agriculture improvement is a global economic issue and ongoing challenge in this covid-19 pandemic that is highly dependent on effectiveness. The recognition of the diseases in plant leaf performs a major role in the agriculture industry and city-side greenhouse farms approximate analysis of the leaf disease this article intends to integrate image processing techniques with the “convolutional neural network”, which is one of the deep learning approaches, to classify and detect plant leaf disease and publicly available plant the late data to help treat the leaf as early as possible, which controls the economic loss. This paper has a set that was used which consists of 10 classes of disease and three classes of a plant leaf, this research offers an effective method for detecting different diseases in plant leaf variations. The model was created to detect and recognize healthy plant kinds, such as tomato and potato, and pepper these three leaves will perform under the algorithm called a convolutional neural network. By modifying the parameters and changing the pulling combination, models that have been used to train and test these types of leaf sample images can be created. leaf disease recognition was based on these 10 different types of classes in three different species tomato, potato, and pepper the classification of sample images has reached diseases identification accuracy.
Train Track Crack Classification Using CNN
Authors: Kumar Manish , Visalakshi P.
Published: February 2023

This research proposes a railway crack detection system. This research describes a classification system that can classify any crack in the railway tracks by using deep learning with convolutional neural networks (CNN). In the railway network of the Indian railways, accidents are one of the major concerns due to the unidentified cracks that are available on the rail tracks. The majority of accidents occur due to railway track cracks, resulting in the loss of precious lives and economic loss. So, it has become necessary to monitor the health condition of the track regularly by using a train track crack classification system. This project prevents the train derailment by classifying cracks in the railway tracks using image processing technologies. To identify the train track crack classification system that uses deep learning with Convolutional Neural Network architecture of different layers along with certain image pre-processing methods has been very successful in the classification of railway track crack has occurred or not. In convolutional neural network, there are a lot of layers available where training of the images are done which are available in the dataset and these layers are made up of lots of neurons. So its have been found that these convolutional neural networks are considered to be able to record the colours and textures of lesions related to corresponding railway track cracks, which is similar to human decision-making.
Building a Recommender System Using Collaborative Filtering Algorithms and Analyzing its Performance
Authors: Jeejoe Akash , Harishiv V. , Venkatesh Pranay , Sangeetha S.K.B.
Published: February 2023

Recommender Systems (RS) systems help users to select items and recommend useful items to target customers who are interested in them, such as movies, music, books, and jokes. Traditional recommendation algorithms are primarily concerned with improving performance accuracy; as a result, these algorithms prefer to promote only popular products. Variability is also an important inaccurate number of personalized recommendations that suggest unfamiliar or different things. Multi objective development strategies, which magnify these contradictory measures simultaneously, are used to measure accuracy and variability. Existing algorithms have an important feature because they are not flexible enough to control competing targets. We suggest creating a recommendation system based on shared filtering. Instead of finding out the preferences and preferences of users openly, we can find out by publicly analyzing historical and real-time data. This is done through a process called matrix factorization. Matrix factorization algorithms work by decomposing the interactive matrix of the user object into a product of two rectangular matrices with a minimum size. This type of recommendation has the added advantage of finding invisible and unmeasured relationships that are not possible with standard content-based filters.
Recognition of American Sign Language with Study of Facial Expression for Emotion Analysis
Authors: Chakraborty Akanksha , Sri Dharshini R.S. , Shruthi K. , Logeshwari R.
Published: February 2023

Sign Language is a medium of communication for many disabled people. This real-time Sign Language Recognition (SLR) system is developed to identify the words of American Sign Language (ASL) in English and translate them into 5 spoken languages (Mandarin, Spanish, French, Italian, and Indonesian). Combining the study of facial expression with the recognition of Sign Language is an attempt to understand the emotions of the signer. Mediapipe and LSTM with a Dense network are used to extract the features and classify the signs respectively. The FER2013 data set was used to train the Convolutional Neural Network (CNN) to identify emotions. The system was able to recognize 10 words of ASL with an accuracy of 86.33% and translate them into 5 different languages. 4 emotions were also recognized with an accuracy of 73.62%.
Face Mask Detection Using OpenCV
Authors: Najeeb Abdul , Sachan Abhishek , Tomer Ashutosh , Prakash Ayushi
Published: February 2023

As a biosafety precaution, the World Health Organization (WHO) introduced the wearing of face masks after the COVID-19 epidemic. This posed challenges to existing facial recognition systems, so this study was born. In this publication, we describe how to create a system that allows you to identify people from images, even when they wear a mask. The face detector in OpenCV is used in conjunction with Based on the Mobile NetV2 architecture, a classification model in this way, it is possible to determine whether the face is wearing a mask and where it is situated. To conduct face recognition, A Face Net model is used as a feature extractor and a multilayer feedforward perceptron is used for training facial recognition models using a collection of about 4000+ photographs. Of the images, 52.9 percent came with a face mask and 47.1 percent were without mask. The outcomes of the tests demonstration that determining whether or not someone is wearing a mask is 99.65% accurate. Face recognition accuracy for ten people wearing masks is 99.52 percent, whereas face recognition accuracy without masks is 99.96 percent.
Weather Image Classification Using Convolution Neural Network
Authors: Nambiar Sanjay , Arjun P , Venkateswar Deepak R. , Rajavel M.
Published: February 2023

A real-world weather prediction system that detects and describes weather condition in image data is becoming prominent subject in machine vision . These systems are designed to address the challenge of weather classification using machine vision. Advances in the fields of Artificial Intelligence and Machine Learning enables applications to take on the image recognition capabilities to identify the input image . Deep learning is a vast field and narrow focusing a bit and takes up the challenge of solving an Image Classification process. Proposed deep learning algorithms by tensorflow or keras by classifying the image of weather reports by convolution neural network. CNN is an artificial neural network which inspires animal neural cortex and built upon it . The images are passed through the neural network which consists of multiple layers and filters and then identified and classified according to the weather type. The algorithm is inspired by the brain and so named as Convolution neural network.
Two-Step Text Recognition and Summarization of Scanned Documents
Authors: Varun V. , Muthukumar Steffina
Published: February 2023

With the explosion of unstructured textual data circulating the digital space in present times, there has been an increase in the necessity of developing tools that can perform automatic text summarization to allow people to get insights from them easily and extract significant and essential data using Automatic Text Summarizers. The readability of documents can be improved and the time spent on researching for information can be improved by the implementation of text summarization tools. In this project, extractive summarization will be performed on text recognized from scanned documents via Optical Character Recognition (OCR), using the TextRank algorithm which is an unsupervised text summarization technique for performing extractive text summarization.
Armament Detection Using Deep Learning
Authors: George Jose Rohan , Rajmohan Rithvik
Published: February 2023

In the recent past there has been an increase in the occurrence of violent incidents involving dangerous objects such as arms and knives. Being able to quickly identify and defuse such situations are of utmost importance in order to preserve peace and to avoid human casualties. One of the most important and commonly used methods to increase security is the usage of surveillance cameras almost everywhere. The benefit of object detection techniques can be used in this field in order to help improve security. Using object detection techniques in order to detect objects of interest in surveillance footage is one method to identify dangerous situations and take necessary steps in order to minimise any damages.This paper uses convolutional neural network (CNN) based YOLO algorithm in its implementation to detect weapons such as knives and pistols
Social Media User Oppression Detection Technique Using Supervised and Unsupervised Machine Learning Algorithms
Authors: Shreya Pradeep T. , Durgadevi P.
Published: February 2023

Social media is the highly gained popular medium of interaction.And because of using the social media in our day to day life more oftenly,the increase in cyberbullying has also increased,especially to the young people who are more into the social media platform.So by cyberbullying detection technique we can make a world or environment more safe.cyberbullying detection technique will categorize tweets or comments as bullying and non bullying words. The previous existing system uses algorithms such as Naïve Bayes which is slow and less accurate in identifying cyber bullying and also high false positives is observed.Instead here CNN and LSTM is proposed to identify cyberbullying comments and to provide good amount of accuracy
Sensitive Data Transaction Using RDS in AWS
Authors: Manohar S. , Vignesh Manohar , Prabhu Gabrielle Margaret
Published: February 2023

Today's and tomorrow's secret data exchange relies heavily on cryptography. Cryptographic applications offer a secure communication channel for safely transferring data. It provides individuals, groups, and organizations with greater privacy and access to communication and other information, as well as the opportunity to restore personal privacy. Nowadays, online users desire to create an account in order to get access to certain websites, such as online tutorials, online purchases, online resource access, hosting services, social networking sites, and so on. However, unless the service provider or authority person ensures that the registration and login processes are genuine, there is a potential that the account might be hacked by a third party using the ordinary user access method. With the growing usage of cloud emails and frequent reports of large-scale leakage occurrences, a security attribute known as forward secrecy becomes desired and necessary for both users and cloud service providers to improve the security of cloud email systems.
Hand Gesture Recognition Used for Functioning System Using OpenCV
Authors: Patel Shubh , Deepa R.
Published: February 2023

Recently much attention has been paid to the design of intelligent and natural user-computer interfaces. Hand Gesture Recognition systems has been developed continuously as its ability to interact with the machines. Now-a-days the news of metaverse ecosystem has increased the number of system in gesture recognition. Gestures are used to communicate with the PCs in a virtual environment. In this project Hand gestures are used to communicate information non-verbally which are free of expression to do a particular task. Here the hand gestures are recognized by using hand skeleton recognition using mediapipe library in python. In this project, the PC camera will record live video and recognizes the hand gestures based on which a particular functionality will take place. This project will present virtual keyboard, calculator and control system’s volume using hand gestures recognition technique by coding in Python using the OpenCV library.
Phishing Website Detection Using Natural Language Processing and Deep Learning Algorithm
Authors: Thirumaran M. , Karthikeyan R.P. , Rathaamani V.
Published: February 2023

Due to rapid growth of the internet most of the people started using internet through mobile and web apps to satisfy their needs. Such as online shopping and banking. Under OWSAP top 10 vulnerabilities, sensitive data exposure is one of the common threats that is identified in recent years and phishing is found to be a key source. Sensitive data exposure is majorly occurring in the internet using various phishing techniques and phishing is found to be a key sources of data stealing. Attackers, not only targeted the financial sectors and e-commerce industries, also in the field of defense and security . To detect the phishing attacks in webpages, many software was used. Some of the method of detection the phishing is, by using the URL of the webpage and by using contents of the webpage. Still, there is no robust and accurate software solution to detect the phishing attacks. The purpose of the research is to use both URL and contents of the webpage to identify the phishing. The proposed work is to build an automated and hybrid model using Random Forest (RF) algorithm in Machine learning with the Convolutional Neural network algorithm (CNN) in Deep Learning is applied to detect and classify the phishing in URL and web page contents in an automated manner .
Security in Cloud Technologies: A Brief Overview
Authors: Rayapati Siri Varshini , Muttavarapu Sravya , Nagasuri Navya , Singhal Sunita
Published: February 2023

Cloud computing, a next-generation computing technique, enables extremely flexible, scalable, and cost-effective computing that is available on demand. Organizations can expect increased agility, improved business interaction, and continuity as a result of cloud technologies. However, with increased demand, the need for data confidentiality, integrity, and availability has become one of the most crucial challenges in cloud computing. Various cryptographic approaches are employed in the cloud to guarantee that these needs are met, such as safeguarding against data breaches and malicious attacks. This paper outlines cloud infrastructure, cloud security requirements, and their challenges. We then present an overview of various cryptographic techniques such as AES, DES, RSA, etc. that have been used to ensure cloud security. We also reviewed hybrid algorithms proposed and implemented by various authors and mentioned their advantages and shortcomings.
Modelling of Boost Converter for PV Cell Using Matrix Topology
Authors: Upadhyay Priya , Yadav Ritupriya , Gupta Sahil , Yadav Dushyant , Pal Surendra Kumar
Published: February 2023

The goal of this work is to use MATLABSimulink to create a simulation of a DC-to-DC boostconverter it, utilizing SPMC topology that is a matrixconverter with only one phase. With the outputboosted more than that the fluctuations are quiet high.In this project work, we have tried to boost up theoutput to five times after keeping the fluctuations aslow as possible. The output voltage is controlled usingthe PWM approach. IGBT is used in four pairs andtaken as the switching device. Four pairs of diodes arealso used with the IGBTs and are placed in parallel andopposite direction. The simulation was performed at aswitching frequency of 10 kHz, and the findings werein excellent agreement with the quadrant operation'sfirst quadrant, and it was able to increase the voltageof itsinput by about five times . In addition, an inverterissuccessfully created, demonstrating the use of matrixtopology.
Using Transfer Learning for Automatic Detection of Covid-19 from Chest X-Ray Images
Authors: Shyni H. Mary , Chitra E.
Published: February 2023

The hasty spread of the perilous coronavirus has resulted in a significant loss of human life and unprecedented public health challenges around the world. Early screening of COVID-19 followed by an immediate medical response can halt the spread of the infection. The deep learning algorithms coupled with chest X-ray images provide fast and accurate results. This study aims to fine-tune seven pre-trained models for COVID-19 detection using chest X-ray radiographs. A sample of 3428 chest X-ray images comprising 1626 COVID-19 images was used to train and validate the models. The Inception V3 model outperformed the other models with an accuracy of 99.42%.
A Novel K Means Biclustering Fusion Based Collaborative Recommender System
Authors: Saravanan S. , Britto Aravind , Prabin S.M.
Published: February 2023

Collaborative Filtering (CF) examines a consumer's interests and delivers automatic and personalized suggestions for purchasing a variety of products. Sparsity, on the other hand, is one of the approach's primary flaws. This difficulty is inherent in the system due to an ever-increasing quantity of people and things. To address the problem of sparsity, many existing techniques have been given. The user-item rating matrix can only provide minimal information to estimate unknown evaluations in both user-based and item-based instances. In scarce data contexts, they are ineffective. In sparse data situations, many clustering-based methods are useless. In sparse situations, traditional similarity measurements like cosine, pearson correlation, and jaccard similarity are ineffective. Although the system is able to analyze similarity in this scenario, there is a chance that the similarity is unreliable due to insufficient information processed. As the accuracy of prediction drops, this has an impact on the performance of a recommender system. As a result, neighbourhood formation is an important phase in collaborative filtering. As a result, a neighbourhood approach that can perform well in a sparse environment is required. A K Means biclustering fusion based technique is proposed to mitigate the sparsity problem by fusing item-based CF with user-based CF. A Mean absolute difference measure is used to find a neighbouring bicluster that has a significant partial similarity with the active user's preferences, which supports the algorithm in locating quality neighbours. The quality of the neighbours improves the accuracy of your recommendations.
Replicare: Real-Time Human Arms Movement Replication by a Humanoid Torso
Authors: Jagannath Vivek , Kumar Shushrut , Visalakshi P.
Published: February 2023

Robotics is a field that has actively been working to reduce the involvement of humans in dangerous environments by automating tasks. In this paper, we propose a method to be able to remotely control a humanoid torso in a telekinetic method. The humanoid torso replicates the pose of the person controlling it remotely by detecting the pose of their arms through an input from an RGB camera in real-time using computer vision techniques based on machine learning algorithms. By detecting the pose, the humanoid’s joints (shoulders and elbows) are positioned to replicate the pose of the person controlling it. This task is achieved by mapping the positions of the joints of the person controlling the robot to a set of equations using vector algebra. Such a systemensures that the movements executed are not only oriented to the end-effector reaching the desired location, but it also ensures that the position of every part of the robot can be controlled to move in the required manner. This level of control eliminates the complexities of collision detection in teleoperated robotic systems and also increases the range of applications such a system can be used in efficiently.
CNN-Based Covid-19 Severity Detection and it’s Diagnosis
Authors: Khan Mohammed Amaan Ahmed , Senthil Kumar G. , Varughese Rohan Rony
Published: February 2023

Coronavirus (COVID-19) has grown to be one of the most dangerous and acute illnesses in recent years, and it has now spread across the globe. In order to prevent COVID-19, early detection of the Coronavirus is necessary. Using a convolutional neural network (CNN) and long short-term memory (LSTM), we have suggested a model for automatically diagnosing COVID-19 from X-ray images. In this model, CNN is used to extract deep features, while LSTM is utilized to identify those features. The proposed method can aid in the diagnosis and treatment of patients with COVID-19. As a final step, this technology will be able to accurately detect the severity of the disease in the lungs and provide it with an automated diagnostic. This model will be hosted on the website so that hospital visits may be minimized and diagnosis can be delivered at home, if necessary, thereby giving a solution for COVID-19 containment.
Implementation of Intrusion Detection Model for Detecting Cyberattacks Using Support Vector Machine
Authors: Ashwini S. , Sinha Megha , Sabarinathan C.
Published: February 2023

A Cyber-attack is a deliberate intent to take illegal access to one’s computer and data. The ascent of the web has turned into the groundwork of the vast majority's day-to-day schedules, and online administration has raised security worries. The rising measure of information, dividing among the cloud and the clients, additionally makes an attack surface. The attack surface has likewise extended with the ascent of organizations and the rising number of individuals utilizing them. The capacity of existing discovery plans to approve the goal and the earlier acknowledgment of assaults is falling apart. In the event that no effective assurance mechanism is carried out, the web will turn out to be substantially more helpless, expanding the gamble of information spillage or hacking. The focus here is to put forward a model (IDS) that detects network intrusions or anomaly detection by classifying all the network traffic packets as non-attack (harmless) or attack (vindictive) classes and also classifying the type of malicious classes using Support Vector Machine algorithm. The machine learning algorithm Support Vector Machine works for classification as well as regression problems. Decision boundaries are usually used in Support Vector Classification (SVC). We have used two different datasets of cybersecurity, namely KDDCUP 1999 and UNSW_NB15. The proposed model has been evaluated using performance metrics, namely accuracy, precision, recall (Detection rate), and F-measure. The test results exhibit that our framework has better identification execution for various cyberattacks. This model achieves an accuracy of 99.8 percent with the KDDCUP 1999 dataset and 98.2 percent with the UNSW_NB15 dataset, and remarkable detection rates of attacks.
An Intensive Investigation of Vehicular Adhoc Network Simulators
Authors: Bharathi S. , Durgadevi P.
Published: February 2023

Over the last several years, vehicular ad-hoc networks (VANETs) technology booming as significant research topic. Vehicular Ad-hoc Networks (VANETs) are a component of Intelligent Transportation Systems (ITS) that are in charge of communication between moving vehicles in a specific environment. VANETs are increasingly recognised as dependable networks that vehicles engage for communication between moving or stationary vehicles (V2V), Road side unit (RSU).The idea of enabling vehicle communication over a cellular connection prompted the creation of the new protocols, equipment, also different application scenarios. Various simulators were created at the time due to the inherent difficulties of evaluating these research contributions on a real-world test platform. The introduction of autonomous cars and the proliferation of new technology have created new research challenges in VANETs (e.g., 5G and edge computing). As a result, VANET simulators must be assessed to determine whether they are still capable of analysing these novel ideas. This study delivers a comprehensive assessment of Vehicular simulators such as Veins, EstiNet, ezCar2X, Eclipse MOSAIC, VanetSim, NetSim, VENTOS,demonstrating their current condition and capabilities for evaluating innovative situations in Vehicular network research. Depends on this demonstration, we highlight the unresolved research issues need to be resolved in current and future Vehicular network simulations.
IOT Based Smart Air Purifier
Authors: Pandey Yash Kumar , Patel Prashant Kumar , Yadav Mohit Singh , Singh Shikha , Yadav Pinki
Published: February 2023

With the development in the technologies and modern equipment the level of the pollution has increased significantly. Air pollution caused due to the factories and the automobiles contributes a large share in this pollution and also one of the most challenging because the Particulate matters are released directly in to the air. So, this project is based on solving the global problem of air pollution by designing an air purifier that brings the increased level of air pollution to its lowest level.
In this air purifier HEPA filters is used to purify the air and to make it smart sensors and IOTs are used. To make this whole process automatic Arduino based microcontroller (Node MCU ESP12e) is used which makes this whole system self-operating .This model will also use air quality sensor (MQ135 ),temperature sensor (DHT11) and humidity sensor (DHT11) for its working. Along-with this for energy requirements solar panels are used but for future scope electricity generation using piezo-electric plates has also been suggested.
A Cumulative Tool for Docker Vulnerability Scanner
Authors: Ram Chilla Jagadeesh , Pavithran Vipin , Nair Varun
Published: February 2023

Docker container technology is a new virtualization technique that is extremely efficient throughout the development and deployment phases. Although Docker container technology is more convenient than traditional virtualization technology (virtual machines); it suffers from weak security due to inexperienced Docker image auditing techniques. To protect the host computer or local Docker containers from malicious Docker containers, it is required to detect potential hazards in Docker images and identify risks when Docker container instances are running on the host computer. This paper proposes a tool to give the cumulative report of the three major open-source vulnerability scanners like Trivy, Clair, and Grype.
Clinical Outcome Future Prediction with Decision Tree and Naive Bayes Models
Authors: Veena S. , Sumanth Reddy D. , Lakshmi Kara C. , Uday Kiran K.A.
Published: February 2023

Clinical decision-making in health care is even now inspired by data-driven computer forecasts or suggestions. A range of machine learning functions has recently been shown in clinical works, particularly for result prediction patterns spanning from humanity to stroke. We investigate the state of the art in relevant subjects such as data point treatment, interpretation, and simulation assessment in the framework of outcome prediction models improved utilizing data as automated health data. We also look at the flaws in widely used modeling assumptions and offer suggestions for further research
Used Car Price Prediction Using Machine Learning
Authors: Alexstan Aarone Steve J. , Monesh Krishna M. , Poonkodi M. , Raj Vineet
Published: February 2023

The increase in new cars and customers' economic inability, global sales of old cars are expanding. As a result, there exists a pressing need for a second-hand automobile method for predicting prices that accurately calculates the value of a car based on number of factors. In the current circumstance, the existing system involves a mechanism in which a seller sets a price at random and the buyer has no knowledge of the car or its value. In fact, the seller doesn't even know the current value of the car or the price at which the car should be sold. To solve this problem, we have developed a very effective model. Regression algorithms are used to provide continuous values as output rather than classified values. This allows for the prediction of the car's real price rather than its price range.
Face Mask and Social Distancing Detection Using Faster Regional Convolutional Neural Network
Authors: Tushar Bansal M. , Rahman Mohammed Saqib , Jaffer Ali S. , Indumathy M.
Published: February 2023

Face masks and social distancing is essential for many infectious diseases that spread through micro-droplets. According to WHO, the preventive measure for COVID-19 is to follow social distancing. Face Detection has expanded as a widespread concern in Computer Vision and Image processing. Many unique algorithms are developed using Convolutional Architectures to curate the algorithm as accurately as possible. First, the person in the video frames is pinpointed with the aid of Deep Learning (DL). The second step is to calculate the span between any two individuals through approaches of image processing. We aim to use a binary face classifier which will help us analyze the frame and help in detecting any face present irrespective of its alignment. Our proposed technique helps in generating accurate face segmentation masks from any random-sized input picture. We start by using an RGB image of any size, the approach uses Predefined Training for feature extraction. Faster Regional Convolutional Neural Networks are used for training. Here we segment out the faces present in the given image or frame semantically. Faster Regional Convolutional Neural Network (FRCNN) processes the given data faster with higher accuracy. The precision and decision-making are very elevated in Faster RCNN compared to others.
Artificial Intelligence Based Chatbot for Healthcare Applications
Authors: Nimal Kumarr A. , Nair Vaishakh V. , Jegdeep R. , Nehru J. Arun
Published: February 2023

During the epidemic, managing the flow of a large number of patients for consultation has been a tough game for hospitals or healthcare workers. It is becoming more difficult to contact a doctor considering the recent situation, especially in rural areas. It's obvious that well-designed and operated chatbots may actually be helpful for patients by advocating precautionary measures and cures, as well as taken to prevent harm inflicted by worry. This paper describes the development of a complicated computer science (AI) chatbot for advising prompt actions when they need to see a doctor. Moreover, offering a virtual assistant may suggest which sort of doctor to consult.
Sentiment Analysis on Food-Reviews Dataset
Authors: Deepa R. , Khan Fardeen , Singh Harsh , Gupta Gundu Jitendra Satya Sai
Published: February 2023

There has been a vast development in the field of the internet, it has led people to express their opinions. It is very important to understand the customer for the successful businesses. Customers express their thoughts in the form of reviews, which can be positive or negative. For successful businesses, it is essential for them to understand a customer and their behavior because it will help them to grow their business more successfully. In this paper, we have proposed sentiment analysis of restaurant review datasets using multinomial naive bayes and logistic regression. This program will help owners quickly determine the customer's sentiments.
Converging Blockchain for Organ Donation
Authors: Kavitha M.S. , Priyadharshini D. , Priyanka K. , Rathina K. Prapa , Rithika M.
Published: February 2023

Allograft has emerged as a preeminent province in medical management. In today's contemporary world, many emerging technologies have evolved and are digitally automatized with the help of Artificial intelligence, Cloud, Blockchain, etc, and it brings innovative solutions for the critical problems to make new vogue [7]. practitioners are given the challenge of forcibly acquiring, and companionating health records [18]. The most highly demanded aspects of the interweb are privacy and security, and this is where blockchain plays a predominant role. It has features such as transparency, decentralization, and immutability that make it indispensable. This technology has captivated remarkable attention from researchers as well as government organizations [6]. By using blockchain, electronic medical records (EMR) and donor details can be stored securely. The electronic medical records are hashed through a distributed IFPS file server that is secure and it is immutable. It is done with the aid of user signs acquired digitally at the time of saving, which cannot be redo anymore. This paper aims to/ design a web portal to collect unused medicine and give those to needy people through hospitals or NGOs that will be beneficial to them. It can be done after checking the medicines and it is approved by the admin or hospitals.
Detection of Bike Riders with no Helmet
Authors: Sai Aditya Ganeshkumar , Swetha Balaji , Meenakshi K. , Hariharan Chandrasekaran
Published: February 2023

Machine literacy refers to the creation of digital mechanisms which could make significant contributions without formal training by interpreting as well as extrapolating from attack patterns using mathematical techniques from various models. It's research wherein the system can improve itself autonomously as a consequence of the interaction and data. It's an example of artificial intelligence in action. In this study, we'll look at recognizing bike riders wearing or not wearing helmets in a video. To get the movies back into circulation, we'll have used the OpenCV Software. The YOLO and CNN models, that are utilized for enabling real-time license plate retrieval for a non-helmeted rider, the above two models are indeed the prescribed strategy for our device. The technology in our approach looks to see if the user who is riding the bike is wearing a helmet. If the rider is not wearing the required protective gear, the license plate is nonetheless extracted so that it could be supplied to activity recognition technology for evaluating and giving the fines to the prosecutor. For this design, we have a truly vast compass. It can be used to detect bikers who are not wearing a helmet. If the individual in the video is not wearing a helmet, the system is designed to detect the license plate of the bike and send an appropriate alert message to the appropriate person. This system is found to be more robust and effective than other algorithms.
Pest Classification with Deep Learning and ReactJS
Authors: Jaya V. Durga , Poornima N.
Published: February 2023

Pests are the biggest problems faced by farmers. The main objective of Pest classification with deep learning and ReactJS paper is to create a website that takes in input in the form of images from the user and tries to classify them into various pests (mainly 9 different classes). For the classification of pests, three deep learning models were chosen and trained. Their performance is compared and the best performing model is deployed in a form of a website with a good and intuitive user interface. The user interface was created with React (javascript framework), Sass, and TensorFowJS (A deep learning library designed especially for javascript developers). The deep learning models that are selected, trained, and evaluated in this paper are VGGnet-16, ResNet-152, and MobileNet. MobileNet has provided the highest accuracy of 99.80%.
Building a New IPv8 Bootstrapper and Network Discovery Strategy for Trusted Chain Identities
Authors: Shagun Attri , Hemalikaa T.V. , Nusair Basha R. , Deivapreetha C.A.S.
Published: February 2023

The Self-Sovereign Identity (SSI) strategy provides user identities that the SSI system cannot serve its purpose without considering user privacy at the network level. The existing systems work to the best of its ability but lack in allowing for a smooth transition of network input/output. This paper addresses the above issue by optimising the Network Discovery Strategy and creating a new Bootstrapper configuration.
Detecting Fake Job Posting Using ML Classifications and Ensemble Model
Authors: Praveen Aadharsh K , Harsita R. , Murali Rachanna Deva , Niveditha S.
Published: February 2023

In this project, we create a fraudulent checker tool to detect fake job postings using NLP (Natural Language Processing) and ML approaches (Random Forest Classifiers, Logistic Regression, Support Vector Machines, and XGBoost Classifiers). These approaches will be compared and then combined into an ensemble model which is used for our job detector. The aim is to predict using machine learning for real or fake job prediction results with the highest accuracy. Dataset analysis is performed by supervised machine learning techniques (SMLT) and collects a variety of information such as variable identification, missing value handling, and data validation analysis. Data cleaning and preparation along with visualization are performed on the entire dataset. The ensemble model is created at the end using ML Algorithms like XGBoost, SVM, Logistic Regression, and Random Forest Classifier by choosing 4 of the best contributing features. The model produced at the end will be implemented in a Flask application for demonstration.
SIGN BOT Extending an Ability to Communicate by Creating an Indian Sign Language
Authors: Kumar S. Sampath , Ajay Kumar V. , Arun Nataraj S. , Devasarathy B. , Hariharan B.
Published: February 2023

There is a communication lag between deaf-mutes and normal people. To overcomethat, we are providing information access and services to deaf-mute people in Indian Sign Language (ISL) and developing a flexible project that can be enlarged to capture the entire lexicon of Indian Sign Language via physical gestures like hand expressions and non-manual signs like facial expressions by developing and building a training model using machine learning algorithms. Sign language recognition uses image-based manual and non-manual gestures. Here we used figure recognition to identify manual and non-manual gestures. Finding expression gestures and analyzing finger movements to determine what the deaf-dumb individual is saying. In Python, the MediaPipe recognizes the hand signs and facial gestures of a person. These modules were developed to assist people with non-identical motions. This paper presents figure identification of Indian Sign Language via hand and facial gestures, as well asits integration with a chatbot as transcript output.
Electric Vehicle’s Charging Stations Allocation System for Metropolitan Cities
Authors: Das Nabhendu , Tiwari Sumit , Malleswari T.Y.J. Naga
Published: February 2023

For years, humanity has been progressing with the cost of harming the environment. And now one of the biggest change and solution being the introduction of electric vehicles. And the past few years’ electric vehicles had shown us it’s environmental and economic advantages, but distribution of the charging stations of these electric vehicles is crucial so that it could meet the needs of the users of these electric vehicles. Numerous attempts have been made to tackle this problem to find an optimize way to allocate the charging stations, but the traditional mathematical equation used are time consuming and suffers when put in new conditions such as different countries as the constants taken changes according to the places. But having the advantage of manipulating large data with the help of machine learning and applying data algorithms which adapts with different situations and bringing out hidden inferential we could take a new way of handling this problem. This paper consists of an exploration of computational ways, using machine learning algorithms to determine an optimal allocation of the electric vehicle’s charging stations in metropolitan cities and creating an interface for ease of use, also a thorough comparison with petrol pumps.
Marking and Traceback Algorithm for IP Traceback with Bitwise Operator
Authors: Kumar S. Sampath , Prithiv M. , Kumar S. Ravi , Kumar K. Senthil , Vishnu K.
Published: February 2023

Forswearing of Service (DoS) and Denial of Service (DDoS) assaults are not kidding strings to the Internet. The recurrence of DoS and DDoS assaults is expanding step by step. Computerized instruments are additionally accessible that empower non-specialized individuals to carry out such assaults without any problem. Consequently, it isn't simply essential to forestall such assaults, yet additionally need to traceback the aggressors. Following back the wellspring of the assaults, which is known as an IP traceback issue is a difficult issue due to the stateless idea of the Internet and parodied Internet Protocol (IP) bundles. Various Internet Protocol traceback strategies present, yet they are have constraints like quantity of bundles needed or capacity computational workloads brought about at switches. A thought of an improved bundle stamping and traceback calculation for IP traceback to recognize an aggressor that works with the traceback of the parodied parcel to its starting point is proposed. The procedure proposed decreases computational time.
Sign Language Detection Using Action Recognition
Authors: Dutta Nishan , Indumathy M.
Published: February 2023

Sign language detection technique is a part of technology which is of extreme importance to the society. Sign languages is used by deaf and dumb people who are unable to communicate directly using sound since they lack the ability to produce or recognize sound waves which enable us to communicate easily. The proposed project aims in decreasing the distance between the sign language detection techniques which only focuses on detecting the meaning of letters like ASL and not actions provided by the users. The project detects sign languages by using key holes as the position locator and then trains the system to detect accordingly. Keyholes are used to find the position of gesture to use LSTM throughout coaching of the information. Experimental results demonstrate the efficaciousness of the planned methodology in sign language detection task
Proposed Music Mapping Algorithm Based on Human Emotions
Authors: Burnwal Harsh Kumar , Mishra Muskan , Annapurani K.
Published: February 2023

Facial recognition based music system plays an important role in the treatment of human psychology. Face recognition system is an extensively used technique in most of the applications such as security system, video processing, in surveillance system and so on. People are often confused while choosing the kind of music they would want to listen. Relatively, this paper focuses on making an efficient music recommendation system which will recommend a suitable music to make the person feel sooth using Facial Recognition Techniques. This system uses FER-2013 dataset for training of the CNN, which is made using mini-xception architecture. Augmentation techniques are used for increasing the number of images in the dataset for training, which helps to increase the accuracy of the prediction. The face is captured using webcam and facial extraction is done using Haarcascade classifier and then sent to the CNN layers. The mini xception algorithm used in these CNN layers makes the system lighter and efficient as compared to existing systems. The accuracy of the proposed model is calculated and found to have reached the barrier threshold of 95% and average accuracy was found to be 90%. The song is recommended to the user using the proposed mapping algorithm.