ISSN: 2455-1821 Scientific Journal Impact Factor (SJIF): 6.056 Index Copernicus (ICV: 56.77) Journal DOI no.: 10.37591/RTPL
STM JOURNALS ( Consortium eLearning Network Pvt Ltd)
A Review: Identification of Credit Card Fraud Using Machine Learning and Anomaly Detection Approach’s on Imbalanced Data
Credit cards are among the most widely used payment methods in today's culture, and online purchases have become quite popular. Credit card fraud has emerged as a problem in this industry as a direct result of its popularity. The issue of credit card fraud is becoming a global problem. Credit card fraud has increased due to the widespread use of these payment methods. Thanks to credit card use, e-commerce has flourished, and the infrastructure for electronic payments has become more user-friendly. To combat fraud, machine learning techniques are being used on a larger scale.When it comes to inspecting customer data, ML algorithms are crucial. Security of credit card transactions and the efficiency of online banking are, therefore, primary concerns for financial organizations. They want to do this by creating more effective methods of detecting fraudulent transactions, which will lead to a reduction in overall fraud. This study aims to define fraud detection, provide fraud detection methods, discuss banking industry fraud difficulties and challenges, and outline contemporary solutions based on ML techniques. This work presents an analysis of the existing literature on machine learning and anomaly detection methods utilized in credit card fraud detection (CCFD).As far as ML is concerned, it is the best way to guarantee privacy while increasing CCFD accuracy.Different class imbalances, machine learning, data mining, as well as anomaly detection techniques have been reviewed and compared in the domain of fraud detection systems.This review provides the overview of CCFD, key features, trends, anomaly detection techniques, machine learning techniques, class imbalance problems and existing work on CCFD.
Copyright (c) 2023 RECENT TRENDS IN PROGRAMMING LANGUAGES
From Text to Revolution: Unfolding ChatGPT's Impact on Library Services
This study explores ChatGPT, an advanced language model by OpenAI, and its potential to revolutionize library services. In order to arouse interest in the opportunities it presents, we examine the advantages, difficulties, and ethical issues associated with incorporating ChatGPT into libraries.As we illustrate ChatGPT's technology, we are able to see how well it performs on various language-based tasks. As a result, the user experience for library reference services will be improved. It also promises lightning-fast responses, improved quality, and less work for librarians.However, we face moral challenges with unfairness and privacy, highlighting the demand for a fair and responsible AI tool. We anticipate a successful relationship by balancing ChatGPT's expertise with the irreplaceable touch of human librarians.It is exciting to see how ChatGPT is used in cataloguing, contributing to academic papers, and reference services. Real-world case studies show its effect and provide us with optimum implementation practices.As we look to the future, AI developments provide more applications in user support and research by scholars. Libraries can embark on an exciting journey into an enhanced future with ChatGPT by merging innovation with ethical responsibility.
The Evolution of Programming Languages: A Survey from Early Achievements to Modern Advancements
Programming languages are fundamental to software development, providing the means for developers to communicate instructions to computers. This study provides a broad overview of programming languages and their history, classifications, key elements, the compiler and interpreter systems that implement them, and notable trends driving innovation. It examines imperative, object-oriented, functional, logical, and scripting languages while exploring syntax, semantics, typing systems, abstraction, variables and bindings, control flow, and runtime environment considerations. The compilation and interpretation processes that translate high-level languages into machine code are delineated. Significant programming paradigms like procedural, structured, modular, declarative, concurrent, and event-driven models are surveyed. Modern developments such as increased support for parallelism and domains like artificial intelligence and data science have become robust areas of focus. The study synthesizes concepts central to understanding the landscape of computer languages spanning from foundational machinations to state-of-the-art advancements.
AI Impact: Unpacking Brain Duplication in Everyday Life
Brain duplication technique is impactful in several industries to reduce the cost and human efforts. Ai-based systems are being implemented in education, health, technology, manufacturing, production and other sectors. Hence, the following work has developed a clear understanding of the brain duplication technique and its impact on human activities.The essential elements have encapsulated the utilization of computer algorithms like machine learning, natural language processing, deep learning, and similar technologies.Hence, the impact on human activities has been produced which developed the statement of how AI has been replacing human jobs as well as finding opportunities for new job sectors.Furthermore, a brief visualization has also displayed the data of recent pictures while comparing with past years as how AI has been implemented in job sectors. On the other hand, implications and discussion have been made to understand the key findings of the research. Moreover, a recommendation has shown the suggestion to improve the abilities in future use.
A Comprehensive Review on Python based NLP Approaches for Sentiment Analysis
Sentiment analysis, also known as opinion mining, is a burgeoning field in natural language processing that involves the computational analysis of textual data to determine and quantify the sentiment expressed within. This study presents a comprehensive exploration of sentiment analysis using Python, focusing on techniques, methodologies, and tools that enable the extraction of sentiment polarity from diverse textual sources. The review commences with an overview of the importance of sentiment analysis in understanding public opinion, customer feedback, and social media dynamics. A significant portion of the study is dedicated to practical implementation using Python, showcasing popular libraries such as NLTK, TextBlob. It also covers portions such as summarization, challenges in sentiment analysis, and applications.
Healthcare Management with QR Code Using Blockchain
The existing healthcare management system is heavily reliant on paper-based records and centralized databases. This makes it difficult to securely share patient data between healthcare providers, resulting in delays in treatment and potential errors in diagnosis. Furthermore, the current system is prone to cyber-attacks and data breaches, putting patient privacy at risk. The lack of transparency also leads to distrust between patients and healthcare providers. A quick response (QR) code is a two-dimensional barcode that can be scanned by a smartphone or a QR code reader on the other hand blockchain technology provides an easy and efficient way to store and retrieve information a decentralized and transparent digital record and verification of transactions on multiple computers the integration of qr codes and blockchain technology in healthcare management offers several advantages. Firstly, it improves the security and privacy of patient information by storing patient information in the blockchain which is resistant to untrusted QR codes and then patient information which ensures the integrity and authenticity of the information used to link to related notebook entries. Secondly, the use of QR code and blockchain technology increases the efficiency of the health care process, for example, when patients visit the clinic, their code can be scanned to access their medical history allergies and other relevant information which eliminates the need for manual documentation and reduce errors or possible errors of interpretation. Healthcare management is an important industry that requires the integration of QR codes and safe and efficient data management as well as technologies known for privacy and accessibility which can greatly improve QR code technology, which is the key to the unique identification of blockchain technology for each patient database provides an immutable and distributed transparent ledger that ensures the integrity of patient data. This paper presents an application that uses two methods to safely manage patient data1 facilitates access to medical records and prescriptions that increases data availability and efficiency and improves patient privacy health management that emphasizes the need to fulfill obligations. It is gaining attention and being explored for various apps. Here is an overview of how the existing system of healthcare management could leverage blockchain: Patient records management: Each patient would have their unique blockchain identifier, and their medical history, test results, prescriptions, and treatment plans could be securely stored in a blockchain. Interoperability: Healthcare blockchains can facilitate data interoperability by providing a standardized format for health data. The ability to access and update patient records seamlessly while maintaining data integrity would be made possible by this.
Enhancement of Security and Performance of Cloud System by Integration of Multi-Tenancy
Cloud computing is a well-liked model for offering flexible and scalable computing resources to both organizations and individuals. However, assuring the performance and security of cloud systems continues to be of significant concern. Multiple users or tenants can share the same cloud infrastructure thanks to multi-tenancy, a crucial notion in cloud computing. This study relates to the incorporation of multi-tenancy to improve the security and functionality of cloud computing systems, the thorough analysis of the literature already in existence on cloud computing, multi-tenancy, and the related security and performance issues. It examines the advantages and disadvantages of multi-tenancy and outlines the most important research gaps that require filling. The article analyzes several security methods and techniques that can be used in a multi-tenant cloud system to address security problems.
Tourism Promotion Website Using Three.js
A tourism promotion website using Three.js can be built using a JavaScript library to create interactive 3D graphics and animations. The website displays popular tourist destinations in the form of 3D models, allowing users to explore and interact with the models in a virtual environment. The website also includes information about destinations such as Historical context and cultural significance. One of the key features of Three.js is its use of WebGL, which allows for hardware-accelerated 3D graphics in the browser. Three.js allows the website to create high-performance and realistic 3D graphics and animations, providing an engaging and interactive way for users to explore destinations and learn about points of interest. Additionally, the tourism website includes other features such as information about accommodations and transportation making it a complete platform for tourism-related information. Overall, this website is a powerful tool for showcasing destinations and landmarks in an interactive and visually appealing way, providing users with an immersive and engaging experience and making it an effective tool for tourism promotion and planning.
This paper introduces a smart operation to find the optimal path. The evolution of finding the shortest path became more useful and systematic in several fields. Hence, the proposed model was created by implementing the A* algorithm associated with the Python language. This algorithm is an approach to search the shortest route between two points, either starting or ending. The experimental results showed a high accuracy to find the optimal path between two obstacles. People have been searching for the best solution to difficulties for a very long time to increase the productivity at work. There are several uses for the problem of shortest path in network technology research in computer science, communications, operations research, geographic information science, and other domains. Designing a problem-solving algorithm and advancing research are crucial steps in solving the problem.
Unleashing the Power of Mathematics in Natural Language Processing: Sentiment Analysis Perspectives
Sentiment analysis (SA), which utilizes natural language processing (NLP), computational linguistics, text analysis, image processing, and video processing to extract and analyze subjective information from the internet, social media, and other sources, is becoming increasingly popular in both the business world and the scientific community. It is even possible to model it so that it focuses on polarity, sentiments and emotions, urgency, and even goals. It is able to distinguish between positive, negative, and neutral data. Document-level sentiment analysis (DLSA), aspect-based sentiment analysis (ABSA), and data-driven sentiment analysis (DSA) are the three levels that are included in sentiment analysis. It is frequently utilized for the classification of text, the translation of speech, the recognition of faces, objects, patterns, and voices, as well as the identification of spam in electronic communications. In recent years, deep learning strategies have become the method of choice for applications that require computer vision and pattern recognition. The convolutional neural network (CNN) modeling technique is one of the deep learning approaches that is utilized most frequently for image processing. Opinion clustering, opinion idea or entity extraction, opinion summarization, and opinion categorization are just some of the actions and processes that are involved in text mining utilizing deep learning. Text categorization is one of the most critical processes in the text mining process that uses machine learning, while working on a project involving machine learning, it is essential to have a solid understanding of the mathematical model and to predefine the feature extraction model before utilizing the choice classification technique. With the help of NLP, X(formally known as twitter)'s representation, and deep learning-based data mining techniques, this study provides an approach called opinion mining that is based on deep learning to analyze Twitter opinion data. The accuracy and reliability of detection systems can be evaluated with the help of confusion matrices by comparing the aggregated opinions of Twitter users through the application of numerical techniques, as well as by building and labeling separate camps of Twitter opinion. The predicted class instances are shown in the form of a list, whereas the actual class instances are presented in the form of a table with rows and columns. In our study, we design CNN-based opinion classification approach and mathematical opinion mining classification model to find sentiment.
In computer science, there are different programming languages but the logic to implement a particular code remains same in every language. Many people have code bases in outdated programming languages like COBOL and switching those bases to more modern ones like Java, C++, or Python requires a significant investment of time and resources. Transcoder can conserve a lot of resources in the situation. This is a pilot project that we designed to translate a program from C++ to Python and vice versa. Two common and reliable programming languages used nowadays are C++ and Python. These two languages each have their own benefits and drawbacks. Python is an interpreted programming language; hence it requires an interpreter to be compiled. C++, on the other hand, is a pre-compiled programming language and does not require interpreter at the time of compilation. Due to Python's phenomenal growth and popularity year after year, this enables straight automatic translation rather than developing a Python program from start.
A Machine Learning Approach to Personalized Movie Recommendations Using Content-based Filtering, Web Scraping and NLP Based Sentiment Analysis
This research work presents an improved approach for movie recommendation using content-based filtering and cosine similarity. The proposed system considers the movies' content information and genre during item similarity calculations to recommend a top 10 list of similar movies based on user preferences. Data for the movies, including title, genre, runtime, rating, and cast, is obtained from the TMDB website using an API key, while reviews are web-scraped from the IMDB website and subjected to sentiment analysis using NLP to predict whether the review is positive or negative. The recommendation system is designed to minimize transaction costs and improve the quality and decision-making process for users. This statement highlights the significance of recommendation systems in today's world, emphasizing their widespread utilization by renowned applications.
Adaptive Huffman Algorithm for Data Compression Using Text Clustering and Multiple Character Modification
Adaptive Huffman algorithm is a popular data compression technique that creates a variable-length binary code for each symbol in a message. However, the original algorithm may not be efficient in compressing text data, particularly when dealing with long sequences of repeated characters. In this study, we propose a novel approach to enhance the compression ratio of the Adaptive Huffman algorithm by utilizing text clustering and multiple character modification. The proposed method first clusters the text data into groups of similar words or phrases. Then, it modifies multiple characters in each group to reduce redundancy and increase the frequency of the most common characters. This modification enables the Adaptive Huffman algorithm to produce shorter codes for the modified characters and effectively compress the clustered text data. Experimental results on a benchmark dataset show that the proposed method achieves better compression ratios than the traditional Adaptive Huffman algorithm and other state-of-the-art compression methods. The proposed method can be applied to various text data, such as documents, emails, and chat messages, and can significantly reduce storage and transmission costs.
A Study on the Intersection of Human and Artificial Intelligence through Augmented Analytics
The amount of exertion put in analytics by humans is colossal. Analytics today predominantly consists of performing the same steps repeatedly manually. Augmenting practices leverage the idea of automating the steps which are repeatedly done by humans and coalesce it with decision making capabilities of mankind. This will furnish us with applications where machines will understand data and make automated business analytics with the help of machine learning and AI. Furthermore, using AI we can amalgamate the science of decision-making capabilities of humans and automated analytics to takeover many of the repetitive procedures in data science and optimize the decision-making capability of machines resulting in dignitary business decisions.
Efficient Security Enhancement Analysis for Cross-site Scripting and Code Injection Attacks for Web Security
SQL code injection and XSSs cross-site scripting are two new fields of computer security flaws introduced by web applications that have surpassed buffer overflows as the most common class of flaws in recent years in both new vulnerability reports and exploit reports. Both SQL injection and XSS are examples of a larger group of vulnerabilities that rely on input validation. Studying the cross-site scripting and SQL server injection vulnerabilities is the main goal of this research, which also proposes a user-centric architecture for secure data transmission. Analysis of a model that provides a framework for symmetric and asymmetric encryption, which is far more dependable than the conventional ways of encryption, is the focus of this study.
Shortest and Fastest Route for Ambulance Identified by Less Congestion on the Road
Due to traffic, many people in India have passed away since they were unable to reach the hospital in time. As a result, we are creating a website for an ambulance service that will lessen the need for patients to rush to the hospital. Our main goal is to hasten the arrival of ambulances at their locations in order to save lives. When users go to our website on their smartphones, they can enter a location and search for nearby ambulances. We will also develop an algorithm-based system dubbed Automatic Ambulance Recovery System that looks for shorter routes between two points. The major goal of this project is to create a straightforward flow that will allow ambulances to arrive at hospitals promptly and thereby reduce propitiation. The primary unit, which offers the ambulance's longest scanning path and ensures its safe arrival at the hospital, controls the ambulance. Through car data, the GPS server also determines the position of the accident scene and travels there via ambulance; find the crash site and assist in hastening the patient's arrival at the hospital by using the completely automated system.
Dijkstra’s algorithm, Floyd’s algorithm, shortest path, geographic information system, transportation
Plant growth is a crucial requirement for farmers since it provides a pathway for their livelihood. Plant damage and growth are correlated with one another. Despite their best efforts, farmers frequently fail to grow healthy crops because of ill plants. Plant disease is currently a dangerous problem for farmers, customers, the environment, and the global economy. Major health problems in plants are caused by excessive pesticide use. Image processing may be the most reliable method of predicting and obtaining precise findings for plant disease diagnosis. Deep convolutional neural networks are used in this research to improve accuracy and training effectiveness. Many uninformed farmers would benefit from our program by receiving accurate disease information that will help them boost their productivity. An online application that can recognize plant illness is something we are fostering. The goal is to identify various plant infections by looking at pictures. We can precisely diagnose plant illness by using the CNN algorithm. The accuracy results demonstrate that this model is superior to any conventional frame.
Plant diseases, deep learning, convolutional neural networks, transfer learning, leaf infection diagnosis
Programming Java Applets in Web Designing
Java applets are small applications, written in the Java programming language, which are compiled into machine Java codes as "HTML name.class" files and are launched from a web page, using HTML files, after which they are executed in the Java Virtual Machine (JVM) in a separate process from the web browser itself (Microsoft Internet Explorer). Java applets appear in a web page frame, in a new application window. Applets are used to provide interactive features to web applications that cannot be provided by HTML alone. They can use mouse input and also have controls such as buttons or checkboxes. In response to user actions, an applet can change the graphical content, it provided, making applets well suited for demonstrating, visualizing, and learning about subjects ranging from physics to cardiac physiology. This study developed by the author on the basis of well-known Java applets of the “Lake”, “Snow” and “SkyLog” Image Java applets, allowing to revive the paintings of famous artists that have lakes with splashing waves, fireworks rising into the sky on the lake with splashing waves background snowfall, as well as clouds moving in the sky. The developed Image Java applets will make it possible to revive the paintings of the great artists of famous virtual galleries: the Louvre, the Metropolitan Museum, the Hermitage, the A.S. Pushkin Museum, Tretyakov Gallery, etc.
Genetic Algorithms Molecular Effect Model Optimization for High-temperature Superconductors Tin and Thallium Classes
Genetic algorithms software was applied in 3D Graphical and Interior Optimization methods for two high-temperature Superconductors (HTSCs) classes. Namely, Tin (Sn) class with (TC>0°), and Thallium (Tl) one subject to (TC˂0°, TC>0°) in Molecular Effect Model (MEM). Results include mathematical Tikhonov Regularization Functionals algorithms for this group of HTSCs without using logarithmic changes. Results also show the contrasts between these two classes for Molecular Effect Model (MEM) hypothesis. Solutions show a series of 2D/3D imaging process charts complemented with a group of numerical results. Applications for MEM in electronics physics are presented, including those for high-temperature superconductors, superconductors, and medical technology.
Cyber Security Implementation Through Deep Learning Based Fraudulent URL Detection
A malicious URL, sometimes known as Spam, malicious ads, and drive-by vulnerabilities may all be found on a site that is malevolent. To find rogue URLs, you need to be quick. Researchers previously used blacklisting, regular expression searching and signature matching in their investigations. These strategies are worthless for discovering new malicious URLs or updated versions of previously problematic URLs. A machine learning-based approach may be developed to overcome this problem. For security objects such as URLs, this type of solution takes a lot of feature engineering and feature representation study. Furthermore, in order to support both versions of existing URLs and wholly new URLs, the resources required for feature engineering as well as feature representation must be updated on a regular basis. Deep learning, machine learning, and artificial intelligence (AI) systems have recently outperformed humans in a number of disciplines, and they have even exceeded human eyesight in certain computer vision applications. They can derive the optimum feature representation automatically, from raw data. Various machine learning techniques, including Decision Tree, KNN, SVM, Random Forest, Logistic Regression, Naive Bayes, RNN-LSTM, and others, benefit from character level embedding of raw URLs, CNN in the cyber security area.
Malicious URL, Cyber Security, Feature Engineering, KNN, Random Forest, CNN, Naïve Bayes, Decision Tree
Copyright (c) 2022 RECENT TRENDS IN PROGRAMMING LANGUAGES
A Case Study Investigating an Appropriate Age and Plugged or Unplugged Approach for Programming at Primary School
Introducing computer programming concepts to children is key in their development. Understanding that what age is most appropriate for their comprehension of subject and discipline specific information is relevant in curriculum development and of particular interest when we look across jurisdictions at the diverse and divergent nature of education internationally. This study presents research investigating the most appropriate methodology and age to introduce computer programming concepts to primary school children. The study was conducted at a private school in Madrid and a variety of educational approaches were used in introducing introductory programming concepts to the children who took part. The results show that there is a need to apply a variety of plugged and unplugged approaches and the results also demonstrate that children in 5th and 6th classes experienced higher learning improvements. The study contributes to the body of knowledge in regard to learning the basics concepts of computer programming and of value to curriculum designers as CS subjects are introduced to global curricula.
Voice Notepad By Using Native File System Api And Web Speech Api In Javascript For Smart Healthcare Services
The gradual increase in the population of the world and simultaneously increase in the number of diseases has made many new pieces of research in the healthcare industry focus on efficiency and quality of treatment. Now people are demanding intelligent healthcare services, applications, and devices. Maintaining patient health data and medicines is a time-consuming activity that must be updated on a daily basis. Voice based Notepad is a simple text editor. And it is superb for writing comparatively short text documents. In today's digital age, everyone uses technology instead of traditional books and pens, and notepads have become well-liked for storing information. As it works on the principle of voice to text, so it will ease the process and save time. This software can also be used from either the doctor's or the patient's perspectives. Patients could use this application to note the doctor's prescription. JavaScript is used to develop this application as it allows us to add dynamic behaviour and event handling.
Notepad, Healthcare records, voice typing, speech to text, Patient healthcare notes, JavaScript
Voice control is a significant emerging element that is revolutionizing how people can live. The voice assistant is frequently utilized in laptops and cellphones. AI-based Operating systems with built-in voices that can detect human speech are known as voice assistants. The voice assistant will collect sounds from the microphone, translate it to text, and send it over GTTS (Google text to speech). The English-language audio file created by the GTTS engine will be played using the play sound module of the Python computer language. This research paper is to know how actually Voice Assistant works in a background and can one make a Voice Assistant by their own by using different python libraries and tried to develop a better interactive version of a Voice Assistant. In this work, we created a conceptual model that includes a new system quality component, namely interaction quality, that we believe may better represent the adoption of AI-based technologies. To validate our model, we used voice assistant system (VAS) technology as an example and evaluated a theory-based model with data from a field survey.
Healthcare Assistant Using NLP (Medibot)
As we all know, the Coronavirus (COVID-19) has become a global pandemic. There is a lot of pressure on the healthcare system and managing the flow of the large number of cases has been one of the biggest issues for hospitals or healthcare professionals. Mainly in the rural or remote areas, where it is becoming very much difficult or unable to consult a doctor or a medical specialist person when the immediate hit of the epidemic has occurred and the infected needs and immediate attention. Thus, it becomes obvious that any help to the health care system will give relief as well as release pressure on them. This study presents the design of a different Artificial Intelligence (AI) chatbot for the purpose of evaluation and recommending better relief measures when patients need medical help or want to track their health-status for a certain or a specific period of time. This work describes the building of a sophisticated artificial intelligence (AI) chatbot for diagnosing individuals who have been exposed to COV-19 and advising quick action. Additionally, a virtual assistant can communicate with registered doctors as symptoms worsen and measure the infection's severity. Also, at the end of the specific period he/she can have a report on his/her medical track for that duration.
A Voice Based Framework in Medical Emergency and Essential Services Using NLP (In Special Reference to People with Writing Disability)
Natural Language Processing (NLP) has become increasingly popular in recent years as a means of organising and structuring knowledge for tasks such as translation, summarization, named entity recognition, relationship extraction, and audio recognition. The processing efficiency of NLP based systems has increased significantly with the introduction of deep learning algorithms. The COVID-19 pandemic has posed a challenge to medical research, information technology operations, corporate procedures, and the global economy. Several techniques are being researched to ensure that lockout and social isolation are successful without creating significant public discomfort. The present developments and uses of NLP can serve as an important support system in the fight against the COVID- 9 epidemic. This research compares various deep learning models for NLP approaches and provides an NLP framework for developing vital and emergency services support systems with the goal of reducing human interactions.
NLG, deep learning, artificial intelligence, name entity recognition, machine translation, illiterate, Disability
Intensifying Security with Smart Video Surveillance
The smart video surveillance system is designed to observe the activities of people nearby for safety concerns. This smart video surveillance can be installed at malls, public areas, banks, businesses, and ATM machines. The field of network surveillance research is rapidly expanding. The cause for this is the global insecurity that is occurring in great extent. As a result, an intelligent monitoring system that gathers data in real time, communicates, analyses, and interprets information about individuals being observed is required. Video surveillance systems are in great demand due to increasing safety concerns. The automated anomaly detection systems are much better since they require almost negligible human intervention and can more accurately detect anomalies. Besides this, the data can be easily stored and retrieved with the help of the video surveillance system. Video surveillance systems may be used for a variety of purposes, including traffic monitoring and anomaly detection. The study describes how three dimensional deep neural networks are used to learn spatiotemporal elements of video feeds that can be used in our smart surveillance system.
Automated anomaly detection, deep neural networks, spatiotemporal, video surveillance system, CNN
In today's society, surveillance is becoming increasingly important in order to maintain a place's decorum and assure the safety and security of its inhabitants. In this case, an aerial monitoring system will be beneficial. This study explains how an unmanned aerial vehicle (drone) can be used to create an aerial surveillance system. We begin by describing the elements of our airborne surveillance system, followed by a discussion of some of the technologies we employed to construct it. Following that, we describe how we integrated those technologies into a drone and made them function in tandem to produce our intended aerial surveillance system. This system will be a practical and cost-effective replacement for present surveillance systems. It can be utilised for both peacekeeping and real-time monitoring of a location at any time of day. The goal is to create economical, rapid, and effective monitoring that may be employed broadly at the private, institutional, and governmental levels.
Drone, unnamed aerial vehicle (UAV), phython programming, raspberry Pi, open CV, android app
A Scrutiny on Coronavirus (COVID-19) and Backing through IT
Coronaviruses are a group of enveloped contagions with on-segmented, single-stranded, and positivesense RNA genomes. Six coronaviruses have been known to infect mortal hosts and cause respiratory conditions after infecting a variety of economically important invertebrates (such as gormandizers and cravens). Among them, several acute respiratory pattern coronavirus (SARS-CoV) and Middle East respiratory pattern coronavirus (MERS-CoV) are zoometric and largely pathogenic coronaviruses that have rebounded in indigenous and global outbreaks Coronaviruses retain a distinctive morphology, the name being deduced from the external borderline, or nimbus of bedded envelope protein. It causes mortal conditions for all of the family members. The replication of the RNA genome is unique in that it occurs through the generation of a nested set of viral mRNA motes. Mortal nimbus-contagion (HCoV) infection results in/causes respiratory conditions ranging from mild to severe. In the last 15 times, we have witnessed the emergence of two zoometric, largely pathogenic HCoVs, severe acute respiratory pattern coronavirus (SARS-CoV) and Middle East respiratory pattern coronavirus (MERS-CoV). HCoV replication is regulated by a number of host factors and causes significant differences in cellular structure and physiology. In this proposed review, we have provided as much information about Corona contagions as possible.
Corona Viruses, 2019-nCoV, respiratory pattern, viral replication, SARS-CoV.
Object and Person Recognition System for Blind
Natural and man-made disasters present a wide range of challenges, which are amplified for people with disabilities. In most cases, assistive technology devices and services assist the disabled in performing daily tasks; yet, these are commonly restricted during and after disasters. One potential approach for people with vision impairment is a cost-effective wearable ‘Object detection’. The object detection provides environmental narratives while also establishing communication between the visually impaired user and a huge online knowledge base system capable of vocalizing narratives. This research is carried out as a proof-of-concept using Python OpenCV and a webcam. The study’s goal is to create assistive technologies that will help visually impaired people navigate their way out of potentially hazardous situations. Object detection is a branch of computer vision that finds instances of semantic objects in images and videos (by creating bounding boxes around them in our case). The annotated text can then be converted into voice responses, which provide the basic positions of the objects in the person’s/camera’s view. We will use our webcam to feed images to this trained model at 30 frames/sec, and we can set it to only process every other frame to speed things up.
Python OpenCV, Web cam, YOLOv3 algorithm, Object detection, Blind people
Importance of Voice Assistant in Real World
Jarvis is a voice assistant for your computer. Main aim of this study is to develop personal assistant for real world applications. It has been designed to give a user-friendly interface for carrying out a variety of tasks by employing certain well-defined commands. It is a software program that helps the users to ease their day-to-day tasks. We can do all the tasks through voice command only and there is no need to use mouse and keyboard. We use speech recognition system, text to speech, speech to text etc. in this software. This will reduce the effort of humans to do the task manually. It helps the user with many daily life activities searching queries on web, listening music, filling the form, searching videos on YouTube, sending messages on WhatsApp and telegram, login on social media account, searching nearest hospital on map, opening editor and help to write the sentences through voice only, Corona details, top 10 news etc.
Desktop voice assistant, text to speech, speech to text, speech recognition, personal assistant, user- friendly.