Optical Character Recognition Development Using Python

Optical Character Recognition (OCR) is a technology used to convert scanned or digital images into editable text. OCR has become an increasingly important tool in the fields of data extraction and information retrieval, allowing for quick and efficient conversion of scanned documents and digital images into text. In this paper, we explore the use of the Python programming language to implement OCR algorithms and systems. We provide a comprehensive overview of existing Python libraries and packages used for OCR, including Tesseract and pytesseract, along with their strengths and limitations. We also examine the different OCR approaches and techniques, including template matching, feature extraction, and encrypting/decrypting the OCR parsed files and discuss their implementation in Python. Finally, we present a case study of a simple OCR system built using Python and evaluate its performance on a sample dataset. The results of our study highlight the potential of Python for OCR implementation and demonstrate its feasibility for real-world applications.

Applications of Fibonacci Sequences and Golden Ratio

The study mainly focuses on the use of the Golden Ratio and the Fibonacci sequence. The connection between them can be clearly visible in nature. With the help of the Fib-onacci sequence scientists have solved many mysteries related to nature. Everything that is around us somehow or other depends on Fibonacci numbers, the Golden Ratio, and the Fibonacci sequence. Some examples are –Flower petals- Lily, Rose, Daisy, Marigold, Sunflower, Iris, Buttercups, wild rose, larkspur Trillium, Bloodroot, Aster, and Susan; Seed heads-Sunflower; Snail; Fruit-Apple, Banana, Pineapple; Human Face; Tree Branches; Cyclone; Pinecones; Shells; Spiral Galaxies; Bees; Famous architecture design – Taj Ma-hal, in Hindu rituals, in decoding-coding the data, in providing security to the sensitive data and all over the world, in mother's womb (about her baby's position), etc. The cur-rent study reflects that there is no limitation to the Fibonacci pattern and Golden Ratio in our surroundings

Implementing Cloud Security through AWS for Blood Bank Application

In today’s time the technologies are seeing a huge shift of work from the on-center to over the internet, which means that a huge number of online services is required, these services could be backup and the data recovery should be available over the internet. Cloud computing is the platform over the internet which provide the services of networking, sharing, storing, etc. on the internet. Not only this shift of data is being ob-served in IT sectors, but also this shift is being observed in the healthcare sector. With the increase in the average age limit of the human beings and new diseases increasing the medical needs. The traditional healthcare is now being replaced by modern and more progressive healthcare. Thus, cloud is providing with several healthcare solutions. How-ever, even after have a lot of benefits of using cloud services it also has some of the risks. The major risk being of the security of the patient’s data. This paper analyses the security issues and their possible countermeasures for the same. The security measures presented in the paper are based on patient data in relation to data storing, access and security of data. In the paper I have worked towards the development of the android application of blood bank, the application uses the Android studio as IDE and the AWS Amplify as the cloud service to host and deploy the application over the cloud. In this paper we will also see the benefits of using the cloud computing, its security and also what are the future works that need to be done in the field

Decentralized Crowdfunding Platform Using Blockchain

Few years back, blockchain was particularly used to support cryptocurrencies, but after some decades, more and more sectors are adopting this brand-new technology. Blockchain will used by the majority of technologies in the future as an effective means of conducting online transactions. Crowdfunding platforms are one of the industries to which blockchain technology may be applied. Although crowdfunding is quite common online, there are still some problems with it. Projects that don't finish on schedule, don't finish at all, or don't provide what they promised cause trust concerns. Additionally, crowdfunding sites serve as intermediaries, so you must put your faith in them to transmit your cash properly. This project solves these problems by integrating Ethereum smart contracts with the crowdfunding platform, scams may be avoided, and it is ensured that projects can be fulfilled within the specified time frame. With the use of blockchain technology, decentralised crowdfunding offers more accessibility, transparency, and reduced fees. The ability to immediately create, watch, and donate to crowdfunding campaigns via the blockchain is the most crucial feature. The decentralised crowdfunding platform is linked to the blockchain and fea-tures authoring solidity code, pairing metamasks, interacting with smart contracts, and sending Ethereum across the network

PlantDoc-Plant Disease Detection using AI

Gardening is a hobby which requires dedication and consistency. It is something more than just watering a plant. Taking care of Garden plants is very important as most of the plants are prone to diseases frequently. Plant Diseases ruin the plant and ultimately may kill it with time so timely identification and treatment of the disease is required for a healthy plant. This also helps to preserve many threatened species of plants. PlantDoc uses Artificial Intelligence model created on Convolution Neural Network algorithm of Deep Learning to solve this problem. The model is trained with images of different plant leaves to identify defected plants. PlantDoc helps in disease detection. It uses computer vision concept of AI to find the disease of plant and provide solution for that automati-cally. PlantDoc uses MERN stack. PlantDoc web application successfully helps to identify plant diseases of various plants by analyzing plant leaf image and suggests cure to treat it. This helps in treatment of plants timely which helps to stop the further spread of dis-ease and provides cure

A Study of Human Physiological Comfort in Lagos Metropolis Using Landsat Satellite Imagery Between 1984 and 2013

Our cities today are exposed more too urban atmospheric conditions that influence man’s comfort, health and behavior. As cities grow changes in land use have altered man’s physiological comfort experienced today. Landsat satellite imagery was used to study human physiological comfort in Lagos metropolis between 1984 and 2013. So, to achieve this remote sensing data acquired from Landsat satellite imagery and Geographic Information System (GIS) were used to derive, namely: land use land cover, land surface and air temperature, and relative humidity index for 1984, 2000 and 2013. While Temperature Humidity Index (THI) was computed from air tem-perature and relative humidity index which was used to assess human physiological out-door comfort in relation to land use activities. In this study, the health implica-tions of physiological stress and thermal sensation were highlighted and addressed from the environmental and health perspectives as it relates to land use. This study thrown open the door of environmental efficiency, well-being and the health of citi-zens which is influenced by physiological comfort through developing and maintain-ing comfortable environmental conditions that will enhance the quality of urban life

A Comparative Analysis of Emotion Detection Techniques

Emotion recognition from facial expressions has become an urgent necessity due to its numerous applications in artificial intelligence, such as human-computer interface, marketing, mental health screening, and sentiment analysis, to name a few areas where emotion detection has become essential. In this paper we present a compara-tive analysis that offers insightful information about two techniques in emotion detection with CK+ and FER2013 datasets in deep learning, assisting researchers, practitioners, and policymakers in making defensible decisions about the selection and application of different methods in diverse applications. It emphasizes how important it is to continue researching and developing in the field of emotion detection in order to make it more reliable, accurate, and equitable in a variety of real-world situations. We focused on the two emotion detection techniques and databases employed, and the contributions that were dealt with. The Cascade Classifier algorithm and the Random Forest technique are thoroughly compared in this research to provide light on their advantages, disadvantages, and suitability for use in various fields. Additionally, the study evaluates the performance of both the Cascade Classifier and Random Forest algorithm on FER2013 and CK+ datasets, considering metrics such as accuracy, precision, f1-score, etc. Finally, the assessment of these methods incorporating the review measures is reported and discussed

Using Machine Learning to Determine the Motorist Somnolence

Traffic accidents pose an increasing threat to society, and researchers are dedicated to preventing accidents and reducing fatalities, as highlighted by the World Health Organ-ization. One significant cause of accidents is drowsy driving, which often leads to severe injuries and loss of life. The objective of this research is to create a fatigue detection sys-tem that can effectively minimize accidents associated with exhaustion. The system uti-lizes facial recognition technology to identify drowsy drivers by analyzing eye patterns through video processing. When the level of fatigue surpasses a predetermined thresh-old, the system alerts the driver and adjusts the vehicle's acceleration accordingly. The implementation of OpenCv libraries, such as Haar-cascade, along with Raspberry Pi fa-cilitates seamless integration of the system. This dissertation evaluates advancements in computational engineering for the development of a fatigue detection system to miti-gate accidents caused by drowsiness. It offers valuable insights and recommendations to enhance comprehension and optimize the system's effectiveness, ultimately leading to safer road travel

Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach

The process of determining if a person, generally a driver, is becoming sleepy or drowsy while performing a task such as driving is known as drowsiness detection. It is a necessary system for detecting and alerting drivers to their tiredness, which might impair their driving ability and lead to accidents. The project aims to create a reliable and efficient system capable of real-time detection of drowsiness using OpenCV, Dlib, and facial landmark detection technologies. The project's results show that the sleepiness detection method can accurately and precisely identify tiredness in real time. The technology is less intrusive and more economical than conventional sleepiness detection techniques. The system is based on a 68 facial landmark detector, which is a highly trained and effective detector capable of recognizing human face points. The detector aids in assessing whether the driver's eyes are closed or open.  The system analyses the data collected by the detector using machine learning methods to discover patterns associated with drowsiness. When drowsiness is detected, the system incorporates a warning mechanism, such as an alarm or a vibration in the steering wheel, to notify the driver. A variety of studies with different drivers and driving conditions were used to evaluate the performance of the real-time driver drowsiness detection system. The results show that the technology can detect tiredness properly and deliver timely warnings to the driver. This method can assist in preventing drowsy driving incidents, enhancing road safety, and saving lives. The results indicated that the algorithm had an average accuracy rate of 94% for identifying tiredness in drivers

Exploring the Potentials of Robotic Process Automation: A Review

We are living in a world where every person wants to speed up their work with min-imal effort. Sometimes we have irksome and repetitive tasks that require a lot of labour and time, and the solution to our trouble is automation. In 1946, the term "Automation" was coined. Now we can see that it has immensely impacted our lives, not a single day goes by without making use of it. This review article explores the ideas, uses, advantages, and difficulties of Robotic Process Automation (RPA) in an effort to give a thorough understanding of the field as it is today. This paper focuses on the revolution of automation in every field and the way it helps boost business, improve customer satisfaction, and relieve staff. It delves into the diverse applications of RPA in different industries. It will elucidate the area of imple-mentation and the use cases, challenges, and benefits. It will also examine the sectors deploying RPA, such as banking and finance, healthcare, education, manufacturing, purchase and supply management, and digital forensics, and the outcomes observed by them. In conclusion, the review paper serves as a comprehensive analysis of the potential of RPA by examining its components, implementation, efficiency, and use cases in dif-ferent industries. It focuses on understanding the idea behind RPA, its implementation, the features of RPA that can help them strengthen their business growth, such as affordability, reliability, scalability, consistency, compliance, and accuracy, and the significant opportunities for streamlining business processes