ISSN: 2395-6704 Scientific Journal Impact Factor (SJIF): 6.118 Index Copernicus (ICV: 57.92) Journal DOI no.: 10.37591/JoOSD
STM JOURNALS ( Consortium eLearning Network Pvt Ltd)
A Paper on The Role of Artificial Intelligence in Sustainable Development
The rise of artificial intelligence (AI) and its expanding influence across various sectors necessitates an evaluation of its impact on the attainment of Sustainable Development Goals. Through a consensus-based expert elicitation method, it is determined that AI has the potential to facilitate the achievement of over half of the targets within all goals, but it may also impede certain objectives. Nevertheless, existing research tends to neglect crucial facets of AI. The rapid advancement of AI demands adequate regulatory understanding and oversight for AI-driven technologies to foster sustainable development. Neglecting this imperative step may lead to deficiencies in transparency, safety, and ethical standards.
Copyright (c) 2023 JOURNAL OF OPEN SOURCE DEVELOPMENTS
Applications of Graph Theory in Computer Science
Graph theory, a branch of mathematics, has found broad applications in different areas, especially in computer science. This study inspects the basic concepts of graph theory and its different applications in computer science. It talks about the role of graphs in modelling real-world networks, algorithmic problem-solving, optimization, data structures, network analysis, and other significant areas within the domain of computer science. Through different illustrations and case studies, this study aims to illustrate the commonsense importance and wide-ranging applications of graph theory in computer science.
An Investigation of Designing for Changes in User Interface (UI), User Experience (UX), and Understanding
Interface technologies have progressed into CUI, GUI, and NUI because of the advancements in Internet of Things, mobile, and cloud computing technologies. A new UI/UX design is anticipated to be created in the future. In this study, we demonstrated our knowledge of the internet, operating systems, contents, and gadgets, as well as our capacity to produce interfaces. For 29 participants, including sophomores, juniors, and seniors in IT departments, a list of 18 items covering four topics was created as part of this study. The results of the understanding test were divided into three categories: excellent, normal, and insufficient, and evaluated. It was discovered that the excellent rate was 19.35%, the average was 42.53%, and the insufficient rate was 38.12%. As a result, just 20% of students had a solid understanding of UI/UX, while the other 80% lacked it. In specifically, the design research area scored 41 points, the design content area scored 43 points, and the design production area scored 16 points, which was the lowest of the four categories in the comparative analysis. Following that, the 10 assessment criteria for the mobile UI/UX build guide were assessed using the same criteria as the UI/UX understanding, with the good score being 16 points, the average being 80 points, and the poor score being 194 points. The evaluation in the UI/UX construction guides was worse than the grasp of UI/UX. Therefore, UI/UX professionals should be supported with professional course organization and systematic curriculum in order to promote UI/UX comprehension and content production capacity.
Identification of Potato Plant Leaf Disease Using Different Artificial Intelligence (AI) Techniques: A Review
One of the nation's most vital industries is agriculture. Employment possibilities are abundant in emerging nations like India. The livelihoods of nearly 70% of the global population depend on agriculture. In this study, we have evaluatednumerous articles that have been released on potato plant leaf disease (PLD)diagnosis and classification utilising machine learning (ML)or Deep Learning (DL)methods. There are several causes of leaf diseases in plants, including bacteria, viruses, fungus, and more. Whether you are a farmer or a scientist, knowing how to spot sick leaves can be a daunting challenge.Farmers spraying their crops with pesticides pose a risk to human health and a financial burden on society. Specialists employ Potato Plant Disease Detection (PDD)methods to help in the fight against and early identification of the disease. This discovery will be a valuable tool for scientists trying to determine whether illnesses affect potato plant leaves by using picture segmentation, ML, DL, and neural network (NN)methods. This document presents an extensive literature review on the diverse diseases affecting potato plant leaves, categorized based on several prominent factors.
Study of Data Science in Online Behavioral Advertising
The proliferation of the COVID-19 pandemic has led to a growing significance of social media in people's lives. Online activity in the digital domain has expanded considerably, showing a higher reliance on social media platforms for communication in both personal and professional situations. Coronavirus has a global impact on e-commerce and hence transformed the nature of business. Despite the COVID-19 issue and economic slowdown, the Indian e-commerce industry exhibited an upward trend after the lockdown, registering a 17% increase in the order volume as of June 2020, when compared to the pre-lockdown period, which continues after the postlockdown period as well.As a result, advertisers took the time to rethink their methods in order to develop the perfect commercials and reassess who their target audience was. The outbreak proved to be the ideal time for this, given how markets and algorithms are constantly shifting, particularly while individuals are stuckat home.Companies began looking for ways to attract buyers for their products utilizing data science methodologies in order to enhance their revenue. We are consolidating our results on how advertisers use data science methodology to acquire customers and the influence on them.
People who are mute or deaf encounter communication challenges when interacting with others. It can be difficult for individuals with similar conditions to effectively convey their thoughts, especially when not everyone understands sign language. This article aims to create a Data Acquisition and Control (DAC) system capable of translating sign language into written text that can be comprehended by a wider audience. This system is referred to as the “Sign Language Translator and Gesture Recognition”. We have designed an intelligent web interface that captures hand gestures and translates them into readable text. The content within this textbook can be wirelessly transmitted to a smartphone or displayed on an embedded TV screen. It is evident from the experimental outcomes that affordable sensors can capture gestures, measuring finger positions and their movements. The current system's performance demonstrates its ability to accurately interpret 20 out of 26 letters, achieving a recognition accuracy of 96%. Sign languages rely on hand and finger shapes, motion, body language, and facial expressions to convey meanings, and they vary from one country to another. As an example, Irish deaf individuals use Irish Sign Language, while in India, people use Indian Sign Language. Different sign languages possess their distinct alphabets, semantics, and vocabularies. Variations in dialects among regions are prevalent, even within a single nation. Variations in some of the signals are employed by various creative groups.
Remote Healthcare Diabetic Retinopathy Detection Using Deep Learning
High blood glucose levels are a hallmark of diabetes mellitus (DM), a metabolic disease. This can give rise to a range of complications, with Diabetic Retinopathy (DR) being among them. DR can impair vision and, if not addressed, may lead to a loss of eyesight. Symptoms include aberrant blood vessels, fluid leaks, exudates, haemorrhages, and retinal microaneurysms. With the advancement of technology, medical imaging has become one of the most useful tools for detecting and treating disorders. Nonetheless, even with these advancements, the procedure is difficult, time-consuming, and not always correct. With more modern technologies, such as Deep Neural Networks, there is a good chance that we can improve accuracy and efficiency while attempting to identify DR using an automated image analysis system. This article aims to create an automated knowledge model for identifying critical characteristics associated with DR from fundus images using OpenCV, Convolutional Neural Networks (CNNs), and Keras. The main phases in this project are to collect and preprocess a diverse collection of fundus photos, annotate them to show the severity of DR, and build a CNN model with Keras. This project requires training the model on this data, monitoring its performance, and evaluating it using measures such as accuracy, precision, and recall. Building trust among healthcare providers requires interpreting the model's predictions and illustrating its decision-making process. Furthermore, the initiative must assure adherence to healthcare legislation and ethical guidelines. Working with medical specialists to assess the model's accuracy and clinical applicability is an important step in this process. Finally, this study has the potential to transform DR diagnosis, making it more efficient and precise, and so improving patient outcomes. Continuous data collection and model development are required to ensure continued progress in this vital healthcare topic.
Revolutionizing Online Exams on a Budget: A Zero-cost, Open-source Approach for Challenging Times
In today's digital era, the importance of an online exam system cannot be overstated. This technology-driven approach offers unparalleled flexibility, accessibility, and efficiency in the assessment process. It empowers individuals to engage in remote learning and evaluation, breaking down geographical and time constraints. Additionally, it streamlines administrative tasks, reduces paper usage, and provides instant grading, contributing to a more sustainable, timely, and adaptable educational and assessment landscape. This article presents an innovative, zero-cost technological solution for online examination systems, utilizing open-source tools. The approach includes the generation of student hall tickets based on information provided through forms, which are then sent to students a week before the exam. Additionally, the Controller of Examination manually creates the exam timetable and establishes Google Classrooms for each program, with topics generated through Google Scripts. Google Forms are generated from a Google Sheet containing MCQ questions and keys submitted by faculty in a prescribed format. A custom Google Script parses the sheet to create corresponding Google Forms. For monitoring and attendance, WebEx is employed, with students receiving two links via email 15 min before the commencement of the examination. The first link allows students to join a WebEx meeting for attendance and monitoring, while the second link directs them to a Google Form containing the MCQ question paper. Remarkably, the Google Form becomes active precisely on time. After the exam, Google Form is disabled and stops accepting responses. The student submissions are manually verified. While this system may not offer a fully robust solution, it provides a cost-effective temporary alternative, particularly relevant during situations such as COVID-19 pandemic.
Detecting Vehicular Congestion Using Fog Computing
Traffic congestion has become a major challenge for modern transportation systems, leading to increased travel time, fuel consumption and air pollution. To address this issue, Internet of Vehicles (IoV) has emerged as a promising technology for detecting traffic congestion in real-time. This study discusses a research investigation that explores the utilization of Fog Computing principles to detect traffic congestion. The proposed system utilizes sensors to capture traffic data and send it to the Fog nodes for processing. The data is analysed using machine learning algorithms to identify congestion patterns and provide real-time alerts to drivers and traffic management centres.
Accelerating Cross-platform Development with Flutter Framework
The need for businesses to spread the word about their wares to a larger audience has led to the rise in popularity of cross-platform mobile application development in recent years. Google created the open-source Flutter framework for creating mobile apps. It has rapidly replaced other options as the method of choice for developing high-quality, cross-platform software. Flutter's cross-platform compatibility across macOS, Linux, and Windows is only one of its many advantages. The framework can also be used to build web apps and desktop software in addition to mobile apps. The purpose of this study is to examine the pros and cons of utilizing Flutter to create cross-platform mobile applications. We will examine the pros and cons of utilizing Flutter to create cross-platform mobile, web, and desktop applications in contrast to other popular cross-platform development frameworks like Xamarin and React Native. We will dive into Flutter's architecture, features, and distinctions from existing cross-platform development frameworks in the context of building applications for several platforms. Case studies of many Flutter-built apps will help us weigh the benefits and drawbacks of this framework for building apps that run on multiple platforms. There will be mobile, online, and desktop apps available, all built for many platforms. We will be rating these applications based on a number of criteria, including how well they operate and how satisfying their users find the whole experience to be. The ultimate goal of this study is to provide readers with an in-depth understanding of the benefits and drawbacks associated with developing cross-platform applications using Flutter. The app's portability between platforms will be part of this comprehension. With this study, we aim to equip developers with the knowledge they need to make informed decisions about the development framework they will employ for future projects by comparing Flutter to other cross-platform development frameworks and investigating the capabilities it offers for a variety of application categories.
Autonomous Car Using Deep Learning and Image Processing Technology
This study showcases a mini version of autonomous cars where it includes both autopilot mode and manual control. Autopilot mode is implemented using deep learning and image processing techniques, such as CNN, Haar cascade model and mobile app for manual control. IoT plays an important role. The Raspberry pi, pi camera and Arduino uno work as the main processors. The high-resolution pi camera, which is of 8 MP, will provide real time information in the form of photos as input to raspberry pi which is mainly responsible for controlling the car based on its input, which in turn results in obstacle detection, traffic sign detection, stop sign detection and lane detection by using deep learning techniques. Along with these features, the car is able to overtake the other car if it comes across a lane with proper indicators.
Digital Sampling and Testing of COVID-19 to Reduce Lab Procedures Further Decreasing Carbon Emission: Review
The COVID-19 pandemic has caused a new virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that primarily infects the lungs and can lead to serious respiratory illnesses such as ARDS and pneumonia caused by coronavirus. Declared a pandemic by the World Health Organization (WHO), the disease belongs to the family of coronaviruses that can cause cold and flu, making it a highly contagious disease. Chest imaging tests such as CT scans, X-rays, and other radiological tests are commonly used to detect and test for COVID-19. However, manual diagnosis of these images can be a time-consuming process that generates considerable biomedical waste, thereby becoming an environmental concern. The goal of this study is to determine whether transfer learning can be used to streamline AI procedures. To achieve this, a deep learning framework using pre-trained deep convolutional neural network models is used. The result of this approach is highly accurate classification of chest radiographs. Results were visualized using pre-trained models (VGG16 and Conv2D). Experimental results showed that the accuracy obtained using VGG16 was 94% with a loss of 7%. However, in validated data, the accuracy achieved using VGG16 was 90% with a loss of 15%.
Novel Perspectives in Imitation Learning: Trends, Challenges, Future Directions
Imitation learning refers to a machine learning approach where an algorithm learns to accomplish a task by emulating the actions and behaviours of a proficient expert. The main aim of imitation learning is to extract knowledge from an expert and train the datasets using the existing knowledge. During training, the algorithm is presented with a large dataset of expert demonstrations and learns to map the input to the correct output based on the examples it has seen. The learned mapping can then be used to perform the task on new inputs that were not present in the training dataset. One example of imitation learning is training an autonomous car to drive by watching and imitating a human driver. In the past few years, various trends in imitation learning have emerged, significantly influencing the trajectory of this field, and shaping its future. These trends include development, integration, and transfer learning. These trends in imitation learning are helping to make this technique more efficient, effective, and applicable to a broader range of tasks and environments. Over the past few years, the imitation learning field has experienced the emergence of multiple trends that have had a substantial impact on its direction and are actively shaping its future. The development of new and more efficient and scalable methods for imitation learning could enable it to be applied to even larger and more complex problems and increase the scope of the problems. In this study, we provide a literature review of trends, challenges, and future developments in imitation learning.
Facial Emotion Recognition Using CNN and OpenCV: A Review
Emotion Recognition Using a Facial Feature Recognition is a prominent area of study in the world of Human Computer Interaction (HCI). Humans may express a wide range of emotions and moods through facial movements and body language. In this project, in order to detect the live emotions from the human facial gesture, we will be using an algorithm that allows the computer to automatically detect the facial recognition of human emotions with the help of Convolution Neural Network (CNN) and OpenCV. Finally, emotion detection involves the integration of data from multiple patterns. If computers can comprehend more human emotions, the gap between humans and computers will be reduced. In this research work, we will show how to read a person's frontal facial expression to accurately identify emotions including neutral, happy, sad, surprised, furious, fear, and disgust.
Online Tracking System for Project Management
Project managers can utilize an online tracking system software to monitor project progress, delegate tasks, and work together with team members in real-time. Such systems often comprise features like task lists, time tracking, and reporting capabilities. The online tracking system for project management (OTS) provides a centralized platform for students, project managers to manage all aspects of all projects, including task assignments, deadlines, progress monitoring, and evaluation. Students can use the system to create and manage their own groups, and the project guide assigned to each group can provide guidance and support throughout the project. The progress chart generated for each group includes information such as task dependencies and timelines which can be used to track progress and identify any potential issues or delays. The project leader can use this information to delegate tasks and ensure that each group is on track to complete their project on time. The criteria used to evaluate each project may differ based on the unique needs of the project and the academic institution involved. However, the system allows for flexibility in defining the evaluation criteria, which can be adjusted as needed throughout the project. The purpose of this research work is to provide an overview of the online tracking system in project management, its benefits and drawbacks, methodologies used in its implementation, and its overall effectiveness.
Music Recommendation System Using Big Data and Machine Learning
In comparison to earlier times, there is now a wealth of music available thanks to the development of music streaming services that can be used from mobile devices. Classifying digital music can be timeconsuming and cause fatigue. Obtaining one’s favorite music from the huge music selection has become a problem. The creation of a music recommendation system that can search through music libraries and make song recommendations to users based on their listening habits is therefore useful. With the aid of music recognition, the system can forecast and then suggest songs to its users based on the traits of the music that they have previously listened to. The system collects information, then it reads the data and identifies the category of the music. Classification works towards the goal by comparing the similarities between the music categories and customer preferences. On the whole, results show that accuracy rate achieved for the single music category user is turned out to be higher than multicategory music user with an average accuracy rate for single category being 50.15%, whereas accuracy for feature recommendation in multicategory is higher than single category with the accuracy being around 42.80%. Based on these results, it shows that although the accuracy for recommendation system is not high, it has its own advantages in the start and can optimize itself for better recommendations.
Copyright (c) 2022 JOURNAL OF OPEN SOURCE DEVELOPMENTS
Enhancing Data Security Via the Application of Back- Propagation Feed-Forward Methods in Encryption
The computer society uses a variety of automated methods for file security and data storage. Several organisations are concerned about the information exchange via an unsecured network for a distributed architecture, such as the time-sharing and real-time system. Probably the most crucial element that contributes to effective security is cryptography. Using a constant weighted factor for boosting the factor, the study aims at extending or updating the earlier presented Artificial Neural Network encryption model. Back-propagation feed-forward methods are a type of artificial neural network (ANN) that can be used in encryption to enhance data security. ANNs are modelled after the structure and function of the human brain and can be trained to recognize patterns and make predictions based on input data. In the context of encryption, back-propagation feed-forward methods can be used to encrypt data by converting it into a series of numerical values, which are then fed into the ANN. The ANN converts the data into an encrypted form that is challenging for unauthorised users to decode, using a series of mathematical operations. The back-propagation algorithm is used to train the ANN to recognize patterns in the input data and adjust the mathematical operations used to transform the data in order to improve the accuracy of the encryption. This allows the ANN to adapt to new types of data and improve its encryption capabilities over time. Using back-propagation feed-forward methods in encryption can provide a number of benefits for data security. For instance, it can aid in preventing unauthorised access to private information, safeguarding against data breaches, and guaranteeing the secrecy and accuracy of data transmissions. However, it is important to note that no encryption method is completely fool proof, and additional security measures may be necessary to protect against sophisticated attacks.
Literature Survey of Finance in Blockchain Technology
Blockchain innovation is a sort of disseminated record innovation and a decentralized record (DLT). Each block in the "chain of blocks" contains time-stepped computerized information, as well as a hash that exceptionally distinguishes it corresponding to the blocks before it. Blockchain technology enjoys an extraordinary benefit in the part of decentration, receptiveness, independence, alter safe data and namelessness. While certain associations are investigating the utilization of blockchain to modernize their organizations, fresher players in the commercial center have had the option to embrace the innovation. Because of the call for papers on the subject of blockchain and computerized banking, a few commitments were gotten. From virtual monetary forms to monetary applications to the more extensive social circle, the scope of blockchain applications has extended. The implementation of blockchain technology has the potential to transform the capital market industry by impacting business strategies, reducing risks, lowering costs, and generating capital savings. This study features the different existing works of blockchain in finance.
In today’s scenario, real estate industry is growing rapidly. Everyone wants to purchase land and a home, either for personal use or as an investment. House prices keep changing day by day and it is hard for a person to choose the best from all of them. There are many technologies that help to analyse the various aspects of buying property. These digital platforms are created using various algorithms like neural network, machine learning, data science etc. Here in this study, we bring to you a review work on the research papers published on these techniques to help you select best.
A Web-based Bug Tracking System to Find and Resolve Bugs
Today’s world is surrounded by network and internet; in this world, “Data is a new Oil”. With the increase of the use of internet, the risk of data getting leaked also gets increased and here the role of cyber security comes to prevent this data to be hacked. Everyone, business, and foundation have personal information that they want to keep private. Cyber Security is essential in every field to protect data from the unauthorized persons like hackers. Hacker takes advantage of bugs on the websites to attack on the system and steal users’ data. A Bug tracking system is a useful tool for software developers to capture the bugs coming in the system and resolve them in a particular time span. Bug tracking system is totally web-based. It can be hosted on Windows or Linux servers, and it requires only a web browser as client software. Users come upon a website and register the bug and can check whether the bug is resolved or not and can also update the bug processing. All this work is done on the Bug tracking website. A web-based bug tracking system is a tool that is used to identify, track, and manage bugs in software. It is a crucial part of software development and helps developers to detect and fix bugs quickly, ensuring that the software performs efficiently. The system allows for easy collaboration among developers, testers, and project managers to ensure that all bugs are addressed and resolved in a timely manner. One of the benefits of a web-based bug tracking system is that it is accessible from anywhere with an internet connection. This means that team members can log in and check the status of bugs and update them from any location, whether they are in the office or working remotely. Additionally, because they are secured by firewalls and other security measures, web-based systems are typically more secure than on-premises systems. In conclusion, a web-based bug tracking system is a crucial tool for teams working on software. It helps to ensure that bugs are detected and resolved quickly, allowing for efficient and effective software development. Its accessibility and integration capabilities make it an asset for any development team looking to streamline their bug tracking process.
Importance of Network Architecture: An Overview
Network architecture is the design of a communications network. It includes the physical and logical layout of the network, the framework of accepted standards and specifications of elements, equipment, services, protocols and functions, growth and change assumptions, and high-level operational principles and procedures. Modern networks encompass a variety of architectural and design principles, which must be understood and articulated by a network organization in order to maintain best practices. Standardized network protocols define how network elements interact, with network architecture design taking the lead on the specification of which protocols are used and how network elements use these cloud computing protocols. A strong network architecture will enable system-level functionality as well as robustness, extensibility, and evolvability in the face of changes in software and hardware components, application functions, security measurement implementation, and external environments. The methodology used in this study is secondary data, where we take information from various books and websites.
Model Studies on Design and Implementation on Secured Code for Interactive Automated Teller Machine
The use of Automated Teller Machine do not need introduction in this global world but its insecurity could make it not to receive it recognition in technology advancement thereby discouraging the urge for cashless economy society. Design and Implementation of Secured and Interactive Automated Teller Machine (ATM) was carried out using JAVA Programming Language. This project brought solution to the menace of insecurity found in Automated Teller Machine. Here, a system was design using Object Oriented-Analysis-Design (OOAD) Methodology. Unlike the previous system where you are required to only input the four (4) digit secret code number and have access for a full transaction, instead more is now required in this new system. The system is now capable of accepting the user details of the four-digit secret code number and if it is correct, the system will now sends information to the user mobile number and the information is a nine (9) digit number and the user in turn gives the same information to the system, only if correct will the user be allowed to continue his/her operations. This system is expected to meet the need for safe self-service, eradicate the menace of ATM insecurity and to encourage the use of cashless economy. This will give its users sure credence that their transactions are safe and secured.
Model studies, implementation, secured code, and automatic teller machines
Model for an Agricultural Information System to Improve Crop Yield Using IoT
The application of Agricultural Information Technology (AIT) in all facets of agriculture has made it the most efficient method and set of instruments for increasing agricultural production and fully using farming methods. The use of Agriculture Information Management technology, a crucial sub- technology of AIT, has a direct impact on the level of agricultural informationalization and the effectiveness of agricultural production decisions. This study provides an integration illustration of the framework in agricultural output. Here we used IoT along with the agriculture application. A simple gathering of the soil at each plant's stems is the soil gathering procedure. To prevent plant degradation, water is delivered slowly and at a moderate level. Ultimately, all plants should have grown nicely after 110 days, at which point it would be time to begin collecting the commodities in the fields. Knowing the fertilizer would enable automatic tracking of the common border length, assuring conformity, including generating automated and thorough documentation of the day's fertilization operations.
Machine Learning Based Intelligent Recommendation System to Recommend Product
Recommender structure (RS) has arisen as a tremendous evaluation interest that plans to assist clients with finding things online by giving contemplations that anxiously match their advantage. . This paper gives a complete report on the RS covering the various suggestion moves close, related issues, and frameworks utilized for data recovery. Because of its far reaching applications, it has incited investigation premium among an essential number of specialists from one side of the world to the other. The standard defense for this paper is to see the examination plan in RS. In excess of 1,000 examination papers, circled by Elsevier, Springer, and ACM beginning around 2011 to the fundamental quarter of 2017, have been thought of. Many enrapturing disclosures have ascended out of this review, that will help to and fro development and future RS scientists to outline and set their examination guide. Also, this paper moreover envisions the inevitable destiny of RS that may call up new investigation direction in this space
Scientific Computing with the KotlinSci Open-source Framework
KotlinSci is a framework for scientific computing for the Java Virtual Machine (JVM). The paper illustrates that KotlinSci can significantly facilitate and simplify the development of complex computational demanded scientific software at the JVM. The work compares Java based approaches with Kotlin ones, and illustrates the significant advantages of the enhanced flexibility of the Kotlin language. We illustrate that one of the more significant drawbacks of Java is its lack of operator overloading. Also, the nominal approach to functional programming of Java is not as flexible and convenient as the structural approach of Kotlin is. Kotlin also offers superb support for concurrency with its coroutines framework. Coroutines are exploited in KotlinSci to provide effective task oriented parallelism. The paper demonstrates that KotlinSci builds an effective and easy to use scientific programming environment by exploring the syntactic fluency and the strong object-functional features of Kotlin. JVM is a superb development framework that is based on advanced technology and algorithms. The advent of improved JVMs supporting more efficiently scientific computation tasks will enrich further the potential of KotlinSci for high-performance computing. Kotlin builds upon the JVM’s framework a modern language capable of confronting effectively a wide set of applications. In turn the presented KotlinSci open-source framework exploits the former ones and implements an easy to use scientific programming environment for the JVM.
Online Assessment and Proctoring for Candidate Recruitment at Workplace
In the present world, human proctoring is the most important approach to evaluation, by either providing the supervisor to visit an examination center or supervising them observably and acoustically while exam using a webcam; however, such methods are manual and costly. So, we aim to develop an automated proctoring system to detect an extensive variety of cheating behavior during an online assessment exam. The system requirement includes a working webcam and mic. This system has four components. User verification, eye-tracking, head-pose-estimation, and person and object detection. Person and object detection is done using the yolo algorithm and eye-tracking using blob algorithm and cascade classification. To develop our purposed system, we collect required data from 10 subjects who also performed various types of cheating while examination/assessment. Vast experimental outcomes indicate our proctoring system's accuracy, validity, and proficiency. By using this system for recruitment, we can increase the chances of getting deserving candidates to the organization.
Proctoring, User verification, object detection, webcam, online assessment exam, OpenCV
Face Mask Detection in Real Time using OpenCV and Python
COVID-19 has brought the entire world to a halt. Doctors, healthcare workers, and many other services were fighting this pandemic on the front lines. Wearing a face mask in public places is more effective, according to the World Health Organization (WHO). According to the reports, wearing the masks while working clearly shows that it reduces the risk of transmission. We will be using the dataset to build a face mask detector with computer vision using Python, OpenCV, TensorFlow and Keras. Our main goal is to determine whether or not the person in an image is wearing a face mask. The system's method is designed in such a way that it uses a video camera to capture images of people and apply detecting algorithms. After successfully implementing face mask detection using a video camera to detect people wearing and not wearing a face mask, the calculation detection percentage can be displayed in a variety of ways using visualization algorithms and thus used for real time detection.
Express-U: Real-time Indian Sign Language Recognition
This study describes a system that recognizes Indian Sign Language (ISL) hand poses and gestures in real time using the Hand Tracking Algorithm and the LSTM Deep Learning model. This approach aims to close the communication gap between those who are deaf or hard of hearing and the rest of society. Existing solutions either have a low level of accuracy or do not operate in real time. On both measures, this system performs admirably. Hand gestures are captured from a smartphone camera and its frames are transmitted to a remote server for processing. It is user-friendly because no external hardware (such as gloves or sensor) is used. Machine learning is an artificial intelligence (AI) application that enables systems to learn and improve without being explicitly programmed. This method does not require a clear background or clean image; it extracts holistic key points and makes predictions.
In computer vision, human body pose estimation is the study that aims at predicting the poses of human body parts and joints. In this project, visual languages are built based on skeleton poses. We present a real-time body tracking pipeline that predicts body posture. Once the joints of the body have been localised to represent a specific part of the human body, we can use them for wider ranges of applications like tracking body movements of some professional athlete or gymnast to understand the various physical techniques and strategies involved to achieve his/her desired goals. It is implemented using MediaPipe, an open-source, cross-platform Machine Learning framework used for building complex and multimodal applied machine learning pipelines. As a result, this system delivers a realtime interface as well as high prediction quality. It might also be utilized to design a smart gym training assistant program that would help individual bodybuilders achieve their intended goals. Following a thorough review of various types of fitness trackers and fitness applications, an evaluation of AI algorithms used in smart fitness scenarios is provided. Finally, a selection of existing gaps and potential future work, as well as extensive lectures on the benefits and potential obstacles of smart fitness, have been identified and offered.
AI Virtual Keyboard and Mouse using OpenCV
These days, PC vision has arrived at its pinnacle, where a PC can recognize its proprietor utilizing a straightforward program of picture handling. In this advanced stage, people are incorporating this vision into various aspects of daily life, such as face recognition, color identification, automatic vehicles, and so on. In this project, computer vision is used to create an optical mouse and console that uses hand signals. The camera of the PC will peruse the picture of various signals performed by an individual's hand and as indicated by the development of the motions the mouse or the cursor of the PC will move, even perform both ways click, utilizing various signals. Essentially, the console capacities might be utilized for a variety of signals, signals, such as utilizing one finger motion for letter set select and four-figure motion to swipe left and right. It will go about as a virtual mouse and console with no wire or outside gadgets. The main equipment part of the undertaking is a webcam, and the coding is done on python, utilizing Anaconda stage. Here, the convex frame deserts are first created, and then a calculation is created using the deformity estimations, and the mouse and console capacities are planned with the imperfections. Planning several of them with the mouse and console, the PC will comprehend the signal shown by the client and act as needs be.