AIML Research Articles

Vision based Accident Detection System Using AI ML and Yolov8 Algorithms

Vision based Accident Detection System Using AI ML and Yolov8 Algorithms

HEALPAL: YOUR PERSONAL HEALTH COMAPANION

In an era where rapid technological advancements intersect with healthcare needs, the development of an intelligent healthcare chatbot has emerged as a promising solution. This study presents a comprehensive exploration into the conceptualization, design, and implementation of an AI-powered chatbot tailored to provide accurate and personalized healthcare information. Integrating Natural Language Processing (NLP) algorithms and Artificial Intelligence Markup Language (AIML), the chatbot aims to bridge the gap between individuals and reliable medical knowledge. Keywords—Chatbot, NLP, Markup Language

COLLEGE ENQUIRY CHAT-BOT SYSTEM

Nowadays, many people are using smartphone with many new applications i.e. technology is growing day by day. Today Artificial Intelligence is playing a major role in a variety of fields ranging from industries in product manufacturing, to customer care in public relations. As there are many online Artificial Intelligence (AI) systems or chat bots which are in existence that help people solve their problems. So, we are going to implement a virtual assistant based on AI that can solve any college related query. This will work as a College Oriented Intelligence machine. This virtual machine will respond the queries of students on college related issues. A chat bot has information stored in its database to identify the sentences and making a decision itself as response to answer a given question. The college enquiry chat bot will be built using algorithm that analyses queries and understand user’s message Chatbots are conversational systems that can do chat interactions with human automatically. It is developed to be virtual assistant, making entertainment for people, helping for answering the questions, getting driving directions, serving as human partner in smart homes etc. Most of the chatbots utilize the algorithms of artificial intelligence (AI) in order to get the required responses. In this paper, we provide the design of a University Chatbot that provides an efficient and accurate answer for any user questions about university information. This is the first University Chatbot for inquiring about school information in Myanmar Language based on Artificial Intelligence Markup Language and uses Pandora bots as the interpreter. This paper introduces approach made for improving the efficiency of the chatbot or artificial conversational entity used in various commercial and banking sector. Humanizing is to improving the response generation ability of the chatbot. Key Words: Artificial Intelligence, Database. Intelligence Machine.

Integration into 3D Printing for Image Processing using AI ML

The challenges of physical measurement for prosthetic development and highlight how our integrated tool offers a remote solution to address these challenges, promoting accessibility, convenience, and active participation in the prosthetic design process. Our Measurement Module for Prosthetic Hand Dimensions is a comparison of Image Processing and Distance Measurement Methods The creation of a module to precisely measure finger dimensions for the creation of prosthetic hands is the main goal of this research project. The module provides distance measurement and image-based input as two different ways to get dimensions. Users place their hands at predetermined distances from a screen in the distance measurement option, while users submit photographs of their hands with reference objects for scale in the image based input option. In order To provide a proper fit and functionality, the collected dimensions are an essential component of the prosthetic hand construction process. The study contrasts the efficiency of distance measurement methods employing cameras or other cameras with image processing techniques like edge recognition and contour analysis. Accuracy, usability, and efficiency are among the factors that are assessed to identify the best method for acquiring dimensions. OpenCV for image processing, TensorFlow for machine learning-based analysis, Tkinter for UI design, Mediapipe for landmark identification and hand tracking, and NumPy for numerical operations are just a few of the open-source libraries that are used in this module. The module's possible influence on improving the creation and customization of prosthetic hands is also covered. By offering insights into effective and precise dimension measurement methodologies for customized prosthetic hand design, the study's findings advance the area of prosthetics

Evolution of Automation to Hyperautomation: Leveraging RPA, AI ML, NLP for Optimal Operational Efficiency

Evolution of Automation to Hyperautomation: Leveraging RPA, AI ML, NLP for Optimal Operational Efficiency

Fuzzy Prosthesis

Artificial Intelligence AI and Machine Learning ML have paved the pathway for a brighter techy future in the field of Prosthetics by acting as a boon for Persons with DisabilityPeoples with Determination PwDs. Biomechanotronics is a branch of mechanotronics and involves four main units. This can be integrated with artificial intelligence for better results and user experiences. We have discussed microprocessor knee joint for better insight into this concept.

Challenges and Limitations of Artificial Intelligence Implementation in Modern Power Grid

Ongoing global decarbonization and energy transition have led to significant changes in the traditional power grid, resulting in new challenges for grid operators. These challenges, which are exacerbated by the increased penetration of distributed energy resources (DER), include power system stability, protection and control system malfunction, and supply and demand balance. To better understand the implications of high DER penetration, we proposed a classification of modern grids based on the DER integration level and identified the specific challenges faced by each category. We defined eight key challenges posed by DER on the grid and examined recent academic research on the application of AI techniques to modern power systems. Despite the potential benefits of AI in terms of power system stability and protection, there are currently no commercial applications of this research. Our study analyzed the limitations and barriers to practical AI implementation in power system protection and stability, including the use of synthetic data, insufficient real measurement data, protection selectivity, and the black-box nature of the models. We emphasize the importance of further research and development in the fields of Explainability AI and physics-informed ML to overcome these challenges and enable the successful implementation of AI in modern power grid stability and protection.

Assessing the Potential of AI–ML in Urban Climate Change Adaptation and Sustainable Development

This study addresses a notable gap in the climate change literature by examining the potential of artificial intelligence and machine learning (AI–ML) in urban climate change adaptation and sustainable development across major global continents. While much attention has been given to mitigation strategies, this study uniquely delves into the AI–ML’s underexplored role in catalyzing climate change adaptation in contemporary and future urban centers. The research thoroughly explores diverse case studies from Africa, Asia, Australasia, Europe, North America, and South America, utilizing a methodological framework involving six-step and five-step models for systematic literature reviews. The findings underscore AI–ML achievements, illuminate challenges, and emphasize the need for context-specific and collaborative approaches. The findings imply that a one-size-fits-all approach is insufficient. Instead, successful adaptation strategies must be intricately linked to the particular characteristics, vulnerabilities, and intricacies of each region. Furthermore, the research underscores the importance of international collaboration, knowledge sharing, and technology transfer to expedite the integration of AI–ML into climate adaptation strategies globally. The study envisions a promising trajectory for AI–ML in the climate adaptation domain, emphasizing the necessity for ongoing research, innovation, and practical AI–ML applications. As climate change remains a defining challenge, this research predicts an increasingly pivotal role for AI–ML in constructing climate-resilient urban centers and promoting sustainable development. Continuous efforts to advance AI–ML technologies, establish robust policy frameworks, and ensure universal access are crucial for harnessing AI–ML’s transformative capabilities to combat climate change consequences.

Artificial Intelligence (AI) Based, Machine Learning (ML) Predicting the Individual Absolute Risk of Acute Graft Versus Host Disease (aGvHD) in a Retrospective International Cohort

Background: HSCT still holds potentially lethal complications such as aGvHD, relapse of malignant disease, graft rejection, and infections. 30 to 50% of all patients undergoing HSCT will develop aGvHD, causing high morbidity and mortality (grade 4 GvHD up to 90%) as well as generating significantly higher treatment costs than a HSCT without aGvHD. MatchGraft.AI is an AI based ML algorithm that predicts individual, absolute risk of aGvHD for every individual HSCT donor-recipient pair, based on data available prior to start of conditioning aiming for optimization of donor selection. Methods: MatchGraft.AI has been built in accordance with best clinical practice (Higgins et al 2021). A random forest is used to predict the probability of aGvHD free survival, GvHD free death and aGvHD. Missing data are imputed, except for the gradient boosted tree, by using the most frequent category for categorical variables and mean value for continuous variables. The performance characteristics are calculated in an aGvHD vs Rest (death or aGvHD free survival) fashion. The data is split into a stratified training and validation set with a ratio of 80% / 20%. Results: The model was trained at Charité University against a retrospective, multi center data set of 1035 German and north American patient/donor sets. We collected 1719 data sets. 684 cases were excluded due to mayor outcome parameters not being available. The following analyzation focuses on the 1035 included stem cell transplantation patient/donor sets (828 training set/ 207 validation set), 356 pediatric patients, 679 adults. M:F = 1,5; age 0 - 76 (median: 39.5 +/- 24.3 years). Indications for transplantation were malignant diseases (hematological neoplasms, lymphomas, solid tumors) in 731 patients and benign diseases (hemoglobinopathies, immune deficiencies, bone marrow failure) in 205 cases, in 99 cases diagnosis was not documented. Transplantations were performed between January 2013 and December 2019. Despite some missing data in our retrospective cohort, model performance was estimated at 0.70 AUC (fig.1). Integrated calibration index was 0,0277. Discussion: MatchGraft.AI predicts the risk of acute GvHD. By providing precise prediction of relevant complications, we expect the pool of possible donors to become larger as well as donor selection to improve. Furthermore, physicians and patients will be able to better prepare and adapt conditioning and treatment plans with MatchGraft.AI predictions. Conclusions: The prediction of individual aGvHD risk, using this AI ML model, is feasible and well calibrated, especially in the medium risk area. Ongoing continuous training, testing and adapting on additional international, multicenter retrospective and prospective data will further improve performance in the low risk area. This study is the basis for further prospective research.

Farm Friendly Chat Bot

This study presents the development of a chat room and a chatbot designed to facilitate discussions on prevalent farming issues among peers and experts. Its primary aim is to provide timely support to farmers in making informed decisions about their farming practices. To create a structured framework for these conversations, a standardized set of questions was formulated through consultations and surveys involving farmers, experts, and other stakeholders. The questions were analyzed to extract 'intents,' representing the specific information or assistance users might seek, and 'examples,' which are concrete instances users provide to express their particular intent. Additionally, 'entities' were identified to represent distinct objects or concepts related to these intents. The model was trained using the Artificial Intelligence Markup Language (AIML) to predict the intent based on the provided examples. This training process enhances the chatbot's ability to understand and respond to user queries effectively. Furthermore, the chatbot was deployed on a cloud platform, reducing the computational resources required on the client end. This approach ensures accessibility and usability for a broader user base without significant hardware constraints.

Implementation Chatbot for SMEs Using Artificial Intelligence Markup Language to Improve Customer Integration and Business Performance

Chatbots have been extensively adopted to produce more positive customer experiences as customers now spend more time in digital surroundings. Despite the technological advancement and benefits of chatbots for client service, exploration of chatbot operations for small and medium-sized enterprises (SMEs) is limited. The absence of exploration explaining the struggles faced by SMEs contributes to the gap in SMEs’ chatbot adoption. This exploration determines the features and rudiments that fit with SMEs’ characteristics and their guests' interactions with chatbots. A mixed-methods approach is used to understand SMEs’ needs. The first study uses interviews with SME business owners and their customers in order to explore the features that chatbots should offer for SME by identifying combinations between chatbots' generic features and SMEs' customer characteristics. The second study tests features identified in SMEs customers to empirically test featured chatbots’ influence on anthropomorphism, perceived enjoyment, perceived ease of use, perceived usefulness, and how they affect SMEs’ customer intentions to use chatbots and their shopping intentions. This paper contributes to the emerging service literature on the use of chatbots for service interactions, particularly for SMEs using Artificial Intelligence Markup Language. The data that has been obtained was analyzed using a Likert scale. As a result, the accuracy of using Artificial Intelligence Markup Language is 84.8%.

Implementation of Chatbot System on Tourism Objects in Banyumas Regency with AIML and Chatterbot

Information technology can be applied to all fields, including tourism. Tourism object information media can be implemented into the chatbot system to make the information search process more flexible. Currently, searching for tourist spot information is often done manually; this makes tourist information services limited in time, while the need for tourism information must always be available. This research aims to build a chatbot system using Artificial Intelligence Markup Language (AIML) and ChatterBot methods. Both methods are accessed from libraries in Python using input in the form of natural language that has been processed into certain patterns. The pattern determination process is carried out by classifying a collection of questions on the chatbot using the Support Vector Machines (SVM) method. Then the classification is divided into five attributes, namely address, ticket price, facilities, description, and access. The SVM model built obtained an accuracy rate of 88%. Based on the testing results on both models that have been tested, the approach with AIML results in an accuracy rate in answering questions correctly of 90%, while ChatterBot is 40%.

Automation of Customer Support System (Chatbot) to Solve Web Based Financial and Payment Application Service

One of the most important features of any online service is the quality of its customer care. However, with the development of NLP tools, businesses are considering automated chatbot solutions to keep up with the increasing demand for their products and services. In view of this, the chatbot was developed using AIML java interpreter library Program AB which helps match input and output predefined in the AIML file. AIML (Artificial Intelligence Markup Language) was used to preprocess and train the bot using ready-made AIML file for FAQ questions. Also, vaadin was used to build a web UI to interact with the trained AIML bot. Finally, a google script was written to translate from any language to English for the bot to understand and send the response in the preferred language of the user. Findings showed that the response time of the bot is dependent of the network, as the design gave a score of 70%, 80%, 90% and 90% for load testing, stability, reliability testing and usability testing, respectively. Also, the bot is compatible with different operating systems, both for forward compatibility and backward compatibility having a score of 95%. The bot was able to answer customer questions, enquiries and complaints and the response time of the bot depends on the strength of the network since it is web based. Hence, the system provided a simple, cheaper, and durable customer financial and payment application service. Since chatbots cannot answer all questions, businesses that decide to implement them should ensure that they have enough protections in place against attacks and that routine requests are standardised to ensure optimal performance.

Eye Facial Gesture System Controller Based on AI ML

Abstract: This paper presents a ground-breaking real-time algorithm for accurately detecting eye blinks in video sequences captured using standard cameras. The algorithm utilizes advanced landmark detectors trained on diverse datasets to ensure robustness against variations in head orientation, illumination, and facial expressions. By employing precise landmark detection techniques, the algorithm estimates the level of eye opening using the eye aspect ratio (EAR) in each frame. An SVM classifier analyses the patterns of EAR values within a short temporal window to identify eye blinks. Comparative evaluations on popular datasets demonstrate the superior performance of the proposed algorithm compared to existing methods. Additionally, the research explores the utilization of eye movements for controlling computer programs, evaluating four distinct approaches in a user-friendly photo viewer. The evaluation process considers various factors such as component sizes, execution time, unintended selections, and gesture repetitions. User experiments reveal that component sizes of 200px provide a convenient and efficient means of application control. The gaze-based method and gestures based on joining points receive positive feedback and exhibit satisfactory performance. This paper contributes significantly to the field of eye-based interaction by introducing an innovative blink detection algorithm and conducting a comprehensive evaluation of eye movement approaches for application control.

AI & ML Powered Autonomous Drone System

The use of drone technology is growing in popularity and has many different industrial uses. Drones are used in agriculture for pesticide and fertilizer application, crop monitoring, and mapping. Drones can be used in the building sector to check job sites and track development. In the movie industry, drones are also utilized for aerial photography and in search and rescue efforts to reach inaccessible places. In general, drone technology has the potential to revolutionize a variety of industries and offer fresh approaches to problems that were previously challenging to solve. Our idea is to develop AI ML based autonomous drone system that can be deployed in many areas for multiple applications such as surveillance, warehouse management etc. The layout includes a PI-CAM V2 camera on a quadcopter using GPS and system powered by Robot Operating System, Open CV and other technologies that gives ability to drone to fly without need of pilot autonomously. Key Word: Multipurpose, Robot Operating System, Simulation, Surveillance, Tracking, Quadcopter, OpenCV, Machine Learning, Object Detection.

E-Commerce Website based on Chatbot

Abstract: Nowadays, traditional markets are gradually are being replaced by many online marketplaces. Fierce competition in the online market demands excellent service from both sellers and buyers, so many online stores offer his 24-hour service. This service would certainly cost a lot of money if done manually. Chat bots can be used as a solution for automated shopping online. Second, the bot must be able to answer accurately and quickly. In this study, we propose an intelligent chatbot system based on AIML (Artificial Intelligence Markup Language) that can be used as an e-commerce assistant. Input questions from users are processed in her three phases: parsing, pattern matching, and AIML crawling the data. In the AIML process, user requests fall into his three categories: general questions, calculations, and inventory checks. Where compute requests intercept the order and payment process

클라우드 기반 플랫폼의 분석 자동화 기술 동향

The demand for data analytics continues to grow as the number of citizen data analysts engaging in analytics activities increases. Data analysis requires a number of complex procedures, all of which can be resource-intensive for analysts to perform manually. This paper compares the technology trends of major cloud-based solutions and discusses automation strategies and technology trends in the cloud environment, with a focus on balancing analytical autonomy and automation, portability of the analytics environment, and improving platform usability. In particular, it compares and analyzes representative platforms such as SageMaker, Vertex AI, and Azure ML to understand the evolution of analytics automation technology and discusses essential components for future advancements. It also discusses how organizations can increase productivity through analytics by using automated platforms for data-driven decision-making.

Voice Assistant and Virtual Mouse using Different AI Techniques and ML

This venture advocates a Human PC Cooperation strategy where cursor development can be controlled through ongoing camera by utilizing human hand stances acknowledgment. This strategy is an option in contrast to current procedures, which incorporate physically squeezing buttons or utilizing actual PC mouse. Instead, it manages various mouse events through camera and computer vision software and can perform any action that a standard computer mouse can. The Virtual Mouse color recognition software will continuously collect photos taken in real time and process them through a number of conversions and filters. Whenever everything is changed over, the application will utilize picture handling to extricate directions of determined variety position from changed over outlines. The process then looks at a list of color combinations, each of which corresponds to a specific set of mouse actions, and compares the current color schemes in the frames. On the off chance that the ongoing variety conspire matches, the application will play out the mouse order, which will be changed over into a genuine mouse order on the user\'s PC. Notwithstanding that creators have likewise fostered a voice right hand to further develop client efficiency by overseeing routine errands of the client and by giving data from online sources to the client since voice colleague is easy to utilize. The objective of this paper is to improve the virtual assistant's performance. In this way, what precisely is a remote helper. Software that understands natural language voice commands and can carry out tasks on your behalf is known as application software, which is also referred to as AI assistants or digital assistants. They are also referred to as virtual assistants. What is the role of a virtual assistant? The list of tasks that virtual assistants can carry out that you can carry out on your own smartphone or personal computer is constantly growing. Meeting scheduling, message delivery, and weather monitoring are just a few of the impressive tasks that virtual assistants typically carry out. Google Assistant and Cortana, two examples of previous virtual assistants, were limited in that they could only perform searches and were not entirely automated. To keep the song's control function intact, these engines, for instance, lack the ability to play and pause the song. They can only have the module to play and search for songs. In order to enhance the project's functionality, we are currently working on an automated Google, YouTube, and numerous other new tasks. Now, a virtual mouse that can only be used to control the cursor and click has been added to make the process easier.

Automated Knowledge Organization AI ML based Subject Indexing System for Libraries

The research study as reported here is an attempt to explore the possibilities of an AI/ML-based semi-automated indexing system in a library setup to handle large volumes of documents. It uses the Python virtual environment to install and configure an open source AI environment (named Annif) to feed the LOD (Linked Open Data) dataset of Library of Congress Subject Headings (LCSH) as a standard KOS (Knowledge Organization System). The framework deployed the Turtle format of LCSH after cleaning the file with Skosify, applied TF-IDF as a language model (backend algorithm), and selected Snowball as an analyzer. The training of Annif was conducted with a large set of bibliographic records populated with subject descriptors (MARC tag 650$a) and indexed by trained LIS professionals. The training dataset is first treated with MarcEdit to export it in a format suitable for OpenRefine, and then in OpenRefine it undergoes many steps to produce a bibliographic record set suitable to train Annif. The framework, after training, has been tested with a bibliographic dataset to measure indexing efficiencies, and finally, the automated indexing framework is integrated with a data wrangling software (OpenRefine) to produce suggested headings on a mass scale. The entire framework is based on open source software, open datasets, and open standards.

Chatbot for Diagnosis of Pregnancy Disorders using Artificial Intelligence Markup Language (AIML)

Artificial Intelligence has evolved in sophistication and widespread use. This study aims to create a chatbot application in the health sector regarding the early diagnosis of pregnancy disorders. Based on basic health research, only 44 percent of pregnant women know the danger signs of pregnancy. The chatbot application developed is expected to facilitate and increase knowledge for pregnant women about the danger signs of pregnancy, especially early diagnosis of pregnancy disorders. The chatbot application was developed with artificial intelligence technology based on Artificial Intelligence Markup Language with the question-answer concept using the Pandorabots framework. The test is carried out in two stages: functional and pattern matching. The functional testing uses the black-box testing method, and the pattern-matching test on the chatbot uses the sentence similarity and bigram methods based on user input and keywords similarity in the bot's knowledge base. The functional testing results show that the chatbot application runs well, with the eligibility criteria reaching 81.4% and the results of the keyword similarity test (pattern matching) are zero to one, in the sense that the value of one has the same similarity between user input and pattern. Meanwhile, the zero value has no similarities, so the bot will respond to it as free input. So it can be concluded that the bot can respond to user questions when the pattern and input have the same level of similarity.