Kind Of Artificial Intelligence Research Articles

A Comprehensive Machine Learning Approach for Advanced Vehicle Detection and Counting

The exponential rise of urban areas and the associated surge in transportation congestion. Consequently, this study offers a thorough method for vehicle recognition and counting via the use of machine learning, as well as an effective system for real-time traffic monitoring, with the aim of reducing traffic. The first step is to develop a model that can identify and follow moving cars in still photos or video. This research delves into the topic of teaching a computer to count automobiles using machine learning, a kind of artificial intelligence. The purpose of this study is to provide a computational model for intelligent vehicle detection and tracking at a given location and time of day, using real-time images of passing cars. This approach uses OpenCV to evaluate the model's car detection and counting capabilities, and convolutional neural networks (CNNs) for object recognition and classification. The techniques laid the groundwork for early improvements, which were often enhanced using machine learning classifiers such as random forests and support vector machines (SVMs). To automate the process and get useful information regarding traffic patterns and management.

Research and Review on Chatbots in Digital Mental Health

In recent years, with advanced technology, various kinds of artificial intelligence (AI) applications in different domains are booming. However, chatbots in the field of digital mental health are still in their early stages and have not yet been widely adopted. Since peoples psychological problems have become increasingly prevalent in society, and many have not been handled, this paper provided comprehensive research and review on the necessity and potential for AI to address the psychological issues faced by the Chinese population through a questionnaire investigation. The questionnaire investigation aimed to gather information on people's attitudes towards using chatbots in digital mental health as a solution. The responses were collected from diverse age groups of participants, including both males and females and varying provinces of residence in China. Overall, the results of the questionnaire investigation showed the possible future of this kind of chatbot among Chinese people.

ChatGPT vs UpToDate: comparative study of usefulness and reliability of Chatbot in common clinical presentations of otorhinolaryngology–head and neck surgery

PurposeThe usage of Chatbots as a kind of Artificial Intelligence in medicine is getting to increase in recent years. UpToDate® is another well-known search tool established on evidence-based knowledge and is used daily by doctors worldwide. In this study, we aimed to investigate the usefulness and reliability of ChatGPT compared to UpToDate in Otorhinolaryngology and Head and Neck Surgery (ORL–HNS).Materials and methodsChatGPT-3.5 and UpToDate were interrogated for the management of 25 common clinical case scenarios (13 males/12 females) recruited from literature considering the daily observation at the Department of Otorhinolaryngology of Ege University Faculty of Medicine. Scientific references for the management were requested for each clinical case. The accuracy of the references in the ChatGPT answers was assessed on a 0–2 scale and the usefulness of the ChatGPT and UpToDate answers was assessed with 1–3 scores by reviewers. UpToDate and ChatGPT 3.5 responses were compared.ResultsChatGPT did not give references in some questions in contrast to UpToDate. Information on the ChatGPT was limited to 2021. UpToDate supported the paper with subheadings, tables, figures, and algorithms. The mean accuracy score of references in ChatGPT answers was 0.25–weak/unrelated. The median (Q1–Q3) was 1.00 (1.25–2.00) for ChatGPT and 2.63 (2.75–3.00) for UpToDate, the difference was statistically significant (p < 0.001). UpToDate was observed more useful and reliable than ChatGPT.ConclusionsChatGPT has the potential to support the physicians to find out the information but our results suggest that ChatGPT needs to be improved to increase the usefulness and reliability of medical evidence-based knowledge.

Classification of cyclones using machine learning techniques

In this article, we provide a method for identifying and categorizing cyclones, both tropical and extratropical. The method is designed with the goal of producing a global labeled dataset for cyclones, and it is based on a set of rigorous criteria. The heuristics are defined from date, time, pressure, wind speed, wind directions, latitude and longitudes. Numerous researchers have confirmed that machine learning, a kind of artificial intelligence, can offer a fresh approach to overcoming the limitations of cyclone classification, whether employing a pure data-driven model or enhancing numerical models with machine learning. This article introduces progress based on machine learning in genesis classification, track records, intensities, and extreme weather forecasts associated with tropical as well as extratropical cyclones (such as strong winds and rainstorms and their disastrous impacts). The challenges of cyclones in recent years and successful cases of machine learning methods in these aspects are summarized and analyzed.

Linguistic mastery: Advances in natural language processing

NLPstands for Natural Language Processing, it is a kind of artificial intelligence. That demonstrate with scrutinize, understanding, and accuse natural human languages. In such a manner that analog procedure would get in touch and human language excluding computer-propel language like programming language such as c, c++, python, javascript. NLP field contains the intellect computer programming into an understanding language which is understandable by humans. So, powerful algorithms can even interpret one language into another language scrupulously. Natural language processing occasionally also known as computational linguistics, it uses semantics and syntax. It assists computers to acknowledge how humans talk or write and also it will know how to deduce meaning of words that they are saying. A language is designate as a synchronize of rules and symbols. These Symbol are amalgamated and used for disseminate the data as well as make contact to other resources. Symbols are potentiate by the rules and regulations.

Prevention of surgical site infections: a personal odyssey

Prevention of surgical site infections: a personal odyssey

Science fact vs science fiction: A ChatGPT immunological review experiment gone awry

Artificial intelligence (AI) has made great progress in recent years. The latest chatbot to make a splash is ChatGPT. To see if this type of AI could also be helpful in creating an immunological review article, I put a planned review on different classes of small RNAs during murine B cell development to the test. Although the general wording sounded very polished and convincing, ChatGPT encountered great difficulties when asked for details and references and made many incorrect statements, leading me to conclude that this kind of AI is not (yet?) suitable for assisting in the writing of scientific articles.

Federated learning with hyper-network—a case study on whole slide image analysis

Federated learning(FL) is a new kind of Artificial Intelligence(AI) aimed at data privacy preservation that builds on decentralizing the training data for the deep learning model. This new technique of data security and privacy sheds light on many critical domains with highly sensitive data, including medical image analysis. Developing a strong, scalable, and precise deep learning model has proven to count on a variety of high-quality data from different centers. However, data holders may not willing to share their data considering the restriction of privacy. In this paper, we approach this challenge with a federated learning paradigm. Specifically, we present a case study on the whole slide image classification problem. At each local client center, a multiple-instance learning classifier is developed to conduct whole slide image classification. We introduce a privacy-preserving federated learning framework based on hyper-network to update the global model. Hyper-network is deployed at the global center that produces the weights of the local network conditioned on its input. In this way, hyper-networks can simultaneously learn a family of the local client networks. Instead of communicating raw data with the local client, only model parameters injected with noise are transferred between the local client and the global model. By using a large scale of whole slide images with only slide-level labels, we mensurated our way on two different whole slide image classification problems. The results demonstrate that our proposed federated learning model based on hyper-network can effectively leverage multi-center data to develop a more accurate model which can be used to classify a whole slide image. Its improvements in terms of over the isolated local centers and the commonly used federated averaging baseline are significant. Code will be available.

I, Robot: the three laws of robotics and the ethics of the peopleless economy

The world economy is currently witnessing an unprecedented surge in the ability of machines and algorithms to replace human labor, a surge with the potential to accomplish gains of efficiency and productivity that were unimaginable just a few years ago. Nonetheless, the wonders of the artificial intelligence economy are ultimately overshadowed by a series of concerns that decision-makers should account for: fast automation is synonymous with job losses, increasing income inequality, demand imbalances, and various sorts of insecurities and anxieties. The countervailing forces associated with the progress of automation raise doubts about whether the transition to the robot economy should advance full speed ahead. Rather than developing any artificial intelligence, it is necessary to develop the right kind of artificial intelligence. In particular, the progress of automation should be framed by fairness and ethical principles. This study discusses the ethical boundaries of the emergent robot economy. The discussion is supported on a theoretical model that is built and simulated with the goal of illustrating the transition to the robot economy. The virtues and flaws of this transition are considered.

The Role of Artificial Intelligence in Graphic Design

The research aims at studying the definition of artificial intelligence. It will present the history of AI. The research also demonstrates the different kinds of artificial intelligence. It also demonstrates the various applications of using AI. The research presents how graphic design uses AI in different purposes. Then the research discussed the impact of using artificial intelligence on the graphic design and how this will impact the profession.

The two kinds of artificial intelligence, or how not to confuse objects and subjects

The two kinds of artificial intelligence, or how not to confuse objects and subjects

DAD-Net: Classification of Alzheimer's Disease Using ADASYN Oversampling Technique and Optimized Neural Network.

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.

Detection Technology of Social Robot: Based on the Interpretation of Botometer Model

In the era of Web 2.0, social media have been a significant place for democratic conversation about social or political issues. While in many major public events like the Russia-Ukraine war or U.S. Presidential election, enormous social bots were found on Twitter and Facebook, putting forward public opinion warfare. By creating the illusion of grassroots support for a certain opinion, this kind of artificial intelligence can be exploited to spread misinformation, change the public perception of political entities or even promote terrorist propaganda. As a result of that, exploiting detection tools has been a great concern since social bots were born. In this article, we focused on Botometer, a publicly available detection tool, to further explain the AI technologies used in identifying artificial accounts. By analyzing its database and combing the previous literature, we explained the model from the aspect of data augmentation, feature engineering, account characterization, and Ensemble of Specialized Classifier (ESC). Considering the consistent evolution of social bots, we propose several optimization suggestions and three other techniques or models to improve the accuracy of social bots detection.

Distributed Intelligent Learning and Decision Model Based on Logic Predictive Control.

By the method of documentation and logical analysis, based on the data, based on logic and based on the knowledge of three kinds of artificial intelligence in the sports education, the intelligent learning system feedback delay are studied, combined with mobile communication which led to the artificial intelligence online sports games teaching, pattern recognition, and virtual technology combined with innovative teaching interaction and experience. Promoting the development of green PE teaching machine learning can identify the types of PE activities and realize efficient PE learning diagnosis. Intelligent decision support system can identify sports talents and improve the effect of personalized PE teaching evaluation. From the perspective of psychological development and education, the key problems to be solved in the integration of artificial intelligence and physical education are examined. Then, the consistent model predictive control for feedback delay of nonlinear sports learning multiagent system with network induced delay and random communication protocol is studied. Under the communication waiting mechanism designed, each agent has a certain tolerance of delay, and this tolerance can be determined by ensuring the stability of the system. At the same time, a random communication protocol is designed to ensure the ordered communication of the multiagent system. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation. To solve the channel competition access problem of the sports intelligent learning system with special structure feedback delay model predictive control, a dual channel awareness scheduling strategy under the model predictive control framework was proposed, and the distributed threshold strategy of sensors and the priority threshold strategy of controllers were designed. It is proved that the sensor will eventually work at Nash equilibrium point under the policy updating mechanism, and the priority threshold strategy of the controller is better than the traditional independent and identically distributed access strategy. By avoiding the data transmission when the channel status is poor, the channel access of the system is efficient and saves energy.

A Computer Vision Platform to Automatically Recognize Surgical Phases in Laparoscopic Pancreatoduodenectomy

Introduction: Laparoscopic pancreatoduodenectomy (LPD) is a complex operation lasting several hours, whose intraoperative videos still require timely manual review and analysis. Computer vision(CV) is a kind of artificial intelligence(AI), which uses convolutional neural networks(CNN) to allow the computer to understand the images and videos. The purpose of our study is to develop a CV platform to recognize and extract surgical phases automatically in LPD videos.

Philosophical Thinking on “AI Face Changing” has Challenged the Highest Value Goal of Human’s Activities

In current world, the development of artificial intelligence is in the ascendant, which also has had a profound and extensive impact on human’s living and development. Among many artificial intelligence, “AI Face Changing” is a relatively new and cutting-edge one in the world. From the perspective of pure technical professionalism, this technology is very powerful. However, from a philosophical point of view, this kind of artificial intelligence seriously has challenged the highest value goal of human’s activities. The highest value goals of human’s activities mainly include three, that is, seeking truth, goodness and beauty. The “AI Face Changing” can be said to bring a serious challenge to the three highest goals of human’s activities. From the perspective of philosophy, according to some basic principles of Marxist philosophy and combined with social reality, this paper mainly analyzed the challenge of “AI Face Changing” to the above-mentioned highest value goal of human’s activities.

Integrating Taguchi method and artificial neural network for predicting and maximizing biofuel production via torrefaction and pyrolysis

Artificial neural network (ANN) is one kind of artificial intelligence in the computing system that aims to process information as the way neurons in the human brain. In this study, the combination of the Taguchi method and ANN are used to maximize and predict biofuel yield from spent mushroom substrate torrefaction and pyrolysis via microwave irradiation. The Taguchi method is utilized to design the multiple factors (particle size, catalyst, power, and magnetic agent) and levels of experiment parameters. The highest total biofuel yield (biochar + bio-oil) is 99.42%, accomplished by a combination of 355 µm particle size, 300 mg·g-SMS-1 catalyst, 900 W power, and 300 mg·g-SMS-1 magnetic agent. ANN with one hidden layer shows the outstanding linear regression predictions for the highest biofuel yields (biochar 0.9999 and bio-oil 0.9998). This high linear regression indicates that ANN with a quick propagation algorithm is an appropriate approach for predicting biofuel conversion.

Big Techs and startups in pharmaceutical R&D – A 2020 perspective on artificial intelligence

We investigated what kind of artificial intelligence (AI) technologies are utilized in pharmaceutical research and development (R&D) and which sources of AI-related competencies can be leveraged by pharmaceutical companies. First, we found that machine learning (ML) is the dominating AI technology currently used in pharmaceutical R&D. Second, both Big Techs and AI startups are competent knowledge bases for AI applications. Big Techs have long-lasting experience in the digital field and offer more general IT solutions to support pharmaceutical companies in cloud computing, health monitoring, diagnostics or clinical trial management, whereas startups can provide more specific AI services to address special issues in the drug-discovery space.

Research on Improved Intelligent Control Processes Based on Three Kinds of Artificial Intelligence

Autotuning and online tuning of control parameters in control processes (OTP) are widely used in practice, such as in chemical production and industrial control processes. Better performance (such as dynamic speed and steady-state error) and less repeated manual-tuning workloads in bad environments for engineers are expected. The main works are as follows: Firstly, a change ratio for expert system and fuzzy-reasoning-based OTP methods is proposed. Secondly, a wavelet neural-network-based OTP method is proposed. Thirdly, comparative simulations are implemented in order to verify the performance. Finally, the stability of the proposed methods is analyzed based on the theory of stability. Results and effects are as follows: Firstly, the proposed control parameters of online tuning methods of artificial-intelligence-based classical control (AI-CC) systems had better performance, such as faster speed and smaller error. Secondly, stability was verified theoretically, so the proposed method could be applied with a guarantee. Thirdly, a lot of repeated and unsafe manual-based tuning work for engineers can be replaced by AI-CC systems. Finally, an upgrade solution AI-CC, with low cost, is provided for a large number of existing classical control systems.

Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure.