Intelligence Research Research Articles

A Survey on Theories and Application for Self Driving Cars on Deep Learning Methods

In today’s, Self-driving cars are one of the hot research topics in technology, which has a great impact on social and economic development. Self-driving cars, powered by deep learning algorithms, represent a groundbreaking fusion of artificial intelligence and transportation technology. Deep learning is also one of the current major areas in artificial intelligence research. It has been widely applied in natural language understanding, image processing, etc. In recent years, more and more deep learning methods have been introduced to the solution in the field of self-driving cars and have achieved excellent results. This survey provides information related to self-driving cars and summarizes the application of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions are analyzed based on deep learning methods, such as lane detection, scene recognition and navigation etc. Also briefly described are some representative approaches to self-driving cars using deep learning methods. Finally, future challenges in applications of deep learning for self-driving cars are described.

Two-Stage Probe-Based Search Optimization Algorithm for the Traveling Salesman Problems

As a classical combinatorial optimization problem, the traveling salesman problem (TSP) has been extensively investigated in the fields of Artificial Intelligence and Operations Research. Due to being NP-complete, it is still rather challenging to solve both effectively and efficiently. Because of its high theoretical significance and wide practical applications, great effort has been undertaken to solve it from the point of view of intelligent search. In this paper, we propose a two-stage probe-based search optimization algorithm for solving both symmetric and asymmetric TSPs through the stages of route development and a self-escape mechanism. Specifically, in the first stage, a reasonable proportion threshold filter of potential basis probes or partial routes is set up at each step during the complete route development process. In this way, the poor basis probes with longer routes are filtered out automatically. Moreover, four local augmentation operators are further employed to improve these potential basis probes at each step. In the second stage, a self-escape mechanism or operation is further implemented on the obtained complete routes to prevent the probe-based search from being trapped in a locally optimal solution. The experimental results on a collection of benchmark TSP datasets demonstrate that our proposed algorithm is more effective than other state-of-the-art optimization algorithms. In fact, it achieves the best-known TSP benchmark solutions in many datasets, while, in certain cases, it even generates solutions that are better than the best-known TSP benchmark solutions.

Automatic Image Caption Generation with Deep Learning

Artificial intelligence research has long focused on the task of automatically defining what is included in a picture or image. This paper presents the CNN and RNN-LSTM models used in the Automatic Caption Generator implementation. It takes into account recent developments in image processing and performing research on translation through automation. The dataset utilized was Flickr8k. We have employed BLEU scores to assess the efficiency of the system that has been described. Based on the scores, one could classify the resulting captions as excellent or awful. The primary uses of this paradigm are virtual assistants, picture indexing, social media, assistive technology for the blind, altering app suggestions, and many more areas. Key Words: Automatic Caption Generator, CNN, RNN-LSTM, Computer vision, Machine translation, Flickr8k dataset, BLEU scores, Performance evaluation, Virtual assistants, Picture indexing, Social media, Assistive technology for the blind, App suggestions.

SHIKSHAKENDRA – The Epitome Of Educational Elegance

In the world of computers, artificial intelligence (AI) is a revolutionary field of computer science that aims to create intelligent systems that can mimic cognitive functions similar to those of humans and make predictions, recommendations, and decisions that affect real or virtual environments. AI is intended to facilitate problem-solving. Machine learning, neural networks, and computer vision are just a few of the many methods and tools that are included in artificial intelligence (AI). With the use of these technologies, machines are able to process enormous volumes of data and identify patterns—often rather accurately. Particular objectives and the application of specific techniques, such as reasoning, knowledge representation, planning, learning, and perception, are the focal points of the many subfields of AI study. Researchers in artificial intelligence (AI) have adopted and combined a variety of approaches to solve problems, such as formal logic, artificial neural networks, search and mathematical optimization, and approaches based on statistics, probability, and economics. Philosophy, neurology, and many other disciplines are also incorporated. Subproblems have been identified within the generation problem of making or imitating intelligence. Key Words: Learning, Chatbot, Physical disabled students, Problem-solving, Planning, Learning, Subscription, Courses, Downloads.

Mapping Emotional Intelligence and Psychological Capital Research: A Bibliometric Analysis and Future Research Agenda

This study explores the scholarly literature on emotional intelligence (EI) and psychological capital (PsyCap) using a comprehensive bibliometric analysis to map the research landscape and propose future directions. Data was collected from the Scopus database using specific search queries related to EI and PsyCap, with inclusion and exclusion criteria established to refine the dataset. A total of 39 articles published between 2007 and 2023 were selected for analysis. Utilizing bibliometric techniques through VOSviewer, patterns of citation and collaboration within the field were quantified, capturing descriptive statistics related to citation data and performing network analysis on authors, countries, publications, citations, and keywords. Key documents, sources, authors, organizations, and countries contributing to the discourse on PsyCap and EI were identified, providing insights into the evolution and trends within this field. The analysis revealed longitudinal trends in research output and explored the intersectionality of EI and PsyCap with other individual differences and personality traits. The findings contribute to a deeper understanding of knowledge growth in positive organizational psychology, informing evidence-based practices, and guiding future research endeavors.

A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR.

The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient's training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient's condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.

A Critical Examination of the Role of ChatGPT in Learning Research:A Thing Ethnographic Study

The rapid evolution of artificial intelligence(AI) in natural language processing has sparked considerable interest in AI Large Language Models(LLM), such as ChatGPT, within the field of education. Despite this attention, there exists a noticeable gap in scholarly discourse regarding the potential impact of ChatGPT on learning research. This study adopts an ethnographic research approach, considering ChatGPT as an active research participant. Guided by four key questions derived from the SWOT analysis framework, the study conducts a comprehensive evaluation of ChatGPT's strengths (Availability and Accessibility, Natural Language Processing, Vast Knowledge Base) and weaknesses (Lack of Deep Understanding and Creative Thinking, Lack of Objectivity and Integrity) in the context of learning research. Special emphasis is placed on the substantial opportunities that ChatGPT introduces for learning research, notably its potential as an Intelligent Research Assistant and its capacity to provide Writing Assistance. Additionally, the study illuminates potential threats posed by ChatGPT to learning research, including Risks to the Development of Critical Thinking and Creativity, Risks to Information Security, and Risks of Deepening Educational Inequities. Through this rigorous critical analysis, the study endeavors to bridge the aforementioned gap in the literature. Furthermore, the research proposes that learning researchers prepare for the impact of ChatGPT on learning research by concentrating on Critical Scrutiny of ChatGPT, Integration of ChatGPT into Research Practices, and Reflective Practices Regarding ChatGPT. By elucidating these aspects, the study advocates for additional empirical research on the role of ChatGPT in learning research, aiming to effectively guide its valuable contributions in the future of learning research.

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images.

Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

Responsible research in artificial intelligence: lessons from the past

Responsible research in artificial intelligence: lessons from the past

Entropic Artificial Intelligence and Knowledge Transfer

An overview of entropy applications to illustrate a few possible uses of entropy in knowledge transmission and artificial intelligence. Artificial intelligence uses entropy as a fundamental concept in many diverse applications, including reinforcement learning, data compression, and decision-making. It assists artificial intelligence models in producing wellinformed forecasts and judgments by assessing uncertainty and information content. As such, the purpose of this work is to highlight the importance of entropy and draw the attention of the artificial intelligence research community to it as a potent tool for advancing artificial intelligence. This work also addresses the importance of knowledge transfer (KT), especially intergenerational KT (IGT), in knowledge management. Knowledge entropy (KE) is a concept that is used to measure the complexity of knowledge distribution within an organisation and evaluate the effectiveness of KT activities. Furthermore, the KT model—which is predicated on the ideas of information content and tacitness—is presented. It blends techniques for customisation and codification. A few challenging open problems are presented along with future study options

Study of Road-based Versus Pattern-based Game Strategies in Connect6

Computer game is an important branch in the field of artificial intelligence research nowadays, and the in-depth study of computer game can not only promote the development of game theory, but also contribute to the development of artificial intelligence. In this paper, for the game search strategy of connect6, according to the two most widely used search strategies and valuation function design, one is the valuation strategy based on the road, and the other is the valuation method based on the chess type, under the PVS search algorithm, the principle and implementation of these two search strategies are introduced in detail, and at the same time, the advantages and disadvantages of the two methods are compared, so as to provide the decision-making policy and the design of valuation function for the game of connect6 a more reasonable reference. Through comparison, it can be seen that in hexachrome, due to the complexity of the evaluation function and game strategy based on chess shapes, it requires the game programme to have a strong recognition of chess shapes, and the complexity of accurately evaluating the advantages and disadvantages of the position is higher. The road-based game strategy has better accuracy in evaluating and predicting the game, and can effectively reduce the game failure due to positional judgement errors.

Analyze and Evaluate the Method Used as Well as the Fallout of OpenAI's Board of Directors Decision to Remove Sam Altman as CEO of the Company

Formed in December of 2015 by such leading lights as Elon Musk and Sam Altman, OpenAI has become a major player on the AI research stage. The OpenAI is committed to developing and Artist, researching investing in creating a technology beneficial for all of mankind. As such, the organization's primary visionaries are working creatively on artificial General Intelligence This latter type refers to systems which would be able perform better than humans at economically significant tasks. OpenAI and my future This study focuses on the course of this endeavor to develop artificial general intelligence, but more important than that is emphasis which Security matters the most fundamental consideration in infusing ethical principles into AI technologies is safety. The most recent in the line--GPT 3.5 -- has been offering impressive abilities related to a wide range of language processing tasks and is reflected by improvements in fields like computer-assisted translation services, automated content generation applications or even virtual assistants. Not only do the GPT models promise a transformative change for natural language processing, their announcement and reception--both within the community of artificial intelligence researchers, as well by people in various sectors of technology industry -- deserve an academic examination.

Artificial intelligence and natural product research.

Artificial intelligence and natural product research.

Convergence analysis of artificial intelligence research capacity: Are the less developed catching up with the developed ones?

AbstractThis study examines whether less developed countries are catching up with developed ones using the log t convergence technique (LCT) and the dynamic spatial ordered probit (DSOP) model. The findings revealed that first, there is no overall convergence in AI research capacity. Second, club clustering analysis showed convergence in four of the five groups of countries on AI research output and in three of the four groups on AI patent grants. Third, the countries are experiencing a slow divergence process in AI research capacity. Fourth, the region, income group and cluster of the countries are influencing the convergence process.

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios.

Descriptive and Predictive Analysis of Artificial Intelligence Research and Innovation in Health

Descriptive and Predictive Analysis of Artificial Intelligence Research and Innovation in Health

The shift of Artificial Intelligence research from academia to industry: implications and possible future directions

The shift of Artificial Intelligence research from academia to industry: implications and possible future directions

Deep reinforcement learning algorithm based on multi-agent parallelism and its application in game environment

Deep reinforcement learning has become a prominent area of research in artificial intelligence in recent years. Its application in solving complex tasks and game environments has garnered significant attention. This study aims to develop a deep reinforcement learning algorithm based on multi-agent parallelism to enhance intelligent decision-making in game environments. The algorithm combines a deep Q-network with a multi-agent cooperation strategy. Through parallel training of multiple agents, the learning process is accelerated, and decision accuracy is improved. The experimental results indicated that the Actor-Critic algorithm, when combined with precision rate, recall rate, and average fitness of multi-agent parallel, achieves a relatively high accuracy rate index, which stabilizes above 0.95. The recall rate index was also above 0.8, and the average fitness was in a relatively high range. The research shows that the deep reinforcement learning algorithm based on multi-agent parallelism performs better and is more effective in game environments. It can learn the optimal strategy faster and obtain higher rewards. This not only provides a new technical means for game development but also offers a useful reference for the application of multi-agent systems in complex environments.

Comparison of Methods of Clinical Trial Emulation Utilizing Data From the Comparison of AMD Treatment Trial (CATT) and the IRIS® Registry

PurposeWe used exact matching and inverse propensity score weighting (IPSW) using real-world data (RWD) from the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight) to emulate the two pro re nata (prn) treatment arms from the Comparison of AMD Treatment Trial (CATT) and to compare the outcomes of the RWD arms to the two monthly treatment arms from the clinical trial. DesignRetrospective cohort study utilizing deidentified electronic health record registry data and patient-level deidentified clinical trial data SubjectsAll treatment-naive patient eyes with neovascular age-related macular degeneration treated with ranibizumab or bevacizumab only for one year from either the CATT or the IRIS Registry MethodsPatients were identified in the IRIS Registry between October 1, 2015 and December 31, 2019. After all non-imaging based inclusion and exclusion criteria from the CATT were applied, patient eyes receiving bevacizumab or ranibizumab only on a prn basis were identified as the eligible cohort. Exact matching and ISPW was applied based on age, gender, and baseline visual acuity. ResultsWe identified 427 eyes treated with ranibizumab prn and 771 eyes treated with bevacizumab prn. Using exact matching, 98% (n= 281) of CATT patient eyes in the bevacizumab monthly treatment arm and 87% (n=261) of CATT patient eyes in the ranibizumab monthly treatment arm were matched to a patient eye in the IRIS Registry. For the ranibizumab prn treatment arm, patient eyes generated using exact matching gained 1.9 letters and those generated using IPSW gained 2.8 letters (Exact Matching: 1.9 letters +/- 14.0 vs. IPSW: 2.8 letters +/- 15.0 letters, p=0.43). For the bevacizumab prn treatment arm, patient eyes generated using exact matching gained 2.4 letters and those generated using IPSW gained 2.1 letters (Exact Matching: 2.4 letters +/- 15.4 vs. IPSW: 2.1 letters +/- 16.0 letters, p=0.79). ConclusionsBoth exact matching and IPSW produced similar results in emulating the prn treatment arms of the CATT using IRIS Registry data and patient-level clinical trial data. Similar to prior real-world studies, the clinical outcomes were significantly worse in the IRIS Registry treatment arms compared to the clinical trial.