Computer-based Intelligence Research Articles

Autonomous AI Systems for Continuous Learning and Real-Time Decision Making

Autonomous AI systems are upsetting navigation by constantly learning and pursuing ongoing choices without human oversight. These frameworks have applications across businesses like medical services, money, and assembling, where ongoing information is fundamental for functional achievement. This paper investigates the parts of independent simulated intelligence frameworks, including constant learning models, continuous information handling structures, and dynamic procedures. We likewise look at the difficulties such frameworks face, including information security, reasonableness, and taking care of edge cases. Through contextual analyses in various enterprises, we show the capability of independent computer-based intelligence frameworks to reshape the fate of savvy navigation. At last, we propose future bearings for research, zeroing in on moral structures and half and half learning models to further develop flexibility and straightforwardness in these frameworks.

Unveiling the Effectiveness of Multi-Classification Algorithms: A Comprehensive Investigation

Multi classes portray alishead to a ton of genuine computer-based intelligence applications that need the ability to perceive numerous different classes immediately. In multiclass arrangement issues, we really want to manage multiple classes which imply the calculation which we are utilizing ought to be equipped for working with different classes. The rear different models accessible for this and a few strategies are likewise accessible that can make support vector machines and Calculated relapse equipped for managing multiple classes. In this examination paper, we present a multiclass grouping strategy from AI (ML) to foresee the six classes of Dermatology sickness expectation. In particular, the One-Refrains One (OVO) and One-Sections Rest (OVR) systems are assessed under Support Vector Machine (SVM) and Logistic Regression (LR) calculations. The current review intends store cognize fitting finding class for dermatology patients through building a multi-class expectation model. Our exploratory outcomes showed that the overall model is LR under the OVR technique accomplishing the most noteworthy Exactness 98.2% when contrasted with LR with OVO and SVM with OVR strategy accomplishing normal precision when contrasted with OVO. This proposed model shows the similar and promising outcomes that can upgrade the expectation execution model formulate classification.

Bias and Fairness in AI Models: How can Bias in AI Models be Identified, Mitigated, and Prevented in Data Science Practices?

Artificial intelligence (AI) and machine learning (ML) systems are progressively used in different areas, going with basic choices that influence individuals' lives. In any case, these frameworks can sustain and try and fuel existing social predispositions, prompting uncalled for results. This paper looks at the wellsprings of predisposition in simulated intelligence models, assesses current methods for distinguishing and relieving inclination, and proposes an extensive structure for creating more pleasant simulated intelligence frameworks. By coordinating specialized, moral, and functional points of view, this exploration plans to add to a more evenhanded utilization of computer-based intelligence across various areas, guaranteeing that artificial intelligence driven choices are fair, straightforward, and socially dependable.

Role Of Artificial Intelligence in Big Database Management

This exploration article digs into the significant effect of (artificial intelligence) AI, on big Data base management in the field of computer science by utilizing the broad abilities of artificial intelligence AI technologies, this study investigates different strategies to improve the oversight and administration of huge information bases. Through an exhaustive assessment of existing academic works, itemized contextual investigations, and master experiences, the article disentangles the mind-boggling combination of Ai’s integration and its important impacts on database management rehearses. Covering an extensive variety of artificial intelligence aspects, for example, AI, regular language handling, and profound realizing, this academic request plans to explain the complex job of computer-based intelligence in the domain of big database management. As a critical commitment to scholarly talk, this research article fills in as an original work, planning the groundbreaking excursion of Ai transformative into the center of data base management system, introducing another time of development and productivity in the computerized scene.

The Role of Explainable AI in Building Trust and Confidence in Automated Business Processe

Computer-based intelligence (AI) has shown significant potential in various applications, but it requires a clear understanding of the local area. This issue falls within the Reasonable simulated intelligence (XAI) field, which is crucial for the practical application of AI models. This article discusses the current work in XAI, defining explainability in machine learning and proposing a new definition. It also discusses ongoing commitments related to AI models, including Profound Learning strategies. The article suggests Dependable Computerized reasoning, a method for large-scale AI execution in real-world contexts with reliability, model logic, and responsibility at its core. The goal is to provide a comprehensive taxonomy for newcomers and professionals to embrace AI's benefits.

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?

The global environmental agenda urgently needs a semantic web of knowledge

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure—i.e., public data and model repositories—is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making.

On human genome manipulation and Homo technicus: the legal treatment of non-natural human subjects

Although legal personality has slowly begun to be granted to non-human entities that have a direct impact on the natural functioning of human societies (given their cultural significance), the same cannot be said for computer-based intelligence systems. While this notion has not had a significantly negative impact on humanity to this point in time that only remains the case because advanced computerised intelligence systems (ACIS) have not been acknowledged as reaching human-like levels. With the integration of ACIS in medical assistive technologies such as companion robots and bionics, our legal treatment of ACIS must also adapt—least society faces legal challenges that may potentially lead to legally sanctioned discriminatory treatment. For this reason, this article exposes the complexity of normalizing definitions of “natural” human subjects, clarifies how current bioethical discourse has been unable to effectively guide ACIS integration into implanted and external artefacts, and argues for the establishment of legal delineations between various ACIS-human mergers in reference to legal protections and obligations internationally.

ROLE OF ARTIFICIAL INTELLIGENCE IN DIABETES MANAGEMENT

According to some of the surveys researchers also claimed that at the end of 2040 there will be marked high in the number of patients worldwide at around 600 million. The vast majority of our day by day exercises have gotten computerized. Computerized wellbeing considers the ever-expanding cooperative energy between cutting edge clinical advancements, development, and computerized correspondence. DM is a condition instigated by unregulated diabetes that may prompt multi-organ disappointment in patients. Because of advances in AI and man-made brainpower which empowers the early discovery and analysis of DM through a computerized procedure which is more favorable than a manual finding. Standards of AI have been utilized to assemble calculations to help prescient models for the danger of creating diabetes or its resulting difficulties. Computer-based intelligence will present a change in perspective in diabetes care from ordinary administration systems to building focused on information-driven exactness care. As per the patient's very own need, an appropriate diabetes care plan requires various fields of experts together to make up the arrangement. So, on the off chance that it is done physically, it would limit the experience and information on these experts and devour bunches of costly clinical assets also.

Fuzzy Logic System Implementation with Mamdani Method in Computer-Based Intelligence Quotient Test to Determining the Type of Intelligence Dimension

A person of a thing can cultivate intelligence in areas of interest, sometimes many people complain because the results of their intelligence quotient test (IQ) show unsatisfactory results. Because they are still looking only lies in those abilities. Intelligence quotient tests are used to explore knowledge about certain individual skills and intelligence. To determine someone’s intelligence, it takes a long time to analyze and get the test output from calculations by certain methods manually. The determination of a person’s dimension intelligence can be done by certain method, this research is one way to make a fuzzy model for the type of intelligence dimension support system based on standardization in psychology The result of this research is expected to explain how to modeling the fuzzy logic system with Mamdani method in a support system to determine the types of intelligence dimensions, namely the festigung / exact or flexibilitat / non-exact.

AN IN-DEPTH ANALYSIS OF THE USABILITY OF ARTIFICIAL INTELLIGENCE TOOLS AND TECHNIQUES IN THE PREVENTION OF BANKING FRAUDS

Banks have given a superior method for peopling to set aside and store cash. With the appearance of banks, installment and store strategies became more helpful than previously. However, robberies torment banks, causing millions of dollars in harm. These robberies have hurt the existence of thousands of organizations from one side of the planet to the other. Robbery is expanding quickly as innovation progresses, yet mechanical ideas, such as computerized reasoning and AI, have helped organizations and people manage these issues. Organizations utilize artificial reasoning to follow these burglaries, from legitimate client verification to different level security checks. Computer-based intelligence is ending up a superior method for combatting these robberies. This article examined the most well-known sorts of robbery and how to forestall them utilizing artificial intelligence.

Knowledge Is Infrastructure.

The article discusses the curiosity-driven basic research. It states that the research brought revolutionary such as rapid development of computer-based intelligence and discovery of genetic basis of life. It notes that it radically alters the deep understanding of the world and the development of new tools and techniques. It mentions that long-term investments in basic research are important and could lead to higher goal.

Pseudodensity Log Generation by Use of Artificial Neural Networks

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 180439, “Pseudodensity-Log Generation by Use of Artificial Neural Networks,” by Wennan Long, University of Southern California; Di Chai, University of Kansas; and Fred Aminzadeh, University of Southern California, prepared for the 2016 SPE Western Regional Meeting, Anchorage, 23–26 May. The paper has not been peer reviewed. The challenges of reservoir characterization can be overcome accurately and efficiently by the use of computer-based intelligence methods such as neural networks, fuzzy logic, and genetic algorithms. This paper will describe how one integrates a comprehensive methodology of data-mining techniques and artificial neural networks (ANNs) in reservoir-petrophysical-properties prediction and regeneration. Introduction ANNs—machine-learning models that provide the potential to establish multidimensional, nonlinear, and complex models—can be powerful tools with which to analyze experimental, industrial, and field data. It is crucial to find the optimal data from one well to build the model with ANNs for pseudowell-log generation of a target well. Manual stratigraphic interpretation, though labor-intensive, is regarded as one approach. Data-mining techniques are another applicable approach, involving the automatic processing of data associated with nonlinearity by use of a statistical method to discover data patterns. One application in petrophysics is facies (or electrofacies) classification, which is widely used to divide well-log data to obtain target information. Clustering analysis, an adjunct to artificial intelligence, can determine electrofacies and categorize lithological profiles quite efficiently. Porosity, one of the more important petrophysical properties, can be obtained from density logs. In this study, a three-step approach was produced. First, the authors apply preprocessing of the log data by use of standardization and dimension reduction [ principal-component analysis (PCA)]. Second, they apply clustering [model-based clustering (MBC)] to recognize specific patterns and interpret stratigraphic information. Finally, a similar pattern is chosen as input to generate a target pseudodensity log by use of ANNs. Well-Log-Data Preprocessing Normalization. Well-log data are constructed in a matrix form whereby each row represents the depth record and each column is the different type of well log. Each well constructs one well-log matrix, and one field that has multiple wells constructs a big data set. The initial step in the first stage is to normalize well-log data. This normalization is necessary because different types of well-log data have different units. For instance, the spontaneous-potential log is given in millivolts, whereas the gamma ray log is given in an API unit.

Working style and extraversion: New insights into the nature of item response latencies in computer-based intelligence testing

With the increasing use of computer-based testing, there is more research interest in possible interpretations of response latencies in various test settings. So far, individual differences in response latencies and the impact of personality variables are seldom investigated. Thus, the aim of the present study is to analyse the impact of Big Five scores on item response latencies. Therefore, 48 reasoning ability items and a personality battery were administrated in a computer-based assessment to 101 students. Mean response latencies on reasoning ability seem to moderate significantly on the relation of extraversion and intelligence. Results are discussed in terms of differential validity of intelligence tests for extraverted persons.

Some musings on four ways humans couple: implications for systems design

Humans interact with their environments in many ways, and that interaction has become the focus of ever-greater interest as technology empowers the physical environment with new sensor and computer-based intelligence. This paper describes four different types of coupling between the human and environment, where "coupling" here refers to two-way interactions which are sufficiently tight that causality between human and environment is obscure and even arbitrary. These are not four mutually exclusive categories, but rather four ways of characterizing the relationship between the human and environment. Some generalizations of these relationships are discussed, along with hints on how we might improve our designs of intelligent technology, especially the interfaces to human users.

Survey findings: Computer‐based intelligence systems experience growth and problems

Competitive Intelligence ReviewVolume 2, Issue 3 p. 31-33 Ci Software Application Survey findings: Computer-based intelligence systems experience growth and problems Leonard M. Fuld, Leonard M. Fuld Fuld & Company, Inc. Leonard M. Fuld is the President of Fuld & Company, Inc., a Cambridge, Massachusetts-based business intelligence firm that specializes in competitive analysis and benchmarking. Fuld & Company also offers competitor intelligence seminars. Mr. Fuld can be reached at (617) 492-5900, or by FAX (617)492-7108.Search for more papers by this author Leonard M. Fuld, Leonard M. Fuld Fuld & Company, Inc. Leonard M. Fuld is the President of Fuld & Company, Inc., a Cambridge, Massachusetts-based business intelligence firm that specializes in competitive analysis and benchmarking. Fuld & Company also offers competitor intelligence seminars. Mr. Fuld can be reached at (617) 492-5900, or by FAX (617)492-7108.Search for more papers by this author First published: Winter 1992 https://doi.org/10.1002/cir.3880020315 Fuld & Company, Inc. Leonard M. Fuld is the President of Fuld & Company, Inc., a Cambridge, Massachusetts-based business intelligence firm that specializes in competitive analysis and benchmarking. Fuld & Company also offers competitor intelligence seminars. Mr. Fuld can be reached at (617) 492-5900, or by FAX (617)492-7108. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume2, Issue3Winter 1992Pages 31-33 RelatedInformation

Affective reactions toward computer-based intelligence testing.

Affective reactions toward computer-based intelligence testing.