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The regulatory uses are real: In 2021, more than 100 drug and biologic applications submitted to the FDA included AI/ML components. Keywords: Algorithms; Artificial Intelligence; Bioengineering; Biologics; Biotechnology; Cybersecurity; Cyborgs; Drug Development; Drug Manufacturing; Drugs and Therapies; Emerging Technologies; FDA; Genetic Engineering; Genetically-Engineered Proteins; Government Agencies Offices and Entities; Health and Medicine; Machine Learning; Office of the FDA Commissioner; Public Health; Technology; U.S. Food and Drug Administration EN Algorithms Artificial Intelligence Bioengineering Biologics Biotechnology Cybersecurity Cyborgs Drug Development Drug Manufacturing Drugs and Therapies Emerging Technologies FDA Genetic Engineering Genetically-Engineered Proteins Government Agencies Offices and Entities Health and Medicine Machine Learning Office of the FDA Commissioner Public Health Technology U.S. Food and Drug Administration 497 497 1 05/22/23 20230523 NES 230523 2023 MAY 22 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Trials Week -- By: Patrizia Cavazzoni, M.D., Director of the Center for Drug Evaluation and Research Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work. [Extracted from the article]

The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]

In this paper, we consider the composition optimization with two expected-value functions in the form of $({1}/{n})\sum _{i = 1}^{n} F_{i}\left({({1}/{m})\sum _{j = 1}^{m} G_{j}(x)}\right)+R(x)$ , which formulates many important problems in statistical learning and machine learning such as solving Bellman equations in reinforcement learning and nonlinear embedding. Full gradient- or classical stochastic gradient descent-based optimization algorithms are unsuitable or computationally expensive to solve this problem due to the inner expectation $({1}/{m})\sum _{j = 1}^{m} G_{j}(x)$. We propose a dualityfree-based stochastic composition method that combines the variance reduction methods to address the stochastic composition problem. We apply the stochastic variance reduction gradient- and stochastic average gradient algorithm-based methods to estimate the inner function and the dualityfree method to estimate the outer function. We prove the linear convergence rate not only for the convex composition problem but also for the case that the individual outer functions are nonconvex, while the objective function is strongly convex. We also provide the results of experiments that show the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

A new report from the University of Florence in Italy provides an extensive review of monitoring methods for electrical power lines, with a focus on high-voltage and medium-voltage systems. The objective of these techniques is to prevent catastrophic failures by detecting partial damage or deterioration of components and allowing for organized maintenance operations. The paper discusses the coordination of protection devices and the implementation of artificial intelligence algorithms to improve the reliability of the network. It also highlights diagnostic techniques, protection devices, and prognostic methods, emphasizing the role of artificial intelligence and offering guidelines for choosing between different approaches. [Extracted from the article]

This article discusses the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cancer research. It highlights how these technologies are transforming the diagnosis, treatment, and prevention of cancer by providing sophisticated analytical capabilities. The paper explores the mathematical foundations and practical implementations of AI and ML techniques in various aspects of cancer research, including detection, diagnosis, prognosis, and personalized treatment strategies. It also reviews influential algorithms such as Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and clustering techniques, and their roles in improving diagnostic accuracy and treatment efficacy. Please note that this preprint has not been peer-reviewed. [Extracted from the article]

Purpose: This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach: The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on "how exactly the matching process is done for evaluating the document." Finally, the e-learners are classified based on their grades. Findings: On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value: This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors' knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners. [ABSTRACT FROM AUTHOR]

Purpose – When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue. Design/methodology/approach – This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model. Findings – The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating indicators, the MFOAGRNN forecast model has better forecast ability than the other three forecasting models. Originality/value – This paper explored the correlation between oil and gold prices, and built oil and gold prices forecasting models. In addition, this paper proposes a modified FOA (MFOA), where an escape parameter Δ is added to Si. The findings showed that the forecasting model that combines MFOA and GRNN has the best ability to forecast the closing price of oil and gold. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) has been widely studied due to its high performance and usability for various applications such as image classification, detection, segmentation, translation, and action recognition. Thanks to the universal applications and high performance of DNN algorithm, DNN is adopted for various AI platforms, including edge/mobile devices as well as cloud servers. However, high-performance DNN requires a large amount of computation and memory access, making it challenging to implement DNN operation on edge/mobile. There have been several ways to solve these problems, including algorithms as well as hardware for DNN. Algorithms that help accelerate DNN in hardware enable much more efficient operation of high-performance AI. This article aims to provide an overview of the recent hardware and algorithm co-design schemes enabling efficient processing of DNNs. Specifically, it will provide algorithm optimization methods for DNN structure, neurons, synapses, and data types. This paper also introduces optimization methods for hardware architectures, PE array, data-path control, and microarchitecture of PE. And we will also show examples of DNN algorithm and hardware co-designed ASICs. [ABSTRACT FROM AUTHOR]

Purpose: Big data are indispensable in scientific endeavours ranging from nuclear research to climate studies. However, there is a growing misperception that congeries of data can be easily reconstructed into competitive business insights. Such notions have been encouraged by a plethora of mainstream techno-utopian forecasts. Design/methodology/approach: This paper investigated such claims and related big data developments, including its "systems-first" and oligopolistic orientations. Due to the paucity of current scholarship on an admittedly pessimistic topic, the paper studied contrarian developments in the industry by relying on secondary data. The study of experts and scholars; industrial trends; and discrepancies and critical gaps in the mainstream data narrative were sourced to prognosticate the likely trajectory of many data giants. Findings: A key finding was that the big data industry faces an untenable market bubble worth trillions of dollars. This will have severe consequences for common digital access and social stability worldwide. Evidence presented also suggests that the data industrial complex may undergo a function creep by facilitating a transition from surveillance capitalism to surveillance society. Research limitations/implications: Primary data for a study of this nature may take years to materialize. This is a "first-pass" study that seeks to illuminate latent dangers facing the big data/AI sector. There is a paucity of scholarly study that even remotely touches on this topic. Therefore, supporting arguments was sourced from contemporary reports and expert study (secondary data). Practical implications: As control of data may have geostrategic implications, balkanization of the wired ecosystem may be underway with Russia and China leading the way. Future superpowers may be defined by the way they handle data. The concentration of data in fewer hands may also affect citizen innovation. Social implications: A break-down of the data industrial complex may lead to social mayhem as the monetization of presently free software, blogs and social media platforms may be unfeasible. Originality/value: This topic has hardly been explored due to the novelty of big data, its applications and the daily hype over its potentials. This paper boldly describes dark countercurrents in the industry. [ABSTRACT FROM AUTHOR]

This paper compares AI (artificial intelligence) algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy, sensitivity, and specificity. Keywords: Algorithms; Artificial Intelligence; Cancer; Emerging Technologies; Gastric Cancer; Gastroenterology; Health and Medicine; Machine Learning; Oncology EN Algorithms Artificial Intelligence Cancer Emerging Technologies Gastric Cancer Gastroenterology Health and Medicine Machine Learning Oncology 427 427 1 03/23/23 20230321 NES 230321 2023 MAR 21 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- A new study on artificial intelligence is now available. [Extracted from the article]

Time and state descriptions form the core of a simulation model representation. The historical influence of initial application areas and the exigencies of language implementations have created a muddled view of the time and state relationships. As a consequence, users of simulation programming languages work in relative isolation; model development, simulation application, model portability, and the communication of results are inhibited and simulation practice fails to contribute to the recognition of an underlying foundation or integrating structure. A model representation structure has been forged from a small set of basic definitions which carefully distinguish the state and time relationships. This paper focuses on the coordination of the time and state concepts using "object" as the link. In addition to clarifying the relationships, the structure relates the concept of ""state-sequenced simulation" to the variations in time flow mechanisms. In conclusion, some speculations are offered regarding alternative algorithms for time flow mechanisms. [ABSTRACT FROM AUTHOR]

In this paper, we continue to explore application of nonsmooth analysis to the study of global asymptotic robust stability (GARS) of delayed neural networks. In combination with Lyapunov theory, our approach gives several new types of sufficient conditions ensuring GARS. A significant common aspect of our results is their low computational complexity. It is demonstrated that the reported results can be verified either by conducting spectral decompositions of symmetric matrices associated with the uncertainty sets of network parameters, or by solving a semidefinite programming problem. Nontrivial examples are constructed to compare with some closely related existing results. [ABSTRACT FROM AUTHOR]

The paper addresses analytical and practical aspects of integration of equation-based (classical) and signal-derived [artificial neural network (ANN)] dynamic models for transient analysis of large-scale dynamical systems. Our signal-based part is based on two ANNs, and is derived from measurements at boundary points. In this paper, we describe this hybrid modeling technique, and focus on: 1) a least square-based mechanism for on-line correction of dynamic variable predictions that is based on actual operating conditions; 2) the resilience of the algorithm to missing measurements due to failed communication links; and 3) a complete two-way interaction between the differential-algebraic equation based subsystem and the ANN-based subsystem. The paper demonstrates the feasibility of implementing our approach in standard power system software by integrating the ANN-based model with the transient analysis toolbox from Matlab. We illustrate capabilities of the proposed approach for transient analysis on a benchmark multi-machine example derived from the New England/New-York interconnected power system. [ABSTRACT FROM AUTHOR]

This article investigates artificial intelligence (AI) fairness in decision-making specifically examining the limitations of this process. The article discusses eight inherent limitations in AI fairness including lack of fairness in human decision-making used to train the AI model, lack of a universal fairness definition, and limited power over the complete decision-making process.

Purpose – The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of multi-dimensional and multi-extreme value functions, the authors would introduce the extended memory factor to the PSO algorithm. Furthermore, the paper aims to improve the convergence rate and precision of basic artificial fish swarm algorithm (FSA), a novel FSA optimized by PSO algorithm with extended memory (PSOEM-FSA) is proposed. Design/methodology/approach – In PSOEM-FSA, the extended memory for PSO is introduced to store each particle’ historical information comprising of recent places, personal best positions and global best positions, and a parameter called extended memory effective factor is employed to describe the importance of extended memory. Then, stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory. Furthermore, the extended memory factor is applied to five kinds of behavior pattern for FSA, including swarming, following, remembering, communicating and searching. Findings – The paper proposes a new intelligent algorithm. On the one hand, this algorithm makes the fish swimming have the characteristics of the speed of inertia; on the other hand, it expands behavior patterns for the fish to choose in the search process and achieves higher accuracy and convergence rate than PSO-FSA, owning to extended memory beneficial to direction and purpose during search. Simulation results verify that these improvements can reduce the blindness of fish search process, improve optimization performance of the algorithm. Research limitations/implications – Because of the chosen research approach, the research results may lack persuasion. In the future study, the authors will conduct more experiments to understand the behavior of PSOEM-FSA. In addition, there are mainly two aspects that the performance of this algorithm could be further improved. Practical implications – The proposed algorithm can be used to many practical engineering problems such as tracking problems. Social implications – The authors hope that the PSOEM-FSA can increase a branch of FSA algorithm, and enrich the content of the intelligent algorithms to some extent. Originality/value – The novel optimized FSA algorithm proposed in this paper improves the convergence speed and searching precision of the ordinary FSA to some degree. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to propose a hybrid-simulated annealing algorithm to address the lacunas in production logistics. The primary focus is laid on the basic understanding of the critical quandary occurring in production logistics, and subsequently research attempts are undertaken to resolve the issue by developing a hybrid algorithm. A logistics problem associated with a flow shop (FS) having a string of jobs which need to be scheduled on m number of machines is considered. Design/methodology/approach – An attempt is made here to introduce and further establish a hybrid-simulated annealing algorithm (NEHSAO) with a new scheme for neighbourhood solutions generation, outside inverse (OINV). The competence in terms of performance of the proposed algorithm is enhanced by incorporating a fast polynomial algorithm, NEH, which provides the initial seed. Additionally, a new cooling scheme (Ex-Log) is employed to enhance the capacity of the algorithm. The algorithm is tested on the benchmark problems of Carlier and Reeves and subsequently validated against other algorithms reported in related literature. Findings – It is clearly observed that the performance of the proposed algorithm is far superior in most of the cases when compared to the other conventionally used algorithms. The proposed algorithm is then employed to a FS under dynamic conditions of machine breakdown, followed by formulation of three cases and finally identification of the best condition for scheduling under dynamic conditions. Originality/value – This paper proposes an hybrid algorithm to reduce makespan. Practical implementation of this algorithm in industries would lower the makespan and help the organisation to increse their profit [ABSTRACT FROM AUTHOR]

The article discusses Explainable artificial intelligence (XAI) and the expectation of artificial intelligence (AI) end users to understand how AI works, including how algorithmic decision making occurs. An overview of AI end users' desire for information on AI methodology on reaching decisions, including in regard to the ethical nature of AI, is provided.

The article focuses on finding community news in the algorithmic era. Topics inlcude the researchers at the University of North Carolina School of Media and Journalism decided to study the exact penetration of news deserts in the U.S., the loss of community information a dearth of local news coverage can lead to dire consequences, and the result was that some of the most-affected counties, which represent some of the most rural and economically has depressed areas in the U.S. INSETS: WHO OWNS ALGORITHMIC NEWS?;CANARIES IN THE AI COAL MINE.

The article discusses research in the age of machines. Topics include data mining and machine-learning as a great boon for political campaigns, scholarly communication librarians' interest for artificial intelligence, and University of California-San Francisco researchers training a machine learning algorithm to decode words and phrases from speech signals in the brain.

In this paper, a biologically inspired, CNN-based, multi-channel, texture boundary detection technique is presented. The proposed approach is similar to human vision system. The algorithm is simple and straightforward such that it can be implemented on the cellular neural networks (CNNs). CNN contains several important advantages, such as efficient real-time processing capability and feasible very large-scale integration (VLSI) implementation. The proposed algorithm also had been widely tested on synthetic texture images. Those texture images are randomly selected from the Brodatz textures database [1]. According to our simulation results, the boundaries of uniform textures can be detected quite successfully. For the nonuniform or nonregular textures, the results also indicate meaningful properties, and the properties also are consistent to the human visual sensation. The proposed algorithm also has been implemented on the CNN Universal Machine (CNN-UM), and yields similar results as the simulation on the PC. Based on the efficient performance of CNN-UM, the algorithm becomes very fast. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS) aims at recovering statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Besides independent component analysis, nonlinear principal component analysis (NPCA) is shown to be another useful tool for solving this problem, but it requires that the measured data be prewhitened. By taking into account the autocorrelation matrix of the measured data, we present in this paper a modified NPCA criterion, and develop a least-mean-square (LMS) algorithm and a recursive least-squares algorithm. They can perform the online BSS using directly the unwhitened observations. Since a natural gradient learning is applied and the prewhitening process is removed, the proposed algorithms work more efficiently than the existing NPCA algorithms, as verified by computer simulations on man-made sources as well as practical speech signals. [ABSTRACT FROM AUTHOR]

The ancient oriental game of Go has long been considered a grand challenge for artificial intelligence. For decades, computer Go has defied the classical methods in game tree search that worked so successfully for chess and checkers. However, recent play in computer Go has been transformed by a new paradigm for tree search based on Monte-Carlo methods. Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players. In this paper, we describe the leading algorithms for Monte-Carlo tree search and explain how they have advanced the state of the art in computer Go. [ABSTRACT FROM AUTHOR]

In this paper, novel call admission control (CAC) algorithms are developed based on cellular neural networks. These algorithms can achieve high network utilization by performing CAC in real-time, which is imperative in supporting quality of service (QoS) communication over packet-switched networks. The proposed solutions are of basic significance in access technology where a subscriber population (connected to the Internet via an access module) needs to receive services. In this case, QoS can only be preserved by admitting those user configurations which will not overload the access module. The paper treats CAC as a set separation problem where the separation surface is approximated based on a training set. This casts CAC as an image processing task in which a complex admission pattern is to be recognized from a couple of initial points belonging to the training set. Since CNNs can implement any propagation models to explore complex patterns, CAC can then be carried out by a CNN. The major challenge is to find the proper template matrix which yields high network utilization. On the other hand, the proposed method is also capable of handling three-dimensional separation surfaces, as in a typical access scenario there are three traffic classes (e.g., two type of Internet access and one voice over asymmetric digital subscriber line. [ABSTRACT FROM AUTHOR]

The article focuses on the use of algorithms and machine intelligence to develop new chemical molecules and retrosynthesis. It comments on researchers Marwin Segler and Mark Waller's development of a neural network which learned the rules of organic chemistry and predicted routes to complex structures that would require six synthetic steps to make. It states chemist Elias Corey developed the concept of retrosynthesis and used it on the molecule longifolene which is found in pine resin.

Recognizing objects in images is an active area of research in computer vision. In the last two decades, there has been much progress and there are already object recognition systems operating in commercial products. However, most of the algorithms for detecting objects perform an exhaustive search across all locations and scales in the image comparing local image regions with an object model. That approach ignores the semantic structure of scenes and tries to solve the recognition problem by brute force. In the real world, objects tend to covary with other objects, providing a rich collection of contextual associations. These contextual associations can be used to reduce the search space by looking only in places in which the object is expected to be; this also increases performance, by rejecting patterns that look like the target but appear in unlikely places. Most modeling attempts so far have defined the context of an object in terms of other previously recognized objects. The drawback of this approach is that inferring the context becomes as difficult as detecting each object. An alternative view of context relies on using the entire scene information holistically. This approach is algorithmically attractive since it dispenses with the need for a prior step of individual object recognition. In this paper, we use a probabilistic framework for encoding the relationships between context and object properties and we show how an integrated system provides improved performance. We view this as a significant step toward general purpose machine vision systems. [ABSTRACT FROM AUTHOR]

Autonomous helicopter flight is widely regarded to be a highly challenging control problem. As helicopters are highly unstable and exhibit complicated dynamical behavior, it is particularly difficult to design controllers that achieve high performance over a broad flight regime. While these aircraft are notoriously difficult to control, there are expert human pilots who are nonetheless capable of demonstrating a wide variety of maneuvers, including aerobatic maneuvers at the edge of the helicopter's performance envelope. In this paper, we present algorithms for modeling and control that leverage these demonstrations to build high-performance control systems for autonomous helicopters. More specifically, we detail our experiences with the Stanford Autonomous Helicopter, which is now capable of extreme aerobatic flight meeting or exceeding the performance of our own expert pilot. [ABSTRACT FROM AUTHOR]

The article focuses on the technological change felt by photography due to algorithms and artificial intelligence (AI).

This article discusses the recent areas of focus at the National Institute of Informatics (NII) in Japan. First, a look at its academic services including SINET6, a backbone network service across Japan and its data platform service, the NII Research Data Cloud. The article also details NII research in computer science with projects in innovative computer vision and engineerable artificial intelligence, as well as applied research projects including a COVID-19 pneumonia diagnosis tool.

This article discusses how the combination of humans and artificial intelligence could target misinformation and polarization, by examining two scenarios- one where computing methods can assist human fact-checkers and one where human search methods can assist in search engine algorithms for personalization. Topics include the Australian Communication and Media Authority research on misinformation in Australia, whether filter bubbles are created by personalization attempts of algorithms, and the various computational methods that can assist fact-checking to prevent misinformation.

Purpose – Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars has been lost by many companies and individuals. The global impact of phishing attacks will continue to be on the increase and thus a more efficient phishing detection technique is required. The purpose of this paper is to investigate and report the use of a nature inspired based-machine learning (ML) approach in classification of phishing e-mails. Design/methodology/approach – ML-based techniques have been shown to be efficient in detecting phishing attacks. In this paper, firefly algorithm (FFA) was integrated with support vector machine (SVM) with the primary aim of developing an improved phishing e-mail classifier (known as FFA_SVM), capable of accurately detecting new phishing patterns as they occur. From a data set consisting of 4,000 phishing and ham e-mails, a set of features, suitable for phishing e-mail detection, was extracted and used to construct the hybrid classifier. Findings – The FFA_SVM was applied to a data set consisting of up to 4,000 phishing and ham e-mails. Simulation experiments were performed to evaluate and compared the performance of the classifier. The tests yielded a classification accuracy of 99.94 percent, false positive rate of 0.06 percent and false negative rate of 0.04 percent. Originality/value – The hybrid algorithm has not been earlier apply, as in this work, to the classification and detection of phishing e-mail, to the best of the authors’ knowledge. [ABSTRACT FROM AUTHOR]

A research report from Soochow University in Suzhou, China, introduces a new algorithm called Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MASDSC) for brain tumor clustering in medical imaging. The algorithm addresses the limitations of traditional unsupervised deep subspace clustering algorithms by incorporating a semi-supervised learning framework and a multi-scale feature extraction mechanism. It also employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters. The MAS-DSC algorithm achieves excellent clustering results on standard datasets and demonstrates robustness and enhanced clustering performance on a brain tumor dataset. The research has been peer-reviewed and published in Computers Materials & Continua. [Extracted from the article]

The article examines the evolving role of Generative AI (Gen AI) in healthcare, contrasting it with traditional artificial intelligence. It states that traditional AI has been used for specific tasks like predictive modeling and decision-making based on structured data. It mentions that Gen AI generates new content and analyzes both structured and unstructured data, showing potential for significant improvements in various healthcare areas, including coding accuracy, and claims management.

As the most important property of neural networks (NNs), the universal approximation capability of NNs is widely used in many applications. However, this property is generally proven for continuous systems. Most industrial systems are hybrid systems (e.g., piecewise continuous), which is a significant limitation for real applications. Recently, many identification methods have been proposed for hybrid system approximation; however, these methods only operate in linear hybrid systems. In this paper, the progressive learning machine—a new learning algorithm based on multi-NNs—is proposed for general hybrid nonlinear/linear system approximation. This algorithm classifies hybrid systems into several continuous systems and can approximate any hybrid system with zero output error. The performance of the proposed learning method is demonstrated via numerical examples and with experimental data from real applications. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to measure the financial risk and optimal capital structure of a corporation. Design/methodology/approach - Irregular disjunctive programming problems arising in firm models and risk management can be solved by the techniques presented in the paper. Findings - Parallel processing and mathematical modeling provide a fruitful basis for solving ultrascale non-convex general disjunctive programming (GDP) problems, where the computational challenge in direct mixed-integer non-linear programming (MINLP) formulations or single processor algorithms would be insurmountable. Research limitations/implications - The test is limited to a single firm in an experimental setting. Repeating the test on large sample of firms in future research will indicate the general validity of Monte-Carlo-based VAR estimation. Practical implications - The authors show that the risk surface of the firm can be approximated by integrated use of accounting logic, corporate finance, mathematical programming, stochastic simulation and parallel processing. Originality/value - Parallel processing has potential to simplify large-scale MINLP and GDP problems with non-convex, multi-modal and discontinuous parameter generating functions and to solve them faster and more reliably than conventional approaches on single processors. [ABSTRACT FROM AUTHOR]

This paper suggests a bidirectional relationship between state-space and problem- reduction representations. It resents a formalism based on multiple-input and multiple-output operators which provides a basis for viewing the two types of representations in this manner. A representation of the language recognition problem which is based on the Cocke parsing algorithm Is used as an illustration. A method for representing problems in first-order logic in such a way that the inference system employed by a resolution-based theorem prover determines whether the set of clauses is interpreted in the state-space mode or in the problem-reduction mode is presented. The analogous concepts in problem-reduction and theorem proving, and the terminology used to refer to them, are noted. The relationship between problem-reduction, input resolution, and linear resolution is is discussed. [ABSTRACT FROM AUTHOR]

For more information on this research see: Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology. Keywords: Algorithms; Artificial Intelligence; Cancer; Cancer Detection; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Pathology; Prostate Cancer; Prostatic Neoplasms; Technology EN Algorithms Artificial Intelligence Cancer Cancer Detection Cyborgs Emerging Technologies Health and Medicine Machine Learning Oncology Pathology Prostate Cancer Prostatic Neoplasms Technology 575 575 1 09/04/23 20230905 NES 230905 2023 SEP 5 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Investigators discuss new findings in artificial intelligence. [Extracted from the article]

Purpose – The purpose of this paper is to provide a way to analyze satellite images using various clustering algorithms and refined bitplane methods with other supporting techniques to prove the superiority of RIFCM. Design/methodology/approach – A comparative study has been carried out using RIFCM with other related algorithms from their suitability in analysis of satellite images with other supporting techniques which segments the images for further process for the benefit of societal problems. Four images were selected dealing with hills, freshwater, freshwatervally and drought satellite images. Findings – The superiority of the proposed algorithm, RIFCM with refined bitplane towards other clustering techniques with other supporting methods clustering, has been found and as such the comparison, has been made by applying four metrics (Otsu (Max-Min), PSNR and RMSE (40%-60%-Min-Max), histogram analysis (Max-Max), DB index and D index (Max-Min)) and proved that the RIFCM algorithm with refined bitplane yielded robust results with efficient performance, reduction in the metrics and time complexity of depth computation of satellite images for further process of an image. Practical implications – For better clustering of satellite images like lands, hills, freshwater, freshwatervalley, drought, etc. of satellite images is an achievement. Originality/value – The existing system extends the novel framework to provide a more explicit way to analyze an image by removing distortions with refined bitplane slicing using the proposed algorithm of rough intuitionistic fuzzy c-means to show the superiority of RIFCM. [ABSTRACT FROM AUTHOR]

A new advanced artificial intelligence (AI) algorithm developed by researchers at Penn State College of Medicine may improve predictions and therapies for autoimmune diseases. The algorithm analyzes the genetic code underlying these conditions to accurately model gene expression and regulation, as well as identify additional genes of risk. The algorithm outperforms existing methodologies and identified 26% more novel gene and trait associations. The researchers also used the algorithm to identify potential therapeutics for autoimmune diseases, pointing to drug compounds that could reverse gene expression in specific cell types associated with these diseases. [Extracted from the article]

A recent study conducted by researchers at the University of California San Diego has shown that AI algorithms, combined with a modified pacifier, can determine if newborns are nursing properly. The study measured the strength and regularity of babies' suckling patterns using eight independent parameters. The results provide objective data that can improve standard assessments and potentially prevent surgical interventions. The study also found that the device-generated data could improve the subjective evaluation of infants' suckling ability by clinicians. The researchers hope that their findings will support parents struggling with breastfeeding and improve long-term health outcomes. [Extracted from the article]

The researchers, part of a group of computer-savvy historians who call themselves the Decrypt Project, have spent the past decade poring over manuscripts from European archives, trying to solve hidden messages. (Bloomberg Businessweek) -- More than 400 years ago, Mary, Queen of Scots was imprisoned in an English castle on charges that she'd conspired against Queen Elizabeth I, her cousin. [Extracted from the article]

The new tool relies on an advanced artificial intelligence (AI) algorithm that helps the AI better identify uncertainty when faced with unexpected data in real-world situations. Keywords: Algorithms; Computer Engineering; Engineering; Government Agencies Offices and Entities; North Carolina State University; Technology EN Algorithms Computer Engineering Engineering Government Agencies Offices and Entities North Carolina State University Technology 5024 5024 1 05/02/23 20230505 NES 230505 2023 MAY 5 (NewsRx) -- By a News Reporter-Staff News Editor at Respiratory Therapeutics Week -- A team of engineering and health researchers has developed a tool that improves the ability of electronic devices to detect when a human patient is coughing, which has applications in health monitoring. [Extracted from the article]

The article offers information on how technology has transformed the financial markets in 2017. A paper presented by economist Andrew Lo models the most relevant human biases, with a view to projecting the forces the human algorithms exert on financial markets. According to lawyer Tom Lin, the toughest questions in finance and artificial intelligence (AI) are about the type of human values and ethics people want to embed in the financial systems.

In this paper, discrete-time blind-source separation (BSS) of instantaneous mixtures is studied. Decorrelation-based sufficient criteria for BSS of stationary and nonstionary sources are derived based on nonstationarity and nonwhiteness. A gradient algorithm is proposed based on these criteria. A batch-data algorithm and an on-line algorithm are developed based on the corollaries of the BSS criteria. These algorithms are especially useful for the separation of nonstationary sources. They are robust to additive white noises if the time-delayed decorrelation and the nonstationarity of the sources are considered simultaneously in the algorithms. Experiment results show the effectiveness and performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a new image-descreening technique based on texture classification using a cellular neural network (CNN) with template trained by genetic algorithm (GA), called GA-CNN. Instead of using the fixed filters for image descreening, we are equipped with a more pliable mechanism for classifications in screening patterns. Using CNN makes it possible to get an accurate texture classification result in a faster speed by its superiority of implementable hardware and the flexible choices of templates. The use of the GA here helps us to look for the most appropriate template for CNNs more adaptively and methodically. The evolved parameters in the template for CNNs can not only provide a quicker classification mechanism but also help us with a better texture classification for screening patterns. After the class of screening patterns in the querying images is determined by the trained GA-CNN-based texture classification system, the recommendatory filters are induced to solve the screening problems. The induction of the classification in screening patterns has simplified the choke of filters and made it valueless to determine a new structured filter. Eventually, our comprehensive methodology is going to be topped off with more desirable results and the indication for the decrease in time complexity. [ABSTRACT FROM AUTHOR]

The promising potential of cellular neural networks (CNN) has resulted in the development of several template design methods. The CNN universal machine (CNN-UM), a programmable CNN, has made it possible to create image-processing algorithms that run on this platform. However, very large-scale integration implementations of CNN-UMs presented parameter deviations that do not occur on ideal CNN structures. Consequently, new design methods were developed aiming at more robust templates. Although these new templates were indeed more robust, erroneous behavior can still be observed. An alternative for chip-independent robustness is chip-specific optimization, where the template is targeted to an individual chip. This paper describes a solution proposal in this sense to automatically tune templates in order to make the chip react as an ideal CNN structure. The approach uses measurements of actual CNN-UM chips as part of the cost function for a global optimization method to find an optimal template given an initial approximation. Further improvements are achieved by generating chip-specific robust templates by doing a search for the best template among the optimal ones. The tuned templates are therefore customized versions that are expected to be much less sensitive to imperfections on the operation of CNN-UM chips. Results are presented for the binary and grayscale cases, including the case of grayscale output. It is expected that as this technique matures, it will give CNN-UM chips enough reliability to compete with digital systems in terms of robustness in addition to advantages of speed. [ABSTRACT FROM AUTHOR]

In this paper, an on-chip implementation of the active contour technique called pixel-level snakes is proposed. This is based on an optimized cellular neural network (CNN) algorithm with capabilities to support changes in the contour topology. The entire algorithm has been implemented on a 64 × 64 CNN universal machine chip-set architecture for which the results of the time performance measurements are given. To illustrate the validity and capabilities of the proposed implementation some on-chip experiments are also included. [ABSTRACT FROM AUTHOR]

The article presents the discussion on Artificial intelligence (AI) and machine learning (ML) increasingly used as technologies. Topics include used for tackling environmental challenges arising from global warming and climate change; and society preparing for climate change adaptation as well as mitigation through actions like reducing the carbon footprint of technology infrastructure.

Keywords: Algorithms; Artificial Intelligence; Biomarkers; Diagnostics and Screening; Emerging Technologies; Eye Diseases and Conditions; Health and Medicine; Machine Learning; Macular Degeneration; Macular Edema; Retinal Degeneration; Retinal Diseases and Conditions EN Algorithms Artificial Intelligence Biomarkers Diagnostics and Screening Emerging Technologies Eye Diseases and Conditions Health and Medicine Machine Learning Macular Degeneration Macular Edema Retinal Degeneration Retinal Diseases and Conditions 222 222 1 03/27/23 20230327 NES 230327 2023 MAR 27 (NewsRx) -- By a News Reporter-Staff News Editor at Diabetes Week -- A new study on artificial intelligence is now available. This paper aims to validate an AI algorithm for identifying and quantifying different major optical coherence tomography (OCT) biomarkers in DME eyes by comparing the algorithm to human expert manual examination.". [Extracted from the article]