Machine Artificial Intelligence Research Articles

Waste-to-energy poly-generation scheme for hydrogen/freshwater/power/oxygen/heating capacity production; optimized by regression machine learning algorithms

Utilization of machine learning techniques in the analysis and enhancement of poly-generation energy systems improves their efficiency and sustainability. Also, waste-to-energy systems propose a hopeful answer for both waste management and sustainable energy and water production. The production of hydrogen and freshwater through these systems not only provides valuable resources but also contributes to environmental sustainability. This research utilizes artificial intelligence's machine learning algorithms to examine and enhance a waste-to-energy system within a poly-generation energy system that generates hydrogen, freshwater, power, oxygen, and hot air. The integrated system consists of a waste combustion chamber, a proton exchange membrane electrolyzer, a supercritical carbon dioxide Brayton cycle, and a desalination system. Based on the findings, the machine learning algorithms exhibiting R-squared values exceeding 99% are considered strong fits. Additionally, all algorithms with high predicted R-squared values have the capability to accurately forecast new data using the provided training data. The emissions of 669.7 g/kWh and an efficiency of 69.62% are achieved when the pressure ratio is 10.73 and the temperature is 863.6 °C under optimal conditions. The accuracy and validity of the machine learning techniques are further confirmed by the strong agreement with thermodynamic modeling.

UTILIZAREA IMAGINILOR SATELITARE ÎN AGRICULTURĂ. STUDIU DE CAZ

All sizes of farms can benefit from satellite imagery, not only big producers. When paired with artificial intelligence (AI) and deep machine learning techniques, satellite photography becomes an effective tool for monitoring agricultural conditions and anticipating issues in the field. As a result, using satellite photos to guide crop farming choices can help determine when to apply nutrients and irrigation. This paper focuses on monitoring through satellite sensors with an emphasis on the facilities offered by the European Copernicus Program through Sentinel-2 satellites the crops from a farm from Calarasi County, Borcea commune.

ENHANCING OIL AND GAS EXPLORATION EFFICIENCY THROUGH AI-DRIVEN SEISMIC IMAGING AND DATA ANALYSIS

This paper delves into the advancements in AI-driven seismic imaging and data analysis techniques aimed at augmenting the efficiency of oil and gas exploration. We explore various AI algorithms and machine learning models that have been deployed to interpret seismic data, predict subsurface structures, and identify potential hydrocarbon reservoirs with unprecedented precision. Furthermore, we discuss the integration of big data analytics and high-performance computing in handling vast volumes of seismic data, thereby facilitating rapid decision-making in exploration projects. Through case studies and empirical evidence, we highlight the tangible benefits and potential challenges associated with the adoption of AI-driven seismic imaging and data analysis in the oil and gas industry. Ultimately, this paper underscores the transformative impact of AI technologies in optimizing exploration workflows and maximizing resource discovery while mitigating risks and reducing operational costs. In the pursuit of optimizing oil and gas exploration, the integration of artificial intelligence (AI) methodologies has emerged as a transformative force. This paper examines the evolving landscape of AI-driven seismic imaging and data analysis techniques, aimed at enhancing efficiency within the exploration domain. By harnessing AI algorithms and machine learning models, seismic data interpretation is propelled to unprecedented levels of accuracy, enabling the prediction of subsurface structures and the identification of potential hydrocarbon reservoirs with enhanced precision. Moreover, the synergistic fusion of big data analytics and high-performance computing facilitates the processing of vast seismic datasets, expediting decision-making processes in exploration endeavors. Through a synthesis of case studies and empirical evidence, this paper elucidates the tangible benefits and potential challenges associated with AI adoption in the oil and gas sector. By amplifying exploration workflows, mitigating risks, and curbing operational costs, AI-driven seismic imaging and data analysis stand poised to revolutionize the landscape of oil and gas exploration, catalyzing sustainable resource discovery in an evolving energy paradigm. Keywords: AI-Driven Seismic Imaging, Oil And Gas Exploration, Data Analysis, Efficiency Optimization, Geophysical Insights, Seismic Interpretation.

CAN ARTIFICIAL INTELLIGENCE ACCURATELY DETECT URINARY STONES? A SYSTEMATIC REVIEW.

To perform a systematic review on artificial intelligence(AI) performances to detect urinary stones. A PROSPERO-registered(CRD473152) systematic search of Scopus, Web of Science, Embase and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume"). Risk-of-bias(RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies and the Checklist for Artificial Intelligence in Medical Imaging(CLAIM). Twelve studies were selected for final review, including 3 multicenter and 9 single-center retrospective studies. 11 studies completed at least 50% of the CLAIM checkpoints and only one presented a high ROB. All included studies aimed to detect on kidney(5/12, 42%), ureter(2/12, 16%) or urinary(5/12, 42%) stones on Non-Contrast Computed Tomography(NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested 2 studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection with sensitivity, specificity and accuracy rates ranging from 58,.7 to 100%, 68,.5 to 100% and 63 to 99,.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated Stone Volume(r=0,.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible. Artificial Intelligence processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stones detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urological foreign bodies (ureteral stent and nephrostomy tubes) cases.

Identifying necrotizing soft tissue infection using infectious fluid analysis and clinical parameters based on machine learning algorithms

BackgroundDetermining the presence of necrotizing soft tissue infection (NSTI) poses a significant hurdle. As of late, there has been a notable increase in the application of artificial intelligence (AI) machine learning techniques in identifying diseases, a shift that can be attributed to their exceptional efficiency, unbiased nature, and high precision. MethodsInformation was gathered from a cohort of 13 patients suffering from NSTI, alongside 12 patients with cellulitis. The construction of NSTI diagnostic machine learning models utilized four different algorithms, specifically random forest, k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression. These models were constructed based on 28 distinctive attributes identified through statistical examination. Following this, the diagnostic efficiency of each algorithms was evaluated. A novel random forest model, streamlined for clinical use, was later developed by focusing on 6 attributes that had the most pronounced influence on the accuracy of our initial random forest model. ResultsThe following data was noted regarding the sensitivity and specificity of the four NSTI diagnostic models:logistic regression displayed 78.2 % and 83.7 %, KNN presented 79.1 % and 87.1 %, SVM showed 83.5 % and 86.3 %, and random forest exhibited 89.6 % and 92.9 %, respectively. In comparison, lactate levels in fluid demonstrated 100 % sensitivity and 76.9 % specificity at an optimal cut-off point of 69.6 mg/dL. Among all four machine learning models, random forest outperformed the others and also showed better results than fluid lactate. A newly constructed random forest model, created using 6 of the 13 identified features, displayed promising results in diagnosing NSTI, having a sensitivity and specificity of 90.2 % and 92.2 %, respectively. ConclusionsDeveloping a diagnostic model for NSTI employing the random forest algorithm has resulted in a diagnostic technique that is more efficient, cost-effective, and expedient. This approach could provide healthcare practitioners with the tools to identify and manage NSTI with greater efficacy.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. PRR1-10.2196/54388.

Boronic Acid-Linked Cell-Penetrating Peptide for Protein Delivery.

Studying functional protein delivery into live cells is important, ranging from fundamental research to therapeutics. Cell-penetrating peptides (CPPs) are known to deliver proteins with applauded efficacy and have gained importance for applications in protein therapeutics and exploration of versatile cellular mechanisms. The primary aim of the work is to design a CPP as a tool and delivery vehicle for macromolecules, including proteins. In this work, boronic acid-linked cyclic deca arginine (cR10) is reported as an efficient CPP that exhibited 3-fold higher delivery of chemically synthesized ubiquitin (Ub) than pristine cR10-linked Ub, examined with live U2OS cells. As a futuristic plan, an artificial intelligence machine learning-based rationale has been designed and proposed.

AI-Powered Revolution: Transforming Industrial Production through Automation

The integration of artificial intelligence (AI) tools has propelled automation in industrial production to new heights, ushering in a wave of transformative advancements. This research delves into the profound impact of AI tools on industrial production, elucidating their pivotal role in driving efficiency, cost reduction, and productivity enhancements. By leveraging AI algorithms and machine learning techniques, manufacturers can optimize operations with unprecedented precision and agility. However, alongside the promises of increased efficiency come significant challenges and opportunities. This paper navigates through the complexities of implementing AI-driven automation in manufacturing processes, addressing issues such as data security, workforce adaptation, and ethical considerations. Through rigorous secondary data analysis and research methodology, this study endeavours to shed light on the current state of automation in industrial production while offering valuable insights into its future prospects and implications for the manufacturing industry.

The Impact of Business Continuity on Supply Chain Practices and Resilience Due to COVID-19

Background: Business continuity entails the potential negative consequences of uncertainty on a firm’s ability to achieve strategic objectives. The COVID-19 pandemic significantly impacted business continuity due to lockdowns, travel restrictions, and social distancing measures. Consequently, firms adopted specific supply chain (SC) practices to effectively navigate this global crisis. Methods: This research adopted a stochastic approach based on Bayesian Networks to evaluate the implications of business continuity on firms’ decisions to embrace SC practices, focusing on omnichannel strategies, SC coordination, and technologies such as artificial intelligence systems, big data and machine learning, and mobile applications. Results: Our findings revealed that firms facing disruption in a single performance area can apply specific strategies to maintain resilience. However, multiple areas of underperformance necessitate a varied approach. Conclusions: According to our empirical analysis, omnichannel strategies are critical when disruptions simultaneously impact quality, inventory, sales, and ROI, particularly during major disruptions such as the COVID-19 pandemic. AI and big data become vital when multiple risks coalesce, enhancing areas such as customer service and supply chain visibility. Moreover, supply chain coordination and mobile app adoption are effective against individual performance risks, proving crucial in mitigating disruption impacts across various business aspects. These findings help policy-makers and business owners to have a better understanding of how business continuity based on performance resistance to disruptions pushes companies to adopt different practices including new technologies and supply chain coordination. Accordingly, they can use the outputs of this study to devise strategies for improving resilience considering their supply chain vulnerabilities.

Abstract LB122: JNJ-87890387, a novel ENPP3 bispecific T-cell redirector (ENPP3xCD3) with tumor selectivity through targeting apical surface antigens

Abstract Clinical success of T-cell redirection therapy for solid tumors has been limited potentially due to a lack of target specificity against cancer, leading to on-target off-tumor toxicity at sub-efficacious doses. ENPP3 (ectonucleotide pyrophosphatase/phosphodiesterase family member 3) is a transmembrane protein that was identified with the help of an artificial intelligence machine learning algorithm. ENPP3 is characterized by apically restricted expression in most normal tissues and elevated, depolarized expression in several solid tumors, thus potentially providing tumor selectivity. ENPP3 expression exhibits a high prevalence in several solid tumors including renal cell carcinoma (RCC) - clear cell (93%) and papillary (78%), lung adenocarcinoma (50%), endometrioid uterine (53%) and ovarian (47%) cancers, colorectal carcinoma (51%), and hepatocellular carcinoma (HCC, 47%). JNJ-87890387 is a fully human bispecific antibody with high affinity and specific binding to ENPP3 on tumor cells and lower affinity binding to cluster of differentiation (CD)3 on T-cells. In vitro, JNJ-87890387 demonstrated potent (pM) T-cell activation and cytotoxicity against tumor cell lines with different levels of endogenous ENPP3 membrane expression, and absence of killing against cells lacking ENPP3 expression, confirming JNJ-87890387 specificity. In vivo, JNJ-87890387 demonstrated ENPP3-expression-dependent potent anti-tumor activity in multiple cell line-derived and patient tumor-derived RCC and HCC xenograft models with complete tumor regressions, and evidence of dose-dependent CD4+ and CD8+ T cell infiltration into the tumors. Preclinical safety assessment in the cynomolgus monkey with a tool molecule using the therapeutic ENPP3 binding arm but with a cynomolgus CD3 cross-reactive arm only identified toxicities consistent with the modality. In summary, these data support JNJ-87890387’s clinical development with an ongoing first-in-human Phase I study to evaluate the safety and preliminary anti-tumor activity in advanced-stage solid tumors with a high prevalence of ENPP3 expression. Citation Format: Smruthi Vijayaraghavan, Pankaj Seth, Annmarie Winkis, Kristen Chevalier, Alexa Marthaler, Katrin Sproesser, Vince Torti, Nirav Shah, Eilyn Lacy, Anna-Lena Frisk, Heather Deutsch, Gerald Chu, Michael Sharp, Jonathan Pabalan, Cholpon Tilegenova, Katherine Thorton, Josh Lauring, Bethany Mattson, Ken Tian, Jacqueline Kinyamu-Akunda, Laurie Lenox, Wan Cheung Cheung, Sheri Moores, Sylvie Laquerre. JNJ-87890387, a novel ENPP3 bispecific T-cell redirector (ENPP3xCD3) with tumor selectivity through targeting apical surface antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB122.

A Machine Learning Algorithm Improves the Diagnostic Accuracy of the Histologic Component of Antibody Mediated Rejection (AMR-H) in Cardiac Transplant Endomyocardial Biopsies.

BackgroundPathologic antibody mediated rejection (pAMR) remains a major driver of graft failure in cardiac transplant patients. The endomyocardial biopsy remains the primary diagnostic tool but presents with challenges, particularly in distinguishing the histologic component (pAMR-H) defined by 1) intravascular macrophage accumulation in capillaries and 2) activated endothelial cells that expand the cytoplasm to narrow or occlude the vascular lumen. Frequently, pAMR-H is difficult to distinguish from acute cellular rejection (ACR) and healing injury. With the advent of digital slide scanning and advances in machine deep learning, artificial intelligence technology is widely under investigation in the areas of oncologic pathology, but in its infancy in transplant pathology. For the first time, we determined if a machine learning algorithm could distinguish pAMR-H from normal myocardium, healing injury and ACR. Materials and MethodsA total of 4,212 annotations (1,053 regions of normal, 1,053 pAMR-H, 1,053 healing injury and 1,053 ACR) were completed from 300 hematoxylin and eosin slides scanned using a Leica Aperio GT450 digital whole slide scanner at 40X magnification. All regions of pAMR-H were annotated from patients confirmed with a previous diagnosis of pAMR2 (>50% positive C4d immunofluorescence and/or >10% CD68 positive intravascular macrophages). Annotations were imported into a Python 3.7 development environment using the OpenSlide™ package and a convolutional neural network approach utilizing transfer learning was performed. ResultsThe machine learning algorithm showed 98% overall validation accuracy and pAMR-H was correctly distinguished from specific categories with the following accuracies: normal myocardium (99.2%), healing injury (99.5%) and ACR (99.5%). ConclusionOur novel deep learning algorithm can reach acceptable, and possibly surpass, performance of current diagnostic standards of identifying pAMR-H. Such a tool may serve as an adjunct diagnostic aid for improving the pathologist's accuracy and reproducibility, especially in difficult cases with high inter-observer variability. This is one of the first studies that provides evidence that an artificial intelligence machine learning algorithm can be trained and validated to diagnose pAMR-H in cardiac transplant patients. Ongoing studies include multi-institutional verification testing to ensure generalizability.

Fire Detection by using DenseNet 201 algorithm and Surveillance Cameras images

Early warning of fires is very important to reduce loss of life and property. It is a technology that has become one of the most important design elements for smart cities in the future. Most current solutions rely on temperature and smoke sensors, which have limited detection ranges and scenarios and long response times. These are used inside some buildings and cannot be used in the external environment, as they are not a preferred solution. In addition to the above, sensor systems are costly because of the hardware and installation requirements, and they require regular upkeep. With the development of artificial intelligence and learning machines, fire detection has been used based on deep learning. Widely. Therefore, a solution to computer vision technology is proposed, which uses a convolutional neural network (CNN), Reducing false alarms. The use of computer vision techniques is certainly better than sensor-based systems, by utilizing security camera footage at specific periods to detect a fire when it breaks out. Our system achieved excellent results with an average predictive accuracy of 98% on the dataset. This accuracy and low cost make it a good alternative to systems that use sensors. The contribution of this study is to improve the detection process by conducting a classification process instead of detecting an object (fire), which requires complex mathematical calculations, large resources, and a long time.

인공지능 기반의 자동화된 통합보안관제시스템 모델 연구

In today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.

Abstract 4915: Identification and validation of novel prognostic genetic markers in HER2-negative advanced gastric cancer (AGC) by artificial intelligence (AI) deep learning and machine learning algorithm

Abstract Introduction: Despite advances in anticancer treatments, there has been urgent need to identify novel prognostic markers for HER2-negative AGC. We propose AI based deep learning and machine learning pipeline to explore genetic markers that predict patient response to chemotherapy in HER2-negative AGC. Methods: We retrospectively assessed 179 stage IV HER2-negative AGC patients who have been treated with first-line chemotherapy in Yonsei Cancer Center, Korea between 2015 and 2021. In-house targeted sequencing panel data (CancerSCANTM and CancerMaster) were used to identify candidate prognostic markers. DeepSurv was mainly used to analyze the progression-free survival (PFS), which is a deep learning algorithm that can investigate prognostic roles. The SHapley Additive exPlanations (SHAP) method was applied to open the deep learning models’ black-box and calculate the importance ranking of gene features. Machine learning models-random survival forest, elastic-net, and lasso-were also investigated to identify potentially meaningful gene variants. To rank the gene features, we calculated variable importance (VIMP) for random survival forest, and penalized regression coefficients for elastic-net and lasso. All deep learning and machine learning models were trained using 5-folds cross-validation in 1,000 iterations of re-sampling bootstrapping with hyperparameter tuning by grid search. To classify responder or non-responder related genetic markers to chemotherapy, we used the signs of averaged penalize coefficients obtained from elastic-net and lasso. Based on the median linear predictor with candidate gene features, patients were classified into high-risk and low-risk group. Results: A total of 7,824 common variants were analyzed and 20 prognostic genetic markers were identified based on the top average values of SHAP, VIMP, and penalized regression coefficients. The top 10 non-responder genetic markers are included as follows: PARP4(p.S873N), CHUK(p.V268I), BARD1(359_365del), CREBBP(p.Y1125F), BARD1(p.P24S), PHLPP2(p.R1312Q), FAT3(p.Q3375R), CASP5(p.T106A), PHLPP2(p.I544V), and PTCH2(p.T988M). Otherwise, the top 10 responder genetic markers are defined as follows: IL7R(p.I66T), NOTCH4(L16delinsLL), FGFR3, BCL2A1(p.G82D), ZNF217(p.T548I), BRCA1(p.K1183R), FANCA(p.A412V), ADGRA2, CDKN2B, and FANCM(p.S175F). The high-risk group had worse PFS compared with low-risk group (median PFS 4.1 vs. 9.1 months; P-value <0.0001). Conclusion: This study showed the potential of AI deep learning and machine learning pipeline that employs an innovate prognostic genetic markers for HER2-negative AGC. Current ongoing analyses of larger cohort with more comprehensive clinical data will be presented. Citation Format: Sejung Park, Seok-Jae Heo, Choong-Kun Lee, Yaeji Lee, Woo Sun Kwon, Jingmin Che, Minseok Kim, Hyun Cheol Chung, Sun Young Rha. Identification and validation of novel prognostic genetic markers in HER2-negative advanced gastric cancer (AGC) by artificial intelligence (AI) deep learning and machine learning algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4915.

PersistVerify: Federated model ownership verification with spatial attention and boundary sampling

Federated learning, known for its emphasis on privacy and resource efficiency, has emerged as a transformative paradigm in the fields of artificial intelligence and industrial machine learning. However, with the advancement and widespread adoption of this technology, the concern of model theft in federated learning models becomes increasingly prominent. While current endeavors concentrate on protecting individual deep learning models, limited attention has been dedicated to those involved in federated learning, especially within decentralized environments. To fill this void, we introduce PersistVerify, an innovative approach for verifying model copyright within the federated learning framework. PersistVerify integrates boundary sample selection and spatial attention mechanisms, enhancing the robustness and confidentiality of neural network backdoor watermarks to ensure secure verification of model ownership. PersistVerify constructs a confidential and robust watermark dataset through three sequential steps: latent representation extraction, boundary sample selection, and spatial attention-based trigger embedding. Our research addresses the pervasive challenge of reliable model ownership verification in federated learning, demonstrating the efficacy of PersistVerify. This study provides valuable insights and methodologies to maintain the confidentiality and reliability of neural network backdoor watermarks in the domains of artificial intelligence and federated learning.

Bridging the artificial intelligence inventorship gap

Objective: to study the gaps in the legal regulation of relations in the sphere of inventions made by artificial intelligence.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological.Results: in Thaler v. Vidal, the U.S. Court of Appeals for the Federal Circuit ruled that an artificial intelligence (AI) machine cannot be an inventor under patent law. This decision leaves open the question of whether a natural person can be the legal inventor of AI-generated inventions. This is a pressing question because it decides whether AI-generated inventions are patentable, as no patent rights can exist without an inventor. Scholars have proposed two doctrines that might resolve this question: the doctrine of simultaneous conception and reduction to practice and the doctrine of first to recognize and appreciate. This article analyzes the two doctrines and argues that neither doctrine readily applies to AI-generated inventions, thereby leaving an “inventorship gap”.Scientific novelty: the article is the first to pose and solve the problem of legal regulation of inventions made with the help of artificial intelligence and to state the need for the U.S. Congress to amend the copyright law in terms of recognizing a physical person who uses artificial intelligence to generate inventions as the author of such inventions. It bridges the gap in legal regulation of relations in the sphere of inventions and patenting and facilitates the goals of the patent system.Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering the issues related to the legal regulation of relations in the sphere of inventions made by artificial intelligence.

Research and Application of Visual Object Recognition System Based on Deep Learning and Neural Morphological Computation

The development of advanced optoelectronic vision sensors for high-level image recognition and data preprocessing is poised to accelerate the progress of machine vision and mobile electronic technology. Compared to traditional sensory computing methods, such as analog-to-digital signal conversion and digital logic computation tasks (i.e., Von Neumann computing), neural morphological vision computing can significantly improve energy efficiency and data processing speed by minimizing unnecessary raw data transmission between front-end photosensitive sensors and back-end processors. Neural morphological vision sensors are typically designed for tasks such as denoising, edge enhancement, spectral filtering, and visual information recognition. These methods can be categorized into approaches using near-sensor and sensor-internal computing processors based on whether preprocessing can be performed in situ. In near-sensor computing approaches, the image sensor for capturing visual information and the memory computing processor for preprocessing captured images are separate. A memory computing processor can simultaneously perform memory and computing tasks based on analog memory functions. Neural morphological vision sensors for in-sensor computing can be constructed using single-element image sensors, enabling both the reception of visual information and the execution of memory computing processes to be achieved in the same device. This represents an ideal scenario for future artificial intelligence machines and mobile electronic devices in visual computing systems.

Harmonising linguistic validation with AI: Precision, efficiency, and the human touch in patient-reported outcome translation

The surge in artificial intelligence (AI) prompts a reassessment of linguistic validation methods for patient-reported outcome (PRO) measures. The robust linguistic process, designed to adapt PRO measures for different cultures and languages, is upheld by regulators and the outcomes research community for its value in maintaining concept equivalence across global trial data. Its methods are entrenched in human translation and review, making it more challenging to integrate AI (machine learning, deep learning, natural language processing) compared to other parts of the global localisation industry. This article provides an overview of the key challenges in integrating linguistic validation and AI. Despite these hurdles, it advocates for the industry to embrace the potential benefits through collaborative and responsible innovation.

Mapping Flood-Prone Areas Using GIS Through as Geo-Artificial Intelligence (Geo-Ai) Approach in Bengkulu City

Bengkulu City is an area prone to flooding due to its proximity to the river estuary. Flooding in Bengkulu City usually occurs during the rainy season, when high rainfall and overflowing rivers can cause inundation in several areas around the city. This research aims to identify flood-prone areas in Bengkulu City through a Geographic Artificial Intelligence (Geo-AI) approach. Geo-AI is an artificial intelligent machine with geospatial data, including satellite images and weather data, whose data analysis is more accurate and efficient in identifying flood-prone areas. Geographic Information System (GIS) can present objects of flood-prone areas from the real world in digital form. Through Google Earth Engine (GEE), satellite imagery data and other geospatial data are processed and analyzed using artificial intelligence algorithms to identify flood patterns and frequently flooded areas. The final result of this research is a map of flood-prone areas in Bengkulu City. On the map, it can be concluded that the red-colored areas including Rawa Makmur, Tanjung Agung, Bentiring, Kebun Tebeng, Penurunan, Sukarami, Pekan Sabtu, and Air Sebakul are areas that are often flooded so that these areas can be confirmed as flood-prone.

Training self-learning circuits for power-efficient solutions

As the size and ubiquity of artificial intelligence and computational machine learning models grow, the energy required to train and use them is rapidly becoming economically and environmentally unsustainable. Recent laboratory prototypes of self-learning electronic circuits, such as “physical learning machines,” open the door to analog hardware that directly employs physics to learn desired functions from examples at a low energy cost. In this work, we show that this hardware platform allows for an even further reduction in energy consumption by using good initial conditions and a new learning algorithm. Using analytical calculations, simulations, and experiments, we show that a trade-off emerges when learning dynamics attempt to minimize both the error and the power consumption of the solution—greater power reductions can be achieved at the cost of decreasing solution accuracy. Finally, we demonstrate a practical procedure to weigh the relative importance of error and power minimization, improving the power efficiency given a specific tolerance to error.