AI-based Strategies Research Articles

Enhancing membrane fouling control in wastewater treatment processes through artificial intelligence modeling: research progress and future perspectives

AbstractMembrane filtration processes have demonstrated remarkable effectiveness in wastewater treatment, achieving high contaminant removal and producing high-quality effluent suitable for safe reuse. Membrane technologies play a primary role in combating water scarcity and pollution challenges. However, the need for more effective strategies to mitigate membrane fouling remains a critical concern. Artificial intelligence (AI) modeling offers a promising solution by enabling accurate predictions of membrane fouling, thus supporting advanced fouling mitigation strategies.This review examines recent progress in the application of AI models, with a particular focus on artificial neural networks (ANNs), for simulating membrane fouling in wastewater treatment processes. It highlights the substantial potential of ANNs, particularly the widely studied multi-layer perceptron (MLP) and other emerging configurations, to accurately predict membrane fouling, thereby enhancing process optimization and fouling mitigation efforts. The review discusses both the potential benefits and current limitations of AI-based strategies, analyzing recent studies to offer valuable insights for designing ANNs capable of providing accurate fouling predictions. Specifically, it provides guidance on selecting appropriate model architectures, input/output variables, activation functions, and training algorithms. Finally, this review highlights the critical need to connect research findings with practical applications in full-scale wastewater treatment plants. Key steps crucial to address this challenge have been identified, emphasizing the potential of AI modeling to revolutionize process control and drive a paradigm shift toward more efficient and sustainable membrane-based wastewater treatment.

AI-based strategies in breast mass ≤ 2 cm classification with mammography and tomosynthesis

PurposeTo evaluate the diagnosis performance of digital mammography (DM) and digital breast tomosynthesis (DBT), DM combined DBT with AI-based strategies for breast mass ≤ 2 cm. Materials and methodsDM and DBT images in 483 patients including 512 breast masses were acquired from November 2018 to November 2019. Malignant and benign tumours were determined by biopsies using histological analysis and follow-up within 24 months. The radiomics and deep learning methods were employed to extract the breast mass features in images and finally for benign and malignant classification. The DM, DBT and DM combined DBT (DM + DBT) images were fed into radiomics and deep learning models to construct corresponding models, respectively. The area under the receiver operating characteristic curve (AUC) was employed to estimate model performance. An external dataset of 146 patients from March 2021 to December 2022 from another center was enrolled for external validation. ResultsIn the internal testing dataset, compared with the DM model and the DBT model, the DM + DBT models based on radiomics and deep learning both showed statistically significant higher AUCs [0.810 (RA-DM), 0.823 (RA-DBT) and 0.869 (RA-DM + DBT), P ≤ 0.001; 0.867 (DL-DM), 0.871 (DL-DBT) and 0.908 (DL-DM + DBT), P = 0.001]. The deep learning models present superior to the radiomics models in the experiments with only DM (0.867 vs 0.810, P = 0.001), only DBT (0.871 vs 0.823, P = 0.001) and DM + DBT (0.908 vs 0.869, P = 0.003). ConclusionsDBT has a clear additional value for diagnosing breast mass less than 2 cm compared with only DM. AI-based methods, especially deep learning, can help achieve excellent performance.

Addressing epistasis in the design of protein function

Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence- and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity.

Advancement Challenges in UAV Swarm Formation Control: A Comprehensive Review

This paper provides an in-depth analysis of the current research landscape in the field of UAV (Unmanned Aerial Vehicle) swarm formation control. This review examines both conventional control methods, including leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field, and advanced AI-based (Artificial Intelligence) methods, such as artificial neural networks and deep reinforcement learning. It highlights the distinct advantages and limitations of each approach, showcasing how conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities. This review underscores the critical need for innovative solutions and interdisciplinary approaches combining conventional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications.

AI-Based Strategies to Improve Resource Efficiency in Urban Infrastructure

AI-Based Strategies to Improve Resource Efficiency in Urban Infrastructure

Artificial Intelligence in Pharmaceutical Industry: Revolutionizing Drug Development and Delivery

Abstract: Artificial Intelligence (AI) has ushered in a profound revolution within the pharmaceutical sector, effectively streamlining the processes of drug development and delivery. The application of AI-driven tools and methodologies, including machine learning and natural language processing, in the realm of pharmaceutical research and development has yielded recent breakthroughs. This accelerated the drug discovery process by meticulously scrutinizing copious data and pinpointing potential drug targets, as expounded upon in this comprehensive review. Furthermore, AI has found utility in optimizing clinical trials, thereby refining trial designs and cost-effectiveness and bolstering patient safety. Notably, AI-based strategies are being harnessed to enhance drug delivery, fostering the creation of intelligent drug delivery systems engineered to target specific cells or organs. This results in heightened efficacy and a concomitant reduction in undesirable side effects. This review also delves into the potential biases residing within AI algorithms and the challenges associated with data quality when integrating AI into the pharmaceutical sphere. The findings of this study underscore the immense potential of artificial intelligence in reshaping the pharmaceutical industry, thereby enhancing the quality of life for patients worldwide.

AI-driven resource management strategies for cloud computing systems, services, and applications

Cloud computing is a way for businesses and individuals to It has changed and revolutionized the way we access and use resources. However, efficient resource management in cloud computing systems remains a major challenge due to the scalability, heterogeneity, and dynamic nature of these environments. To address these challenges, artificial intelligence (AI) technology has emerged as an effective solution to improve resource management efficiency. This paper provides an overview of AI-based strategies for efficient resource management in cloud computing systems, services, and applications. This paper first reviews resource management challenges in cloud computing, including scalability, heterogeneity, quality of service requirements, and cost optimization. Below is an overview of the various AI techniques used for resource management. B. Algorithms for machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms. Next, this paper considers specific AI-based strategies for efficient resource management. These strategies include automated resource provisioning and scaling, intelligent workload planning and task allocation, predictive maintenance and fault detection, and energy-efficient resource management. We also present case studies and applications of AI-driven resource management in various cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container environments. This paper describes evaluation metrics and performance analysis techniques to evaluate the effectiveness of AI-based resource management approaches. It highlights the importance of ethical considerations, transparency, and explainability in AI-powered resource management systems. Additionally, the integration of AI technologies into existing resource management frameworks is discussed, and future directions are identified, including B. real-time resource optimization and coordination.

Applications of artificial intelligence in biliary tract cancers.

Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northernIndia. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis.

Artificial intelligence (AI) in diagnostic imaging.

In the last few years artificial intelligence (AI) has increasingly become a topic of interest, especially in diagnostic imaging. There are two main expected potential benefits: workflow effectiveness and diagnostic support systems, particularly as far as quality assurance is concerned. To meet these objectives, it is necessary to define what artificial intelligence in medicine means and to discuss which detailed steps should be fulfilled, e. g., for MSK imaging in the daily routine. In addition, this article provides an overview of what is necessary for an efficient IT-based workflow including the clinical question, the choice of modalities and investigation protocols, structured reports, and clinical classification. This is particularly interesting for potential providers, who are keen to apply new soft skills to support imaging diagnostic processes. The use of AI-supported diagnostic imaging could result in a paradigm shift from a modality-oriented to a clinical problem-oriented workflow. In order to streamline trauma, degeneration, inflammation, and oncology-MSK diagnostic imaging, the potential benefits of AI-based workflow optimization should be taken advantage of. The following article describes a five-point plan combining patient expectations and specialized radiological standards with respect to investigation protocols and reports. Moreover, this AI-based strategy could help to improve interdisciplinary networking, e. g., boards etc., and facilitate the required leap in quality towards "personalized precision medicine" for the patient. According to the global discussion, there is a need to point out that it is not currently realistic to replace doctors with AI. · AI as support-system has a paramount clinical impact. · AI in the daily routine could be for work-flow-support (processing) - a physician-replacement is un-realistic yet. · Standardization of work-flow by AI could be an important contribution of quality assurance. · Braunschweig R, Kildal D, Janka R. Artificial intelligence (AI) in diagnostic imaging . Fortschr Röntgenstr 2024; DOI: 10.1055/a-2208-6487.

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence.

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

Investigating the feasibility of genetic algorithms in predicting the properties of eco-friendly alkali-based concrete

This study employed an AI-based strategy to determine the accurate parameters in alkali-activated concrete (AAC) mix design that contribute to optimal performance. The data mining approach was used to generate a dataset comprising seven crucial input parameters and three outputs. Gene expression programming (GEP) models and multi-expression programming (MEP) models were developed to predict the rheological characteristics (in terms of static/dynamic yield stress and plastic viscosity) of AAC. R2 values, statistical checks, Taylor's diagram, and the difference between experimental and anticipated rheological parameters were used to assess the suitability of the created models. All prediction models developed using the MEP approach were found to be highly accurate (R2 > 0.90), while all GEP models were found to be in the acceptable range of accuracy (R2 near 0.90). Moreover, the greater accuracy of MEP models over GEP models was also confirmed by error evaluation by statistical tests. The empirical equations presented by the models might be useful for comprehending the mix design of AAC and the effect of each input parameter. Precursor content was found to have a significantly positive impact on ACC's rheological properties, as determined by the sensitivity analysis.

Empowering Deaf-Hearing Communication: Exploring Synergies between Predictive and Generative AI-Based Strategies towards (Portuguese) Sign Language Interpretation.

Communication between Deaf and hearing individuals remains a persistent challenge requiring attention to foster inclusivity. Despite notable efforts in the development of digital solutions for sign language recognition (SLR), several issues persist, such as cross-platform interoperability and strategies for tokenizing signs to enable continuous conversations and coherent sentence construction. To address such issues, this paper proposes a non-invasive Portuguese Sign Language (Língua Gestual Portuguesa or LGP) interpretation system-as-a-service, leveraging skeletal posture sequence inference powered by long-short term memory (LSTM) architectures. To address the scarcity of examples during machine learning (ML) model training, dataset augmentation strategies are explored. Additionally, a buffer-based interaction technique is introduced to facilitate LGP terms tokenization. This technique provides real-time feedback to users, allowing them to gauge the time remaining to complete a sign, which aids in the construction of grammatically coherent sentences based on inferred terms/words. To support human-like conditioning rules for interpretation, a large language model (LLM) service is integrated. Experiments reveal that LSTM-based neural networks, trained with 50 LGP terms and subjected to data augmentation, achieved accuracy levels ranging from 80% to 95.6%. Users unanimously reported a high level of intuition when using the buffer-based interaction strategy for terms/words tokenization. Furthermore, tests with an LLM-specifically ChatGPT-demonstrated promising semantic correlation rates in generated sentences, comparable to expected sentences.

A novel hybrid physical AI-based strategy for fault severity estimation in spur gears with zero-shot learning

Fault diagnosis of gears by vibration analysis has undergone significant growth in recent years. The traditional approaches for gear diagnostics in the past were focused mainly on fault detection. Improved understanding of physics of gear interaction, together with significant progress in dynamic modelling and the new era of artificial intelligence, make it possible for researchers to take on more challenging tasks such as fault severity estimation (FSE) and gear prognosis. This study presents a novel, hybrid strategy for combining physics of failure of machine elements with AI, specifically FSE of tooth breakage faults in spur gears, by a unique fusion of dynamic modelling, experimental characterization, feature extraction, and unsupervised machine learning algorithms. The novelty of the proposed strategy is its logic flow, which starts with a fundamental and deep understanding of the physical phenomena in realistic simulations. The extracted features are selected based on physical insights. The training dataset includes healthy measured data and simulations of healthy and faulty gear to compensate the lack of faulty data in real case scenarios. Discrepancies between the simulations and reality are reduced by passing the simulated signal through transfer functions estimated from experiments. The severity of the fault is estimated according to a “traffic-light” classification strategy, in which, for the first time, the fault geometry is employed to set thresholds for health indicators (HIs). This study shows that a wide set of sensitive features should be used for an early detection, while a smaller and more basic set of features is sufficient for fault severity classification. The suggested strategy is demonstrated on data from an actual testbed; and it is shown to detect breakage faults and estimate their severity, both by zero-shot learning (that is, without training on any measured faulty instances). The strategy can be applied to other systems and likely generalized, thus showing the power of hybrid approaches that combine physical insights with data-driven models.

Strategies based on artificial intelligence for the detection of fake news

The article examines artificial intelligence (AI) strategies to combat fake news, highlighting the rise in misinformation, especially during the pandemic, and its negative impact on public decision-making. The accelerated spread of fake news in the face of truth underlines the urgency of effective detection methods. Through a systematic literature review, the use of machine learning, deep learning, and natural language processing (NLP) to automate the identification of fake news is explored, highlighting key data sets such as BuzzFeedNews, LIAR, and BS Detector, among others, essential to train detection algorithms. The study discusses various AI approaches and techniques applied to detection, including convolutional neural networks (CNN), bidirectional LSTM, and the combination of CNN with LSTM, showing significant improvements in accuracy and efficiency. However, the limitations of these techniques are pointed out, such as the volatility of the training data and the difficulty of adapting models to different misinformation contexts. The conclusion highlights AI as a vital tool against fake news, emphasizing the need to advance research and develop more sophisticated technologies to strengthen disinformation detection and protect information integrity in society. The fight against fake news is complex, but AI-based strategies show a promising path toward practical solutions.

English

Artificial Intelligence (AI) and deep learning have the capacity to reduce losses in crop production, such as low crop yields, food insecurity, and the negative impacts on a country&rsquo;s economy caused by crop infections. This study aims to find the knowledge and technological gaps associated with the application of AI-based technologies for plant disease detection and pest prediction at an early stage and recommend suitable curative measures. An evidence-based framework known as the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology was used to conduct systematic reviews of the state-of-the-art of AI and deep learning techniques for crop disease identification and pest prediction in developing countries. The results demonstrate that conventional methods for plant disease management face some challenges, such as being costly in terms of labour, having low detection and prediction accuracy, and some are not environmentally friendly. Also, the rapid increase in data-intensive and computational-intensive tasks needed for plant disease classification using traditional machine learning methods poses challenges such as high processing time and storage capacity. Consequently, this paper recommends a deep learning and AI-based strategy to enhance the detection, prediction and prevention of crop diseases. These recommendations will be the starting point for future research. Key words: Plant diseases detection, pest prediction, pesticide recommendation, artificial intelligence, machine learning.

Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications

Recent advancements and developments in artificial intelligence (AI) based approaches have shifted the manufacturing practices towards the fourth industrial revolution, considered as Industry 4.0 practices. A positive impact of AI-based techniques on sustainability can be seen in manufacturing organisations’ at the system, product and process levels. Adopting AI-based strategies in manufacturing improves decision making, productivity and system performance. Despite sustainability and other benefits, the adoption of AI-based approaches in manufacturing organisations is still limited due to employees’ knowledge and digital skills. In the present time, due to the digitalisation of manufacturing activities, intelligent sensors, and supply chain activities, industries are facing challenges with the generation of high volume, different variety and velocity of data. This data can be helpful for manufacturing organisations to enhance their performance and sustainability. However, managing this big data is still a significant challenge due to a lack of knowledge and limited literature. Deep learning (DL) based models can be a suitable choice to provide advanced analytics tools for manufacturing data processing and analysis. However, literature on the DL is still limited in the manufacturing context with its relationship to sustainability. The present study discusses the evolution of DL approaches in manufacturing and different DL-based models. This study also highlights how DL-based approaches can enhance the sustainability performance of industries. In the study, primary research areas, i.e., fault diagnosis, quality management, and predictive maintenance, have been discussed. Finally, a conceptual DL-based framework is proposed for the manufacturing industries to enhance their sustainability performance in manufacturing activities.

AI-Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns.

Seismograms always result from mixing many sources and medium changes that are complex to disentangle, witnessing many physical phenomena within the Earth. With artificial intelligence (AI), we isolate the signature of surface freezing and thawing in continuous seismograms recorded in a noisy urban environment. We perform a hierarchical clustering of the seismograms and identify a pattern that correlates with ground frost periods. We further investigate the fingerprint of this pattern and use it to track the continuous medium change with high accuracy and resolution in time. Our method isolates the effect of the ground frost and describes how it affects the horizontal wavefield. Our findings show how AI‐based strategies can help to identify and understand hidden patterns within seismic data caused either by medium or source changes.

Artificial intelligence to complement rather than replace radiologists in breast screening

Artificial intelligence to complement rather than replace radiologists in breast screening

Computer Vision and Artificial Intelligence Based Puzzle Solver

This document gives formatting instructions for authors preparing papers for publication in the International Journal. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type you’re In the last decade or so, solving the Sudoku puzzle has become everyone's passion or you can say hobby. The simplicity of the puzzle's structure and the low requirement of mathematical skills caused people to possess enormous interest in accepting challenges to unravel the puzzle. Therefore, developers have tried to hunt out algorithms so on get the variability of puzzles for human players so as that they could be even solved by programming. AI-based real-time Computer Vision and puzzle solver using webcam allows providing the pc system with the Sudoku puzzle in real-time and provides the optimum solution thereto. The system not only involves the use of AI but also makes use of Computer vision, thus combining the 2 major concepts. During this system the pc tries to analyse the environment by capturing the multiple-image bursts from the important time, and from those images, it might detect the Sudoku grid lines. For the detection of the grid, the use of the Hough Transform technique has been made. Then the numbers are detected using OCR technology i.e. Optical Character Recognition. The system gets the whole knowledge of the puzzle and then computes the last word solution by making the use of AI-based strategies for getting the optimal solution in efficient manner to the Sudoku Puzzle Problem.

Design of detectors at the electron ion collider with artificial intelligence

Artificial Intelligence (AI) for design is a relatively new but active area of research across many disciplines. Surprisingly when it comes to designing detectors with AI this is an area at its infancy. The electron ion collider is the ultimate machine to study the strong force. The EIC is a large-scale experiment with an integrated detector that extends for about ±35 meters to include the central, far-forward, and far-backward regions. The design of the central detector is made by multiple sub-detectors, each in principle characterized by a multidimensional design space and multiple design criteria also called objectives. Simulations with Geant4 are typically compute intensive, and the optimization of the detector design may include non-differentiable terms as well as noisy objectives. In this context, AI can offer state of the art solutions to solve complex combinatorial problems in an efficient way. In particular, one of the proto-collaborations, ECCE, has explored during the detector proposal the possibility of using multi-objective optimization to design the tracking system of the EIC detector. This document provides an overview of these techniques and recent progress made during the EIC detector proposal. Future high energy nuclear physics experiments can leverage AI-based strategies to design more efficient detectors by optimizing their performance driven by physics criteria and minimizing costs for their realization.