Artificial Intelligence Techniques Research Articles

Ischemic Stroke Segmentation by Transformer and Convolutional Neural Network Using Few-Shot Learning

Stroke is a major factor in causing disability and fatalities. Doctors use computerized tomography (CT) and magnetic resonance imaging (MRI) scans to assess the severity of a stroke. Automatic image segmentation can help doctors diagnose strokes more quickly and accurately, but it is challenging due to the variability of stroke lesions and the limited availability of labeled data. Deep learning is the cutting-edge technique of machine learning and artificial intelligence, which needs an extensive labeled dataset for effective training. Unfortunately, in the medical domain, the availability of labeled data is severely limited, posing a challenge for conventional deep learning approaches. In this paper, we introduce a system that utilizes deep learning in the form of fusing transformer-based and convolutional neural network (CNN)-based features and few-shot learning techniques to segment ischemic strokes in multimedia MRIs. To accomplish this, we employ two different methods. The first method involves parallel fusion, where we combine CNN-based and transformer-based features. The second method utilizes serial fusion, combining CNN-based and transformer models using few-shot learning. Through the integration of transformer and CNN models, we can extract both global and local features and enhance the system's performance. Moreover, we tackle the issue of limited labeled data by integrating few-shot learning techniques. Additionally, our system optimizes efficiency by selecting only the slices with lesions, disregarding unlesioned slices. The system under consideration is trained with the BraTS2020 dataset, evaluated on the ISLES 2015 dataset, and contrasted the performance with cutting-edge systems. The suggested system attains a dice coefficient score of 0.76, surpassing the scores of previous cutting-edge systems by a substantial margin.

From biological potency absorbed components to artificial intelligence mining: A review of analytical strategies for the discovery and validation of quality marker in traditional Chinese medicine in the past decade

Quality control of Traditional Chinese Medicine (TCM) is fundamental to ensuring its clinical efficacy, with TCM formulas being the primary form used in clinical practice. Current quality control methods for TCM formulas often rely on pharmacopoeial standards for individual medicinal materials, typically encompassing only characteristic or partial active ingredients. These methods fail to fully reflect the clinical efficacy of TCM formulas. Consequently, exploring the multiple efficacious components in TCM formulas and establishing the correlation between multi-component content and efficacy has become an urgent issue in the modern quality assessment of TCM formulas. The quality marker (Q-marker) has emerged as a crucial standard in this field, achieving notable success in recent years. This paper reviews recent progress in the development of the Q-marker system in TCM by highlighting strategies based on the correlation between efficacy and constituents, using analytical techniques to investigate the material basis and efficacy of TCM. However, the aforementioned methods inevitably involve human selection factors. With the widespread application of artificial intelligence (AI) learning algorithms, it is now possible to develop a modern quality evaluation method for the multi-component “efficacy-quality” correlation in TCM formulas. This approach leverages AI techniques to explore novel and quantifiable methods for scientific and rational quality control in TCM formulas. In this paper, important future directions and questions in this field are also discussed.

Condition assessment and predictive maintenance for contact probe using health index and encoder‐decoder LSTM model

AbstractContact probe is broadly used for the continuous monitoring of microelectronic components in manufacturing industries. False rejection of fine product due to defective contact probe significantly reduces the yield in production. Traditionally, defect detection for contact probes heavily depends on a valid range manually defined by engineers over the measured value of certain parameters. However, the subjective range defined according to engineer experience is prone to trigger a high rate of false alarms due to the inherent noise in the measured parameters. To address this issue, we construct a health index (HI) with the contact resistance‐directly‐related features to help monitor and assess the condition of contact probe. Based on the established HI, we develop Long Short‐Term Memory (LSTM) encoder‐decoder machine learning model to assess the condition of contact probe by forecasting the HI value in the future. Encoders from LSTM and convolutional neural network (CNN) are selected as the encoder‐decoder architecture for the sequence‐to‐sequence prediction due to their advantage in extracting the correlation of features at different scales. An explainable Artificial Intelligence (XAI) technique named Local interpretable model‐agnostic explanations (LIME) is used to quantify the contribution of each feature to the model prediction. The encoder from CNN is found to outperform the LSTM encoder in extracting the inter‐feature correlation. Finally, the predicted HI is used to signal the alarm for the maintenance action of contact probe when its value is below a predefined threshold. Comparison between the action alarm triggered by the developed HI and the actual maintenance records suggests that the proposed approach achieves at least 75% accuracy for the triggered alarm in the next 15 mins.

Blueprints of Architected Materials: A Guide to Metamaterial Design for Tissue Engineering.

Mechanical metamaterials are rationally designed structures engineered to exhibit extraordinary properties, often surpassing those of their constituent materials. The geometry of metamaterials' building blocks, referred to as unit cells, plays an essential role in determining their macroscopic mechanical behavior. Due to their hierarchical design and remarkable properties, metamaterials hold significant potential for tissue engineering; however their implementation in the field remains limited. The major challenge hindering the broader use of metamaterials lies in the complexity of unit cell design and fabrication. To address this gap, a comprehensive guide is presented detailing the design principles of well-established metamaterials. The essential unit cell geometric parameters and design constraints, as well as their influence on mechanical behavior, are summarized highlighting essential points for effective fabrication. Moreover, the potential integration of artificial intelligence techniques is explored in meta-biomaterial design for patient- and application-specific design. Furthermore, a comprehensive overview of current applications of mechanical metamaterials is provided in tissue engineering, categorized by tissue type, thereby showcasing the versatility of different designs in matching the mechanical properties of the target tissue. This review aims to provide a valuable resource for tissue engineering researchers and aid in the broader use of metamaterials in the field.

The Degree to Which Public School Principals Possess Artificial Intelligence Applications in the Karbala Governorate Education Directorate

The study aims to identify the degree of possession of government school principals in the Karbala Governorate Education Directorate of artificial intelligence applications. The descriptive analytical approach was used. To achieve the study objectives, a questionnaire which contained (34) items was developed, divided into five dimensions. The study community was (114) male and female principals. The study sample consisted of (94) male and female managers, and the sample was selected randomly. The results of the study showed that the degree of possession of government school principals in the Karbala Governorate Education Directorate of artificial intelligence applications was average. The results of the study also showed that there were no statistically significant differences at the significance level (α=0.05) attributed to the study variables (gender, academic qualification, experience in artificial intelligence techniques). In light of the study results, the researchers recommended several recommendations, most notably: paying attention to training courses in the field of information and communications technology, especially in the subject of artificial intelligence, by organizing meetings and workshops with the aim of raising awareness among individuals and society about the importance of artificial intelligence, encouraging the Iraqi Ministry of Education to its teaching staff and providing them with incentives to employ artificial intelligence applications in their administrative and educational work, introducing artificial intelligence applications into curricula and courses and paying attention to them, and providing specialized teaching staff in this field.

An empirical study of AI techniques in mobile applications

The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. Despite existing research exploring how mobile apps exploit AI techniques, they have the following main limitations: (1) Most existing studies focus on DL-based apps, with limited research on ML-based apps. (2) Existing research typically focuses on investigating the apps and the technologies utilized in the apps, lacking user-level analysis. (3) The number of apps studied is limited, with only 1,000 to 2,000 ML/DL apps identified after filtering. To fill the gap, in this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real-world AI applications, focusing on three crucial perspectives: (1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; (2) Framework and model analysis, where we analyze AI framework usage and AI model protection; (3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.

Understanding Public Opinion towards ESG and Green Finance with the Use of Explainable Artificial Intelligence

This study leverages explainable artificial intelligence (XAI) techniques to analyze public sentiment towards Environmental, Social, and Governance (ESG) factors, climate change, and green finance. It does so by developing a novel multi-task learning framework combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning to extract nuanced insights from a large corpus of social media data. Our approach integrates state-of-the-art models, including the SenticNet API, for sentiment analysis and implements multiple XAI methods such as LIME, SHAP, and Permutation Importance to enhance interpretability. Results reveal predominantly positive sentiment towards environmental topics, with notable variations across ESG categories. The contrastive learning visualization demonstrates clear sentiment clustering while highlighting areas of uncertainty. This research contributes to the field by providing an interpretable, trustworthy AI system for ESG sentiment analysis, offering valuable insights for policymakers and business stakeholders navigating the complex landscape of sustainable finance and climate action. The methodology proposed in this paper advances the current state of AI in ESG and green finance in several ways. By combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning, our approach provides a more comprehensive understanding of public sentiment towards ESG factors than traditional methods. The integration of multiple XAI techniques (LIME, SHAP, and Permutation Importance) offers a transparent view of the subtlety of the model’s decision-making process, which is crucial for building trust in AI-driven ESG assessments. Our approach enables a more accurate representation of public opinion, essential for informed decision-making in sustainable finance. This paper paves the way for more transparent and explainable AI applications in critical domains like ESG.

The Impact of Artificial Intelligence on Project Management Efficiency

This research paper examines the influence of Artificial Intelligence (AI) on the efficiency of project management, concentrating on the ways in which AI tools and techniques improve different facets of project management. The study initiates with a comprehensive examination of project management and the increasing significance of AI in various domains, emphasizing the shortcomings of conventional methodologies and the capacity of AI to tackle these issues. The study seeks to evaluate the impact of AI on project processes, pinpoint effective AI tools, and investigate the advantages and obstacles associated with AI integration. A thorough examination of existing literature investigates AI tools, including machine learning, natural language processing, and predictive analytics, along with their applications in planning, scheduling, resource allocation, risk management, communication, and performance monitoring. Case studies and empirical evidence illustrate the beneficial effects of AI on project outcomes, while also highlighting existing gaps in the literature that warrant further investigation. The mixed-methods approach integrates qualitative and quantitative data sourced from surveys, interviews, and case studies, employing both thematic and statistical analysis. The findings indicate that AI plays a crucial role in enhancing project efficiency through the reduction of timelines, improvement of cost control, optimization of resources, and facilitation of proactive risk management, in addition to enhancing communication and collaboration. Nonetheless, factors such as initial investment, skill requirements, resistance to change, and ethical considerations are acknowledged. The discussion outlines actionable strategies for project managers, highlighting the importance of training, incremental integration, ensuring data quality, and promoting innovation. The study concludes by offering recommendations for future research regarding the long-term effects of AI, conducting cross-industry analyses, and creating AI tools specifically designed for project management. This approach aims to provide empirical evidence and strategic insights into the integration of AI for enhanced project outcomes.

VMCNN: Integrating Vedic Mathematics for Enhanced Convolutional Neural Network Performance in Image Classification Tasks

In the rapidly advancing field of deep learning, Convolutional Neural Networks (CNNs) have emerged as a cornerstone for various image classification tasks. However, the existing CNN architectures and optimization methods often grapple with trade-offs between computational efficiency, accuracy, and generalizability. This work addresses these limitations by integrating principles of Vedic Mathematics, an ancient Indian mathematical system known for its efficiency and simplicity, into CNN optimization. Traditional optimization techniques often fall short in balancing the computational load with the accuracy of the model, particularly in diverse and complex datasets. To overcome these challenges, we propose a novel model that incorporates specific Vedic Mathematical Sutras, namely Urdhva-Tiryakbhyam, Anurupyena, Nikhilam Navatashcaramam Dashatah, Shunyam Saamyasamuccaye, Paravartya Yojayet, Sankalana-vyavakalanabhyam, Ekadhikina Purvena, and Gunitasamuchyah, for optimizing internal operations of CNNs. These Sutras were meticulously selected for their potential to simplify computational processes, enhance parallel processing capabilities, and optimize training algorithms. The application of these sutras has led to a remarkable improvement in the performance of CNNs across various datasets, including ImageNet, CIFAR, ChestXRay8, and Architectural Heritage Datasets. The optimized CNN models demonstrated a significant enhancement in classification accuracy (9.5% increase), precision (8.3% increase), recall (8.5% increase), area under the curve (AUC) (4.9% increase), MAE (5.5% improvement), and reduction in delay (6.5% decrease) compared to existing methods. The integration of Vedic Mathematics into CNN optimization not only paves the way for more efficient and accurate image classification models but also opens new avenues for interdisciplinary research, blending ancient mathematical wisdom with contemporary artificial intelligence techniques. This advancement has profound implications for various applications, including medical imaging, autonomous vehicles, and heritage conservation, thereby contributing significantly to the field of AI and computational efficiency.

CANCEL: A feature engineering method for churn prediction in a privacy-preserving context

This paper proposes a solution for predicting churn with privacy preservation by using edge computing. With the increasing popularity of smartphones, users are becoming more demanding regarding mobile app usage. Installing and removing an app are frequent routines and the ease of uninstallation can facilitate churn, which is customer abandonment. Companies seek to minimize churn since the cost of acquiring new customers is much higher than retaining current ones. To predict possible abandonment, organizations are increasingly adopting artificial intelligence (AI) techniques. Nevertheless, customers are becoming more concerned about their data privacy. In this context, we propose a technique called CANCEL, which creates attributes based on users' temporal behavior, with edge computing to predict churn locally, without transmitting users' data. The paper presents the evaluation of CANCEL in comparison to baseline solutions, the development of a mobile app integrated with the proposed method and deployed as an edge computing solution.

Emotional dialogue generation model of electronic commerce intelligent customer service based on topic expansion

Current electronic commerce (e-commerce) customer service systems often rely on retrieval-based methods, resulting in impersonal and emotionally flat responses. These systems struggle to handle diverse expressions and emotional nuances in user queries, leading to poor user experience. In this paper, we applied artificial intelligence techniques, notably the Gated Recurrent Unit model, to generate emotionally resonant dialogues in e-commerce customer service systems. We integrate intent recognition, emotion recognition, and topic expansion for accurate intent identification and nuanced responses. The model is trained on a customer service dataset from Jing Dong, a leading Chinese e-commerce platform. Through extensive experiments, we conducted comparative analyses against benchmark model Gated Recurrent Unit neural network. The results demonstrate that our model significantly outperforms existing approaches, achieving fluency, rationality, and emotionality scores of 62%, 58% and 61% respectively. These findings indicate that our approach generates responses with greater consistency to user intent and richer emotional content. The significance of these results lies in their potential to enhance user experience in e-commerce customer service interactions by providing more personalized and emotionally resonant automated responses.

A generalizable and interpretable model for early warning of pest-induced crop diseases using environmental data

Pest infestations and resulting crop diseases threaten global food security. Traditional pest and disease monitoring methods are time-consuming and prone to delays, thus necessitating the development of effective prediction strategies that facilitate early and timely detection. In response to this challenge, this research proposes a generalizable and interpretable machine learning model to predict two major rice pests—Green Leafhopper and Yellow Stem Borer—and a No Pest class using environmental data collected from various regions in India. The dataset, comprising factors such as temperature, humidity, and rainfall, underwent rigorous preprocessing. Key features like temperature difference, humidity difference, and vapor pressure deficit were engineered to enhance the model’s performance. Twelve baseline models were trained and their performance was evaluated using F1 scores and AUC values due to the imbalanced nature of the dataset. Through statistical analysis of baseline models, Random Forest, Balanced Random Forest, XGBoost, and CatBoost models, were selected for hyperparameter tuning via Optuna. The tuned CatBoost model demonstrated superior performance, achieving AUC values of 0.99 for Green Leafhoppers, 0.98 for Yellow Stem Borers, 1.00 for No Pest class and an overall F1 score of 0.9414 with a mean AUC value of 0.9912 across all classes. Additionally, Explainable Artificial Intelligence techniques, particularly SHAP, were employed to interpret the model’s predictions, identifying the relative importance of environmental factors in pest occurrence. This interpretability aligns the model’s predictions with established agronomic knowledge, enhancing its practical utility for early pest and disease detection. The generalizability of the proposed model suggests it can be adapted to other crops and regions, offering a valuable tool for early warning systems in agriculture, promoting sustainable practices, and reducing crop losses due to pests and diseases.

How Can Generative Artificial Intelligence Techniques Facilitate Intelligent Research into Ancient Books?

How Can Generative Artificial Intelligence Techniques Facilitate Intelligent Research into Ancient Books?

A prediction model for improving virtual machine live migration performance in cloud computing using artificial intelligence techniques

Over recent years, virtualization has worked as the powerhouse of the data centers. To positively influence datacenter utilization, power consumption, and management, live migration presents a technique which must be employed. Live migration refers to the method whereby an online virtual machine or an application is moved from one physical server to another without shutting down the client or the program. However, a migration’s qualities, which define service quality, have to be evaluated prior to a migration. In this paper, we propose a novel approach using predictive machine learning and deep learning models to forecast key metrics such as total migration time, downtime, and total data transferred for two migration types: pre-copy migration and post-copy migration. It builds on methods from regression machine learning and deep learning, and hyperparameter optimization, and feature engineering to push past reported solutions. The novelty of this work is the development of the hybrid model that includes the positive aspects from both of the machine learning and the deep learning with an emphasis on the increased accuracy of the model’s predictions. Concretely, the proposed deep learning framework based on GRU provided outstanding performance with 99.6% in total migration time where the transferred data GRU achieved 99.9% Moreover, in Down time target GRU reached 98.3%. This outperforms the other machine learning model and prior studies to become the best model for live migration prediction.

Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles

The integration of IoT systems into automotive vehicles has raised concerns associated with intrusion detection within these systems. Vehicles equipped with a controller area network (CAN) control several systems within a vehicle where disruptions in function can lead to significant malfunctions, injuries, and even loss of life. Detecting disruption is a primary concern as vehicles move to higher degrees of autonomy and the possibility of self-driving is explored. Tackling cyber-security challenges within CAN is essential to improve vehicle and road safety. Standard differences between different manufacturers make the implementation of a discreet system difficult; therefore, data-driven techniques are needed to tackle the ever-evolving landscape of cyber security within the automotive field. This paper examines the possibility of using machine learning classifiers to identify cyber assaults in CAN systems. To achieve applicability, we cover two classifiers: extreme gradient boost and K-nearest neighbor algorithms. However, as their performance hinges on proper parameter selection, a modified metaheuristic optimizer is introduced as well to tackle parameter optimization. The proposed approach is tested on a publicly available dataset with the best-performing models exceeding 89% accuracy. Optimizer outcomes have undergone rigorous statistical analysis, and the best-performing models were subjected to analysis using explainable artificial intelligence techniques to determine feature impacts on the best-performing model.

Integration of big data analytics in the investigation of the relationship between acromegaly and cancer

ObjectiveTo evaluate the association between acromegaly and cancer and different types of cancer by using natural language processing systems and big data analytics. Material and methodsWe conducted an observational, retrospective study utilizing data from the electronic health records (EHRs) of Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. Information from the EHRs was extracted using artificial intelligence techniques and analyzed using Savana Manager 4.0 software. ResultsOut of a total of 708,047 registered patients (54.7% females), 544 patients (0.08%; 330 women, 60.7%; mean age at diagnosis 53.0±15.8 yr) were diagnosed with acromegaly. The incidence of cancer was higher in patients with acromegaly vs those without this condition (7.7% vs 3.9%, p<0.001; OR, 2.047, 95%CI, 1.493–2.804). Male acromegalic patients had a higher prevalence of cancer vs females (57.1% vs 42.9%, p=0.012). A significantly higher prevalence of colorectal cancer (2.9% vs 1.4%, p=0.006), bladder cancer (1.1% vs 0.3%, p=0.005), and lymphoma (1.1% vs 0.3%, p=0.009) was observed in patients with acromegaly vs those without the condition. Acromegalic men had significantly higher prevalence rates of colorectal cancer (4.7% vs 1.3%, p=0.001), bladder cancer (2.8% vs 0.4%, p<0.001), breast cancer (0.9% vs 0.2%, p=0.042), gastric cancer (0.9% vs 0.1%, p=0.011), lymphoma (1.4% vs 0.3%, p=0.037), and liver cancer (0.9% vs 0.1%, p=0.012) vs non-acromegalic men. On the other hand, acromegalic women showed a higher prevalence of thyroid cancer (1.2% vs 0.4%, p=0.043) vs non-acromegalic women. ConclusionOur study, based on artificial intelligence techniques and analysis of real-world data and information, revealed a significant association between acromegaly and cancer in our hospital population, mainly acromegalic men, with a higher frequency of colorectal cancer, bladder cancer and lymphoma in particular.

Unleashing the Potential of Artificial Intelligence (AI) Tools in Phytogeographical studies

Phytogeography, the study of the geographic distribution of plants, is important for understanding ecosystem dynamics, biodiversity, and ecological processes. Over the past few years, advances in technology, especially artificial intelligence (AI), have revolutionized various scientific fields, including ecology and environmental science. In recent years, AI techniques have been increasingly applied in phytogeography, providing new opportunities to increase our understanding of plant distribution patterns and improve conservation efforts. The study of the role of artificial intelligence in phytogeography focuses on how AI techniques such as machine learning, remote sensing, and spatial analysis are being used to analyse large-scale plant distribution data. By leveraging AI, researchers can gain valuable insights from vast and complex datasets, identify patterns and predict future changes in plant distributions with greater accuracy. Furthermore, AI-driven approaches have the potential to address important challenges in phytogeography, such as species distribution modelling, habitat mapping, and biodiversity conservation. By integrating AI with traditional ecological methods, more effective strategies can be developed to manage and conserve plant species and their habitats. AI-driven phytogeography research, provides an overview of recent progress, discusses potential applications of AI techniques in ecological studies, and the opportunities and challenges associated with the use of AI in understanding and conserving plant biodiversity. Ultimately, the integration of AI with phytogeography has the potential to revolutionize our understanding of plant distributions and inform more sustainable conservation practices in the face of global environmental change.

HcGAN: Harmonic conditional generative adversarial network for efficiently generating high-quality IHC images from H&E

Generating high quality histopathology images like immunohistochemistry (IHC) stained images is essential for precise diagnosis and the advancement of computer-aided diagnostic (CAD) systems. Producing IHC images in laboratory is quite expensive and time consuming. Recently, some attempts have been made based on artificial intelligence techniques (particularly, deep learning) to generate IHC images. Existing IHC stained image generation methods, still have a limited performance due to the complex structures and variations in cells shapes, potential nonspecific staining and variable antibody sensitivity. This paper proposes a novel technique known as harmonic conditional generative adversarial network (HcGAN) for generating high quality IHC-stained images. To generate the IHC images, the HcGAN model is fed with the widely available hematoxylin and eosin (H&E) images that contain cellular and morphological underlying structures of diverse cancer tissues. Such approach helps generate high quality IHC images mimicking the real ones that highlight the positive cells. The proposed HcGAN model is based generative adversarial learning with generator and discriminator networks. In HcGAN, harmonic convolution based on discrete cosine transform filter banks is employed in the generator and discriminator networks instead of the standard convolution in order to improve visual quality of the generated images and address the issue of overfitting. Our qualitative and quantitative results demonstrate that the proposed HcGAN achieved the highest performance over state-of-the-art methods using two publicly available datasets.

A Comprehensive Hybrid Approach for Predicting Oral Health Status Based on Different Factors Through New Artificial Intelligence Techniques

A Comprehensive Hybrid Approach for Predicting Oral Health Status Based on Different Factors Through New Artificial Intelligence Techniques

Performance Improvement of Photovoltaic Panels in Solar Systems using Artificial Intelligence Techniques

Performance Improvement of Photovoltaic Panels in Solar Systems using Artificial Intelligence Techniques