Field Of Artificial Intelligence Research Articles

Raman and Mid-Infrared Spectroscopy Coupled With Machine–Deep Learning for Adulterant Detection in Ground Turmeric

The intricate nature of the global food supply chain and the presence of regulations spanning multiple jurisdictions contribute to an increased likelihood of food adulteration. This underscores the need for effective monitoring methods to guarantee the safety and nutritional quality of our food. In this context, the application of infrared spectroscopy-based techniques emerges as an environmentally friendly, non-invasive, and waste-minimizing solution for authenticating food products. Infrared spectra serve as unique molecular fingerprints, offering a multidimensional representation of how chemical bonds in the material interact with infrared light. Chemometrics, which are primarily linear-based models, play a crucial role in extracting essential information from spectral data, enabling dimensionality reduction, classification, and predictive analysis. Recent progress in the field of big data science and artificial intelligence has brought forth machine learning and deep learning algorithms explicitly designed to uncover features from complex multidimensional data, encompassing both linear and nonlinear relationships. These advancements have the potential to enhance the detection of adulterants in food products. This study assesses the accuracy of various shallow machine learning models and a deep learning model based on a one-dimensional convolutional neural network (1D CNN). The evaluation is conducted using Raman and infrared spectral data obtained from ground turmeric samples that were deliberately adulterated with five distinct substances. The study highlights the improved classification accuracy achieved through the implementation of the 1D CNN model.

The Role of Artificial Intelligence in the Diagnosis and Treatment of Ulcerative Colitis

Background and objectives: This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. Method: A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Results: Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. Conclusions: When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

OPTIMIZATION OF CNN + MOBILENETV3 FOR INSECT IDENTIFICATION: TOWARD HIGH ACCURACY

Developments in the field of artificial intelligence and deep learning, particularly Convolutional Neural Networks (CNN) techniques, have expanded the research potential and applications in ecology, including efficient and accurate insect classification. However, there are challenges in achieving high levels of accuracy with similar computational efficiency. In response, the efficient MobileNetV3 architecture was investigated to improve the insect pest classification process. Through an analytical descriptive quantitative approach and insect datasets from Kaggle, this study tested the effectiveness of CNN models optimized with MobileNetV3. The results indicated that the optimized model achieved classification accuracy of up to 90%, with consistent performance between training and validation data and significant loss reduction. With high precision and processing efficiency, this discovery makes a substantial contribution to deep learning applications in the field of intelligent agriculture, promising methodological improvements for other classification problems. Despite offering a promising solution, this study recognizes the limitation in dataset diversity and suggests further exploration with more varied datasets to strengthen the model's application in actual agricultural practices.

The white paper on artificial intelligence as a source for the formation of European Union legislation in the field of artificial intelligence

The white paper on artificial intelligence as a source for the formation of European Union legislation in the field of artificial intelligence

Predicting blood transfusion following traumatic injury using machine learning models: A systematic review and narrative synthesis.

Haemorrhage is a leading cause of preventable death in trauma. Accurately predicting a patient's blood transfusion requirement is essential but can be difficult. Machine learning (ML) is a field of artificial intelligence that is emerging within medicine for accurate prediction modelling. This systematic review aimed to identify and evaluate all ML models that predict blood transfusion in trauma. This systematic review was registered on The International Prospective register of Systematic Reviews (CRD4202237110). MEDLINE, Embase and the Cochrane Central Register of Controlled Trials were systematically searched. Publications reporting a ML model that predicted blood transfusion in injured adult patients were included. Data extraction and risk of bias assessment was performed using validated frameworks. Data was synthesised narratively due to significant heterogeneity. Twenty-five ML models for blood transfusion prediction in trauma were identified. Models incorporated diverse predictors and varied ML methodologies. Predictive performance was variable but eight models achieved excellent discrimination (AUROC >0.9) and nine models achieved good discrimination (AUROC >0.8) in internal validation. Only two models reported measures of calibration. Four models have been externally validated in prospective cohorts: the Bleeding Risk Index, Compensatory Reserve Index, the Marsden model and the Mina model. All studies were considered at high risk of bias often due to retrospective datasets, small sample size and lack of external validation. This review identified twenty-five ML models developed to predict blood transfusion requirement after injury. Seventeen ML models demonstrated good to excellent performance in-silico but only four models were externally validated. To date ML models demonstrate the potential for early and individualised blood transfusion prediction but further research is critically required to narrow the gap between ML model development and clinical application. Systematic Review Without Meta-Analysis, Level IV.

Developing Time Series Forecasting Models with Generative Large Language Models

Nowadays, Generative Large Language Models (GLLMs) have made a significant impact in the field of Artificial Intelligence (AI). One of the domains extensively explored for these models is their ability as generators of functional source code for software projects. Nevertheless, their potential as assistants to write the code needed to generate and model Machine Learning (ML) or Deep Learning (DL) architectures has not been fully explored to date. For this reason, this work focuses on evaluating the extent to which different tools based on GLLMs, such as ChatGPT or Copilot, are able to correctly define the source code necessary to generate viable predictive models. The use case defined is the forecasting of a time series that reports the indoor temperature of a greenhouse. The results indicate that, while it is possible to achieve good accuracy metrics with simple predictive models generated by GLLMs, the composition of predictive models with complex architectures using GLLMs is still far from improving the accuracy of predictive models generated by human data scientists.

Adding External Artificial Intelligence (AI) into Internal Firm-Wide Smart Dynamic Warehousing Solutions

This study advances knowledge in the AI field. It provides deep insight into current industry generative AI inclusion systems. It shows both literature and practical leading industry operations can link, overlap, and complement each other when it comes to AI and understanding its complexities. It shows how to structurally model and link AI inclusions towards delivering a suitable sustainability positioning. It shows approaches to integrate external AI contributions from one firm into another firm’s intelligences developments. It shows how to track, and maybe benchmark, the progress of such AI inclusions from either an external or an integrated internal software developer perspective. It shows how to understand and create a more sustainable, AI-integrated business positioning. This study considers firm artificial intelligence (AI) and the inclusion of additional external software developer engineering as another AI related pathway to future firm or industry advancement. Several substantive industrial warehousing throughput areas are discussed. Amazon’s ‘smart dynamic warehousing’ necessitates both digital and generative ongoing AI system prowess. Amazon and other substantive, digitally focused industry warehousing operations also likely benefit from astute ongoing external software developer firm inclusions. This study causally, and stagewise, models significant global software development firms involved in generative AI systems developments—specifically ones designed to beneficially enhance both warehouse operational productivity and its ongoing sustainability. A structural equation model (SEM) approach offers unique perspectives through which substantive firms already using AI can now model and track/benchmark the relevance of their prospective or existing external software developer firms, and so create rapid internal ‘net-AI’ competencies incorporations and AI capabilities developments through to sustainable operational and performance outcomes solutions.

Post-hoc vs ante-hoc explanations: xAI design guidelines for data scientists

The growing field of explainable Artificial Intelligence (xAI) has given rise to a multitude of techniques and methodologies, yet this expansion has created a growing gap between existing xAI approaches and their practical application. This poses a considerable obstacle for data scientists striving to identify the optimal xAI technique for their needs. To address this problem, our study presents a customized decision support framework to aid data scientists in choosing a suitable xAI approach for their use-case. Drawing from a literature survey and insights from interviews with five experienced data scientists, we introduce a decision tree based on the trade-offs inherent in various xAI approaches, guiding the selection between six commonly used xAI tools. Our work critically examines six prevalent ante-hoc and post-hoc xAI methods, assessing their applicability in real-world contexts through expert interviews. The aim is to equip data scientists and policymakers with the capacity to select xAI methods that not only demystify the decision-making process, but also enrich user understanding and interpretation, ultimately advancing the application of xAI in practical settings.

FedSKF: Selective Knowledge Fusion via Optimal Transport in Federated Class Incremental Learning

Federated learning has been a hot topic in the field of artificial intelligence in recent years due to its distributed nature and emphasis on privacy protection. To better align with real-world scenarios, federated class incremental learning (FCIL) has emerged as a new research trend, but it faces challenges such as heterogeneous data, catastrophic forgetting, and inter-client interference. However, most existing methods enhance model performance at the expense of privacy, such as uploading prototypes or samples, which violates the basic principle of only transmitting models in federated learning. This paper presents a novel selective knowledge fusion (FedSKF) model to address data heterogeneity and inter-client interference without sacrificing any privacy. Specifically, this paper introduces a PIT (projection in turn) module on the server side to indirectly recover client data distribution information through optimal transport. Subsequently, to reduce inter-client interference, knowledge of the global model is selectively absorbed via knowledge distillation and an incomplete synchronization classifier at the client side, namely an SKS (selective knowledge synchronization) module. Furthermore, to mitigate global catastrophic forgetting, a global forgetting loss is proposed to distill knowledge from the old global model. Our framework can easily integrate various CIL methods, allowing it to adapt to application scenarios with varying privacy requirements. We conducted extensive experiments on CIFAR100 and Tiny-ImageNet datasets, and the performance of our method surpasses existing works.

THE OPPORTUNITIES, LIMITATIONS, AND CHALLENGES IN USING MACHINE LEARNING TECHNOLOGIES FOR HUMANITARIAN WORK AND DEVELOPMENT

Novel digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development and can contribute to monitoring and mitigating humanitarian problems. The potential of applying novel technologies to solving some of humanity’s most pressing issues has garnered interest outside the traditional disciplines studying and working on international development. Today, scientific communities in fields like Computational Social Science, Network Science, Complex Systems, Human Computer Interaction, Machine Learning, and the broader AI field are increasingly starting to pay attention to these pressing issues. However, are sophisticated data driven tools ready to be used for solving real-world problems with imperfect data and of staggering complexity? We outline the current state-of-the-art and identify barriers, which need to be surmounted in order for data-driven technologies to become useful in humanitarian and development contexts. We argue that, without organized and purposeful efforts, these new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.

On the fusion of soft-decision-trees and concept-based models

In the field of eXplainable Artificial Intelligence (XAI), the generation of interpretable models that are able to match the performance of state-of-the-art deep learning methods is one of the main challenges. In this work, we present a novel interpretable model for image classification that combines the power of deep convolutional networks and the transparency of decision trees. We explore different training techniques where convolutional networks and decision trees can be trained together using gradient-based optimization methods as usually done in deep learning environments. All of this results in a transparent model in which a soft decision tree makes the final classification based on human-understandable concepts that are extracted by a convolutional neural network. We tested the proposed solution on two challenge image classification datasets and compared them with the state-of-the-art approaches, achieving competitive results.

Visible-light photoelectric performance and bending stability of flexible LaCoO3/Mica thin films

New energy sources such as solar energy have been developed and utilized due to the shortage of non-renewable energy, and photoelectric materials have attracted more attention, accordingly. Moreover, flexible optoelectronic devices are crucial in the fields of wearable artificial intelligence, portable energy, and smart medical care. At present, photoelectric devices based on traditional perovskite cobalt oxide rigid films have problems such as inability to bend and deformation, which limits their application environment. Here, natural mica with atomically flat surface, high thermal stability, high transparency and good mechanical flexibility was selected as the substrate for preparing a series of LaCoO3 flexible photoelectric films. These films exhibit excellent photoelectric properties and flexible properties. More importantly, the flexible film still has fast response speed and reproducible light response after 500 bending cycles. It reveals that all-inorganic perovskite LaCoO3 is a promising candidate for designing high-performance, flexible and wearable optoelectronic devices.

Freestanding 3D graphite foam prepared by compressed growth template for superior electro-photo thermal performance

As a promising three-dimensional (3D) graphene architecture, graphene foam has excellent structural, electrical and thermal properties. However, due to the growth template limitation of commercial nickel/copper foam, graphene foam prepared by chemical vapor deposition (CVD) has a fixed 3D framework architecture, which limits its performance and application scenarios. In this paper, independent 3D graphite foam (GF) with different thickness and longitudinal compressibility was prepared by CVD method, and it was confirmed that mechanical compression of nickel foam template before graphite film deposition had a positive effect on the electrothermal, photothermal and mechanical properties of GF. At a low driving voltage of 3 V, the heating temperature of compressed GF with a low density of 8.9 mg/cm3 and a high porosity of 99.59 % can reach nearly 600 °C, which is nearly 250 °C higher than that of uncompressed GF. The excellent electrothermal performance of compressed GF is mainly attributed to the high crystallinity and thicker thickness (150–350 nm) of the graphite layers that make up the 3D framework, thus resulting in higher electrical conductivity. Under 1 and 2 sun illumination, the temperature of compressed GF can reach 83 °C and 109 °C respectively, which is 4–7 °C higher than that of the original GF. Compressed GF has better electrothermal, photothermal and radiant heating properties than traditional graphene/graphite foam and 2D graphene/graphite film. It not only has great application potential in high-efficient and energy-saving high-temperature electric heaters and photothermal converters, but also can be used as conductive supports or fillers with better performance, and has broad application prospects in flexible electronics, household appliances, artificial intelligence, medical care and other fields.

Counting the Cells Based on Their Size Using Fuzzy Logic and the Image J System

     Medical diagnosis is the process of identifying the presence of a specific disease. Medical images play an important and major role in helping the doctor and specialist in the process of diagnosing the patient's condition. As a result of the technological development taking place in the fields of artificial intelligence, which processes data quickly and efficiently by responding to various conditions and situations, providing a great deal of flexibility and accuracy,  it is widely used in helping to diagnose diseases. Fuzzy logic was used to find the number of cells in medical images, where the increase or decrease in the number of cells beyond the normal limit indicates the presence of a specific disorder or disease. This paper aims to use fuzzy logic and the Image J system to determine the appropriate mechanism and method for counting cells according to their size in the medical image, where a set of basic steps are implemented on the image by using the Image J system. This includes converting to mask, outlining, filling holes, watershedding, setting measurements, and analyzing particles. After that, using fuzzy logic to determine the best appropriate methods for counting cells in medical images based on size, enter a single random value for size, and cells smaller than that value are ignored. The area and perimeter of cells are accounted for.

中国面部识别技术发展考察

Recently, the rapid development of facial recognition technology, which is an important field of artificial intelligence, has attracted more and more attention worldwide due to the integration of big data and artificial intelligence technology. This technology has been widely applied in various fields and continues to develop with many advantages and convenience. However, the concerns and potential risks associated with the widespread use of facial recognition technology are also significant. Therefore, this study aims to comprehensively and critically review the development and risks of this technology, focusing on the current situation in China. It is intended to offer guidance and support for the ethical advancement of contemporary facial recognition technology as well as to promote responsible oversight and regulation. In this paper, first, the development process of facial recognition technology such as the technological origin and core technological innovation in the world and China is reviewed. Next, it also examines the application and convergence of big data and artificial intelligence algorithms in facial recognition technology. Furthermore, it analyzes the latest development of facial recognition technology in China, including the driving role of the government and companies and trends in major application fields. Simultaneously, this discourse delves into the extensive risks and challenges associated with facial recognition technology, such as privacy, data security, potential misuse, ethics, and legal regulations. To put it another way, the practical risks of facial recognition technology are focused on its specific applications in the fields of public safety, commerce, and social management, highlighting the resulting technical vulnerabilities, privacy threats, and abuse of social monitoring. Finally, this study summarizes the development status of facial recognition technology, points out the major problems and risks facing it once again, and presents appropriate countermeasures and policy recommendations.

Image Caption Generator using Deep Learning

Image Caption Generation has always been a study of great interest to the researchers in the Artificial Intelligence department. Being able to program a machine to accurately describe an image or an environment like an average human has major applications in the field of robotic vision, business and many more. Automatic caption generation with attention mechanisms aims at generating more descriptive captions containing coarse to fine semantic contents in the image. This has been a challenging task in the field of artificial intelligence. In this paper, we present different image caption generating models based on deep neural networks, focusing on the various CNN techniques and analyzing their influence on the sentence generation. We have also generated captions for sample images and compared the different feature extraction and encoder models to analyse which model gives better accuracy and generates the desired results

Predictive Analytics and Machine Learning in Disease Diagnosis: A Review of Recent Advances

Artificial intelligence (AI) refers to the utilisation of computer information to exhibit intelligent behaviour with minimal human intervention, whereas machine learning (ML) is seen as a subset of AI methodologies. Typically, this form of intelligence is widely recognised to have originated with the advent of robotics. Given the slow progression of diseases, it is crucial to make early predictions and administer appropriate medication. Hence, it is imperative to present a decision model that may aid in the diagnosis of chronic illnesses and forecast future patient prognoses. The primary objective of this study is to investigate the application of Predictive Analytics and ML in the field of Disease Diagnosis. The study will focus on reviewing the latest advancements in this area. This work specifically emphasises the significance of ML prediction models in disease diagnosis within the AI field, amidst other approaches available. The study utilises a qualitative research methodology. ML has been prominent in the medical field as it offers methods for analysing disease-related data, as indicated by this study. ML algorithms are crucial in attaining early disease detection. Another crucial finding in this research is that the accuracy and performance of the model can be enhanced by employing an alternative technique to generate a single ensemble model.

Double-Compressed Artificial Neural Network for Efficient Model Storage in Customer Churn Prediction

In the rapidly evolving field of Artificial Intelligence (AI), efficiently storing and managing AI models is crucial, particularly as their complexity and size increase. This paper explores the strategic importance of AI model storage, focusing on performance, cost-efficiency, and scalability within the realm of customer churn prediction, utilizing model compression technologies. Deep learning networks, integral to AI models, have become increasingly large, necessitating millions of parameters. These parameters make the models computationally expensive and voluminous in storage requirements. Addressing these issues, the paper discusses the application of model compression techniques—specifically pruning and quantization—to mitigate the storage and computational challenges. The experimental results demonstrated the effectiveness of the proposed method. These techniques reduce the physical footprint of AI models and enhance their processing efficiency, making them suitable for deployment on resource-constrained devices. Using these models in customer churn prediction in telecommunications illustrates their potential to improve service delivery and decision-making processes. By compressing models, telecom companies can better manage and analyze large datasets, enabling more effective customer retention strategies and maintaining a competitive edge in a dynamic market.

Foreword

As technology advances, there are growing arguments about artificial intelligence, which is still controversial, with both welcome and pessimistic views regarding its role in the future of human life. What was revealed about the development of artificial intelligence is considered very simple in the eyes of specialists and observers, especially in areas that have not been explored, and this constitutes a source of fear for many. The vast leaps witnessed in recent years in the field of artificial intelligence have become a source of anxiety for most people, especially those working in institutions and industrial facilities, even though this anxiety stems from losing their jobs and replacing them with robots. This concern came from statements that the next two decades will witness the introduction of vast quantities of robots to the market. OpenAI was initially established in 2015 by Sam Altman, Elon Musk, and others to advance digital intelligence. On November 30, 2022, OpenAI released ChatGPT (Generative Pre-trained Transformer. ChatGPT uses a large language model to provide users with quick and reliable answers and give enough information about most of the topics. Furthermore, ChatGPT allows users to have more in-depth conversations. The concern about the impact role of ChatGPT on educational and social institutions has led to some reactions from academic institutions, and school districts were hasty and took action to block artificial intelligence platforms from schools, which they quickly retracted in the aftermath. On the other hand, some scholarly academic journals, including our Arab World English Journal, have announced guidelines for article authors. Authors must disclose the use of generative AI and AI-assisted technologies in the writing process. While we are editing the final draft of this volume (April 2024), OpenAI CEO Sam Altman announced in a statement that OpenAI has opened its first office in Asia, Japan, and is providing a GPT-4 custom for the Japanese language; it is three times faster its predecessor. OpenAI aims to expand ChatGPT’s global presence. After releasing ChatGPT, which is backed by Microsoft, OpenAI has emerged as a leading company in the history of computing. In this special issue, we addressed the impact of ChatGPT and AI-assisted technologies, an essential topic that will constitute the main features of education and publications in the twenty-first century. The issue contains 21 articles from eight countries (China, Kuwait, Iraq, Oman, Malaysia, Morocco, Saudi Arabia, and Ukraine). It covers a broad spectrum of related topics: Academic writing, the pedagogical potential of infinigoChatIC, Grammar Teaching and Learning in EFL Classes, English Language Proficiency in EFL Classrooms, Grammar Teaching and Learning in EFL Classes, EFL Essay Writing, Distance Learning Environment, Evaluation of the Translation, Improve the Research Writing Process, Role of Artificial Intelligence in Education, Language Learners’ Critical Thinking, Naruto Fan Fiction Writing, and the Impact of ChatGPT on Future Academics. We seize this opportunity to extend our heartfelt thanks to the authors who have shared their research with our Arab World English journal. We anticipate that the articles in this volume will attract a diverse readership and deepen our understanding of ChatGPT’s role in learning and teaching and its associated challenges. We also hope these papers will provide a thought-provoking perspective for faculty members and students to consider ChatGPT’s impact on writing and related assignments.

Two-Stage Probe-Based Search Optimization Algorithm for the Traveling Salesman Problems

As a classical combinatorial optimization problem, the traveling salesman problem (TSP) has been extensively investigated in the fields of Artificial Intelligence and Operations Research. Due to being NP-complete, it is still rather challenging to solve both effectively and efficiently. Because of its high theoretical significance and wide practical applications, great effort has been undertaken to solve it from the point of view of intelligent search. In this paper, we propose a two-stage probe-based search optimization algorithm for solving both symmetric and asymmetric TSPs through the stages of route development and a self-escape mechanism. Specifically, in the first stage, a reasonable proportion threshold filter of potential basis probes or partial routes is set up at each step during the complete route development process. In this way, the poor basis probes with longer routes are filtered out automatically. Moreover, four local augmentation operators are further employed to improve these potential basis probes at each step. In the second stage, a self-escape mechanism or operation is further implemented on the obtained complete routes to prevent the probe-based search from being trapped in a locally optimal solution. The experimental results on a collection of benchmark TSP datasets demonstrate that our proposed algorithm is more effective than other state-of-the-art optimization algorithms. In fact, it achieves the best-known TSP benchmark solutions in many datasets, while, in certain cases, it even generates solutions that are better than the best-known TSP benchmark solutions.