Research on Artificial Intelligence: Deep Learning to Identify Plant Species

Artificial Intelligence (AI) and its tools are being widely used worldwide. In the area of agriculture, AI is being widely studied and expanding. The use of AI in agriculture is being widely studied and expanding from pre-harvest to post-harvest. The increase in world population has triggered the need to increase food production. This need has triggered a search for solutions that promote increased food production and quality. One way to increase food production and quality is pest control. AI and its tools have proven to be a growing and rising solution in controlling and combating pests. This research focuses on reviewing and demonstrating the advances in combating and controlling pests using AI tools and images. It stands out: the classification of pests; insect identification; use and capture of Unmanned aerial vehicle (UAV) footage; using Deep Learning (DL) and Convolutional Neural Network (CNN). A search engine was applied to 5 databases. Cutting criteria were applied in 3 stages, and there were 71 papers at the end. The 71 went through 3 quality assessment questions, leaving 47 works for final analysis. This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions. Another tool in recent studies associated with CNN is the attention mechanism, improving pest identification results. Identification of insects through leaf images using CNN requires.

Cancer remains a major global health challenge, characterized by high incidence and mortality rates. Accurate early diagnosis and treatment are essential for improving patient survival. Medical imaging technologies, such as X‐ray, computed tomography (CT), positron emission tomography (PET), ultrasound (US), magnetic resonance imaging (MRI), and mammography, play a key role in identifying the location, size, shape, density, and structure of tumors, as well as assessing their spread. The diverse types and large volume of medical images place a heavy workload on radiologists, potentially leading to increased misdiagnosis rates and prolonged diagnostic periods. This situation underscores the growing need for computer‐aided diagnostic systems in clinical practice. Deep learning, a subfield of artificial intelligence (AI), demonstrates significant potential in analyzing and interpreting complex imaging data, enabling precise tumor characterization and improved diagnostic outcomes. This review examines the integration of AI techniques—such as machine learning, deep learning and convolutional neural networks in medical imaging, which have notably enhanced diagnostic accuracy, early detection rates, and workflow efficiency in cancer diagnostics. By applying automated feature extraction and analysis across various imaging modalities, AI systems assist in clinical decision‐making and reduce human oversight. However, challenges remain in areas like data quality, privacy concerns, and model generalizability, which constrain the broader application of AI. Future research should address these challenges to support more robust, reliable, and accessible AI‐driven imaging solutions for cancer diagnosis.

Chapter 4 - Deep Learning in Texture Analysis and Its Application to Tissue Image Classification

Classification is one of the most popular topics in image process. In this paper, it provides the specific process of convolutional neural network in deep learning. Here builds typical convolution neural network which parameters and the connection mode can be adjusted. In addition, we present our preliminary classification results. Through the experiments of a convolutional neural network on the Mixed National Institute of Standards and Technology database (MNIST), we compared with the classification results and analyzed in the experimental results with the parameters. The experimental results show that image classification effect is very good used by convolutional neural network.

The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Use of Artificial Intelligence and Machine Learning in Critical Care Ultrasound.

Major mineral discoveries have declined in recent decades, and the natural resource industry is in the process of adapting and incorporating novel technologies such as machine learning and artificial intelligence to help guide the next generation of exploration. One such development is an artificial intelligence architecture called VNet that uses deep learning and convolutional neural networks. This method is designed specifically for use with geoscience data and is suitable for a multitude of exploration applications. One such application is mineral prospectivity in which the machine is tasked with identifying the complex pattern between many layers of geoscience data and a particular commodity of interest, such as gold. The VNet algorithm is designed to recognize patterns at different spatial scales, which lends itself well to the mineral prospectivity problem of there often being local and regional trends that affect where mineralization occurs. We test this approach on an orogenic gold greenstone belt setting in the Canadian Arctic where the algorithm uses gold values from sparse drill holes for training purposes to predict gold mineralization elsewhere in the region. The prospectivity results highlight new target areas, and one such target was followed up with a direct-current induced polarization survey. A chargeability anomaly was discovered wherein the VNet had predicted gold mineralization, and subsequent drilling encountered a 6 g/t Au intercept within 10 m of drilling that averaged more than 1.0 g/t Au. Although most of the prospectivity targets generated from VNet were not drill tested, this first intercept helps validate the approach. We believe this method can help maximize the use of existing geoscience data for successful and efficient exploration programs in the future.

The research focus was motivated by the limited understanding of cognitive technologies and the growing gap between artificial intelligence (AI) and human intelligence. The research is a literature review, and its purpose is to simplify the meaning and processes behind cognitive technologies, notably, the fundamentals of machine learning (ML) and computer vision with the intention to briefly address the alleged threat of AI taking over the job market. The research is a review of peer-reviewed articles retrieved from comparative studies, systematic reviews, meta-analysis, service research, reports, conference proceedings, experimental studies, literature reviews, scientometric analyses, books, and multi-case studies, dating from the years of 2018 to 2024. This literature review defines machine learning (ML), artificial intelligence (AI), computer vision, and convolutional neural networks (CNNs). It also compares machine learning to traditional programming and reveals the types of learning in ML models’ training. ML and its correlation with AI are also discussed and details about theory of mind, self-aware AI, reactive machines, and limited memory AI are shared. The literature expounds computer vision, particularly convolutional neural network (CNN) and CNN layers. Recent cutting-edge applications of artificial intelligence including generative AI models and autonomous systems are also incorporated. Finally, the literature briefly addresses the alleged threat of AI taking over the job market. The findings of this literature review reveal that AI is becoming the new way of operating. The conclusion shows that AI models require significant computation to allow computers to learn autonomously. Thus, understanding mathematical models of data and perfecting the process of writing software could be the key to remaining employable as more jobs are expected to be shifted due to AI and tasks automation. Keywords: Cognitive technology, artificial intelligence, machine learning, computer vision, convolutional neural networks

This review explores the transformative role of artificial intelligence (AI), especially through deep learning (DL) and convolutional neural networks (CNNs), in the early detection of esophageal squamous cell carcinoma (ESCC). ESCC presents significant diagnostic challenges due to its aggressive nature and high mortality, often diagnosed at advanced stages. Traditional endoscopic methods, while essential, suffer from limitations like interobserver variability, especially for subtle early lesions. AI-based systems have demonstrated high sensitivity in identifying these early neoplasms, reducing diagnostic discrepancies and enhancing precision. Through a bibliographic review, the article highlights recent AI advancements, particularly in utilizing CNNs to identify early-stage ESCC. These tools have shown the potential to improve diagnostic accuracy even among less experienced endoscopists and provide critical support in clinical decision-making, including biopsy guidance and lesion mapping in chromoendoscopy and narrow-band imaging (NBI). Despite promising results, challenges persist in AI’s practical application, such as the need for extensive data for training, clinical validation, and standardization across different endoscopic equipment. The study underscores the need for large-scale multicenter studies and standardization to solidify AI’s role in ESCC diagnosis. It envisions a future where AI not only serves as a diagnostic support tool but becomes integral to endoscopic practices, requiring continued technological development, ethical considerations, and regulatory frameworks to maximize clinical benefit.

The recent advancement of Artificial Intelligence (AI) affords ambition to exploit this revolution in multiple fields. Computer-assisted teaching and learning creates a very important area of AI application. Consequently, this last will be able to revolutionize this field. In research conducted by our laboratory, we are interested to explore AI trends to teaching and learning technologies. As part of this, we aim to study learner’s behaviors in education and learning environment, thus we aim to analyze the student through the front camera, as a first step we intend to develop a model that classify face’s images based on deep learning and Convolutional Neural Networks (CNN) in particular. Model development of images classification can be realized based in several technologies, we have chosen for this study to use IBM solutions, which are provided on the cloud. This paper describes the training experiment and the model development based on two alternatives proposed by IBM where the goal is to generate the most precise model. It presents a comparative study between the two approaches and ends with result discussing and the choice of the accurate solution for deployment in our teaching and learning system.

Agriculture is the most critical part of the Indian economy so to prevent production loss, disease attacks must be predicted and treated early. In plants with differing climatic conditions, disease is very normal and natural which decreases the crop productivity. Study into the use of image processing techniques for plant disease identification has become a hot subject in order to address these issues. Deep learning (DL) is the golden age of machine learning (ML), and it is now helping to identify and classify plant diseases early. This review investigates and analyzed recent methods on Deep learning, Transfer learning and convolution neural network for crop disease detection. First, a look at the deep learning architectures, data sources and various image processing techniques that were used to process the imaging data. Many DL architectures have recently been adopted, along with visualization tools, which are critical for identifying signs and classifying plant diseases. We also go through some of the unsolved issues that must be tackled in order to create functional automated plant disease recognition systems that can be used in the field.

The concept of Artificial Intelligence (AI) has changed with time, and with the advent of Big Data, Machine Learning, Deep Learning, and Artificial Neural Networks, the fundamentals of AI and the way it is perceived in today’s life have undergone a sea-change. However, along with this, the perception of Data and Security has also come a long way. Security earlier meant being in a secure, safe, risk-free zone; it has now metamorphosed itself into a concept of perimeter-driven layers of security – which changes from person to person. Against this backdrop, some of the pertinent questions that face us today are: Does AI aim to replace Human Intelligence? Would AI overtake Human Intelligence? Can AI ‘learn’ on its own – thereby teaching itself new concepts and mechanisms – while on the fly? This article takes us through the various concepts of AI, its types and modes of growth and expected future development, leading to its impact on society, businesses, culture and people. Further, this study leads to impact points of AI development and progress on points like data and security and possible costs of security and whether they would be restricted to financial costs alone. Towards the end, the authors put forth thoughts to ponder on whether Big Data and AI are the answers to all questions that lie ahead; and how AI can play a role in the betterment of Data and Security available to global citizens of future societies; with a responsible impact on businesses, economies, people and culture.

BackgroundHuntington’s disease is one of the rare neurodegenerative diseases caused because of genetic mutation of the Huntingtin gene. The major hallmarks of the condition include motor impairment, cognitive decline, and psychiatric symptoms. With no cure and only symptomatic treatments available, early detection and personalized therapy are warranted for managing the disease effectively. Artificial Intelligence has emerged as a transformational tool in healthcare, revolutionizing many parts of medical practice and research, thus holding the potential in detecting, monitoring, and managing Huntington’s disease.Main body of abstractArtificial Intelligence’s role in Huntington’s disease includes a variety of applications like medical image analysis and predictive analytics. AI-driven algorithms are utilized to analyze brain imaging data in medical image analysis. Deep learning and convolutional neural networks (CNNs) aid in the detection of subtle brain changes and the identification of illness biomarkers, allowing for the early diagnosis of the disease. Additionally, the predictive analytics capabilities of AI are used to analyze disease development and forecast clinical outcomes. AI models can identify illness patterns, estimate the rate of functional decline, and assist doctors in making educated decisions about treatment methods and care planning by analyzing patient data.ConclusionsWith clinical practice and research integrated with Artificial Intelligence technologies, we can significantly improve the quality of life of individuals affected with Huntington’s disease. This integration holds the potential to develop effective personalized interventions. Nevertheless, collaborative efforts among doctors, researchers, and technology sound developers would be key to the successful implementation of AI in HD.Graphical

Inflammatory bowel disease (IBD) causes chronic inflammation of the colon and digestive tract, and it can be classified as Crohn's disease (CD) and Ulcerative colitis (UC). IBD is more prevalent in Europe and North America, however, since the beginning of the 21st century it has been increasing in South America, Asia, and Africa, leading to its consideration as a worldwide problem. Optical colonoscopy is one of the crucial tests in diagnosing and assessing the progression and prognosis of IBD, as it allows a real-time optical visualization of the colonic wall and ileum and allows for the collection of tissue samples. The accuracy of colonoscopy procedures depends on the expertise and ability of the endoscopists. Therefore, algorithms based on Deep Learning (DL) and Convolutional Neural Networks (CNN) for colonoscopy images and videos are growing in popularity, especially for the detection and classification of colorectal polyps. The performance of this system is dependent on the quality and quantity of the data used for training. There are several datasets publicly available for endoscopy images and videos, but most of them are solely specialized in polyps. The use of DL algorithms to detect IBD is still in its inception, most studies are based on assessing the severity of UC. As artificial intelligence (AI) grows in popularity there is a growing interest in the use of these algorithms for diagnosing and classifying the IBDs and managing their progression. To tackle this, more annotated colonoscopy images and videos will be required for the training of new and more reliable AI algorithms. This article discusses the current challenges in the early detection of IBD, focusing on the available AI algorithms, and databases, and the challenges ahead to improve the detection rate.