Understanding Semantic Analysis NLP

The 2.5D-based method uses 2D convolutional kernels and takes in multiple slices as input. The slices can either be a stack of adjacent slices using interslice information [167, 168], or slices along three orthogonal directions (axial, coronal, and sagittal) [67, 68, 148, 169], which is shown in Fig. Zhou et al. [170] segmented each 2D slice using FCN by sampling a 3D CT case on three orthogonally oriented slices and then assembled the segmented output (i.e., 2D slice results) back into 3D.

Ji et al.[232] introduced a novel CSS framework for the continual segmentation of a total of 143 whole-body organs from four partially labeled datasets. Utilizing a trained and frozen General Encoder alongside continually added and architecturally optimized decoders, this model prevents catastrophic forgetting while accurately segmenting new organs. Some studies only used 2D images to avoid memory and computation problems, but they did not fully exploit the potential of 3D image information. Although 2.5D methods can make better use of multiple views, their ability to extract spatial contextual information is still limited. Pure 3D networks have a high parameter and computational burden, which limits their depth and performance.

Obtaining simultaneous annotations for multiple organs on the same medical image poses a significant challenge in image segmentation. Existing datasets, such as LiTS [213], KiTS (p19) [214], and pancreas datasets [215], typically provide annotations for a single organ. How to utilize these partially annotated datasets to achieve a multi-organ segmentation model has arisen increasing interest. To obtain high-quality datasets for multi-organ segmentation, numerous research teams have collaborated with medical organizations.

Understanding these terms is crucial to NLP programs that seek to draw insight from textual information, extract information and provide data. It is also essential for automated processing and question-answer systems like chatbots. These features make AI/BI a significant step towards true self-service BI, significantly broadening the range of analytics that everyday users can perform. Additionally, AI/BI integration with Databricks’ Data Intelligence Platform ensures unified governance, lineage tracking, secure sharing, and top-tier performance at any data scale. As researchers attempt to build more advanced forms of artificial intelligence, they must also begin to formulate more nuanced understandings of what intelligence or even consciousness precisely mean.

This proficiency goes beyond comprehension; it drives data analysis, guides customer feedback strategies, shapes customer-centric approaches, automates processes, and deciphers unstructured text. Semantics gives a deeper understanding of the text in sources such as a blog post, comments in a forum, documents, group chat applications, chatbots, etc. With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text. MonkeyLearn makes it simple for you to get started with automated semantic analysis tools. Using a low-code UI, you can create models to automatically analyze your text for semantics and perform techniques like sentiment and topic analysis, or keyword extraction, in just a few simple steps.

Ethical AI: Towards responsible and fair technology

For example, Chen et al. [129] integrated U-Net with long short-term memory (LSTM) for chest organ segmentation, and the DSC values of all five organs were above 0.8. Chakravarty et al. [130] introduced a hybrid architecture that leveraged the strengths of both CNNs and recurrent neural networks (RNNs) to segment the optic disc, nucleus, and left atrium. The hybrid methods effectively merge and harness the advantages of both architectures for accurate segmentation of small and medium-sized organs, which is a crucial research direction for the future. While transformer-based methods can capture long-range dependencies and outperform CNNs in several tasks, they may struggle with the detailed localization of low-resolution features, resulting in coarse segmentation results. This concern is particularly significant in the context of multi-organ segmentation, especially when it involves the segmentation of small-sized organs [117, 118].

Chen et al. [165] developed a multi-view training method with a majority voting strategy. Wang et al. [171] used a statistical fusion method to combine segmentation results from three views. Liang et al. [148] performed context-based iterative refinement training on each of the three views and aggregated all the predicted probability maps to obtain final segmentation results. These methods have shown improved segmentation results compared to the three separate views. The coarse-to-fine-based methods first input the original image and its corresponding labels into the first network to obtain probability map.

To mitigate the impact of pseudo labels, they assessed the reliability of pseudo labels through outlier detection in latent space and excluded the least reliable pseudo labels in each self-training iteration. It is widely recognized that the choice of loss function is of vital importance in determining the segmentation accuracy. In multi-organ segmentation tasks, choosing an appropriate loss function can address the class imbalance issue and improve the segmentation accuracy of small organs.

Its applications have multiplied, enabling organizations to enhance customer service, improve company performance, and optimize SEO strategies. In 2022, semantic analysis continues to thrive, driving significant advancements in various domains. Tang et al. [172] proposed a novel method which combines the strengths of 2D and 3D models.

For example, Lee et al. [247] developed a method that employed a discriminator module, which incorporated human-in-the-loop quality assurance (QA) to supervise the learning of unlabelled data. Raju et al. [248] proposed an effective semi-supervised multi-organ segmentation method, CHASe, for liver and lesion segmentation. CHASe leverages co-training and hetero-modality learning within a co-heterogeneous training framework. This framework can be trained on a small single-phase dataset and can be adapted for label-free multi-center and multi-phase clinical data. Zhou et al. [175] proposed a Prior-aware Neural Network (PaNN) that guided the training process based on partially annotated datasets by utilizing prior statistics obtained from a fully labeled dataset. Fang and Yan [233] and Shi et al. [234] trained uniform models on partially labeled datasets by designing new networks and proposing specific loss functions.

It organizes and interprets various aspects of the research topic to reveal meaningful insights. Natural language processing helps computers understand human language in all its forms, from handwritten notes to typed snippets of text and spoken instructions. Start exploring the field in greater depth by taking a cost-effective, flexible specialization on Coursera. Although natural language processing might sound like something out of a science fiction novel, the truth is that people already interact with countless NLP-powered devices and services every day. You can foun additiona information about ai customer service and artificial intelligence and NLP. Improved conversion rates, better knowledge of the market… The virtues of the semantic analysis of qualitative studies are numerous. Used wisely, it makes it possible to segment customers into several targets and to understand their psychology.

The increasing accessibility of generative AI tools has made it an in-demand skill for many tech roles. If you’re interested in learning to work with AI for your career, you might consider a free, beginner-friendly online program like Google’s Introduction to Generative AI. As products have evolved, pushing the boundaries of performance has become increasingly challenging. Industrial companies that can rapidly innovate and bring higher-performing products to market faster are much more likely to gain market

share and win in their market segments. As a result, systems are redesigned with each new project but overlook opportunities to reuse parts, driving up costs and increasing supply chain complexity.

Introduction to Semantic Analysis

Then, four networks were trained to distinguish each target organ from the background in separate refinements. Zhang et al. [133] developed a new cascaded network model with Block Level Skip Connections (BLSC) between two networks, allowing the second network to benefit from the features learned by each block in the first network. By leveraging these skip connections, the second network can converge more quickly and effectively. Moreover, by enabling gradients to be backpropagated from the loss layer to the entire network, the RSTN facilitates joint optimization of the two stages. Ma et al. [154] presented a comprehensive coarse-to-fine segmentation model for automatic segmentation of multiple OARs in head and neck CT images. This model used a predetermined threshold to classify the initial results of the coarse stage into large and small OARs, and then designed different modules to refine the segmentation results.

In DeepLearning.AI’s AI For Good Specialization, meanwhile, you’ll build skills combining human and machine intelligence for positive real-world impact using AI in a beginner-friendly, three-course program. When researching artificial intelligence, you might have come across the terms “strong” and “weak” AI. Though these terms might seem confusing, you likely already have a sense of what they mean. Learn what artificial intelligence actually is, how it’s used today, and what it may do in the future. Since the complexity of products and operating conditions has exploded, engineers are struggling to identify root causes and track solutions.

This application helps organizations monitor and analyze customer sentiment towards products, services, and brand reputation. By understanding customer sentiment, businesses can proactively address concerns, improve offerings, and enhance customer experiences. Understanding user intent and optimizing search engine optimization (SEO) strategies is crucial for businesses to drive organic traffic to their websites. Semantic analysis can provide valuable insights into user searches by analyzing the context and meaning behind keywords and phrases. By understanding the intent behind user queries, businesses can create optimized content that aligns with user expectations and improves search engine rankings. This targeted approach to SEO can significantly boost website visibility, organic traffic, and conversion rates.

It can leverage the inherent self-attentiveness of the network and is especially useful for multi-organ segmentation tasks [101, 187]. There are several kinds of attention mechanisms, such as channel attention, spatial attention, and self-attention, which can be used to selectively emphasize the most informative features. While GAN can enhance accuracy with its adversarial losses, training a GAN network is challenging and time-consuming since the generator must achieve Nash equilibrium with the discriminator [99]. Natural language processing (NLP) is a form of artificial intelligence (AI) that allows computers to understand human language, whether it be written, spoken, or even scribbled.

Two researchers independently reviewed these articles to determine their eligibility. Among them, 67 articles did not meet the inclusion criteria based on the title and abstract, and 45 complete manuscripts were evaluated separately. The early segmentation process relies heavily on manual labeling by physicians, which is labour-intense and time-consuming. For example, mapping 24 OARs in the head and neck region takes over 3 h, resulting in potential long waits for patients, especially in cases of patient overload [6]. Due to a shortage of experienced doctors, the mapping process becomes even more time-consuming, potentially delaying the patient’s treatment process and missing the optimal treatment window [7]. Furthermore, the labeling results obtained by different physicians or hospitals exhibit significant variability [8,9,10,11].

As a result, companies are highly dependent on

pattern recognition by experienced engineers and spend a lot of time trying to re-create issues in lab environments in an attempt to get to the root cause. Immerse yourself in the data by reading and re-reading it to become deeply familiar with its content and context. Collect qualitative data through various methods such as interviews, focus groups, observations, or document analysis. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success. So far, we’ve looked at types of analysis that examine and draw conclusions about the past.

By analyzing the dictionary definitions and relationships between words, computers can better understand the context in which words are used. Semantic analysis has revolutionized market research by enabling organizations to analyze and extract valuable insights from vast amounts of unstructured data. By analyzing customer reviews, social media conversations, and online forums, businesses can identify emerging market trends, monitor competitor activities, and gain a deeper understanding of customer preferences. These insights help organizations develop targeted marketing strategies, identify new business opportunities, and stay competitive in dynamic market environments.

Zhao et al. [153] proposed a flexible knowledge-assisted framework that synergistically integrated deep learning and traditional techniques to improve segmentation accuracy in the second stage. Dong et al. [102] employed a GAN framework with a set of U-Nets as the generator and a set of FCNs as the discriminator to segment the left lung, right lung, spinal cord, esophagus and heart from chest CT images. The results showed that the adversarial networks enhanced the segmentation performance of most organs, with average DSC values of 0.970, Chat GPT 0.970, 0.900, 0.750, and 0.870 for the above five organs. Tong et al. [100] proposed a Shape-Constraint GAN (SC-GAN) for automatic segmentation of head and neck OARs from CT and low-field MR images. It used DenseNet [108], a deep supervised fully convolutional network, to segment organs for prediction and uses a CNN as the discriminator network to correct the prediction errors. The results showed that combining GAN and DenseNet could further improve the segmentation performance of CNN by incorporating original shape constraints.

Semantic analysis helps businesses gain a deeper understanding of their customers by analyzing customer queries, feedback, and satisfaction surveys. By extracting context, emotions, and sentiments from customer interactions, businesses can identify patterns and trends that provide valuable insights into customer preferences, needs, and pain points. These insights can then be used to enhance products, services, and marketing strategies, ultimately improving customer satisfaction and loyalty. Driven by the analysis, tools emerge as pivotal assets in crafting customer-centric strategies and automating processes. Moreover, they don’t just parse text; they extract valuable information, discerning opposite meanings and extracting relationships between words.

Furthermore, FocusNet [105, 147] presented a novel neural network that effectively addresses the challenge of class imbalance in the segmentation of head and neck OARs. The small organs are first localized using the organ localization network, and then high-resolution features of small organs are fed into the segmentation network. Liang et al. [146] introduced a multi-organ segmentation framework that utilizes multi-view spatial aggregation to integrate the learning of both organ localization and segmentation subnetworks. Trullo et al. [72] proposed 2 deep architectures that work synergistically to segment several organs such as the esophagus, heart, aorta, and trachea. In the first stage, probabilistic maps were obtained to learn anatomical constrains.

Semantic analysis aids search engines in comprehending user queries more effectively, consequently retrieving more relevant results by considering the meaning of words, phrases, and context. It’s used extensively in NLP tasks like sentiment analysis, document summarization, machine translation, and question answering, thus showcasing its versatility and fundamental role in processing language. Consider the task of text summarization which is used to create digestible chunks of information from large quantities of text.

They annotated spleen, liver, kidney, stomach, gallbladder, pancreas, aorta, and inferior vena cava in 8,448 CT volumes. The proposed active learning process generated an attention map, highlighting areas that radiologists need to modify, reducing annotation time from 30.8 years to 3 weeks and accelerating the annotation process by 533 times. These algorithms process and analyze vast amounts of data, defining features and parameters that help computers understand the semantic layers of the processed data. By training machines to make accurate predictions based on past observations, semantic analysis enhances language comprehension and improves the overall capabilities of AI systems.

It is precisely to collect this type of feedback that semantic analysis has been adopted by UX researchers. By working on the verbatims, they can draw up several persona profiles and make personalized recommendations for each of them. Analyzing the meaning of the client’s words is a golden lever, deploying operational improvements and bringing services to the clientele. With a semantic analyser, this quantity of data can be treated and go through information retrieval and can be treated, analysed and categorised, not only to better understand customer expectations but also to respond efficiently.

Thematic analysis identifies and interprets patterns (themes) within qualitative data. Write up the findings in a coherent and persuasive report that presents the themes and supports them with data extracts. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Practice working with data with Macquarie University’s Excel Skills for Business Specialization.

The organ location in the first stage can be obtained through registration or localization network, with reference in [105, 142,143,144,145,146,147,148,149,150,151,152,153]. This paper adopts the method proposed by the PRISMA guidelines [35] to determine the articles included in the analysis. Using the keywords “multi-organ segmentation” and “deep learning”, the search covered the period from January 1, 2016, to December 31, 2023, resulting in a total of 327 articles. We focused on highly cited articles, including those published in top conferences (such as NeurIPS, CVPR, ICCV, ECCV, AAAI, MICCAI, etc.) and top journals (such as TPAMI, TMI, MIA, etc.).

Likewise, the word ‘rock’ may mean ‘a stone‘ or ‘a genre of music‘ – hence, the accurate meaning of the word is highly dependent upon its context and usage in the text. It helps understand the true meaning of words, phrases, and sentences, leading to a more accurate interpretation of text. Semantic analysis, on the other hand, is crucial to achieving a high level of accuracy when analyzing text. Capturing the information is the easy part but understanding what is being said (and doing this at scale) is a whole different story.

Enroll for Data Warehousing, Analytics and BI sessions at the Data + AI Summit, or watch the on-demand recordings online after the event. AI is still in relatively early stages of development, and it is poised to grow rapidly and disrupt traditional problem-solving approaches in industrial companies. These use cases help to demonstrate the concrete applications of these solutions as well

as their tangible value.

Datasets

For example, once a machine learning model has been trained on a massive amount of information, it can use that knowledge to examine a new piece of written work and identify critical ideas and connections. Semi-supervised multi-organ segmentation often employs multi-view methods to leverage information from multiple image planes and improve the reliability of pseudo-labels. Zhou et al. [243] proposed the DMPCT framework, which incorporated a multi-planar fusion module to iteratively update pseudo-labels for different configurations of unlabeled datasets in abdominal CT images. Xia et al. [244] proposed the uncertainty-aware multi-view collaborative training (UMCT) method, which employed spatial transformations to create diverse perspectives for training independent deep networks.

ChatGPT is a chatbot powered by AI and natural language processing that produces unusually human-like responses. Recently, it has dominated headlines due to its ability to produce responses that far outperform what was previously commercially possible. Natural language processing (NLP) is a subset of artificial intelligence, computer science, and linguistics focused on making human communication, such as speech and text, comprehensible to computers.

The automated process of identifying in which sense is a word used according to its context. You understand that a customer is frustrated because a customer service agent is taking too long to respond. Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly

interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the

most exciting work published in the various research areas of the journal.

By analyzing the context and meaning of search queries, businesses can optimize their website content, meta tags, and keywords to align with user expectations. Semantic analysis helps deliver more relevant search results, drive organic traffic, and improve overall search engine rankings. Semantic analysis, powered by AI technology, has revolutionized numerous industries by unlocking the potential of unstructured data.

Semantic analysis is a process that involves comprehending the meaning and context of language. It allows computers and systems to understand and interpret human language at a deeper level, enabling them to provide more accurate and relevant responses. To achieve this level of understanding, semantic analysis relies on various techniques and algorithms. Using machine learning with natural language processing enhances a machine’s ability to decipher what the text is trying to convey. This semantic analysis method usually takes advantage of machine learning models to help with the analysis.

Where is data analytics used?‎

By leveraging this powerful technology, companies can gain valuable customer insights, enhance company performance, and optimize their SEO strategies. Since 2019, Cdiscount has been using a semantic analysis solution to process all of its customer reviews online. This kind of system can detect priority axes of improvement to put in place, based on post-purchase feedback. The company can therefore analyze the satisfaction and dissatisfaction of different consumers through the semantic analysis of its reviews.

Various large models for medical interactive segmentation have also been proposed, providing powerful tools for generating more high-quality annotated datasets. Therefore, acquiring large-scale, high-quality, and diverse multi-organ segmentation datasets has become an important direction in current research. Due to the difficulty of annotating medical images, existing publicly available datasets are limited in number and only annotate some organs. Additionally, due to the privacy of medical data, many hospitals cannot openly share their data for training purposes. For the former issue, techniques such as semi-supervised and weakly supervised learning can be utilized to make full use of unlabeled and partially labeled data.

But this method typically includes multiple steps, therefore, the performance of this method may be influenced by various relevant factors involved in each step. Moreover, due to the use of fixed atlases, it is challenging to manage the anatomical variation of organs between patients. In addition, it is computationally intensive and takes a long time to complete an alignment task. The statistical shape model uses the positional relationships between different organs, and the shape of each organ in the statistical space as a constraint to regularize the segmentation results. However, the accuracy of this method is largely dependent on the reliability and extensibility of the shape model, and the model based on normal anatomical structures has very limited effect in the segmentation of irregular structures [23].

For example, Ren et al.[156] focused on segmenting small tissues like the optic chiasm and left/right optic nerves. Qin et al.[254] considered the correlation between structures when segmenting the trachea, arteries, and veins, including the proximity of arteries to airways and the similarity in strength between airway walls and vessels. Additionally, some researchers [255] took into account that the spatial relationships between internal structures in medical images are often relatively fixed, such as the spleen always being located at the tail of the pancreas. These prior knowledge can serve as latent variables to transfer knowledge shared across multiple domains, thereby enhancing segmentation accuracy and stability. However, this method requires initializing the network encoder and decoder with the training weights of the Swin transformer on ImageNet.

When studying literature, semantic analysis almost becomes a kind of critical theory. The analyst investigates the dialect and speech patterns of a work, comparing them to the kind of language the author would have used. Works of literature containing language that mirror how the author would have talked are then examined more closely. Our team of experienced writers and editors follows a strict set of guidelines to ensure the highest quality content. We conduct thorough research, fact-check all information, and rely on credible sources to back up our claims. In addition to polysemous words, punctuation also plays a major role in semantic analysis.

This technology is already in use and is analysing the emotion and meaning of exchanges between humans and machines. Read on to find out more about this semantic analysis and its applications for customer service. The amount and types of information can make it difficult for your company to obtain the knowledge you need to help the business run efficiently, so it is important to know how to use semantic analysis and why. Using semantic analysis to acquire structured information can help you shape your business’s future, especially in customer service. In this field, semantic analysis allows options for faster responses, leading to faster resolutions for problems. Additionally, for employees working in your operational risk management division, semantic analysis technology can quickly and completely provide the information necessary to give you insight into the risk assessment process.

In multi-organ segmentation tasks, weak annotation not only includes partial annotation, but also includes other forms such as image-level annotation, sparse annotation, and noisy annotation [235]. For example, Kanavati et al. [236] proposed a weakly supervised method for the segmentation of liver, spleen, and kidney based on classification forests, where the organs were labeled through scribbles. Shape prior has been shown to be particularly effective for medical images due to the fixed spatial relationships between internal structures. As a result, incorporating anatomical priors in multi-organ segmentation task can significantly enhance the segmentation performance. In recent years, many deep learning-based step-by-step segmentation methods have emerged. For example, Zhao et al. [161] first employed the nnU-Net to segment the kidneys and then to segment kidney tumors based on the segmentation results of the kidneys.

Semantic analysis plays a crucial role in transforming customer service experiences. By analyzing customer queries, sentiment, and feedback, organizations can gain deep insights into customer preferences and expectations. This enables businesses to better understand customer needs, tailor their offerings, and provide personalized support. Semantic analysis empowers customer service representatives with comprehensive information, enabling them to deliver efficient and effective solutions.

The Tversky loss [202] is an extension of the Dice loss and can be fine-tuned by adjusting its parameters to balance the rates of false positives and false negatives. The focal loss [203] was originally proposed for object detection to highlight challenging samples during training. Similarly, the focal Tversky loss [208] assigns less weight to easy-to-segment organs and focuses more on difficult organs. Inspired by the exponential logarithmic loss (ELD-Loss) [209], Liu et al. [189] introduced the top-k exponential logarithmic loss (TELD-Loss) to address the issue of class imbalance in head and neck OARs segmentation. Results indicate that the TELD-Loss is a robust method, particularly when dealing with mislabeling problems. In addition to the methods combining CNN and transformer, there are some other hybrid architectures.

In this example, the meaning of the sentence is very easy to understand when spoken, thanks to the intonation of the voice. But when reading, machines can misinterpret the meaning of a sentence because of a misplaced semantic analysis definition comma or full stop. Besides, Semantics Analysis is also widely employed to facilitate the processes of automated answering systems such as chatbots – that answer user queries without any human interventions.

Check out the Natural Language Processing and Capstone Assignment from the University of California, Irvine. Or, delve deeper into the subject by complexing the Natural Language Processing Specialization from DeepLearning.AI—both available on Coursera. To become an NLP engineer, you’ll need a four-year degree in a subject related to this field, such as computer science, data science, or engineering. If you really want to increase your employability, earning a master’s degree can help you acquire a job in this industry.

By studying the relationships between words and analyzing the grammatical structure of sentences, semantic analysis enables computers and systems to comprehend and interpret language at a deeper level. Milletari et al. [90] proposed the Dice loss to quantify the intersection between volumes, which converted the voxel-based measure to a semantic label overlap measure, becoming a commonly used loss function in segmentation tasks. Ibragimov and Xing [67] used the Dice loss to https://chat.openai.com/ segment multiple organs of the head and neck. However, using the Dice loss alone does not completely solve the issue that neural networks tend to perform better on large organs. To address this, Sudre et al. [201] introduced the weighted Dice score (GDSC), which adapted its Dice values considering the current class size. Shen et al. [205] assessed the impact of class label frequency on segmentation accuracy by evaluating three types of GDSC (uniform, simple, and square).

This data is the starting point for any strategic plan (product, sales, marketing, etc.). A semantic tagger is a way to “tag” certain words into similar groups based on how the word is used. The word bank, for example, can mean a financial institution or it can refer to a river bank. “There is no set of agreed criteria for establishing semantic fields,” say Howard Jackson and Etienne Zé Amvela, “though a ‘common component’ of meaning might be one” (Words, Meaning and Vocabulary, 2000). The arrangement of words (or lexemes) into groups (or fields) on the basis of an element of shared meaning.

Finally, it analyzes the surrounding text and text structure to accurately determine the proper meaning of the words in context. Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature

Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for

future research directions and describes possible research applications. We believe compound AI systems that can draw insights about your data from its full lifecycle will be transformative to the world of business intelligence.

• It can leverage the inherent self-attentiveness of the network and is especially useful for multi-organ segmentation tasks [101, 187].
• How to utilize these partially annotated datasets to achieve a multi-organ segmentation model has arisen increasing interest.
• They introduced a CLIP-driven universal model for abdominal organ segmentation and tumor detection.
• By experimenting with AI applications now, industrial companies can be well positioned to generate a tremendous amount of value in the years ahead.
• In the localization and segmentation-based method, the first network provides location information and generates a candidate frame, which is then used to extract the Region of Interests (ROIs) from the image.

For example, Zhang et al. [197] utilized a pool of anisotropic strips with three directional receptive fields to capture spatial relationships between multiple organs in the abdomen. Compared to network architectures, network modules have gained widespread use due to their simple design process and ease of integration into various architectures. One significant reason for the limited availability of data for multi-organ segmentation is the issue of data privacy. Many institutions are unable to share their data for training due to privacy concerns. Federated learning is a distributed learning approach in machine learning aimed at training models across multiple devices or data sources without centralizing the dataset in a single location. In federated learning, model training occurs on local devices, and then locally updated model parameters are sent to a central server, where they are aggregated to update the global model [52].

AI can help through its ability to consider a multitude of variables at once to identify the optimal solution. For example, in one metals manufacturing plant, an AI scheduling agent was able to reduce yield losses by 20 to 40 percent while significantly improving on-time delivery for customers. Rather than endlessly contemplate possible applications, executives should set an overall direction and road map and then narrow their focus to areas in which AI can solve specific business problems and create tangible value. As a first step, industrial leaders could gain a better understanding of AI technology and how it can be used to solve specific business problems. In 2018, we explored the $1 trillion opportunity for artificial intelligence (AI) in industrials.1Michael Chui, Nicolaus Henke, and Mehdi Miremadi, “Most of AI’s business uses will be in two areas,” McKinsey, March 7, 2019.

However, radiation therapy can pose a significant risk to normal organs adjacent to the tumor, which are called organs at risk (OARs). Therefore, precise segmentation of both tumor and OARs contours is necessary to minimize the risk of radiation therapy [4, 5]. Thematic Analysis is a method for identifying, analyzing, and reporting patterns (themes) within data. A thematic statement captures the main ideas and insights derived from the data, providing a clear narrative for the study. Significance of the Study Qualitative Research lies in its flexibility and ability to uncover complex phenomena from detailed textual data. Qualitative data, including interviews, focus groups, and open-ended survey responses, are ideal for Thematic Analysis as they provide in-depth insights into participants’ perspectives and experiences.

As AI-powered devices and services become increasingly more intertwined with our daily lives and world, so too does the impact that NLP has on ensuring a seamless human-computer experience. Research on the user experience (UX) consists of studying the needs and uses of a target population towards a product or service. Using semantic analysis in the context of a UX study, therefore, consists in extracting the meaning of the corpus of the survey. For us humans, there is nothing more simple than recognising the meaning of a sentence based on the punctuation or intonation used. To improve the user experience, search engines have developed their semantic analysis. The idea is to understand a text not just through the redundancy of key queries, but rather through the richness of the semantic field.