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LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population
Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data. The LRE-MMF method utilizes the complementary strengths of sMRI and rs-fMRI: sMRI provides detailed anatomical information, while rs-fMRI captures functional connectivity patterns. We applied this approach to a dataset consisting of 20 PD patients and 20 healthy controls (HC), all scanned with a 3 T MRI. The primary objective was to determine whether the integration of sMRI and rs-fMRI through the LRE-MMF method improves the classification accuracy between PD and HC subjects. LRE-MMF involves the division of imaging data into localized regions, followed by feature extraction and dimensionality reduction using Principal Component Analysis (PCA). The resulting features were fused and processed through a neural network to learn high-level representations. The model achieved an accuracy of 75 %, with a precision of 0.8125, recall of 0.65, and an AUC of 0.8875. The validation accuracy curves indicated good generalization, with significant brain regions identified, including the caudate, putamen, thalamus, supplementary motor area, and precuneus, as per the AAL atlas. These results demonstrate the potential of the LRE-MMF method for improving early diagnosis and understanding of PD by effectively utilizing both sMRI and rs-fMRI data. This approach could contribute to the development of more accurate diagnostic tools. 2024 The Authors -
LSTM-MGTO: a novel early breast cancer detection using long short term memory based modified gorilla troops optimization algorithm
One of the most prevalent and severe tumors in women, breast cancer, remains a major global health issue despite a notable increase in incidence over the last ten years. It is the second leading cause of cancer-related death among women. Identifying breast cancer in its early stages has the potential to save lives; however, current screening techniques for the illness require several laboratory procedures involving medical experts. Automated solutions with rapid and reliable diagnostic capabilities are needed to minimize human error and expedite breast cancer diagnosis. The projected accuracy of cancer diagnosis remains far from matching the precision offered by existing approaches, even with the research on automated systems for the disease being studied. This work suggests a long short-term memory-based modified Gorilla troop optimization (LSTM-MGTO) method for breast cancer classification in order to address these issues. The Mastectomy Koibra Dataset (BCCD) and Wisconsin Diagnostic Mastectomy (WDBC) datasets were used to test the suggested methods. First, the proposed system employs contrast-limited adaptive histogram equalization (CLAHE) to enhance the quality of digital mammograms. Furthermore, employ a semantic deep learning (SDL) model to extract features. After the feature selection process, a recursive feature elimination technique was implemented to determine the crucial WDBC and BCCD characteristics that are relevant to breast cancer detection. Moreover, recommend a modified U-Net architecture for partitioning in both unmapped and guided contexts. The experimental findings indicate that the newly developed partitioning model surpasses existing advanced techniques, yielding superior results in both Dice and IoU score evaluations. On the WDBC and BCCD datasets, the suggested U-Net segmentation produces maximum Dice scores of 97.65% and 96.24%, respectively. Additionally, the model obtained the greatest IoU scores of 95.43% and 90.65% on the WDBC and BCCD datasets, respectively, according to the experimental findings. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025. -
LULC Analysis of Green Cover Loss in Bangalore
Urbanization of the cities especially the Indian City of Bangalore has led to the creation of an important discourse concerning development and conservation. The study carries out a detailed LULC study with special reference to Green Cover Loss in city of Bangalore. Using satellite images from 2014 to 2023 period and machine learning tools, the study establishes declines in green spaces with economic, environmental and health consequences of the city's uncontrolled expansion. The innovations afforded to the study regard methodologically on the use of ResNet50 for accurate LULC classification with an accuracy of 92% Hence the study reveals the interaction between urbanization and conservation, the efficiency of which requires policy adjustments that depend on existing knowledge. The study not only accustomizes the progression in the geography of Bangalore but it also shapes the technology and methodology for the further geospatial research in the areas under rapidly urbanizing in the future. 2024 IEEE. -
Luminescence and energy storage characteristics of coke-based graphite oxide
The substantial escalation in both energy consumption and ecological crisis prompts the utilization of conventional pollution-causing energy resources towards a proficient mode of energy production and storage. The most polluting fossil fuel like coal possesses a highly ordered sp2 carbon clusters, that can be easily tailored into graphene derivatives promising for energy-related applications. However, the impact of crystallinity and quality of the precursor coke on the physicochemical characteristics of extracted carbon nanostructures need to be identified. Herein, we have prepared graphite oxide structures (GrO) from high-quality coal, coke via Improved Hummers' method eliminating the need for toxic NaNO3. The inherent defect states own by coke are also of high significance as it performs the role of various photoluminescence emission centers. The sp2 domains and different surface defects promote excitation independent and dependent luminescence substantiating the distinct multi-emission property of GrO. The extent of functionalization during the oxidation process has also significantly affected the thermal stability of the carbogenic structure. The symmetric galvanostatic charge-discharge curves and lower internal resistance present superior stability and fatser ion transport of as-synthesized GrO. A specific capacitance of 193F/g was obtained at 0.2A/g with excellent capacitance retention over 2500 cycles. The versatile attributes of the coke derived GrO validate its realizable optoelectronics and energy storage applications. 2020 Elsevier B.V. -
Lung Cancer Classification from CT-Scan Images Using an Enhanced VGG16 Model
Lung cancer has been one of the most common and deadly types of cancer around the globe, for which early detection is quite crucial for patient survival. In this research work, a deep learning-based method for four-class classification of chest CT-scan images, such as Squamous Cell Carcinoma, Large Cell Carcinoma, Adenocarcinoma, and Normal, is presented. With a modified VGG16 architecture, adding Squeeze-and-Excitation (SE) blocks and residual connections, the enhanced SERES_VGG16 model enhances feature representation and classification accuracy. The dataset we used here contains preprocessed chest CT-scan images divided into a training set, validation set, and test set. It is trained with augmentation techniques in the data to improve generalization. Its performance is evaluated using measures of standard performances, such as F1-score, recall, precision, accuracy and confusion matrices. The model achieved over 95% accuracy, class-wise precision ranging from 94 to 99%, recall ranging from 88 to 99%, F1-score from 93 to 96%. The presented approach reached over 95% accuracy on the test set and can be a trusted second opinion for radiologists to assist with early and accurate lung cancer subtype classification. However, this study is constrained by the small size of the dataset and the lack of other clinical parameters like genetic information. Future studies will concentrate on expanding the dataset and integrating multi-modal clinical information for enhanced robustness. This work in this study justifies the importance of deep learning in the classification of the medical images and points out further ways toward improving automated diagnostic systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm
Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada Boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GBM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably. 2023 IEEE. -
Lung cancer detection and classification with optimal feature selection and two-fold-deep-learning-classifiers
The respiratory system is undoubtedly hampered by lung disorders. Also, one of the important reasons for death among people all around the world has been lung cancer. Early discovery can advance human survival probabilities. As a result, a unique ensemble-deep-learning paradigm for lung cancer detection and classification is established in the present research effort. The projected model includes five major phases: (a) image augmentation, (b) pre-processing, (c) segmentation, (d) feature extraction, (e) feature selection, and (f) lung cancer detection and classification, respectively. The collected raw CT images are augmentation with SMOTE. The augmented images are pre-processed via Median Filtering (for noise removal) and Contrast-limited adaptive histogram equalization (CLAHE) (for image contrast enhancement). Subsequently, from the pre-processed data, the ROI is identified via optimized U-NETS. The activation function (hyper-parameter) of U-NETS is optimized via a new hybrid optimization model-Digging Tunaswarm Optimizer (DTO). This DTO is the conceptual amalgamation of two standard meta-heuristic optimization models, namely Honey Badger Algorithm (HBA) and Tuna Swarm Optimization (TSO) models, respectively. Then, from the selected ROI area, the features like texture features (Manhattan Distance-based-GLCM, GLRM), Color features (Color Histogram), and Shape features (Moments, Area, Perimeter) are extracted. Among the extracted features, the optimal features are selected using DTO. This optimal feature selection reduces the computational complexity of the projected model. Finally, using these extracted optimal features, the two-fold-deep-learning-classifier framework is trained. This two-fold-deep-learning-classifiers framework encapsulates the Bidirectional long-short term memory (Bidirectional LSTM) and the Recurrent Neural Network (RNN) and the Modified Convolutional Neural Network (M-CNN). In the first phase, the Bi-LSTM and RNN are clamped, and they are trained with the selected optimal factors. The outcome from Bi-LSTM and also RNN was fed as input to M-CNN. Final detected findings based on the existence or absence of lung cancer are acquired from the M-CNN, whose loss function has been modified with RMSE. Finally, a comparative evaluation is undergone to validate the efficiency of the projected model. The proposed model has a higher overall accuracy (92.4%) detecting modelling accuracy (96.3%) and classification accuracy (92.4%) compared to other models such as HBA, TSO, CNN, 3D CenterNet, and TSCNN. The use of a two-fold deep learning framework is responsible for these improvements, and the model also has lower failure rates (FPR and FNR) in detecting lung cancer. It is suggested that the proposed approach is effective in early-stage lung cancer detection. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Lung cancer detection using image processing techniques
Lung cancer is one of the hazardous disease which leads to high death rates in the world. A cancer is an irregular growth of cells that can be characteristically derived from a single irregular cell and that may spread to whole part of the lung. So, it is necessary to find it at the earlier stages and take basic steps to cure.CT scan is one of the sensitive method used in the medical field for treating the patients. The quality of the image is very important for detection of lung cancer. Pre-processing of an image is a necessary process, as there is a difficulty in detecting cancer cells in an image due to the presence of noise and low-quality of images. To reduce the volume of these problems, diagnosis of lung cancer steps like image enhancement, image segmentation, feature extraction methods can be used. For processing and implementation of these methods Matlab tool has been used. This paper focuses on improving the quality of image and to optimise the work. Implementation is done using image processing toolbox that is available in Matlab tool.The whole idea of this research is to show the improved work in the existing system and to get more agreeable results. RJPT All right reserved. -
Lung Cancer Diagnosis from CT Images Based on Local Energy Based Shape Histogram (LESH) Feature Extration and Pre-processing
Lung cancer as of now is one of the dreaded diseases and it is destroying humanity never before. The mechanism of detecting the lung cancer will bring the level down of mortality and increase the life expectancy accuracy 13% from the detected cancer diagnosis from 24% of all cancer deaths. Although various methods are adopted to find the cancer, still there is a scope for improvement and the CT images are still preferred to find if there is any cancer in the body. The medical images are always a better one to find with the cancer in the human body. The proposed idea is, how we can improve the quality of the diagnosis form using pre-processing methods and Local energy shape histogram to improve the quality of the images. The deep learning methods are imported to find the varied results from the training process and finally to analyse the result. Medical examination is always part of our research and this result is always verified by the technicians. Major pre-processing techniques are used in this research work and they are discussed in this paper. The LESH technique is used to get better result in this research work and we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. The construction of the proposed method will include smoothing of the images with median filters, enhancement of the image and finally segmentation of the images with LESH techniques. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Lung cancer prediction with advanced graph neural networks
This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
Lung cancer prediction with advanced graph neural networks
This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
Lung tuberculosis detection using x-ray images
This research work is based on the various experiments performed for the detection of lung tuberculosis using various methods like filtering, segmentation, feature extraction and classification. The results obtained from these experiments are discussed in this paper. Lung tuberculosis is a bacterial infection that causes more deaths in the world than any other infectious disease. Two billion people are infected with tuberculosis all around the world. Lung tuberculosis is a disease caused by a bacteria known as Mycobacterium tuberculosis or Tubercle bacillus. This research work strives to identify methods by which patients, who require second opinion for an already identified result, can save a lot of money. Once we receive X-ray image an input, pre-processing methods like Gaussian filter, median filter is applied. These filters help to remove unwanted noise and aid to get fine textural features. The output obtained from this is taken as an input and applied to water shed segmentation and gray level segmentation which helps to focus on the lung area of the obtained results. Output from these segmentation methods is fused to get a Region of Interest (ROI). From the ROI, the statistical features like area, major axis, minor axis, eccentricity, mean, kurtosis, skewness and entropy are extracted. Finally, we use KNN, Sequential minimal optimization (SMO), simple linear regression classification methods to detect lung tuberculosis. The results obtained in this paper suggests KNN classifier performs well than the other two classifiers. Research India Publications. -
LungDxNet: AI-Powered Low-Dose CT Analysis for Early Lung Cancer Detection
Early and accurate diagnosis, however, is still lacking for the most common form of lung cancer, and this remains one of the leading cancers leading to mortality. CT scans are widely used for lung cancer screening; however, their manual interpretation is time-consuming and prone to variability. This study introduces LungDxNet, a deep learning-based framework that integrates transfer learning to enhance diagnostic accuracy and efficiency. Using a large dataset of Low Dose CT (LDCT) scans, the system is built with fine-tuned pre-trained Convolutional Neural Networks (CNNs) such that feature extraction is reliable though minimal reducing radiation exposure. Consequently, LungDxNet involves the integration of component segmentation techniques that have been used to isolate the lung regions and discriminate the cancerous nodules from the malignant and benign cases. Very rigorous evaluations were performed on the model against both conventional machine learning and state of the art deep learning architectures. Results show that there is a substantial reduction of false positive and false negative resulting in a superior accuracy (98.88), sensitivity, and specificity. This design is to be scaled, robust and clinically applicable, making it a potential real world lung cancer diagnosis tool. Deep learning and transfer learning has excellent power to transform lung cancer detection, and this research brings awareness of how far we can optimise and integrate into clinical workflow. The model is enhanced for future work and adapted for real time diagnostic applications. 2025, Sakarya University. All rights reserved. -
Luxury consumption: The decision process
This chapter explores the luxury consumer decision process, highlighting the psychological, social, and cultural factors that influence luxury purchases. Consumers are driven by emotional and social motivations, such as self-expression and status, which shape their buying behaviors. Social and cultural differences require marketers to adapt strategies to diverse regional preferences. As digital technologies transform the luxury market, brands face the challenge of maintaining exclusivity while embracing e-commerce, social media, and influencer marketing. To remain competitive, luxury brands must leverage psychological insights and digital tools to create personalized, engaging experiences. The future of luxury consumption will depend on brands' ability to build meaningful connections with consumers, fostering loyalty and ensuring long-term success in an evolving market landscape. 2025, IGI Global Scientific Publishing. All rights reserved. -
Lyrics of longing: Exploring the role of music in the lived experience of homesickness among college students
The study investigates the multifaceted role of music during homesickness among first-year college students in India. As compared to other mental health outcomes, homesickness is a relatively understudied phenomenon, yet noteworthy due to its direct association with depression and anxiety. Although empirical evidence about music highlights its therapeutic potential for managing stress and anxiety, few studies have explored its role in connection with homesickness. The data for this study were collected through semi-structured interviews with 10 students about their perception of using music during homesickness. Through interpretative phenomenological analysis, the emerging themes pointed to a mixed influence, highlighting the bittersweet nature of music during homesickness. While music validates feelings and boosts confidence and motivation, it also triggers restorative nostalgia and serves as an escape from confronting homesickness. Moreover, native songs fostered an appreciation for ones culture and helped students connect with their roots. The study contributes to understanding how music is a versatile tool for students dealing with homesickness, offering solace and potential challenges. It serves as a guide to future intervention studies that could explore musics long-term influences. Recognising the diverse ways students perceive and respond to music provides valuable insights for developing tailored interventions and support systems. The Author(s) 2024. -
Lyrics of longing: Exploring the role of music in the lived experience of homesickness among college students
The study investigates the multifaceted role of music during homesickness among first-year college students in India. As compared to other mental health outcomes, homesickness is a relatively understudied phenomenon, yet noteworthy due to its direct association with depression and anxiety. Although empirical evidence about music highlights its therapeutic potential for managing stress and anxiety, few studies have explored its role in connection with homesickness. The data for this study were collected through semi-structured interviews with 10 students about their perception of using music during homesickness. Through interpretative phenomenological analysis, the emerging themes pointed to a mixed influence, highlighting the bittersweet nature of music during homesickness. While music validates feelings and boosts confidence and motivation, it also triggers restorative nostalgia and serves as an escape from confronting homesickness. Moreover, native songs fostered an appreciation for ones culture and helped students connect with their roots. The study contributes to understanding how music is a versatile tool for students dealing with homesickness, offering solace and potential challenges. It serves as a guide to future intervention studies that could explore musics long-term influences. Recognising the diverse ways students perceive and respond to music provides valuable insights for developing tailored interventions and support systems. The Author(s) 2024 -
m-quasi-?-Einstein contact metric manifolds
The goal of this article is to introduce and study the characterstics of m-quasi-?-Einstein metric on contact Riemannian manifolds. First, we prove that if a Sasakian manifold admits a gradient m-quasi-?-Einstein metric, then M is ?-Einstein and f is constant. Next, we show that in a Sasakian manifold if g represents an m-quasi-?-Einstein metric with a conformal vector field V, then V is Killing and M is ?-Einstein. Finally, we prove that if a non-Sasakian (?, )-contact manifold admits a gradient m-quasi-?-Einstein metric, then it is N(?)-contact metric manifold or a ?-Einstein. Kumara H.A., Venkatesha V., Naik D.M., 2022. -
Machinability and surface integrity for Mg AZ61A alloy composite by employing Taguchi integrated grey relational analysis
The present experimental study seeking to identify the optimal processing parameters in WEDM of Mg AZ61A-ZrB2 composite using Taguchi integrated grey relational analysis (GRA). Wire-cut electric discharge machining (WEDM) is the most effective method of metal removing process which is utilized in a variety of industries, like defence, biomedical, automotive, and aerospace. It is widely used in the machining of conductive and hard materials like composites and super alloys. In this experiment, the Mg AZ61A alloy composite reinforced with 12 wt% ZrB2 particles was fabricated through stir casting method. The scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) mapping ensured the presence of matrix elements and reinforcement in the developed composite. The material removal rate (MRR) and surface roughness (Ra) were examined with relation to the processing factors such as pulse current (Ip), pulse on-time (Ton), and pulse off-time (Toff). The machining was conducted in compliance with Taguchi's L9 array. According to the GRA results, the optimal ranges of factors for achieving the better MRR and Ra were found at 4 amps of Ip, 15 ?s of Ton, and 45 ?s of Toff. The ANOVA results confirm that Ip was the most dominating factor that contributing to 37.58 %, next by Ton (30.96 %) and Toff (12.85 %), respectively. The confirmation test was demonstrated that the actual and predicted GRG values are fairly close to one another with 13.36 % improvement. The morphology of the machined surface was examined and it was shows the formation of a recast layer and the existence of flaws. 2025 The Authors -
Machine intelligence security : A methodological blend of fuzzy logic in industry 4.0 algorithms
The way things are made has changed a lot because of Industry 4.0. It has also led to a time with great technology and relationships. The paper discusses way to improve security in Machine Intelligence in the setting of Industry 4.0. The study uses a mix of methods to combine Fuzzy Logic with cutting-edge Industry 4.0 algorithms in order to deal with new hacking problems. Because fuzzy logic can deal with doubt and imprecision, it can be used to make current methods more reliable. This creates a complex and flexible security structure. The merger was carefully planned to make the methods for finding anomalies, reducing threats, and responding to incidents work better. The suggested method aims to make machine intelligence systems more resistant to complex cyber dangers by combining the best parts of Fuzzy Logic with Industry 4.0 algorithms. This study adds to the growing conversation about how to keep smart factory settings safe by focusing on a proactive and dynamic security model. The effects of this mix of methods could be felt in many different industries, making it possible to use advanced technologies in a safer and more reliable way in the age of Industry 4.0. 2024, Taru Publications. All rights reserved. -
Machine Intelligence: Computer Vision and Natural Language Processing
Machines are being systematically empowered to be interactive and intelligent in their operations, offerings. and outputs. There are pioneering Artificial Intelligence (AI) technologies and tools. Machine and Deep Learning (ML/DL) algorithms, along with their enabling frameworks, libraries, and specialized accelerators, find particularly useful applications in computer and machine vision, human machine interfaces (HMIs), and intelligent machines. Machines that can see and perceive can bring forth deeper and decisive acceleration, automation, and augmentation capabilities to businesses as well as people in their everyday assignments. Machine vision is becoming a reality because of advancements in the computer vision and device instrumentation spaces. Machines are increasingly software-defined. That is, vision-enabling software and hardware modules are being embedded in new-generation machines to be self-, surroundings, and situation-aware. Machine Intelligence emphasizes computer vision and natural language processing as drivers of advances in machine intelligence. The book examines these technologies from the algorithmic level to the applications level. It also examines the integrative technologies enabling intelligent applications in business and industry. Features: Motion images object detection over voice using deep learning algorithms Ubiquitous computing and augmented reality in HCI Learning and reasoning in Artificial Intelligence Economic sustainability, mindfulness, and diversity in the age of artificial intelligence and machine learning Streaming analytics for healthcare and retail domains Covering established and emerging technologies in machine vision, the book focuses on recent and novel applications and discusses state-of-the-art technologies and tools. 2024 Taylor & Francis Group, LLC.