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Advanced Lung Cancer Diagnosis and Treatment with Artificial intelligence: Applications, Methods, and Future Directions 3

Advanced Lung Cancer Diagnosis and Treatment with Artificial...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chaudhari, Pawan Dilip Gudadhe, Amit Maharashtra Wardha442001 India Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

AI technology revolutionizes the treatment of lung cancer and other related lung cancer diagnoses, hence becoming the game-changing technology in the diagnosis and treatment of this serious disease. As one of the most prevalent and deadliest cancers globally, early identification, precise diagnosis, and efficient planning for the treatment of lung cancer have always been difficult. The therapy using AI-based methods is introducing another novel way of addressing the problems concerning lung cancer via advanced imaging analysis, predictive modeling, and tailored plans of treatment, the most important among which is of course, imaging analysis. What is certainly most exciting among the applications of AI in lung cancer treatment is in the realm of imaging analysis. AI has been seen to be extremely clever in the interpretation of images rather than using machine learning or deep learning but has been much faster and more accurate than a human being while doing it. Besides, these systems also increase the general accurateness of diagnosis through a reduction of the error in diagnosis and consistent results. Alluring promises are made for AI soon for the treatment of lung cancer. In this paper, we discuss how AI has been transformed into imaging, diagnosis, and treatment planning and how this will improve patient outcomes and completely transform the way healthcare is delivered. We also consider barriers still to be cleared for realizing fully the potential benefits of AI. These include problems such as the quality of available data used in model interpretation and generalizability across different healthcare settings. Therefore, we end and suggest some future research on collaboration by emphasizing the safe, effective, and ethical entry of AI into normal clinical practice. © 2025 IEEE.

关键词： Lung cancer

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Unlocking Raw Data: The Power of Micro-Inspection in Handwriting Analysis 4th

Unlocking Raw Data: The Power of Micro-Inspection in Handw...

引用

4th international conference on pattern recognition and Artificial intelligence, ICPRAI 2024

作者： Xu, Yan Tang, Yufang Suen, Ching Y. Concordia University MontrealQCH3G 1M8 Canada Shandong Normal University Shandong Jinan250014 China

ISBN: (纸本)9789819787043

In the field of handwriting analysis, deep neural networks seemed to perform not as well as the classic machine-learning methods (such as the support vector machine). Thus, we started the inspection from the source, that is raw data, the input of deep neural networks. One is random splits, it has a positive effect on test accuracy and loss, solving the small sample problem by creating more samples to make up for the lack of training samples. The other is preprocessing. We carried out experiments with and without preprocessing, and the results show that preprocessing operation is necessary especially binarization operations. Furthermore, the order of preprocessing operation also affects the final prediction result. Finally, we compared with state-of-the-art methods, and obtained competitive experimental results, discovering the power of micro-inspection in handwriting analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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Predicting Sensory Integration Disorder in 3- to 6-Year-Old Children: Application of machine Learning Models 3

Predicting Sensory Integration Disorder in 3- to 6-Year-Old ...

引用

3rd international conference on Artificial intelligence and Education, ICAIE 2024

作者： Wang, Xiaofen Tang, Bin Chengyi College Jimei University Fujian Xiamen China Zhundong Network Technology Fujian Xiamen China

ISBN: (纸本)9798400712692

The aim of this study is to develop predictive models of sensory integration disorder (SID) in children, alongside identifying key factors of SID using machine learning algorithms that by establishing could help in formulating early health interventions. We conducted a stratified random sampling method to assess 1,032 3-6-year-old children (541 boys and 491 girls) using the Child Sensory Integration Scale in Fujian Province, China. Demographics, personal and family living conditions, and social environment were potential risk factors. Univariate analysis was performed to select key predictive variables. We implemented four different machine learning algorithms: eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector machine (SVM), and Random Forest (RF) algorithms to build predictive models that were subsequently evaluated for performance across both training and testing data sets. The areas under the ROC curve (AUC) in the training set of 0.828, 0.758, 0.759, and 0.801 for XGBoost, LR, SVM and RF, respectively, along with an accuracy of 0.755, 0.667, 0.685, and 0.696 were achieved in the training set respectively. The AUCs in the test set was 0.725, 0.703, 0.707, and 0.690, with accuracies of 0.696, 0.686, 0.686, and 0.623, respectively. In conclusion, XGBoost was the most accurate, stable and outperformed each of the other algorithms. Sleep quality, age, screen time, emotional warmth, sleep duration were the top five factors identified by the XGBoost model. The results indicate the importance of the XGBoost model for the early detection of children with the risk of SID, which is a valid foundation for the early intervention work. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： 3-6-year-old children machine Learning Predictive Model Risk Factors SID

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Speech Measuring Following Semiconductor Feature for Acoustic using Biodegradable Material 3

Speech Measuring Following Semiconductor Feature for Acousti...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Upadhyay, Shrikant Kumar, Rakesh Kumar, Binod Kumari, Barkha Lakra, Ankita Mandal, Sampurna Department of Electronics and Communication Engineeing MLR Insitute of Technology Hyderabad India Wipro Engineering Edge Hyderabad India Facultyt of CSE & IT Jharkhand Rai University Ranchi India GH Raisoni College of Engineering and Management Pune India Faculty of Computing and IT Usha Martin Univerity Ranchi India Department of Computer Science and Engineering-Artifical Intelligence Brainware University Kolkata India

ISBN: (纸本)9798331527549

This research paper primarily focuses to identify the various feature removal presence used for addresser recognition and validation. The current template i.e. classical and typical are considered to examine its role in different acoustic surroundings and also try to measure feature removal in acoustic condition considering factors like jitters, noise, echo etc. which truly a factor that affect the speech performance and degrades the overall performance of any system. Effort has been put to inspect the attainment of various feature removal approach with the help of classifier considering different structures. The behavior of different feature extraction removal simulated using Hindi samples and it will be analysed in terms of efficiency and error rate. The result may be useful for suitable for existing as well as any proposed model by changing its classifiers. Speech of different ages as well groups are used to analysed and implementation using 16-bit TMS 320 C5X semiconductor devices with the feature extraction and algorithm may improve the overall efficiency of the proposed system. Bio-degradable material can also help to reduce the acoustic level at different of speaker identifications. The feature of semiconductor helps to understand and also add the improvements in proper selection of feature extraction that consume less time in evaluating the algorithm and provide better efficiency in different acoustic conditions. Biodegradable material has not used so far in various studies and biodegradable paly a very important role in acoustic condition as we are dealing with acoustic condition and it is very important to take care of acoustic to come up with better efficiency. © 2025 IEEE.

关键词： Semiconductor devices

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Smart intelligence-Driven Deep Learning Framework for Reliable Detection and Accurate Classification of Skin Cancer Diseases 3

Smart Intelligence-Driven Deep Learning Framework for Reliab...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Rathish Babu, T.K.S. Chandramohan, S. Be, Hajira Niroshini Infantia, H. Srm Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai89 India Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya Department of Computer Science Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Computer Applications Maduranthakam Taluk India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331509675

The interest in skin cancer detection is growing remarkably because of the prevalence of this health condition worldwide. It improves the chances of survival and enhances general treatment outcomes when diagnosis has been done early. Most the traditional methods for skin lesion detection involve visual inspection performed by dermatologists;hence, it is time-consuming and, to some extent, subjective. Therefore, with the rise of deep learning-Artificial intelligence, auto-detection of skin cancer has gained a foothold in improving diagnostic accuracy, hence treatment outcomes. The presented paper proposes a GAN-based approach for skin cancer detection by deep learning analysis of dermoscopic images, classifying them as malignant and benign. The proposed approach uses the generator and discriminator parts of GAN to obtain an effective discrimination between cancerous and non-cancerous lesions with high precision. The proposed method incorporates the native abilities of GANs for boosting diagnostic precision. GANs consist of two major components: a generator and a discriminator. These two adversaries collaborate with each other in a way to encourage the classification performance of the framework. The generator synthesizes the realistic data representations by augmenting the dataset with various variations of dermoscopic images. This helps not only to compensate for the insufficiency of annotated data but also to make the model generalize more robustly to diverse skin lesion patterns. At the same time, the discriminator learns to distinguish malignant from benign lesions with high accuracy by using the adversarial nature in GAN training. These results clearly indicate the effectiveness of the proposed GAN-based approach over state-of-the-art traditional deep learning models. GAN makes not only the detection system more robust but also reduces its dependency on large annotated datasets. © 2025 IEEE.

关键词： Adversarial machine learning

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Evaluating Llama-3.1 for Adverse Drug Event Entity and Relationship Extraction Across Prompting Techniques 3rd

Evaluating Llama-3.1 for Adverse Drug Event Entity and Rel...

引用

3rd international conference on Advances in Computing Research, ACR 2025

作者： Prioleau, Howard Aryal, Saurav K. Burge, Legand Electrical Engineering and Computer Science Howard University 2400 6th St NW WashingtonDC20059 United States

ISBN: (纸本)9783031876462

Post-market adverse drug effects (ADEs) are often found in the clinical notes of Electronic Health Records. We evaluate open-weight large language model, Llama-3.1, for detection of ADEs, Named Entity recognition, and Entity Linkage tasks across varying prompting techniques suited for text decomposition including, but not limited to zero-shot, few-shot and tree of thought. Finally, we propose a novel improvement to the tree of thought prompting technique which combines consistency and thought merging. Our proposed approach is then evaluated and compared with the other approaches on these tasks using standard metrics for each task (f1, precision and recall). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adverse Drug Events Artificial intelligence Clinical Notes Large language models Llama n2c2

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AI Driven Classification of Diabetes Patients' Health Behavior 3

AI Driven Classification of Diabetes Patients' Health Behavi...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Gurung, Jennifer Nandy, Parvaty Sharma, Bibeth Saha, Saibal Kumar Jha, Ajeya Sikkim Manipal Institute of Technology Department of Management Studies Sikkim India Sikkim Manipal Institute of Medical Sciences Department of General Medicine Sikkim Gangtok India School of Business and Management Chrit University Bangalore India

ISBN: (纸本)9798331523893

This study aims to predict the health-seeking behavior among diabetic patients based on their Emotional intelligence and Health Locus of Control with the help of artificial intelligence and Internet of Things. Health-seeking behavior is a critical determinant of healthcare outcomes. It is influenced by various factors like sociodemographic characteristics, emotional intelligence, and internal/external locus of control. While the interplay between these variables has been explored in prior research, this study uniquely combines them to classify patients into high or low health-seeking behavior categories. The research is exploratory in nature. 600 diabetic patients in Sikkim, India were surveyed with the help of Brief Emotional intelligence Scale-10 and the Multidimensional Health Locus of Control Scale. machine learning algorithm - neural network was used for classification of the patients. Results indicate a classification accuracy exceeding 90%, with the area under the ROC curve surpassing 0.9 which demonstrates the effectiveness of model. These findings underscore the potential of artificial intelligence and the Internet of Things in predicting health-seeking behavior. It can be used by healthcare providers in tailoring interventions for improved health outcomes. This novel approach helps to bridge the gaps in healthcare accessibility and fosters advancements in patient-centered care strategies. © 2025 IEEE.

关键词： artificial intelligence diabetic patients Health Locus of Control health seeking behavior Internet of Things neural network

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Artificial intelligence in Mathematics Education: A Bibliometric Analysis in Scopus Database (2000–2024) 3rd

Artificial Intelligence in Mathematics Education: A Bibliome...

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3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Son, Le Ngoc Tuyen, Nguyen Thi Thanh Tinh, Phan Thi Tu, Nguyen Xuan Dinh, Nguyen Thi Huyen, Luu Thi Thu Trang, Nguyen Huyen Hung Vuong University Phu Tho Viet Nam

ISBN: (纸本)9783031809422

Artificial intelligence is considered a useful technological solution in mathematics education within the context of the modern world’s educational reforms. In this study, we provide an overview of the development of collaboration networks and research trends in the field of artificial intelligence in mathematics education through the analysis of scientific documents extracted from the Scopus database. We filtered and obtained 431 documents published between 2000 and 2024 that met the research criteria. The analysis results show that research trends in artificial intelligence in mathematics education are quite diverse, focusing on integrating intelligent technologies in teaching mathematics, measuring and evaluating learning outcomes, and improving students interest in mathematics through technological applications. We present the collaboration network among countries, the most cited publications, and influential authors in this research field in our results. However, the study has some limitations as it only extracts documents from the Scopus database, the data filtering process is manual, and the research quality depends on the input data quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Comprehensive Survey of Technologies for Wildlife Detection and Accident Prevention 3

A Comprehensive Survey of Technologies for Wildlife Detectio...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Anjali, Kadali Reddy, Dumpa Charmika Prasanthi, B.V. Hussain, S Mahaboob Veenadhari Ch, Lakshmi Budithi, Ravindra Bharathi Vishnu Institute of Technology Department of CSE Bhimavaram India

ISBN: (纸本)9798331523893

Wildlife collisions on roads and railways pose significant threats to animal populations and human safety, making wildlife detection and accident prevention critical research areas. This comprehensive survey reviews recent technological advancements to mitigate wildlife-vehicle and wildlife-train collisions. It covers the utilization of IoT (Internet of Things) devices such as cameras, sensors, and motion detectors, alongside AI (Artificial intelligence) technologies like machine learning, image processing, and deep learning. These studies address a variety of factors, including environmental parameters, animal behaviour, road and rail track conditions, and traffic patterns, to predict and prevent wildlife collisions. Solutions for railways focus on preventing accidents using sensors and AI-driven models to monitor animal presence near tracks, while road-based systems emphasise wildlife crossing identification, behaviour analysis, and driver warning systems. With detection accuracies ranging from 75% to 93%, these technologies show potential for real-time alert systems and proactive wildlife conservation efforts. This survey highlights the advancements, challenges, and future directions for integrating smart technologies into transportation infrastructure to reduce wildlife-related accidents. © 2025 IEEE.

关键词： accident prevention artificial intelligence driver warning system machine learning wildlife detection

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Potential of Vision Transformer in Leukemia Detection 3

Potential of Vision Transformer in Leukemia Detection

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ahmed, Rayan Nadeem, Laraib Shafique, Maryam Saleem, Zameera Iqbal, Ayesha Iqbal, Hafsa National University of Sciences and Technology (NUST) School of Electrical Engineering and Computer Science (SEECS) Islamabad Pakistan Shaukat Khanum Memorial Cancer Hospital & Research Center Lahore Pakistan

ISBN: (纸本)9798331523893

Leukemia, a bone marrow-related disease, arises from the abnormal proliferation of cells. It is the predominant form of childhood cancer, representing about one-third of cases annually. This study focuses on incorporating AI models to enhance the early detection of Leukemia. A public dataset is employed for testing purposes which comprises of 15,114 images in total. Advancing the field, this research employs vision transformer techniques, previously unexplored in leukemia detection, showing promising efficiency for such applications. Emphasizing the critical role of precision in medical imaging, particularly in the context of Leukemia detection, this study contributes to the ongoing efforts to improve the diagnostic accuracy and patient outcomes. © 2025 IEEE.

关键词： artificial intelligence automated diagnosis image analysis leukemia machine learning medical imaging vision transformer

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