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检索条件"机构=Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Sawangi"

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Breast Cancer Detection using Ultrasound Images based and Convolutional Neural Network 6

Breast Cancer Detection using Ultrasound Images based and Co...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Langote, Meher Hiwase, Kunal Wande, Radha Tale, Abhay Kalekar, Harsh Gundewar, Swapnil Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Sawangi India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Sawangi India

ISBN: (纸本)9798331522667

Breast cancer forms one of the most common types of cancer among women worldwide;therefore, it highlights the importance of having rapid and timely diagnosis methods. Here, we will survey the use of convolutional neural networks (CNNs) for mutually exclusive cancer detection in ultrasound images using detectors. A dataset of benign and malignant breast tumors visualized via ultrasound is taken as the base for the training and testing of the CNN model. The CNN architecture is arranged to select valuable features from the ultrasound pictures only, but not malignant lesions. The performance and the evaluation of the CNN model are done through metrics like accuracy, precision, recall, and F1 score. The findings confirm the efficiency of using the CNN here, as it shows the extent and solidity of reading textures with excellent precision and quick performance in breast cancer screening. This investigation is the advancement of computer-aided diagnostic tools for breast cancer screening. As medical practice sees these benefits, the efficiency of healthcare delivery and patient outcomes are bound to be optimized. © 2025 IEEE.

关键词： Ultrasonic imaging

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics 3

Enhancing Interpretability: The Role of Explainable AI in He...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Zade, Nikita Langote, Meher Verma, Prateek Faculty of Engineering & Technology Department of Artificial Intelligence & Data Science Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Artificial Intelligence & Machine Learning Maharashtra Sawangi442001 India

ISBN: (纸本)9798331509675

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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A Review on the Role of artificial intelligence in Personalized learning

A Review on the Role of Artificial Intelligence in Personali...

引用

2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Qureshi, Rukaiyya Hajare, Shital Verma, Prateek Sawangi Meghe Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Sawangi Meghe Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798350361537

This article explores the revolutionary role of artificial intelligence (AI) in personalized learning, focusing on its potential to revolutionize educational processes and improve learning outcomes. This paper provides insights into how AI technologies can be used to tailor instruction, identify learning needs, and provide specific strategies that cater to different students' psychological, cognitive, and motivational preferences. The article examines case studies and examples to demonstrate different uses of AI in personalized learning settings, such as autonomous tutoring systems, personalized education channels, and analysis based on data. However, the use of AI in education raises ethical, technological, and policy concerns that require careful study and proactive actions to assure fair access, preserve learner privacy, and eliminate algorithmic biases. Ultimately, finally, the article emphasizes AI's transformative potential in personalized learning and advocates for coordinated efforts among educators, academics, and policymakers to leverage its benefits while tackling accompanying obstacles. © 2024 IEEE.

关键词： Federated learning

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Addressing Data Imbalance in machine learning: Challenges and Approaches 6

Addressing Data Imbalance in Machine Learning: Challenges an...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Langote, Meher Zade, Nikita Gundewar, Swapnil Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Sawangi India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Sawangi India

ISBN: (纸本)9798331522667

A key difficulty of dealing with imbalanced datasets is that machine learning models are trained to favor the majority class and therefore perform poorly when it comes to a minority class classification. In this paper, we consider the following methods of tackling the issue of the over-representation of a class;random oversampling, undersampling, SMOTE, cost-sensitive learning, and ensemble methods. Comparing them, we assess the performance of each of these techniques within different classifiers. Techniques including SMOTE together with combined methods including Random Forests also show higher capacity in minority class predictions. However, problems such as overfitting and the increase in the computational load are not escapable yet. We briefly recap the strengths and weaknesses of each approach and propose potential refinements including a combination of both methods and dynamic resampling methods to serve as the next steps toward improving the model. Thereby, our results draw attention to the necessity of accurate methods for handling the class imbalance, which will further yield more accurate and fair machine learning solutions across a variety of applications. © 2025 IEEE.

关键词： Adversarial machine learning

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A Review on the Applications of Natural Language Processing in Healthcare 4

A Review on the Applications of Natural Language Processing ...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Warbhe, Mohan K. Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Design Sawangi Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

In healthcare, Natural Language Processing (NLP) is a technique to improve clinical decision support, patient care, and medical research. By automating clinical recording, NLP enhances the calibre of medical records and professional communication. It makes Electronic Health Records (EHRs) easier to use by facilitating quick and easy data entry, retrieval, and access to vital patient data that helps with precise diagnosis and efficient treatment planning. Through the analysis of patient-specific data, NLP supports clinical decision support by delivering prompt recommendations. Chatbots and virtual assistants managing repetitive duties like booking appointments also improve patient connection. NLP makes it easier to analyse vast amounts of literature for medical research, facilitating the extraction and summary of important discoveries. Healthcare data processing requires text preparation, sentiment analysis, Named Entity Recognition (NER), semantic parsing, and Natural Language Generation (NLG). However, there are obstacles to overcome, like bias, ethical concerns, system integration, privacy, and data quality. The healthcare industry can use NLP to enhance patient outcomes, promote personalized medication, and streamline healthcare delivery by guaranteeing interoperability and setting standards. © 2024 IEEE.

关键词： Challenges of NLP in Healthcare Future treads of NLP for healthcare Healthcare NLP

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Review of machine learning and Deep learning algorithms for Personality traits classification 2

Review of Machine Learning and Deep Learning algorithms for ...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bhadane, Satish N. Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Nowadays, people deal with major psychological issues on a daily basis, often in the absence of a desired support system. In the field of psychology, focus is on the thoughts, feelings, behaviors, goals, and interests of individuals. The psychologist conventionally considers various types of personality traits, like the Big 5 personality traits, the Myers-Briggs Type Indicator (MBTI), the OCEAN Model (Openness, Conscientiousness, Agreeableness, Extraversion, Neuroticism), and other methods also considered for measuring personality. To forecast personality traits like extraversion, agreeableness, openness, conscientiousness, and neuroticism, consider the name "Big Five personality traits"It is a universal model to forecast these personality traits. Studies on personality prediction that identify personality disorders from user content on social media sites like Facebook, Twitter, and WhatsApp primarily rely on user behavior on these platforms. A number of popular Deep learning (DL) techniques have advanced recently, including Deep Belief Networks (DBNs), Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Examine these methods in terms of single and multiple modalities, including psychological, audio, video, text, and image information. Researchers currently focus on predicting personality traits using single-modality text features, static facial images, audio, and video clips, including interview recordings and YouTube videos. Therefore, the current study highlights that personality prediction mostly relies on written text, audio and video datasets used in machine learning (ML) and DL models. Additional investigation is needed to resolve ethical concerns and justify the use of psychological datasets and DL models to evaluate personality traits. By combining personality traits with a psychological dataset, this study imagines a time when people everyday struggles can be effectively addressed. The integration of ML and DL in the development of pe

关键词： Adversarial machine learning

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The Future of Driving: A Review on the Combination of artificial intelligence and Autonomous Vehicles 2

The Future of Driving: A Review on the Combination of Artifi...

引用

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Zade, Nikita Gawande, Mayur Verma, Prateek Gundewar, Swapnil Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The convergence of artificial intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and machine learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability. © 2024 IEEE.

关键词： Automotive industry

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Enhancing Pneumonia Treatment in Children by machine learning Techniques 4

Enhancing Pneumonia Treatment in Children by Machine Learnin...

引用

4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Gajbhiye, Ashwini Gundewar, Swapnil Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Pneumonia is a health challenge that affects many people more so the children, thus leading to high death rates across the world. There is a persuading evidential proof which shows that the techniques in the application of the machine learning (ML) have helped in improving the diagnosis and treatment of pneumonia. This paper discusses a number of ML techniques such as CNNs, SVMs and Random Forest that can be used to enhance the reliability and effectiveness of detecting pneumonia from enhanced chest x-ray images. The classification shows how other methods like Min-Max normalization and a specific type of image pre-processing called Contrast-Limited Adaptive Histogram Equalization (CLAHE) can make image quality even better as well as increase the accuracy of the model. Here, by using the sophisticated dalliance of ML models it is possible to dramatically minimize the incidence of diagnostic errors and enhance the results of pneumonia treatments thus witnessing an important evolutionary leap in the specialty. © 2025 IEEE.

关键词： Convolutional neural networks

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Web-Based Smart Healthcare System 2

Web-Based Smart Healthcare System

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Daigavhane, Mansvi Kishorrao Gundewar, Swapnil Gadkari, Ayush Pravin Anpan, Sakshi Prakash Faculty of Engineering and Technology Wardha India Faculty of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Wardha India

ISBN: (纸本)9798331528713

In today's healthcare sector, utilizing information technology is essential for improving hospital operations. This study presents the creation and deployment of a web-based Smart Healthcare system (SHS) aimed at optimizing various administrative and clinical tasks within a healthcare setting. Some of the components are patient portal for signing in, practice calendar, assessment tools, invoicing, and reporting using webbased platform. Web based technologies and keeping a centralized database leads to secure data management, real time information and effective communication between different departments. This paper focuses on the description of the system's layout, structure and basic operations identifying it as a tool for raising organizational productivity, improving data accuracy and promoting patient care benefits. © 2024 IEEE.

关键词： Electronic health record

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A Review on the Applications of machine learning Algorithms for Mental Stress Detection 2

A Review on the Applications of Machine Learning Algorithms ...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Motghare, Renuka Bhoyar, Shivam Verma, Prateek Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Stress, a widespread issue affecting both individual well-being and societal productivity, has attracted significant research interest. Defined as a state of mental or emotional strain, stress arises from various factors such as work, relationships, and health concerns, manifesting physically, emotionally, and behaviorally. This paper provides a review of machine learning (ML) techniques to detect mental stress using physiological signals like ECG, GSR, and EEG. Through a review of existing studies, the paper highlights the progress made in this area and the challenges that remain, particularly concerning data quality, algorithm accuracy, and the practical deployment of stress monitoring devices. The study underscores the potential for real-time monitoring and personalized interventions, especially in daily activities. Future research should focus on enhancing model robustness, integrating multiple data sources, and addressing real-world applicability. This paper emphasizes the importance of early stress identification and personalized treatment to improve mental health outcomes © 2024 IEEE.

关键词： Adversarial machine learning

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