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Biomedical Materials and Devices 2025年 1-23页

作者： Shinde, Sumit Sudhakar Ghotkar, Archana Santosh Department of Computer Engineering Society for Computer Technology and Research’s Pune Institute of Computer Technology Savitribai Phule Pune University Maharashtra Pune India Department of Information Technology Society for Computer Technology and Research’s Pune Institute of Computer Technology Savitribai Phule Pune University Maharashtra Pune India

In recent years, modernization, physical work scenarios technology-wise, lifestyle, culture, and personal environments contribute to the stressed state of individuals. However, the early evaluation of long-term mental stress conditions is essential as it triggers several chronic disorders and affects the mental health of affected individuals. In traditional techniques, the multifaceted symptoms and comorbidities introduce difficulty in diagnosis, posing a risk of misdiagnosis. However, the existing techniques often failed to capture the relevant features and neglected to observe the notable shifts in various bio-signals caused by mental stress resulting in inaccurate detection. In addition, medical professionals are skeptical about the adoption of AI-assisted diagnosis due to their inability to be transparent in decision-making processes. In this regard, Explainable Artificial Intelligence (XAI) has surfaced to address the computational black box issue with AI systems by offering transparency and interpretability for model predictions. Consequently, this research proposes the Ensemble Optimization enabled Explainable Convolutional Neural Network (EO-ECNN) for mental stress detection by offering insights into its decision-making process which in turn enhances the system interpretability and transparency. The proposed model exploited the ECNN improves the effectiveness of the stress detection model in conjunction with Ensemble optimization, which combines the traits of the coyote’s and wolf’s individual and group huts, respectively. The high detection accuracy is made possible by the optimization that is being used, which increases the classifier’s slow convergence rate. The multimodal input data for the study still consist of text, images, and audio. The audio features are extracted with the help of the VGGish feature extractor, while the visual input is processed by Residual Network (ResNet). The experimental results demonstrate the superior performance of the multi

关键词： Convolutional neural network Ensemble optimization Explainable artificial intelligence Mental stress detection Residual network

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Hair Loss Stage Classification by Deep Learning 1

Hair Loss Stage Classification by Deep Learning

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1st International Conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Joshi, Advait Ketkar, Suvrat Jain, Rinit Mahajan, Tirthraj Kulkarni, Amey Kale, Geetanjali. V. Computer Engineering Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798350355611

Hair loss in males is a growing issue in every age group. Experts suggest remedies based on the stage, that is, the severity of hair loss. There is a discrepancy in the total number of stages in which hair loss can be classified. We have considered five stages for classification: normal (no significant hair loss), stage 1, stage 2, stage 3 and bald. We are classifying a person into these stages by analyzing the hairline visible in the image of the face. We have used deep learning techniques to build the classification model. The model will aid users in determining their correct stage accurately. We have developed a web and a mobile application which allows a user to connect to a dermatologist to gain insights about remedies. © 2025 IEEE.

关键词： Contrastive Learning

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Dynamic Indian Sign Language Sentence Captioning System Using Machine Learning 5th

Dynamic Indian Sign Language Sentence Captioning System Usin...

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5th International Conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Kulkarni, Vedant Dattatray Bajaj, Anushree Shrigopal Marathe, Himanshu Deshmukh, Mandar Sreemathy, R. Jagdale, Jayashree Department of Information Technology Pune Institute of Computer Technology Pune India Department of Electronics and Telecommunication Engineering Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819791118

Sign language serves as a vital mode of communication for the deaf and hard of hearing community, yet access to sign language content remains limited due to the lack of accurate and timely captioning. In this paper, a comprehensive comparison has been made between Long Short-Term Memory (LSTM)- and Support Vector Machine (SVM)-based neural networks for sign language recognition. Our approach uses LSTM networks to effectively recognize and translate sign language gestures into textual descriptions. We leverage a large dataset of sign language videos paired with English captions for training and evaluation. Through extensive experimentation, we demonstrate the effectiveness of our system in accurately captioning sign language videos, thereby enhancing accessibility for individuals with hearing impairments. Our self-curated dataset includes 28 unique sentences on which our LSTM-based model achieved an accuracy of 90% whereas it was 97% for SVM-based approach. Our research contributes to advancing the field of accessibility technology and holds promise for real-world applications in education, entertainment, and communication for the deaf and hard of hearing community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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The Rehab Reco—A Therapy Recommender System for Dementia 5th

The Rehab Reco—A Therapy Recommender System for Dementia

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5th International Conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Pore, Pritish Bhagwat, Sharvari Desai, Yash Rinke, Prutha Deshpande, Arati Das, Soubhik Marne, Pushkraj Pune Institute of Computer Technology Pune India Manastik Pune India

ISBN: (纸本)9789819791118

Dementia is a progressive neurological condition marked by cognitive decline affecting memory, thinking, and behavior. It poses significant challenges for patients, caregivers, and healthcare systems. As individuals age, the risk of developing dementia increases. This marks the urgent need for solutions that integrate medical expertise with technological advancements. The Dementia Application and Diagnosis and Tracking (DADT) app serves as a comprehensive platform connecting medical professionals with dementia patients, offering a range of tailored therapies. This paper introduces a hybrid recommender system embedded in the DADT app, combining collaborative, and demographic filtering techniques. Utilizing patient electronic health records (EHR) and app engagement data, the system aims to provide personalized therapy recommendations, reducing medical professional’s workload and enhancing patient care. Preliminary results showcase the system’s efficacy in generating personalized recommendations, with improved precision and recall over demographic filtering alone, and competitive performance compared to collaborative filtering. This highlights its potential to optimize dementia care workflows and enhance patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Optimizing User-Content Creator Engagement via Semantic Comment Clustering, Automated Chatbots, and Sentiment Analysis 1

Optimizing User-Content Creator Engagement via Semantic Comm...

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1st International Conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Sharma, Sahil Waghmare, Mayur Darade, Ankita Malusare, Nikhil Mahajan, Jayshree Pune Institute of Computer Technology Affliated To SPPU Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

In the rapidly evolving digital landscape, social media platforms play a critical role in fostering interactions between users and content creators. However, the sheer volume of user comments presents challenges in identifying common concerns, providing timely responses, and understanding audience sentiment. This study examines three key technologies - comment clustering, chatbots for FAQs, and sentiment analysis - as tools to enhance the efficiency and quality of social media interactions. The study explores various clustering algorithms that group semantically related comments, enabling content creators to streamline their responses to recurring queries. Additionally, we review chatbot systems designed to automate responses to frequently posed questions, reducing response times and improving user satisfaction. Furthermore, sentiment analysis techniques are assessed for their effectiveness in categorizing user feedback as positive, negative, or neutral, providing creators with actionable insights into audience sentiment. By studying these approaches, this paper demonstrates how the integration of clustering, chatbots, and sentiment analysis can transform social media communication, making it more efficient, responsive, and personalized. This integrated approach offers significant benefits for both users and creators by improving engagement and fostering meaningful dialogue. © 2025 IEEE.

关键词： Latent semantic analysis

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Navigation With Augmented Reality 1

Navigation With Augmented Reality

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1st International Conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Pawar, Urmila S. Kulkarni, Rutuja A. SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

Augmented Reality (AR) and Virtual Reality (VR) are transforming user interactions, computation, market strategies, and businesses at an unprecedented pace. While VR immerses users in entirely virtual environments, AR overlays digital objects onto the real world, visible on devices such as smartphones or tablets. Mixed Reality (MR) integrates AR and VR, enabling interaction between the physical and virtual environments. AR's potential spans diverse fields, from gaming to education, enhancing our perception and interaction with the world. This paper presents a novel AR-based 'Navigation System' designed to provide intuitive and immersive guidance in unfamiliar environments. It aims to explore the feasibility and effectiveness of AR technology for navigation purposes, focusing on enhancing user engagement and usability. © 2025 IEEE.

关键词： Virtual environments

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An Integrated Agricultural Financial Inclusion System Leveraging Supervised Machine Learning & Data Analytics 6

An Integrated Agricultural Financial Inclusion System Levera...

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

作者： Rathi, Snehal Khedkar, Vijayshri Jadhav, Rutuja Kamate, Mugdha Potdar, Apurva Kamble, Prachi Computer Department Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798331522667

Agriculture is backbone of many economies but still farmers often face many challenges in accessing financial services or available resources for them, making informed crop choices and participating in government initiatives. Because of many farmers are not aware about all these things, to address these concerns, the proposed "Agricultural Financial Inclusion System" provides a comprehensive platform which integrates essential services with aim of improving farmers’ production and their financial awareness. Our system has a crop recommendation feature that helps farmers to choose the best suitable crop for their soil in the farm by taking climate and soil type into account. In order to make sure that farmers are aware about various available services/resources or any government schemes and able to take advantage of them, the platform also offers essential information on a variety of government initiatives, saving plans etc. By combining all these features, the Agricultural Financial Inclusion System encourage farmers to make smart choices which help them to enhance agricultural productivity and financial stability. In order to create more successful and sustainable agriculture sector, this integrated approach hopes to fill the gap between farmers and technology & resources they need. © 2025 IEEE.

关键词： Smart agriculture

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Mitigating Risks in Vehicle Fires: An Integrated Approach for Early Detection and Real-Time Alert System 4th

Mitigating Risks in Vehicle Fires: An Integrated Approach fo...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Suryawanshi, Ranjeetsingh Bhagat, Harshita Karande, Aryan More, Parth More, Vaishali Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819751990

Every year, there are about 170,000–220,000 car accidents each year that involve fire. The cause of the fire ranges from collision, engine malfunction, electric short circuits, and many more. Irrespective of the cause, the damage incurred is close to $2 billion in direct property damage and in severe accidents, even the passengers’ lives. The rapid detection and timely notification of fire incidents are crucial for minimizing property damage and ensuring public safety. The current vehicle fire safety system relies on manual notification, leading to delayed responses and potential risks, especially in low-density areas and nighttime scenarios. Additionally, the lack of proactive fire detection technology hinders early identification, increasing the likelihood of extensive damage and harm to passengers. This paper proposes an integrated alert system automatically notifies fire stations, hospitals, and police upon fire detection, streamlining coordination and reducing response times. Rigorous evaluations are undertaken to assess the system's accuracy, sensitivity, and reliability under various conditions, solidifying its efficacy in addressing vehicle fire incidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fires

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Sentiment and Tone Analysis of Reddit Posts Using Python 4

Sentiment and Tone Analysis of Reddit Posts Using Python

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

作者： Amod Agarkar, Aarti Betgeri, Santushti Nimbalkar, Aryaraje Nikam, Vedant Rane, Chinmay Motade, Pruthviraj Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798331523923

The volume of user-generated content that the social media space, particularly that of Reddit, produces is unprecedented, which now can be considered an excellent source of analysis for the study of public opinion and communication trends. However, such huge unstructured text pieces also pose several problems for analytics that are exacerbated in the presence of noise in the texts, non-standard variations of language usage, sarcasm, and so forth, as well as the requirements for scalable modes of computation. Although new advances in Natural Language Processing(NLP) like BERT and RoBERTa do much better than their previous transformer-based models, at a great computational cost, there is also an enormous labeled dataset to fine-tune. Light in weight but falling flat where the situation might be ambiguous based on the context or subtlety in the expression, there are lexicon-based traditional methods like VADER and TextBlob. A Python-based system that uses advanced NLP techniques for sentiment and tone analysis of Reddit posts. The study categorizes the sentiments as positive, negative, and neutral. The tones were also kept in mind, differentiating formal posts from informal ones. The methodology undertaken was a step-by-step approach, fetching data from the Reddit API, preprocessed removal of noise, and text that is absolutely irrelevant. VADER and TextBlob have been employed to perform sentiment analysis. Developing a tone detection method depends on linguistic features such as vocabulary complexity, sentence structure, and punctuation usage. It was written with respect to computational efficiency and scalability, using Python libraries such as NumPy, NLTK, MatplotLib and spaCy. This work contributes to social media analytics through an inclusive and scalable framework for potential application in analyzing complex unstructured textual data. This maps the generalization to brand reputation management, public policy planning, and content management activities, all of w

关键词： Decision making

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Stationary wavelet transform and SpinalNet trained light spectrum Tasmanian devil optimization enabled DR detection using fundus images

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Multimedia Tools and Applications 2025年第4期84卷 1597-1626页

作者： Tamboli, Mubin Shoukat Dhumane, Amol Prasad, Rajesh Prasad, Jayashree Rajesh Ranjan, Nihar M. Computer Engineering Pimpri Chinchwad College of Engineering Pradhikaran Maharashtra Pune411044 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune India Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Diabetes is a common disease that causes complications in the eyes known as Diabetic Retinopathy (DR). It aids in discovering the DR and tends to be the main cause behind people’s blindness amidst the previous decades. For prevent the complications of DR and to control the level of blood Glucose, the timely and crucial diagnosis is essential. A model is provided for detecting the DR. Here, Light Spectrum Tasmanian Devil Optimization (LSTDO) is utilized with SpinalNet for DR detection. The method undergoes an adaptive median filter and Region of interest mining process to generate a pre-processed image. The Sparse Fuzzy C-Means (FCM) is adapted for segmenting the optic disks followed by feature extraction. Then, the Hough line transformation with Tyler Coye algorithm (HLTCA) is used for blood vessel segmentation. Concurrently, the Stationary Wavelet Transform (SWT) is adapted to input image to overwhelm the deficiency of translation-invariance issue and then the feature mining is done. The feature vector is generated by unifying the outputs of the three-feature extraction phases and is employed to detect DR. The accuracy, sensitivity, and specificity obtained by the LSTDO-based SpinalNet are 91.6%, 89.9% and 92.1%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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