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检索条件"机构=Dept of Computer Science and Computer Engineering"

共 38701 条记录，以下是121-130 订阅

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DoConnect: Patients Health Care Application 3

DoConnect: Patients Health Care Application

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Arun Kumar, S. Deepak Sai, L. Vinay, V.S. Nikith, J. Gokul, L. Vedanth Gowda, K. Presidency University Dept of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350381931

In recent years, the healthcare sector has witnessed transformative changes through the integration of cutting-edge information technology. This study introduces 'DoConnect,' an innovative web application meticulously crafted with Java Servlets, JSP, HTML, and CSS. DoConnect serves as a comprehensive platform, facilitating seamless patient-doctor interaction by simplifying appointment scheduling, prescription management, and medication delivery. The platform, designed to be user-friendly, employs Java Servlets and JSP to efficiently manage appointments, enabling patients to select convenient time slots and fostering secure communication with healthcare providers. Notably, DoConnect streamlines medication delivery, dispatching prescriptions directly to patients' doorsteps, enhancing convenience and operational efficiency. The integration of a judicious rating system allows patients to provide feedback, fostering transparency and accountability in the healthcare community. HTML and CSS contribute to an aesthetically pleasing and intuitive user interface, ensuring a positive user experience. This research paper meticulously details DoConnect's architecture, design principles, and implementation intricacies, highlighting the crucial role of technological interventions in bridging the gap between patients and doctors. DoConnect exemplifies a paradigmatic application of Java-based technologies in healthcare, showcasing the potential for elevated patient care through inventive digital solutions. © 2024 IEEE.

关键词： HTML

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Deep Learning Model for Detecting the Presence of Neurological Conditions in MRI and CT Scans 2

Deep Learning Model for Detecting the Presence of Neurologic...

引用

2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Kanchan, Jayapadmini Ananya, H.N. Anoksha, N.A. Hegde, Sarika Dept. of Computer Science and Engineering Nitte Karkala India

ISBN: (纸本)9798350354461

An AI-powered MRI/CT analysis solution for medical professionals can significantly enhance diagnostic accuracy and efficiency. By harnessing deep learning algorithms, this solution can minimize human error and accelerate image interpretation. The incorporation of a Convolutional Neural Network (CNN) model empowers the solution to identify abnormalities in MRI/CT images with high consistency and reliability. This solution supports intricate case evaluations and integrates effortlessly with electronic health records, making high-quality diagnostic capabilities available to a wider range of healthcare professionals. The solution is meticulously designed to detect various abnormalities, including tumors, Alzheimer's, and hemorrhages from MRI images, as well as COVID-19 and checking for squamous conditions from CT images. Ultimately, this solution aims to improve patient outcomes through precise and prompt diagnoses, while also serving as an educational resource and contributing to ongoing medical research and innovation. The brain tumor MRI model achieved an accuracy of 87.5% after 100 epochs, while the chest CT scan model attained 95.65% accuracy in just 50 epochs. Brain hemorrhage MRI model accomplished 88.45% accuracy in 30 epochs, and the COVID-19 dataset model reached 78.9% accuracy in 40 epochs. Lastly, the Alzheimer's model obtained an accuracy of 80.70% after 40 epochs. © 2024 IEEE.

关键词： Convolutional neural networks

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Affordable Navigation Aid: A Smart Walking Stick for the Visually Impaired 6

Affordable Navigation Aid: A Smart Walking Stick for the Vis...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Muhammed Suhair, K. Malayil, Shanid Deepak, A.V. Faslu Rahman, T.P. Abdul Kabeer, Marwan Pookatil Mea Engineering College Dept: Computer Science Perinthalmanna India

ISBN: (纸本)9798350381689

An innovative effort to improve mobility and freedom is the Low-Cost Smart Walking Stick, which is intended for those with vision impairments. This inventive walking stick offers an effective navigation solution for a variety of locations by integrating affordable technologies like the ESP8266, ultrasonic sensor, buzzer, and vibrator. Users are greatly enhanced by being instantly notified about barriers, changes in topography, and potential risks through real-time notifications and haptic feedback. With the help of this affordable smart walking stick, people who are blind or visually impaired can now confidently and independently traverse their daily lives. It is a major technological advancement. © 2024 IEEE.

关键词： Ultrasonic sensors

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Predictive Modeling of Parkinson's Disease Using Machine Learning Techniques 6

Predictive Modeling of Parkinson's Disease Using Machine Lea...

引用

6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Helali, Sad Shariar Based, Md. Abdul Rahman, Md. Zubair Rahman, Md. Arafat Dhaka International University Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331531973

Parkinson's disease (PD) is a common neurodegenerative disorder that affects millions of individuals worldwide. Early and accurate diagnosis is crucial for managing symptoms and improving patient outcomes. This research paper presents a comprehensive approach to predicting Parkinson's disease using the MDVP (Multidimensional Voice Program) dataset by machine learning models integrated with a user-friendly web application. The research uses eight machine learning methods, including Decision Tree, K-Nearest Neighbors, AdaBoost, Logistic Regression, Gradient Boosting, Random Forest, Nearest Centroid, and Naïve Bayes. Among them, Gradient Boosting and Random Forest show the best result with 100.00% accuracy and additionally, the AdaBoost and Decision Tree classifiers also demonstrated high performance, achieving 98.31% accuracy. These findings suggest the potential of these models for distinguishing between Parkinson's disease and healthy individuals. Also, the web application we developed allows users to input voice data and get a prediction about having Parkinson's disease. This tool is designed to be user-friendly, providing quick and reliable results which ultimately lead to better patient care and more efficient use of healthcare resources. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Smart Garden Watering and Lighting System Integrated with Machine Learning Model to Maintain Urban Gardens

Smart Garden Watering and Lighting System Integrated with Ma...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shahapure, Arsheyee Siddha, Rishika Tewary, Tanmay Shahapure, Aniruddha Mantri, Shamla Kamble, Aparna Dept. Computer Science and Engineering MIT-WPU Pune India

ISBN: (纸本)9798331512965

Garden maintenance may be a difficult task, particularly in urban settings. Many people are not skilled enough to determine their plants' precise requirements for lighting and watering. Conventional gardening techniques frequently result in underwatering or overwatering, wasting resources, and producing unhealthy plants. Our completely automated smart garden watering and lighting system offers a fully automated solution to these problems. With its sophisticated sensors, this system keeps an eye on the quantity of moisture in the soil and the amount of sunshine it receives, making sure plants get the correct amount of both for healthy growth. Traditional gardening involves guesswork and physical effort, which our solution avoids by automating the irrigation and lighting operations. You may have the advantages of a flourishing garden without the trouble of ongoing upkeep with our intelligent gardening system. Our method offers a dependable and effective solution to care for your plants and improve your urban green space, regardless of experience level. © 2025 IEEE.

关键词： Support vector machines

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Event Identification and Chronology Model of Ramayana 8

Event Identification and Chronology Model of Ramayana

引用

8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Paul, Apurba Mishra, Rishabh Padia, Nayan Das, Dipankar IEM Kolkata UEM Kolkata Dept. of Computer Science & Engineering Kolkata India IEM Kolkata Dept. of Computer Science & Engineering Kolkata India Jadavpur University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

The paper introduces an event chronology model for Hindu mythological texts, with a focus on the Ramayana's Bala-kanda. It captures and structures sentence-level events based on their chronological order to better understand narrative progression and character interactions. A manually crafted dataset of 1217 events, involving 214 characters across 75 chapters, is used to generate a knowledge graph. The graph organizes events by sequence, linking characters, actions, and chapters within the storyline. The event chronology is mapped by establishing relationships between events, ensuring they are connected in the proper sequence to reflect the narrative flow. This approach enables a deeper understanding of how each event contributes to the overarching story, providing insights into both the individual actions of characters and their significance within the broader mythological narrative. Transformer-based models (BERT, DistilBERT, and RoBERTa) were utilized for predicting subsequent events, with RoBERTa showing the highest accuracy in preserving the correct event chronology. This structure is key to analyzing the sequential development of the Ramayana's storyline. © 2025 IEEE.

关键词： event chronology Hindu mythological texts knowledge graph Ramayana sentence-level event extraction transformer models

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A Comprehensive Approach to Song Popularity Forecasting 2

A Comprehensive Approach to Song Popularity Forecasting

引用

2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Kishore, N. Santhiya, P. Mohamed Askar Asib, M. Kirubaaharan, S. Kongu Engineering College Dept. Computer Science India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Assessing the potential success of a song based on its acoustic characteristics is a crucial task within the music industry. Traditionally, this has been approached by leveraging audio features independently. In this study, we take a novel approach by jointly exploiting audio features, framing as a regression on prediction task. Our methodology employs a wide architecture, enabling a comprehensive analysis. Additionally, we enhance feature sets by incorporating information about track release years. Evaluation on the Million Song Dataset, using Billboard Hot 100 inclusion as a metric for song success, reveals that our approach surpasses baseline methods and those utilizing low- or high-level features in isolation. Notably, the inclusion of release year alongside mood and vocal features further enhances predictive accuracy, highlighting the efficacy of our comprehensive model in gauging song popularity. © 2024 IEEE.

关键词： Music

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Optimising Vertical Mobility: Using Machine Learning to Reduce Passenger Wait Time in Elevators 5

Optimising Vertical Mobility: Using Machine Learning to Redu...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Talwandi, Navjot Singh Walia, Navpreet Kaur Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Elevators are essential in today's urban infrastructure but struggle to keep pace with increasing demand, resulting in long passenger wait times This research aims to transform vertical transportation through better machine learning each on to increase the efficiency of the elevator proposes an innovative way to streamline elevator operation, ultimately reducing passenger waiting time and improving overall user satisfaction. © 2024 IEEE.

关键词： Efficiency Optimization Elevators Machine Learning Passenger Wait Times Predictive Models Scheduling Algorithms Traffic Analysis Vertical Transportation

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Comparative Analysis of Lung Cancer Detection Using CNN, KNN, and SVM Algorithms: Evaluating Accuracy and Performance

Comparative Analysis of Lung Cancer Detection Using CNN, KNN...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Jalaja, S. Prerita, H.G. Bangalore India Dept of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350385199

Lung cancer remains a leading cause of mortality, emphasizing the crucial need for early detection to improve survival rates. Computed tomography (CT) imaging stands out as a pivotal strategy in this endeavor. However, the escalating volume of CT images necessitates efficient and accurate automated detection methods to mitigate manual labeling efforts and ensure consistent results. This research paper employs a comprehensive strategy, merging traditional machine learning models (including KNN and SVM) with state-of-the-art feature extraction methods such as PCA, HOG, and VGG16 transfer learning, as well as utilizing a dedicated Convolutional Neural Network (CNN) for classification. By amalgamating these methodologies, the study seeks to comprehensively evaluate their efficacy in accurately diagnosing lung cancer. The anticipated outcomes aim to contribute novel insights to medical image analysis, particularly in advancing early cancer detection. Through this research, valuable contributions are expected to be made towards the enhancement of medical image analysis techniques, thereby pushing the boundaries of early cancer detection and ultimately improving patient outcomes. The best result of the proposed model shows the accuracy percentage of the SVM classifier varies from 90% to 96%, the accuracy of the KNN classifier varies from 95% to 100%, and the accuracy of the CNN classifier varies from 96% to 99%. © 2024 IEEE.

关键词： Lung cancer

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Cyberbullying Detection using Deep Learning Algorithms 3

Cyberbullying Detection using Deep Learning Algorithms

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nirmala Devi, K. Jayanthi, P. Rajasekar, Vani Rathinasamy, Rahul Dhandapani, Pradeep Rasil Risam, F. Dept. of Computer Science Kongu Engineering College Erode India

ISBN: (纸本)9798331527549

Social media's widespread use has made it easier for people to communicate openly, but it has also fuelled the growth of cyberbullying, which has seriously harmed victims' emotional and mental health. The intricacy of human language and the variety of abusive behaviours make it difficult to detect cyberbullying. To solve this problem, numerous studies have used deep learning (DL) and machine learning (ML) algorithms over time. However, deep learning (DL) algorithms have transformed text analysis by automatically extracting features and comprehending contextual dependencies in language, while machine learning (ML) techniques are limited in their ability to handle complex and contextual linguistic patterns. Building upon this framework, this study investigates the use of transformer-based models—more especially, BERT—for the detection of cyberbullying. Out of the proposed models, DistlBERT gives the best accuracy, precision, recall, and F1-score of 98.87%,98.87%, 98.87 and 98.86% respectively. © 2025 IEEE.

关键词： Adversarial machine learning

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