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检索条件"机构=Computer Science Engineering with Specialization in Artificial Intelligence & Machine Learning"

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Classifying Parkinson's Disease Using Speech Features 2

Classifying Parkinson's Disease Using Speech Features

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Soliman, Hala A. Elshourbagi, Mohammed S. Dahy, Malak A. Osman, Mena M. Kamal, Bassant A. Elaziz, Mohamed Abd El-Rashidy, Nora Galala University Faculty of Computer Science and Engineering Suez435611 Egypt Intelligence-Kferelshikh University-kaferelshikh Machine learning and information retrieval-faculty of artificial Egypt

ISBN: (纸本)9798350362633

This scientific paper focuses on the detection of Parkinson's Disease in individuals using vocal/speech features. Specifically, the study records an individual's pronunciation of the short 'a' sound (\\ \\\ \\\ae) and applies various Signal Processing Algorithms to process the recorded data. To address the data's class imbalance, both oversampling and undersampling techniques are implemented, and their results are compared. Additionally, a feature selection algorithm is employed to eliminate noise from the dataset while retaining the relevant features necessary for the model. Logistic regression, Support Vector machines (SVM), decision trees, and Ensemble learning methods are utilized to determine the likelihood of an individual having Parkinson's Disease. Furthermore, SHAP (SHapley Additive exPlanations) analysis is conducted to gain a deeper understanding of the impact of features on class determination for each instance. The Random Forest model achieves an accuracy of approximately 95% when trained on the oversampled dataset, surpassing the accuracy obtained from the undersampled dataset. Additionally, a graphical user interface (GUI) is successfully developed to predict the diagnosis of Parkinson's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Deep learning Approaches for Strengthening Network Intrusion Detection Systems 7

Deep Learning Approaches for Strengthening Network Intrusion...

引用

7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kaulgud, Sanjeev P Chempavathy, B. Tejeswar, Nama Sai Vigneshwar, Chekka Kesava Kumar, Akuthota Rakesh Eswar, Akula New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India New Horizon College of Engineering Department of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331527822

Strengthening network security is a must in today's digital landscape. Existing Intrusion Detection Systems commonly make use of deep learning techniques such as Deep Neural Networks to identify malicious & anomaly activities. However, there are serious problems of overfitting and huge demand for computational resources in this system that hinder the efficiency and scalability of this system. It introduces an advanced form of an Intrusion Detection System (IDS) which integrates a hybrid machine learning algorithm with Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) along with other machine learning techniques to improve threat detection. This system would realize the recognition of established patterns as well as new anomalies about attacks by means of enhanced feature engineering and real-time AI-driven threat intelligence. It also addresses scalability for more extensive and complex configurations of the network as well as response mechanisms that automatically neutralize threats quickly. Then there is user behavioral analytics that helps in identifying insider threats. It is aimed at providing an all-embracing security solution that is strong and adaptable, greatly improving overall protection of the network. Hence, the effective execution of this project will thus provide a new benchmark for the effectiveness of IDS in delivering reliable security across different sectors as well as paving the way for further improvements in cybersecurity around the globe. © 2024 IEEE.

关键词： Network intrusion

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Automated Unified System for LPG Refill Booking & Lekage Detection: A Pervasive Approach 15

Automated Unified System for LPG Refill Booking & Lekage Det...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Suraksha, P. Aishwarya Kumari, Kavana L. Dept.of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Dept.of Artificial Intelligence and Machine learning Vidyavardhaka College of Engineering Karnataka Mysuru India

ISBN: (纸本)9798331300579

Civilization is becoming increasingly aware of natural resources, it results in the use of propane and butane, and hence the use of *** India most people use the LPG *** per current government regulations, intense demand but shortage in production of LPG cylinder, once a new cylinder is booked we need to wait for some days to get it *** no cost-efficient technique is available which identifies when cylinder is about to get *** this paper we designed, developed, a proven Automated Unified System using smart Pervasive sensors, where LPG retailer requests for a new cylinder using Short Message Service (SMS) once it identifies cylinder will be empty in few days and also provides detection of gas leakage for better service to the consumers and reduce the scope of irregularities. © Grenze Scientific Society, 2024.

关键词： Liquefied petroleum gas

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Investigating Youtube's Educational Videos: Patterns, User Conduct and Suggestion Techniques

Investigating Youtube's Educational Videos: Patterns, User C...

引用

2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sundar, Koushik Eugene Berna, I. Vijayakumar, R. Ambeth Kumar, V.D. Vijay, K. Citibank NA Jersey CityNJ United States Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Design Chennai India Mizoram University Department of Computer Engineering Mizoram Aizawl India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331522988

This research voyage drew us far into the world of instructional videos on YouTube, a platform that promotes learning and discovery. Our goal was to identify patterns and trends in instructional films so that we might improve the user experience by suggesting better content. We conducted an analysis of data that we directly gathered from YouTube using certain technologies. The varied lifespans of trending videos across nations were one fascinating discovery. Certain videos were more well-known for a longer period of time than others. We were captivated by the cultural diversity found in video lifespans. We also looked at likes, dislikes, views, and comments to find robust user involvement worldwide. Through engagement data, different countries demonstrated their preferences for expressing oneself. It was discovered that a video's popularity was greatly influenced by the timing of the content's release. Content producers can stay relevant by keeping in mind this correlation. Sentiment analysis also uncovered emotional overtones connected to video tags, offering perceptions into what appeals to viewers. We also identified the optimal time, based on user involvement and behavior, to watch instructional videos. Ultimately, we used recommendation algorithms to tailor the YouTube learning experience by fusing content-based and collaborative filtering. All things considered, clarifies the complex world of instructional content and offers advice to users and producers on this dynamic platform. Our research will continue to influence the direction of more interesting instructional content as YouTube develops. © 2024 IEEE.

关键词： Video analysis

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Enhanced Image Processing for Automobile Accident Detection Using Deep learning 3

Enhanced Image Processing for Automobile Accident Detection ...

引用

3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Usharani, S. Dhanushkumar, J. Manju Bala, P. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning Villupuram India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350391565

Road safety can be creatively increased by utilizing systems for reporting and detecting accidents use the YOLO algorithm. Yolo, which stands for "You Only Look Once,"is a sophisticated object recognition system that can identify and pinpoint objects in live video streams. By identifying accidents and notifying emergency services, the system lowers the reaction times and increases the possibility of saving lives by utilizing the YOLO algorithm. The object detection and alarm systems are the two primary parts of the proposed system. The YOLO method is employed by the object recognition module to look for accidents in live video broadcasts. A module of accident photos was used to teach the system to accurately identify incidents. When an accident is discovered, an alert system is activated. The location of the accident and a brief account of what transpired are communicated to the emergency services by the alert system. This information is communicated to emergency services through a wireless communication network, which expedites response times and increases the likelihood of saving lives. Positive results were obtained by testing the system using the images and an accident module. It was shown that the warning system could react in a couple of seconds and that the YOLO algorithm could identify accidents having a precision of around 94. Highways, busy intersections, and other high-risk areas may have systems in place to increase traffic safety and lower the number of accidents. Developing crash detection and warning systems using deep learning using the YOLO technique is one way to potentially increase road safety. With the use of technology, problems may be precisely identified in real-time video feeds, alerting emergency services and potentially increasing survival rates and reaction times. © 2024 IEEE.

关键词： Image enhancement

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Efficient Chromosome Abnormal Detection using Yolo-V8 model 3

Efficient Chromosome Abnormal Detection using Yolo-V8 model

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Umarani, V. Isaac, Augustian Premnath, K. Kannan, G. Sanjay, A. Hari Haran, L.K. Saveetha Engineering College Dept of Computer Science and Engineering TamilNadu Chennai India Saveetha Engineering College Dept of Artificial Intelligence & Machine Learning TamilNadu Chennai India

ISBN: (纸本)9798331523893

Human chromosomes exhibit complex and intricate patterns, making their analysis and data interpretation challenging. Studying chromosomes is crucial for identifying genetic disorders, such as Down syndrome, and various types of cancer. Traditional karyotyping methods depend heavily on manual method, which causes human error and high cost. To address these challenges, a deep learning-based approach is proposed to automate the karyotyping process, improving both accuracy and efficiency while minimizing human involvement and errors. This research leverages advanced deep learning techniques, specifically the YOLOv8 object detection framework, to enhance chromosomal analysis. From experiments, the proposed YOLO V8 model achieves a 95.2% accuracy, surpassing other models such as DCNN, CNN, and VGG-19. This work represents a significant step toward developing a more reliable and efficient diagnostic tool for detecting genetic disorders. © 2025 IEEE.

关键词： Deep learning

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Detection of Medicinal Plants Using machine learning 12th

Detection of Medicinal Plants Using Machine Learning

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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Early Melanoma Detection and Classification Using CNN and Confusion Matrix Analysis

Early Melanoma Detection and Classification Using CNN and Co...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dinesh Kumar, K. Deepthi, K. Jaya Saravanakumar, S. Balamurugan, S. Govindharaj, I. Reddeppa, Pulikandla Akshay Vel Tech University Department of Computer Science and Engineering Chennai India Learning Mohan Babu University Department of Artificial Intelligence and Machine Tirupati India Paavai Engineering College Department of Information Technology Namakkal India Bengaluru India Learning Vel Tech University Department of Artificial Intelligence and Machine Chennai India

ISBN: (纸本)9798331510022

This article brings up a new method for detecting and classifying skin cancers using Convolutional Neural Networks (CNN) and Confusion Matrix analysis. The main work of this work evolves around detection of Melanoma and Non Melanoma cells. The large dataset on skin cancers is used to teach the CNN model to accurately classify different types of skin lesions into different malignant and non-cancerous groups. The integration of Confusion Matrix, in this case, allows for accurate identification of the classification errors made by the model as well as possible areas for improvement hence making it possible to comprehensively evaluate how well the model performs. Therefore, early recognition is indispensable to successful treatment as skin cancer is a typical yet fatal condition. Results shows that the suggested proposed model achieves 95.5% accuracy and performance in comparison with other methods found in literature. Additionally, Modified DenseNet201 model has a sensitivity of 95.96% as well as a specificity of 98.03%. © 2024 IEEE.

关键词： Lung cancer

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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

Enhanced Model for Mango Detection and Quality Classificatio...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Aryan, Adla Mohammed, Abdul Aleem Chabra, Manish Rasheed, Syed Saarib Adnan, Mohammed Raoof, Mohammed Abdul Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Muffakham Jah College of Engineering and Technology Department of Computer Science and Engineering Hyderabad500034 India Methodist College of Engineering and Technology Department of Artificial Intelligence & Data Science Telangana Hyderabad500001 India

ISBN: (纸本)9798331529833

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process. © 2025 IEEE.

关键词： Fruits

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A Comparative Study of DenseNet121 and InceptionResNetV2 model for DeepFake Image Detection 3

A Comparative Study of DenseNet121 and InceptionResNetV2 mod...

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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Manoranjitham, R. Swaroop, Somu Santhi Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The rise of deep fake technology poses a significant challenge to societal integrity, necessitating robust detection methods. This research study investigates the effectiveness of DenseNet121 and InceptionResNetV2 deep learning models in detecting deep fake images. The study starts with the curation of the dataset and then applies different preprocessing methods to improve the quality of the dataset like scaling, normalization, and data augmentation. The DenseNet121 and InceptionResNetV2 models are trained to differentiate authentic and manipulated images based on distinctive features. DenseNet121's dense connectivity facilitates feature reuse, enabling effective extraction of discriminative features crucial for identifying manipulated content. In contrast, InceptionResNetV2's sophisticated architecture exploits both inception and residual connections, enhancing its ability to capture intricate patterns indicative of deep fake manipulation. The comparative analysis of performance metrics reveals InceptionResNetV2's superiority, achieving an impressive accuracy rate of 99.78% compared to DenseNet121. These findings underscore the efficacy of leveraging existing architectures for identifying deep fake images, thereby contributing to the ongoing efforts to combat misinformation and safeguard digital content integrity. © 2024 IEEE.

关键词： Deep learning

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