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

共 8635 条记录，以下是311-320 订阅

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm 9

Plagiarism Detection and Similarity Checking Program using M...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Bhavana, Munugapati Rao, Koppula Srinivas Koduru, Gagan Kumar Vatsal, K.Vijay Krupa Parvathi, S. Sravani, Mogiligidda Mlr Institute of Technology Affiliated to Jntu Dept. of Computer Science & Information Technology Hyderabad India Mlr Institute of Technology Affiliated to Jntu Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Hyderabad India Malla Reddy University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Dept.of Computer Science & Information Technology Hyderabad India Mvsr Engineering College Department of Information Technology India

ISBN: (纸本)9798350377972

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment. © 2024 IEEE.

关键词： Contrastive Learning

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Performance and Efficiency Comparison of VisionFive V1 Board and Raspberry Pi 4B

Performance and Efficiency Comparison of VisionFive V1 Board...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Kam, Kai-Heng Wong, Chee-Siang Sor, Kean-Vee Ooi, Boon-Yaik Hong, Zeng-Wei Universiti Tunku Abdul Rahman Department of Computer Science Kampar Malaysia Feng Chia University Dept. of Information Engineering and Computer Science Taichung Taiwan

ISBN: (纸本)9798350324174

This paper presents a comparison of the performance and efficiency of two single-board computers, VisionFive V1 Board and Raspberry Pi 4B model, in the context of Industry 4.0 and IoT applications. Our study focuses on evaluating the compression speed, compute speed, power consumption and performance efficiency of both devices. Our results show that the Raspberry Pi 4B model outperforms the VisionFive V1 Board in all performance metrics. Nevertheless, we note that the VisionFive V1 Board development community is still in a relatively infancy stage compared to the well-developed Raspberry Pi. Our findings contribute to the ongoing discussion on selecting appropriate single-board computers for Industry 4.0 and IoT applications and provide valuable insights for engineers and researchers working in this field. © 2023 IEEE.

关键词： Benchmarking

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Using Pyramid Vision Transformer for Assisting Glaucoma Diagnosis with Small Datasets 21

Using Pyramid Vision Transformer for Assisting Glaucoma Diag...

引用

21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wu, Qinhao Yao, Shuaiyu Ouyang, Wendi Huang, Jingzhe Kang, Qi Eindhoven University of Technology Dept. of Mathematics and Computer Science Eindhoven Netherlands Coll. of Electronic and Information Engineering Tongji University Shanghai China Coll. of Economics and Management Nanjing University of Aeronautics and Astronautics Nanjing China King's College London Dept. of Mathematics London United Kingdom

ISBN: (纸本)9798350365221

Glaucoma is one of the leading causes of blindness worldwide. Patients with open-angle glaucoma have difficulty realizing the graduate vision loss. However, the optic nerve head's morphological change can manifest in the retinal image. Therefore, many studies offer computer-aided glaucoma diagnosis based on retinal image analysis. With the trend of deep learning, there have been several attempts to utilise deep-learning-based glaucoma classification in practice. However, the limited size of the dataset is a problem when adapting deep-learning models to glaucoma classification. Therefore, we test three vision transformer models to handle this problem and demonstrate their performance in glaucoma classification. Also, based on their performance, we give suggestions for their usage in clinical practice. In this paper, we fine-tuned three transformer models for glaucoma classification using two datasets, RIM-ONE-DL and Drishti-GS. Based on our experiments, the pyramid vision transformer (PVT) model achieves the best performance, with good scores in terms of accuracy and sensitivity. This suggests that the PVT can catch the missing detail and can be used to support the glaucoma diagnosis. © 2024 IEEE.

关键词： Ophthalmology

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An Efficient Approach to Deal with Cyber Bullying using Machine Learning: A Systematic Review 2

An Efficient Approach to Deal with Cyber Bullying using Mach...

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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Naveen Kumar, M.R. Vishwachetan, D. Mushtaq Ahmed, D.M. Dayananda, K.J. Manasa, K.J. Nanda, K.V. Vidyavardhaka College of Engineering Information Science and Engineering Mysuru India Ramaiah Institute of Technology Information Science and Engineering Bengaluru India Gsss Ssfgc Dept. of Computer Application Mysuru India MUSoE Dept. of Aiml Manasagangothri Mysuru India Sri Siddhartha Institute of Technology Compter Application Tumkur India

ISBN: (纸本)9798350328202

The prominence of social media platforms like Facebook, WhatsApp, and Instagram have given rise to Cyber crime, notably in the form of Cyber bullying. This unique manifestation of harassment involves individuals exploiting social networks to engage in harmful behaviors such as posting toxic comments or sharing embarrassing content, often causing severe reputation damage to victims, particularly among teenagers. This Paper gives a comprehensive survey of technical approach to tackle this escalating issue, employing advanced machine learning algorithms such as Support Vector Machines (SVM), Convolution Neural Networks (CNN), and J48. The objective is to review models capable of accurately detecting Cyber bullying, thereby contributing to the creation of robust systems that promote a safer online environment, focusing on safeguarding vulnerable demographics like teenagers and also possible future direction. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Diabetes Detection: A Weighted Averaging Approach for Combined Model Accuracy 9

Enhancing Diabetes Detection: A Weighted Averaging Approach ...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sami, Areesha Naseer, Anum Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Dept. of Computer Science Pakistan University of Central Punjab Faculty of Information Technology Department of Computer Science Lahore Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan University Lahore Information Technology Pakistan

ISBN: (纸本)9798350394474

Diabetes, marked by prolonged high blood sugar levels, poses a significant global health challenge. Precise early prediction is vital but faces hurdles due to limited data and complexities like outliers. Uncontrolled diabetes heightens severe complications, driving research into advanced machine and deep learning techniques. Healthcare professionals recognize the crucial role of Machine Learning Algorithms (MLA) in enhancing disease identification. This study integrates deep and machine learning models through techniques like K-means, CNN, NB, and SVM. Combining their predictions using weighted averaging yields a 79.5% accuracy, improving diabetes prediction globally. © 2024 IEEE.

关键词： Support vector machines

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Designing of Creator's information Hub using Machine Learning 2

Designing of Creator's Information Hub using Machine Learnin...

引用

2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gabhane, Diksha Gawande, Ujwalla Gaikwad, Sanjana Gajarlewar, Swati Bhawalkar, Rita Yeshwantrao Chavan College of Engineering Dept. of Computer Science & Design Nagpur India Yeshwantrao Chavan College of Engineering Dept. of Information Technology Nagpur India NCPharmacy Wanadongri Nagpur India G H Raisoni College of Engineering and Management Dept. of Cyber Security and Data Science Pune India

ISBN: (纸本)9798331528713

The proliferation of educational content on platforms like YouTube has transformed digital learning, yet it presents challenges in effective creation, dissemination, and impact assessment. This paper proposes a strategic initiative to collaborate with 6,000 active educational YouTubers to enhance global educational outreach. Central to this initiative is the compilation and analysis of a comprehensive dataset containing vital information about content creators and their audiences. Leveraging advanced machine learning algorithms, the analysis aims to identify collaboration opportunities, optimize engagement strategies, enhance content relevance and impact, facilitate knowledge sharing, and measure outcomes. The problem statement highlights fragmentation in educational content, lack of data-driven insights, and suboptimal collaboration opportunities. Addressing these challenges requires a structured approach to connect creators, leverage data analytics, and establish a collaborative ecosystem for enriching educational experiences worldwide. © 2024 IEEE.

关键词： Adversarial machine learning

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Prediction of Cyclone with Metrological Factors using Gradient Boosting Algorithm 4

Prediction of Cyclone with Metrological Factors using Gradie...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Durai Arumugam, S.S.L. Praveenkumar, R. Shreya, P. Selvakumar, J. Easwari Engineering College Dept. of Information Technology Chennai India Easwari Engineering College Dept. of Electronics and Communication Engineering Chennai India Sri Ramakrishna Engineering College Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300093

Innovations in computer vision and satellite imaging technologies have made fully automatic weather forecasting a reality. The cyclone prediction system is developed with the goal of reducing both natural disasters and human casualties. Machine Learning object recognition system is created to pinpoint the cyclone, which contains more information about the storm's activity. This is crucial work because it pinpoints the exact spot where the cyclone's formation. The accuracy of cyclone forecasts from many machine learning and deep learning models is 86%-95% or higher, while the accuracy of cyclone eye detection is 87% or higher from the same models. The impressive findings show that the experiment can surpass both established methods and previous attempts. © 2023 IEEE.

关键词： Weather forecasting

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Early Cancer Detection and Diagnosis using Deep Learning Models in Medical Imaging 4

Early Cancer Detection and Diagnosis using Deep Learning Mod...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sikarwar, Shailendra Singh Rathore, Rachna Kumar, Sharma Sonu Monalisha, Mitali Satapathy, Lopamudra Nisha, M. Jiwaji University Dept. of Computer Science Pgv College Gwalior India Jiwaji University Dept. of Mathematics Gwalior India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Paramedical Science Ranchi India Usha Martin University Dept. of Agriculture Ranchi India Dhanalakshmi Srinivasan University Dept. of Cse Coimbatore India

ISBN: (纸本)9798350364729

Breast cancer is a leading cause of mortality among women globally. While its exact cause remains uncertain, no definitive methods exist for its prevention or treatment. However, early detection through mammography plays a crucial role in improving management strategies and increasing the likelihood of recovery. Mammography aids not only in early cancer detection but also in identifying various conditions and distinguishing between cancer types - benign, malignant, or normal. This study explores an advanced machine learning and image processing framework for detecting and classifying breast cancer. The approach incorporates machine learning techniques alongside feature extraction, selection, and image preprocessing to classify breast cancer and related skin diseases effectively. Key advancements include the use of a geometric mean filter to enhance image quality and AlexNet for feature extraction. The relief algorithm is employed for feature selection, and classification is performed using machine learning models such as random forest, K-nearest neighbors (KNN), least square support vector machine (LS-SVM), and Naïve Bayes. This innovative methodology provides an accurate and efficient solution for breast cancer detection through advanced image analysis techniques. © 2024 IEEE.

关键词： Lung cancer

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Real-time Human Movement Recognition and Interaction in Virtual Fitness using Image Recognition and Motion Analysis 12

Real-time Human Movement Recognition and Interaction in Virt...

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12th International Conference on Awareness science and Technology, iCAST 2023

作者： Yeh, ChunHsiu Shen, Wei-Cheng Ma, Chi-Wei Yeh, Qiu-Tong Kuo, Chung-Wei Chen, Jong-Shin Dept. of Information Engineering Computer Science Feng Chia University Taichung Taiwan CollegeofComputerScience Jimei University Xiamen China Dept. of Information Communication Engineering ChaoYang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This research focuses on the application of image recognition, motion detection, and artificial intelligence techniques to achieve real-time identification and analysis of human movements in a dynamic manner. The primary goal is to establish a seamless association between captured human movements and corresponding actions performed by virtual avatars. The implementation involves the utilization of image processing methodologies from python's OpenCV and pygame libraries. MediaPipe technology is employed to capture users' images and facilitate accurate detection of specific movement patterns. By calculating the angles between users' limbs and torso, the correctness of certain movements can be assessed. The research is practically developed and applied to a simulated fitness system, where an interactive exercise game platform is devised. The system is characterized by its engaging user interface, enhancing the ease and enjoyment of fitness activities. The seamless integration of interactive virtual avatars and real users within physical activities serves to stimulate greater participation, particularly among the elderly. The amalgamation of image recognition and motion detection technologies presents opportunities for combining fitness and entertainment in diverse life scenarios, offering an enhanced experience of physical exercise and enjoyment. © 2023 IEEE.

关键词： Image recognition

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Data Pre-Processing Framework for Kannada Vachana Sahitya

Data Pre-Processing Framework for Kannada Vachana Sahitya

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Lavanya, C.B. Swamy, H.S. Nagendra Pavan Kumar, M.P. University Of Mysore Dept. Of Studies In Computer Science Mysore India Dr. Ait Bangalore India J.N.N College Of Engineering Dept. Of Information Science & Engg. Shimoga India

ISBN: (纸本)9798350371567

Advancements in Natural Language Processing (NLP) driven by machine learning, deep learning, and artificial intelligence have significantly broadened its scope and improved interactions between humans and computers. Despite these advancements, NLP systems encounter challenges arising from incomplete and error-prone data, which can result in biased model outputs. Technical domains present further hurdles, necessitating domain-specific fine-tuning and the development of custom lexicons. Additionally, many languages lack robust NLP support, limiting accessibility. In this context, innovative NLP data pre-processing and tokenization methods tailored for Kannada Vachana Sahitya texts are investigated. The majority of documents or text utilized in any language processing applications consists of raw text, in which some of the words are not represented in the standard form. There is a necessity of pre-processing the input text before building the translation model. In this paper, the main goal is to pre-process the source text with essential steps like Cleaning, Parsing, Tokenization, Padding, Stemming and Lemmatization. During this process, Kannada source text - Vachana Sahitya is tested on bilingual corpus and approximately 35% of non-standard input text is considered for experimental analysis. © 2024 IEEE.

关键词： Natural language processing systems

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