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检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

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UZNER: A Benchmark for Named Entity Recognition in Uzbek 12th

UZNER: A Benchmark for Named Entity Recognition in Uzbek

引用

12th National CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Yusufu, Aizihaierjiang Jiang, Liu Ainiwaer, Abidan Teng, Chong Yusufu, Aizierguli Li, Fei Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Information Management Wuhan University Wuhan China School of Computer Science and Technology Xinjiang Normal University Urumqi China

ISBN: (纸本)9783031446924

Named entity recognition (NER) is a key task in natural language processing, and entity recognition can provide necessary semantic information for many downstream tasks. However, the performance of NER is often limited by the richness of language resources. For low-resource languages, NER usually performs poorly due to the lack of sufficient labeled data and pre-trained models. To address this issue, we manually constructed a large-scale, high-quality Uzbek NER corpus of Uzbek, and experimented with various NER methods. We improved state-of-the-art baseline models by introducing additional features and data translations. Data translation enables the model to learn richer syntactic structure and semantic information. Affix features provide knowledge at the morphological level and play an important role in identifying oversimplified low-frequency entity labels. Our data and models will be available to facilitate low-resource NER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Dynamic Face Interaction: Simulating 3D Spatial Relationships with Mesa and OpenCV

Dynamic Face Interaction: Simulating 3D Spatial Relationship...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Ravi Charan Podupuganti Hemanth Injeti Ashish Paul Kandula Rishu Jaiswal Anantha Hothri Inuguri Manju Khanna Department of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The dynamic face interaction model simulates real-time spatial connections by calculating distances between recognized faces and a camera by merging Mesa and OpenCV. It leverages OpenCV’s Haar cascades to offer dynamic face interactions depending on proximity in 3D space through the use of FaceAgent and FaceDistanceModel classes. It is useful for social dynamics, surveillance, and human-computer interaction. It employs LBPH facial recognition and Haar cascades to precisely modify agent placements. The purpose-built dataset addresses difficulties in comprehending social dynamics and strengthens the originality of research while promoting ethical research practices. This creative method highlights the transformational potential of combining simulation and AI technologies, and it encourages researchers to carefully examine the dataset in quest of significant breakthroughs while being aware of biases.

关键词： Human computer interaction Technological innovation Three-dimensional displays Power measurement Face recognition Surveillance Real-time systems Time measurement Artificial intelligence Complex systems

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Enhancing Web Security with Two-Way Authenticated SSL Communication

Enhancing Web Security with Two-Way Authenticated SSL Commun...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Akshith Kosanam Neeharika Ayalasomayajula G Vivek C.R Kavitha Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The use of two-way SSL or Secure Socket Layer communication becomes the key for effectively creating a secure two-way communication between the servers and the clients in web applications. This research delves into an understanding of the objectives and importance of two-way SSL authentication with a focus on security, encryption, as well as protection against most threats including man in the middle attacks. The use of two-way SSL is also demonstrated in a Flask integrated secure login system example that demonstrates how mutual authentication and encryption are implemented so as to provide confidentiality, integrity, and authenticity of data that are being transmitted between the server and different clients. Therefore, this research draws attention to two major areas of concern through demonstrating the relevance of two-way SSL when it comes to issues of security and defending data from unauthorized access and hacking in current complex web platforms.

关键词： Privacy Sockets Prevention and mitigation Authentication Information leakage Encryption Security Servers Protection computer crime

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A Comparative Analysis of Black-Box and White-Box Models for IOT Botnet Detection

A Comparative Analysis of Black-Box and White-Box Models for...

引用

International Conference for Convergence of Technology (I2CT)

作者： Richa Vivek Savant Spoorthi M Penumarty Krishna Mohan Sreebha Bhaskaran Shinu M. Rajagopal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Botnets pose a significant threat to network security, especially to the potential server crashes they can cause. The proposed study focuses on methods for identifying the presence of and the type of botnet activity, by implementing machine learning techniques, and consecutively comparing the black-box and white-box approaches employed. Utilizing the CTU-13 and N-BaIoT datasets, the study involves data preprocessing and a comparative analysis of various models. The LGBM Classifier emerges as the top-performing model for detecting the presence of a botnet, while the XGBoost Classifier excels in identifying the specific type of botnet attack based on different performance evaluation metrics.

关键词： Performance evaluation Analytical models Botnet Data preprocessing Closed box Machine learning Network security

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Advancing Ophthalmic Diagnostics: Employing Deep Learning for Precision Detection of Retinal Damage in OCT Images

Advancing Ophthalmic Diagnostics: Employing Deep Learning fo...

引用

Data science and Network Security (ICDSNS), International Conference on

作者： M Rithani R Prasanna Kumar Veda Chatiyode Janani Srinivasan Anusha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Retinal optical coherence tomography imaging is an important diagnostic tool for early detection and monitoring of various retinal disorders. This study aims to improve diagnostic accuracy by leveraging deep learning methods such as VGG16 and InceptionV3 models to classify retinal OCT images automatically, ultimately enhancing ocular diagnostics and clinical decision-making. Utilizing a dataset comprising approximately 85,000 high-resolution images across primary retinal condition categories including diabetic macular edema (DME), age-related macular degeneration (AMD), Choroidal Neovascularization (CNV), and healthy retinas, the efficacy of these deep learning models is assessed in this comprehensive study. Results demonstrate that VGG16 excels in CNV classification and InceptionV3 is proficient in identifying normal retinal conditions. Comparative analysis underscores their distinct strengths, with both models achieving an impressive overall accuracy of 92.76%. These findings demonstrate the potential of deep learning for automating OCT image interpretation and enhancing ocular diagnostics, thereby improving patient outcomes and clinical decision-making resulting in advanced healthcare practices.

关键词： Deep learning Accuracy Optical coherence tomography Decision making Refining Medical services Network security Retina Monitoring Diseases

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Scrutinization of Threats from a Cloud-based Network Traffic Environment

Scrutinization of Threats from a Cloud-based Network Traffic...

引用

International Conference on computing for Sustainable Global Development (INDIACom)

作者： Shraddha Chopra A. G. Sreedevi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

In this age of digitalization, securing computer networks is of utmost importance to protect sensitive data and critical infrastructure. Threat scrutinization plays an integral role in identifying and thwarting cyber risks. This study focuses on a pioneering strategy to bolster the network’s reliability by harnessing Artificial Intelligence (AI) techniques in conjunction with cloud computing infrastructure, particularly AWS SageMaker. The focus lies in leveraging a comprehensive and realistic cyber dataset, CSE-IDS-2018, to assess the efficacy of the proposed model. The study commences by architecting a threat scrutinization mechanism that harnesses the feasibility of machine learning algorithms, deep learning models, and cloud computing resources. Specifically, the Random Forest algorithm is employed for the multi-class classification of seven types of attacks within the CSE-CICIDS2018 dataset, facilitated by AWS SageMaker’s machine-learning capabilities.

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Enhancing Multimedia Security through Advanced Image Authentication and Encryption

Enhancing Multimedia Security through Advanced Image Authent...

引用

International Conference for Convergence of Technology (I2CT)

作者： Kotha Chakradhar K Thulasi Tharun Shinu M. Rajagopal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

With this flood of multimedia data, protecting it is an absolute necessity. This paper describes a complete method for securing images that combines encryption and watermarking. A technique of the sort suggested further prolongs picture encryption, by interweaving a Rubik's idea with advanced encryptions like those based on Triple DES and AES. As further, a brand new strong watermarking algorithm is displayed. In this way, a high degree of protection for the copyright and genuineness is hoped to be achieved. The proposed method has encouraging results ensuring harvested photographs 'accessibility, confidentiality and integrity.

关键词： Limiting Authentication Watermarking Real-time systems Hybrid power systems Encryption Reliability

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Thyroid Disease Prediction Using Machine Learning Techniques

Thyroid Disease Prediction Using Machine Learning Techniques

引用

Electronic Systems and Intelligent computing (ICESIC), International Conference on

作者： Sompalli Pranath Kumar Pasuladi Narsimha Reddy V Nivethitha Suthir Sriram Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Thyroid disorders are primarily observed in people in the age range of 30 to 60 years; however, the disease is said to be very prevalent in women. Some of the risk factors for thyroid disorders include other diseases that are autoimmune in nature, a family history of thyroid disease, and past exposure to radiation of the neck. More correctly, thyroid diseases are meaningful metabolic disorders and determinants of health. Toward this end, this study used a large dataset from the patient medical record to predict the onset of thyroid disease. The models that are used in this work are K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression, and Naive Bayes. Further each of the models’ performances is also assessed based on recall, accuracy, and precision. The MLP model developed a good predictive ability in comparison with the other models, along with an accuracy of 99.4 percent. Therefore, this study puts forward a very important message concerning the vital importance of machine learning.

关键词： Support vector machines Logistic regression Accuracy Predictive models Nearest neighbor methods User interfaces Bayes methods Medical diagnostic imaging Diseases Thyroid

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A Comprehensive Comparison of Machine Learning Techniques for Heart Disease Prediction

A Comprehensive Comparison of Machine Learning Techniques fo...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Devika Madhusoodanan R Vismaya Hridyalakshmi Santhosh Manju Venugopalan Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Heart disease, affecting millions worldwide, poses an alarming threat to global health due to its high prevalence and significant impact on mortality rates. Despite advances in medical technology, early detection remains crucial for effective intervention and prevention strategies. Machine learning aids in heart disease detection by analyzing patient data to identify patterns and predict risk factors, facilitating early intervention and personalized treatment. In the proposed work, a comprehensive range of classifiers, including AdaBoost, SVM, Naïve Bayes, CatBoost, Random Forest, Perceptron, Decision Tree, XGBoost, kNN, Bagging, Extra Trees, Logistic Regression, Stacking, and Multi-layer Perceptron, were extensively employed to evaluate their effectiveness. Notably, Random Forest exhibited superior performance with an F1 measure of 0.98, endorsing its selection as the primary classifier in our model.

关键词： Heart Support vector machines Logistic regression Prevention and mitigation Stacking Forestry Bayes methods Random forests Regression tree analysis Diseases

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Pedestrian Intention Detection for Autonomous Driving: A Novel Object Detection Approach

Pedestrian Intention Detection for Autonomous Driving: A Nov...

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International Conference on Electrical Energy Systems (ICEES)

作者： A Padmavathi Dheeraj Reddy Pullela Shaik Nazeer Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

Detecting pedestrian intentions is crucial for safer interactions between autonomous driving and pedestrians. This study addresses this challenge by proposing an object detection model for autonomous driving using YOLOv8. Our model identifies pedestrians and categorizes their intentions as "waiting" or "crossing" based on bounding box analysis. Utilizing YOLOv8, the model recognizes pedestrians in real-time video streams and interprets their actions to determine their intentions. The model's performance is evaluated through metrics such as precision, recall, and F1 score, achieving high accuracy. This approach enables proactive decision-making based on pedestrian intentions, significantly enhancing autonomous driving and driver assistance system safety. The results highlight the model's potential to improve pedestrian safety in traffic, contribute to autonomous driving technology, and benefit connected vehicles.

关键词： Pedestrians Laser radar Connected vehicles Transfer learning Measurement by laser beam Object detection Real-time systems Safety Autonomous vehicles Streams

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