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machine Learning based Classifier and Feature Extractor for Intrusion Detection 3

Machine Learning based Classifier and Feature Extractor for ...

引用

3rd international conference on Artificial intelligence For Internet of Things, AIIoT 2024

作者： Srivastav, Nidhi Singh, Rajiv Banasthali Vidyapith Department of Computer Science Rajasthan India Swami Keshvanand Institute of Science & Technology Management & Gramothan Rajasthan India Centre for Artificial Intelligence Banasthali Vidyapith Department of Computer Science Rajasthan India

ISBN: (纸本)9798350372120

Cyberattacks, or attacks on computer networks, now affect practically every device linked to the Internet on a daily basis. Unseen Cyberattacks create a huge problem in these devices. Thus, some methods or techniques tried to be employed that can encounter unseen attack along with known attacks. ML and DL based IDS are being used to encounter them. This research will show a variety of methods used to protect against various types of cyberattacks. After being applied to datasets, the classification algorithms are compared in accordance with precision, accuracy, recall, F1 score. The classification algorithms used in the paper are the LR, Naïve Bayes and KNN as classifier and PCA as feature extractor. Importance of feature extraction is also presented in the paper by taking performance of specified classifier for different feature values in account. Deep learning classification algorithms have been critically reviewed using specified performance criteria. © 2024 IEEE.

关键词： Intrusion detection

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ADMM Based Semi-Structured pattern Pruning Framework for Transformer 3

ADMM Based Semi-Structured Pattern Pruning Framework for Tra...

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3rd international conference on Artificial intelligence, Internet of Things and Cloud Computing Technology, AIoTC 2024

作者： Wang, Tianchen Fudan University Shanghai China

ISBN: (纸本)9798331530280

NLP (natural language processing) has achieved great success through the transformer model. However, the model has hundreds of millions or billions of parameters, which is a huge burden when deployed on personal computers or small-scale servers. To deal with it, we either make the model's weight matrix relatively sparser or compress the attention layer. pattern pruning, one of the most important pruning methods, permits selecting and pruning a fixed number of parameters in each divided pattern block. However, the effect of pattern pruning is strictly limited by the sparsity within a region of weights in each layer. In this paper, we first introduced the Alternating Direction Method of Multipliers (ADMM) based pattern pruning framework to reshape the distribution of the activation map. Specifically, we propose to formulate the pattern pruning on the transformer as a constrained optimization and use ADMM to optimize the problem. In this way, the initial dense feature maps are transformed into rather regionally sparsified ones. Therefore, we can then achieve a higher compression ratio with better performance based on the pattern pruning method. Additionally, this paper provides theoretical derivations of the ADMM with local sparsity. Finally, we also extend the proposed ADMM-based framework with Sparse-Refined Straight-Through Estimator (SR-STE) to demonstrate its generalization and to avoid the gradient vanishing problem. We conduct extensive experiments on classification tasks over GLUE datasets. Significantly, we achieve a 50% compression ratio while maintaining an overall score of 80.1 on the GLUE dataset. © 2024 IEEE.

关键词： Constrained optimization

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Applying Artificial intelligence and Deep Learning to Identify Neglected Tropical Skin Disorders 3

Applying Artificial Intelligence and Deep Learning to Identi...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Pattnayak, Parthasarathi Mohanty, Arpeeta Das, Tulip Patnaik, Sanghamitra Kiit Deemed to Be University School of Computer Applications Bhubaneswar India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Sri Sri University Faculty of Emerging Technologies Cuttack India Kiit Deemed to Be University School of Law Bhubaneswar India

ISBN: (纸本)9798350381931

Visual inspection plays a significant role in the identification of skin diseases, albeit it is not the only method. Teledermatology techniques are thus particularly suitable for the diagnosis and treatment of these disorders. In vision tasks, deep learning (DL), a subset of machine learning (ML) and/or artificial intelligence (AI), has produced outstanding results. There has been few research in this field, and even fewer that have focused on dark skin, despite the growing interest in using this technology to improve diagnostics for neglected tropical diseases affecting the skin (skin NTDs). In this work, we used clinical photos to create AI models based on deep learning. In order to determine whether or not various models with training patterns can increase diagnosis accuracy. The five skin NTDs in which data were gathered were yaws, scabies, mycetoma, leprosy, and Buruli ulcer. © 2024 IEEE.

关键词： Diagnosis

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TST: Time-Sparse Transducer for Automatic Speech recognition 1

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3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Zhang, Xiaohui Liang, Mangui Tian, Zhengkun Yi, Jiangyan Tao, Jianhua Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (数字)9789819991198

ISBN: (纸本)9789819991181;9789819991198

End-to-end model, especially Recurrent Neural Network Transducer (RNN-T), has achieved great success in speech recognition. However, transducer requires a great memory footprint and computing time when processing a long decoding sequence. To solve this problem, we propose a model named time-sparse transducer, which introduces a time-sparse mechanism into transducer. In this mechanism, we obtain the intermediate representations by reducing the time resolution of the hidden states. Then the weighted average algorithm is used to combine these representations into sparse hidden states followed by the decoder. All the experiments are conducted on a Mandarin dataset AISHELL-1. Compared with RNN-T, the character error rate of the time-sparse transducer is close to RNN-T and the real-time factor is 50.00% of the original. By adjusting the time resolution, the time-sparse transducer can also reduce the real-time factor to 16.54% of the original at the expense of a 4.94% loss of precision.

关键词： speech recognition human-computer interaction computational paralinguistics

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Fashion Label Relation Networks for Attribute recognition 3rd

Fashion Label Relation Networks for Attribute Recognition

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3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Wang, Tongyang Huang, Yan Qian, Jianjun Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Socia Nanjing Peoples R China

ISBN: (纸本)9789819988495;9789819988501

Clothes attribute recognition has played a distinguished role in the fashion industry. A large number of attributes and the intrinsic relations between attributes make this task challenging. Most prior methods employ the landmark detection to improve performance. However, there has been little research devoted to analyzing the relationships between fashion labels. To address the above problem, this paper proposes an effective Fashion Label Relation (FLR) network, including the local label relation (LLR) and the global label relation (GLR). LLR uses an attention mechanism to focus on local label relations in conjunction with a predicted label correlation matrix for refining the label representation. To fully characterize the relationships among all attributes, GLR performs a pairwise label co-occurrence prediction task in label co-occurrence prediction (LCP) module and constructs a new label feature distribution in label relation smoothing (LRS) module to avoid overfitting. The comprehensive experimental results strongly demonstrate that our proposed framework outperforms state-of-the-art algorithms.

关键词： Fashion recognition label relation attention mechanism

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Artificial intelligence Techniques for Cardiovascular Disease Classification using 12-Lead ECG 3

Artificial Intelligence Techniques for Cardiovascular Diseas...

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3rd international conference on Embedded Systems and Artificial intelligence, ESAI 2024

作者： Hadzine, Younes Jbari, Atman Najoui, Mohamed Bahatti, Lhoussain ENSET of Mohammedia Hassan II University EEIS Laboratory Mohammedia Morocco ENSAM of Rabat Mohammed v University in Rabat E2SN Laboratory Rabat Morocco ENSAM of Rabat Mohammed v University E2SN Laboratory Rabat Morocco

ISBN: (纸本)9798331522056

Cardiovascular disorders, including atrial fibrillation and bundle branch blockages, have a significant impact on global health and are associated with higher mortality rates. The accurate categorization of these disorders through ECG data is essential in order to improve the results for patients. This investigation addresses the challenge of classifying major cardiac conditions by utilizing seven ECG datasets and employing machine learning techniques. We seek to classify patients exhibiting atypical ECGs through advanced methodologies. We combined datasets, incorporating 12 leads, and utilized preprocessing techniques to enhance data quality. By employing various models, such as CNN, SVM, KNN, random forest, and logistic regression, our CNN combined with logistic regression attained an accuracy of 94% and a sensitivity of 94% on a test set comprising 1500 ECGs. This comprehensive approach minimizes false negatives and enhances diagnostic precision. The integration of multiple datasets and sophisticated preprocessing improves the dependability of our findings, highlighting the importance of data quality and comprehensive analysis in cardiac diagnostics. The results demonstrate how machine learning can improve cardiac diagnoses through thorough data integration and analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial intelligence-Based on Automatic Detection of Diabetic Eye Diseases : A Systematic Review 4th

Artificial Intelligence-Based on Automatic Detection of Diab...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Ba, Mandicou Mbaye, Amadou Diop, Idy Bah, Alassane Cheikh Anta Diop Univ UCAD Med Imaging & Bioinformat Lab LIMBI Polytech Higher Sch ESP Dakar Senegal

ISBN: (纸本)9783031821554;9783031821561

The diagnosis of eye diseases, especially those related to diabetes, has long posed enormous challenges for ophthalmologists in developing countries. In Africa, the main difficulty stems from the limited number of technologies and/or equipment available. Nowadays, with the advancement of technology and the proliferation of artificial intelligence models, the detection and analysis of eye diseases are becoming increasingly easier. It is clear that existing prediction systems can diagnose eye disorders such as glaucoma, cataracts, diabetic retinopathy, etc., but sometimes with very low accuracy. Manual diagnosis of fundus images by ophthalmologists also constitutes a slow, expensive, tedious task and may even be prone to errors. However, despite this, it is worth noting that some doctors still continue to practice this method. This paper highlights the crucial role of artificial intelligence systems, particularly those based on machine learning or deep learning, in the early detection of diabetes-related eye disorders in Africa. In a continent where the prevalence of diabetes is increasing, but resources are limited, these technologies offer significant potential to improve access to ocular healthcare and reduce the workload of healthcare professionals. This also underscores the importance of promoting research in the field of artificial intelligence in ophthalmology, especially in the African context.

关键词： Artificial intelligence machine Learning Deep Learning Diabetic Retinopathy Glaucoma Cataracts

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Sports Game Classification and Detection Using ResNet50 Model Through machine Learning Techniques Using Artificial intelligence 3

Sports Game Classification and Detection Using ResNet50 Mode...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Gill, Kanwarpartap Singh Anand, Vatsala Malhotra, Sonal Devliyal, Swati Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350381931

The intention of artificial intelligence (AI) sports analysis is to use AI and machine learning to get useful insights and enhance sports performance. Data from sporting events, players, and welfare equipment are processed and analysed using AI methods. machine learning, computer vision, natural language processing, and other forms of artificial intelligence analysis are used to improve every facet of sports, from player efficiency to audience participation. It's a fast-paced, ever-changing industry that's revolutionising the way athletes, coaches, and fans interact with the sporting world. The ResNet-50 model and other machine learning techniques are at the bleeding edge of research into sports analysis and performance enhancement. The deep learning model ResNet-50 is used to analyse video and stills from sporting events. Application areas include player monitoring, injury prediction, and game result predicting, all of which are made possible by the use of transfer learning to collect subtle details in sports data. This approach ushers in a new age of data-driven decision making in the sports industry while also revolutionising sports analytics, providing access to invaluable information for coaches, players, and spectators. The primary objective of this research was to categorise nine distinct sports-related picture classifications and predict their expected accuracy, which came in at 93 percent. Future researchers in this field of study will benefit from the high level of accuracy shown in this work. © 2024 IEEE.

关键词： Sports

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pattern Reconfigurable Antenna using Programmable Metasurface 3

Pattern Reconfigurable Antenna using Programmable Metasurfac...

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3rd international conference on Artificial intelligence and Signal Processing, AISP 2023

作者： Suryapaga, Venkataswamy Khairnar, Vikas V. Andhra Pradesh Amaravati India

ISBN: (纸本)9798350320749

In this paper, a pattern reconfigurable antenna operating at 3.5 GHz frequency is proposed. A square-shaped patch serves as the radiator for this antenna while the superstrate layer is made up of a metasurface layer that contains a 4× 4 programmable square ring resonator. To control the radiation beam in the H-plane four PIN diodes are installed in each cell to alter the electric field intensity within the programmable metasurface layer. Performance of the proposed pattern reconfigurable antenna is analyzed in three operational states. Main beam of the antenna can be discretely switched from -30 ° to +30°. In all the operating modes the antenna shows good impedance matching with overall bandwidth from 3.37 to 3.54 GHz. A stable gain of 6.9 dBi is observed in all the operational states. © 2023 IEEE.

关键词： Slot antennas

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Remote Sensing Object Detection Based on Improved Detection Transformer 3

Remote Sensing Object Detection Based on Improved Detection ...

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3rd international conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Hu, Yuanzhong Hu, Po Wang, Haihui Wang, Yuzhuo School of Computer Science and Engineering School of Artificial Intelligence Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798331506582

Nowadays, the accuracy of remote sensing image target recognition has greatly improved. It enables humans to efficiently monitor crop growth, track climate changes and natural disasters, and even provide critical strategic intelligence for national defense. However, due to the challenges posed by the large number of objects and the immense size of remote sensing images, there is still considerable room for improvement in recognition accuracy and efficiency. Detection Transformer (DETR) framework utilizes the Transformer architecture, change the object detection task to a set prediction problem, thus simplifying the traditional post-processing workflow. Through its attention mechanisms, DETR effectively captures object features in intricate scenes by modeling global contextual information. Nonetheless, there is still potential for improvement in extracting fine-grained details. In this study, three modifications are proposed to the DETR framework to boost remote sensing object detection accuracy. First, a multi-scale feature extraction approach is employed to capture deeper and more detailed image features. Second, a bidirectional learning path is introduced to enhance the contextual relationships between image features. Experimental results demonstrate that our proposed method has achieved strong competitiveness compared to other approaches © 2024 IEEE.

关键词： Optical remote sensing

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