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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Ramachandran, Raji Yamunakrishnan Govind, G. Devkrishna, M.K. Anil, Abhiram A. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350305258

Machine Learning (ML) is a modern fast-growing technology. It has extensive applications in disciplines like computer vision, bioinformatics, the medical field, finance, fraud detection, and so on. As we've seen, real-world data are used to train machine learning models, which can be used to process the data for purposes like prediction, classification, image processing, etc. The number of attributes in a dataset is known as its dimension. Real-world data sets have a lot of variables, which increases their dimension. It becomes increasingly challenging to envision and work on the training set as the number of variables increases. Sometimes the correlation between most of these factors makes them unnecessary. The benefits of using dimensionality are that when it comes to data compression and gaining less data space, dimensionality reduction is a great instrument. It drastically cuts down on computation time and it makes it simple to remove redundant characteristics from datasets. This project aims to provide a comparison of different dimensionality reduction algorithms. Dimensionality reduction uses a variety of techniques that yield varied outcomes. To compare and analyze the same, we have chosen four algorithms: The linear discriminant analysis algorithm (LDA), the CUR matrix decomposition algorithm, the singular value decomposition algorithm (SVD), and the principal component analysis algorithm (PCA). We are assessing the performance of these algorithms using the classifiers: Decision Tree classifier, Gaussian Naïve Bayes classifier, and the Random-Forest classifier, based on the parameter's accuracy, recall, precision, and F-measure. © 2023 IEEE.

关键词： CUR matrix decomposition algorithm Decision Tree classifier Dimensionality reduction Gaussian Naïve Bayes classifier linear discriminant analysis algorithm (LDA) Principal component analysis algorithm (PCA) Random-forest classifier Singular value decomposition algorithm (SVD)

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Impact of Fuzziness for Skin Lesion Classification with Transformer-Based Model 6

Impact of Fuzziness for Skin Lesion Classification with Tran...

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6th International Conference on Computing, Electronics and Communications Engineering, iCCECE 2023

作者： Yasmin, Israt Sultana, Suriya Begum, Syeda Jobaida Patwary, Muhammed J. A. Almohamad, Tarik Adnan Salam, Iftekhar International Islamic University Chittagong Department of Computer Science and Engineering Chattogram Bangladesh Karabuk University Electrical-Electronics Engineering Karabuk Turkey Xiamen University Malaysia School of Computing and Data Science Sepang Malaysia

ISBN: (纸本)9798350340921

Skin lesion is one of the most commonly encountered illnesses that need to be detected and treated at an early stage. Numerous Convolutional Neural Network (CNN) classifiers were utilized to identify skin conditions using visual features. When it comes to computational effectiveness and accuracy, the recently released Vision Transformers (ViT) have been shown to outperform CNNs. Since the ViTs perform well in classifying images, we use ViT models in our approach. This research proposes a fuzziness-based transformer model to classify images. The divide-And-conquer technique is employed to examine the effectiveness of fuzzy logic and classifying images more accurately. To increase the effectiveness of image classification, the testing dataset has been divided into three groups, including low, medium, and high-fuzzy instances, based on the degree of fuzziness for each sample. For better classification of images, we propose a straightforward mechanism that consists of a fuzzy divide-And-conquer strategy with ViT. Experiments are carried out to compare the model's performance with and without using this proposed mechanism. We noticed that retraining classifiers with low fuzzy samples can enhance image classifier performance. The classification performance on the PAD-UFES-20 dataset using this strategy revealed substantial improvements in the classifier's performance. © 2023 IEEE.

关键词： Image enhancement

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques 5

Weapon Detection Identification and Classification for DCNN ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ashok Kumar, J. Dhivya, G. Selvaraj, P. Karthikeyan, V. Ganesh Ramesh, S.P. Lakshmi, S. Jeya Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science and Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computing SASTRA Deemed to Be University Thanjavur Tamil Nadu Thirumalaisamudram India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification. © 2024 IEEE.

关键词： Median filters

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Deep Learning based Recognition of Auto Covariance Signatures through Protein Interaction Model for Amino Acid Sequence 8

Deep Learning based Recognition of Auto Covariance Signature...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Balamurugan, K.S. Geetha Rani, K. Sadish Sendil, M. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Department of Computer Science and Engineering Bangalore560069 India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Vel Nagar Avadi Chennai India Jeppiaar Institute of Technology Sriperumudur Department of Electronics and Communication Engg Tamilnadu India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798350367904

The detection of protein interaction partners using amino acid sequences is a challenging task in bioinformatics and molecular biology. This study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to improve the accuracy of protein interaction prediction. Auto-covariance signatures are employed to capture the positional relationships between amino acids and protein sequences, facilitating the extraction of structural and functional features. CNNs are used to automatically extract both short-and long-range dependencies, while RNNs handle temporal patterns. The model incorporates self-supervised learning to handle unlabeled data, improving generalization. With a benchmark dataset, the system achieved an impressive accuracy of 98.9%, outperforming existing methods in protein interaction prediction. The approach also highlights the importance of optimization techniques in managing protein sequence structures and functions effectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Machine learning of organic solvents reveals an extraordinary axis in Hansen space as indicator of spherical precipitation of polymers

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Aggregate 2023年第5期4卷 207-213页

作者： Yuta Ihara Hiroshi Yamagishi Masanobu Naito Yohei Yamamoto Department of Materials Science Faculty of Pure and Applied Sciencesand Tsukuba Research Center for Energy Materials Science(TREMS)University of TsukubaTsukubaJapan Research and Services Division of Materials Data and Integrated System(MaDIS) National Institute for Materials Science(NIMS)TsukubaJapan Program in Materials Science and Engineering Graduate School of Pure and Applied SciencesUniversity of TsukubaTsukubaJapan

Machine learning is an emerging tool in the field of materials chemistry for uncovering a principle from large ***,we focus on the spherical precipitation behavior of polymers and computationally extract a hidden trend that is orthogonal to the availability bias in the chemical *** constructing a dataset,four polymers were precipitated from 416 solvent/nonsolvent combinations,and the morphology of the resulting precipitates were *** dataset was subjected to computational investigations consisting of principal component analysis and machine learning based on random forest model and support vector ***,we eliminated the effect of the availability bias and found a linear combination of Hansen parameters to be the most suitable variable for predicting precipitation *** predicted appropriate solvents are those with low hydrogen bonding capability,low polarity,and small molecular ***,we found that the capability for spherical precipitation is orthogonal to the availability bias and forms an extraordinary axis in Hansen space,which is the origin of the conventional difficulty in identifying the *** extraordinary axis points toward a void region,indicating the potential value of synthesizing novel solvents located therein.

关键词： chemical space computational chemistry machine learning polymers precipitation

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Block Broyden's Methods for Solving Nonlinear Equations 37

Block Broyden's Methods for Solving Nonlinear Equations

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Chengchang Chen, Cheng Luo, Luo Lui, John C.S. Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong Shanghai Key Laboratory of Trustworthy Computing East China Normal University China School of Data Science Fudan University China

ISBN: (纸本)9781713899921

This paper studies quasi-Newton methods for solving nonlinear equations. We propose block variants of both good and bad Broyden's methods, which enjoy explicit local superlinear convergence rates. Our block good Broyden's method has a faster condition-number-free convergence rate than existing Broyden's methods because it takes the advantage of multiple rank modification on Jacobian estimator. On the other hand, our block bad Broyden's method directly estimates the inverse of the Jacobian provably, which reduces the computational cost of the iteration. Our theoretical results provide some new insights on why good Broyden's method outperforms bad Broyden's method in most of the cases. The empirical results also demonstrate the superiority of our methods and validate our theoretical analysis. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Nonlinear equations

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Towards Accurate Auscultation Sound Classification with Convolutional Neural Network

Towards Accurate Auscultation Sound Classification with Conv...

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2023 IEEE International Conference on Innovative data Communication Technologies and Application, ICIDCA 2023

作者： Abhishek, S. Mannava, Mahima Chowdary Ananthapadmanabhan, A.J. Anjali, T. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350397208

Deep learning algorithms have been incorporated into conventional auscultation procedures, advancing the area of respiratory sound analysis. Convolutional Neural Network and Gated Recurrent Unit deep learning models were used in a study to increase the precision and effectiveness of respiratory sound categorization (GRU). The work involves developing the models using a sizable dataset of auscultation sounds and assessing how well they classified the sounds into six categories-healthy, bronchiectasis, bronchiolitis, COPD, pneumonia, and URTI-by using auscultation sounds as input. The outcomes indicated that the accuracy of the CNN model was 95%, while the accuracy of the GRU model was 93%. This effort has significantly aided in creating a reliable and effective respiratory sound categorization system. © 2023 IEEE.

关键词： Deep neural networks

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The Development and Testing of a Model for Converting American Sign Language (ASL) Sentences Using Natural Language Processing Techniques

The Development and Testing of a Model for Converting Americ...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Kaewprom, Alongkorn Suriyapattanapong, Aritat Sanmano, Smith Songmuang, Pokpong Malakul, Sivakorn Suksanareewittaya School Bangkok Thailand Streesmutprakan School Bangkok Thailand Chitrala School Bangkok Thailand Thammasat University Department Of Computer Science Pathum Thani Thailand The Institute For The Promotion Of Teaching Science And Technology Bangkok Thailand

ISBN: (纸本)9798350370096

This research addresses the global prevalence of people with hearing disabilities, recognizing the increasing numbers within this demographic. The communication challenges encountered by these individuals drive the development and evaluation of a language translation model. The primary goal is to design a model specifically tailored for translating sign language sentences into English, considering the unique word order in sign language. The study involves comparing eight models that utilize various techniques. Evaluation will be conducted through the bleu score, with 100 randomly selected sentences in each of the 10 rounds. Consistent inputs will be used across rounds to calculate the bleu score, offering a comprehensive assessment of model performance. © 2024 IEEE.

关键词： Natural language processing systems

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Cocoon morphological Features Based Silk Quality Prediction Using XG Boost Algorithm

Cocoon morphological Features Based Silk Quality Prediction ...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Nivetha, M. Sudha, I. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348460

Silk quality is a critical determinant in the silk industry, significantly influencing the market value of silk products. This study proposes an innovative method for forecasting silk quality by leveraging cocoon morphological features through the application of the XG Boost algorithm. The morphological characteristics of the cocoon, including dimensions, shape, and colour, are identified as key indicators of silk quality. The XG Boost algorithm, known for its effectiveness in handling complex datasets, is employed for building a predictive model. A comprehensive dataset is assembled, encompassing a diverse set of cocoon morphological features obtained from a representative sample of silkworms. These features comprise cocoon size, length, width, shape parameters, and colour attributes. The dataset is then utilized to train and validate the XG Boost model, fine-tuning its parameters to ensure accurate prediction of silk quality. The outcomes of this study showcase the effectiveness of the proposed approach, demonstrating high predictive accuracy in determining silk quality based on cocoon morphological features. Moreover, the XG Boost algorithm facilitates the identification of crucial features that significantly contribute to the prediction, offering valuable insights into the factors influencing silk quality. In conclusion, the integration of cocoon morphological features and the XG Boost algorithm presents a promising avenue for accurate silk quality prediction. This research contributes to the advancement of silk production methodologies, introducing innovative technologies to the silk industry and fostering a pathway for the adoption of cutting-edge practices. © 2024 IEEE.

关键词： data Security Deep Learning Machine Learning Quality Prediction Silk Quality XGBoost Algorithm

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Review of DDoS Attack Detection in Big data with Cloud using Machine Learning 2

Review of DDoS Attack Detection in Big Data with Cloud using...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Radhika, P. Kamalakkannan, S. Kavitha, P. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798350340235

Cloud Computing (CC) is a massive breakthrough in Information Technology (IT) that provides end users to access flexible andvirtualizedsources at affordable infrastructure cost and management. One of the most significant technologies in the big-data era is the CC data Centres (DCs). The Distributed Denial of Service (DDoS) attacks are one of the most serious issues when it comes to the privacy of DC. DDoS attacks using Transmission Control Protocol (TCP) traffic are taken into consideration, which are becoming more prevalent but challenging to identify. The DDoS attack is the focus of this study, along with the technique used to prevent it and lessen the vulnerability of the big data server side. The system entails the delivery of packets in the form of DDoS attacks to cloud-based websites and even addresses the real-time prediction of software layer DDoS attacks using various Machine Learning (ML) and Deep Learning (DL) techniques. As a result, it stands apart among numerous hosts. Additionally, the objective of this paper was to offer a succinct introduction to attack detection approaches for early researchers working on cloud-based big data applications. As a result, these approaches are categorised according to how they function, their strengths and shortcomings are reviewed, and finally, several research papers that used each method are examined. © 2023 IEEE.

关键词： Big data Cloud computing Deep learning Distributed Denial of Service attacks Machine Learning

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