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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Panigrahy, Smita Dash, Sachikanta Padhy, Sasmita Kumar, Naween Dash, Yajnaseni Computer Science And Engineering Giet University Odisha Gunupur India School Of Computing Science And Engineering Vit Bhopal University MP Bhopal India School Of Computer Science Engineering And Technology Bennett University Uttar Pradesh Greater Noida India School Of Computer Science Engineering And Technology Bennett University Uttar Pradesh India

ISBN: (纸本)9798350366846

Chronic diabetes Mellitus (DM) leads to severe consequences such as nephropathy, neuropathy, and retinopathy. Various learning algorithms are employed to forecast and categorize eight issues, encompassing metabolic syndrome and dyslipidemia. Machine learning is revolutionizing various domains by enabling algorithms to extract models from data. The main objective of this proposed study is to utilize binary classifiers to predict and categorize eight problems associated with DM. For this study we have used the dataset obtained from the Jeevandeep Hospital and Research Centre (JHRC) in Baripada, Mayurbhanj, Odisha, India. It includes 1296 records and has nine input variables and eight output categories that indicate different issues. Multiple models were constructed to address problems like Nephropathy, neuropathy, dyslipidemia, diabetic foot, metabolic syndrome, retinopathy, hypertension, and obesity. Different preprocessing operations on missing data, unbalanced data, and feature selection are employed. Binary classifiers were developed for each type of complication, with the number of records varied for each. Subsequently, an evaluation was conducted. The models achieved a maximum of 95.7% accuracy and 97.9% F1 score. The study demonstrates the effectiveness of using selected features to build accurate classifiers for predicting diabetes complications. © 2024 IEEE.

关键词： Learning algorithms

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Credit Card Fraud Detection Using Machine Learning- A Comprehensive Review 3

Credit Card Fraud Detection Using Machine Learning- A Compre...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jaswant, T. Venkata Manoj, G. Sai Vamisdhar, V. Aravind, A. Reddy, S. Vasudeva Patni, Jagdish Chandra Bahadure, Nilesh Kumar, Rohit Alliance School of Advanced Computing Alliance University Dept. of Computer Science Engineering Bengaluru India School of Technology Gsfc University Department of Computer Science and Engineering Gujarat India

ISBN: (纸本)9798331518592

Credit card fraud is an omnipresent threat in the digital era that cannot be effectively managed without complex detection and prevention strategies. Machine learning has become instrumental for the industry, principally, due to providing tools to analyze transactional information and identify patterns of fraud. The article dwells on the methods, problems, and prospects of their future use. The algorithms are analyzed according to their potential applicability, scalability, and interpretability for fraud detection. This study includes convolutional neural networks and recurrent neural networks are used for utilization of anomaly exposure detection methods to detect unseen fraud with models. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Detection of Hate Speech in Hindi using Supervised Learning Models

Automated Detection of Hate Speech in Hindi using Supervised...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Rajak, Nikhil Phulre, Ajay Kumar Chandra Patel, Subhash Patel, Narottam Das Khekare, Ganesh Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350375442

This study addresses the pressing issue of hate speech on online platforms. Traditional text-based analysis is no longer adequate for identifying hate speech due to the growth of internet platform data. The project aims to create scalable automated methods for web platforms to detect hate speech in real-time. To find the best algorithms for recognizing hate speech, the study compares many machine learning models using a large dataset in Hindi from Twitter. Where the study matters is in the creation of advanced content filtering systems that offer a more secure and friendly online environment. Support Vector Machine (SVM) has the best performance, with outstanding precision (0.9468) and F1-score (0.944), showcasing the top-performing ones and talking about the difficulties and ways ahead in this field. The study recommends improving Hindi hate speech detection systems. With problems such as imbalanced data sets and the dynamic nature of online language, language research using hybrid models, constant model adjustments, and language trend monitoring are all necessary. © 2024 IEEE.

关键词： Adversarial machine learning

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A Robust Convolutional Neural Network Architecture for Automated Pneumonia Detection from Chest X-ray Images 3

A Robust Convolutional Neural Network Architecture for Autom...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Babu, Tina Naik, Pramod Kumar Nair, Rekha R Pallavi, K. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354171

Pneumonia is a significant global health concern, especially for vulnerable populations such as children under five. Timely and accurate diagnosis is critical for effective treatment and improved patient outcomes. This project proposes a robust solution leveraging Convolutional Neural Networks (CNNs) for automated pneumonia detection from chest X-ray images. The model, trained on a meticulously preprocessed dataset of 5,216 chest X-ray images, demonstrated exceptional performance, achieving a training accuracy of 98.16% and a validation accuracy of 98.45% after 50 epochs. The architecture comprises five Conv2D layers with ReLU activation, followed by max-pooling layers, and a fully connected artificial neural network for classification. The model's success in distinguishing between normal and pneumonia-infected cases underscores its potential as a valuable tool for healthcare professionals, offering a reliable and efficient means of pneumonia identification. The model's high accuracy and efficiency make it a valuable asset, potentially reducing the workload on radiologists and enabling quicker diagnoses, ultimately leading to more timely interventions and improved patient outcomes. Comprehensive evaluation, including testing on a separate dataset and real-world image inferences, validates the robustness of the proposed solution. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Security: A Hybrid Encryption Algorithm for Effective Key Generation 2

Enhancing Security: A Hybrid Encryption Algorithm for Effect...

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2nd International Conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Jacob, Jeevan John Kiran, P.S. Shenoy, Jaigovind B Basavaraju, Sagar Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350366570

The abstract Data security is of most importance, in the present day- the introduction of high-level wireless communication channels has led to generation and transfer of massive amounts of data. Data security must keep up with it. Data breach may cause disastrous outcomes. Previously, hardware security modules (HSM) were used to perform cryptographic actions- storing keys and for authentication. This is no longer feasible as HSMs are physical devices and become inconvenient to carry around all the time. The unavailability of HSM poses a serious security threat (for cryptographic keys). This paper aims at a novel software solution that compensates for hardware. As the algorithm focuses on securing keys used for encryption. It may be referred to as key security scheme (KSS). The paper proposes a way to generate keys instead of storing it and assumes performing of cryptographic actions on the available hardware in the system. The proposed method can secure data, both in transit and while at rest. © 2024 IEEE.

关键词： Encryption algorithms

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Regularized Hybrid Deep Learning for DDoS Attack Prediction in Software Defined Internet of Things (SD-IoT) 3

Regularized Hybrid Deep Learning for DDoS Attack Prediction ...

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3rd International Conference on Power, Control and computing Technologies, ICPC2T 2024

作者： Deepthy, J. Radhika, N. Denny, Alosh Department of Electronics & Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India School of Engineering Ernakulam Cochin University of Science and Technology Division of Computer Science and Engineering India

ISBN: (纸本)9798350349207

The availability of online resource is seriously threatened by the fatal DDoS (Distributed Denial of Service) attacks, which can earnestly break up a targeted network or website by flooding it with traffic. DDoS attacks can use a variety of tactics and can be launched from different points around the world, making it difficult to predict when they will occur. In order to improve accuracy by minimizing overfitting of data in CNN, this work proposes an efficient method by combining CNN with Bi-LSTM for predicting DDoS attacks based on a modified loss function with regularization. We test our method on the CICDDOS2019 dataset of network traffic and demonstrate that it can accurately and successfully predict DDoS attacks with an accuracy of 99.8% and an F-score of 97.29%. © 2024 IEEE.

关键词： Denial-of-service attack

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D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 755-769页

作者： Arfat Ahmad Khan Rakesh Kumar Madendran Usharani Thirunavukkarasu Muhammad Faheem Department of Computer Science College of ComputingKhon Kaen UniversityKhon KaenThailand Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiIndia Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiIndia School of Technology and Innovations University of VaasaVaasaFinland

Epilepsy is considered as a serious brain disorder in which patients frequently experience *** seizures are defined as the unexpected electrical changes in brain neural activity,which leads to *** researches made an intense effort for predicting the epileptic seizures using brain signal ***,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's *** addition,the existing prediction models have severe issues,such as overfitting and false positive *** overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain *** neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict *** multi‐network design enhances the trained model's generalisability and stability ***,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal *** earlier warning of epilepsy with the proposed model obtains the auxiliary *** data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing *** be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%***,the numerical results unveil that the proposed work outperforms the existing models.

关键词： artificial intelligence techniques classification learning(artificial intelligence)

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Divide and Conquer: Legal Concept-guided Criminal Court View Generation

Divide and Conquer: Legal Concept-guided Criminal Court View...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Xu, Qi Wei, Xiao Yu, Hang Liu, Qian Fei, Hao School of Computer Engineering and Science Shanghai University China School of Computer Science University of Auckland New Zealand School of Computing National University of Singapore Singapore

ISBN: (纸本)9798891761681

The Criminal Court View Generation task aims to produce explanations that inform judicial decisions. This necessitates a nuanced understanding of diverse legal concepts, such as Recidivism, Confess, and Robbery, which often coexist within cases, complicating holistic analysis. However, existing methods mainly rely on the generation capability of language models, without paying enough attention to the important legal concepts. To enhance the precision and depth of such explanations, we introduce Legal Concept-guided Criminal Court Views Generation (LeGen), a three-stage approach designed for iterative reasoning tailored to individual legal concepts. Specifically, in the first stage, we design a decomposer to divide the court views into focused sub-views, each anchored around a distinct legal concept. Next, a concept reasoning module generates targeted rationales by intertwining the deconstructed facts with their corresponding legal frameworks, ensuring contextually relevant interpretations. Finally, a verifier and a generator are employed to align the rationale with the case fact and obtain synthesized comprehensive and legally sound final court views, respectively. We evaluate LeGen by conducting extensive experiments on a real-world dataset and experimental results validate the effectiveness of our proposed model. Our codes and dataset are available at https://***/xuqi220/LeGen. © 2024 Association for Computational Linguistics.

关键词： Crime

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A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5733-5745页

作者： Tivive, Fok Hing Chi Bouzerdoum, Abdesselam Phung, Son Lam Le, Hoang Thanh Baali, Hamza University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Hamad Bin Khalifa University Information and Computing Technology Division College of Science and Engineering Doha Qatar

Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that have achieved remarkable success in accurately counting crowds. However, many of the existing deep learning methods, which have large model sizes, are unsuitable for deployment on edge devices. This article introduces a novel network architecture and processing element designed to create an efficient and compact deep learning model for crowd counting. The processing element, referred to as the shunting inhibitory neuron, generates complex decision boundaries, making it more powerful than the traditional perceptron. It is employed in both the encoder and decoder modules of the proposed model for feature extraction. Furthermore, the decoder includes alternating convolutional and transformer layers, which provide local receptive fields and global self-attention, respectively. This design captures rich contextual information that is used for generating accurate segmentation and density maps. The self-attention mechanism is implemented using convolution modulation instead of matrix multiplication to reduce computational costs. Experiments conducted on three challenging crowd counting datasets demonstrate that the proposed deep learning network, which comprises a small model size, achieves crowd counting performance comparable to that of state-of-the-art techniques. Codes are available at https://***/ftivive/SINet. © 2024 IEEE.

关键词： Deep learning

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Deep Convolutional Neural Networks for Automated Screening and Diagnosis of Ophthalmic Diseases 3

Deep Convolutional Neural Networks for Automated Screening a...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Babu, Tina Naik, Pramod Kumar Nair, Rekha R Janhavi, P.G. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350354171

This project focuses on developing a deep learning algorithm to classify eye diseases from images. The dataset comprises images depicting various eye conditions, including glaucoma, cataracts, normal eyes, and diabetic retinopathy. We adopt a comprehensive approach by utilizing pre-trained architectures such as ResNet-18, GoogleNet, AlexNet, VGG19, and ResNet-50 as the backbone of our models, fine-tuning each on our dataset. During the training process, we employed data augmentation techniques to enhance the dataset, improving the models' generalization ability across diverse eye conditions. The evaluation of the models involves a combination of cross-entropy loss and accuracy metrics to assess performance. Notably, the individually trained models exhibited varying accuracies, with ResNet-18 achieving the highest accuracy of 92.7% on the validation set. Additionally, we conducted an analysis of model performance using metrics such as precision, recall, and F1-score, further enhancing our understanding of the models' capabilities. The visualization of results is facilitated through the presentation of confusion matrices, providing insights into the models' classification behavior across different eye diseases. This study opens up a promising new path for improving ophthalmology screening and diagnosis by demonstrating the prospective benefits of deep learning in automating the classification of eye diseases. © 2024 IEEE.

关键词： Ophthalmology

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