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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Veena, K. Meena, K. Mercy Theresa, M. Hemalatha, C. Rajalakshmi, D. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600119 India Department of Computer Science and Engineering GITAM School of Technology GITAM Bangalore561203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India Computer Science and Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (纸本)9798350398496

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds. © 2023 IEEE.

关键词： Network security

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Prediction of AIDS/HIV Infection using Custom Artificial Neural Networks Model 1

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sneha, B.J. Shriyashreshta Kodipalli, Ashwini Martis, Roshan Joy Ushasree, A. Devamane, Shridhar B. Global Academy of Technology Department of Ai & Data Science Bangalore India Manipal Institute of Technology Department of Electronics & Communication Engineering Bangalore India Gokaraju Lailavathi Engineering College Department of Computer Science & Engineering Hyderabad India Amrita Vishwavidyapeetam School of Computing Department of Cs Mysuru India

ISBN: (纸本)9798331529246

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The AIDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/AIDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer. © 2025 IEEE.

关键词： ANN dropouts HIV/AIDS optimizers

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Predicting Cervical Cancer Using Deep Learning Mutation-based Atom Search Optimization (MASO) Algorithm 2

Predicting Cervical Cancer Using Deep Learning Mutation-base...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Mayuri, A.V.R. Selvekumar Subash, K. Aeron, Anurag Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Sehore India Bahir Dar University Bahir Dar Institute of Technology Department of Electrical and Computer Engineering Ethiopia Department of Data Science Trichy India Miet Meerut Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350384277

Worldwide, women are compressed by cervical cancer, which is a prevalent malignancy. This disease, which is currently the fourth leading cause of death for women, shows no symptoms when it first arises. Cells that cause cervical cancer multiply gradually at the cervix. If this cancer is detected early enough, treatment can be effective. Presently, it is difficult for medical workers to detect this kind of cancer before it spreads rapidly. This study used a number of machine learning classification algorithms with risk markers to predict cervical cancer. These study suggestions mutation-based Atom Search Optimization (MASO) with a Deep Convolutional Neural Network (DCNN) to offer a original method for automated diagnosis of uterine cervical cancer. Though DCNN is used to extract features from medical imaging data, MASO is used to increase the optimization of the diagnostic model. Composed, these technologies have the potential to rise cervical cancer diagnosis accuracy and efficiency, offering an automated approach for early detection and prompt intervention. Our proposed model established a strong 95% accuracy rate for generalization. This study explores the difficulties caused by missing values and class imbalance in the specific dataset with the goal of assisting medical professionals in the early detection of cervical cancer and improving treatment for people that are impacted by the illness. © 2024 IEEE.

关键词： Convolutional neural networks

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AstroFusion: A GAN-Augmented Approach for Exoplanet Detection

AstroFusion: A GAN-Augmented Approach for Exoplanet Detectio...

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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Aakash Suresh Lakshmi C Gopirajan PV K Dhanasekaran School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Humanity's exploration of the cosmos spans billions of years, prompting contemplation of our position in the universe. This study advocates for proactive communication with potential intelligent species, driven by uncertainty about their existence. The focus is on exploring Generative Adversarial Networks (GANs) to enhance exoplanet detection through data augmentation. Utilizing the extensive Kepler dataset, the study evaluates the accuracy of conventional machine learning techniques: Logistic Regression, Decision Trees, and Random Forests. (RF)-trained on original data. Subsequently, it assesses the effectiveness of models trained solely on GAN-generated synthetic data and compares their performance with that of models trained on original data. Lastly, it merges original and synthetic data to create a composite dataset, evaluating the ML model's accuracy. The results indicate that models trained solely on synthetic data achieve comparable accuracy to those trained on original data. Furthermore, the combined dataset yields improved accuracy-87%, 95%, and 96% in Logistic Regression, Decision Tree, and RF, respectively-highlighting the potential of GAN-based data augmentation in enhancing exoplanet detection. This study advances space exploration by integrating technology with astronomical research, facilitating more precise and reliable exoplanet detection methods. Leveraging GANs for data augmentation promises novel insights and discoveries about the universe.

关键词： Radio frequency Logistic regression Accuracy Uncertainty Computational modeling Heuristic algorithms Exoplanet data augmentation data models Synthetic data

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An Endless Outpainting Tool Based on Stable Diffusion

An Endless Outpainting Tool Based on Stable Diffusion

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Peter Garrity Leonidas Deligiannidis School of Computing and Data Science Wentworth Institute of Technology Boston MA USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

We developed a tool that utilizes the capabilities of machine learning to explore the possibilities of image outpainting. Our tool is built around an image generation model based on Stable Diffusion and provides an easy-to-use command-based interface. The command-line interface allows us to generate an endless set of images. It starts with a given image, and after the outpainting process, it feeds the generated image as input to the next iteration. To preserve the original image's dimensions, before feeding an image as input to the next iteration, we scale it down so that the generated images are the same dimensions as the original image. By using several iterations like this, we can repeatedly create new details around the edge of an input image.

关键词： Scientific computing Image synthesis Image edge detection Machine learning Feeds Computational intelligence

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Explaining Grover's Quantum Algorithm to College Students

Explaining Grover's Quantum Algorithm to College Students

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Computer science, Computer Engineering, & Applied computing (CSCE), Congress in

作者： Leonidas Deligiannidis School of Computing and Data Science Wentworth Institute of Technology Boston MA USA

Wentworth institute of Technology is a baccalaureate degree granting institution and in recent years we have also developed several Master's programs. Our department offers several degrees leading to a Bachelor of science in Computer science, Computer Networking, Information Technology, Cybersecurity, and data science. All our programs are designed to be hands-on and expose students to a wide skill set. All our majors need to take several security-related courses. One of the latest security related courses we developed is in Quantum computing with a focus on security. Quantum computing is a groundbreaking technology that exploits the laws and properties of quantum mechanics. It can dramatically speed up computations of certain problems and surpass the computation power of modern supercomputers. One notable quantum algorithm we cover in our newly developed course is Grover's algorithm. It is used to search an unstructured database while providing quadratic speedup, compared to its classical counterpart. While demonstrating Grover's algorithm in the classroom, the students raise many questions and explaining all the details of the algorithm is challenging. In this paper, we explain and illustrate the theoretical and implementational challenges and provide examples and solutions.

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Feature selection using modified chaotic satin bowerbird algorithm with deep transfer learning for Multispectral Image Classification

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International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Rajakani, M. Kavitha, R.J. Rajesh, S. Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Electronics and Communication Engineering University College of Engineering Panruti Panruti 607106 India Department of Information Technology Mepco Schlenk Engineering College Sivakasi India

Multispectral image classification has received significant attention among research communities and academicians. Recently, several artificial intelligence (AI) models can be used for the extraction of prominent features. Besides, deep learning (DL) methods become more familiar and gained interest in the remote sensing community for the classification of multispectral and hyperspectral images. With this motivation, this paper presents an optimized feature learning with deep transfer learning enabled multispectral image classification (OFLDTL-MSIC) technique. The proposed OFLDTL-MSIC technique aims to categorize the different class labels of the multispectral images. Besides, the OFLDTL-MSIC technique applies multi-level discrete wavelet transform (DWT) based image decomposition technique. Moreover, the EfficientNet technique is applied as a feature extractor to generate a collection of features. Furthermore, selecting optimal features extracted by a EfficientNet involves identifying and retaining the most relevant and informative features The improved chaotic satin bowerbird optimization (CSBO) algorithm which uses dynamic population size is proposed for feature selection and kernel extreme learning machine (KELM) model is applied for the classification process. In order to ensure the enhanced performance of the OFLTDL-MSIC technique, a wide range of simulations take place using the Madurai LISS IV multispectral images and the results are examined under several aspects. The extensive comparative study highlighted the better performance of the OFLTDL-MSIC technique over the recent methods. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Deep learning Image classification Multispectral images Parameter tuning Remote sensing

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ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation 25

ABXI: Invariant Interest Adaptation for Task-Guided Cross-Do...

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34th ACM Web Conference, WWW 2025

作者： Bian, Qingtian de Carvalho, Marcus Li, Tieying Xu, Jiaxing Fang, Hui Ke, Yiping College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Northeastern University Shenyang China Research Institute for Interdisciplinary Sciences Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9798400712746

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains. One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it. Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings. Another challenge lies in aligning the domain-specific and cross-domain sequences. Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains. In such cases, the domain-specific knowledge carried by the current tokens may degrade performance. To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI). Specifically, leveraging LoRA’s effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation. First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics. Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain. Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths. Experimental results on three datasets demonstrate that our approach outperforms other CDSR counterparts by a large margin. © 2025 Copyright held by the owner/author(s).

关键词： cross-domain sequential recommendation

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FedSM: A Practical Federated Shared Mobility System 50th

FedSM: A Practical Federated Shared Mobility System

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50th International Conference on Very Large data Bases, VLDB 2024

作者： Wei, Shuyue Zhang, Yuanyuan Zhou, Zimu Zhang, Tianlong Xu, Ke SKLCCSE Lab FBPC Beijing Advanced Innovation Center Beihang University China North China Institute of Computing Technology China School of Data Science City University of Hong Kong Hong Kong SKLCCSE Lab Beihang University China

Shared mobility leverages under-utilized vehicles to offer on-demand transport services by sharing vehicles among users. It strives to match supply with demand via a series of data-intensive opera- tions such as supply prediction and task assignment. However, its full potential is often compromised in practice as most shared mobility platforms operate in isolation, leading to sub-optimal re- source utilization. In this demonstration, we advocate a federated approach to shared mobility, which enhances its effectiveness by enabling optimizations across platforms while retaining their au- tonomy. We develop privacy-preserving operators and incentive mechanisms dedicated to supply prediction and task assignment in shared mobility and implement generic interfaces that support diverse prediction and assignment algorithms. We showcase the shared mobility system with real-world ride-hailing applications. © 2024, VLDB Endowment. All rights reserved.

关键词： Arctic vehicles

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