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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Buvaneswari, P.R. Madhura, G.K. Alasady, Heba Alagarraja, K. Soni, Mukesh School of Electrical and Electronics Engineering Vit Bhopal University Bhopal India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India University Centre for Research & Development Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798331508456

In recent years, network intrusion refers to unauthorized access to systems or networks by external attackers or insiders aimed at stealing information or deploy malicious software. Traditional approaches for anomaly-based intrusion detection systems have faced several challenges which include high computational overhead and limited capability in detecting zero-day attacks due to reliance on predefined attack signatures or handcrafted features. To address these limitations, this research proposes a Machine Learning-based Stacking Ensemble Model (ML-SEM) for anomaly-based intrusion detection, effectively reducing computational overhead and enhancing detection of previously unseen attacks. Initially, data is collected from University of Nevada, Reno Intrusion Detection dataset (UNR-IDD) which consists of network traffic data and various types of attacks. Then, the collected data is preprocessed by using standard scaler normalization which ensures that all features have a mean of zero and a standard deviation of one. After that, the features are selected using Principal Component Analysis (PCA) which reduces dimensionality and enhances model efficiency by eliminating redundant and less significant features. Finally, anomaly-based intrusion detection is done by using proposed ML-SEM. The proposed ML-SEM achieved better results in terms of accuracy (98.6%), precision (95.5%) and recall (98.3%) when compared with existing Random Forest (RF). © 2025 IEEE.

关键词： Dimensionality reduction

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Innovative Healthcare Advancements: Harnessing artificial and Human intelligence for Bionic Solutions

Innovative Healthcare Advancements: Harnessing Artificial an...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Samruddhi Sapkal Sulaxan Jadhav P Mallikarjun Rejuwan Shamim Atowar Ul Islam Kalyan Bamane Artificial Intelligence and Machine Learning D Y Patil International University Akurdi Pune India D Y Patil International University Akurdi Pune India Department of Electrical and Electronic Engineering Malla Reddy Engineering College JNTUH Hyderabad School of data science Maharishi University of Information Technology Noida India Department of Computer Science and Electronics University of Science and Technology Meghalaya Ri-Bhoi Techno city Kiling Road Baridua Meghalaya Department of Information Technology D Y Patil College of Engineering Akurdi Pune

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

According to initial data, individuals who have been diagnosed with type 2 diabetes (T2DM) appear to be at a more chances of evolving breast cancer compared to those who have not received a T2DM diagnosis. The primary goal of the research was to estimate the efficacy of three dissimilar methods in forecasting the probability of breast cancer in T2DM patients with diverse attributes. To achieve this objective, a danger expectation model was created for breast cancer in individuals with T2DM using the primary *** ensure a comprehensive analysis, we also gathered information on potential factors that may predict the growth of breast cancer. As the population sample size was restricted, we utilized Synthetic Minority Oversampling Technology to amplify the quantity of accessible data. Random assignment of data points to training or test sets was conducted at a ratio of roughly 39 to 1. Three distinct models, specifically artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), were assessed for effectiveness using a range of performance criteria, including as $F 1$ score, area under the receiver operation characteristic curves, recalled, and correctness. (AUC). AUC values for these models were as follows: LR had an AUC of 0.834, ANN had an AUC of 0.865, and RF had an AUC of 0.959, with RF having the greatest AUC. According to our study, the Random Forest model outperformed the LR and ANN models in correctly estimating the incidence of breast cancer in people with T2DM.

关键词： Radio frequency Training Technological innovation Biological system modeling artificial neural networks Receivers Predictive models

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Effect of Cost on Internet of Things-Based Integrated Remote Electronic Health Surveillance, Self-Regulating Home, and Security Alert System: A Review Analysis

Effect of Cost on Internet of Things-Based Integrated Remote...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Anjali Naudiyal Shobha Aswal Harishchander Anandaram Kapil Joshi Kawerinder Singh Sidhu Ajay Singh School of Applied and Life Sciences Uttaranchal University Dehradun India School of Computing Graphic Era Hill University Dehradun Uttarakhand India Centre for Computational Engineering and Networking Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Computer Science and Engineering Uttaranchal University Dehradun Uttarakhand India Uttaranchal University Dehradun Uttarakhand India

Auto-control and the internet of things have brought about significant changes around the globe. With each passing day, the expansion and revolution in the field of contemporary technology brought on by the Internet of Things make our life simpler and more convenient by automating formerly manual tasks. In the modern period, a person's level of risk to their health is not dependent on their age because hectic schedules and erratic lifestyles are common. This study focuses on the price of IOT gadgets with sensors in various industries. It has been made clear through this article that it is difficult to integrate smart devices across all industries and nations due to their high cost.

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A 1D Plug-and-Play Synthetic data Deep Learning For Undersampled Magnetic Resonance Image Reconstruction

A 1D Plug-and-Play Synthetic Data Deep Learning For Undersam...

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IEEE International Conference on Image Processing

作者： Min Xiao Zi Wang Jiefeng Guo Di Guo Xiaobo Qu Institute of Artificial Intelligence Xiamen University Xiamen China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen China Department of Microelectronics and Integrated Circuit Xiamen University Xiamen China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Magnetic resonance imaging (MRI) plays a pivotal role in modern medical diagnosis yet is often hindered by the long imaging time. MRI imaging can be accelerated through undersampling, but the introduced aliasing artifacts should be removed during image reconstruction. While deep learning reconstruction methods excel at image de-aliasing, they may yield suboptimal results when training sampling settings differ from those at the time of reconstruction. To decouple from specific sampling settings, we propose using synthetic data to generate a substantial training dataset and pre-train a 1D deep denoiser. We then integrate the trained deep denoiser into the iterative reconstruction process as a replacement for the approximation operator within the deep plug-and-play framework. In vivo results indicate that the proposed method exhibits robust and visually appealing image reconstruction when there is a mismatch between the training and reconstruction undersampling settings, such as different undersampling patterns and sampling rates.

关键词： Training Deep learning In vivo Magnetic resonance imaging Magnetic resonance Training data Trajectory

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Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs

arXiv

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arXiv 2023年

作者： Shi, Guangsi Zhang, Daokun Jin, Ming Pan, Shirui Yu, Philip S. Department of Chemical and Biological Engineering Faculty of Engineering Monash University ClaytonVIC3800 Australia Department of Data Science and Artificial Intelligence Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University SouthportQLD4215 Australia Department of Computer Science University of Illinois at Chicago ChicagoIL60607-7053 United States

The great learning ability of deep learning facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour both in academia and industry. However, the complex laws of the physical world pose significant challenges to the learning based simulations, such as the varying spatial dependencies between interacting particles and varying temporal dependencies between particle system states in different time stamps, which dominate particles’ interacting behaviour and the physical systems’ evolution patterns. Existing learning based methods fail to fully account for the complexities, making them unable to yield satisfactory simulations. To better comprehend the complex physical laws, we propose a novel model – Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE) – that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework. Through training with real-world particle-particle interaction observations, GNSTODE can simulate any possible particle systems with high precisions. We empirically evaluate GNSTODE’s simulation performance on two real-world particle systems, Gravity and Coulomb, with varying levels of spatial and temporal dependencies. The results show that GNSTODE yields significantly better simulations than state-of-the-art methods, showing that GNSTODE can serve as an effective tool for particle simulation in real-world applications. © 2023, CC BY.

关键词： Ordinary differential equations

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RGB-D Indiscernible Object Counting in Underwater Scenes

arXiv

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arXiv 2023年

作者： Sun, Guolei Cheng, Xiaogang An, Zhaochong Wang, Xiaokang Liu, Yun Fan, Deng-Ping Cheng, Ming-Ming Gool, Luc Van Computer Vision Lab ETH Zurich Zurich8092 Switzerland Pioneer Centre for Artificial Intelligence University of Copenhagen Copenhagen1350 Denmark School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Jiangsu Province Nanjing210003 China College of Computer Science Nankai University Tianjin300350 China

Recently, indiscernible/camouflaged scene understanding has attracted lots of research attention in the vision community. We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundings. Due to a lack of appropriate IOC datasets, we present a large-scale dataset IOCfish5K which contains a total of 5,637 high-resolution images and 659,024 annotated center points. Our dataset consists of a large number of indiscernible objects (mainly fish) in underwater scenes, making the annotation process all the more challenging. IOCfish5K is superior to existing datasets with indiscernible scenes because of its larger scale, higher image resolutions, more annotations, and denser scenes. All these aspects make it the most challenging dataset for IOC so far, supporting progress in this area. Benefiting from the recent advancements of depth estimation foundation models, we construct high-quality depth maps for IOCfish5K by generating pseudo labels using the Depth Anything V2 model. The RGB-D version of IOCfish5K is named IOCfish5KD. For benchmarking purposes on IOCfish5K, we select 14 mainstream methods for object counting and carefully evaluate them. For multimodal IOCfish5K-D, we evaluate other 4 popular multimodal counting methods. Furthermore, we propose IOCFormer, a new strong baseline that combines density and regression branches in a unified framework and can effectively tackle object counting under concealed scenes. We also propose IOCFormer-D to enable the effective usage of depth modality in helping detect and count objects hidden in their environments. Experiments show that IOCFormer and IOCFormer-D achieve state-of-the-art scores on IOCfish5K and IOCfish5K-D, respectively. The resources are publicly available at ***/GuoleiSun/Indiscernible-Object-Counting. Copyright © 2023, The Authors. All rights reserved.

关键词： Image annotation

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Anomaly-Based Intrusion Detection Systems by using Machine Learning based Stacking Ensemble Model

Anomaly-Based Intrusion Detection Systems by using Machine L...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： P R Buvaneswari Madhura G K Heba Alasady K. Alagarraja Mukesh Soni School of Electrical and Electronics Engineering VIT Bhopal University Bhopal India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering University Centre for Research & Development Chandigarh University Mohali India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

关键词： Radio frequency Analytical models Adaptation models Accuracy Stacking Intrusion detection Telecommunication traffic Integrated circuit modeling Standards Principal component analysis

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Delivery Time Prediction Using Large-Scale Graph Structure Learning Based on Quantile Regression

Delivery Time Prediction Using Large-Scale Graph Structure L...

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International Conference on data engineering

作者： Lei Zhang Xin Zhou Zhiwei Zeng Yiming Cao Yonghui Xu Mingliang Wang Xingyu Wu Yong Liu Lizhen Cui Zhiqi Shen School of Software Shandong University Jinan China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University Jinan China Alibaba-NTU Singapore Joint Research Institute Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Alibaba Group China

Predicting Estimated Time of Arrival (ETA) for packages is a critical problem in e-commerce. The prediction is often made based on spatial (sending and receiving addresses), temporal (payment time), and context (merchants) attributes. Existing methods usually formalize this task as an Origin-Destination (OD) ETA prediction problem and exploit the attribute relations with graph learning. However, most existing methods make use of fixed and manually defined graph structures, which are often not optimal for downstream ETA task and hence lead to unsatisfactory prediction results. In addition, current ETA models tend to focus on prediction accuracy without considering fulfillment rate. This may lead to a low fulfillment rate in practice, i.e., actual delivery time is much longer than estimations provided by models, which consequently exacerbates the frustrating experiences for users. To address these issues, we propose a novel Graph Structure Learning-based Quantile Regression (GSL-QR) model for e-commerce ETA prediction in this paper. Specifically, we utilize graph structure learning to dynamically update the spatial and temporal relation graphs of orders and learn optimal graph structures and graph embeddings guided by downstream ETA prediction task. To guarantee both prediction accuracy and order fulfillment rate, we design a multi-objective quantile regression in GSL-QR that can find the Pareto solution of the problem. In order to extend GSL to large-scale real-world graphs, we devise a Fast Sampling-based Graph Structure Learning (FS-GSL) method, which can significantly reduce the computational complexity of graph structure learning. Finally, we conduct comprehensive experiments on three industrial datasets collected from Alibaba e-commerce platform. The results demonstrate that the proposed model can significantly outperform baselines on both ETA prediction accuracy and order fulfillment rate.

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Cloud Computing-Enhanced and Raspberry Pi-Powered Solution for Remote Ear, Nose, and Throat Consultations with AI-Driven Diagnostics

Cloud Computing-Enhanced and Raspberry Pi-Powered Solution f...

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Emerging Technology (INCET), International Conference for

作者： Giriprasad. S T. Karpagam V. Nalini Nilamadhab Mishra N. Mohankumar N. Malathi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Chennai Tamil Nadu India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capabilities of Raspberry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to be performed without interruption while securely transmitting data to the cloud. On a web-based platform, interactive patient-professional consultations are made possible via real-time communication, which is made possible by WebRTC. Cloud services are very important because of their ability to provide scalable storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from beginning to finish. This integrated approach promises scalability, reliability, and advanced diagnostic capabilities, and it reshapes the landscape of remote ENT care with accessible and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions.

关键词： Cloud computing Privacy Telemedicine Scalability Nose Medical services Ear

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A data-Driven Approach Towards Software Regression Testing Quality Optimization

A Data-Driven Approach Towards Software Regression Testing Q...

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Information Technology (ACIT)

作者： Abdallah Moubayed Nouh Alhindawi Jamal Alsakran MohammadNoor Injadat Mohammad Kanan Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Department of Software Engineering Jadara University Irbid Jordan Fairleigh Dickinson University Canada Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan Department of Industrial Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees.

关键词： Software testing Machine learning algorithms Software algorithms Software quality Software systems Software reliability Regression tree analysis Optimization Standards Software development management

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