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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Saqer S. Alja'afreh Amjaad T. Altakhaineh Mallak H. Al-shamaileh Aser M Matarneh Omar R. Daoud Mohammad Al-Khawaldah I Electrical and Computing Engineering School of Engineering and Computing American International University Al-Jahra Kuwait Data Science and Machine Learning Dept. Pioneers International Academy Amman Jordan Communications and Computer Engineering Al-Ahliyya Amman University Amman Jordan

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

A first-hand, low-profile, two-port planar inverted-F antenna is proposed for current 4G/5G sub-3 GHz hand-portable multiple-input and multiple-output (MIMO) applications. The design features a profile of about 5 mm and dual-coplanar feeds, which are isolated using only a modified top plate, while the shared ground plane is not perturbed as in previous designs. An open-edge slot on the top of the PIFA provides a 3λ/4 current path. The proposed antenna covers approximately a 15% impedance bandwidth fraction in the range of 2.45-2.85 GHz. The simulated results, including scattering parameters, far-field radiation patterns, and the envelope correlation coefficient, all exhibit excellent features.

关键词： Wireless communication Correlation coefficient Simulation Bandwidth Specific absorption rate Impedance MIMO communication

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Application of Convolutional Neural Networks for Cervical Cancer Detection in Women’s Uterus

Application of Convolutional Neural Networks for Cervical Ca...

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International Conference on Networking, Embedded and Wireless Systems (ICNEWS)

作者： Janjhyam Venkata Naga Ramesh J. Visalakshi Ganjikuntla Uday Kishore John Samuel Vikram Neerugatti Yashika S Department of CSE Graphic Era Hill University Uttarakhand India Dept. of CSE -AIML CMR Institute of Technology Telngana India Dept. of CSE-Data Science CMR College of Engineering & Technology Telngana India Department of CSE MLR Institute of Technology Telngana India Dept. of CSE Jain (Deemed – to – be Univeristy) Banglore India

ISBN: (数字)9798350367461

ISBN: (纸本)9798350367478

HPV infection is frequently the cause of cervical cancer, the fourth most prevalent type of cancer that mostly affects women’s cervixes. Early vaccination of women may be the key to its eradication, but this effort is fraught with difficulties, particularly in middle-class or lower-income nations where access to healthcare is scarce. In this paper, a novel method for detecting cervical cancer using medical image processing techniques and Convolutional Neural Networks (CNNs) is presented. A serious health risk is cervical cancer, especially in areas with poor access to medical facilities. By using CNNs to analyze medical pictures, such as pap smears and cervical scans, the suggested approach seeks to overcome this difficulty by enabling the early identification of abnormalities in the cervical region that may be signs of cancer or precancerous lesions. data collection, preprocessing, segmentation, feature extraction, model training, and evaluation are all steps in the workflow. The CNN model shows promising performance in reliably diagnosing cervical cancer by automatic feature extraction from labelled datasets. The efficacy of the suggested strategy is confirmed by evaluation measures including area under the ROC curve, sensitivity, specificity, and accuracy.

关键词： Wireless communication Training Sensitivity Uterus Feature extraction Vaccines Convolutional neural networks Reliability Cervical cancer Medical diagnostic imaging

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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things Computer security Protection Robots

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Faithful Polarization Entanglement Distribution in the SeQUeNCe Quantum Network Simulator

Faithful Polarization Entanglement Distribution in the SeQUe...

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2024 Frontiers in Optics, FiO 2024

作者： Singal, Ansh Zang, Allen Ramesh, Anirudh Kolar, Alexander Chung, Joaquin Kettimuthu, Rajkumar Kumar, Prem Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Pritzker School of Molecular Engineering University of Chicago 5801 S Ellis Ave ChicagoIL60637 United States Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States

We simulate polarization entanglement distribution with the Fock state representation in the open-sourced quantum network simulator SeQUeNCe. Simulations are validated by comparing with experimental entanglement distribution results and unknown experimental parameters are estimated. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Polarization

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Prompting for Automatic Log Template Extraction

arXiv

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arXiv 2023年

作者： Xu, Junjielong Yang, Ruichun Huo, Yintong Zhang, Chengyu He, Pinjia Shenzhen China Dept. of Computer Science & Engineering The Chinese University of Hong Kong China ETH Zurich Switzerland Shenzhen Research Institute of Big Data China

Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing supervised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose DivLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates a target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance. © 2023, CC BY.

关键词： Semantics

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Toward Interpretable Graph Neural Networks via Concept Matching Model

Toward Interpretable Graph Neural Networks via Concept Match...

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IEEE International Conference on data Mining (ICDM)

作者： Tien-Cuong Bui Wen-Syan Li Dept. of Electrical and Computer Engineering Seoul National University Seoul South Korea Graduate School of Data Science Seoul National University Seoul South Korea

Graph Neural Networks have achieved notable success, yet explaining their rationales remains a challenging problem. Existing methods, including post-hoc and interpretable approaches, have numerous limitations. Post-hoc methods treat models as black boxes and can mislead users, while interpretable models often overlook user-centric explanations. Furthermore, most existing methods do not carefully consider the user’s perception of explanations, potentially resulting in explanation-user mismatches. To address these problems, we propose a novel interpretable concept-matching model to enhance GNN interpretability and prediction accuracy. The proposed model extracts frequent concepts from input graphs using the graph information bottleneck theory and modified constraints. These concepts are managed in an in-memory concept corpus for efficient inference lookups and explanation generation. Various explanation construction features are implemented based on the concept corpus and the discovery module, aiming to fulfill diverse user preferences. Extensive experiments and a user study validate the performance of the proposed approach, showcasing its potential for improving model accuracy and interpretability.

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UAVC: Unmanned Aerial Vehicle Communication Using a Coot Optimization-Based Energy Efficient Routing Protocol

UAVC: Unmanned Aerial Vehicle Communication Using a Coot Opt...

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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： E. S. R. Ravi Kumar Suresh Babu Chandolu K. Parish Venkata Kumar Muvva Venkateswara Rao V. Muralidhar Kaja Nagarjuna Sai Srinivas Vellela Dept. of Mathematics V. R. Siddhartha Engineering College Vijayawada India Dept. of CSE Dhanekula Institute of Engineering and Technology Vijayawada India Dept. of Computer Applications V. R. Siddhartha Engineering College Vijayawada India Dept. of CSE NRI Institute of Technology Vijayawada India Dept. of CSM VVIT-Nambur Guntur India Dept. of Information Technology Stanley College of Engineering & Technology for Women Hyderabad India Dept. of CSE-Data Science Chalapathi Institute of Technology Guntur India

Because of their ability to be deployed in open environments with a high degree of flexibility and mobility, Unmanned Aerial Vehicles (UAVs) have garnered a considerable amount of attention in both military and civilian applications with the goals of enabling ubiquitous connectivity and fostering dynamic communications. The challenge is caused by aspects other than mobile ad hoc networks (MANET), specifically aerial mobility in three-dimensional space and frequently shifting topology. One and the same node in the UAV network is responsible for all three functions. The communication path is typically made up of multiple hops, and the routing decision has a substantial impact on how well the network performs. When choosing the most effective routing system, performance analysis should receive a significant amount of attention and effort. As a result of this impetus. The COAER-UAVC approach that has been given creates efficient communication pathways between the UAVs. To a large extent, it is predicated on the behaviors of Coot in their natural environments, such as fighting and hunting iguanas and evading predators. In addition, the COAER-UAVC technique that has been provided places an emphasis on the development of fitness functions with the goal of minimizing both energy consumption and communication delay. In order to show the optimum performance of the COAER-UAVC system, a diverse collection of simulations have to be carried out. The results of the experiments demonstrated that using the COAER-UAVC strategy resulted in significantly enhanced performance in comparison to using other methods.

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Explaining Agent-Based Modeling Outputs Using Subgroup Discovery: A Case Study in Marketing

SSRN

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SSRN 2024年

作者： Romero, Elena del Jesús, Cristóbal José Carmona Cordón, Oscar del Jesus, María José Damas, Sergio Chica, Manuel Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Computer Science and Artificial Intelligence DECSAI University of Granada Granada18071 Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Jaen Jaen23071 Spain Leicester School of Pharmacy De Montfort University LeicesterLEI 9BH United Kingdom Dept. of Computer Science University of Jaen Jaen23071 Spain Dept. of Software Engineering University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia

Agent-based modeling (ABM) is a popular tool for simulating complex systems. During the simulation, ABM generates valuable information about the agents' roadmap, but the large volume of generated data is difficult to analyze and understand. Improving the understanding of ABM outputs is a key challenge, which can also lead to increase the model understanding. However, most studies analyzing model outputs have mainly focused on model validation or identifying relationships between input and output, with limited emphasis on extracting insights about agent behavior from simulated data. We propose the application of Explainable Artificial Intelligence techniques, specifically Subgroup Discovery (SD), to improve model transparency and interpretability. We provide a methodology for applying SD to ABM for extracting useful knowledge of agents' behavior with respect to a variable of interest. We compare the performance of different SD algorithms and show the effectiveness of our methodology through a case study in the marketing area. We analyze the obtained set of rules from the point of view of a marketing expert. The results show that our methodology can provide a deeper understanding of consumer behavior and market dynamics, providing potential areas of opportunity for marketers. © 2024, The Authors. All rights reserved.

关键词： Consumer behavior

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A Fast and Scalable Method for Inferring Phylogenetic Networks from Trees by Aligning Lineage Taxon Strings

arXiv

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arXiv 2023年

作者： Zhang, Louxin Abhari, Niloufar Colijn, Caroline Wu, Yufeng Dept. of Mathematics Centre for Data Science Machine Learning National University of Singapore 119076 Singapore Dept. of Mathematics Simon Fraser University BurnabyBCV5A 1S6 Canada Dept. of Computer Science and Engineering University of Connecticut StorrsCT06269 United States

The reconstruction of phylogenetic networks is an important but challenging problem in phylogenetics and genome evolution, as the space of phylogenetic networks is vast and cannot be sampled well. One approach to the problem is to solve the minimum phylogenetic network problem, in which phylogenetic trees are first inferred, then the smallest phylogenetic network that displays all the trees is computed. The approach takes advantage of the fact that the theory of phylogenetic trees is mature and there are excellent tools available for inferring phylogenetic trees from a large number of biomolecular sequences. A tree-child network is a phylogenetic network satisfying the condition that every non-leaf node has at least one child that is of indegree one. Here, we develop a new method that infers the minimum tree-child network by aligning lineage taxon strings in the phylogenetic trees. This algorithmic innovation enables us to get around the limitations of the existing programs for phylogenetic network inference. Our new program, named ALTS, is fast enough to infer a tree-child network with a large number of reticulations for a set of up to 50 phylogenetic trees with 50 taxa that have only trivial common clusters in about a quarter of an hour on *** Codes 05C30 © 2023, CC BY.

关键词： Phylogenetic

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GrabQC: Graph based Query Contextualization for automated ICD coding

arXiv

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arXiv 2022年

作者： Chelladurai, Jeshuren Santhiappan, Sudarsun Ravindran, Balaraman Dept. of Computer Science and Engineering Indian Institute of Technology Madras India Robert Bosch Centre for Data Science and AI India

Automated medical coding is a process of codifying clinical notes to appropriate diagnosis and procedure codes automatically from the standard taxonomies such as ICD (International Classification of Diseases) and CPT (Current Procedure Terminology). The manual coding process involves the identification of entities from the clinical notes followed by querying a commercial or non-commercial medical codes Information Retrieval (IR) system that follows the Centre for Medicare and Medicaid Services (CMS) guidelines. We propose to automate this manual process by automatically constructing a query for the IR system using the entities auto-extracted from the clinical notes. We propose GrabQC, a Graph based Query Contextualization method that automatically extracts queries from the clinical text, contextualizes the queries using a Graph Neural Network (GNN) model and obtains the ICD Codes using an external IR system. We also propose a method for labelling the dataset for training the model. We perform experiments on two datasets of clinical text in three different setups to assert the effectiveness of our approach. The experimental results show that our proposed method is better than the compared baselines in all three settings. © 2022, CC BY.

关键词： Search engines

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