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15th IEEE Annual Computing and communication Workshop and conference, CCWC 2025

作者： Guan, Saiqing University of South Alabama Health Care Access Mobile United States

ISBN: (纸本)9798331507695

New case breast cancer has steadily increased since 2012 and contributes to highest treatment costs among cancers. While current literature widely applies machine learning (ML) and Artificial Intelligence (AI) in breast cancer prediction using patient features. However, latent relationships between diseases and patients remain unexplored. This study utilizes National Health Interview Survey (NHIS) 2023 and focused on chronic diseases participants. 738 breast cancer patients and 733 non-breast cancer patients are randomly selected. Patient-centric network attributes and patient features are both included into seven machine-learning risk prediction models. XGBoost model outperform among others having an Area Under Curve (AUC) of 94%. Notably, eigenvalue centrality and obesity are the most important features. This novel approach represents a significant improvement over current literature and provides promising applications in disease prediction. © 2025 IEEE.

关键词： Lung cancer

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Application of Crawler Algorithm for Situational Awareness in network Security 5th

Application of Crawler Algorithm for Situational Awareness i...

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5th international conference on Data Science, machine learning and Applications

作者： Jagadish, Sripelli Madanan, Mukesh SR Univ Sch CS & AI Warangal Telangana India Dhofar Univ Salalah Dept Comp Sci Salalah Oman

ISBN: (纸本)9789819780334;9789819780310;9789819780303

A key idea in network security is situational awareness, which enables prompt detection and reaction to cybersecurity threats. This study investigates the use of crawler algorithms to improve situational awareness in network security. In this article, we look at the theoretical underpinnings of awareness of situations, the function of crawler algorithms, how they're used in security of networks, and how they affect threat and incident response. The network protection event database was created and constructed using data gathered from multiple Zhiming network security incident websites using the Scrappy web crawler framework, which improved the situational awareness research's data. In this study, a text-based network safety event evaluation instrument is built and executed in order to analyze the evaluation and interpretation of network security incidents, which is a critical component in the process of security awareness and perception. The crawler technique was used to build a network vulnerability event database that has 45,820 sets of data, which is an increase in capacity of 14.36% and 30.53% over the old approach and a reduction in reading time of 65.2% and 88.4%.

关键词： Crawler algorithm network security Situational Awareness

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Effective communication Using Generative AI 5th

Effective Communication Using Generative AI

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5th international conference on Data Science, machine learning and Applications

作者： Hegde, Nagaratna P. Vikkurty, Sireesha Sriperambuduri, Vinay Kumar Srikanth, Bingi Rao, Kakumanu BalaSubba Vasavi Coll Engn Dept CSE Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Artificial Intelligence (AI) has experienced substantial progress in recent times, resulting in the birth of ChatGPT, a cutting-edge conversational AI model. Fuelled by advanced learning techniques and natural language processing (NLP), ChatGPT has completely altered the way humans engage with machines. This research document presents a thorough examination of ChatGPT, delving into its framework, training approach, uses, and moral concerns. Additionally, it spotlights the potential obstacles and forthcoming directions for enhancing ChatGPT's capabilities [1], with the aim of achieving even more authentic and dependable conversational interactions.

关键词： ChatGPT Artificial Intelligence Prompt Data Set Transformer Model OpenAI

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Design and Development of Traffic VANETs for Safety and Accident Avoidance 5th

Design and Development of Traffic VANETs for Safety and Acci...

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5th international conference on Data Science, machine learning and Applications

作者： Kadiayla, Ramana Tanam, Aparna Manvita, K. Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad 500075 Telangana India G Narayanamma Inst Technol & Sci Dept Informat Technol Hyderabad 500104 Telangana India G Narayanamma Inst Technol & Sci Dept Comp Sci & Technol Hyderabad 500104 Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Vehicular ad-hoc networks (VANETs) are one of the recent interests in research industry that have advances in wireless communication and networking models. The ultimate goal of the VANETS is to enable the authorized messaging services towards the vehicles and public networks. To reduce the complexity of the service authorization, it uses proxy vehicles to reduce the complex computational problems. During the authentication of proxy vehicles, it sends multiple messages to frequently get response from the road side units in a more effective way. In the proposed system, proxy based unique authentication scheme is discussed where it cannot provide message authenticity and also it will not resist the mediate attacks. Sometimes, the system accepts the false signatures. Secondly, we proposed a unique identity-based authentication scheme using latest proxy vehicles (ID-MAP). To reconfirm the authentication assurance, the signature is adaptively verified against the chosen message. A robust authentication model is evaluated using the Elliptic Curve discrete Logarithm problem represented as (ECDLP) in the system oracle model.

关键词： Vehicular Ad-hoc networks Secure network Wireless network technologies Safety Accident Avoidance

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A Comparative Study of Deep learning Algorithms for Glaucoma Classification Using Retinal Images 5th

A Comparative Study of Deep Learning Algorithms for Glaucoma...

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5th international conference on Data Science, machine learning and Applications

作者： Swapna, T. Varshitha, Y. Sai Raja Sudeepthi, K. L. Manavika, B. Saishree, T. G Narayanamma Inst Technol & Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

A group of eye conditions known as glaucoma impair the optic nerve, which is in charge of sending visual data from the eye to the brain. Glaucoma impacts 3.54% of adults aged 40 to 80 around the world. Early detection of glaucoma is crucial as it can prevent total optic nerve damage, which would cause irreversible vision loss. It is possible for specialists to diagnose glaucoma medically, but treatment options are either expensive or time-consuming and requires ongoing care from medical professionals. There have been numerous initiatives at streamlining all components of the glaucoma categorization process, however these models are challenging for users to comprehend the key predictors, resulting in them being unreliable for use by medical experts. The study uses eye fundus images to classify glaucoma patients using three distinct Deep learning techniques: Convolutional neural network, Visual Geometry Group 16 (VGG16), and Global Context network (GC-Net). In addition, several data pre-processing techniques are used to avoid overfitting and achieve high accuracy. This research compares and analyses the performance of various architectures using the aforementioned techniques. The CNN model had the best accuracy of 83% when in contrast to the other deep learning models.

关键词： Biomedical image processing Glaucoma Blind ness machine learning Visual geometry group 16 Convolutional neural network Global context network

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7th international conference on Wireless communications, networking and Applications, WCNA 2023

7th International Conference on Wireless Communications, Net...

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7th international conference on Wireless communications, networking and Applications, WCNA 2023

ISBN: (纸本)9789819624089

The proceedings contain 38 papers. The special focus in this conference is on Wireless communications, networking and Applications. The topics include: EffiNet: An Efficient, Low-Complexity Neural network for Monocular Depth Estimation on Embedded Devices;adaptive communication Spectrum Sensing Algorithm Based on Energy Detection;a Cache Scheduling Method Based on Adaptive Expiration for Data Process System;Research on Airport communication and Navigation Equipment Inspection Assistance System Based on AR Technology;application of Quality Function Deployment in Assessing Unmanned Assault Capabilities for Mountain Counter-Terrorism Operations;research on the Application of Internet of Things Technology in Power Grid Limited Space Security Monitoring;research on the Application of Computer Vision Based on Deep learning in Autonomous Driving Technology;research on Cybersecurity System of Scientific Research Institutions Based on National Cybersecurity Classified Protection 2.0: Take the Chinese Academy of Agricultural Sciences as an Example;Research on DOA Estimation Method of Underwater Acoustic Signal Based on machine learning;Design and Implementation of Smart Home Control System Based on STM32;communication Anti-jamming System Based on Deep Reinforcement learning;fault Diagnosis of Infrared Sensor Based on Convolutional Neural network;graph Neural network Knowledge Graph Recommendation Model Integrating Deep Domain Information and Important Domain Information;an Approach to Microenvironment-Based Particle Swarm Optimization Algorithm;a Verifiable Ciphertext Retrieval Scheme for Smart Grids;panoptic Semantic Mapping Method for Tomato Growing Environment Based on K-Net and OctoMap;An Adversarial Attack Method for Multivariate Time Series Classification Based on AdvGAN;recognition and Calculation of Fish Rafts in Mariculture on the Basis of Artificial Intelligence;digital Twin for Power Load Forecasting.

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Third international conference on Green communication, network, and Internet of Things, CNIoT 2023

Third International Conference on Green Communication, Netwo...

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3rd international conference on Green communication, network, and Internet of Things, CNIoT 2023

ISBN: (纸本)9781510668867

The proceedings contain 89 papers. The topics discussed include: research on decoding technology for surface-well fluid pulse communication in oil and gas wells;fault root cause location based on machine learning in 5G core network;communication security between SDN controllers and switches based on IPsec;a communication resource optimization method for multi-QoS requirements in sharing electricity;outage analysis for wireless-powered relaying radio frequency-underwater wireless optical communication systems;design and realization of line interrupt monitor module in the local area network;research and implementation of link aggregation technology based on multi-network router;hybrid forwarding strategy pressure-based routing for underwater wireless sensor network;efficient routing algorithms for maximizing network lifetime in chain-type wireless sensor networks using multiple sinks;and research on server energy consumption optimization technologies.

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machine learning Meets Encrypted Search: The Impact and Efficiency of OMKSA in Data Security

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international JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期2025卷

作者： Wei, Zhongkai Su, Ye Zhang, Xi Yang, Haining Qin, Jing Ma, Jixin Shandong Univ Sch Math Jinan Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Hebei Normal Univ Coll Comp & Cyber Secur Shijiazhuang Peoples R China Hebei Normal Univ Hebei Prov Key Lab Network & Informat Secur Shijiazhuang Peoples R China Univ Greenwich Sch Comp & Math Sci London England

The convergence of machine learning and searchable encryption enhances the ability to protect the privacy and security of data and enhances the processing power of confidential data. To enable users to efficiently perform machine learning tasks on encrypted data domains, we delve into oblivious keyword search with authorization (OKSA). The OKSA scheme effectively maintains the privacy of the user's query keywords and prevents the cloud server from inferring ciphertext information through the searching process. However, limitations arise because the traditional OKSA approach does not support multi-keyword searches. If a data file is associated with multiple keywords, each keyword and corresponding data must be encrypted one by one, resulting in inefficiency. We introduce an innovative approach aimed at enhancing the efficiency of search processes while addressing the limitation of current encryption and search systems that handle only a single keyword. This method, known as the oblivious multiple keyword search with authorization (OMKSA), is designed for more effective keyword retrieval. One of our important innovations is that it uses the arithmetic techniques of bilinear pairs to generate new tokens and new search methods to optimize communication efficiency. Moreover, we present a detailed and rigorous demonstration of the security for our proposed protocol, aligned with the predefined security model. We conducted a comparative experiment to determine which of the two schemes, OKSA and OMKSA, is more efficient when querying multiple keywords. Based on our experimental results, our OMKSA is very efficient for data searchers. As the number of query keywords increases, the computational overhead of connected keyword searches remains stable. Finally, as we move into the 5G era, the potential applications of OMKSA are huge, with clear implications for areas such as machine learning and artificial intelligence. Our findings pave the way for further exploration and deplo

关键词： authorization machine learning oblivious transfer searchable encryption

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3rd international conference on Computing and communication networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd international conference on Computing and communication networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and communication networks. The topics include: Enhancing Security in Wireless Sensor networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor networks;Using Wireless Sensor network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite Image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire Detection Using Image Processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical networks;improved Markov Decision Process in Wireless Sensor network for Optimal Energy Consumption.

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11th EAI international conference on Advancement of Science and Technology, ICAST 2023

11th EAI International Conference on Advancement of Science ...

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11th EAI international conference on Advancement of Science and Technology, ICAST 2023

ISBN: (纸本)9783031641503

The proceedings contain 23 papers. The special focus in this conference is on Advancement of Science and Technology. The topics include: Breast Cancer Stage Identification Using machine learning;deep learning-Based Emotion Classification for Amharic Texts;bilingual Word-Level Language Identification for Omotic Languages;ChatGPT in Education: Opportunities and Concerns for Functional English Sentence Structure Analysis for Automatic Question Generation;artificial Intelligence-Based Secured Smart Mirror Control Systems with Enhanced Vehicle and Signboard Detection;music Genre Classification Using Deep Neural network with Feature Selection and Optimization via Evolutionary Algorithm;collaborative Filtering-Based Recommender System for Ethiopian Tourism Sites;prediction of Compaction Parameters Based on the Atterberg Limit by Using a machine learning Approach;prediction of Fatigue Life of Materials Using machine learning Approach: A Review;Adaptive Particle Swarm Optimization-Based Tuning of Fractional Order PID for Moisture Control in an Industrial Rotary Dryer System;trajectory Tracking Control of Three-Degree-of-Freedom Articulated Robot Manipulator Using Neural network Controller;modeling and Trajectory Tracking Controller Design for Nonholonomic Wheeled Mobile Robots;ABF and PBF Optimization for IRS-Based WCS with Practical Phase Shifts;BiLSTM Based on Indoor Localization by Using Smartphones and Wi-Fi with RSSI Values;ethiopic Cryptography: An Efficient Hybrid Encryption Algorithm for Reducing Cybersecurity Attack;Dual-Channel MEMS Actuated Tunable Optical Filter Based on Polarization Insensitive Compound Ring Resonator for DWDM networks;Voltage Profile Improvement and Loss Minimization in a Transmission Line Using FLC-Based UPFC at Bahir Dar Substation-II 230 kV Line;Simulation of a 100 MW Grid-Connected Solar Power Plant and MPPT Control Using the PSO-P&O Technique.

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