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arXiv 2023年

作者： Kazerouni, Amirhossein Azad, Reza Hosseini, Alireza Merhof, Dorit Bagci, Ulas School of Electrical Engineering Iran University of Science and Technology Iran Institute of Imaging and Computer Vision RWTH Aachen University Germany School of Electrical and Computer Engineering College of Engineering University of Tehran Iran Faculty of Informatics and Data Science University of Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Department of Radiology Northwestern University Chicago United States

Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. However, existing INRs face limitations in capturing fine-grained details, handling noise, and adapting to diverse signal types. To address these challenges, we introduce INCODE, a novel approach that enhances the control of the sinusoidal-based activation function in INRs using deep prior knowledge. INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function. Through a task-specific pre-trained model, INCODE adapts the task-specific parameters to optimize the representation process. Our approach not only excels in representation, but also extends its prowess to tackle complex tasks such as audio, image, and 3D shape reconstructions, as well as intricate challenges such as neural radiance fields (NeRFs), and inverse problems, including denoising, super-resolution, inpainting, and CT reconstruction. Through comprehensive experiments, INCODE demonstrates its superiority in terms of robustness, accuracy, quality, and convergence rate, broadening the scope of signal representation. Please visit the project’s website for details on the proposed method and access to the code. © 2023, CC BY.

关键词： Inverse problems

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Application of Artificial Intelligence - based Multiple Regression Algorithm to indicate the Human Development in the Workspace

Application of Artificial Intelligence - based Multiple Regr...

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Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： Tanyaa Singh Anshul Mandawat L Mohit Reddy Aakash Panchagnula Darsh Pandya Tanvi Salian Department of Computer Science and Engineering Indira Gandhi Delhi Technical University For Women Delhir India Department of Biochemical Engineering and Biotechnology IIT Delhi Delhi India Department Computer Science and Business systems VIT Vellore Vellore India Department of Mechatronics and Engineering NMIMS Mumbai India Department of information technology K J SOMAIYA COLLEGE OF ENGINEERING Mumbai India Department of Computer Science and Engineering K. C.College of Engineering and Management studies Thane India

ISBN: (纸本)9781665489638

Human instinct puts a high worth on development, and there is a natural desire to continue extending and changing in all features of our life. This improvement is unsurprising and stable in small kids and youngsters, simplifying it to notice. Is is actually required to develop and create as years progress, in spite of the fact that it could seem somewhat unique. As people spend most of their time at work, it is highly important to develop and grow expertly. Many individuals will like to look for another work in the event that the open door emerges on the off chance that there aren't opportunities for proficient headway at work. In a working environment where representatives are urged and upheld to work on their relational, close to home, and expert capacities, development and improvement are available. Such organizations offer different inside and outside choices for representatives to grow their range of abilities. This will help them with their current work and furthermore assist them with being prepared for expected future ones. A calling in human advancement oftentimes calls areas of strength for and correspondence capacities to more readily address an individual's issues and help in their improvement or mending. There are many positions and vocation ways you can take in the field of human turn of events, from helping specialists and clinical experts in a medical clinic to helping understudies in making scholarly progress. This research study characterize a lifelong in human turn of events and provide a rundown of 15 potential choices. The main aim of this research work is to use the regressive algorithms and compare it with the decision tree to analyze the performance and improve the accuracy of the regression algorithm.

关键词： Organizations Artificial intelligence Regression tree analysis Logistics

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Advancing Robustness and Privacy in Federated Learning for Secure Autonomous Vehicle systems

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IEEE Transactions on Consumer Electronics 2025年

作者： Moudoud, Hajar Houda, Zakaria Abou El Brik, Bouziane Jan, Mian Ahmad Alshawi, Bandar Département d'informatique et d'ingénierie Gatineau Canada University of Quebec RSEC Umr Inrs-UQO INRS-EMT GatineauQCJ8Y 3G5 Canada Sharjah University College of Computing and Informatics Computer Science Department Sharjah United Arab Emirates Umm Al-Qura University College of Computing Makkah Saudi Arabia

The rapid development of Autonomous Vehicle systems (AVS) is transforming transportation, enabling safer, more efficient mobility. However, ensuring the security and privacy of sensitive data generated by AVS remains a major challenge. Federated Learning (FL) has emerged as a promising solution for AVS by enabling distributed machine learning across connected vehicles without sharing raw data, thereby enhancing privacy. Despite these advantages, FL faces critical challenges in autonomous driving environments, including high communication overhead, latency, and vulnerability to adversarial attacks. To address these challenges, we propose SecureFL, a novel framework designed to enhance the robustness and privacy of FL in autonomous vehicle systems. SecureFL consists of two main modules. The first module is a novel client selection strategy that integrates various factors to ensure efficient and robust client participation. First, we design a novel mechanism that uses an ensemble of classifiers to identify and mitigate adversarial attacks that attempt to corrupt the global learning model. Then, we propose a novel clustering scheme that groups devices exhibiting similar behaviors into clusters. This approach optimizes data transmission and reduces latency, ensuring efficient communication across the AVS network. The second module is a Graph Neural Network (GNN)-based reputation system that evaluates the reliability of vehicles based on data quality, prioritizing contributions from trustworthy sources, and dynamically adjusting participation in the FL process. SecureFL's effectiveness is validated through simulations with real-world AVS-based attacks, demonstrating SecureFL's capability to ensure security, privacy, and communication efficiency in federated learning for autonomous vehicles. © 1975-2011 IEEE.

关键词： Differential privacy

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Collaborative Authentication for 6G Networks: An Edge Intelligence based Autonomous Approach

arXiv

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arXiv 2023年

作者： Fang, He Xiao, Zhenlong Wang, Xianbin Xu, Li Hanzo, Lajos The School of Electronic and Information Engineering Soochow University Soochow215301 China The Department of Informatics and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada The College of Computer and Cyber Security The Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou350117 China School of Electronics and Computer Science University of Southampton SO17 1BJ United Kingdom

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Authentication

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Dual role of sprouty2 as an inhibitor of RAS/ERK-driven proliferation and a promoter of cancer invasion in KRAS wild-type colorectal cancer

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Molecular Carcinogenesis 2023年第7期62卷 951-962页

作者： Lee, Chung-Ta Chu, Chien-An Wang, Yu-Ming Wang, Yi-Wen Chen, Yi-Lin Ho, Chung-Liang Yeh, Yu-Min Lin, Peng-Chan Lin, Bo-Wen Chen, Po-Chuan Chen, Shang-Hung Chan, Ren-Hao Chang, Chen Chow, Nan-Haw Department of Pathology College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Pathology National Cheng Kung University Hospital Dou-Liou Branch Yunlin County Douliu City Taiwan College of Medicine Institute of Molecular Medicine National Cheng Kung University Tainan Taiwan Department of Oncology College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Genomic Medicine College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan College of Electrical Engineering and Computer Science Institute of Medical Informatics National Cheng Kung University Tainan Taiwan Department of Surgery College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan College of Medicine Institute of Basic Medical Sciences National Cheng Kung University Tainan Taiwan

Sprouty2 (SPRY2) is known to inhibit the RAS/MAPK/ERK pathway, and is a potential study target for cancer. The effect of SPRY2 in colorectal cancer (CRC) and whether it is influenced by KRAS mutation are not known. We manipulated SPRY2 gene expression and used an activating KRAS-mutant plasmid to determine its effect on CRC cell function in vitro and/or in vivo. We performed SPRY2 immunohistochemical staining in 143 CRC specimens and analyzed the staining results with various clinicopathological characteristics in relation to KRAS mutation status. SPRY2 knockdown in Caco-2 cells carrying the wild-type (WT) KRAS gene upregulated phosphorylated ERK (p-ERK) levels and increased cell proliferation in vitro, but inhibited cell invasion. However, SPRY2 knockdown in SW480 cells (activating KRAS mutant) or Caco-2 cells transfected with KRAS-mutant plasmid did not significantly alter p-ERK levels, cell proliferation, or invasion. The xenografts of SPRY2-knockdown Caco-2 cells were larger with less deep muscle invasion than those of control cells. The clinical cohort study revealed a positive association of SPRY2 protein expression with pT status, lymphovascular invasion, and perineural invasion in KRAS-WT CRCs. However, the associations were not observed in KRAS-mutant CRCs. Interestingly, high SPRY2 expression was related to shorter cancer-specific survival in both KRAS-WT and KRAS-mutant CRC patients. Our study demonstrated the dual role of SPRY2 as an inhibitor of RAS/ERK-driven proliferation and as a promoter of cancer invasion in KRAS-WT CRC. SPRY2 may promote the invasion and progression of KRAS-WT CRC, and might also enhance KRAS-mutant CRC progression through pathways other than invasion. © 2023 Wiley Periodicals LLC.

关键词： cetuximab colorectal cancer invasion KRAS sprouty2

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Quantifying Harm

arXiv

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arXiv 2022年

作者： Beckers, Sander Chockler, Hana Halpern, Joseph Y. Cluster of Excellence in Machine Learning University of Tübingen Germany causaLens and Department of Informatics King’s College London United Kingdom Computer Science Department Cornell University United States

In a companion paper (Beckers et al. (2022)), we defined a qualitative notion of harm: either harm is caused, or it is not. For practical applications, we often need to quantify harm;for example, we may want to choose the least harmful of a set of possible interventions. We first present a quantitative definition of harm in a deterministic context involving a single individual, then we consider the issues involved in dealing with uncertainty regarding the context and going from a notion of harm for a single individual to a notion of "societal harm", which involves aggregating the harm to individuals. We show that the "obvious" way of doing this (just taking the expected harm for an individual and then summing the expected harm over all individuals) can lead to counterintuitive or inappropriate answers, and discuss alternatives, drawing on work from the decision-theory literature. © 2022, CC BY.

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An Improved Fuzzy Logic Based Alcohol Detection System to Preserve Road Safety using Smart Sensors Association

An Improved Fuzzy Logic Based Alcohol Detection System to Pr...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： V. R. Kavitha Annamalai Muthu B. Geetha V. Samuthira Pandi M Dinesh B. Anni Princy Department of Computer Science and Business Systems R.M.K. Engineering College Chennai Tamil Nadu India Department of Engineering University of Technology & Applied Science Nizwa Oman Department of Science & Humanities Prince Shri Venkateshwara PadmavathyEngineering College Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India

The increased risk of being involved in a traffic collision is directly proportional to the rate at which the number of vehicles on the road is growing. The majority of people in the world believe that driving under the influence of alcohol is a significant factor in the number of people killed in car accidents. The fundamental purpose of this investigation is to develop a device that is able to determine the degree of drunkenness that is present in the driver of a motor vehicle. The purpose of the proposed system is to discourage users from getting behind the wheel while under the influence of alcohol or drugs in order to reduce the number of accidents that are caused by intoxicated drivers. As part of this investigation, we propose the Fuzzy Assisted Alcohol Detection Mechanism (FAADM), which is a logical fuzzy-based alcohol detection mechanism. It uses a Direct Current (DC) motor as the vehicle's engine and a push button as its ignition system. This mechanism detects the presence of alcohol using a fuzzy-logic approach. The efficiency of this system is always being evaluated to ensure that it performs as designed. If this system were put into place, there would be a reduction in the number of accidents on the roads of smart cities that were caused by drivers who were under the influence of alcohol. It is strongly suggested that this system be fitted and installed inside the vehicle. In the event of an unexpected emergency, the engine should be turned off and the alarm should be activated. If there has been no drinking or accidents, the security system can be disarmed and the vehicle's engine should be started. If alcohol is found, the car will be turned off and an alarm will ring. As the number of incidents caused by intoxicated drivers continues to rise, the implementation of a system that combines alcohol detection and vehicle immobilization has been proposed as a potential solution. Engineers have developed a gadget that can detect the amount of alcohol in a d

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Advancements in Multi-Cloud Applications for Enhanced E-Healthcare Services

Advancements in Multi-Cloud Applications for Enhanced E-Heal...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： P Ramesh Naidu Dankan Gowda Parismita Sarma Dinesh Arora Sampathirao Suneetha Sandip Ramesh Patil Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of Electronics & Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Information Technology Gauhati University Assam Guwahati Assam India Department of Electronics and Communication Chandigarh Engineering College Mohali Punjab India Department of Computer Science & Systems Engineering Andhra University Visakhapatnam Andhra Pradesh India Department of Communication Skill Bharati Vidyapeeth College of Engineering Navi Mumbai India

E-Health Record Security Research on a Cloud-Based Multi-Layer Framework reaches its climax in a string of noteworthy discoveries, demonstrating how the framework may transform cloud health data security. The framework's multi-tiered design proved to be an effective barrier against a wide range of cyber threats, protecting the privacy and security of patients' medical records. An important factor in the varied healthcare industry is the fact that it can be easily scaled and adjusted to meet the needs of healthcare providers of varied sizes and capabilities. Nevertheless, there are obstacles that need to be addressed, according to the report. These include the difficulty of implementation and the ongoing need for upgrades and modifications to address changing cyber threats and technical advances. Healthcare firms must continuously implement educational programs due to the reliance on user compliance and training. With an eye toward the future, this structure lays the groundwork for more sophisticated studies in the subject. Potential research directions include improving cross-platform compatibility, optimizing resource utilization to reduce performance implications, and integrating AI and ML for automated threat response and predictive analytics.

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Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

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computers, Materials & Continua 2021年第12期69卷 3531-3548页

作者： Nora Shoaip Amira Rezk Shaker EL-Sappagh Tamer Abuhmed Sherif Barakat Mohammed Elmogy Department of Information Systems Faculty of Computers and InformationMansoura UniversityEgypt Centro Singular de Investigaci’on en Tecnolox’ias Intelixentes(CiTIUS)Universidade de Santiago de Compostela Santiago de Compostela15782Spain Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Department of Computer Science and Engineering College of ComputingSungkyunkwan UniversityRepublic of Korea Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia *** is a global health problem.99%of clinical trials have failed to limit the progression of this *** risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical *** for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological *** this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive *** an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very ***,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning *** models are random forest(RF),JRip,and J48,which have been used along with the voting *** dataset was used for this *** proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference ***,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.

关键词： Mild cognitive impairment Alzheimer’s disease knowledge based semantic web rule language reasoning system ADNI dataset machine learning techniques

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Improving Math Proficiency Prediction in computer-Based International Large-Scale Assessments with Data Augmentation

Improving Math Proficiency Prediction in Computer-Based Inte...

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International Symposium on Intelligent systems and informatics (SISY)

作者： Aleksandar Pejić Piroska Stanić Molcer Dávid Fa Doctoral School of Computer Science University of Szeged Szeged Hungary Informatics department Subotica-Tech College of Applied Sciences Subotica Serbia ICBTECH Ltd Subotica Subotica Serbia

ISBN: (纸本)9781665489898

This study explores the possibility to improve students' math proficiency prediction with a multi-class neural network model by augmenting the training dataset with synthetic data. The original training dataset was based on the publicly released PISA 2012 computer-based database. Three math proficiency classes were established: low, mediocre and high. Minority class with the least number of samples was the high proficiency class. SMOTE, VAE and CTGAN methods were used to augment the minority class with additional data samples. G-mean was used as a performance measure for observing the enhancement to the prediction of the minority class.

关键词： Training Databases Computational modeling Neural networks Predictive models Data models Generators

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