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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Vidhya, R.G. Seetha, J. Ramadass, Sudhir Dilipkumar, S. Sundaram, Ajith Saritha, G. Hkbk College of Engineering Department of Ece Bangalore India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Data Analyst Sterck Systems Chennai India Vit Department of Analytics Scope Vellore India School of Business Selection Grade Kochi India Sri Sai Ram Institute of Technology Department of Ece Tamilnadu Chennai India

ISBN: (纸本)9798350397840

Cancer that originates in the breast tissue then spreads to the chest wall is called breast cancer. Doctors routinely examine mammograms for signs of cancer;however, aberrant macrocalcifications and microcalcifications might appear on mammograms when the picture quality is subpar. Always get checked out if you see anything out of the ordinary, especially if it involves your breasts, such abnormal calcium deposits. For this mammographic deposit to be properly interpreted, top-notch picture quality is necessary. Many different breast cancer screening methods and the many breast cancer phases are still the subject of active study. In order to construct effective medical image processing systems, experts use methods including the Ant Colony Algorithm (ACA), the Improved Adaptive Fuzzy C-Means (IAFCM), and TNM (the size of the breast tumor (T), the lymph nodes around the tumor, and metastasized). Classes were determined using an MPIG, or a modified Poisson inverse gradient classifier. More than five hundred picture modalities are used across all methods. Medical professionals that rely on images to establish diagnoses or treatments might find the results of this research useful. © 2022 IEEE.

关键词： Deposits

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things

A Secured and Optimized Broadcast Authentication Scheme for ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Syed Waleed Riaz Fazlullah Khan Insaf Ullah Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China Institute for Analytics and Data Science University of Essex Colchester UK Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes.

关键词： Performance evaluation Computational modeling Authentication Surgery Internet of Medical Things Public key cryptography Real-time systems Information management Monitoring Read only memory

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Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 859-876页

作者： Ahmad Chaddad Qizong Lu Jiali Li Yousef Katib Reem Kateb Camel Tanougast Ahmed Bouridane Ahmed Abdulkadir School of Artificial intelligence Guilin Universiy of Electronic TechnologyGuilin 541004China Laboratory for Imagery Vision and Artificial IntelligenceEcole de Technologie SuperieureMontrealQuebec H3C 1K3Canada College of Medicine Taibah UniversityMadinah 42353Saudi Arabia College of Computer Science and Engineering Taibah UniversityMadinah 42353Saudi Arabia Laboratory of Design Optimization and Modeling SystemsUniversity of LorraineMetz 57070France Center for Data Analytics and Cybersecurity(CDAC) University of SharjahSharjah 27272United Arab Emirates Laboratoire de recherche en neuroimagerie Centre Hospitalier Universitaire VaudoisMont Paisible 161011LausanneSwitzerland

Artificial intelligence(AI)continues to transform data analysis in many *** in each domain is driven by a growing body of annotated data,increased computational resources,and technological *** medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of *** this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each *** models increase confidence if the results appear plausible and match the clinicians ***,the absence of a plausible explanation does not imply an inaccurate *** in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple *** example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health *** centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health *** narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.

关键词： Domain adaptation explainable artificial intelligence federated learning

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OSN Rumor Control Model Based on Community Immunization

OSN Rumor Control Model Based on Community Immunization

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Ming Dong Zhiqiang Sun Zhenhua Tan Bin Zhang School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

This paper proposes a rumor control model based on community immunization. Based on the community division and the trust network inference algorithm, the model redefines the standard to measure the importance of nodes in the network. First, the model uses the Louvain clustering algorithm based on the Ochiai coefficient to discover the network community and then presents the trust network inference algorithm. By analyzing the key factors that affect trust transfer between nodes, the trust evaluation between unfamiliar nodes is inferred, and important nodes with a high degree of trust in the network community are calculated. Finally, combined with the characteristics of inner degree and outer degree centrality of nodes in the network community, five types of important nodes in the network are screened out. To avoid repeated selection of nodes, this paper identifies a group of key nodes in the network community for local immunization by means of deduplication and taking intersection, so as to realize effective control of rumors in the network.

关键词： Distributed processing Clustering algorithms Inference algorithms Standards Open data

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Optimization Study of Renal CT Image Classification Using Convolution Neural Networks

Optimization Study of Renal CT Image Classification Using Co...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Afnaan. K K. V Nagaraja Tripty Singh Bhanu Prakash KN Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Department of Mathematics Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Principal Investigator Research Scientist Clinical Data Analytics and Radiomics Bioinformatic Institute (BII) A*STAR Singapore

computer tomography (CT), also known as computed tomography, is a medical imaging method that generates detailed and precise horizontal or axial images of targeted regions of the body for diagnostic purposes. In this particular study, the focus was on performing image classification of CT scan images of the Kidney. This organ plays an essential role in detoxification, fluid balance, and maintaining electrolyte levels in the human body. The dataset used for the study contained 12,446 images into four labeled classes - Cyst, Tumor, Stone, and Normal. Convolution Neural Network (CNN) model is built for binary (Normal and Tumor) and multi-image Classification to classify the Renal CT images into four classes. CNN uses ReLU as an activation function for classifying these images. The Model is evaluated and compared based on Accuracy and Loss performance metrics by compiling the CNN model with different deep learning TensorFlow Keras optimizers for Version 2.11.0 for Binary Image Classification at 10,20, and 50 epochs. The CNN model is assembled with Adam Optimizer and Follows the Regularized Leader (FTRL) Optimizer. The Testing and training Accuracy and Losses achieved are evaluated with a learning rate of 0.001 and 0.0001. Adam Optimizer outperforms to be the best Optimizer for Binary and Multi-Image Classification.

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Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text

arXiv

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arXiv 2023年

作者： Xu, Fangzhi Liu, Jun Lin, Qika Zhao, Tianzhe Zhang, Jian Zhang, Lingling School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D National Engineering lab for Big Data Analytics Xi’an China

Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: (1) How is the zero-shot capability of the models (train on seen types and test on unseen types)? (2) How to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes both the heuristic input reconstruction and the contrastive learning to improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset. © 2023, CC BY.

关键词： Benchmarking

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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

arXiv

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arXiv 2022年

作者： Albert, Paul Saadeldin, Mohamed Narayanan, Badri Fernandez, Jaime Namee, Brian Mac Hennessy, Deirdre O'Connor, Noel E. McGuinness, Kevin School of Electronic Engineering Dublin City University Ireland School of Computer Science University College Dublin Ireland Insight Centre for Data Analytics Ireland Teagasc Ireland VistaMilk Ireland

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then use the enhanced drone images to train a semi-supervised algorithm that uses ground-truthed, ground-level images as the labelled data together with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way for automate

关键词： Agriculture

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A New Integrated Fuzzy Optimization Approach for Sustainable Supply Chain Planning Subjected to Sustainability and Uncertain Environments

A New Integrated Fuzzy Optimization Approach for Sustainable...

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computer science and data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Noppasorn Sutthibutr Kunihiko Hiraishi Navee Chiadamrong Suttipong Thajchayapong School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology (SIIT) Thammasat University Pathum Thani Thailand School of Information Science Japan Advanced Institute of Science and Technology (JAIST) Ishikawa Japan Data Science and Analytics Research National Electronics and Computer Technology Center Pathum Thani Thailand

This study proposes an integrated fuzzy optimization approach for sustainable supply chain planning subjected to sustainability and uncertain environments. The proposed approach combines the concept of Chance-Constrained Programming (CCP) with Intuitionistic Fuzzy Linear Programming (IFLP) that decision makers can control risk violation of fuzzy constraints as well as consider satisfaction and non-satisfaction levels simultaneously. The effectiveness of the proposed approach is demonstrated through a case study by minimizing total costs and maximizing supplier's social and environmental scores where costs and customer demands are imprecise. Our comparative result clearly shows that the obtained solution outperforms the result from the traditional integrated fuzzy optimization approach. The advantages of the proposed approach can assist decision-makers to prepare and take necessary actions for future uncertainty, leading to sustainability.

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An Analytical Study of Internet Key Exchange (IKE) Protocol for Wireless Network Security

An Analytical Study of Internet Key Exchange (IKE) Protocol ...

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Communication & Computing (WCONF), World Conference on

作者： Vaishali Singh Pratik Pandey Santosh Chowhan S. Hemelatha Divya N Yogesh D. Deshpande Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Data Analytics and Mathematical Sciences Jain (Deemed to be University) Bangalore Karnataka Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Information Technology Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

This analytical examine investigates the safety of net Key change (IKE) Protocol for wireless networks. It specializes in how IKE works and the unique additives that it involves so one can provide relaxed key trade. The paper examines the IKE protocol primarily based at the maximum current requirements and gives a complete review of ways the protocol works, its key components, the safety mechanisms it is predicated on, its blessings, and its weaknesses. thru our examine, it's miles discovered that even though IKE is considered to be one of the most relaxed protocols for Wi-Fi networks, there are nevertheless certain regions within the protocol that have ability for improvement. The paper concludes with numerous tips on a way to maximize the security furnished by using IKE while minimizing its potential for weakness.

关键词： Protocols Additives Reviews Wireless networks Authentication Organizations Internet Security Protection Wireless fidelity

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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

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

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

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