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IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction

Computers, Materials & Continua 2022年第6期71卷 6219-6237页

作者： Tanzila Saba Rabia Javed Mohd Shafry Mohd Rahim Amjad Rehman Saeed Ali Bahaj Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Johor BahruMalaysia Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan MIS Department College of Business Administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

The Internet ofMedical Things(IoMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent *** is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel ***,detection and treatment at an early stage can result in a higher chance of *** classical methods of detection are expensive and ***,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of *** current improvement in computer-aided systems is providing very encouraging results in terms of precision and *** this article,we propose an improved region growing technique for efficient extraction of the lesion *** analysis and detection ofMelanoma are helpful for the expert *** CNN features are extracted using the pre-trained VGG-19 deep learning *** the end,the selected features are classified by *** proposed technique is gauged on openly accessible two datasets ISIC 2017 and *** the evaluation of our proposed framework,qualitative and quantitative experiments are *** suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on ISIC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 *** results are notably better than the results of prevalent methods available on the same *** machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of *** proposed method detects and classifies melanoma far better than other ***,our framework gained promising results in both segmentation and classification phases.

关键词： Deep features extraction lesion segmentation melanoma detection SVM VGG-19 healthcare IoMT public health

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Stress Prediction in Higher Education Students Using Psychometric Assessments and AOA-CNN-XGBoost Models 4

Stress Prediction in Higher Education Students Using Psychom...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sharma, Sarthak Vij, Suman Praveen, R.V.S. Srinivasan, S. Yadav, Dharmendra Kumar Raj Kumar, V.S. Shri Guru Ram Rai University Uttarakhand Dehradun India School of Management and Commerce Studies Shri Guru Ram Rai University Uttarakhand Dehradun India LTIMindtree Limited Telangana Hyderabad India Amet University Department of Advanced Computing Sciences Tamilnadu Chennai India Lalit Narayan Mithila University University Department of Mathematics Bihar Darbhanga India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India

ISBN: (纸本)9798331540364

Research backs up the common perception that the years preceding up to college are the most difficult, showing that students report higher levels of discomfort and worse self-esteem during this switch. Positive personality qualities such as optimism, hope, and happiness were examined in this system along with 215 first-semester Israeli university students' ratings of functional impairment, psychological distress, and self-esteem. The three stages that make up this suggested method are data preprocessing, model training, and feature selection. In order to process data that would be infeasible to process without data preprocessing, the data can be adjusted to meet the specifications of each data mining technique. Beginning with different forms of feature classification or aggregation and progressing to individual activity levels, several levels of feature granularity were investigated in order to employ a feature selection technique. The proposed approach used an AOA-CNN-XGBoost for the whole model training phase. This novel method outperforms CNN and AOA with an average accuracy of 89.27%. © 2024 IEEE.

关键词： Students

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Designing Dissemination and Validation of a Framework for Teaching Cloud Fundamentals 21

Designing Dissemination and Validation of a Framework for Te...

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2021 Working Group Reports on Innovation and Technology in Computer science Education, ITiCSE-WGR 2021

作者： Paterson, James Adams, Joshua White, Laurie Csizmadia, Andrew Erdil, D. Cenk Foster, Derek Hills, Mark Kazmi, Zain Kuber, Karthik Nazir, Sajid Sakr, Majd Stott, Lee Department of Computing Glasgow Caledonian University Glasgow Scotland United Kingdom School of Computing Artificial Intelligence Robotics Data Science CS Department Saint Leo University Saint LeoFL United States

ISBN: (纸本)9781450392020

Three previous Working Groups (WGs) met at ITiCSE conferences to explore ways to help educators incorporate cloud computing into their courses and curricula by mapping industry job skills to knowledge areas (KAs). These WGs identified, organized, and grouped together student learning objectives (LOs) and developed these KAs and LOs in a repository of learning materials and course exemplars. This WG focused on the sustainability of the work of its predecessors through dissemination, community building and validation of the framework of KAs and LOs and its contribution to curriculum development. Firstly, a case study is presented which analyzed the implementation of a new Masters program which was based on the KAs and LOs. It was found that these provide a useful basis for program development and approval and demonstrate that successful program development of this nature can provide a valuable opportunity to communicate the work of the previous WGs. Thereafter, a plan was formulated for dissemination of the work done in order to drive adoption and to encourage instructors with an interest in teaching cloud computing to participate and grow the community. While the strategy included a range of dissemination methods, the importance of interaction with users was a guiding principle. Initial pilots of webinar and workshop activities have been implemented. Approaches to validating that a cloud computing course designed around the KAs and LOs can meet the needs of industry have been outlined with further iterations being considered. A research plan has been designed for a study to be implemented over the coming year in order to perform this validation. © 2021 ACM.

关键词： Cloud computing

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Vader protocol based sentiment analysis technique using LSTM for Stock Trend prediction 14

Vader protocol based sentiment analysis technique using LSTM...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Ticku, Amrita Tripathy, Niva Mishra, Pankaj Kumar Sinha, Anurag Jadon, Shivangi Raj, Aditya Kulshrestha, Harsh Rai, Vipin Shamim, Rejuwan Bharti Vidyapeeth's College of Engineering India Driems Autonomous Engineering College Cuttack India Rungta College of Engg. & Technology R1 Department of Electronics and Telecommunication Engg. CG Bhilai India Ignou School of Computing and Information Science New Delhi India Galgotias University Dept. of Computer Science and Engineering Noida India Galgotias University Department of Computer Science and Engineering Noida India Maharishi University of Information Technology Dept. of Computer Science and Engineering with Data Science Noida India

ISBN: (纸本)9798350335095

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& P 500 dataset reveal that it outperforms traditional stock prediction approaches. © 2023 IEEE.

关键词： Forecasting

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Contrastive Graph Auto-Encoder for Graph Embedding

SSRN

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SSRN 2023年

作者： Zu, Shuaishuai Li, Li Shen, Jun Tang, Weitao School of Computer and Information Science Southwest University China School of Computing and Information Technology University of Wollongong Australia Department of Data Science & Artificial Intelligence Monash University Australia

Graph embedding aims to embed the information of graph data into low-dimensional representation space. Prior methods generally suffer from an imbalance of preserving structural information and node features due to their pre-defined inductive biases, leading to unsatisfactory generalization performance. In order to preserve the maximal information, graph contrastive learning (GCL) has become a prominent technique for learning discriminative embeddings. However, in contrast with graph-level embeddings, existing GCL methods generally learn less discriminative node embeddings in a self-supervised way. In this paper, we ascribe above problem to two challenges: 1) graph data augmentations, which are designed for generating contrastive representations, hurt the original semantic information for nodes. 2) the nodes within the same cluster are selected as negative samples. To alleviate these challenges, we propose Contrastive Graph Auto-Encoder (CGAE) and Contrastive Variational Graph Auto-Encoder (CVGAE). Specifically, we first propose two distribution-dependent regularizations to guide the paralleled encoders to generate contrastive representations following similar distribution, followed by theoretical derivations to verify the equivalence of the above regularizations. Then, we utilize truncated triplet loss, which only selects top-k nodes as negative samples, to avoid over-separate nodes affiliated to the same cluster. Furthermore, we give theoretical analysis of the effectiveness of our models. Experiments on several real-world datasets show that our models advanced performance over all baselines in link prediction, node clustering, and graph visualization tasks. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Block Broyden's methods for solving nonlinear equations 23

Block Broyden's methods for solving nonlinear equations

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Chengchang Liu Cheng Chen Luo Luo John C.S. Lui Department of Computer Science & Engineering The Chinese University of Hong Kong Shanghai Key Laboratory of Trustworthy Computing East China Normal University School of Data Science Fudan University

This paper studies quasi-Newton methods for solving nonlinear equations. We propose block variants of both good and bad Broyden's methods, which enjoy explicit local superlinear convergence rates. Our block good Broyden's method has a faster condition-number-free convergence rate than existing Broyden's methods because it takes the advantage of multiple rank modification on Jacobian estimator. On the other hand, our block bad Broyden's method directly estimates the inverse of the Jacobian provably, which reduces the computational cost of the iteration. Our theoretical results provide some new insights on why good Broyden's method outperforms bad Broyden's method in most of the cases. The empirical results also demonstrate the superiority of our methods and validate our theoretical analysis.

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Digital Twin System for Smart Campuses

Digital Twin System for Smart Campuses

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IEEE International Conference on Control science and Systems Engineering (CCSSE)

作者： Huijie Wang Yuanyuan Cai Fabu Yue Hao Luo Sina Abdipoor Say Leng Goh School of Big Data BaoShan University BaoShan Yunnan China Department of Computer Science Universiti Putra Malaysia Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan Malaysia

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff.

关键词： Uniform resource locators Three-dimensional displays databases Virtual reality Real-time systems Digital twins Maintenance Servers Monitoring Thermal stability

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Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

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Computers, Materials & Continua 2021年第9期68卷 4125-4142页

作者： Abdul Rehman Mohd Fadzil Hassan Yew Kwang Hooi Muhammad Aasim Qureshi Tran Duc Chung Rehan Akbar Sohail Safdar Computer and Information Science Department Centre for Research and Data Science(CeRDaS)Universiti Teknologi PETRONAS32610Seri IskandarPerak Darul RidzuanMalaysia Department of Computer Science Bahria UniversityPakistan Computing Fundamental Department FPT UniversityHoa Lac Hi-Tech ParkHanoiVietnam Department of Information Systems Uiversiti Tunku Abdul RahmanMalaysia Information Technology Department College of IT AHLAI UniversityBahrain

Trust is one of the core components of any ad hoc network security *** management(TM)has always been a challenging issue in a vehicular *** such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart *** originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV *** security standards are insufficient to meet the extremely dynamic and rapidly changing IoV *** plays a vital role in ensuring security,especially during vehicle to vehicle *** networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust *** TM schemes are inflexible and *** scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle *** vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical *** vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security ***,internet involvement makes it more vulnerable to *** proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV *** learning is the best solution to utilize the big data produced by vehicle *** handle the uncertainty Bayesian machine learning statistical model is *** proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter *** results indicated better performance than existing TM ***,for future work,a high-level machine learning model is proposed.

关键词： Internet of vehicles(IoV) trust management(TM) vehicular ad hoc network(VANET) machine learning context awareness bayesian learning

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Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT 24

Decoding Urban Industrial Complexity: Enhancing Knowledge-Dr...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Siqi Liang, Chao Gao, Yunfan Liu, Yang Li, Jing Wang, Haofen Tongji University Shanghai China Department of Computing The Hong Kong Polytechnic University Hong Kong Guangdong Guodi Institute of Resources and Environment Guangzhou China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China College of Design and Innovation Tongji University Shanghai China Research Centre on Data Science & Artificial Intelligence Hong Kong

ISBN: (纸本)9798400706868

Industrial parks are critical to urban economic growth. Yet, their development often encounters challenges stemming from imbalances between industrial requirements and urban services, underscoring the need for strategic planning and operations. This paper introduces IndustryScopeKG, a pioneering large-scale multi-modal, multi-level industrial park knowledge graph, which integrates diverse urban data including street views, corporate, socio-economic, and geospatial information, capturing the complex relationships and semantics within industrial parks. Alongside this, we present the IndustryScopeGPT framework, which leverages Large Language Models (LLMs) with Monte Carlo Tree Search to enhance tool-augmented reasoning and decision-making in Industrial Park Planning and Operation (IPPO). Our work significantly improves site recommendation and functional planning, demonstrating the potential of combining LLMs with structured datasets to advance industrial park management. This approach sets a new benchmark for intelligent IPPO research and lays a robust foundation for advancing urban industrial development. The dataset and related code are available at https://***/Tongji-KGLLM/IndustryScope. © 2024 ACM.

关键词： Semantics

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Empirical Analysis of EIP-1559: Transaction Fees, Waiting Times, and Consensus Security 22

Empirical Analysis of EIP-1559: Transaction Fees, Waiting Ti...

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28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022

作者： Liu, Yulin Lu, Yuxuan Nayak, Kartik Zhang, Fan Zhang, Luyao Zhao, Yinhong SciEcon Cic London United Kingdom Center on Frontiers of Computing Studies Peking University Beijing China Department of Computer Science Duke University DurhamNC United States Department of Computer Science Yale University New HavenCT United States Data Science Research Center and Social Science Division Duke Kunshan University Suzhou China

ISBN: (纸本)9781450394505

A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs. © 2022 Owner/Author.

关键词： Blockchain

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