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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862922

the proceedings contain 28 papers. the special focus in this conference is on Advancements in Smart computing and Information Security. the topics include: Prediction of Anti-Cholinesterase Inhibitor for Alzheimer Disease Using Machine Learning Techniques;scrutinize Search Engine Optimization Strategies with Artificial Intelligence to Rank a Website;a Study on Machine Learning algorithms: Forecasting Crop Prices;improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features;extraction of Normal and Abnormal Region in Colposcopy Image to Support Cervical Cancer Clinical Decision;optimizing Pricing Strategies: A Comprehensive Framework Using Bayesian Inference and Game theory;hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics;a Review on Machine Learning algorithms for Real-Time Traffic Management;hyperparameter-Tuned Intention Mining for Mental Health Diagnosis Using Logistic Regression;machine Learning for Accessible and Precise Assessment in Smart Monitoring systems;an Efficient Model for Academic Performance Prediction of the University Students;comprehensive Comparative Study on Data Mining and Machine Learning Approaches for Fraud Detection in Financial Services;prediction of Stock Price and Detection of Stock Market Trends Using Adaptive Learning Techniques;asthma Prediction Using Fuzzification and Machine Learning Based Ensemble Classifier;detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma;a Hybrid Deep Learning Framework for Uncertain Supply Chains: An Optimization Approach;a Comprehensive Review of Machine Learning Techniques in Recommender System for E-Commerce Platform;Ethics of AI in the Educational Sector - Navigating the Moral Landscape;international Law and AI Interface.

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V-SBERT: A Mixture Model for Closed-Domain Question-Answering systems Based on Natural Language Processing and Deep Learning 6

V-SBERT: A Mixture Model for Closed-Domain Question-Answerin...

引用

6th international conference on Data Science and Information Technology, DSIT 2023

作者： Zhuang, Yuan Liu, Boyan Lin, Xiaotao Xu, Canhao BNU-HKBU United International College Guangdong Provincial Key Laboratory IRADS Zhuhai519087 China Zhuhai519087 China

ISBN: (纸本)9798350304442

Smart question answering (QA) systems have been the focus of research for many years. this is known to be a challenging task due to the randomness of user input. As deep learning technology is evolving fast, closed-domain QA systems based on frequently asked questions (FAQs) have already become a mature field. In the project, the team designed a mixture model based on deep learning and natural language processing (NLP) for closed-domain QA tasks. We construct a novel dataset containing admission FAQs for a Chinese university1. then we propose an ensemble system using the average voting method based on TF-IDF and fine-tuned versions of SBERT variation models (V-SBERT). Because both the query and questions in the knowledge base are formulated in natural language, it is important to use NLP approaches to represent the messages. then, the matches between the user query and standard questions are scored. the accuracy and performance of several different encoding models are tested on the collected dataset, including Term Frequency - Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), Sentence-BERT (SBERT), Robustly Optimized BERT pre-training Approach (RoBERTa), Decoding-enhanced BERT with disentangled attention (DeBERTa), and OpenAI's text-embedding-ada-002. the evaluation shows our V-SBERT system based on TF-IDF and SBERT variations outperforms other algorithms for this task. © 2023 IEEE.

关键词： Deep learning

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Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation 7

Boundary-aware Contrastive Learning for Semi-supervised Nucl...

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7th international conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Zhang, Ye Wang, Ziyue Wang, Yifeng Bian, Hao Cai, Linghan Li, Hengrui Zhang, Lingbo Zhang, Yongbing School of Computer Science and Technology Harbin Institute of Technology 518055 China School of Science Harbin Institute of Technology 518055 China Tsinghua Shenzhen International Graduate School Tsinghua University 518071 China Faculty of Computing Harbin Institute of Technology 150001 China

Semi-supervised segmentation methods have demonstrated promising results in natural scenarios, providing a solution to reduce dependency on manual annotation. However, these methods face significant challenges when directly applied to pathological images due to the subtle color differences between nuclei and tissues, as well as the significant morphological variations among nuclei. Consequently, the generated pseudo-labels often contain much noise, especially at the nuclei boundaries. To address the above problem, this paper proposes a boundary-aware contrastive learning network to denoise the boundary noise in a semi-supervised nuclei segmentation task. the model has two key designs: a low-resolution denoising (LRD) module and a cross-RoI contrastive learning (CRC) module. the LRD improves the smoothness of the nuclei boundary by pseudo-labels denoising, and the CRC enhances the discrimination between foreground and background by boundary feature contrastive learning. We conduct extensive experiments to demonstrate the superiority of our proposed method over existing semi-supervised instance segmentation methods. © 2024 CC-BY 4.0, Y. Zhang, Z. Wang1, Y. Wang2, H. Bian3, L. Cai1, H. Li4, L. Zhang3 & Y. Zhang1.

关键词： Contrastive Learning

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Digital Trust in Industry 4.0 & 5.0: Impact of Frauds

Digital Trust in Industry 4.0 & 5.0: Impact of Frauds

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international conference on ent computing and Control systems (ICICCS)

作者： Joy Chatterjee Madhavi Damle Avinash Aslekar Symbiosis Institute of Digital and Telecom Management Symbiosis International (Deemed University) Pune India

Digital trust in Industry 4.0 and 5.0 is a crucial aspect. the digital age is working in real-time; agility is required in this digital era. the digital nature of businesses has bought significant changes such as transparency, advancement in technological usage, and, most notably, customer experience. the focus of this study is the impact of fraud that may occur in Industry 4.0 & 5.0 as an element of digital trust that must be secured. A secondary research method to understand digital trust, industry 4.0 & 5.0 and fraud, with ramifications on repute while organisations try their best to protect data and information. Maintaining digital trust achieved by mitigating the risk of fraud using various techniques is discussed here by its priority and digital transformation of technologies to achieve trust with discussion on types of fraud and their mitigation practices. the implications are far-reaching for organisations and society where the bar is raised to keep unwanted attention and frauds kept at bay in the standardisation by way of technological transformations for the satisfaction of consumers and have a sense of responsibility as the practitioners, policymakers and organisations.

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7th international conference on Control, Decision and Information Technologies, CoDIT 2020

7th International Conference on Control, Decision and Inform...

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7th international conference on Control, Decision and Information Technologies, CoDIT 2020

ISBN: (纸本)9781728159539

the proceedings contain 212 papers. the topics discussed include: implementation of the algorithm for improving the frontier in DEA models;a decision model and method for the bi-objective parallel machine ScheLoc problem;deep imitation learning for broom-manipulation tasks using small-sized training data;performance and usage frequency based policy for products recovery and remanufacturing;voltage oriented control of indirect matrix converter applied to wind energy conversion system using PMSM generator;optimal trajectories synthesis of a mobile robots group using Cartesian genetic programming;minimization of an aircraft's energy;a multi-objective and multi-decision maker approach for the balancing market in distribution grids in presence of aggregators;actor-critic algorithm for optimal synchronization of Kuramoto oscillator;and estimation of the distance to moving vehicles in a traffic stream.

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IntrusionHunter: Detection of Cyber threats in Big Data

IntrusionHunter: Detection of Cyber Threats in Big Data

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12th international conference on Data Science, Technology and Applications, DATA 2023

作者： Mohamed, Hashem El Bolock, Alia Sabty, Caroline Informatics and Computer Science German International University Cairo Egypt

ISBN: (纸本)9789897586644

the rise of cyber-attacks has become a serious problem due to our growing reliance on technology, making it essential for both individuals and businesses to use efficient cybersecurity solutions. this work continues on previous work to improve the accuracy of intrusion detection systems by employing advanced classification techniques and an up-to-date dataset. In this work, we propose IntrusionHunter, an anomaly-based intrusion detection system operating on the CSE-CICIDS2018 dataset. IntrusionHunter classifies intrusions based on three models, each catering to different purposes: binary classification (2C), multiclass classification with 7 classes (7C), and multiclass classification with 15 classes (15C). Four main classification models were used: Random Forest, Extreme Gradient Boosting, Convolutional Neural Networks, and Deep Neural Networks. the results show that Random Forest and XGBoost algorithms outperformed state-of-the-art intrusion detection systems in binary and multiclass classification (15 classes). the findings also show that the dataset imbalance needs to be addressed to improve the performance of deep learning techniques. © 2023 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Network intrusion

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17th international conference on Mobile systems and Pervasive computing, MobiSPC 2020

17th International Conference on Mobile Systems and Pervasiv...

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17th international conference on Mobile systems and Pervasive computing, MobiSPC 2020

the proceedings contain 108 papers. the topics discussed include: deep learning-based data augmentation for hydraulic condition monitoring system;mobile light storage: make the light smarter;enhanced cloud acknowledgement scheme for a node network;concepts and solutions of the digital teams platform to support mobile work and virtual teams;time-series data augmentation based on interpolation;an weighted CNN ensemble model with small amount of data for bearing fault diagnosis;segmentation process and spectral characteristics in the determination of musical genres;analysis of crowd behavior through pattern virtualization;unbalanced data processing using oversampling: machine learning;and analysis of behavior of automatic learning algorithms to identify criminal messages.

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Study of Image Data Security with Cloud

Study of Image Data Security with Cloud

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Pronika Chawla Harshita Parihar Ankit Mathur Department of Computer Science & Engineering Faculty of Engineering & Technology Manav Rachna International Institute of Research and Studies Faridabad India

the on-demand availableness of computer systems resources, especially storage of data (cloud storage) and rectifying power, with no direct ongoing administration by the user is what they call cloud computing. Large cloud functions are usually spread among several locations, each of which constitutes a data center. Customers may save money on capital expenses by using cloud computing, which frequently takes a “pay-as-you-go” approach. Coherence in cloud computing is achieved by sharing resources. In today's world of the Internet, demand for cloud services is increasing drastically leading to the production of new services day by day. As the services increase, the data gets primarily targeted by spiteful users who attempt to steal the data for their own atrocious and unethical activities. Users and trustworthy applications are considering more security and privacy and services get more in demand. Moreover, this study has reviewed several algorithms such as CPE-ABE (Ciphertext policy attribute-based encryption), ABE, KP-ABE (Key policy attribute-based encryption) CSP (Constraint satisfaction problem), PKG, AES (Advanced Encryption Standards), SHA-1 (Secure Hash Algorithm), Photo encryption, Photo decryption, PRE, IDEA (international Data Encryption algorithm) and LSBG (Least Significant Bit Grouping) for image data security. As already discussed, the increasing threats and frauds around the world, safe and secure applications and services should be created to resolve this problem so that people can store data on a platform that can be relied on. this research study has discussed about the concept of what are the paradigms required for securing and protecting the data and securing the image data at an encrypted level. this study has reviewed several existing research works, studied different algorithms which have been used in different research articles, and compared their strengths and drawbacks accordingly. Different research works have been summarized in a form of ta

关键词： Cloud computing Privacy Data centers Image resolution Production Market research Encryption

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proceedings - 26th Pan-Hellenic conference on Informatics, PCI 2022

Proceedings - 26th Pan-Hellenic Conference on Informatics, P...

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26th Pan-Hellenic conference on Informatics, PCI 2022

ISBN: (纸本)9781450398541

the proceedings contain 63 papers. the topics discussed include: performance benchmarking of visual human tracking algorithms for UAVs;community detection at scale: a comparison study among Apache spark and Neo4j;security operations center in education: building an educational environment for attack and defense scenarios;smart business processing in industry 4.0;a survey on vulnerability prediction using GNNs;design for smart home devices: an exploratory eye-tracking study;intelligent emergency evacuation systems for buildings;better solutions for the thesis defense timetabling problem using a three-phase approach;self-adaptive privacy in cloud computing: an overview under an interdisciplinary spectrum;evaluation of the relationship between the smart packaging technology and disability in Greece;an exact based approach for the post enrollment course timetabling problem;and a neuro-symbolic approach for fault diagnosis in smart power grids.

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PECCS 2020 - proceedings of the 10th international conference on Pervasive and Parallel computing, Communication and Sensors

PECCS 2020 - Proceedings of the 10th International Conferenc...

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10th international conference on Pervasive and Parallel computing, Communication and Sensors, PECCS 2020

ISBN: (纸本)9789897584770

the proceedings contain 7 papers. the topics discussed include: directional-IPeR: enhanced direction and interest aware PeopleRank for opportunistic mobile social networks;ESP32 based edge devices to bridge smart devices to MQTT broker for healthcare purposes in the COVID scenario;analysis of free time intervals between buyers at cash register using generating functions;internal and external factor analysis in bottleneck detection in shop sales: the case of grocery shops in Lithuania;AHP-based evaluation of the acceptance of autonomous driving;and systematic crowd mobility management to avoid catastrophic disasters of human stampedes: the case of Mina Hajj.

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