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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Mittal, Shikha Monga, Chetna Upreti, Kamal Kumar, Nishant Raut, Ranjana Dinkar Alam, Mohammad Shabbir Chitkara University Institute of Engineering and Technology Punjab India Dr. Akhilesh das Gupta Institute of Technology and Management Department of Computer Science and Engineering India Amity University Amity School of Business Uttar Pradesh Noida India Sant Gadge Baba Amravati University Department of Applied Electronics Maharashtra India Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Cloud computing is currently a popular research topic among academics. It is an internet-based resource pool with a wide range of resources. The cloud environment is extremely dependable in terms of making resources available to internet users when they are needed. Reliable computing services can be provided without the need for any additional infrastructure, making it a cost-effective alternative. The majority of the businesses relied on cloud computing to host their apps. The health-care unit service is the most important service for the population. It is critical to keep sensitive information about a patient's medical history in a secure location. As a result, the most secure encryption and decryption should be used. The maintenance of Electronic Health Records is one of the advanced technologies in cloud computing. There is very little research and development in the areas of personal health records and electronic health records. The ultimate goal of this work is to offer a system for exchanging personal health information in a safe cloud setting. The medical information about a patient is likewise kept in this EHR by cloud environment in a dispersed manner. The saved information of user can be collected, shared, exchanged, and organised by other users. In addition, this research study demonstrates an effective way for securing an e-health cloud system in a cloud computing environment utilising identity-based cryptographic technique. © 2022 IEEE.

关键词： Cryptography

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Development of AI Chatbot for Tourism Promotion: A Case Study in Ranong and Chumphon, Thailand

Development of AI Chatbot for Tourism Promotion: A Case Stud...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Nasith Laosen Kanjana Laosen Piyawat Sriprasert Department of Digital Technology Faculty of Science and Technology Phuket Rajabhat University Phuket Thailand Andaman Intelligent Tourism and Service Informatics Center College of Computing Prince of Songkla University Phuket Thailand Computer Engineering Institute of Engineering Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Ranong and Chumphon provinces in Thailand pos-sess significant tourism potential due to their natural beauty and archaeological heritage. However, tourism development in the region remains limited due to inadequate information infrastructure and limited technology integration. This research presents a chatbot designed to promote tourism in these locations. The chatbot leverages a graph database built using data from the University to Tambon (U2T) project. Functioning as a virtual travel companion, it provides information on tourist attractions, accommodations, and dining options. Advanced natural language processing and machine learning enable the chatbot to under-stand user inquiries and effectively retrieve relevant information. Evaluation demonstrates strong performance with an F1-score of 99.56 for intent classification and 99.96 for named entity recognition. Testing with user-designed test cases confirmed the chatbot's functionality.

关键词： Adaptation models Databases Training data Prototypes Machine learning Chatbots Data models

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Machine Learning-Based Security Mechanism for Detecting Phishing Attacks

Machine Learning-Based Security Mechanism for Detecting Phis...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2024

作者： Bhatt, Pragya Obaidat, Mohammad S. Dangwal, Gagan Das, Ashok Kumar Wazid, Mohammad Sadoun, Balqies Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248 002 India University of Science and Technology Beijing Beijing100083 China SRM University Kattankulathur603203 India Amity University Noida201301 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India College of Engineering Al-Balqa' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Phishing attacks pose a serious threat to online users by impersonatina trustworthy entities and stealing sensitive data. This can result in reputational damage, financial loss, ransomware, or additional spyware outbreaks. Phishing scams take into consideration roughly 22% of all data violations, making it one of the most prominent cybercrimes, in accordance with the FBI's 2021 IC3 report. According to phishing email statistics, roughly 1.2% of all emails received are fraudulent, resulting in 3.4 billion phishing emails per day. One out of every 4,200 emails sent is almost certainly a phishing attempt. Cognitive methods, such as machine learning (ML) and deep learning (DL), are widely used in cybersecurity to extract information and detect possible intrusions and phishing attempts. This research investigates the procedure of employing machine learning methods for recognizing URLs that are used for phishing attacks. We have used the Pristine and Malicious URLs Dataset to train, test, and evaluate the performance of a set of algorithms to develop a robust binary class URL phishing attack detection mechanism. The obtained results show that the proposed mechanism outperforms the other existing competing mechanisms with 99.6% accuracy. © 2024 IEEE.

关键词： Phishing

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Statistical Analyses for Key Risk Factor Identification and Prediction of Chronic Kidney Disease *

Statistical Analyses for Key Risk Factor Identification and ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ananya Samanta Soham Bandyopadhyay Debasis Samanta Sponsored Research and Institute Consultancy Indian Institute of Technology Kharagpur India Advanced Technology Development Center Indian Institute of Technology Kharagpur India Department of Computer Science & Engineering Indian Institute of Technology Kharagpur India

Chronic renal disease, also known as chronic kidney disease (CKD) is a common disease and is a concern of public health management. Effective techniques for early CKD prediction are desirable. Given a set of biomarkers, Machine learning techniques are known for predicting CKD. This work aims of predicting CKD given clinical data. The proposed work suggests a methodology that includes data prepossessing (i.e. data cleaning, addressing null values and normalizing), applying statistical methodologies for finding key risk factors. Finally, using the most significant risk factors, machine learning techniques is applied to prognosticate the onset of CKD. The proposed approach has been tested with two data sets and proves to be fast, cost-effective and accurate compared to the existing state of the art *** relevance— Prognosticating CKD with a higher accuracy using a minimum number of risk factors is a significant aspects of healthcare informatics, where the treatment cost and predictive results both are optimized towards the betterment of patients.

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A Secure and Trustworthy Biometric Data Ecosystem for Cross-border Suspect Identification

A Secure and Trustworthy Biometric Data Ecosystem for Cross-...

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IEEE International Conference on Big Data

作者： Katerina Kyriakou Apostolos Apostolaras Polychronis Velentzas Georgios Benos Konstantinos Koutsoukos Chrysostomos Symvoulidis Kaitai Liang Zeshun Shi Asterios Leonidis Kyriaki Miniadou Eleni Veroni Spyridon Evangelatos Georgios Th. Papadopoulos Thanasis Korakis Department of Electrical and Computer Engineering University of Thessaly Volos Greece CERTH The Centre for Research & Technology Hellas Volos Greece Research & Development Thridium Ltd. London United Kingdom Delft University of Technology Delft The Netherlands Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Crete Greece Research & Innovation Development Netcompany-Intrasoft S.A. Luxembourg Luxembourg Dept. of Electronic Engineering Hellenic Mediterranean University Crete Greece Department of Informatics and Telematics Harokopio University of Athens Athens Greece

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

关键词： Biometrics Law enforcement Space technology Terrorism Ecosystems Regulation Blockchains Resource management Spectrogram Indexing

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Evolutionary Trends of Intelligent IoT Networking for Commercial and Industrial Use Cases

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中国通信（英文版） 2023年第8期20卷前插1-前插4页

作者： Shuai Han Rong Bo Nadra Guizani Sai Xu Ruoyu Su Liang Xiao Cheng Li Department of Electronics and Communication Engineering Harbin Institute of Technology Communications Research Centre Canada Engineering program and the computing Research Association School of Cybersecurity Northwestern Polytechnical UniversityXi'anChina Nanjing University of Posts and Telecommunications Department of Informatics and Communication Engineering Xiamen Univer- sityFujianChina Department of Electrical and Computer Engineering of Memorial University St.John'sCanada

The intelligent Internet of Things(IIoT)is one of the key enablers for the digital transformation of traditional industries towards Industry *** services have diversified requirements due to the nature of their commercial and industrial *** various devices in IIoT environments have their unique properties in terms of resource limita-tions,network lifetimes,and application Quali-ty-of-Service(QoS)requirements which affect the performance under such applications ***,IoT devices typically have constrained power,storage,computing,high latency,massive multiple access and communications resources which limit the performance of IIoT for commercial and industrial Use *** the other hand,devices used in IIoT were not initially designed with security in mind and they had been considered *** response to the above challenges,domestic and foreign scholars have gradually carried out research on network architecture,collaborative scheduling strategy,energy supply technology,resource man-agement,and ***,the research on IIoT is still in its infancy,and has not yet formed comprehen-sive and systematic theoretical methods and technical standards.

关键词： Special issues and sections Internet of Things Intelligent systems Commercialization Requirements engineering Industrial engineering

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Efficient Classification for Neural Machines Interpretations Based on Mathematical Models

Efficient Classification for Neural Machines Interpretations...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： Rahul Kumar Monit Kapoor Neeraj Varshney Manish Saraswat Akhil Sankhyan Devendra Singh Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Centre of Research Impact and Outreach Chitkara University Rajpura Punjab India Department of Computer Engineering and Applications GLA University Mathura India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India Department of Computer Science & Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

This study explores mathematical models to enhance our understanding of information generated by neural machines. We tested various models, including Statistical Machine Translation (SMT), Neural Machine Translation (NMT), Mathematical Transformation Recurrent Neural Networks (M.T RNN), and Simple Recurrent Units (SRU). Our research indicates that these models significantly improve the coherence and understanding of computer-generated writing, revolutionizing natural language processing and machine learning. Proper categorization facilitates the organization and utilization of interpreted data, opening avenues for diverse applications across academic fields. For more information, click on this link. These findings empower researchers and professionals to choose effective interpretation strategies for specific language combinations, enhancing machine translation intelligibility and accuracy.

关键词： Recurrent neural networks Accuracy Statistical analysis Computational modeling Organizations Machine learning Coherence

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An Improved Hierarchical Control Strategy for Power Management in Islanded Hybrid Microgrids

An Improved Hierarchical Control Strategy for Power Manageme...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Jeba Singh O K. Anish Pon Yamini Rajesh Sharma R Sheila Mahapathra Centre for Academic Research Alliance University Bengaluru India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Alliance University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bengaluru India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Power management and control through a hierarchical control strategy in a Hybrid Micro Grid (HMG) consisting of multiple AC and DC small Micro Grids (MG) with different voltage ratings are focussed of this research. The ultimate aim of the proposed power management strategy is to limit the continuous operation of Interlink (IL)in different load conditions. Hence, the power loss at the IL converters are reduced, orelse it may affect the operational stability of the HMG. The hierarchical control strategy for power management and coordination for islanded mode of HMG is implemented and analysed. The designed system consists of primary and secondary control levels, that provides an accurate power transfer among the small MGs. The new methodology ensure a continuous power flow within the small MGs with minimal operation of IL converter.

关键词： Accuracy Microgrids Power system stability Frequency conversion Hybrid power systems Stability analysis Information and communication technology Voltage control Frequency control Load flow

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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Transref: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting

SSRN

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SSRN 2024年

作者： Liu, Taorong Liao, Liang Chen, Delin Xiao, Jing Wang, Zheng Lin, Chia-Wen Satoh, Shin'ichi School of Computer Science Wuhan University Hubei Wuhan430072 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan

Image inpainting for completing complicated semantic environments and diverse hole patterns of corrupted images is challenging even for state-of-the-art learning-based inpainting methods trained on large-scale data. A reference image capturing the same scene of a corrupted image offers informative guidance for completing the corrupted image as it shares similar texture and structure priors to that of the holes of the corrupted image. In this work, we propose a Transformer-based encoder-decoder network for Reference-guided image inpainting, named TransRef. Specifically, the guidance is conducted progressively through a reference embedding procedure, in which the referencing features are subsequently aligned and fused with the features of the corrupted image. For precise utilization of the reference features for guidance, a reference-patch alignment (Ref-PA) module is proposed to align the patch features of the reference and corrupted images and harmonize their style differences, while a reference-patch transformer (Ref-PT) module is proposed to refine the embedded reference feature. Moreover, to facilitate the research of reference-guided image restoration tasks, we construct a publicly accessible benchmark dataset containing 50K pairs of input and reference images. Both quantitative and qualitative evaluations demonstrate the efficacy of the reference information and the proposed method over the state-of-the-art methods in completing complex holes. Code and dataset can be accessed at: https://***/Cameltr/TransRef. © 2024, The Authors. All rights reserved.

关键词： Embeddings

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