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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Saleem, Muhammad Haris, Muhammad Naeem, Mehwish Abro, Abdul Basit Ahmad, Maaz Ahad, Abdul Iqra University Main Campus Dept. of Software Engineering Karachi Pakistan University Libre de Bruxelles Brussels Belgium Indus University Dept. of Computer Science Karachi Pakistan Iqra University Main Campus Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

Most financial lending systems in use today are based on a single service platform, transactions are opaque, and the majority of system participants can be tracked back to Mechanisms for protecting data privacy are not strong enough to withstand cyberattacks. We suggest lending over the block chain (LoC), a cutting-edge finance and credit facility built on smart contracts on a block chain hyper ledger fabric, as a solution to these problems. We use the poverty level for Chinese loans as an example. We create a digital accounting model to transfer assets between decentralized and centralized ledgers and suggest smart contract closing and unlocking methods. To safeguard the privacy of data, we established digital signatures and oracles. The opening rule's and the chain rule's performance evaluation demonstrates the applicability of our system in actual loan situations. © 2024 IEEE.

关键词： Smart contract

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Transformer Models for Enhanced Natural Language Processing in Medical Records Management 4

Transformer Models for Enhanced Natural Language Processing ...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Vasani, Vaibhav Prakash Pawar, Swapnil Chandrakant Sowmya, P. Ahamad, Shahanawaj Sahu, Aradhana Talele, Gokul Somaiya Vidyavihar University Dept. of Computer Engineering Maharashtra Mumbai India Nmamit Dept. of Cse Nitte India College of Cse University of Hail Dept. of Software Engineering Hail City Saudi Arabia Rungta College of Engineering and Technology Dept. of Cse Chhattisgarh Bhilai India Accenture Pune India

ISBN: (纸本)9798350387490

The use of transformer models to enhance NLP in MR administration is investigated. Renowned for their ability to understand contextual links along with efficiently handle large datasets, transformer designs provide clear improvements in the processing along with analysis of medical language. Work improves the extraction, classification, along with summarising of data from complex medical records by use of models like BERT, GPT, along with variants. The study looks at how well these models may simplify data retrieval, increase the accuracy and relevance of CR, along with support decision making in medical environments. The results imply that the use of transformer-based NLP techniques might greatly boost the medical record administration, thereby improving patient care, accelerating procedures, along with optimising data usage. Transformer models such as BERT are used in the research approach aiming at enhancing NLP in medical record handling. Literature, preprocessing EHRs, fine-tuning and assessment along with pilot testing all plays a part. Maintaining ethical issues, a user interface is given for interaction. This method improves medical data handling effectiveness. BERT has showed promise in handling medical records in terms of enhancing identification and categorisation of medical entities found in data. Various medical entities including diseases, medications, procedures, and symptoms have been specifically tailored into the strong language paradigm. Confusion matrix performance measures reveal how well model controls medical records. Future studies could investigate combining BERT with other advanced NLP methods, extending dataset to include a wider range of medical records, using TLT in case of particular medical domains, including real time data processing and feedback systems, and investigating ethical issues along with data privacy concerns in health care. © 2024 IEEE.

关键词： Records management

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Computational Model of Swarm Algorithm for Optimizing Process of Keywords Extraction from Text Information Presented as Graph 7

Computational Model of Swarm Algorithm for Optimizing Proces...

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Kravchenko, Yury Kuliev, Elmar Kravchenko, Daniil Rodzin, Sergey Pavlov, Nikolay Rodzina, Lada Southern Federal University Dept. of Computer Aided Design Taganrog Russia Southern Federal University Department of Software Engineering Taganrog Russia

ISBN: (纸本)9798350371369

The article describes the scientific problem of optimizing the processes of acquiring knowledge when processing textual information presented in the form of a graph, based on the development of a modified bioinspired algorithm for extracting keywords. The solution proposed by the authors differs from the known ones by introducing new attractive operators and behavioral patterns of bacterial agents into the computational model of the bacterial swarm algorithm. This approach allows you to balance the ratio of the speed of convergence of the algorithm with the diversification of the search space for solutions. The article also presents an analytical study of the features of two types of parsing: constituency parsing and dependency parsing. As part of the study, algorithms for syntactic parsing of dependencies between elements of text information and a graph-based dependency parsing algorithm are described. The formulation of the problem of keyword extraction is given. The task of achieving a balance between accuracy and speed of a linguistic parser is an open topic that requires further research due to the importance of this problem for improving the efficiency of text analysis, especially in applications that require real-time accuracy. To increase the efficiency of solving the problem, the authors propose a modified bioinspired algorithm for extracting keywords. The computational experiment carried out allows us to conclude that the modified bacterial optimization method proposed by the authors is in a leading position, which confirms the need to continue research into the applicability of bioinspired methods for solving problems of acquiring knowledge when processing text information presented in the form of a graph. This article is focused on the direction of study in the master's program "Informatics and computer Science", as well as the scientific specialty of postgraduate training "Artificial Intelligence and Machine Learning". © 2024 IEEE.

关键词： Text processing

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EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mode Selection to Classify Upper Limb Complex Movements Using Statistical Features 20

EMD and VMD in Pre-Movement EEG Signal Analysis: A Hybrid Mo...

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20th IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2023

作者： Khalid, Beenish Hassan, Ali Munir, Ehsan Ullah Niazi, Imran Khan Dept. of Computer and Software Engineering Islamabad Pakistan Comsats University Islamabad Dept. of Computer Science Pakistan College of Chiropractic Centre of Chiropractic Research Auckland New Zealand

ISBN: (纸本)9798350331110

Electroencephalogram (EEG) signals, inherently non-stationary and non-linear, present significant challenges in their processing and interpretation. This paper presents a hybrid mode selection approach using two advanced decomposition methods: Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to analyze these signals, targeting their application in the classification of upper limb complex movements for enhanced prosthetic limb control and rehabilitation therapy assessment. Using optimized statistical features extracted from selected modes, Intrinsic mode functions (IMFs) via EMD and modes via VMD, we seek to better distinguish neural activities in pre-movement EEG signal. Our methodology involves the following two strategies: straightforward extraction of statistical features from modes yielded by EMD and VMD;a genetic algorithm (GA) feature selection technique to select the most optimal set from these statistical features. These derived features train machine learning (ML) classifiers to differentiate limb movements. The results, derived from proprietary dataset from Aalborg University, Denmark, comprising five distinct upper limb movements, demonstrate the effectiveness of our hybrid approach. The usage of EMD and VMD significantly enhanced the discriminatory power of the extracted features, leading to improved classification performance. Furthermore, our hybrid approach yielded classification accuracies of 93.1 % and 95.6% with EMD and VMD respectively when the K-NN classifier was deployed with a 10-fold cross-validation. K-NN classifier outperformed traditional ML classifiers in terms of computational time, highlighting its potential as lightweight yet robust algorithm for classification of complex movements. The primary goal is to present and validate a hybrid mode (IMFs/modes) selection approach through EMD and VMD to analyze EEG signals associated with upper limb complex movements. © 2023 IEEE.

关键词： Empirical mode decomposition

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Enhancing Autism Education: Exploring Interactive Videos and AI Integration for Effective Teaching 15

Enhancing Autism Education: Exploring Interactive Videos and...

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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Raeesi, Fatima Al Kuwaiti, Mariam K. Al Berengueres, Jose United Arab Emirates University Dept. of Software Engineering Al Ain United Arab Emirates United Arab Emirates University Dept. of Computer Science & Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350382396

This research project proposes the use of interactive videos to enhance teachers' training in educating autistic children. Effective teaching methods for students with autism require teachers to comprehend the condition and employ tailored instructional strategies, including adapting assignments, aiding those with language difficulties, and utilizing visual aids for better organization and focus. Traditional teacher training methods can be both expensive and time-consuming. In contrast, interactive videos provide a proactive and flexible way to access training content, empowering teachers to engage with the material dynamically and take control of their learning experiences. Future work will explore the integration of AI-driven ChatGPT to offer personalized support and create a dynamic training program, with the goal of improving inclusivity and educational quality in autism settings while benefiting teachers, students, and the education system at large. © 2023 IEEE.

关键词： Students

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Laser-assisted Multidimensional Imaging Modalities for Early Detection of Tumours in Bio Tissues: An In Vitro Study

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Lasers in engineering 2025年第4-6期59卷 199-215页

作者： Akhtar, M. Ullah, H. Aslam, K. Siddique, M.A. Akram, A. Majid, M.A. Abdullah-Al-Wadud, M. Aleem, A. Farhan, M. Batool, Z. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

The idea, upon which this research work depends, is the early detection of tumours in different biological soft tissues like bladder, respiratory and thigh tissues. Imaging and spectroscopic technique that were used to study healthy and cancerous tissues were white light microscopy and Fourier-transform infrared radiation (FTIR) spectroscopy. The observation and calculations collected through all these imaging techniques prove as helping tools to differentiate healthy and cancerous body tissues. Soft tissues from patients who had been diagnosed with bladder, respiratory, and thigh cancer were gathered from the Bahawalpur Victoria Hospital, Pakistan while keeping in mind the ethical research perspective. Two-dimensional (2-D) white light microscopy images showed that healthy and cancerous tissues have a different kind of shapes, sizes and structure arrangements. Normal tissues images show a regular pattern of structure arrangement, but cancerous tissues show an irregular shapes, sizes, and structures. Similarly, FTIR spectroscopy graphs represents different peaks of stretching vibrations of C-H, O-H, C-C, N-H, CH2 double and triple bonding of different biological tissues like bladder, respiratory and thigh tissues. This research work will help in the medical field for early detection of tumour which helps to diagnose the tumour and also helpful for better treatment planning in early stage. ©2025 Old City Publishing, Inc.

关键词： Fourier transform infrared spectroscopy

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A Comparative Study of the Performance of Real time databases and Big data Analytics Frameworks 7

A Comparative Study of the Performance of Real time database...

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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Mustafa, Syed Muhammd Nabeel Farooq, Muhammad Umer Zehra, Syeda Sundus Noronha, Jospeh Peter Terenece Ned University of Engineering and Technology Dept. of Computer Science & I.T Karachi Pakistan Sir Syed University of Engineering and Technology Dept. of Software Engineering Karachi Pakistan

ISBN: (纸本)9798350338461

The amelioration of information technology and the infiltration of it almost all the sectors of world have resulted in massive efflux of data. The rampant increase in the generation of data has introduced to a new term called, Big data. Big data is letting industry set the pace of future research and development. The need rose to further refine the data and get most out of it. Scientists and researchers performed analytic on the Big data to analyze it for patterns and behaviors hidden inside the data. Big data analytics aid in understanding the data through various tools, algorithms and frameworks. It allows stakeholders to make business plans and strategies according to the revelations from data. There are various tools and technologies available to perform analytics on the data. The most popular among scientists and researchers are real-time database and Big data analytics frameworks. In the paper, we evaluate the performance of the tools and technologies of Big data analytics. Mongodb and Firebase are real-time databases implemented in this study and Impala and Hive are the BDA frameworks. The parameter to perform the comparison is the query execution time. It is found out that Impala and Hive gave lesser execution time than Mongodb and Firebase. The query execution time of Impala on 50 mb dataset and 100 mb dataset was recorded to be 4 ms and 14 ms respectively. Hence, Big data Analytics Frameworks are better performing analytics tools for Big data Analytics. © 2023 IEEE.

关键词： Big data

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SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in Satellite IoT 10

SCHC over DtS-IoT: Performance of SCHC Confirmation Modes in...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Munoz-Lara, Rodrigo Cespedes, Sandra Diaz, Marcos University of Chile Dept.of Electrical Engineering Santiago Chile Concordia University Dept. of Computer Science and Software Eng. MontrealQC Canada

ISBN: (纸本)9798350373011

Static Context Header Compression and Fragmentation (SCHC) is a standard defined as an adaptation layer for supporting IPv6, UDP, and CoAP protocols in low power wide area network (LPWAN) technologies. SCHC has fragmentation modes that use confirmation mechanisms to deliver reliable communications. Direct-to-satellite Internet of Things (DtS-IoT) directly communicates between end devices and the satellite in an IoT environment. If SCHC is implemented in a DtSIoT environment, applications using IPv6 could have global coverage through the Internet of Things (IoT). Unfortunately, it is not known whether SCHC can operate with the current acknowledgement mechanisms in a DtS-IoT environment, and if so, it is not known which one performs better. This work evaluates SCHC acknowledgement modes for ACK-on-Error fragmentation mode, including the new SCHC Compound ACK mode. It determines which mode has the lowest transfer delay for different LoRa data rates and frame error rates in a DtS-IoT environment with LEO satellites. © 2024 IEEE.

关键词： Internet of things

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A Study on Traceable Group Signature Scheme for Blockchain 8

A Study on Traceable Group Signature Scheme for Blockchain

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8th International Conference on Information and Network Technologies, ICINT 2023

作者： Song, Hyo-Jin Lee, Im-Yeong Soonchunhyang Univ. Dept. of Software Convergence Asan-si31538 Korea Republic of Soonchunhyang Univ. Dept. of Computer Software Engineering Asan-si31538 Korea Republic of

ISBN: (纸本)9798350301458

A blockchain is a decentralized database system in which a ledger containing transactions is stored and maintained identically by all through consensus among network participants without a central server. Instead of having a central server, network participants make sure that everyone stores and maintains the same ledger containing transactions through consensus. Integrity and reliability of the data stored in the blockchain are guaranteed, but on the other hand, privacy issues may arise. The pseudonymity of the public key reused in transactions causes a privacy invasion of the user's behavioral pattern, asset size, and offline identity. Therefore, instead of assigning unique public keys to everyone, they are provided through a single signature in a blockchain environment. It can also be described as a method for ensuring anonymity by having members of the same group use the same public key in a transaction while employing the group signature, which has been studied in this paper. This paper defines the key used for group signature is defined as a composition of a 'group private key' and a 'partial key'. Mainly among them, we consider the leakage of the partial key. In the proposed scheme, it is possible to trace the leaker and the signer from the signature generated by the outsider with the leaked partial key. In this paper, we provide a secure traceable group signature scheme to cope with the leakage of the group signing key. © 2023 IEEE.

关键词： Blockchain

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Intrusion Detection System (IDS) of Multiclassification IoT by using Pipelining and an Efficient Machine Learning 9

Intrusion Detection System (IDS) of Multiclassification IoT ...

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9th International Conference on engineering and Emerging Technology, ICEET 2023

作者： Hazim, Layth Rafea Jasim, Abdulrahman Ahmed Ata, Oguz Ilyas, Muhammad College of Computer Science and Mathematics Tikrit University Tikrit Iraq Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey College of Engineering Al-Iraqia University Baghdad Iraq Altinbas University Istanbul Dept. of Software Engineering Turkey Altinbas University Istanbul Dept. of Computer Engineering Turkey

ISBN: (纸本)9798350316926

The Internet of Things (IoT) has quickly advanced and been incorporated into many different fields. With the use of IoT technology, gadgets can receive, process, and send data automatically. IoT has been rapidly accepted in many important fields since it makes life easier and increases service quality, yet it still faces significant privacy and security problems. An Intrusion Detection System (IDS) could be implemented as a security feature to protect IoT networks from a variety of cyberattacks. This study suggests using IDS to defend against a wide range of cyberattacks on IoT systems. The suggested approach makes use of the Multi-layer Perceptron (MLP) as well as Extra Trees (ExT) as efficient algorithms of classification. Also, the study uses the pipeline to put together several cross-validated phases while selecting various parameters to increase the detection rate. One dataset is utilized for evaluating and analyzing the performance outcomes so as to validate the efficiency of the suggested IDS approach. The evaluation findings show that the suggested IDS methods may greatly increase detection performance results concerning accuracy rate, precision, F1-score, and recall while also improving detection efficiency. © 2023 IEEE.

关键词： Network security

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