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Frontiers of Information technology (FIT)

作者： Muhammad Umar Khan Sumair Aziz Khushbakht Iqtidar Raul Fernandez Rojas Department of Electronics Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Computer Software Engineering National University of Sciences and Technology Islamabad Pakistan Faculty of Science & Technology University of Canberra Canberra Australia

ISBN: (纸本)9798350345940

Preterm deliveries are an important cause of mortality and morbidity in newborns. Accurate and early prediction of a premature delivery can prove helpful in providing proper medication and treatment. Recording of electrical activity known as Electrohysterogram (EHG) from the abdominal surface of pregnant women corresponds to the uterus contractions. A new direction is open using EHG signals for the diagnosis of preterm births. In this research, we present a new method for the accurate classification of preterm and term EHG signals. The proposed method first filters a three-channel EHG signal using bandpass filters. Next, we combined the filtered three-channel EHG into one signal using an accumulation operation. The accumulated EHG signal was post-processed through variational mode decomposition (VMD). VMD algorithm splits the input signal into finite modes using center frequencies known as intrinsic mode functions (IMFs). An energy-based intelligent signal reconstruction approach is designed to combine IMFs having an energy level above the computed threshold. Next, the reconstructed EHG signals were split into continuous windows, and time, frequency, and Hjorth features were extracted. These features were fused to construct a distinct feature representation and were reduced using the ReliefF algorithm. We trained an artificial neural network (ANN) to obtain 98.8 % average accuracy using 10-fold cross-validation.

关键词： Band-pass filters Time-frequency analysis Artificial neural networks Filtering algorithms Feature extraction Signal reconstruction Classification algorithms

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Research and development of an information system for speech recognition based on neural network algorithms metrics

Research and development of an information system for speech...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Maksat Kanashev Assel Mukasheva Didar Yedilkhan Sabina Rakhmetulaeva Sergiy Gnatyuk Mirambayeva Nazym Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Information Systems International University of Information Technologies Almaty Kazakhstan Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields.

关键词： Performance evaluation Adaptation models Vocabulary Accuracy Data visualization Speech recognition Libraries

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Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

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Artificial Intelligence in Medicine 2024年 149卷 102779-102779页

作者： Aminizadeh, Sarina Heidari, Arash Dehghan, Mahshid Toumaj, Shiva Rezaei, Mahsa Jafari Navimipour, Nima Stroppa, Fabio Unal, Mehmet Medical Faculty Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul34060 Turkey Tabriz University of Medical Sciences Faculty of Medicine Tabriz Iran Urmia University of Medical Sciences Urmia Iran Tabriz University of Medical Sciences Faculty of Surgery Tabriz Iran Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Department of Mathematics School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.

关键词： Quality of service

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PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yijia Chen Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Singapore Faculty of Engineering The University of Sydney Australia

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model’s accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively.

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Sinhala Named Entity Recognition Model: Domain-Specific Classes in Sports

Sinhala Named Entity Recognition Model: Domain-Specific Clas...

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Advancements in Computing (ICAC), International Conference on

作者： W.M.S.K. Wijesinghe Muditha Tissera Department of Statistics and Computer Science Faculty of Science University of Kelaniya Sri Lanka Department of Software Engineering Faculty of Computing & Technology University of Kelaniya Sri Lanka

ISBN: (纸本)9798350398106

Named Entity Recognition (NER) is one of the crucial and vital subtasks that must be solved in most Natural Language Processing (NLP) tasks. However, constructing a NER system for the Sinhala Language is challenging. Because it comes under the category of low-resource languages. Therefore, the proposed approach attempted designing a mechanism to identify specific named entities in the sports domain. Firstly, a domain-specific corpus was built using Sinhala sport e-News articles. Then a semi-automated, rule-based component named as “Class_Label_Suggester” was built to annotate pre-defined named entities. After auto annotation, the outcome was further validated manually with a little effort. Finally, it was trained using the annotated data. Linear Perceptron, Stochastic Gradient Descent (SGD), Multinomial Naive Bayes (MNB), and Passive Aggressive classifiers were used to train the NER model. Though, the above Machine Learning (ML) algorithms showed approximately 98% accuracy, the MNB model demonstrated highest accuracy for the identified class labels of which, 99.76% for ‘Ground’, 99.53% for ‘School’, 98.55% for ‘Tournament’, and 97.87% for ‘Other’ classes. Additionally, high precision values of the above classes were 81%, 72%, 62%, and 98% respectively. An accurately annotated Sinhala dataset and the trained Sinhala NER model are main contributions of the study.

关键词： Machine learning algorithms Annotations Computational modeling Stochastic processes Machine learning Approximation algorithms Natural language processing

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Identification of the Diabetic Retinopathy Using ResNet-18

Identification of the Diabetic Retinopathy Using ResNet-18

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Cyber Resilience (ICCR), International Conference on

作者： Fahad Ahmed Areej Fatima Muhammed Mamoon Shan Khan Department of Computer Science National College of Business Administration economics Lahore Pakistan Department of Computer Science Lahore Garrison University Lahore Pakistan Department of Software Engineering Islamia University Bhawalpur Bahawalpur Pakistan FOIT(Faculty of Information Technology) University of Central Punjab Lahore Pakistan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

In the realm of healthcare, trauma, aging, and diseases like diabetes can cause a variety of abnormalities in human eyes. Diabetic retinopathy stands out as a prevalent global cause of blindness. Early identification and diagnosis of diabetic retinopathy is essential for prompt treatment and blindness prevention. The analysis of medical images can contribute to the identification of diabetic retinopathy, assisting in the diagnostic process. This study uses a deep learning (DL) model, ResNet-18, to identify diabetic retinopathy. The dataset used in this study acquired from Kaggle is divided into training and testing. ResNet-18 accomplished a training accuracy of 99.91% and a testing accuracy of 96.65%. The findings demonstrate the effectiveness of utilizing DL models like ResNet-18 for the early identification of diabetic retinopathy, potentially revolutionizing the screening process in clinical settings. By streamlining the diagnostic process, this proposed model has the potential to significantly enhance early intervention and treatment strategies, ultimately mitigating the risk of blindness in diabetic patients. Incorporating such technology into the healthcare system could improve patient outcomes and resource allocation.

关键词： Training Deep learning Diabetic retinopathy Blindness Medical services Resource management Medical diagnostic imaging

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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International Conference on Informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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QuIP: A P4 Quantum Internet Protocol Prototyping Framework

arXiv

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arXiv 2024年

作者： Kozlowski, Wojciech Kuipers, Fernando A. Smets, Rob Turkovic, Belma QuTech Delft University of Technology Delft2628 CD Netherlands The Networked Systems Group Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands SURF Utrecht3511 EP Netherlands TNO The Hague2595 DA Netherlands

Quantum entanglement is so fundamentally different from a network packet that several quantum network stacks have been proposed;one of which has even been experimentally demonstrated. Several simulators have also been developed to make up for limited hardware availability, and which facilitate the design and evaluation of quantum network protocols. However, the lack of shared tooling and community-agreed node architectures has resulted in protocol implementations that are tightly coupled to their simulators. Besides limiting their reusability between different simulators, it also makes building upon prior results and simulations difficult. To address this problem, we have developed QuIP: a P4-based Quantum Internet Protocol prototyping framework for quantum network protocol design. QuIP is a framework for designing and implementing quantum network protocols in a platform-agnostic fashion. It achieves this by providing the means to flexibly, but rigorously, define device architectures against which quantum network protocols can be implemented in the network programming language P416. QuIP also comes with the necessary tooling to enable their execution in existing quantum network simulators. We demonstrate its use by showcasing V1Quantum, a completely new device architecture, implementing a link- and network-layer protocol, and simulating it in the existing simulator NetSquid. © 2024, CC BY.

关键词： Network architecture

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Joint Clustering and Analyze Single Cell Multi-omics Data by scMOGAN

Joint Clustering and Analyze Single Cell Multi-omics Data by...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Congcong An Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore SG Faculty of Engineering The University of Sydney Australia

Advancements in single-cell multi-omics sequencing technologies have dramatically transformed the analysis of cellular states at single-cell resolution. Cluster analyses leveraging single-cell transcriptome and epigenome data have enabled the characterization of cellular states and the delineation of transcriptomic regulatory programs associated with cellular heterogeneity. However, the high dimensionality, sparsity, and heterogeneity inherent to multi-omics data present significant challenges to their clustering analysis. In this study, we introduced single-cell Multi-Omics Generative Adversarial Networks (scMOGAN), an unsupervised clustering model specifically tailored for single-cell transcriptome and epigenome data. scMOGAN accomplishes this by efficaciously modeling the omics data and mining potential features through neural networks, ultimately utilizing Gaussian mixture models for cell type identification. To validate and scrutinize the performance of scMOGAN, we performed analyses on two actual multi-omics datasets and one simulated dataset. The results demonstrate that scMOGAN surpasses other existing methods in terms of clustering performance, underscoring its efficacy in navigating the complexities of multi-omics data.

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Dogodo: IoT Based Mobile Application to Provide Essential Health Services to Dogs 3

Dogodo: IoT Based Mobile Application to Provide Essential He...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Thilakarathne, L.V.I.S. Salay, M.S. Wijethilaka, M. G. R Chethana Fernando, T.S. Sriyaratna, Disni Rupasinghe, Shalini Sri Lanka Institute of Information Technology Faculty of Computing Department of Infotmation Tchnology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Voice of dogs can be heard by people who listen to them. The more you listen, the more you learn about the dogs. This study proposes a platform to identify and observe dogs' behavior and their activities by using the newest technologies. The proposed system will mainly cover the relevant areas that are supposed to be covered to full fill the pet owners' expectations by providing necessary services such as internal health, voice recognition, and emotion translations and external issues such as skin diseases, breeding patterns, and breeding outcomes. Our priority is to emphasize necessary services in the mobile application and provide fluid services with fewer interruptions. Primarily research will offer an IoT device and a mobile application that covers the dog's internal health and external health consecutively. The study revealed current developments and the challenges and possibilities related to the system. © 2021 IEEE.

关键词： Speech recognition

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