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Nanoinformatics: Opportunities and challenges in the development and delivery of healthcare products in developing countries 4

Nanoinformatics: Opportunities and challenges in the develop...

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4th International Conference on Science and Sustainable Development: Advances in Sciences and Technology for Sustainable Development, ICSSD 2020

作者： Nwankwo, Wilson Ukhurebor, Kingsley Eghonghon Software Engineering and Cyberphysical Systems Unit Department of Computer Science Edo University Iyamho Edo State Nigeria Environmental AndTelecommunication Unit Department of Physics Edo University Iyamho Edo State Nigeria

The world is witnessing sustained effects of information technologies across all works of life. Though some of these influences are markedly negative and detrimental to the socio-economic prospects and progress of the society, the positive effects are often impressive especially where they are integrated for the betterment and greater good of the larger society. One of the fastest growing technologies is nanotechnology. Nanotechnology has numerous prospects and applicability across various sectors ranging from electronics, telecommunications, agriculture and food production, biotechnology and genetics, oil prospecting and production, remote sensing, drug production, to name a few. This novel technology readily finds usefulness and several researches are ongoing, geared at developing new tools and techniques that would improve its acceptance across the concerned domains. It is this quest that has culminated into the ongoing effort in nanoinformatics, an interdisciplinary study and a subdomain of informatics simply regarded as the conscientious application of informatics tools and technologies to the analysis, design and development of systems on the broad spectrum of nanomaterials including their physicochemical and environmental characteristics as well as their interactions, interrelationships, and applications within a given domain. This paper presents a review of some opportunities for individuals, experts, and the society especially in the production of cost-effective nanotechnology-based healthcare products. Notwithstanding the aforesaid opportunities which could be harnessed and sustained in any developing country like Nigeria, this paper identifies and buttresses core challenges that could confront the adoption of good nanoinformatics methodologies. This paper concludes that a developing country (Nigeria in perspective) could benefit from nanoinformatics if there are stronger ties among the key stakeholders involved in healthcare products delivery in the socie

关键词： Developing countries

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Semi-supervised trajectory classification using convolutional auto-encoders 1

Semi-supervised trajectory classification using convolutiona...

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1st ACM SIGSPATIAL International Workshop on on Animal Movement Ecology and Human Mobility, HANIMOB 2021

作者： Makris, Antonios Kontopoulos, Ioannis Psomakelis, Evangelos Tserpes, Konstantinos Department of Informatics and Telematics Harokopio University of Athens Distributed Knowledge and Media Systems Group National Technical University of Athens Athens Greece Harokopio University of Athens Department of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9781450391221

Massive volumes of high-frequency and high-volume data are constantly being generated by the vast amount of available tracking sensors of moving objects. This phenomenon can be strongly observed in the maritime domain since most of the vessels transmit their location periodically. Automated methodologies able to extract meaningful information from vessel data is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Supervised deep learning approaches such as convolutional neural networks (CNNs) require a large-scale annotated image dataset. Thus, semi-and unsupervised feature learning algorithms which learn image features from unlabelled data are able to mitigate this problem. To this end, in this work we propose a semi-supervised convolutional autoencoder (CAE) model for trajectory classification which is able to provide high-precision classification of mobility patterns. Experimental results demonstrated that the vessel activity classification performance can reach an F1-score of over 94%. © 2021 ACM.

关键词： Learning algorithms

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities

The Reliability of Information Sources: Student Perceptions ...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources; social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape.

关键词： Economics Power engineering Accuracy Social networking (online) engineering profession Education Reliability engineering Information filters Information age Reliability

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Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

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BMRMIA:A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

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computer Modeling in engineering & Sciences 2022年第5期131卷 851-863页

作者： Yankun Cao Lina Xu Zhi Liu Xiaoyan Xiao Mingyu Wang Qin Li Hongji Xu Geng Yang School of Software Shandong UniversityJinan250101China School of Information Science and Engineering Shandong UniversityQingdao266237China School of Computer Science and Technology Shandong Jianzhu UniversityJinan250012China Department of Nephrology Qilu Hospital of Shandong UniversityJinan250012China State Key Laboratory of ASIC&Systems The School of MicroelectronicsFudan UniversityShanghai200433China Shenzhen Institute of Information Technology Shenzhen518172China

Contribution:This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis *** platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system,and strengthen the teaching practice of deep learning related courses in hospitals,so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary ***:In recent years,deep learning has been widely used in academia,industry,*** increasing number of companies are starting to recruit a large number of professionals in the field of deep *** numbers of colleges and universities also offer courses related to deep learning to help radiologists learn automated medical image analysis *** now,however,there is no practical training platform that can help radiologists learn automated medical image analysis ***:The platform proposes the basic learning,model combat,business application(BMR)concept,including the learning guidance system and the assessment training system,which constitutes a closed-loop learning guidance mode of“learning-assessment-training-learning”.Findings:The survey results show that most of radiologists met their learning expectations by using this *** platform can help radiologists master deep learning techniques quickly,comprehensively and firmly.

关键词： BMR deep learning three dimensional medical decision support system deep learning engineer standard

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QSroute : A QoS Aware Routing Scheme for software Defined Networking

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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International Conference on software engineering and computer systems (ICSECS)

作者： Imran Edzereiq Kamarudin Mohamed Ariff Ameedeen Muhamad Idaham Umar Ong Azlee Zabidi Department of Computer Systems and Networking Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field.

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SER Analysis -An Approach to enhance Human Health

SER Analysis -An Approach to enhance Human Health

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Anusha N C.M. Mohana Akshat Sharma Aryan Sharma Garv Mittal Navadhesh. M Department of IoT School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore TamilNadu India Department of Software Systems SCOPE Vellore Institute of Technology Vellore Tamil Nadu India Department of Computational Intelligence SCOPE Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

This work focuses on Speech Emotion Recognition (SER) for extracting emotional states from speech signals, encompassing universal emotions like Happiness, Anger, Neutral, Calm, Sadness, Disgust, Fearful, and Surprised. The methodology involves extracting features such as Chromogram, Mel- frequency cepstral coefficients (MFCC), Mel scaled spectrogram, Tonal Centroid and Spectral contrast, followed by classification using Artificial Neural Networks (ANN). A health-oriented aspect, is integrated in this model by providing personalized guided meditation sessions based on the user's emotional state to promote mental wellness. The main aim of this work is to broaden its reach and impact through ongoing refinement of the model and exploration of new implementation strategies, ultimately contributing to a healthier and happier society. The results of this work demonstrate promising accuracy, with the trained deep neural network (DNN) model achieving approximately 81.25% accuracy in recognizing speech emotions in comparison with the existing algorithms. DNN shows a 11%, 8%, 14% increase in accuracy with RNN & LSTM, SVM, GMM respectively.

关键词： Support vector machines Emotion recognition Accuracy Speech recognition Speech enhancement Feature extraction Cognitive informatics Mel frequency cepstral coefficient Spectrogram Long short term memory

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Audio Adversarial Example Detection Using the Audio Style Transfer Learning Method

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IEEE Access 2023年 1-1页

作者： Kwon, Hyun Lee, Kangjun Ryu, Junyeol Lee, Jun Department of Artificial Intelligence and Data Science Korea Military Academy Seoul Republic of Korea Department of Software Science and Engineering Kunsan National University South Korea Department of systems & Mechanical Engineering Korea Military Academy Seoul Korea Division of Computer Information and Science Hoseo University Asan-si South Korea

In this paper, we propose a method for defending against audio adversarial examples that operates by applying audio style transfer learning. The proposed method has the effect of maintaining the classification result produced by the target model and removing the adversarial noise by changing only the style while maintaining the content of the input audio sample. In an experimental evaluation using the Mozilla Common Voice dataset as the test data source and TensorFlow as the machine learning library, the proposed method improved the target model’s accuracy on the adversarial examples from 2.1% to 79.2% while maintaining its accuracy on the original samples at 81.4%. Author

关键词： Speech recognition

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Development of a Query Delay Injection System for the MEC Simulator of the LWMECPS Platform

Development of a Query Delay Injection System for the MEC Si...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Ivan Filianin Vladimir Bogatyrev Matvey Lesonen Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

In this paper the problem of testing decision making systems for MEC platforms was formulated. Methods and means of organizing the introduction of network delays as part of the emulation system of MEC platforms LWMECPS were analyzed. Test applications mec-test-app and mec-test-client are developed for testing and evaluation of the introduced network delays. Developed mec-orch-app to provide test application management and metrics collection. Experiments have been conducted to estimate the insertion latency and manage the test applications using mec-orch-app. Special attention was paid to the organization of all parts of the system as a future laboratory bench for testing decision systems using reinforcement learning machine learning algorithms.

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Machine Learning Algorithms for Intrusion Detection Performance Evaluation and Comparative Analysis

Machine Learning Algorithms for Intrusion Detection Performa...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： B. Md. Irfan V. Poornima S Mohana Kumar Upendra Singh Aswal N. Krishnamoorthy Ramya Maranan Department of Information Technology Nalsar University of Law Hyderabad Telengana India Department of Computer Science VISTASPallavaram Chennai Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Software Systems and Engineering Vellore Institute of Technology Vellore Campus Vellore Tamilnadu Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The security of computer networks is increasingly difficult to maintain due to the rising complexity and frequency of cyber-attacks. Important tools for finding and neutralizing these dangers are intrusion detection systems. This study sets out to do a thorough examination and comparison of the efficacy of several machine learning algorithms for use in intrusion detection. This research study evaluates the efficacy of several machine learning algorithms in correctly categorizing instances of network traffic as normal or invasive via extensive experiments performed on representative datasets. Algorithms like random forests, decision trees, SVMs, DL models and NNs are all being tested and rated. Effectiveness is measured and compared using a variety of performance indicators including accuracy, recall, precision, false positive rate, and F1-score. The results of this study emphasize the potential of deep learning models and Random Forests for use in intrusion detection and add to the body of knowledge around machine learning methods for this task. Professionals in the field of network security might use the results to their advantage when building intrusion detection systems. Future research areas are also mentioned, which will hopefully lead to even greater improvements in the field and safer, more reliable intrusion detection systems.

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