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19th EAI International Conference on computer science and Education in computer science, CSECS 2023

作者： Rusevski, Ivan Todorovska, Ana Vodenska, Irena Chitkushev, Ljubomir Trajanov, Dimitar Faculty of Computer Science and Engineering Ss. Cyril and Methodius University in Skopje Skopje Macedonia Computer Science Department Metropolitan College Boston University BostonMA United States Administrative Sciences Department Financial Management Metropolitan College Boston University BostonMA United States

ISBN: (纸本)9783031446672

In a rapidly changing world where no market or economy is secure, interconnectivity is becoming a key feature of almost all social and economic systems. As a brand-new digital asset, cryptocurrencies are quickly taking over the world economy. Cryptocurrencies are becoming alternative investments as a store of value compared to precious metals. The purpose of this study is to examine the connections between cryptocurrencies, precious metals, and energy resources using structured numerical data. To achieve this goal, two approaches are employed to create networks that reveal relationships between cryptocurrencies (Bitcoin, Ethereum, Cardano, Chainlink, Litecoin, Stellar, and Ripple), precious metals (Gold, Silver, and Platinum), and energy resources (Oil and Natural Gas) based on their daily prices and returns. The interdependence of the networks is further examined to reveal similarities between the created networks. The proposed methodology facilitates an understanding of the dynamics of cryptocurrency markets, the factors that influence them, and the potential for future research to expand and include additional cryptocurrencies. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2023.

关键词： Energy resources

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Control of Master-Slave DC Microgrid with Optical Fiber Communication Link 15

Control of Master-Slave DC Microgrid with Optical Fiber Comm...

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15th International Renewable Energy Congress, IREC 2025

作者： Alfergani, Asma Shaltami, Farhat M. Alfaitor, Khalid Ateea Khalil, Ashraf Mohamed, Faisal University of Benghazi Electrical and Electronics Engineering Department Benghazi Libya College of Electrical and Electronics Technology - Benghazi Benghazi Libya University of Benghazi Faculty of Information Technology Department of Computer Networks and Communications Benghazi Libya Dtu Engineering Technology Technical University of Denmark Kongens LyngbyDK-2750 Denmark Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (纸本)9798331516772

In this paper, a model for the master-slave DC Microgrid over a fiber optic link is proposed. The delay margin is calculated according to Lyapunov-Krasovskii functional using the LMI toolbox in Matlab with the binary iteration algorithm. The optical fiber link is implemented using optisystem simulator. The optical fiber link is set to transmit the digitalized reference current from master controller to the slave controller. The link tested at 100Mbps, 1Gbps and 2.5Gbps data rate values and several optical fiber lengths and wavelength of 1550nm and 1400nm. The results show that the performance of the link is sufficient at received power more than -30dBm for all data rate values at 1550nm. While at 1400nm, the attenuation rate is very high. Therefore, the link must be 10km to achieve acceptable performance. This wavelength is used in this simulation to avoid the C band and O band, which used for telecommunication purposes. © 2025 IEEE.

关键词： MATLAB

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The Relationship of Paradoxical Middle Turbinate on Sinonasal Pathologies and Nasal Septal Deviation

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SN Comprehensive Clinical Medicine 2025年第1期7卷 1-6页

作者： Keskin, Nevin Köremezli Ertuğrul, Süha Calik, Elif Department of Radiology Karabük University Training and Research Hospital Faculty of Medicine Karabük University Karabük Turkey Department of Otorhinolaryngology Faculty of Medicine Karabük University Karabük Turkey Department of Medical Engineering Faculty of Engineering Karabük University Karabük Turkey School of Computer Science College of Science and Engineering ADAPT Research Centre University of Galway Galway Ireland

This study aims to investigate the concomitant presence of the paradoxical middle turbinate and paranasal sinus pathologies, sinonasal variations, and nasal septal deviation in the tomography. The paranasal sinus computed tomography images of a total of 602 patients were analyzed retrospectively. Seventy-eight patients with unilateral or bilateral paradoxical middle turbinate constituted the study group, and 80 patients with normal middle turbinate constituted the control group. Both groups were compared with respect to the presence of sinus pathology (sinusitis, mucosal thickening, retention cyst), paranasal sinus variations (Haller cell, Agger nasi cell), and nasal septal deviation. The relationship between the localization of the paradoxical middle turbinate and the direction of nasal septal deviation was investigated. The prevalence of paradoxical middle turbinate was 12.9%. The most common sinus pathology accompanying paradoxical middle turbinate was in the maxillary sinus (43.6%). There was no significant difference between the groups in terms of the prevalence of sinus pathologies. The prevalence of sphenoid sinus pathology was significantly less in the paradoxical middle turbinate group than in the control group (p = 0.006). The prevalence of nasal septal deviation was significantly higher than the control group (p = 0.023). No significant relation was found between the localization of the paradoxical middle turbinate and the direction of the nasal septal deviation (p = 0.202). The prevalence of sphenoid sinus pathology was significantly less, and the nasal septal deviation was significantly higher in the patients with paradoxical middle turbinate.

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Speech Detection and Comparison using Different Feature Extraction Method for Correct Verification 3

Speech Detection and Comparison using Different Feature Extr...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Upadhyay, Shrikant Kumar, Binod Singh, Rajan Alam, Md. Irfan Kumari, Barkha Hinz, Binita Roshima Mlr Insitute of Technology Department of Electronics and Communication Engineeing Hyderabad India Jharkhand Rai University Facultyt of Cse & It Ranchi India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Jharkhand Rai University Faculty of Cse & It Ranchi India Pune India Rtc Institute of Technology Department of Computer Science and Engineering-Cyber Security Ranchi India

ISBN: (纸本)9798331528201

Mental status through physical appearance identification is not possible as it does not give complete status of any human. Using voice and considering quality extraction of talked it is quite easy to predict the mental behavior and internal variation that will help the doctor related with medical treatment that support them to give proper treatment to person. Hindi is the natural language of India and large community of population talk and speaks this language to exchange and communicate. Various inputs like specific feature, images and vital report of serious person and it is one of the important facts sources for deep training or gaining systems. In this research paper we try to identify the speech signal under different scenario like gun noise, car noise etc. following different feature extraction approaches and compare it for its proper verification. This verification may help to enhance the quality of speech in different adverse scenario and make the speech detection easy. Here, the comparison has been done with various feature extraction method and the results reflects that MFCC has good efficiency of 95% and with a low error rate of 2% compared to other approach. For analysis Hindi voice sample were used for the experimental analysis for 50 samples. © 2024 IEEE.

关键词： Speech enhancement

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Transforming Cybersecurity in the Digital Era: The Power of AI 2

Transforming Cybersecurity in the Digital Era: The Power of ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Mahfuri, Mahmoud Ghwanmeh, Sameh Almajed, Rsha Alhasan, Waseem Salahat, Mohammed Lee, Jin Hie Ghazal, Taher M. International University For Science And Technology Computer And Information Technology Department Damascus Syria American University In The Emirates Department Of Computer Scienc Dubai United Arab Emirates School Of Computer Science And Informatics De Montfort University Dubai Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Miracle Clinic Seoul Korea Republic of Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

In the midst of the digital revolution of the 21st century, cybersecurity has come to be a primary social situation, requiring revolutionary and Innovative solution. To find the proper answer for this pressing demand, AI (AI) has developed as an innovative catalyst that has fundamentally transformed the cybersecurity landscape. The power of AI is exemplified by its ability to process and interpret large and heterogeneous cybersecurity data sets optimizing critical functions such as threat detection, asset prioritization, and vulnerability management beyond human capabilities swiftly and accurately. This transformation is redefining our cybersecurity approach. This paper presents a full exploration of AI's extreme influence on cybersecurity, delving deep into how AI tools not only enhance but often surpass human-mediated processes. By unraveling the intricacies of integrating AI into the realm of cybersecurity, we vividly illustrate AI's potential to anticipate, detect, and proactively mitigate cyber threats, empowering organizations to bolster their digital security. However, it's vital to recognize the essential difficulties of AI. We underline the critical necessity for non-stop human administration and involvement to confirm that cybersecurity processes are in proportion and powerful. Additionally, we mention the probable ethical concerns and underscore the importance of strong authority structures to accelerate accountable and transparent AI usage in cybersecurity. This paper elucidates how AI is revolutionizing cybersecurity techniques, contributing to a more secure and greater secure digital future. It additionally lays the base for current investigation and discourse on the role of AI in cybersecurity - a conversation of growing importance in our unexpectedly advancing digital age. © 2024 IEEE.

关键词： Cybersecurity

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Integrating Deep Learning in Project Management: Improving Efficiency and Effectiveness in Project Planning and Execution 4

Integrating Deep Learning in Project Management: Improving E...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Shukla, Sunil Singh, Jagendra Sahu, Hemant Kanthalia, Shilpa Taufikin Elangovan, Muniyandy Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Department of Cs & It Udaipur India Faculty of Management Studies Udaipur India Kudus Department of Postgraduate Kudus Indonesia Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biosciences Chennai India

ISBN: (纸本)9798350354218

The purpose of this research is to explore the use of deep learning technologies, namely the K-Nearest Neighbors model within the textile manufacturing process to test the improvement of the said process. To properly implement the model, we must use real-time data from the process and evaluate the improvement depending on a number of factors: how much does the maintenance cost decrease, how accurate is the inventory management, and how well the program forecasts demand, all of which should lead to an improvement of profit. First, the analysis of the results proved that the implementation of KNN model leads to a significant decrease of maintenance costs: while without the model the reduction was 15%, with it cost maintenance cost a 25% decrease. Second, the usage of a model improves inventory management accuracy: before the use of KNN, it was 85%, while after it rose to 95%. Third, demand forecasting becomes more accurate, as the difference was 75% to the positive without the model and 90% with it. The use of a model improved all aspects, and the most improved was profit: before the implementation of the KNN model, profit was 10%, and it was 40% after, which led to the conclusion that the implementation of deep learning models, namely the KNN model, improves the effectiveness of textile manufacturing process and positively affects the profitability and the production process as a whole. © 2024 IEEE.

关键词： Inventory control

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IoT Based Monitoring System for Health During Menstruation

IoT Based Monitoring System for Health During Menstruation

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Chinmaya Khadse Shital Hajare Nishad Sheikh Roshni Dahule Priya Kate Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawani Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The integration of IoT and ML brought forth new prospective solutions in menstrual health management, pertaining to continuous monitoring of physiological signals and personalization of insights for women. This article discusses the usage of IoT, smartphones, and cloud-based systems for tracking vital signs, temperature, pulse rate, and flow of menstruation that will enable pro-active management of the cycle. The use of ML algorithms, including supervised models-like Random Forest and Logistic Regression-and complex techniques like RNNs and LSTM networks-offers added advantages for predicting menstrual cycles and ovulation. These technologies offer personalized health recommendations, early diagnosis of menstrual disorders, and help in communicating with healthcare providers in a very short span of time. While offering huge potential to improve women's health, concerns around data privacy, ethical considerations, and equitable access to these digital health solutions are highlighted. The existing systems showed improvements yet they encountered problems because of insufficient data diversity and health system integration as well as insufficient personalization and imprecise cycle predictions. Data privacy issues and security threats that stem from ML models along with constant bias concerns prevent broad acceptance of ML systems in general use. This research study analyzes IoT and ML applications in menstrual health today and introduces their advantages as well as the barriers that require resolution before mass acceptance can occur. The proposal introduces an approach to create menstrual tracking software through a combination of rule-based systems with ML algorithms that generates precise forecasts alongside tailored information for women who have PCOS or PCOD.

关键词： Data privacy Ethics Software algorithms Prediction algorithms Software Internet of Things Biomedical monitoring Wearable devices Smart phones Monitoring

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Research and Analysis of IT Specifications of Good Practices in the Area of Artificial Intelligence 16

Research and Analysis of IT Specifications of Good Practices...

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16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023

作者： Kostadinova, I. Dimitrov, G. Martsenyuk, V. Rancic, D. Dirgova-Luptakova, I. Jovancevic, I. Tsvetkova, P. Rasheva-Yordanova, K. Petrova, P. Petrov, P. Faculty of Information Science of University of Library Studies and Information Technologies Sofia Bulgaria Bulgarian Academy of Sciences Acad. Institute of Robotics Bulgaria Department of Computer Science and Automatics Poland Faculty of Electronic Engineering Serbia Faculty of Natural Sciences Slovakia Montenegro University of Economics-Varna Bulgaria

ISBN: (纸本)9798350347029

This article is a contribution within the Erasmus+ project titled "The Future Lies in Applied Artificial Intelligence"(FAAI) and examines research of collected IT specifications of good practices in Area of Artificial Intelligence (AAI). The article describes research conducted, the purpose of which is to find IT specifications of good practices in AI and describe their characteristics, like an area of implementation of the AI solution, the result of processing the data, the source of data, Data processing, and quality, what tools are used for processing data, and others. AAI application cases and the technologies used for implementation are reviewed. The specifics of the data and the applications used are described. The examination of these technologies will provide insight into which ones are favored and provide an overview of what is commonly referred to as "best practices"in this particular *** research encompassed a global examination of cases. The analysis of the data offers valuable insights in various directions:•Application area of ML/AI•Type of machine learning problems in described good practices in Artificial Intelligence•Type of models were developed within the projects•What is the area of implementation of AI solution•Used AI libraries (frameworks).•Source of data•Data characteristics•Tools are used to store data•What platform solution is used•What type of storage is usedThe research and analysis done provide a clear picture of the used IT specifications of good practices in AI. The obtained results will guide in what areas to organize the practical training in the preparation of specialists in the AAI. Also, this research would help future AI application developers. © 2023 IEEE.

关键词： Artificial intelligence

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A Robust Finite Time Longitudinal Speed Control for a Catenary-based Electric Vehicle

A Robust Finite Time Longitudinal Speed Control for a Catena...

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IEEE Conference on Decision and Control

作者： Gianmario Rinaldi Antonella Ferrara Prathyush P Menon Department of Engineering Faculty of Environment Science and Economy University of Exeter United Kingdom Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The electrification of the transportation sector plays a crucial role in reducing the global carbon footprint. Among the various solutions, catenary-based electric vehicles have attracted a great deal of interest from researchers and practitioners. This article considers a vehicle powered by a battery energy storage system (BESS) and a catenary system and proposes a robust finite-time longitudinal speed control strategy. Drawing inspiration from the Second-Order Sliding Mode (2-SOSM) methodology, the approach ensures that the control input remains within the limits set by the maximum torque of the motor. The strategy demonstrates robustness against uncertainties and disturbances, eliminating the need to know the model parameters. The simulation case study considers the effects of real road data, including slope variations, on power management. A comparative analysis with the conventional Proportional Integral (PI) method highlights the superior performance of the proposed strategy.

关键词： Uncertainty Torque Roads Power system management Velocity control Transportation Electric vehicles Motors Robustness Regulation

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Development of a Smart Technique for Mobile Web Services Discovery

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computers, Materials & Continua 2021年第11期69卷 1483-1501页

作者： Mohamed Eb-Saad Yunyoung Nam Hazem M.El-bakry Samir Abdelrazek Department of Information Systems Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Web service(WS)presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited *** the increasing demand for mobile WS(MWS),the WS discovery process has become a significant challenging point in the WS lifecycle that aims to identify the relevant MWSs that best match the service *** discovery process is a resource-consuming task that cannot be performed efficiently in a mobile computing environment due to the limitations of mobile ***,a cloud computing can provide rich computing resources for mobile environments given its unlimited and easily scalable *** paper proposes a semantic WS discovery and invocation framework in mobile environments based on cloud and a relationship-aware matchmaking *** discovery algorithm enriches MWS and user requests semantically with the functional and non-functional properties of Ontology Web Language for Services,such as Quality of Web Service,device context,and user *** WS repository is filtered based on logical reasoning and a parameter-based matching algorithm to minimize the matching space and improve runtime *** cosine similarity between the user request and services repository is then assessed to generate the most relevant *** relationships among concepts in the ontology are considered to improve the recall and precision *** the WS discovery process,users can invoke and test these services in a mobile environment through a dynamic user *** interface of the invocation process is changed according to the WS description *** application prototype is also developed to evaluate the framework based on a Cordova cross-mobile development framework.

关键词： Cloud web service web service discovery semantic matching mobile web service discovery

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