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Silver-coated whispering gallery mode resonator for bio-sensing applications

Optoelectronics Letters 2024年

作者： HUDA ADNAN ZAIN SULAIMAN WADI HARUN MALATHY BATUMALAY HAZLI RAFIS A.RAHIM RETNA APSARI Department of Electrical Engineering， Universiti Malaya Faculty of Data Science and Information Technology， INTI International University Faculty of Electronic and Computer Engineering， Universiti Teknikal Malaysia Melaka Department of Physics， Faculty of Science and Technology， Airlangga University

This letter presents a biosensor utilizing a whispering gallery mode resonator characterized by azimuthal symmetry and crescent-shaped coatings of silver. The study investigates the impact of the coupling gap on the extinction ratio and Q-factor of the setup. The resonator is coated with silver in crescent shapes， ranging from 40 to 65 nm thick. Coupling is achieved with a silica waveguide， simulating the tapered fiber coupling method. Notably， the resonator exhibits a maximum sensitivity of 220 nm/RIU when coated with 55 nm silver in conjunction with a four nm-thick layer of thiol-tethered DNA. This biosensor holds promise for biomolecule detection applications.

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The Effectiveness of SPBE Application and Infrastructure Audit Tools in Enhancing Information Technology Governance Using COBIT 2019

The Effectiveness of SPBE Application and Infrastructure Aud...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Irzavika, Nindy Mahda, Friyogi Resvy Universitas Pembangunan Nasional Veteran Jakarta Faculty of Computer Science Jakarta Indonesia Badan Riset Dan Inovasi Nasional Research Center for Data and Information Sciences Jakarta Indonesia

ISBN: (纸本)9798350307306

Information Technology (IT) has become the core of nearly every aspect of business and organizations. Effective IT governance is vital for achieving organizational goals and managing risks. This study uses COBIT 2019 to assess the SPBE application and infrastructure audit tool effectiveness. SPBE Application and Infrastructure Audit Tools are used in Indonesian central and local government agencies. SPBE aims to create a clean, efficient, transparent government system and trusted public services. The SPBE Audit Tools assist in auditing SPBE applications and infrastructures. SPBE Audit tools play a crucial role in monitoring and evaluating SPBE applications and infrastructure in Indonesia, contributing to improved IT Governance. However, effective governance requires risk management to address potential threats and vulnerabilities. COBIT 2019 is designed to support digital transformation and adapt to the dynamic changes in the IT landscape. This study uses COBIT 2019 as a framework for assessing the effectiveness of SPBE application and infrastructure audit tools in enhancing information technology governance because COBIT has more advantages compared to other frameworks. Based on the data, applications that conduct audits using SPBE Audit Tools show an improvement in the maturity level acquired each year. This suggests that SPBE Audit Tools contribute to achieving SPBE monitoring and evaluation objectives, including measuring progress, enhancing SPBE quality, and improving public services at IPPD. © 2023 IEEE.

关键词： Risk management

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Maize Plant Disease Prediction of UAV Images for Precision Agriculture Using Fusion of Multimodal 3

Maize Plant Disease Prediction of UAV Images for Precision A...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Mishra, Vinay Naik, Nenavath Srinivas Kumar, Santosh Alsamhi, Saeed Hamood Saif, Abdu Curry, Edward -Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Galway Insight Centre for Data Analytics Galway Ireland Faculty of Engineering & Information Technology Taiz Yemen

ISBN: (纸本)9798350321487

The agriculture sector is vital to a country's economic and productive development. With technological advancements, we can identify plant diseases in their early stages, similar to how human diseases are diagnosed, and avoid significant financial loss. For example, a new technique has been developed to detect diseases in maize crops, a widely used raw product in the food industry and precision agriculture. Furthermore, farmers ensure the fields do not remain wet for extended periods to prevent fungus infection. Currently, machine learning and deep learning approaches are being utilized, making agriculture one of the most researched topics in AI. This study proposes a novel framework for predicting maize plant disease using a fusion of multi-deep learning models. The proposed framework uses a four-stage pipeline for detecting disease in maize plants by fusion of Xception, DenseNet-121, and ensembling of Xception and DenseNet-121 models to achieve excellent results on the OSF drone image dataset, which consists of 1821 high-resolution images of maize plants taken from unmanned aerial vehicles (UAVs). The accuracy of our proposed approach is 96.95%, which is highly effective, considering the challenges posed by drone images. © 2023 IEEE.

关键词： Drones

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Leveraging Machine Learning to Assess Factors Influencing Undernourishment in Nigeria 1st

Leveraging Machine Learning to Assess Factors Influencing Un...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mobark, Nada Elfiky, Noha Ahmedin, Ahmed Ibrahim, Ameera Abdel-Aziz, M. Hamad, Safwat Faculty of Computers and Information South Valley University Qena83523 Egypt Business Analytics and Data Science Department School of Economics and Business Administration Saint Mary’s College of California MoragaCA94575 United States Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University South Sinai46511 Egypt

ISBN: (纸本)9783031823763

Notwithstanding economic progress, Nigeria continues to countenance significant food security issues. This study investigates the root causes of this problem and attempts to develop predictive models to address hunger in Nigeria. The research covering data from 2000 to 2022, sourced primarily from the World Bank and other institutions. The trend analysis, along with the linear model, indicates that investment in farming is crucial for reducing undernourishment. Additionally, there is a strong relationship between agricultural expenditure, exports, access to electricity, and unemployment in relation to undernourishment. In this paper, a linear model, correlation analysis, ARIMA, and trend analysis are utilized to demonstrate how undernourishment, representing hunger and food insecurity, is influenced by other factors. As a result, it is recommended to maintain the unemployment rate between 3.5% and 3.7%, while ensuring that exceeds 5% of government spending is recommended. In other words, to combat hunger in Nigeria, it is important to focus on investing in agriculture, creating jobs, and improving infrastructure. This aligns with our findings and with another research around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ARIMA Nigeria predictive model Undernourishment

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Comparison of IDS in IoT, Mobile and Cloud based Systems 12

Comparison of IDS in IoT, Mobile and Cloud based Systems

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Gok, Gorkem Boyaci, Aytug Ulas, Mustafa Firat University Faculty of Technology Department of Software Engineering Elaziǧ Turkey Air Force Academy National Defence University Department of Computer Engineering İstanbul Turkey Firat University Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

This study is meant to research the evolution of intrusion detection and network monitoring within computer, cloud-based systems, IIoT, and mobile environments. The source has outlined the novel technologies in IDS, from 'Fast Learning Network' to hybrid classifiers aiming at protecting cloud computing. The findings highlight innovation IDS technologies that provide flexible and peculiar solutions to ward off potential cyber threats. In addition, it presented with the use of Nagios, NConf, the state of the art in network traffic monitoring, and deep learning as tools for advanced management and network protection. Besides, the research also reviewed concepts of Big data Analytics and Federated Learning in bettering direct integration and anomaly detection in IIoT. A good comparison of the two would show that recent technological advancements are much needed for network efficiency and its cybersecurity. Based on the findings, the scope of networked system security develops dynamically amid current challenges. © 2024 IEEE.

关键词： Cybersecurity

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images 4

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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

作者： Bhatnagar, Roheet Hassan, Esraa Saber, Abeer Shams, Mahmoud Y. Elbedwehy, Samar Manipal University Jaipur Department of Computer Science and Engineering Rajasthan India Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial Intelligence Information Technology Dept. Damietta34517 Egypt Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Data Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350387490

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%. © 2024 IEEE.

关键词： Classification Deep Learning Diagnosis Monkeypox

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Deep Learning based Brain Tumor Classification for MR Images using ResNet50 12

Deep Learning based Brain Tumor Classification for MR Images...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Kokcam, Omer Mirac Boyaci, Aytug Colak, Muhammed Emre Firat University Faculty of Technology Department of Software Engineering Elaziǧ Turkey National Defence University Department of Computer Engineering Air Force Academy İstanbul Turkey Firat University Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

Brain tumors are abnormal cell growths that occur in various parts of the brain, and the accurate classification of these tumors plays a critical role in determining treatment methods. Classification and diagnosis of brain tumors based on artificial intelligence and deep learning models have been made possible due to advances in medical image processing technologies. For instance, such technologies facilitate quick detection of tumors by health experts thereby improving early diagnosis rate and hence enhancing treatment outcomes. Importantly, however, one considers improvement or breakthroughs in artificial intelligence-based classification systems as a crucial step forward towards brain tumor diagnosis as well as its treatment strategy. In this study we use Google Research's ImageNet-21k pre-trained ResNet50 model for classifying brain tumor cases. This model is a deep learning-based image classification tool and is capable of learning from large data sets. The model is specifically trained for high-resolution image recognition and is designed to achieve high accuracy rates. The results showed a classification accuracy of 99.9 percent, which is an exceptional accuracy rate for this model. This accuracy rate indicates that the model is highly effective in detecting and classifying brain tumors. High accuracy rates allow for early diagnosis of patients' diseases and, as it is often said in medicine, 'Early Diagnosis Saves Lives'. This result is one of the many proofs of how effective artificial intelligence and deep learning are in medical image analysis. © 2024 IEEE.

关键词： Medical imaging

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Pattern Discovery in an EEG database of Depression Patients: Preliminary Results 14

Pattern Discovery in an EEG Database of Depression Patients:...

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14th International Conference on Measurement, MEASUREMENT 2023

作者： Hlavackova-Schindler, Katerina Pacher, Christina Plant, Claudia Lazarenko, Mykola Palus, Milan Hlinka, Jaroslav Kathpalia, Aditi Brunovsky, Martin University of Vienna Data Mining and Machine Learning Research Group Faculty of Computer Science Vienna Austria Czech Academy of Sciences Institute of Computer Science Department of Complex Systems Prague Czech Republic National Institute of Mental Health Clinical Research Programme Klecany Czech Republic

ISBN: (纸本)9788097262976

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient's brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants"ongoing at the University of Vienna, the Czech Academy of sciences and the National Institute of Mental Health in the Czech Republic. © 2023 Institute of Measurement science, Slovak Academy o.

关键词： data mining

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ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents

arXiv

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arXiv 2023年

作者： Amitai, Yotam Avni, Guy Amir, Ofra Faculty of Data & Decision Science Technion - I.I.T Faculty of Computer Science University of Haifa

As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL) methods generate a static explanation depicting their developers’ intuition of what should be explained and how. In contrast, literature from the social sciences proposes that meaningful explanations are structured as a dialog between the explainer and the explainee, suggesting a more active role for the user and her communication with the agent. In this paper, we present ASQ-IT – an interactive tool that presents video clips of the agent acting in its environment based on queries given by the user that describe temporal properties of behaviors of interest. Our approach is based on formal methods: queries in ASQ-IT’s user interface map to a fragment of Linear Temporal Logic over finite traces (LTLf), which we developed, and our algorithm for query processing is based on automata theory. User studies show that end-users can understand and formulate queries in ASQ-IT, and that using ASQ-IT assists users in identifying faulty agent behaviors. © 2023, CC BY-SA.

关键词： User interfaces

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Navigating Energy: Unveiling Power Consumption Patterns in Autonomous Vehicles 1

Navigating Energy: Unveiling Power Consumption Patterns in A...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Saif, Abdu Shah, Nor Shahida Mohd Ameen, Ali Alnoamani, Aiman Al-Qatf, Majjed Alsamhi, Saeed Hamood Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University P.O. Box 6803 Taiz Yemen Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Computer Science Lincoln University College Kuala Lumpur Malaysia Insight Center for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9798331533557

Energy consumption is critical in self-driving cars and is set to change how transportation works. In this paper, we address the challenges of vehicle energy consumption by examining how the location of self-driving cars affects their energy use over time, looking at two- and three-dimensional aspects. Using models and real-world data, we examine the energy usage patterns of vehicle systems within MATLAB to reveal insights into their design and operation. In this paper, the power dynamics of cars that significantly contribute to the discussions of practical and sustainable self-driving transportation are introduced. The main goal is to demonstrate how power management may be revolutionized for self-driving car systems, ensuring maximum performance and dependability in today’s dynamic mobility scene. The findings show that enhancing autonomous vehicles’ design and operational strategies ensures their reliability and optimizes performance in today’s rapidly evolving transportation landscape. © 2024 IEEE.

关键词： Autonomous vehicles

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