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13th IEEE International Conference on System engineering and technology, ICSET 2023

作者： Hadi, Aya Khalid Mosleh, Mahmood Farhan Azeez, Adham R. Abd-Alhameed, Raed A. University of Middle Technique Department of Computer Techniques Engineering Baghdad Iraq University of Information Technology and Communications Department of Media Technology and Communications Engineering College of Engineering Baghdad Iraq University of Bradford Faculty of Engineering and Informatics Bradford United Kingdom

ISBN: (纸本)9798350340891

This article presents a miniaturized size Microstrip-patch sensor structure based on meander-line slot for water-quality and salinity measurement applications. The HFSS-software tool is employed to design and simulate the proposed sensor. The proposed sensor is printed on 60× 50× 1mm3 a dielectric material of FR-4 type. The meander-line slot resonator is inserted in the patch-sensor for miniaturize with the sensitivity enhancement. In order to contain the water sample that wants to be tested, a receptacle with dimensions of 47.75× 50× 4mm3 is mounted on the sensor structure. The receptacle is made from PTFE-Teflon dielectric material. The miniaturize sensor is noticed resonant at 2.74 GHz for unloaded-state. Furthermore, it's found resonant at;2.44 GHz, 2.49 GHz, and 2.5 GHz for DI-water, fresh-water and sea-water respectfully. The miniaturized-sensor is showed good sensing values that equals to 0.901 for DI-water, 0.751 for fresh-water, and 0.721 for Sea-water. Finally, a good agreement is showed between the measurement results and those from simulation. © 2023 IEEE.

关键词： Dielectric materials

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Provably Efficient Multi-Cancer Image Segmentation Based on Multi-Class Fuzzy Entropy

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Informatica (Slovenia) 2023年第8期47卷 77-88页

作者： Jasim, Hend Muslim Ghrabat, Mudhafar Jalil Jassim Abdulrahman, Luqman Qader Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen Abduljaleel, Iman Qays Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Iraqi Commission for Computers and Informatics The Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Science Department Al-Turath University College Baghdad10013 Iraq College of Health Science Hawler Medical University Erbil Iraq Jaramogi Oginga Odinga University of Science and Technology Bondo40601 Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Technical Computer Engineering Department AL-Kunooze University College Basrah6100 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah61004 Iraq

One of the segmentation techniques with the greatest degree of success used in numerous recent applications is multi-level thresholding. The selection of appropriate threshold values presents difficulties for traditional methods, however, and, as a result, techniques have been developed to address these difficulties multidimensionally. Such approaches have been shown to be an efficient way of identifying the areas affected in multi-cancer cases in order to define the treatment area. Multi-cancer methods that facilitate a certain degree of competence are thus required. This study tested storing MRI brain scans in a multidimensional image database, which is a significant departure from past studies, as a way to improve the efficacy, efficiency, and sensitivity of cancer detection. The evaluation findings offered success rates for cancer diagnoses of 99.08%, 99.87%, 94%;97.08%, 98.3%, and 93.38% sensitivity;the success rates of in particular were 99.99%;98.23%, 99.53%, and 99.98%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Image segmentation

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Maximum Correntropy Criterion Kalman Filter for Indoor Quadrotor Navigation under Intermittent Measurements

arXiv

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arXiv 2023年

作者： Hadjiloizou, Loizos Makridis, Evagoras Charalambous, Themistoklis Deliparaschos, Kyriakos M. Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Electrical and Computer Engineering and Informatics Department Cyprus University of Technology Limassol Cyprus

We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy Criterion Kalman Filter (MCC-KF) that can manage intermittent observations. The MCC-KF is designed to improve the performance of the estimation process when is done with a Kalman Filter (KF), since KFs are likely to degrade dramatically in practical scenarios in which noise is non-Gaussian (especially when the noise is heavy-tailed). To evaluate the performance of the MCC-KF, we compare it with a previously designed Kalman filter by the authors. Through this comparison, we aim to demonstrate the effectiveness of the MCC-KF in handling indoor navigation missions. The simulation results show that our presented framework offers low positioning errors, while effectively handling intermittent sensor measurements. Copyright © 2023, The Authors. All rights reserved.

关键词： Kalman filters

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Maximum Correntropy Criterion Kalman Filter for Indoor Quadrotor Navigation under Intermittent Measurements

Maximum Correntropy Criterion Kalman Filter for Indoor Quadr...

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Mediterranean Conference on Control and Automation (MED)

作者： Loizos Hadjiloizou Evagoras Makridis Themistoklis Charalambous Kyriakos M. Deliparaschos Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Electrical and Computer Engineering and Informatics Department Cyprus University of Technology Limassol Cyprus

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Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

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computer Systems Science & engineering 2023年第1期44卷 235-247页

作者： R.Bhaskaran S.Saravanan M.Kavitha C.Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science Faculty of Engineering&InformaticsUniversity of BradfordBradfordUnited Kingdom English Department University CollegeTarabaTaif UniversityTaif21944Saudi Arabia

Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so *** has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source *** current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and *** proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible ***,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of ***,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.

关键词： Sentiment analysis data classification machine learning red deer algorithm extreme learning machine natural language processing

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Language Models for Online Depression Detection: A Review and Benchmark Analysis on Remote Interviews

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ACM Transactions on Management Information Systems 2025年第2期16卷 1-35页

作者： Qin, Ruiyang Yang, Kai Abbasi, Ahmed Dobolyi, David Seyedi, Salman Griner, Emily Kwon, Hyeokhyen Cotes, Robert Jiang, Zifan Clifford, Gari Cook, Ryan A. Computer Science University of Notre Dame Notre Dame United States College of Economics Shenzhen University Shenzhen China IT Analytics and Operations University of Notre Dame Notre Dame United States University of Colorado Boulder Boulder United States Emory University Atlanta United States Biomedical Engineering Georgia Institute of Technology Atlanta United States Biomedical Informatics Emory University Atlanta United States Biomedical Engineering Georgia Institute of Technology Atlanta United States

The use of machine learning (ML) to detect depression in online settings has emerged as an important health and wellness use case. In particular, the use of deep learning methods for depression detection from textual content posted on social media has garnered considerable attention. Conversely, there has been relatively limited evaluation of depression detection in clinical environments involving text generated from remote interviews. In this research, we review state-of-the-art feature-based ML, deep learning, and large language models for depression detection. We use a multidimensional analysis framework to benchmark various language models on a novel testbed comprising speech-to-text transcriptions of remote interviews. Our framework considers the impact of different transcription types and interview segments on depression detection performance. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of future detection methods. © 2025 Copyright held by the owner/author(s).

关键词： mHealth

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Coordinated Operational Optimization of Water and Power Systems Under Emergency Conditions

SSRN

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SSRN 2024年

作者： Perelman, Gal Shmaya, Tomer Navon, Aviad Vrachimis, Stelios Panteli, Mathaios Eliades, Demetrios G. Ostfeld, Avi Faculty of Civil and Environmental Engineering Technion – Israel Institute of Technology Israel Faculty of Electrical Engineering and Computer Engineering Technion – Israel Institute of Technology Haifa Israel KIOS Research and Innovation Center of Excellence University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

This research explores the integrated management of Water Distribution Systems (WDS) and Power Distribution Systems (PDS) for improved operational efficiency and resilience under extreme scenarios. Traditionally, these systems are operated independently leading to sub-optimal operational efficiency. Alternatively, some studies proposed a joint operation of WDS and PDS but assumed that decisions are made simultaneously by a single decision-maker, which is impractical. This study offers a novel approach for emergency control based on sparse communication between WDS and PDS operators to enhance resilience and improve the integrated systems’ functionality. The primary objective is to minimize the load shedding in the PDS through strategic demand management in the interconnected WDS, demonstrating the potential of collaborative decision-making between these two critical infrastructure systems. The paper highlights the significance of cross-sectoral collaboration, presenting a viable pathway for managing local infrastructure systems under extreme conditions while ensuring uninterrupted service delivery to communities. © 2024, The Authors. All rights reserved.

关键词： Decision making

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IoT for Sustainable Hospitality: A Systematic Review of Opportunities and Challenges for the Hospitality Industry Revolution

IoT for Sustainable Hospitality: A Systematic Review of Oppo...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Marios S. Poullas Elena Kakoulli Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Cell & Developmental Biology University College London London UK Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

technology has influenced the hospitality industry, and the Internet of Things (IoT) has become a focus for sustainability, revenue growth, and problem-solving. This systematic review examined the opportunities and challenges of implementing IoT technology for a sustainable hospitality. Through a thorough analysis of 28 studies, we found that IoT technology has great potential to address sustainability issues in the hospitality industry. However, there are still significant challenges that need to be addressed for the successful implementation of IoT technology in the hospitality industry. Overall, this study highlights the potential of IoT technology to revolutionize the hospitality industry and make it more sustainable. Further research is needed to address the challenges of IoT implementation in the hospitality industry and to evaluate the effectiveness of IoT technology in achieving sustainability goals.

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Performance and Radiation Testing of the Coral TPU Co-processor for AI Onboard Satellites

Performance and Radiation Testing of the Coral TPU Co-proces...

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2023 European Data Handling and Data Processing Conference for Space, EDHPC 2023

作者： Lentaris, George Leon, Vasileios Sakos, Chronis Soudris, Dimitrios Tavoularis, Antonios Costantino, Alessandra Polo, Cesar Boatella University of West Attica Department of Informatics Computer Engineering Greece National Technical University of Athens School of Electrical Computer Engineering Greece European Space Research Technology Centre European Space Agency Netherlands

ISBN: (纸本)9789090379241

COTS HW and SW components become crucial for advancing AI in space applications. The CAIRS21 ESA project considers Coral TPU as a candidate AI co-processor in avionics and examines its suitability in terms of performance, programmability/productivity, and radiation tolerance. The current paper focuses on our custom methodology for evaluating TPU, including TID and SEE effects. We assume representative AI benchmarks and irradiation profiles for LEO missions. We define a series of actions and measurements to derive thorough conclusions while avoiding increased time/budget procedures. Additionally, the paper summarizes key findings and a preliminary analysis of results from actual tests. Overall, TPU shows significant benefits when executing embedded AI, as well as promising Krad(Si) and SEE cross-section values. © 2023 ESA.

关键词： Space applications

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