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International Conference on Internet of Things and Applications (IoT)

作者： Aref Azizian Behnam Yousefimehr Mehdi Ghatee Dep. of Math. & Computer Science Amirkabir University of Technology (Tehran Polytechnic) Iran

ISBN: (数字)9798350374674

ISBN: (纸本)9798350374681

Urbanization and the emergence of smart cities have led to increased industrial activities, raising concerns about safety and environmental hazards. Detecting and monitoring gas leaks accurately is crucial due to the significant risks they pose to public health and the environment. In this study, we propose a novel approach that combines thermal imaging with sensor data to enhance gas detection accuracy in urban and industrial settings. By employing advanced algorithms and analyzing extensive datasets, our methodology achieves an impressive accuracy rate of 99.32%, setting a new standard in gas leak detection. Our innovation lies in the utilization of a unique oversampling technique called Normal Synthetic Minority Oversampling Technique (NSMOTE), coupled with the simultaneous integration of thermal imaging and sensor data within a multimodal framework. This breakthrough not only underscores the potential of integrating diverse data sources for environmental monitoring but also represents a significant stride in mitigating the risks associated with industrial gas emissions.

关键词： Technological innovation Accuracy Smart cities Soft sensors Surveillance Imaging Thermal sensors

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Usage of Blockchain technology for Improving computer Security 25

Usage of Blockchain Technology for Improving Computer Securi...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Almomani, Adai Al Refai Mohammed, N. Aburomman, Ahmad Alidmat, Omar Khair Alla Saber, Qotadeh Alshariedeh, Fuad Khouj, Mohammed Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Department of Data Science and A Zarqa Jordan Univeristy of Business and Technology Department of Electronic Enginnering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Cyber threats have grown increasingly sophisticated, surpassing the capabilities of traditional security measures. Blockchain technology, with its decentralized and immutable structure, offers a potential solution to these challenges. This study examines the role of blockchain tools such as smart contracts, decentralized identity management, and secure data sharing in enhancing cybersecurity. A review of key literature and analysis of survey data from cybersecurity professionals demonstrate blockchain's effectiveness in improving data integrity, access control, and secure communications. The results show significant correlations between the adoption of blockchain technologies and the mitigation of threats like data breaches and unauthorized access. While blockchain presents promising advancements, scalability, regulatory barriers, and energy consumption remain challenges. The study provides valuable insights for researchers, practitioners, and policymakers on how blockchain can be integrated into modern cybersecurity strategies to create more secure digital environments. © 2024 IEEE.

关键词： Cybersecurity

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Improving ADHD Detection with Cost-Sensitive LightGBM

Improving ADHD Detection with Cost-Sensitive LightGBM

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International eConference on computer and Knowledge Engineering (ICCKE)

作者： Behnam Yousefimehr Mehdi Ghatee Ali Heydari Dep. of Math. & Computer Science Amirkabir University of Technology (Tehran Polytechnic) Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

One common neurodevelopmental issue that needs prompt and precise diagnosis for successful treatment is attention-deficit/hyperactivity disorder (ADHD). In this study, we present a novel approach using a cost-sensitive LightGBM (Light Gradient Boosting Machine) model to enhance the prediction of ADHD. Our key contribution lies in the integration of cost-sensitive learning, which prioritizes minimizing the misclassification costs associated with false negatives and false positives, critical in medical diagnostics. Additionally, we leverage the computational efficiency and speed of LightGBM, a gradient boosting framework known for its high performance in handling complex data structures. Our results demonstrate that the cost-sensitive LightGBM model achieves a predictive accuracy of 94% and an F1-score of 94%, while significantly reducing the time required for training and inference. This makes it a viable tool for real-world ADHD screening applications. This study highlights the importance of incorporating cost-sensitive algorithms in medical predictions to achieve a balanced trade-off between accuracy and resource efficiency.

关键词： Training Knowledge engineering Accuracy Computational modeling Predictive models Boosting Prediction algorithms Inference algorithms Medical diagnosis Medical diagnostic imaging

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Predictions of COVID-19 Spread by Using Supervised Data Mining Techniques

Predictions of COVID-19 Spread by Using Supervised Data Mini...

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2020 Ibn Al-Haitham International Conference for Pure and Applied sciences, IHICPS 2020

作者： Awadh, Wid Akeel Alasady, Ali Salah Mustafa, Hadeel Ismail Dep. of Computer Information System College of Computer Science and Information Technology University of Basrah Iraq Dep. of Computer Science College of Computer Science and Information Technology University of Basrah Iraq

In the wake of the outbreak of the new coronavirus, the countries in the world have fought to combat the spread of infection and imposed preventive measures to compel the population to social distancing, which led to a global crisis. Important strategies must be studied and identified to prevent and control the spread of coronavirus COVID-19 disease 2019. In this paper, the effect of preventive strategies on COVID-19 spread was studied, a model based on supervised data mining algorithms was presented and the best algorithm was suggested on the basis of accuracy. In this model, three classifiers (Naive Bayes, Multilayer Perceptron and J48) dep.nded on the questionnaires filled out by Basra City respondents. The questionnaires consisted of 25 questions that covered fields most related to and that affect the prevention of COVID-19 spread, including demographic, psychological, health management, cognitive, awareness and preventive factors. A total of 1017 respondents were collected. This model was developed using Weka 3.8 tool. Results showed that quarantine played an important role in controlling the spread of the disease. By comparing the accuracy of the algorithms used, the best algorithm was found to be J48. © Published under licence by IOP Publishing Ltd.

关键词： COVID-19

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Precision Diagnosis in Pandemics: Machine Learning and Cough Sound Analysis for COVID-19

Precision Diagnosis in Pandemics: Machine Learning and Cough...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Iyad Chehili Skander Hamdi Dep. of Artificial Intelligence and Data Science University of Science and Technology Houari Boumediene Algiers Algeria Dep. of Computer Science University of Ferhat Abbes Setif I Setif Algeria

ISBN: (数字)9798350367928

ISBN: (纸本)9798350367935

The unprecedented global crisis caused by the COVID-19 pandemic has significantly impacted public health, economies, and daily routines. A critical challenge in managing the pandemic is promptly and precisely identifying COVID-19 cases. As a result, there is an urgent need for alternative, non-invasive, and swift diagnostic techniques to identify infected individuals and mitigate the spread of the virus. In this study, we introduce a machine-learning method for early COVID-19 diagnosis by analyzing cough sounds. The study utilizes Mel-frequency cepstral coefficients, extracted from cough audio. Extensive experiments using the COUGHVID cough dataset demonstrate the feasibility and effectiveness of the approach which uses the Extra-Tree classifier. The classification model outperforms other models with a testing accuracy of 90.05%, sensitivity of 86.01%, and an area under the curve of 90.08%, outperforming other similar works, in terms of performance and computation.

关键词： COVID-19 Training Accuracy Sensitivity Pandemics computer viruses Computational modeling Random forests Standards Testing

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Performance assessment of low-cost sensors under representative indoor air conditions 17

Performance assessment of low-cost sensors under representat...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Alonso, Maria Justo Jørgensen, Rikke Bramming Madsen, Henrik Mathisen, Hans Martin NTNU Dep. Energy and Process Engineering Trondheim Norway NTNU Dep. Industrial Economics and Technology Management Trondheim Norway DTU Applied Mathematics and Computer Science Kgs. Lyngby Denmark

Recently many indoor air quality (IAQ) low-cost sensors (LCS) have arrived in the market. Being cost-effective encourages sensor installation in many different situations for IAQ-monitoring and sometimes for control of HVAC systems. However, very often these sensors are delivered with incomplete information and there are many doubts about their performance, reliability, and response to aging or drift. In this article, an evaluation of the performance of eight equal in-house mounted IAQ stations is presented. Pre-calibrated LCS measuring CO2, temperature, RH, and particulate matter were connected to a RaspberryPi. Some tests were repeated with 6 months of difference and some with one year. In general, all stations responded similarly to the events to which they were exposed, the inter-sensor correlations values were higher than 0.95. LCS reacted similarly to the sources (trends) but with quantitative disparities. Sensor calibration is needed but when included, the tested LCS seem fit for IAQ measurements. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Carbon dioxide

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Pollutant correlation analysis in measurements at four classrooms in four Norwegian schools 17

Pollutant correlation analysis in measurements at four class...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Alonso, Maria Justo Jørgensen, Rikke Bramming Madsen, Henrik Mathisen, Hans Martin Dep. Energy and Process Engineering Trondheim Norway Dep. Industrial Economics and Technology Management Trondheim Norway Technical University of Denmark Applied Mathematics and Computer Science Kgs. Lyngby Denmark

Demand-controlled ventilation (DCV) is often used in school buildings to save energy by modulating the airflow rates based on the demand. However, other pollutants and indoor air parameters may affect health and are not necessarily correlated to CO2. In this study investigation of correlations between PM2.5, formaldehyde, absolute humidity, CO2 and temperature are performed. Measurements of these parameters were performed between two months and one year, positioned in the supply air in four CO2-based DCV classrooms at four different schools. The time series of measurements were de-trended to avoid overestimating cross-correlation functions. From the measurements we have concluded the need to measure: 1) pollutants related to human activities (CO2) 2) pollutants that infiltrate from processes like combustion or traffic outdoors (PM2.5), and 3) parameters related to other "non-combustion-related activities" indoors, heat sources and moisture loads (temperature). Formaldehyde from building materials seems to be mostly degassed in these older than one-year buildings, but this study should be repeated on newer buildings. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Formaldehyde

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Intelligent Recognition of Immune Cells: Analysis and Research 19

Intelligent Recognition of Immune Cells: Analysis and Resear...

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19th IEEE International Conference on e-Business Engineering, ICEBE 2023

作者： Li, Chih-En Liu, Wei-Lun Lai, Kun-Yi Chiang, Yu-Hsiang Yan, Xiaowei Chen, Hao-Xiang Li, Hsu-Ching Cheng, Wan-Shu National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung Taiwan Institute for Systems Biology Seattle Washington United States Providence University Dep. of Computer Science and Information Management Taichung Taiwan

ISBN: (纸本)9798350325553

Immunotherapy utilizes the potential of the patient's immune system to fight disease. T-lymphocytes have become the central focus of the immune system in the fight against cancer. One of the fundamental studies in immunotherapy is how to accurately identify various types of immune cells. Therefore, the study of immune-related cells will be an important reference for the treatment of COVID19 or other related diseases. In this paper, a series of experiments were conducted with COVID-19 patients as the starting point to design a strategy to identify the important subtypes of CD$4^{+}$T cells. In this paper, an intelligent machine learning algorithm is proposed according to the results of different experiments of linear and nonlinear dimension reduction algorithms, integrating the important characteristics of these two algorithms and improving the performance. Our experimental results show that the algorithm designed based on the front-end experiments can effectively distinguish the important lymphocytes. The proposed algorithm not only improves the performance, but also the dimension reduction procedure keeps the important features of both global and regional data. The experimental results of this paper will be beneficial for the subsequent analysis of the clustering in identifying important subtypes of CD$4^{+}$T cells. Most importantly, this wm significantly improve the procedure of manually integrating and labeling the recorded data after several tedious and monotonous analyses with external software. © 2023 IEEE.

关键词： COVID-19

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On the classification of weather based on the production of photovoltaic installations

On the classification of weather based on the production of ...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Paweł Parczyk Tobiasz Puślecki Robert Burduk Dep. of Systems and Computer Networks Wrocław University of Science and Technology Wrocław Poland

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

In recent years, there has been an energy transition in which fossil fuel-fired power plants are being replaced by renewable energy sources such as photovoltaics. While the influence of factors such as location and method of installation or the sun’s position above the horizon is deterministic, the weather factor is rather random. Weather data can be easily obtained from nearby weather stations, but some weather phenomena are so dynamic that data obtained in this way can become useless. When considering the problem of classifying weather conditions based on production data of photovoltaic installations, we propose two new features extracted from the production data that describe the overall level of sunshine and the variability of sunlight. Three supervised learning methods were used to classify the dataset: CNN, Random Forest classifier and Decision Tree Classifier. Reference values were obtained by generating classification results from the raw output and compared with those generated from the two new features. The experiments showed a statistically significant advantage for the models fitted to the data extended with the new features.

关键词： Photovoltaic systems Renewable energy sources Clouds Supervised learning Production Feature extraction Data models Data mining Random forests Meteorology

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Design of Quality Improvement Technique Through Ridgelet Transform on Watermarked Video

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第1期4卷 204-210页

作者： Ahmed, Rasha I. Ali, Sura Mazin Computer science Dep. University of Technology-Iraq Baghdad Iraq Mustansiriyah University Baghdad Iraq

with the rapid spread of the use of video files cross the world before and during the pandemic, and the availability of capabilities and tools to create, store and transmit video files of high quality. The latest studies illustrate that video files are the highest data types that had been transmitted over the internet, during video transmission or broadcast from one point to the other or over the internet, videos may be damaged for several reasons, such as packet loss or jitter, in this paper, a proposed approach of Ridgelet transformation is presented to improve received video file quality, based on embedded watermark quality present in video frames. It is implemented by extracting three-color channels of video frames, i.e., Red (R), Green (G), and Blue (B). A selected watermark is included in blue channel (B) along with transmitting the video file from source to destination. However, the quality of watermark is measured with execution regarding watermark. Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) were adopted as measurement criteria. The evaluation results show interesting comparative values without noise, in comparison to applying salt, pepper, and Gaussian noise to video frames. © 2023 Iraqi Journal for computer science and Mathematics. All rights reserved.

关键词： Mean square error

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