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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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A Latent Causal Inference Framework for Ordinal Variables

arXiv

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arXiv 2025年

作者： Scauda, Martina Kuipers, Jack Moffa, Giusi Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Dep. of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom Dep. of Biosystems Science and Engineering ETH Zurich Basel Switzerland

Ordinal variables, such as on the Likert scale, are common in applied research. Yet, existing methods for causal inference tend to target nominal or continuous data. When applied to ordinal data, this fails to account for the inherent ordering or imposes well-defined relative magnitudes. Hence, there is a need for specialised methods to compute interventional effects between ordinal variables while accounting for their ordinality. One potential framework is to presume a latent Gaussian Directed Acyclic Graph (DAG) model: that the ordinal variables originate from marginally discretizing a set of Gaussian variables whose latent covariance matrix is constrained to satisfy the conditional indep.ndencies inherent in a DAG. Conditioned on a given latent covariance matrix and discretisation thresholds, we derive a closed-form function for ordinal causal effects in terms of interventional distributions in the latent space. Our causal estimation combines naturally with algorithms to learn the latent DAG and its parameters, like the Ordinal Structural EM algorithm. Simulations demonstrate the applicability of the proposed approach in estimating ordinal causal effects both for known and unknown structures of the latent graph. As an illustration of a real-world use case, the method is applied to survey data of 408 patients from a study on the functional relationships between symptoms of obsessive-compulsive disorder and dep.ession. © 2025, CC BY-NC-SA.

关键词： Directed graphs

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Real-Time Audio Analysis for Detection of Apnea Intervals in Health-Care System

Real-Time Audio Analysis for Detection of Apnea Intervals in...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Alireza Vahdani Mahdieh Rahmati Mehdi Sharifi Mehdi Ghatee Dep. Of Math. And Computer Science Amirkabir University of Technology Iran Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Accurate detection of respiratory pauses is crucial in patients undergoing cataract surgery under sedation, as undetected pauses can lead to serious complications. Conventional monitoring techniques, such as pulse oximetry and capnography, often fail to detect respiratory pauses in a timely manner, limiting their effectiveness in high-stakes clinical settings. To address this gap, we propose an automated method leveraging tracheal sound analysis using a deep learning model based on ResNet18. Our approach incorporates signal processing techniques, noise reduction, Mel-spectrogram extraction, and data augmentation, to optimize the model's performance. The model was evaluated using multiple performance metrics, including AUC of the precision-recall curve, F1 score, etc. It achieved an AUC of 92.5% and an F1 score of 89% for the apnea class, demonstrating the system's potential for precise, real-time respiratory monitoring in clinical environments.

关键词： Time-frequency analysis Accuracy Smoothing methods Noise reduction Surgery Data augmentation Data models Real-time systems Data mining Monitoring

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Enhanced Multi-Modal Gas Leakage Detection with NSMOTE: A Novel Over-sampling Approach

Enhanced Multi-Modal Gas Leakage Detection with NSMOTE: A No...

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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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System for Talent Acquisition: Integrating AI, Automation, and Data Analysis in HR 6

System for Talent Acquisition: Integrating AI, Automation, a...

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6th International Conference on Artificial Intelligence in information and Communication, ICAIIC 2024

作者： Abitova, Gulnara Serikov, Ayanbek Nikulin, Vladimir Rakhimzhanova, Mira Shuteyeva, Gulnur Kulniyazova, Korlan Astana IT University Dep. Computer Engineeering Astana Kazakhstan Binghamton University Dept. Electical Engineering and Computer Science Binghamton United States Eurasian National University Dept. System Anslysis and Managemnt Astana Kazakhstan

ISBN: (纸本)9798350344349

This article presents a novel system designed for human resources (HR) companies, aiming to predict an individual's personality based on resume analysis through the integration of advanced information technology tools. At the core of this system is Airable, serving as the central database, which is seamlessly I integrated with various other tools such as *** for document handling, GitHub for code repository management, and OpenAI for advanced AI algorithms. The integration and automation of these diverse tools are skillfully orchestrated using Make, a no-code solution that streamlines the workflow and enhances efficiency. This innovative approach allows HR companies to leverage technology for more accurate personality predictions, thereby improving their recruitment processes and decision-making strategies. © 2024 IEEE.

关键词： Machine learning

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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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A Region-Wide Cost-Benefit Assessment of Residential V2G Participation in Demand Response

A Region-Wide Cost-Benefit Assessment of Residential V2G Par...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Khunsha Nasr Hany Farag Shivam Saxena Dep. Of Electrical Engineering and Computer Science York University Toronto Canada Dep. Of Electrical Engineering and Computer Engineering University of New Brunswick Fredericton Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Participation of Electric Vehicles (EVs) in providing grid services is becoming popular for the economic benefits to their stakeholders, i.e., Local Distribution Companies (LDCs), ratepayers, and EV owners. Battery Electric Vehicles can be effectively used for power system applications such as demand response and peak shaving. However, there is a lack of region-wide cost-benefit analysis of Vehicle to Grid (V2G) participation in providing grid services. Thus, this paper introduces a model that could be utilized to perform residential V2G mainly to evaluate its effect on non-participating ratepayers as a measure of fairness. The developed model yields the benefit-to-cost ratios (BCRs) for LDCs (administrators), ratepayers (those who don’t participate in V2G), and EV owners (participants).

关键词： Vehicle-to-grid Sensitivity analysis Particle measurements Demand response US dep.rtment of Defense Power systems Stakeholders

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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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Detecting 5G Signal Jammers with Autoencoders Based on Loose Observations

Detecting 5G Signal Jammers with Autoencoders Based on Loose...

引用

IEEE Globecom Workshops

作者： Matteo Varotto Stefan Valentin Stefano Tomasin Dep. of Information Engineering University of Padova Padova Italy Dep. of Computer Science Darmstadt University of Applied Sciences Darmstadt Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Padova Italy

Fifth-generation (5G) networks are prone to jamming attacks, which are particularly dangerous in mission-critical applications such as factory automation. In this paper, we present a simple method to detect jamming attacks with machine learning techniques operating on in-phase and quadrature (IQ) modulated signals. In particular, a convolutional autoencoder (CAE) learns the structure of the clean signal to distinguish it from jammed signals in real time. This approach requires only a loose synchronization to the OFDM symbol, while equalization and decoding are not necessary. Despite its simplicity, our technique has shown high detection rates in experiments on a 5G testbed.

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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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