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2024 ASEE Annual Conference and Exposition

作者： Tsakalerou, Mariza Evangelou, Semira Maria Xenos, Michalis School of Engineering and Digital Sciences Nazarbayev University Kazakhstan Computer Engineering and Informatics Department University of Patras Greece

This paper presents a comprehensive exploration of students' experiences with low-code platforms, which emphasize high-level logic and functionality over intricate coding. This is achieved by utilizing pre-test and post-test questionnaires, Mendix Studio Pro as an exemplar of such platforms, and structured experimentation tasks. The pre-test captures participants' baseline perspectives, while the post-test examines shifts after hands-on interaction. The study addresses research questions concerning demographic correlations, coding backgrounds, attitude changes, coding performance, and qualitative insights. The experiment protocol includes an instructional video, demographic surveys, and three tasks. Descriptive statistics and statistical tests provide insights. Performance discrepancies between IT and non-IT backgrounds are statistically significant. Feedback indicates positive perceptions of low code. © American Society for engineering Education, 2024.

关键词： Students

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AI-Based Predictive Ramp-Metering Control for Freeway Traffic Systems 27

AI-Based Predictive Ramp-Metering Control for Freeway Traffi...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Binjaku, K. Mece, E.K. Pasquale, C. Siri, S. Sacone, S. University of Genova Department of Informatics Bioengineering Robotics and Systems Engineering Italy Polytechnic University of Tirana Department of Computer Engineering Albania

ISBN: (纸本)9798331505929

Traffic congestion in freeways poses significant challenges, impacting travel times and environmental sustainability. This paper proposes a novel approach to enhance ramp metering control using predictive traffic insights derived from physics-informed LSTM (Long Short-Term Memory) models. By integrating predictive capabilities with established control strategies like ALINEA, the method dynamically adjusts on-ramp flow rates based on anticipated traffic conditions. Real-world traffic data are used to evaluate the effectiveness of the approach, demonstrating improved performance compared to the adoption of conventional controllers. © 2024 IEEE.

关键词： Travel time

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Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

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computers, Materials & Continua 2023年第6期75卷 5471-5499页

作者： Azimbek Khudoyberdiev Mohammed Abdul Jaleel Israr Ullah DoHyeun Kim Department of Applied Informatics Kimyo International University in TashkentTashkent100121Uzbekistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Engineering Jeju National UniversityJeju63243Korea

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource *** of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish *** objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding *** paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish *** proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption *** farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted *** logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy *** evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried *** results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

关键词： Smart fish farming internet of things(IoT) predictive optimization objective function fuzzy logic control(FLC)

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Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Deep Neural Networks 14

Forecasting Stock Market Alternations Using Social Media Sen...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Saravanos, Christina Kanavos, Andreas University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350318067

Forecasting stock market volatility is a challenging task, primarily due to the influence of non-financial factors such as public opinion and sentiment. Social media platforms, particularly Twitter, have the potential to sway public sentiment and impact stock market prices. In this paper, we present a novel approach for predicting the volatility of multiple indices in the U.S. stock market by integrating historical closing prices with sentiment analysis of Twitter data using deep neural networks. Our approach involves extracting features from tweet metadata and analyzing the overall presence of authors on the platform to estimate future changes in index closing prices. Through our experiments, we observed a strong correlation between the predicted closing prices of an index, the sentiment expressed in tweets, and the extracted features. Despite the sparsity of the dataset, our applied neural networks exhibited robust per-formance. This study contributes to the expanding research on predicting stock market volatility through the utilization of social media sentiment analysis and deep learning techniques. The proposed approach holds promise for applications in financial decision-making and risk management. © 2023 IEEE.

关键词： Sentiment analysis

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Development of a machine learning model for predicting abnormalities of commercial airplanes

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Data Science and Management 2024年第3期7卷 256-265页

作者： Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh Department of Computer Engineering Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Intelligent System Research Group(ISysRG) Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia Department of Informatics Faculty of Computer ScienceUniversitas Sriwijaya30662Indonesia College of Engineering Studies Universiti Teknologi MARA(UiTM)Shah Alam40450Malaysia

Airplanes are a social necessity for movement of humans,goods,and *** are generally safe modes of transportation;however,incidents and accidents occasionally *** prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast *** study combined data-quality detection,anomaly detection,and abnormality-classification-model *** research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and *** data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial *** results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关键词： Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model

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Integration of data science with the intelligent IoT(IIoT):Current challenges and future perspectives

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Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon UniversitySeongnamSujeong-gu 13120Republic of Korea University of Electronic Science and Technology of China Chengdu 611731China College of Information Science and Engineering Hohai University213000China Department of Computer Science University of SalernoItaly Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1ChofugaokaChofu-shiTokyo182-8585Japan

The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate *** science is the science of dealing with data and its relationships through intelligent *** state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their ***,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various *** paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based *** insights into IoT data security,privacy,and challenges are visualized in the context of data science for *** addition,this study reveals the current opportunities to enhance data science and IoT market *** current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.

关键词： Data science Internet of things(IoT) Big data Communication systems Networks Security Data science analytics

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Simulating inexact DNNs for use in Custom Hardware Architectures

Simulating inexact DNNs for use in Custom Hardware Architect...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Danopoulos, Dimitrios Zervakis, Georgios Soudris, Dimitrios National Technical University of Athens School of Electrical and Computer Engineering Athens Greece University of Patras Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350318845

In response to the growing complexity of Deep Neural Network (DNN) models, the paradigm of approximate computing has emerged as a compelling approach to strike a balance between computational efficiency and model accuracy. Approximate computing involves intentionally allowing errors using inexact computations, exploiting the inherent resilience of many applications to such inaccuracies. While current state-of-the-art approaches leverage approximate multipliers for approximate DNN accelerators, there has been a notable difficulty in emulating such DNNs on common frameworks such as PyTorch, as they do not inherently support inexact arithmetic. This paper introduces a seamless PyTorch plugin, to evaluate inexact DNN models while having GPU acceleration to enable rapid emulation. Additionally, we support approximate-aware retraining and perform evaluation on several popular convolutional neural networks on Cifar10 and ImageNet datasets. © 2024 IEEE.

关键词： Deep neural networks

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Improved Accuracy of Decision Tree with XGBoost Technique in Cat Skin Disease Diagnosis 4

Improved Accuracy of Decision Tree with XGBoost Technique in...

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4th International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Safrina, Nanda Lydia, Maya Silvi Nasution, Benny Benyamin Universitas Sumatera Utara Master of Informatics Faculty of Computer Science and Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798350376111

This research aims to develop an expert system for initial diagnoses of skin diseases in cats using the Decision Tree method. It assists cat owners in identifying skin diseases based on observed symptoms. Data from expert veterinarian drh. Ismi Azima, including various types of cat skin diseases and their symptoms, were used. The research employs 80% training data and 20% testing data to develop the classification model. Testing results indicate the Decision Tree model achieves up to 90% average accuracy in diagnosing cat skin diseases based on 50 datasets. This expert system aids in diagnosis and provides treatment and prevention solutions, beneficial for cat owners, especially in areas with limited veterinary access. In conclusion, this Decision Tree-based expert system effectively provides initial diagnoses of skin diseases in cats with high accuracy. © 2024 IEEE.

关键词： Cat Skin Diseases Decision Tree Expert System XGBoost

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Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques

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Annals of Data Science 2024年第1期11卷 217-242页

作者： Das Guptta, Sumitra Shahriar, Khandaker Tayef Alqahtani, Hamed Alsalman, Dheyaaldin Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & ampTechnology Chittagong4349 Bangladesh Unit of Cybersecurity Department of Computer Science Center of Artificial Intelligence King Khalid University Abha Saudi Arabia School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

In this paper, we mainly present a machine learning based approach to detect real-time phishing websites by taking into account URL and hyperlink based hybrid features to achieve high accuracy without relying on any third-party systems. In phishing, the attackers typically try to deceive internet users by masking a webpage as an official genuine webpage to steal sensitive information such as usernames, passwords, social security numbers, credit card information, etc. Anti-phishing solutions like blacklist or whitelist, heuristic, and visual similarity based methods cannot detect zero-hour phishing attacks or brand-new websites. Moreover, earlier approaches are complex and unsuitable for real-time environments due to the dependency on third-party sources, such as a search engine. Hence, detecting recently developed phishing websites in a real-time environment is a great challenge in the domain of cybersecurity. To overcome these problems, this paper proposes a hybrid feature based anti-phishing strategy that extracts features from URL and hyperlink information of client-side only. We also develop a new dataset for the purpose of conducting experiments using popular machine learning classification techniques. Our experimental result shows that the proposed phishing detection approach is more effective having higher detection accuracy of 99.17% with the XG Boost technique than traditional approaches. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Search engines

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Temperature and Humidity Control System to Address Filament Brittleness in 3D Printers 2

Temperature and Humidity Control System to Address Filament ...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Erlina, Tati Violani, Salsa Kiki Suwandi, Rifki Nugroho, Yusuf Sulistyo Islam, Syful Universitas Andalas Computer Engineering Department Padang Indonesia Universitas Muhammadiyah Surakarta Informatics Engineering Surakarta Indonesia BSMRSTU Computer Science and Engineering Gopalganj Bangladesh

ISBN: (纸本)9798350389241

Additive manufacturing, mainly 3D printing, has emerged as a transformative technology with widespread applications across various industries. Despite its advancements, filament brittleness remains a significant challenge, undermining the quality and reliability of printed objects. In order to address this challenge, this study built a specialized temperature and humidity management system specifically for 3D printing applications. The system utilizes a Proportional-Integral-Derivative (PID) control technique to regulate the temperature within the filament storage environment, mitigating the impact of thermal fluctuations and moisture absorption. The system has been carefully developed and tested to assure optimal printing conditions, exceptional filament quality, and uniform print accuracy and precision. Since hardware testing confirms minimum disparities between sensor readings and reference measurements, reliability in environmental management is ensured for optimized print outcomes. Moreover, software testing ensures the PID controller's efficient functionality by providing real-time monitoring and control capabilities. Functional testing reveals the system's effectiveness in maintaining optimal storage conditions for various filament types, improving the quality of prints, and minimizing waste. Incorporating essential components such as sensors, actuators, and user interface elements guarantees seamless operation and intuitive engagement. © 2024 IEEE.

关键词： Humidity control

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