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International Conference on Informatics and Computing (ICIC)

作者： Andri Roni Yunis Djoni Ng Poi Wong Robin Darwin Program of Computer Science Department Universitas Mikroskil Medan Indonesia Program of Information Systems Department Universitas Mikroskil Medan Indonesia Program of Computer Science Department University of Pelita Harapan Tangerang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Reducing dropout rates is crucial for enhancing human capital and education standards. Existing methods, such as Random Forest with Chi-Square and SMOTE-ENN, effectively addressed class imbalance and improved prediction accuracy for dropout data. However, there is still a research gap in achieving optimal model performance. This study addresses the gap by incorporating hyperparameter tuning alongside ChiSquare for feature selection and SMOTE-ENN for handling class imbalance. The dataset was segmented into training and evaluation subsets through the implementation of 10 -fold cross-validation. The testing was conducted with seven variations, namely building and implementing a Random Forest model using the default parameters from the Weka tool and applying six different hyperparameter tuning techniques. The results showed that Hyperband, along with other techniques like TPE, RandomSearch, and BO-TPE, led to substantial improvements in model accuracy, precision, and F-measure, and achieved perfect AUC scores. However, BO-GP and Nevergrad did not improve model performance. These findings suggest that the combination of SMOTE-ENN, Chi-Square, and hyperparameter tuning can enhance the effectiveness of dropout prediction models, with potentially positive implications for early intervention strategies in educational institutions.

关键词： Training Accuracy Training data Predictive models Feature extraction Reliability Tuning Random forests Standards Testing

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Automatic Smart Crawling on Twitter for Weather information in Indonesia

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Procedia computer Science 2023年 227卷 795-804页

作者： Kartika Purwandari Reza Bayu Perdana Join W.C. Sigalingging Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta 10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate.

关键词： Social media Twitter SVM smart crawling weather

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Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance

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computers, Materials & Continua 2021年第9期68卷 3465-3481页

作者： Ghulfam Zahra Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Fayez Eid Alazemi Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science and Information Systems College of Business StudiesPAAET12062Kuwait

:In recent years,video surveillance application played a significant role in our daily *** taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the *** reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery,object detection,target killing,and *** remove fog and enhance visibility,a number of visibility enhancement algorithms and methods have been proposed in the ***,these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer vision *** existing techniques do not perform well when images contain heavy fog,large white region and strong atmospheric *** research work proposed a new framework to defog and dehaze the image in order to enhance the visibility of foggy and haze *** proposed framework is based on a Conditional generative adversarial network(CGAN)with two networks;generator and discriminator,each having distinct *** generator network generates fog-free images from foggy images and discriminator network distinguishes between the restored image and the original fog-free *** are conducted on FRIDA dataset and haze *** assess the performance of the proposed method on fog dataset,we use PSNR and SSIM,and for Haze dataset use e,r−,andσas performance *** results shows that the proposed method achieved higher values of PSNR and SSIM which is 18.23,0.823 and lower values produced by the compared method which are 13.94,0.791 and so *** results demonstrated that the proposed framework Has removed fog and enhanced the visibility of foggy and hazy images.

关键词： Video surveillance degraded images image restoration transmission map visibility enhancement

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Effective Dimension in Bandit Problems under Censorship

arXiv

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arXiv 2023年

作者： Guinet, Gauthier Amin, Saurabh Jaillet, Patrick AWS AI Labs United States MIT United States Dept. of Civil and Environmental Engineering Laboratory for Information and Decision Systems United States Dept. of Electrical Engineering and Computer Science & Laboratory for Information and Decision Systems United States

In this paper, we study both multi-armed and contextual bandit problems in censored environments. Our goal is to estimate the performance loss due to censorship in the context of classical algorithms designed for uncensored environments. Our main contributions include the introduction of a broad class of censorship models and their analysis in terms of the effective dimension of the problem – a natural measure of its underlying statistical complexity and main driver of the regret bound. In particular, the effective dimension allows us to maintain the structure of the original problem at first order, while embedding it in a bigger space, and thus naturally leads to results analogous to uncensored settings. Our analysis involves a continuous generalization of the Elliptical Potential Inequality, which we believe is of independent interest. We also discover an interesting property of decision-making under censorship: a transient phase during which initial misspecification of censorship is self-corrected at an extra cost;followed by a stationary phase that reflects the inherent slowdown of learning governed by the effective dimension. Our results are useful for applications of sequential decision-making models where the feedback received depends on strategic uncertainty (e.g., agents’ willingness to follow a recommendation) and/or random uncertainty (e.g., loss or delay in arrival of information). © 2023, CC BY.

关键词： Decision making

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Hybrid Signal Integrity Modeling and Analysis of Heterogeneous Integrated System With Neuromorphic Darwin Chip

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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A Comparative Study on the Efficiency of the Modified COMET in Decision-Making 28th

A Comparative Study on the Efficiency of the Modified COMET ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Paradowski, Bartosz Olender, Pawel Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland

Decision-making is a high-risk area and, simultaneously, a process that cannot be ignored. Risks often arise under challenging situations where ranking the options under consideration is a complex problem. For such purposes, multi-criteria decision-making methods are resorted to, which, despite continuous improvement, still have some weaknesses, often due to their characteristics. One such method that has seen many modifications in the literature is the Characteristic Objects METhod (COMET). It allows a simplified comparison of alternatives considered in a given problem, compared to the Analytic Hierarchy Process (AHP), but has a relatively high computational complexity. In this study, we present a modification of the COMET method that simplifies the procedure for assigning preference values to characteristic objects. Furthermore, a comparison with the classical approach is given regarding the obtained preference values, rankings, and criteria weights, which occur implicitly in this method. The study showed that the proposed modification has grounds for broader use and offers similar results to classical COMET while reducing the number of required operations to obtain preference values. © 2024 The Authors.

关键词： Comets

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Adversarial attack method with adaptive step size for ID card text recognition 4

Adversarial attack method with adaptive step size for ID car...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Feng, Weidong Zhang, Chunjie Zheng, Xiaolong Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (纸本)9798350349252

In the field of text recognition models, existing adversarial attack algorithms mainly target white-box scenarios, which are usually limited to single-color backgrounds and short text length. However, with the complex color textures of ID card images and long text length, these methods are not satisfactory when performing adversarial attacks on ID card text data. To overcome these challenges, this paper introduces a novel black-box attack method tailored for ID card text recognition models. The proposed approach accounts for the complexities of intricate image backgrounds, extended text data, and the limitations inherent in black-box conditions. The proposed algorithm introduces an adaptive step-size search method based on line search, aiming to overcome the limitations of traditional methods that rely on fixed step sizes and projection techniques for perturbation generation. This refinement aims to enhance the success rate of black-box attacks while maintaining efficacy in white-box scenarios. Spatial transformations and random noise are incorporated to fortify the robustness of adversarial samples. Experimental results, conducted under various norms, demonstrate that the fusion of the traditional method with the adaptive step-size search method yields superior attack performance. © 2024 IEEE.

关键词： Adversarial machine learning

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OSM: Leveraging model checking for observing dynamic 1 behaviors in aspect-oriented applications

arXiv

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arXiv 2024年

作者： AlSobeh, Anas Mohammad Ramadan Department of Information Systems Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan Program of Information Technology School of Computing College of Engineering Computing Technology and Mathematics Southern Illinois University CarbondaleIL United States

In the intricate domain of software systems verification, dynamically model checking multifaceted system characteristics remains paramount, yet challenging. This research proposes the advanced observe-based statistical model-checking (OSM) framework, devised to craft executable formal models directly from foundational system code. Leveraging model checking predicates, the framework melds seamlessly with aspect-oriented programming paradigms, yielding a potent method for the analytical verification of varied behavioral attributes. Exploiting the transformative capacity of OSM framework, primary system code undergoes a systematic metamorphosis into multifaceted analysis constructs. This not only simplifies the model verification process but also orchestrates feature interactions using an innovative observing join point abstraction mechanism. Within this framework, components encompassing parsing, formal verification, computational analytics, and rigorous validation are intrinsically interwoven. Marrying the principles of model checking with aspect-oriented (AO) modularization, OSM framework stands as a paragon, proficiently scrutinizing and affirming system specifications. This ensures the unyielding performance of electronic health record systems amidst shifting preconditions. OSM framework offers runtime verification of both object-oriented and AO deployments, positioning itself as an indispensable open-source resource, poised to automate the enhancement of system performance and scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Aspect oriented programming

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IoT-Sustainability Protection:Water Level MonitoringWith Micro Leak Detection and AutomaticWater Restoration 5

IoT-Sustainability Protection:Water Level MonitoringWith Mic...

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5th International Conference on Smart Sensors and Application, ICSSA 2024

作者： Zainuddin, Ahmad Anwar Bharudin, Mohamad Syafiq Asyraf Kamarudin, Saidatul Izyanie Hussin, Amir ‘Aatieff Amir Subramaniam, Krishnan Tamrin, Mohd. Izzuddin Mohd. Handayani, Dini Kamal, Nik Nor Muhammad Saifudin Nik Mohd Department of Computer Science Kulliyyah of ICT International Islamic University Malaysia Kuala Lumpur Malaysia Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mechanical Engineering Mila University Negeri Sembilan Nilai Malaysia Department of Information Systems Kulliyyah of ICT International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350354294

Efficient water resource management stands as a linchpin for sustainable development initiatives worldwide. To this end, this work endeavours to pioneer a comprehensive water level monitoring system with a focus on leak detection and automatic water restoration. Tailored with specialized sensors with cutting-edge automation technology to monitor water levels against predefined thresholds, promptly triggering restoration processes when necessary and identifying leaks in real-time. Central idea of the design is the incorporation of a dedicated rain sensor module, bolstering the micro leak detection capabilities while an ultrasonic sensor ensures precise measurement of water levels in tanks. In the event of a detected leak, rain sensor promptly initiates a signal, process by the main board which also known as the "brain" of the system and triggers an automatic cessation of water input to prevent wastage. Moreover, the system ensures seamless data logging by uploading pertinent information to a dedicated IoT cloud database, facilitating comprehensive monitoring and analysis. This innovative solution underscores not only its viability but also its practical efficacy, showcasing its potential to optimize water utilization and conservation efforts. Specifically, it excels in detecting micro droplets indicative of leaks in the tank. This work aims to spearhead the development of an advanced water level monitoring system, leveraging state-of-the-art of Arduino hardware and a diverse array of sensors, including ultrasonic and rain sensors as well as presenting a holistic framework poised to revolutionize water management practices. © 2024 IEEE.

关键词： Restoration

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Smart Greenhouse Control System using Multi-Objective Adaptive Policy Gradient and Neural Meta Controller 4

Smart Greenhouse Control System using Multi-Objective Adapti...

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4th International Conference on Ubiquitous Computing and Intelligent information systems, ICUIS 2024

作者： Himabindu, C.H. Sreeramu, M. Chaganti, Koushik Reddy Gutta, Pruthvi Krishna Nagesh, C. Ruksana, S. Vignan Institute of Technology and Science Dept of Mba Hyderabad India University of the Cumberlands Dept. of Computer and Information Systems KY United States Tesla Dept of It AustinTX United States Srinivasa Ramanujan Institute of Technology Department of Cse Anantapur India Gates Institute of Technology and Science Department of Cse Anantapur India

ISBN: (纸本)9798331529635

This confluence of escalating energy costs and upsurges in the demand for high-quality crop yields under uncertain environmental conditions makes a strong case for the urgent optimization of greenhouse operations. The most currently used control systems for smart greenhouses are founded upon static or even narrowly focused optimization approaches, which severely limit their adaptability and efficiency in dynamic settings. These methodologies fail to address the challenge of multiple objectives, such as the consumption of energy and crop yield, with unpreparedness for disturbances caused by external influences and computational bottlenecks. This paper brings about a collective framework for three of the most advanced RL methodologies: Multi-Objective Adaptive Policy Gradient with Environmental Feedback, Hierarchical Reinforcement Learning with Temporal Decision Decomposition, and Neural Meta-Controller with Self-Organizing Sub-Networks. MAPGEF utilizes real-time environmental feedback to dynamically adjust policy weights, thus achieving up to a 25% reduction in energy use and an 18% increase in crop yield through accurate environmental tuning. The HRL-TDD utilizes hierarchical RL combined with temporal decomposition in order to handle tough tasks of a greenhouse, thus improving a decision by up to 30%, with energy efficiency improved by 20% and crop yield stabilized with 15%. NMCSOS introduces a neural meta-controller coordinating self-organizing sub-networks, ensuring robust responses to external disturbances with 33% faster recovery, 22% better energy optimization, and 18% reduced yield variability levels. Together, these methodologies address the pressing need for adaptable, scalable, and energy-efficient greenhouse control systems. By integrating multi-objective learning, temporal decomposition, and self-organizing networks, this work significantly advances greenhouse management, promoting sustainable agriculture and resilience in the face of environmental uncert

关键词： Adaptive Policy Gradient Multi-Objective Reinforcement Learning Neural Meta-Controller Scenarios Smart Greenhouse Sustainable Agriculture

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