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22nd IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2024

作者： Ghosh, Partha Charit, Arnab Banerjee, Hindol Bandhu, Debanwesa Ghosh, Agniv Pal, Ankita Goto, Takaaki Sen, Soumya Academy of Technology Department of Computer Science and Engineering Adisaptagram India Toyo University Faculty of Information Sciences and Arts Saitama Japan A.K.Choudhury School of Information Technology University of Calcutta Kolkata India

ISBN: (纸本)9798350391343

Addressing the persistent challenge of student dropout, particularly prevalent in developing countries like India, Bangladesh, etc. are of paramount importance. Factors such as poverty, natural calamities, and early marriages exacerbate this issue. High student dropout rates can negatively impact a country by diminishing its economic productivity, increasing social inequalities, and perpetuating a cycle of poverty. Addressing dropout issues requires comprehensive strategies to ensure a skilled and educated workforce, fostering societal well-being and global competitiveness. This research focuses on analysing comprehensive data on students who have dropped out. Thereafter, a machine learning based methodology is used to discern the underlying causes of student attrition in various schools. Furthermore, it allows for efficient monitoring of the state's educational landscape, with the ability to drill down to granular levels when necessary to identify specific regional challenges. The effectiveness of this approach is validated through the utilization of real-world datasets. © 2024 IEEE.

关键词： Students

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Optimization of Feature Selection to Improve Order Transaction Performance in the Manufacturing Industry

Optimization of Feature Selection to Improve Order Transacti...

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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： Ery Hartati Deris Stiawan Wijang Widhiarso Faculty of Engineering Universitas MDP Faculty of Engineering Universitas Sriwijaya Palembang Palembang Indonesia Computer Engineering Department Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Computer Engineering Department Faculty of Computer Science Universitas Multi Data Palembang Palembang Indonesia

The development of business processes that occur in Industry 4.0 requires the optimization of processes in the system, especially in the process of order transactions. To optimize the transaction process, the assistance of the rank and weight system on dataset features, called the feature selection process, can be employed. Feature selection aims to improve the elimination of redundant and irrelevant features, which can reduce computation time and improve accuracy. In this research using multivariate datasets. Feature selection is used to eliminate some attributes that are irrelevant to the label class, resulting in improved performance of the classification algorithm. The author compares three algorithms: information gain, ratio gain, and chi-square. These are algorithms to find the rank and weight of data. In this research to measure feature selection performance using the Naïve Bayes classifier algorithm. The evaluation is performed by observing the level of classification accuracy generated without feature selection versus the classification accuracy generated after the implementation of feature selection. The experimental results showed that 14 features were included in the selection of features, and four relevant and best features were produced using the Chi-square binary classification algorithm. Classification using naïve Bayes algorithm reached 97.56%, while using ratio gain and information gain resulted in classification values of 75.57% and 92.69%, respectively.

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Automated Glaucoma Detection based on LBP Representation and GLRLM Feature Extraction Method

Automated Glaucoma Detection based on LBP Representation and...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Ahmed, Zakia Lawgali, Ahmed Abdalla, Mohamed University of Omar Al-Mukhtar Faculty of Sciences Department of Computer Science Albayda Libya University of Benghazi Faculty of Information Technology Department of Computer Science Benghazi Libya University of Benghazi Faculty of Information Technology Department of Software Engineering Benghazi Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

Glaucoma is a chronic and degenerative disease that causes irreversible damage to the nerve system of an eye, due to an increase in the Intra-ocular pressure in the retina. It reduces the vision area and leads to blindness. Early detection is critical to prevent permanent vision loss. The automated analysis of medical images contributes to the increase in the performance of classifying retinal images to pathological and nonpathological. This paper presents an automated glaucoma diagnosis approach by analyzing the texture of fundus images. The analysis was performed using Local Binary Pattern descriptor to represent grayscale and channels of retinal images and applied Gray Level Run-Length Matrix to describe patterns of texture. The classification was performed using two classifiers. The proposed approach provided the best classification results with 98%, 91%, and 97% of the three versions of RIM-ONE databases using Support Vector Machine classifier. Findings showed that local binary pattern representations and concatenated gray level run length matrix features achieved high results. Consequently, proving this approach is reliable and promising. © 2022 IEEE.

关键词： Local binary pattern

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A Study on Impact-Aware Aerial Robots Colliding with the Environment at Non-Vanishing Speed

A Study on Impact-Aware Aerial Robots Colliding with the Env...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Gayatri Indukumar Alessandro Saccon Antonio Franchi Chiara Gabellieri Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands Department of Mechanical Engineering Eindhoven University of Technology Eindhoven The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Enabling aerial robots to handle dynamic contacts happening at non-vanishing speeds can enlarge the range of their applications. In this work, we propose an impactaware strategy to allow aerial multirotor robots to recover from impacts. The method leverages a reactive strategy not requiring low-level changes to the motion controller commonly implemented onboard quadrotors, which might be not viable or not desirable for most users. Extensive simulation tests show that the proposed strategy considerably increases the tolerated velocity at impact in tasks in which the robot either picks an object up or collides against an object to clear its way. Preliminary experimental results using Crazyflie UAVs are also presented.

关键词： Trajectory tracking Dynamics Lead Autonomous aerial vehicles Stability analysis Robustness computer crashes Trajectory Robots Quadrotors

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Absorptive Microwave Filters: From Narrowband to Wideband Operation

Absorptive Microwave Filters: From Narrowband to Wideband Op...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Jiaqian Cui Lei Zhu Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

This paper presents a method to control the out-ofband performance of absorptive filters in both narrowband and wideband cases. To verify the method, a narrowband absorptive filter is designed with wideband matching, and a wideband one is proposed to achieve an infinite low-reflection range. Finally, the narrowband filter is implemented in microstrip-line platform, not only attaining symmetrical geometry but also improving impedance-matching performance.

关键词： Resistors Microstrip filters Filters Simulation Prototypes Reflection Reflection coefficient Microwave filters Wideband Narrowband

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Performance of different Machine Learning Techniques for the Prediction of Heart Diseases

Performance of different Machine Learning Techniques for the...

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2021 International Conference on Forensics, Analytics, Big Data, Security, FABS 2021

作者： Sarveshvar, M.R. Gogoi, Anindita Chaubey, Amrit Kumar Rohit, S. Mahesh, T.R. Department of Computer Science and Engineering Faculty of Engineering and Technology Bangalore India

ISBN: (纸本)9781665420051

Machine Learning (ML), which is the most well known sub area Artificial Intelligence, is revolutionizing the area of research. ML is primarily an subset of Artificial Intelligence(AI) that has been a crucial component of digitalization solutions that has gotten a lot of buzz in the digital world. In this work, ML is utilized to determine whether or not a person has cardiac disease. ML may be used to determine if a person has a cardiovascular illness based on particular characteristics such as chest discomfort, cholesterol levels, age, and other factors. Cardiovascular disease diagnosis can be simplified using ML classification algorithms that are based on supervised learning. To distinguish those with cardiac illness from those who do not, many ML algorithms are being used including Naïve Bayes Classifier, Logistic Regression Classifier and Random Forest.. The dataset contains certain irrelevant features that are removed during the data cleaning stage, and the data is also standardized for better results. The results and analyses of the publicly available ML Heart Disease dataset are being compared in this paper using different ML methods. The accuracy as well as confusion matrix are also being used to validate a good amount of promising outcomes. © 2021 IEEE.

关键词： Machine learning

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Generalizable and Robust Spectral Method for Multi-view Representation Learning

arXiv

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arXiv 2024年

作者： Yacobi, Amitai Lindenbaum, Ofir Shaham, Uri Department of Computer Science Bar-Ilan University Faculty of Engineering Bar-Ilan University

Multi-view representation learning (MvRL) has garnered substantial attention in recent years, driven by the increasing demand for applications that can effectively process and analyze data from multiple sources. In this context, graph Laplacian-based MvRL methods have demonstrated remarkable success in representing multi-view data. However, these methods often struggle with generalization to new data and face challenges with scalability. Moreover, in many practical scenarios, multi-view data is contaminated by noise or outliers. In such cases, modern deep-learning-based MvRL approaches that rely on alignment or contrastive objectives present degraded performance in downstream tasks, as they may impose incorrect consistency between clear and corrupted data sources. We introduce SpecRaGE, a novel fusion-based framework that integrates the strengths of graph Laplacian methods with the power of deep learning to overcome these challenges. SpecRage uses neural networks to learn parametric mapping that approximates a joint diagonalization of graph Laplacians. This solution bypasses the need for alignment while enabling generalizable and scalable learning of informative and meaningful representations. Moreover, it incorporates a meta-learning fusion module that dynamically adapts to data quality, ensuring robustness against outliers and noisy views. Our extensive experiments demonstrate that SpecRaGE outperforms state-of-the-art methods, particularly in scenarios with data contamination, paving the way for more reliable and efficient multi-view learning. Our code is available at: https://***/shaham-lab/SpecRaGE. © 2024, CC BY.

关键词： Contrastive Learning

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A study of the leverage effect in Bitcoin Conditional Heteroskedasticity prediction 10

A study of the leverage effect in Bitcoin Conditional Hetero...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Abarghouie, Masoud Omidvari Alizadeh, Sasan H. Khademzadeh, Ahmad Department of Computer Engineering Science and Research branch Islamic Azad University Tehran Iran Faculty of Information Technology Iran Telecommunication Research Center Tehran Iran Iran Telecommunication Research Center Tehran Iran

ISBN: (纸本)9798350348873

In today's world, countries establish large companies specifically designed to effectively manage the risks associated with volatility exchange rates and leveraged assets. Bitcoin is widely recognized as the leading cryptocurrency, making it highly appealing to investors and traders. The recorded data reveals a growing number of transactions conducted using Bitcoin cryptocurrency. Consequently, accurately predicting the volatility of Bitcoin's conditional heteroskedasticity holds significant importance for the financial markets. Cryptocurrencies present a challenging situation due to their complex dynamics, extreme observations, asymmetry, and various other non-linear features. Modeling these cryptocurrencies accurately becomes difficult using statistical models, as they fail to consider the leverage effect and provide precise predictions. In this paper, we have employed a hybrid modeling approach by utilizing MLP neural network and GARCH statistical model. The experiments conducted on Bitcoin time series data have yielded promising results, showcasing a notable reduction in the prediction error of conditional heteroskedasticity volatility. ©2024 IEEE.

关键词： Forecasting

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controlled Out-band Device to Device Communication in Cellular Networks Using Backup Channel in Television White Space 18

Controlled Out-band Device to Device Communication in Cellul...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Rahman, Saifur Shah, Syed Luqman Mursal, Salim Nasar Faraj Abbas, Ziaul Haq Usman, Muhammad Irfan, Muhammad Muhammad, Fazal College of Engineering Nairan University Electrical Engineering Department Najran61441 Saudi Arabia Gik Institute of Engineering Sciences and Technology TeleCoN Research Lab Topi23640 Pakistan Gik Institute of Engineering Sciences and Technology Faculty of Electrical Engineering Toni 23640 Pakistan University of Engineering and Technology Department of Electrical Engineering Mardan23200 Pakistan University of Engineering and Technology Department of Computer Science Mardan23200 Pakistan

ISBN: (纸本)9798350328172

In this article, we address the problem of spectrum scarcity in cellular networks (CNs). We propose a backup channel (BuC) for cellular users (CUs) who are in the same macro-cell under the control of a single macro base station (eNB). This BuC operates in the television white space and is detected by the CUs through a cognitive radio energy detection channel sensing technique with a certain probability of success. When the regular channels with the cellular eNB are all occupied, the CUs within the same coverage area of the macro eNB can utilize the sensed BuC to establish a controlled out-band device-to-device link for communication. The BuC bypasses the eNB and reduces the burden on the core of the CN. This leads to improvements in the capacity of the cellular eNB. In the proposed system model, each CU and eNB is equipped with two antennas for communication in two separate bands, i.e., cellular and TV bands. Through simulations, we have observed significant reductions in the blocking probability and probability of call delays. © 2023 IEEE.

关键词： Cognitive radio

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Image Denoising for Smart Laser Osteotomy Using Deep Learning-based Fast Optical Coherence Tomography (OCT) 2

Image Denoising for Smart Laser Osteotomy Using Deep Learnin...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Ghate, Shubhangi N. Kalpanadevi, D. Amudha, K. Velayutham, Priya Balu, S. Shah, Mohd Asif D. Y. Patil Deemed to Be University Ramrao Adik Institute of Technology Department of Electronics and Telecommunication Engineering Maharashtra Navi Mumbai Nerul India Kalasalingam Academy of Research and Education Department of Computer Applications Krishnan Koil Tamil Nadu Srivilliputhur India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Thottiyam Tamilnadu Trichy India Paavai Engineering College Department of Computer Science and Engineering Tamilnadu Namakkal India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode Tamilnadu Namakkal India Chandigarh University Adjunct Faculty University School of Business Gharuan Punjab Mohali India

ISBN: (纸本)9798350346640

With laser osteotomy, patients may expect clean incisions and minimal damage to bone structure. With the smart laser osteotomy method, this study has suggested using Optical Coherence Tomography (OCT) to track the ablation as it occurred. The OCT image is useful for distinguishing different types of tissue and giving the ablation laser guidance on where to focus its energy to avoid damaging vital structures like bone marrow and nerves. Furthermore, the application relies on the quality of the OCT picture to determine the tissue classifier's exactness. Because of this, establishing a reliable feedback system depends heavily on picture denoising. The frame averaging approach is widely utilized as a means of picture denoising in Optical Coherence Tomography (OCT). The more photographs that are used, the better the final image will seem. However, this method requires more time to acquire data and makes the system more vulnerable to motion abnormalities. To get around these restrictions, a deep learning denoising algorithm is used to simulate the frame-averaging approach. The generated image was of higher quality than traditional digital filtering techniques while maintaining the same level of detail as when using a frame-averaging approach. This study also analyzes how the proposed strategy impacts the precision of the tissue classifier model to inform the ablation laser's adjustments. After applying image denoising, a significant improvement has been observed in the tissue classifier's performance. It is also evident that the classifier trained with deep learning denoised images was as accurate as the classifier trained with frame-averaged images. © 2023 IEEE.

关键词： Optical tomography

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