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International computer Science and engineering Conference (ICSEC)

作者： Khomkris Mathiang Kulwarun Warunsin Phuwitson Phumsaranakhom School of Information and Communication Technology University of Phayao Phayao Thailand Department of Computer Engineering Faculty of Engineering Ramkhamhaeng University Bangkok Thailand

ISBN: (纸本)9781665462730

Producing premium beef cattle requires a good quality roughage and feed containing nutritional value appropriate to the age of the cattle. One strategy for producing high-quality beef cattle is smart nutrition management in the feed of fattening. Optimal feed ration for beef cattle is a challenging task under the multiple constrains. Therefore, this research aims to compare the efficiency of 4 optimal methods: SLSQP, COBYLA, Simplex, and Primal-Dual via the 10 beef cattle feed formulas. Using a database from the Bureau of Animal Nutrition Development, Department of Livestock Development, by comparing the accuracy and processing time. According to the experimental findings, all strategies produced answers that were equally accurate. However, the time required to process SLSQP is minimal, followed by the Simplex and Primal-Dual. Due to the iteration loop exceeding our limit, COBYLA is negligees.

关键词： Databases Animals Cows Programming Agriculture Feeds Task analysis

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Parameterized Problems Complete for Nondeterministic Fpt Time and Logarithmic Space

SSRN

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SSRN 2023年

作者： Bodlaender, Hans Leo Groenland, Carla Nederlof, Jesper Swennenhuis, Céline Department of Information and Computing Sciences Utrecht University Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Technical University Delft Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

Let XNLP be the class of parameterized problems such that an instance of size n with parameter k can be solved nondeterministically in time $f(k)n^{O(1)}$ and space $f(k)\log(n)$ (for some computable function f). We give a wide variety of XNLP-complete problems, such as List Coloring andmPrecoloring Extension with pathwidth as parameter, Scheduling of Jobs with Precedence Constraints, with both number of machines and partial order width as parameter, Bandwidth and variants of Weighted CNF-Satisfiability. In particular, this implies that all these problems are W[t]-hard for all t. This also answers a long standing question on the parameterized complexity of the Bandwidth problem. © 2023, The Authors. All rights reserved.

关键词： Bandwidth

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VADDOT: Vocal Analysis to Detect Diseases of Throat

VADDOT: Vocal Analysis to Detect Diseases of Throat

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Ipsita Senapati Animesh Pradhan Lipsita Senapati Rajendra Prasath Tripti Swarnkar Department of Computer Science and Engineering ITER Siksha ‘0’ Anusandhan University Bhubaneswar Odisha Mercedes Benz Research and Development India Bengaluru Karnataka Faculty of Computer Science and Engineering Indian Institute of Information Technology Sri City Andhra Pradesh

Pharyngeal diseases, or throat diseases, are rampant- owing to pollution, allergies, and pathogens. The current diagnostic practices for such diseases are invasive, time-taking, and prone to subjectivity. Aiming to develop a patient-friendly mechanism for easy and efficient detection of throat diseases, reducing the time taken for diagnosis, VADDOT describes a throat disease identification system which utilises machine learning to detect diseases based on vocal parameters. It aims to provide an alternative, noninvasive way for the early detection of various throat diseases. VADDOT consists of Data Collection Module (DCM), Feature Learning Module (FLM), and Machine Learning Model (ML). DCM collects vocal samples by asking the concerned patients to record their voices while pronouncing various phonetics. FLM finds vocal parameters such as frequency, intensity, jitter, pitch, etc. from the voice. The Machine Learning Model works on Support Vector Machine trained by vocal parameter datasets of various throat diseases. Research in this field has shown promising results on diseases such as Parkinson's, Alzheimer's, and COVID-19. Since, the research is relatively new, datasets available to train the machine learning model are limited, causing difficulties in widening the spectrum of diseases that can be successfully detected through VADDOT. It is non-invasive, affordable, easy to use and reduces the diagnosis time. With further development and testing, the system has the potential to be widely used in healthcare facilities, enabling early detection and treatment of diseases, ultimately leading to better patient outcomes. It is simple and requires minimal training, making it an accessible tool for healthcare providers. Accuracy scores of 90.3% (Parkinson's Disease) and 97% (COVID-19) have been achieved so far by VADDOT.

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Recent Advances in Mobile Communication Network Security

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中国通信（英文版） 2022年第6期19卷前插1-前插3页

作者： Kaiping Xue Jianqing Liu Liehuang Zhu David S.L.Wei Rongxing Lu School of Cyber Science and Technology USTC Department of Electrical and Computer Engineering at the University of Alabama in Huntsville School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijingChina Computer and Information Science Department at Fordham University Faculty of Computer Science(FCS) Univer-sity of New Brunswick(UNB)Canada

Thelandscape of mobile communication networks has been evolving so *** the last decades,3G/4G achieved unprecedented success thanks to its advancement in IP for all,and high-speed,mobile and ubiquitous Internet *** with 3G/4G,5G now makes significant advances in terms of connectivity,data rate and latency,which shifts from the conventional people-to-people to the modern people-to-things and things-to-things communication *** the next standardization focus,6G will further build a ubiquitous network that realizes the integrated communication of air,space,earth and sea,boost the data rate 100 X faster than that of 5G,enable machine intelligence through AI,and many ***,6G will have much broader appli-cation prospects such as virtual or augmented reality,Internet of Things,Industrial Internet,unmanned driving,and smart factories.

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A Perceptual Data Cleansing Model (SDCM) for Reducing the Dirty Data

A Perceptual Data Cleansing Model (SDCM) for Reducing the Di...

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Smart Computing and Application (ICSCA), International Conference on

作者： Mohammad Azmi Al-Madi Ahmed Abdel-Wahab Mohammed AlShanty Shaikhan Bawazeer Mohammed AlZahrani Department of Computer Sciences Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Faculty of Computer Studies Arab Open University Riyadh Saudi Arabia Deprtment of Systems and Computers Engineering Al-Azhar University Cairo Egypt

Data cleansing approaches aim at revealing and reducing different types of outsourced errors. Such errors introduce a major issue as data cleansing often involves costly computations and time consumption. Data cleansing is complicated as most of the errors within the obtained data emerge in different forms, such as typos, duplicates, noncompliance with business rules, outdated data, and missing values. In this paper, the Supervised Dataset Cleaning Model (SDCM) is proposed in order to detect and reduce different types of outsourced errors that are stored in the data repository according to the supervised cleaning rules that are practiced from the previous cleaning processes. The findings indicates that the cleaning execution time can be reduced with this supervised model and the accuracy of the model is also increased when it covers the entire training rules and classified error types derived from the data warehouse. There are some expected future directions, which include: implementing full scale of SDCM and comparing the results obtained with different current methods. By comparing different data cleaning algorithms with SDCM, a knowledge gap is likely to arise in the search for further future improvements.

关键词： Training Computational modeling Supervised learning Clustering algorithms Data warehouses Cleaning Data models

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A new approach for solving global optimization and engineering problems based on modified Sea Horse Optimizer

arXiv

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arXiv 2024年

作者： Hashim, Fatma A. Mostafa, Reham R. Khurma, Ruba Abu Qaddoura, Raneem Castillo, P.A. Faculty of Engineering Helwan University Egypt University of Sharjah Sharjah27272 United Arab Emirates Department of Information System Faculty of Computers and Information Sciences Mansoura University Mansoura35516 Egypt MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan School of Computing and Informatics Al Hussein Technical University Amman Jordan Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

Sea Horse Optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named mSHO. The enhancement primarily focuses on bolstering SHO’s exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm’s search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon’s rank-sum and Friedman’s tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and effic

关键词： Global optimization

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Anticipation of Parking Vacancy during Peak/Non-peak Hours using Convolutional Neural Network-YOLOv3 in University Campus

Anticipation of Parking Vacancy during Peak/Non-peak Hours u...

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2021 International Conference on Green Energy, Computing and Sustainable technology, GECOST 2021

作者： Davan, Jasvina Mae A.P. Ealn Koh, Tieng Wei Tong, Dong Ling Tseu, Kwan Lee First City University College Artificial Intelligence Lab Faculty of Computing and Engineering Petaling Jaya Malaysia University of Putra Malaysia Faculty of Computer Science and Information Technology Selangor Malaysia Universiti Tunku Abdul Rahman Centre of IoT Big Data Faculty of Information and Communication Technology Perak Malaysia

ISBN: (纸本)9781665438650

Searching for a publicly available parking space has become a nightmare to many drivers. With the constant development of global urbanization, human population has increased drastically in the past decades. Searching for a publicly available parking space in a highly populated area can be daunting and time consuming. No matter how much time is spent to find a vacant parking space, it always causes traffic congestion in the area. To alleviate these problems, it is of utmost importance to have a system that can detect and display the vacant parking spaces in real-time. This paper has conducted a study of anticipation of parking vacancy using convolutional neural network called YOLOv3 in a university campus. Image data is gathered from the video capture of the university's campus open space parking lot. The YOLOv3 algorithm is used to train and predict whether the space is vacant or occupied. Results showed that YOLOv3 has been able to correctly predict the vacant space. The result of the rendering video will then be transformed into an image and is sent to the students via a Telegram group. © 2021 IEEE.

关键词： Convolutional neural networks

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The Significance of the Torso Electrodes for Selected Cardiac Regions

The Significance of the Torso Electrodes for Selected Cardia...

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International Conference on Measurement

作者： Beata Ondrusova Peter Tino Jana Svehlikova Institute of Measurement Science Slovak Academy of Sciences Bratislava Slovakia Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia School of Computer Science University of Birmingham Birmingham UK

The significance of the torso electrodes for the solution of the inverse problem of electrocardiography using a single dipole depends on the position of the true origin of the cardiac activity, i.e., ground truth. However, the ground truth is not known in clinical practice. Therefore, we studied whether similar electrode significances would be computed for the dipole corresponding to the ground truth, the computed inverse solution, and dipoles within the 30 mm radius around the inverse solution. The significance of torso electrodes was computed for 8 datasets of patients with a known position of the ground truth. The significance assessment of torso electrodes was performed using algebraic properties of the transfer matrix computed for the given dipole position. We studied three variants of the significance assessment of torso electrodes and for two of them similar electrode significances were obtained for all positions of dipoles within 30 mm of the inverse solution. For those two variants, the most significant electrodes created a subset of 47 ± 6% and 40 ± 4% from the full set of electrodes. The results suggest that the electrode significances for a given patient can be established from the inverse solution if the inverse solution lies within 30 mm of the ground truth.

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A Hybrid Learning Approach for Automatic Data Labelling and Anomaly Detection in IoT Networks

A Hybrid Learning Approach for Automatic Data Labelling and ...

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Artificial Intelligence (ICAI), International Conference on

作者： Rimsha Kanwal Umara Noor Zahid Rashid Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul South Korea

Internet of Things (IoT) is an environment in which digital equipment is augmented with sensors to share and receive data through network. When devices share data it can be effected by anomalies or any attack due to corrupted data or by any other uncertainty and ambiguity in data. The data can also be corrupted through a damage in device. These attacks or anomalies damage the working of the IoT networks. The anomalous data can be detected through detection techniques however most anomaly detection techniques depend upon labelled data but for IoT datasets, usually class labels are not available. Labeling is performed by a manual process which is time consuming and also costly. As data in IoT increases day by day so there is a need to label and classify data for future unseen data. In this paper a hybrid algorithm is proposed in which both clustering and classification techniques are applied for automatic labeling and classifying on IoT dataset. The model contains two function. In the first phase k-means clustering is employed for labelling dataset instances as normal or anomalous. In the second phase labelled dataset is used to train Random Forest model to detect anomalies in IoT networks. The results show that the proposed model is detecting anomalies in IoT networks with an accuracy 98%, precision 98 %, recall 98%, and F-meausre 0.98%.

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AlphaPIG: The Nicest Way to Prolong Interactive Gestures in Extended Reality 25

AlphaPIG: The Nicest Way to Prolong Interactive Gestures in ...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Li, Yi Fischer, Florian Dwyer, Tim Ens, Barrett Crowther, Robert Kristensson, Per Ola Tag, Benjamin Faculty of Information Technology Monash University Melbourne VIC Australia Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Science The University of British Columbia Okanagan Campus Kelowna BC Canada School of Science and Technology University of New England Armidale NSW Australia School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (纸本)9798400713941

Mid-air gestures serve as a common interaction modality across Extended Reality (XR) applications, enhancing engagement and ownership through intuitive body movements. However, prolonged arm movements induce shoulder fatigue - known as "Gorilla Arm Syndrome"- degrading user experience and reducing interaction duration. Although existing ergonomic techniques derived from Fitts' law (such as reducing target distance, increasing target width, and modifying control-display gain) provide some fatigue mitigation, their implementation in XR applications remains challenging due to the complex balance between user engagement and physical exertion. We present AlphaPIG, a meta-technique designed to Prolong Interactive Gestures by leveraging real-time fatigue predictions. AlphaPIG assists designers in extending and improving XR interactions by enabling automated fatigue-based interventions. Through adjustment of intervention timing and intensity decay rate, designers can explore and control the trade-off between fatigue reduction and potential effects such as decreased body ownership. We validated AlphaPIG's effectiveness through a study (N=22) implementing the widely-used Go-Go technique. Results demonstrated that AlphaPIG significantly reduces shoulder fatigue compared to non-adaptive Go-Go, while maintaining comparable perceived body ownership and agency. Based on these findings, we discuss positive and negative perceptions of the intervention. By integrating real-time fatigue prediction with adaptive intervention mechanisms, AlphaPIG constitutes a critical first step towards creating fatigue-aware applications in XR. © 2025 Copyright held by the owner/author(s).

关键词： Adaptive Interaction Gorilla Arm Mid-air Interaction Shoulder Fatigue

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