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Autism Screening Disorder : Early Prediction 4

Autism Screening Disorder : Early Prediction

引用

4th IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Rashme, Tamanna Yesmin Islam, Linta Prova, Ayesha Aziz Jahan, Sohely Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh Central Women's University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Brishal Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728199511

Autism Spectrum Disorder (ASD), which is a developmental disability due to neuro-development disorder. This disorder is a brain growth disorder that affects how an individual perceives and interacts with others, resulting in social contact and communication difficulties. Though this disorder is the result of genetic or in nature but earlier detection allows earlier intervention, which is generally more effective. Based on ASD history research, This ASD problem begins with childhood and as time goes this disorder progresses into adolescence and adulthood. Thus, in this paper, a model has been proposed for the early prediction and analysis of ASD problems in children, adolescents, and adults. Principal Component Analysis (PCA) is used for the feature reduction method and then various machine learning algorithms have been applied to three ASD datasets relating to Children, adolescents, and adults. 10-fold cross-validation is used to evaluate the performance of applied machine learning algorithms. The result produces from our proposed model shows that Logistic Regression shows outperform comparing to all other benchmark machine learning algorithms in terms of accuracy, precision, recall, and F1-score during the prediction of ASD cases. © 2021 IEEE.

关键词： Principal component analysis

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Internet of Things Control Center 4

Internet of Things Control Center

引用

4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Thomas, Vibha Terence, Sebastian Agrawal, Bishnu Immaculate, Jude Karunya Instittue of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Sales - Mcaps Microsoft Technology Center Bangalore India Karunya Institute of Technology and Sciences Dept. of Mathematics Coimbatore India

ISBN: (纸本)9798350300772

The Internet of Things (IoT) is a global network of 'smart gadgets' that can sense their environment, connect to them, and communicate with people and other systems. This articles presents an IoT control center that provides a comprehensive solution for managing and controlling Io devices in a network. The control center offers a user-friendly interface that enables administrators to monitor and control a wide range of devices, from a single dashboard. The IoT control center uses machine learning algorithms to analyze real-time data, enhancing device performance. The IoT Control center also ensures the security of the devices and the data they generate, with the capability to identify anomalies and suspicious activity in real-time. The IoT control center in this paper emphasizes on two main use-cases namely, Smart Agriculture System, Spatial Analysis, but isn't confined to just these two use cases, in fact, proselytizes as many use cases possible into a single control center. The system provides a centralized interface for administrators to monitor, analyze and automate connected devices, enabling efficient and effective device management. © 2023 IEEE.

关键词： Object detection

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Arabic Sentiment Analysis of Mobile Banking Services Reviews 10

Arabic Sentiment Analysis of Mobile Banking Services Reviews

引用

10th International Conference on Social Networks Analysis, Management and Security, SNAMS 2023

作者： Khabour, Safaa M. Mustafa, Dheya Althebyan, Qutaibah Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Al Ain University Dept. of Cybersecurity Al Ain United Arab Emirates Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9798350318906

Online customer reviews have developed into a significant source of information about a business's performance. Due to shifting consumer expectations and growing internet penetration, the Middle East, especially Jordan, is seeing an increase in the usage of online banking. This propensity toward digital transition has been accelerated by the COVID-19 pandemic. Mobile banking service companies aim to effectively use customers' feedback to identify areas for improvement in customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on mobile banking reviews in Arabic, exploiting machine learning, natural language processing, and resampling methods to obtain subjective feedback, determine polarity, and identify customers' feelings in the banking domain. This work walks the reader through detailed steps of cleaning and preprocessing a manually annotated dataset of online banking reviews in Arabic, followed by features extraction and modeling, dataset splitting, and balancing training data. Finally, we examined classification algorithms including Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Ensemble hard and soft voting ML methods were also evaluated. We achieved a maximum accuracy of about 94% using the LR classifier and a maximum precision of 94% using the LR and hard voting methods. Our methodology will enable banks to acquire insights on how to develop their online presence and meet the demands of customers and stakeholders. © 2023 IEEE.

关键词： Sentiment analysis

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Causal Structure Learning from Imperfect Longitudinal data in Healthcare 11

Causal Structure Learning from Imperfect Longitudinal Data i...

引用

11th IEEE International Conference on Healthcare Informatics, ICHI 2023

作者： Yang, Haoyu Tourani, Roshan Li, Jia Caraballo, Pedro Steinbach, Michael Kumar, Vipin Simon, Gyorgy University of Minnesota Dept. of Computer Science and Engineering Minneapolis United States University of Minnesota Institute for Health Informatics Minneapolis United States Mayo Clinic Dept. of Internal Medicine Rochester United States

ISBN: (纸本)9798350302639

Causal Structure Discovery (CSD) is the task of learning the set of underlying causal relationships from observational data. Due to their computational scalability and flexibility, a recently developed class of CSD methods, NOTEARS, based on a formulation allowing for continuous optimization, is gaining popularity. However, this formulation can lead to incorrect edge orientations when little/no likelihood advantage is conferred upon any edge orientation, e.g., a → b → c versus c → b → a. In longitudinal data, like electronic health records (EHRs), temporal relationships are observable among many pairs of variables. Such temporal relationship is imperfect but still suggest an orientation since causal effects cannot travel backwards in time. Following this idea, we propose methods to incorporate precedence constraints into continuous optimization-based CSD methods. Experiments on both a synthetic and two real-world datasets validate the effectiveness of the proposed precedence constraints. © 2023 IEEE.

关键词： Machine learning

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A Deep Learning-Based Spectral Efficiency Maximization in Multiple Users Multiple STAR-RISs Massive MIMO-NOMA Networks 14

A Deep Learning-Based Spectral Efficiency Maximization in Mu...

引用

14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van An, Beongku Hongik University Dept. of Software and Communications Engineering Graduate School Korea Republic of International University Vietnam National University School of Computer Science and Engineering HCM City Viet Nam Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350335385

In this paper, we investigate a deep learning-based spectral efficiency maximization in multiple users multiple simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks, where multiple STAR-RISs are deployed to assist a base station (BS) in communicating with multiple near and far users simultaneously. To improve spectral efficiency (SE), we formulate a joint optimization of the precoding matrix, the phase shift of reflection and transmission of the STAR-RIS problem subject to the individual quality of services (QoS) of each user, maximum power budget at BS and the discrete phase shift. The formulated problem belongs to the class of mixed-integer non-convex optimization problem, which is difficult to solve optimally. We first relax the formulated problem and then transform it into an equivalent non-convex one but with a more tractable form. We then apply the inner approximation method to approximate the non-convex parts iteratively. Towards real-time optimization, we propose a novel deep learning framework with a convolutional neural network (CNN) model to predict the downlink SE based on users' position information and channel gains. Numerical results show that the proposed approach improves the SE compared to the massive MIMO systems with conventional RIS. Besides, the DL framework for the proposed scheme predicts the optimal SE within a short time compared to the conventional approach. © 2023 IEEE.

关键词： Spectrum efficiency

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Latency Analysis of In-Vehicle Network for Advanced Driver Assistance System

Latency Analysis of In-Vehicle Network for Advanced Driver A...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Hanyoung Park Minji Cho Yongjae Jang Ji-Woong Choi Dept. of Electrical Engineering & Computer Science Daegu Gyeongbuk Institute of Science & Technology (DGIST) Daegu Korea

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Considering the advancements in autonomous driving technologies, the necessity for an advanced driver assistance system (ADAS) to incorporate a multitude of sensors for enhanced precision has become paramount. Consequently, the increase of sensors correlates directly with the escalation of autonomous driving capabilities. However, this escalation causes a larger volume of data traffic and an increase in the latency within the in-vehicle network (IVN). As the IVN latency increases, the overall latency of ADAS from the sensor to the actuator increases. This increased latency can affect the safety of ADAS. Hence, latency analysis for various sensors is required. In this paper, we struct Ethernet-based zonal architecture considered for future automotive architecture and analyze the factor of end-to-end latency for the safety-related ADAS with multi-type sensors, including IVN latency.

关键词： Simulation computer architecture Switches Sensor systems Sensors Topology Safety Advanced driver assistance systems Autonomous vehicles Queueing analysis

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Leveraging Community Software in CS Education to Avoid Reinventing the Wheel 22

Leveraging Community Software in CS Education to Avoid Reinv...

引用

27th ACM Conference on Innovation and Technology in computer science Education, ITiCSE 2022

作者： Blanchard, Jeremiah Hott, John R. Berry, Vincent Carroll, Rebecca Edmison, Bob Glassey, Richard Karnalim, Oscar Plancher, Brian Russell, Seán University of Florida Dept. of Engineering Education GainesvilleFL United States University of Virginia Dept. of Computer Science CharlottesvilleVA United States LIRMM - Univ Montpellier CNRS Polytech Engineering School Montpellier France Full Sail University Dept. of Computer Science Winter ParkFL United States Virginia Tech Dept. of Computer Science BlacksburgVA United States KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden University of Newcastle School of Information and Physical Sciences CallaghanNSW Australia Barnard College Columbia University Dept. of Computer Science New York CityNY United States University College Dublin School of Computer Science Dublin Ireland

ISBN: (纸本)9781450392006

Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. Many are then community or individually maintained. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the CS Education community. We hypothesize that this is due in part to accessibility, discoverability, and adaptability challenges, among others. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to utilize community developed solutions that often lack a centralized authority. This working group will explore what solutions are currently available, what instructors need, and reasons behind the above-mentioned phenomenon. This will be accomplished via a literature review and survey to identify the tools that have been developed by the community;the solutions that are currently available and in use by instructors;what features are needed moving forward for classroom and research use;what support for extensions is needed to support further CS Education research;and what institutional challenges instructors and researchers are currently facing or have faced in the past in developing, deploying or otherwise using community software solutions. Finally, the working group will identify factors that limit adoption of solutions and ways to integrate and improve the accessibility, discoverability, and dissemination of existing community projects, as well as manage and overcome institutional challenges. © 2022 Owner/Author.

关键词： community software educational tools

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Scattering and Absorption Efficiency Analysis of Gold Nanospheres for Optoelectronic Applications

Scattering and Absorption Efficiency Analysis of Gold Nanosp...

引用

IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Chandan Upadhyay Kamalesh Tripathy Mitradip Bhattacharjee Dept. of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

Metal nanoparticles show the phenomena of localized surface plasmon resonance (LSPR) and lead to significant optical signal enhancement in their vicinity. Here, we investigate optical-cross sections of gold nanospheres (diameter ranges from 20 nm to 100 nm) as a function of excitation wavelength, particle size, and refractive index of the surrounding medium. Scattering and absorption efficiencies are also studied to assist in choosing the optimum scattering environment for LSPR. The resonance wavelength is observed distinctly for gold nanosphere diameter greater than 30 nm and found to be varying from 502-523 nm.

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System “Stationary 3-Axis Fire-Emergency Response Base”

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Shyantan Kundu Swarajit Dey Md. Mustafa Abdullha Gobinda Sen Malay Gangopadhyay Dept. of Computer Science & Engineering Institute of Engineering & Management Kolkata India Dept. of Electronics & Communication Engineering Institute of Engineering & Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named “Stationary 3-Axis Fire-Emergency Response Base” (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management.

关键词： Industries Projectiles Forestry Flame retardants Hazards Real-time systems Production facilities Time factors Reliability Internet of Things

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Deadline Constrained Least Laxity Time Based Cloudlet Scheduling

Deadline Constrained Least Laxity Time Based Cloudlet Schedu...

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Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Lipika Datta Dept. of Computer Science and Engineering College of Engineering and Management Kolaghat West Bengal India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

Users send their requests to cloud service providers, who then deliver the services over networked systems. With a high volume of requests, it is crucial to schedule cloudlets effectively to ensure that most meet their specified deadlines for optimal performance. This paper focuses on calculating Both slack time and laxity time for cloudlets across all virtual machines and scheduling them accordingly. It also considers the rearrangement of cloudlets already assigned to virtual machines to accommodate new cloudlets. The CloudSim simulation environment is utilized to schedule cloudlets by evaluating their slack time and laxity time across various virtual machines. This approach helps in selecting the most suitable virtual machine for executing the cloudlets. By incorporating laxity time into the scheduling decisions, the system becomes more balanced, reduces makespan, and thus enhances overall performance. The proposed method has been shown to reduce task rejection by at least 10% and the makespan by an average of 17% when compared to the EFD algorithm.

关键词： Cloud computing Schedules Mechatronics Processor scheduling Virtual machines Time factors Intelligent systems

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