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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of 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 FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, Science, and technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Thabet, Hassaan Th. H. Al-Hilali, Aqeel A. Al-Safi, Mohammed G. S. Thabit, Thabit H. Mahmood, Firas M. Al-Hadba University College Dept. of Computer Technology Engineering Mosul Iraq Al-Farahidi University Dept. of Medical Instruments Engineering Techniques Baghdad Iraq Al-Esraa University College Dept. of Accounting Baghdad Iraq Ninevah University Dept. of Computer Engineering and Information Mosul Iraq Cihan University-Erbil Dept. of Informatics and Software Engineering Kurdistan Region Iraq

ISBN: (纸本)9798350369359

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper. © 2023 IEEE.

关键词： Vehicles

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Using Object Detection to Select Grasp Type & Control Functional Electrical Stimulation for Hand Rehabilitation 10

Using Object Detection to Select Grasp Type & Control Functi...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Bhagat, Nikunj Arunkumar Ruppa, Manikanta Indian Institute of Science Dept. of Electrical Engineering Bengaluru India Indian Inst. of Inf. Tech. & Mgmt Dept. of Computer Science Thiruvananthapuram India

ISBN: (纸本)9781665490993

Unilateral muscle weakness and hand paralysis is the most common outcome after a stroke. Functional electrical stimulation (FES) is effective in assisting hand prehension, but conventional stimulators require users to manually select the grasp type (e.g. by pressing a button), which is challenging for patients with severe paralysis. Also, patients need to frequently divert attention from their task to operate the stimulator, which is cumbersome and reduces their engagement in the therapy. In this study, we develop a novel deep learning-based object detection approach to select multiple grasp types and control an electrical stimulator, in order to assist grasping. Object detection was performed using a state-of-the-art YOLOv5 algorithm, which achieved above 93% mean average precision. The algorithm tracked the positions of the hand and objects and selected a grasp type based on the object nearest to the hand. Once the grasp type was selected, a custom-built FES stimulator was activated to execute pre-defined stimulation sequences and allow a person to grasp the nearest object. This contactless, vision-based solution is beneficial for patients opting for homebased rehabilitation since it doesn't require additional setup time or help from caregivers. The future scope of this work includes testing the object detection-based FES on stroke patients and determining its efficacy in restoring hand movements. © 2023 IEEE.

关键词： Object detection

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Virtual Illusions: Unleashing Deepfake Expertise for Enhanced Visual Effects in Film Production 11

Virtual Illusions: Unleashing Deepfake Expertise for Enhance...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Sonu Joshi, Mukesh Vats, Prashant Upadhayay, Govind Murari Vats, Shailender Kumar Kumar, Shalendra Amity University Dept. of Aiit Uttar Pradesh Noida India School of Computing Graphic Era Hill University Bhimtal Campus Uttarakhand India Manipal University Jaipur Faculty of Engineering Dept. of Cse Rajasthan Jaipur India Manipal University Jaipur Dept. of Computer Applications Rajasthan Jaipur India Dept. of Computer Applications Uttar Pradesh Noida India New Delhi Inst. of Mgmt. Ggsipu Dept. of Computer Applications New Delhi India

ISBN: (纸本)9798350350357

As technology continues to evolve, the realm of visual effects in film production has witnessed a significant transformation. Deepfake technology, once associated with controversial uses, has found a legitimate and innovative application within the filmmaking industry. This paper explores the integration of deepfake expertise into film production processes, focusing on its potential to revolutionize visual effects and storytelling. By harnessing deep learning algorithms, filmmakers can create hyper-realistic digital assets, seamlessly blending fiction with reality. This paper delves into the ethical considerations, creative possibilities, and technical challenges associated with the utilization of deepfake technology in filmmaking. Furthermore, it examines case studies and industry trends to provide insights into the current landscape and prospects of incorporating deep fake expertise for enhanced visual effects in film production. © 2024 IEEE.

关键词： Blending

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Photonic Signal Transport on Aerospace Platforms: Is Analog Signaling Ever Useful? 6

Photonic Signal Transport on Aerospace Platforms: Is Analog ...

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6th Annual IEEE Research and Applications of Photonics in Defense Conference, RAPID 2023

作者： Hoff, Michael T. Ralph, Stephen E. Stevens, Rick C. Georgia Inst of Technology Lockheed Martin Corporation Advanced Technology Laboratories Dept. of Electrical and Computer Engineering AtlantaGA United States Georgia Inst of Technology Dept. of Electrical and Computer Engineering AtlantaGA United States Lockheed Martin Corporation Advanced Technology Laboratories EaganMN United States

For photonic signal transport in multi-GHz, waveform-sensitive RF transport applications, it will be shown that digital-over-fiber fundamentally enables superior SNR performance versus analog-over-fiber. However, for ... 详细信息

ISBN: (纸本)9781665423526

关键词： Fibers

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Mining Road Traffic Accident Data for Prediction of Accident Severity

Mining Road Traffic Accident Data for Prediction of Accident...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Bahiru, Tadesse Kebede Manjula, V.S. Akele, Tadesse Birara Tesfaw, Engdaw Ayalew Belay, Tadesse Destaw Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information Technology Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Pg Dept of Computer Science Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information System Amhara Kombolcha Ethiopia Wachamo University School of Computing and Informatics College of Engineering and Technology Dept. of Information Technology SSNPR Hosaena Ethiopia

ISBN: (纸本)9781665474511

As World Health Organization reports show more than 1.3 million people die each year from traffic accidents and more than twenty to fifty million people are harmed by non-fatal accidents. Understanding and identifying the major factors of road traffic accidents can help the stakeholders to take appropriate measures to minimize the accidents. Support Vector Machine (SVM), J48, and Naïve Bayes (NB) classification techniques are implemented to build a model that predicts accident severities and to identify road traffic accident factors. As experimental findings showed weather conditions, number of lanes, road lighting conditions, and speed limit are the determinant factors that cause more accident severity. Finally, the classification and prediction performance of the models are compared using various performance evaluation techniques. J48 classifier has better classification performance than Support Vector Machine and Naïve Bayes classifiers. Naïve Bayes is the classifier that showed the lowest performance than others, but it has good performance in classifying fatal accident severities rather than serious and slight accident severities. © 2023 IEEE.

关键词： Support vector machines

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Optimizing Machine Learning Algorithms for Temperature Forecasting and Energy Management Systems

Optimizing Machine Learning Algorithms for Temperature Forec...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chandra Sekhar, G. Kedhari Sailaja, P. MadhusudhanaRao, G. Srinivas, N. Sai Thrinath, B.V. Akash, G. Dept. of Computer Science Engg Aeronautical Engineering Hyd Visakhapatnam Dept of Computer Science Engg Institute Visakha Inst. of Engg. & Tech Dept of Electrical and Electronics Engg Vardhaman College of Engg Hyd. Dept of Electrical and Electronics Engg Mohan Babu University Tirupati Dept of Computer Science Engg Woxsen University Hyderabad

ISBN: (纸本)9798350373783

Cloud data centers strive to provide efficient, scalable, and reliable computing resources while addressing challenges related to security, privacy, cost, and more. Their objectives include delivering value to users through flexibility, availability, innovation, and environmental responsibility. These Data centers have observed tremendous growth, utilizing massive energy for computer resources and cooling systems. A combination of time series regression models, machine learning, and other optimization techniques may be used to develop more robust and adaptive resource management policies for complex data center environments. Additionally, continuous monitoring and feedback loops are essential to ensure that the models remain accurate and effective as conditions evolve. This paper studied existing machine learning algorithms and methods for temperature forecasting and adequate energy consumption for data centers. The investigations have been analyzed that the impact of energy consumption on computer resource workloads significantly improved, and Virtual Machines scheduling algorithms will optimize the forecasting of thermal effects. This paper also compares the regression model with the Machine Learning algorithms, which will provide the best methodology for forecasting thermal effects and improving efficient energy. © 2024 IEEE.

关键词： Cloud Computing Data Centres Index: Energy Efficiency Privacy Security Thermal Forecast Virtual Machines

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Toward EEG-Based Brain State Recognition for Personalized Neuromodulation 15

Toward EEG-Based Brain State Recognition for Personalized Ne...

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15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Chang, Yu-Cheng Chao, Pin-Hsuan Chen, Sin-Horng Wei, Chun-Shu NYCU Dept. of Computer Science Taiwan Inst. of Bme NYCU Taiwan Dept. of Electronics and Electrical Engineering Taiwan

ISBN: (纸本)9781665464994

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive antidepressant neuromodulation therapy for treatment-resistant depression (TRD). However, the remission rate of patients remains unsatisfactory possibly due to the suboptimal configuration of conventional rTMS protocol. This work aims to design a close-loop TMS system and validate the practicability of brain-state-dependent stimulation based on real-time monitoring of electroencephalogram (EEG). We propose a novel method of phase estimation to enhance the precision of EEG phase-triggered firing of TMS. Our implementation supports subsequent studies on personalized brain-state-dependent neuromodulation for clinical applications. © 2022 IEEE.

关键词： Electroencephalography

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Generating Automated Assistance Mechanism in Android Programming Self-learning System Using Automatic Testing Tools

Generating Automated Assistance Mechanism in Android Program...

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2023 International Conference on Innovation and Intelligence for informatics, Computing, and Technologies, 3ICT 2023

作者： Syaifudin, Yan Watequlis Liliana, Dewi Yanti Kaswar, Andi Baso Fatmawati, Triana Funabiki, Nobuo Siradjuddin, Indrazno State Polytechnic of Malang Dept. of Information Technology Malang Indonesia State Polytechnic of Jakarta Dept. of Informatics Engineering Jakarta Indonesia State University of Makassar Dept. of Computer Engineering Makassar Indonesia Okayama University Dept. of Elec. and Comm. Engineering Okayama Japan State Polytechnic of Malang Dept. of Electrical Engineering Malang Indonesia

ISBN: (纸本)9798350307771

Nowadays, computer-assisted learning (CAL) has experienced a significant increase in demand for self-learning purposes with the mechanism of automated assistance to facilitate assessments and provide inst.nt feedback to learners. Using automatic testing, test cases can be executed repeatedly, providing informative feedback to assist students in correcting the written code referring to the assignment specifications. This research experiences an automated testing mechanism in an Android programming self-learning system to serve several important functions for learning experience enhancement. By adopting the TDD method and utilizing automatic software testing tools, the testing process combines unit testing with JUnit and integration testing with Robolectric. It ensures the quality and functionality of the application under test based on learning assignment's specifications. Utilizing the assertion methods realizes the code verification referring to expected behavior and potentially produces informative feedback that can be captured to provide automated assistance. Evaluation through the test code implementation in the Basic Application learning topic shows that automatic testing tools cover the automated assistance feature in the validations of project configuration and UI components. © 2023 IEEE.

关键词： Specifications

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