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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Lase, Yuyun Yusnida Lubis, Arif Ridho Elyza, Fauziah Syafli, Sekar Arini Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798350345162

Mental health disorders can affect a person's emotions and behavior and can impact their health and daily activities. The most serious consequence of poor mental health is death by suicide. The problem in this research is about mental health which can be analyzed using the field of natural language processing with the science of sentiment analysis on the developing TikTok platform. Content contained on the TikTok platform will have comments made by fellow users, then these comments are collected for sentiment analysis. This study utilizes a classification algorithm, namely naïve Bayes. The dataset obtained is preprocessed by text first, then the process from TF-IDF will be used to be able to see the appearance of words in the data. The data used in this study is 6300 data. The results of this study are accuracy as measured by a metric evaluation which produces 80.95%. The sentiment in this study focuses on positive and negative sentiments. With visualization, words that often appear are tired, sick, tired, and hurt in negative comments. Meanwhile, the words that often appear in positive comments are able, passionate, strong, and happy. © 2023 IEEE.

关键词： mental health naïve bayes social media tiktok

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Blockchain Application in the Waste Trading 7

Blockchain Application in the Waste Trading

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Dana, Ian Rachman Hidayati, Anita Ismail, Iklima Ermis Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798350342154

Waste is a complex and severe problem with detrimental environmental impacts if not appropriately managed. The process of buying and selling waste is essential in waste management. With its smart contracts, blockchain allows transactions to be carried out automatically without involving third parties, thereby speeding up the process and increasing efficiency and security. This research applies blockchain to record waste buying and selling transactions. We selected Binance Smart Chain (BSC) as a blockchain platform and Solidity 0.8.0 to build smart contracts containing structures, events, and functions that manage transactions. Autotask connected to the relayer records transactions on the blockchain network without requiring direct intervention from the user. We set up credentials from apiKey and apiSecret to connect to OpenZeppelin Defender, then enter the contract address deployed to the BSC network. After that, we create a variable to accommodate the deployed smart contract's Application Binary Interface (ABI). System testing uses the black box and System Usability Scale (SUS). Of the ten test scenarios in the black box, everything ran smoothly. System testing using SUS resulted in a score of 76 and a Net Promoter Score (NPS) value of 70%. This score indicates that the system is deemed acceptable and included in the good category. This system can guarantee transparency in the waste buying and selling system, hoping to increase accountability and trust in the community. © 2023 IEEE.

关键词： Smart contract

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Optimal Design of a Planning and Contracting Framework to Enable Vehicle to Building and Grid Services via Demand Response

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7542-7556页

作者： Abd Elaziz Elsayed, Ahmed Saxena, Shivam Essa Zidan Farag, Hany York University Lassonde School of Engineering Electrical Engineering and Computer Science Department Keele Campus TorontoONM3J 1P3 Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B5A3 Canada

Bidirectional electric vehicle (EV) charging enables stored energy to reduce peak loads for vehicle to buildings (V2Bs) and the vehicle to grid (V2G). However, building owners investing in V2B infrastructure while generating revenue from V2G services face challenges in planning and coordinating with EV owners due to uncertainties in their schedules and profit-sharing expectations. Additionally, misaligned building and grid peak times can create conflicts between V2B and V2G goals, which may negatively impact the building electricity bill. Unlike previous studies that used a contract-free approach for aggregating V2B and V2G, resulting in inconsistent participation, this article proposes a novel planning and contracting framework that enables building owner to determine the optimal contract parameters with EV owners. These parameters include minimum participation time in DR events, minimum arrival state of charge (SoC), and permitted emergency departure hours. The framework supports V2B aggregation with on-site distributed energy resources (DERs) for DR and V2G services, ensuring transparency and fairness through shared profits and performance-based penalties, while compensating building electricity bills due to V2G activities. The framework is composed of a tri-stage optimization process that uses Monte Carlo simulations to generate EV owner profit assessments, select optimal EV candidates based on charger availability, estimate contract parameters with profit/penalty sharing, and assign contracts between power system operators (PSOs), building owners, and EV owners under multiple virtual scenarios. Simulation results validate the contract parameter assessment and its performance via a 3-day case study with real-world datasets that demonstrates net revenue generation of {\} 209 for each EV owner and {\} 950 for building owners during DR events with a return on investment (ROI) of 134.5% and 130.7% for the EVs and Building owners, respectively. © 2015 IEEE.

关键词： Vehicle-to-grid

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Gait Optimization for Legged Systems Through Mixed Distribution Cross-Entropy Optimization 23

Gait Optimization for Legged Systems Through Mixed Distribut...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Tsikelis, Ioannis Chatzilygeroudis, Konstantinos University of Patras Computer Engineering and Informatics Department PatrasGR-26504 Greece Department of Electrical & Computer Engineering PatrasGR-26504 Greece Department of Mathematics PatrasGR-26110 Greece

ISBN: (纸本)9798350373578

Legged robotic systems can play an important role in real-world applications due to their superior load-bearing capabilities, enhanced autonomy, and effective navigation on uneven terrain. They offer an optimal trade-off between mobility and payload capacity, excelling in diverse environments while maintaining efficiency in transporting heavy loads. However, planning and optimizing gaits and gait sequences for these robots presents significant challenges due to the complexity of their dynamic motion and the numerous optimization variables involved. Traditional trajectory optimization methods address these challenges by formulating the problem as an optimization task, aiming to minimize cost functions, and to automatically discover contact sequences. Despite their structured approach, optimization-based methods face substantial difficulties, particularly because such formulations result in highly nonlinear and difficult to solve problems. To address these limitations, we propose CrEGOpt, a bi-level optimization method that combines traditional trajectory optimization with a black-box optimization scheme. CrEGOpt at the higher level employs the Mixed Distribution Cross-Entropy Method to optimize both the gait sequence and the phase durations, thus simplifying the lower level trajectory optimization problem. This approach allows for fast solutions of complex gait optimization problems. Extensive evaluation in simulated environments demonstrates that CrEGOpt can find solutions for biped, quadruped, and hexapod robots in under 10 seconds. This novel bi-level optimization scheme offers a promising direction for future research in automatic contact scheduling. © 2024 IEEE.

关键词： Cost functions

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Exploring the Use of Augmented Reality in Teaching History to Students with Attention-Deficit Hyperactivity Disorder 3rd

Exploring the Use of Augmented Reality in Teaching History t...

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Fraggista, Chrysa Krouska, Akrivi Troussas, Christos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9783031440960

Augmented reality is an emerging technology that aims to enrich the elements of the real world with virtual information through the use of appropriate devices. These new technological devices, namely smart mobile devices such as smartphones and tablets, can serve as a link between augmented reality and the user. Augmented reality technology has not left the education industry unscathed, attracting the interest of many teachers and researchers, since it is seen as an important, cutting-edge learning and educational tool. Augmented reality provides students with unique learning experiences by supplementing the sensory stimuli of sight and hearing with three-dimensional images, sounds, videos, etc. The purpose of this study is to investigate the effectiveness of augmented reality in high school history classrooms for the teaching of students with attention-deficit hyperactivity disorder (ADHD). The evaluation was conducted with semi-structured interviews with students who have been diagnosed with ADHD, using a questionnaire at the end of the procedure. Qualitative analysis was mainly used to conclude. The method used to carry out this work is the case study. Research findings show that augmented reality technology can make a positive contribution to the learning process. Through the answers of most students, it seemed that they accepted augmented reality and considered it essential for learning better. Furthermore, student behaviors such as attention, engagement and learning motivation showed an improvement. Finally, the educational objectives of this didactic intervention were achieved to a satisfactory degree according to the results of the test. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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Empowering Responsible Digital Citizenship Through an Augmented Reality Educational Game 1

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Iakovidis, Marios Papakostas, Christos Troussas, Christos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (数字)9783031441462

ISBN: (纸本)9783031441455

As big data analytics becomes increasingly prevalent, fostering a comprehensive understanding of digital information ownership and responsibility is crucial. This paper presents an educational game that uses mobile augmented reality and a server backend to promote responsible digital citizenship among students. The mobile app uses ArUco markers to locate virtual characters, which represent digital citizens and are role-played by the participants. In the game, students can assign keywords to their characters and create text based on the keywords of nearby characters. The server backend facilitates communication between the characters and sets the interaction range for collecting keywords. The paper details the system’s architecture and a proposed use-case as an educational scenario that employs the game. This educational game holds the potential to equip students with the skills to become conscientious digital citizens and seeks to enhance understanding of data ethics, digital information ownership, and responsible data practices. An initial evaluation of the developed prototype has been performed, yielding encouraging results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Weighted Average Ensembled Probability Pipeline of Pre-trained CNN Models for Hand Gesture Classification 15

Weighted Average Ensembled Probability Pipeline of Pre-train...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gunti, Rahul Rohan Reddy, B. Swathi, Gunti Sharath Kumar Reddy, G. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

The automatic detection of sign language from hand gesture images is fundamental for effective human-computer interaction, especially for individuals with hearing and speech disorders. Achieving accurate detection and classification of sign language gestures is paramount. However, accurately recognizing these gestures frequently presents challenges due to dull backgrounds, variations in gesture orientation, and fluctuations in lighting conditions. Previous approaches tried to overcome these challenges but still the model performance lacks recognition accuracy and visual quality. To address this need for accuracy, we proposed a Weighted Average Ensembled Probability Pipeline composed of EfficientNetB7, ResNet-50, and Xception models. Leveraging the Extensive Hand Gesture Recognition Database (HGRD), comprising 20,000 images, our individual models achieved notable accuracies: 96.498 % for EfficientNetB7, 93.2 % for ResNet-50, and 90.175 % for Xception. Through ensemble learning, we integrate these models to attain an impressive overall accuracy of 98.95 %. This research contributes to robust gesture recognition system and underscores the significance of ensemble techniques in enhancing performance for Gesture recognition systems. © 2024 IEEE.

关键词： Gesture recognition

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Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models

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International Journal of Intelligent Networks 2024年第1期5卷 351-363页

作者： Anand, R. Andrew, J. Makki, Ihab Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka 576104 India Department of Nouvelle Aquitaine France

The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affected by war are characterized by significant devastation and human suffering. In order to effectively tackle this matter, it is imperative to use proactive strategies that focus on the identification and mitigation of these perilous substances prior to their potential infliction of harm. Nevertheless, the majority of current solutions exhibit significant deficiencies, such as exorbitant expenses, inefficiencies, and apprehensions over accuracy. These drawbacks are further compounded by the inherent trade-offs that exist between these elements, where improvements in one area often come at the expense of another. Contrarily, recent breakthroughs in the areas of deep learning, unmanned aerial vehicles, and sensor technologies are being recognized as potentially transformative elements in the domain of landmine identification and removal. This paper presents a thorough examination of recent scholarly investigations that integrate computerized technology in the field of landmine detection. To the extent of our current understanding, there has been no prior investigation that has thoroughly examined this particular domain. The main aim of this study is to investigate the incorporation of machine learning based regression methods in the field of landmine detection. The study specifically emphasizes the identification and resolution of existing issues that hinder the development of efficient automated solutions, hence enhancing performance optimization. The Sum of Sine Curve Fit Regression Model is proposed and proved a powerful and adaptable tool for extracting relevant information from this hyperspectral images. © 2024 The Authors

关键词： Unmanned aerial vehicles (UAV)

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DESIGN OF RF FEEDING NETWORK [1:8] USING UNEQUAL AMPLITUDE HYBRID COUPLER FOR LOW SLL X-BAND RADAR APPLICATIONS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 1-19页

作者： Kothapudi, Venkata Kishore Sunayana, Lella Divya, Shaik Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St. ColumbiaSC29204 United States

The design simulation and manufacturing of an x-band frequency uneven amplitude 90° hybrid coupler are described in this paper. This hybrid coupler is used to create a feeder network with eight output ports operating at 9.3 GHz. The overall goal is to design and analyze an uneven power feeding network (1:8) for low SLL x-band radar applications employing a hybrid coupler. The specific goal is to create a hybrid coupler with equal amplitude, a hybrid coupler with uneven amplitude [Taylor’s amplitude = –17 dB], and a feeding network using hybrid couplers. The goal of this research is to improve the ability of a single transmitter to provide power to more than one antenna, all at a time either equally or unequally, and this can be achieved by designing a desired number of hybrid couplers and connecting them to the specific number of antennas at the output end and to the single transmitter at the input end. © 2025 by Begell House, Inc.

关键词： Transmitters

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Multi-type Vehicle Detection and Classification Using YOLOV5

Multi-type Vehicle Detection and Classification Using YOLOV5

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2023 International Conference on informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Liliana, Dewi Yanti Nalawati, Rizki Elisa Iswara, Ratna Widya Pradito, Reza Nova Heryana, Aldy Mochamad Andayani, Gresia Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798350307306

In the modern era marked by technological advancement, vehicle recognition using computer vision has become increasingly important. The ability to accurately identify and classify vehicles has far-reaching implications, from efficient traffic management to better road safety. Some research on vehicle classification have been performed, but for multi-type vehicles still needs to be carried out due to the limited efforts on it, especially in vehicles variation, not only on specific vehicle such as cars, but also for two-wheeled and more. The problems of automated vehicle recognition are the variety of vehicle types on the roads;the ability to recognize many vehicles in real-time;as well as the accuracy of classifying vehicles where many obstacles and occlusions appears in the real-world. Hence, this research develops a web-based application that can recognize multi-type vehicles in real-time. The YOLOv5 method is used because of its advantages in fast recognition time and high accuracy, especially for surveillance data. Testing on dataset of multi-type vehicles including cars, trucks, motorcycles, and bicycles gave results with the accuracy rate of 93.23% and F1 score of 0.93. This research contributes to produce a web-based application for fast detecting and classifying multiple types of vehicles with the superior accuracy rate. © 2023 IEEE.

关键词： computer vision

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