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International Conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Rahmad Fauzi Muhammad Zarlis Herman Mawengkang Poltak Sihombing Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (纸本)9798350320725

Quantum encoding is a process to transform classical information into quantum states. It plays a crucial role in using quantum algorithms to solve classical problems, especially in quantum machine learning tasks. There are many QE methods. It is very difficult to determine which QE method to choose to improve classification accuracy. Therefore, this paper will analyze several QE methods. Training and testing on Iris flower datasets were performed in a architecture quantum circuit and some performances parameters were evaluated. The expected result is that we can compare the classification accuracy of some of these Quantum encodings.

关键词： Training Quantum computing Quantum algorithm computer architecture Transforms Quantum state Encoding

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A systematic literature review to implement large language model in higher education: issues and solutions

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Discover Education 2025年第1期4卷 1-25页

作者： Guizani, Sghaier Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Bibi, Afsha Hamam, Habib College of Engineering Alfaisal University Riyadh 11533 Saudi Arabia School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan Department of Computing School of Arts and Creative Technology University of Greater Manchester Manchester BL15AB United Kingdom Department of Computer and Software Technology University of Swat Swat 19200 Pakistan Faculty of Engineering Uni de Moncton Moncton E1A3E9 NB Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Artificial intelligence-driven Chatbots, especially large language models (LLMs) like GPT-4, represent significant progress in digital education. These models excel in mimicking human-like text and transforming learning and teaching methods. This study examines the development, application, and impact of LLMs in education. It highlights their role in automating instructional tasks and promoting personalized learning experiences. Despite integration concerns and ethical debates, LLMs showcase the potential of AI to improve educational practices. Our research concludes that LLMs offer transformative opportunities for education. However, their incorporation requires careful ethical considerations, data privacy measures, and a balance between human educators and AI technologies. The findings suggest strategies for integrating LLMs into educational frameworks to enhance learning outcomes while preserving educational integrity. © The Author(s) 2025.

关键词： Artificial intelligence Education Large language models Learning systems Natural language processing systems Neural networks

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UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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Classification of Time Series Using FCM-Based Forecasting Models

Classification of Time Series Using FCM-Based Forecasting Mo...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Miłosz Wrzesień Mariusz Wrzesień Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Kraków Poland Faculty of Applied Informatics University of Information Technology and Management Rzeszów Poland

In this study, a method of time series classification is considered. Classification is performed using forecasting models. It is assumed that processed time series are of different natures, i.e., they belong to different classes. Each class has its forecasting model. Thus, an unknown time series is presented to the models to evaluate forecasting errors. The classified time series is assigned to the class with the winning forecasting model. In the study, Fuzzy Cognitive Maps are used to build forecasting models. Prior to forecasting, the processed raw time series are preprocessed. Six different error functions having the most significant influence on classification are used. The error functions come from root mean square error and mean percentage error.

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Customer Segmentation for Targeted Marketing: A Comparative Analysis of Clustering Techniques

Customer Segmentation for Targeted Marketing: A Comparative ...

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IEEE International Conference on Multi Topic

作者： Rasikh Ali Tayyaba Farhat Sanya Abdullah Sheeraz Akram Faculty of Computer Science and Information Technology The Superior University Lahore Pakistan Information Systems Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia

In the contemporary landscape of data-driven decision-making, businesses are increasingly harnessing customer segmentation as a strategic tool for tailoring their marketing endeavors. his research employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to investigate customer segmentation for targeted marketing. It encompasses phases such as data understanding, preprocessing, modeling, evaluation, deployment, and monitoring. Our study applies K-Means and Hierarchical Clustering algorithms to create customer segments. While Hierarchical Clustering provides a visually insightful segmentation structure, K-Means excels in terms of the Silhouette score, a crucial clustering metric. Overall, K-Means Clustering emerges as the superior choice due to its interpretability and comprehensive utility. This research contributes to data-driven marketing by offering insights for businesses seeking to enhance marketing strategies, elevate customer engagement, and boost revenue.

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Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained Timescales

arXiv

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arXiv 2024年

作者： Dagdanov, Resul Andrejević, Milan Liu, Dikai Lin, Chin-Teng The Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia The Psychology Discipline Graduate School of Health Faculty of Health University of Technology Sydney UltimoNSW2007 Australia The Australian Artificial Intelligence Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

When interacting with each other, humans adjust their behavior based on perceived trust. However, to achieve similar adaptability, robots must accurately estimate human trust at sufficiently granular timescales during the human-robot collaboration task. A beta reputation is a popular way to formalize a mathematical estimation of human trust. However, it relies on binary performance, which updates trust estimations only after each task concludes. Additionally, manually crafting a reward function is the usual method of building a performance indicator, which is labor-intensive and time-consuming. These limitations prevent efficiently capturing continuous changes in trust at more granular timescales throughout the collaboration task. Therefore, this paper presents a new framework for the estimation of human trust using a beta reputation at fine-grained timescales. To achieve granularity in beta reputation, we utilize continuous reward values to update trust estimations at each timestep of a task. We construct a continuous reward function using maximum entropy optimization to eliminate the need for the laborious specification of a performance indicator. The proposed framework improves trust estimations by increasing accuracy, eliminating the need for manually crafting a reward function, and advancing toward developing more intelligent robots. The source code is publicly available. © 2024, CC BY.

关键词： Intelligent robots

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Impact of Massive Tourist and Vehicles Flow on Air and Water Quality of Uttarakhand

Impact of Massive Tourist and Vehicles Flow on Air and Water...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Bhupesh Rawat Ankur Singh Bist Hardjanto Nusantoro Muhammad Dede Soleman Po Abas Sunarya Iqbal Desam Girinzio Faculty of Computer Applications Graphic Era Hill University Uttarakhand India Faculty of Computer Science and Engineering Graphic Era Hill University Uttarakhand India Master of Information Technology University of Raharja Tangerang Indonesia Faculty of Science and Technology University of Raharja Tangerang Indonesia

The degradation of air quality in hilly regions due to the influx of tourists and vehicular traffic is a significant global concern. This research focuses on assessing the impact of these factors specifically in the Uttarakhand region. While various other factors contribute to the problem, our study aims to quantify the proportion of these two inputs. To achieve this, we employ data collection methods and develop innovative deep learning techniques tailored to the issue. The novelty of our work lies in the application of these novel methods to address the problem at hand. By conducting this study, researchers have gained insights into the extent of the impact and its implications for the region. The findings of our research contribute to the existing body of knowledge and provide valuable information for policymakers, environmentalists, and stakeholders involved in addressing air quality concerns in hilly regions.

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Trust aware secure energy efficient hybrid protocol for MANET

Trust aware secure energy efficient hybrid protocol for MANE...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Jha, Anuradha Vivek, V. Gupta, Poonam Joshi, Rutvij Singh, Prabhdeep Iyengar, N.Ch.Sriman Narayana University School of Law and Legal Studies Uslls Ggsip University Dwarka India Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India Sanskriti University Department of Management Mathura India Parul University Department of Ece Piet Vadodara India Graphic Era Deemed to Be University Department of Cse Dehradun India Sreenidhi Institute of Science and Technology Department of Information Technology Hyderabad India

ISBN: (纸本)9798350322309

Transmitters (MANETs) are particularly appropriate for uses like special outings, satellite communications in regions with no radio electric grid, crises and things in the environment, as well as diagnostic interventions for the armed forces. Despite the fact that MANETs have evolved into infrastructure-free, soul, and quickly moveable wireless technologies. Privacy may be the key IoT weak point due to the frameworks' flexibility and the constantly changing node mobility. Due to this, it is especially susceptible to numerous assaults, such as software modification, tunneling, and espionage. On MANET, security threats are more serious than problems with the quality of service (QoS). Invasion monitoring, which alters your system to identify further breach holes, is therefore the best way to protect MANET privacy. The ability to detect intrusions is essential for providing protection and acting as an extra layer of restrictions. The loss of the node's energy source might also affect the cellular station's capacity to send messages, which is solely dependent on system life. As a result, the protocol was created and this connection was chosen as the best, most dependable way to extend the MANETs for travel. It is challenging to provide secure and energy-efficient routing in this sort of network due to its dynamic topology and resource limitations. To address the problems of energy safety and security, we provide a hybrid technique of cat slapped solo algorithms (C-SSA), which selects the finest leaps in route advancement. This approach would enable confidence secure efficient energy traveling in MANETs. Fuzzy clustering is utilized initially, and number of clusters (CHs) are selected according to the importance of recent, implicit, and direct trust. Also discovered were nodes depending on the trust threshold value. Even the CHs participate in wireless multi-hop routing, and the best routes are selected here based on lag, throughput, and connection throughout this context

关键词： Quality of service

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Classification of Television Programs Based on Public Opinion in Social Media Using Random Forest and Decision Tree

Classification of Television Programs Based on Public Opinio...

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computer science, information technology and Engineering (ICCoSITE), International Conference on

作者： Dede Kurniadi Shopi Nurhidayanti Indri Tri Julianto Teguh Wahyono Yosep Septiana Hetty Rohayani Department of Computer Science Institut Teknologi Garut Garut Indonesia Faculty of Information Technology Satya Wacana Cristian University Salatiga Indonesia Faculty of Science and Technology Muhammadiyah Jambi University Jambi Indonesia

One of the electronic media that provides a source of entertainment and information for its audience is television programs. The quality of television programs is assessed based on ratings. In addition, public opinion also has a crucial role, and this is because this opinion can be used as data to carry out sentiment analysis by classifying whether the opinion is included in the category of positive or negative sentiment. The problem requires an efficient decision-making system, such as a machine learning algorithm. This study aims to develop a classification of sentiment analysis using Random Forest and Decision Tree algorithms. The dataset used is a public opinion tweet dataset on Twitter for four television programs with five attributes: the number of retweets, id, sentiment, tv show, and tweet text. The results of testing the two algorithm models show that the Random Forest and Decision Tree models have different performance levels. The Decision Tree has lower performance with an accuracy of 74.66%, precision of 76.13%, recall of 78.75%, and an f1-score of 76.73%. Meanwhile, Random Forest has higher performance with an accuracy of 84.06%, precision of 83.65%, recall of 82.80%, and f1-score of 82.32% with a comparison of test data and training data of 20:80. Based on these results, the Random Forest model has the best performance compared to the Decision Tree in classifying television program sentiment based on user opinions in social media.

关键词： Training Sentiment analysis TV Machine learning algorithms Social networking (online) Training data Classification algorithms

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

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computer Systems science & Engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.Alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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