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16th International Symposium on Electronics and Telecommunications, ISETC 2024

作者： Hazim, Layth Rafea Ata, Oguz Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Altinbas University Dept. of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350390865

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models' robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications. © 2024 IEEE.

关键词： Logistic regression

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EV Allocation and Charging within Parking Lots Using a Locational Marginal Pricing Mechanism

EV Allocation and Charging within Parking Lots Using a Locat...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Arias, Nataly Bañol Giraldo, Juan S. López, Juan Camilo Hoogsteen, Gerwin Hurink, Johann Dept. of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Netherlands

ISBN: (纸本)9798350396782

Electrifying parking lots (PLs) is becoming increasingly challenging as the number of electric vehicle (EV) chargers operating within PLs increases, putting a strain on the internal electricity grid. Reinforcing the local grid can be an expensive solution. Therefore, an alternative approach is to design a mechanism that optimizes the distribution of incoming EVs along the PL infrastructure, minimizing operating costs for the PL owner and the charging costs for EV users. This paper proposes a user-aware pricing mechanism for EV charging in PLs based on locational marginal prices (LMPs), which consists of a base tariff and a location-dependent tariff. A mixed-integer second-order programming model is developed to determine the optimal charging spot for each EV upon arrival at the PL. Results show that the proposed LMP-based allocation mechanism distributes the strain more evenly throughout the local grid, increasing the EV hosting capacity of the PL, while minimizing charging costs for users. Compared to a random allocation approach, the proposed mechanism resulted in 30.9% less energy not served and 17.5% lower charging costs. © 2023 IEEE.

关键词： Electric vehicles locational marginal prices optimization parking lots pricing mechanism

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Robust and Predictive Charging of Large Electric Vehicle Fleets in Grid Constrained Parking Lots 14

Robust and Predictive Charging of Large Electric Vehicle Fle...

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14th IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2023

作者： Hoogsteen, Gerwin Winschermann, Leoni Nijenhuis, Bart Arias, Nataly Banol Hurink, Johann L. University of Twente Dept. of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9781665455541

Grid congestion forms an obstacle for the adoption of electric vehicles (EVs) by companies for their sustainability goals. This paper presents a robust approach to smart charging of large fleets of EVs that only utilizes currently available technologies and data, such that it can be applied in practice. The method relies only on available data at charge point operators. The presented approach uses offline aggregated planning to determine a robust fill-level. Subsequently, this fill-level is used in online control to maximise the quality of service to users while being able to provide flexibility to the energy system. Simulation results using real data show that the self-consumption of PV energy can be improved from 72.0% to 85.4%. Furthermore, the energy not served is reduced from 5.9% to 2.8%. © 2023 IEEE.

关键词： Charging (batteries)

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Brain-computer Interfaces: A Key to Neural Communication's Limitless Possibilities 1

Brain-Computer Interfaces: A Key to Neural Communication's L...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Saxena, Shilpi Ranjan, Mritunjay Kr. Sattar, Arif Md. School of Computer Science and Engineering Sandip University Nashik India A.M. College Dept. of Computer Science & Information Technology Gaya India

ISBN: (纸本)9798350361780

Brain-computer Interfaces (BCIs) have revolutionized human-machine interaction by establishing a direct communication pathway between the brain and an external machine. These systems rely on neural signals, acquired invasively through implanted electrodes or non-invasively using scalp sensors. Signal processing algorithms extract meaningful information from these signals, such as motor intentions or cognitive states. BCIs have applications in various domains, including healthcare, neurorehabilitation, gaming, virtual reality, and augmented reality. However, BCI development faces challenges such as improving signal acquisition and decoding techniques, enhancing reliability and robustness, and ensuring user safety and privacy. The translation of BCI technology from the lab to real-world applications requires addressing ethical, legal, and societal considerations. Standardized evaluation metrics and protocols are needed to compare and validate different BCI approaches effectively. In conclusion, BCIs have the potential to revolutionize human-machine interaction across various domains, but challenges remain in technology refinement, user acceptance, and ethical considerations. Future research directions include enhancing spatial and temporal resolution of neural signals, exploring novel electrode materials, and investigating long-term implantation methods. © 2024 IEEE.

关键词： Augmented reality

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Suitable and Style-Consistent Multi-Texture Recommendation for Cartoon Illustrations

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第7期20卷 1-26页

作者： Wu, Huisi Wang, Zhaoze Li, Yifan Liu, Xueting Lee, Tong-Yee College of Computer Science and Software Engineering Shenzhen University No. 3688 Nanhai Road Nanshan District Guangdong Shenzhen518060 China Dept. of Computer Science and Information Engineering National Cheng-Kung University No. 1 University Road Tainan70101 Taiwan

Texture plays an important role in cartoon illustrations to display object materials and enrich visual experiences. Unfortunately, manually designing and drawing an appropriate texture is not easy even for proficient artists, let alone novice or amateur people. While there exist tons of textures on the Internet, it is not easy to pick an appropriate one using traditional text-based search engines. Although several texture pickers have been proposed, they still require the users to browse the textures by themselves, which is still labor-intensive and time-consuming. In this article, an automatic texture recommendation system is proposed for recommending multiple textures to replace a set of user-specified regions in a cartoon illustration with visually pleasant look. Two measurements, the suitability measurement and the style-consistency measurement, are proposed to make sure that the recommended textures are suitable for cartoon illustration and at the same time mutually consistent in style. The suitability is measured based on the synthesizability, cartoonity, and region fitness of textures. The style-consistency is predicted using a learning-based solution since it is subjective to judge whether two textures are consistent in style. An optimization problem is formulated and solved via the genetic algorithm. Our method is validated on various cartoon illustrations, and convincing results are obtained. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Textures

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Exploring Explainable AI in Large Language Models: Enhancing Transparency and Trust 11

Exploring Explainable AI in Large Language Models: Enhancing...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Ijas, H.A. Jo, Ashly Ann Raj, Ebin Deni FACTS-H Lab Indian Institute of Information Technology Dept. of Computer Science & Engineering Kottayam India

ISBN: (纸本)9798350379136

Large Language Models (LLMs) are at the forefront of technological evolution, significantly enhancing digital interactions and automating complex processes across various sectors. While these models facilitate advancements in data analytics, content generation, and strategic decision-making, their opaque nature poses challenges regarding user trust and model comprehension. Addressing the critical need for transparency, this paper focuses on enhancing LLM explainability. We propose a framework to demystify the internal mechanisms of these models, facilitating a deeper understanding that aligns with stringent ethical standards. Our approach integrates advanced explanatory tools that elucidate model decisions and foster accountability and fairness in Artificial Intelligence applications. Through rigorous analysis and the development of novel interpretative methodologies, we aim to bridge the gap between LLM capabilities and ethical AI practices, ensuring that these powerful tools are leveraged responsibly and transparently in critical infrastructures. © 2024 IEEE.

关键词： Accountability AI Applications Ethical AI Explainable AI Fairness Large Language Models (LLMs) Model Interpretability Transparency

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Security of Examination Question Paper Through Blockchain – SecureQ

Security of Examination Question Paper Through Blockchain –...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Solomon Raju, G. Sowmya, K.N. Chennamma, H.R. Dept. of Computer Applications JSS Science and Technology University Mysuru India Dept. of Information Science and Engineering JSS Academy of Technical Education Bengaluru India

ISBN: (纸本)9798331505134

Ensuring the security of confidential information is a daunting task for any concerned authority. Proving the authenticity and integrity of exam question papers is a prime responsibility of examination authorities. Storing and sharing question papers on open networks makes them vulnerable to unauthorized access and alterations. The proposed approach redefines question paper management using the Interplanetary File System (IPFS) and blockchain technology, providing a secure, decentralized, and tamper-resistant platform. This solution addresses long-standing challenges and ensures security, accessibility, and data integrity, establishing new standards for examination management. A user-friendly text editor in the framework allows paper setters to create, modify, and format questions effortlessly while preserving content integrity. The integration of this editor streamlines question paper preparation and enhances both efficiency and security. By leveraging a decentralized framework, the proposed solution ensures that the critical tasks of any concerned authority are handled securely and efficiently, overcoming limitations of conventional methods. © 2024 IEEE.

关键词： Data integrity

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Microstrip Patch Antenna for Single Resonant Frequency Using Various Types of Slots

Microstrip Patch Antenna for Single Resonant Frequency Using...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Islam, Md Amirul Stea, Giovanni Uddin, Md Ashraf Mahmud, Sultan Rahma, Kh. Mustafizur University of Pisa Dept. of Information Engineering Pisa Italy Jagannath University Dept. of Computer Science & Engineering Dhaka Bangladesh World University of Bangladesh Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350322972

Microstrip patch antennas are widely utilized in WLAN applications, and body-centric wireless communication relies on multiple network links to get messages through. But the existing antenna size is big with less bandwidth, which increases the cost. This paper has taken a rigorous approach to design an antenna that will provide a single resonant frequency at the targeted bandwidth (2.45GHz). The patch size in the proposed antenna is 28mm by 27mm and energized by a 50 ohm microstrip feed line with dimensions 3.137mm by 18.35mm. The dielectric substrate of permittivity Er = 4.3 is used in the fabrication process. The optimal size and other antenna characteristics are found by finite difference time domain method based CST Studio Suite. Other parameters like return loss, input impedance, and bandwidth of the microstrip patch antennas are calculated using VNA and WATS software to verify the proposed work. Finally, the proposed slot-loaded antenna attains an improved bandwidth and gain while providing a single resonant frequency. © 2023 IEEE.

关键词： Bandwidth

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Scheduling of Flexible Manufacturing System with Penalty Cost Using Simulated Genetic Algorithm 15

Scheduling of Flexible Manufacturing System with Penalty Cos...

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

作者： Singh, Vijai Singh, Varsha Inderprastha Engineering College dept. of Computer Science and Engineering Uttar Pradesh Ghaziabad India Indian Institute of Information Technology Allahabad dept. of Information Technology Uttar Pradesh Prayagraj India

ISBN: (纸本)9798350370249

This paper discuss about the scheduling problem for flexible manufacturing systems (FMSs) using the genetic algorithm. In FMSs, the operation of a job is performed on more than one machine. Therefore, FMS scheduling is considered to be a computationally hard problem. In this paper, various approaches are reviewed that are applied to the FMS scheduling problem. This paper also attempts to find a possible solution to the scheduling problem of FMS. To identify the order in which the various jobs would be assigned to a number of machines, this paper uses a genetic algorithm with Simulated Annealing to generate a machine sequence. In this, the developed tool requires tabular input. To obtain a schedule with minimum make span, many iterations of the genetic algorithm are applied. In this system, an optimal schedule is given for FMS by taking the makespan time and minimum penalty cost, and therefore calculating the minimum combined objective function. © 2024 IEEE.

关键词： Scheduling algorithms

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-dept. examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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