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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Martins, João Conceição, Filipe Gomes, Marco Silva, Vitor Dinis, Rui Instituto de Telecomunicações Portugal University of Coimbra Department of Electrical and Computer Engineering Coimbra3030-290 Portugal Universidade Nova de Lisboa Faculty of Science and Technology Caparica2829-516 Portugal

ISBN: (纸本)9798350368369

The ability to provide reliable data rates across the several intended coverage areas has made massive multiple-input multiple-output (m-MIMO) cell-free (CF) a crucial technology for future sixth-generation (6G) systems. However, CF networks also introduce new vulnerabilities, specifically in terms of security and network integrity. For that, to complement the conventional cryptographic algorithms, physical layer security (PLS) can be an effective strategy for acquiring essential wireless network information in order to create important authentication mechanisms against spoofing attacks. To this end, we propose leveraging the wireless channel and the access point selection (APS) allocation schemes as authentication methods to prevent network attacks that attempt to impersonate legitimate user communications. Our results show that monitoring these parameters provides valuable insights about the network performance and the impact under active eavesdropping influence. Consequently, this approach enables a statistical detection of illegitimate spoofing attempts through alarms, which can be further reinforced by upper-layer authentication protocols to enhance the security performance of wireless networks. © 2025 IEEE.

关键词： Authentication Protocol

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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Fine-Tuning Transformer-Based Representations in Active Learning for Labelling Crisis Dataset of Tweets

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SN computer science 2023年第5期4卷 1-22页

作者： Paul, Nayan Ranjan Balabantaray, Rakesh Chandra Sahoo, Deepak Department of Computer Science and Engineering IIIT Bhubaneswar Gothapatna Odisha Bhubaneswar 751003 India Department of Faculty of Emerging Technologies Sri Sri University Godisahi Odisha Cuttack 754026 India

Supervised machine learning-based models are generally used for classifying tweets related to crisis. A labelled tweet dataset is a major requirement for training the models. Labelling huge quantities of text data manually is a time-consuming and costly process. Active learning reduces some of the work necessary to use vast volumes of unlabelled data for machine learning tasks without fully labelling them. During the active learning process, the representation strategy employed for tweets has a substantial impact on the process’ effectiveness. The representations like Bag-of-Words and representations based on pre-trained word embeddings like GloVe have been used in the active learning process and have proven to be effective in representing tweets. The introduction of pre-trained transformer-based models like BERT, XLNet, and GPT-2 is prevalent in natural language processing tasks. These transformer-based models can also be used to represent embeddings of tweets but are not yet explored fully as an alternative to other embeddings used in active learning. This work offers a complete evaluation of the usefulness of representations for active learning, based on transformer-based language models. This study also demonstrates that transformer-based models, particularly BERT-like models, which have yet to be widely used in active learning, outperform more regularly used vector representations such as Bag-of-Words or other traditional word-embeddings such as GloVe. This work also compares the usefulness of representations based on the “[CLS]” token and aggregated representations generated using BERT-like models. The effectiveness of representations based on different types of BERT such as DistilBert, Roberta, and Albert is also investigated in this work. In this work finally, we propose a method called adaptive fine-tuning active learning, which is fine-tuning the representations produced by BERT-like models during the active learning process. The results show that the mini

关键词： Active learning BERT Transformer Tweet labelling Word embeddings

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging 18

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khalid, Hassan Bin Siddique, Abu Bakar Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain. © 2023 IEEE.

关键词： Hidden Markov models

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Mm-Wave to Sub-THz Frequencies for Non-Invasive Melamine Detection in Food Safety

Mm-Wave to Sub-THz Frequencies for Non-Invasive Melamine Det...

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2023 IEEE Conference on Antenna Measurements and Applications, CAMA 2023

作者： Mai-Khanh, Nguyen Ngoc Takada, Shitaro Inagaki, Keizo Alkasimi, Ahmad Pham, Anh-Vu Higo, Akio Nguyen, Bich-Yen Kawanishi, Tetsuya Tokyo Japan Waseda University Faculty of Science and Engineering Tokyo Japan Tokyo Japan King Abdulaziz University Department of Electrical and Computer Engineering Jeddah Saudi Arabia University of California Electrical and Computer Engineering DavisCA United States Soitec France Waseda University Tokyo Japan

ISBN: (纸本)9798350323047

This paper presents the exploration of the detection of melamine, a harmful substance, across a wide mm-wave frequency range for food safety applications. The experiment investigates the presence of melamine impurities in rice and milk powders. To conduct the experiment, two different approaches are utilized: a 77-GHz radar approach and a vector network analyzer connected to frequency extenders and standard horn antennas. These approaches cover two frequency bands, namely 220-330 GHz and then 330-500 GHz. The substances involved in the measurements are melamine, milk, rice, and their mixtures. In addition, we propose a technique for the quantitative determination of melamine in mixtures. This method was validated through measurements taken mm-wave frequency range. The results demonstrate the feasibility of determining melamine concentrations in mixtures across diverse mm-wave and sub-THz frequency bands. © 2023 IEEE.

关键词： Horn antennas

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Indonesian Sign Language (SIBI) Detection: A Landmark-Based Approach with Scikit-Learn Utilizing Random Forest Classifier 9

Indonesian Sign Language (SIBI) Detection: A Landmark-Based ...

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9th International Conference on Informatics and Computing, ICIC 2024

作者： Raharja, Agung Rachmat Putra, Vito Hafizh Cahaya Kanugrahan, Ghanim Tho, Cuk Bandung University Faculty of Health and Engineering Bandung Campus Bandung 40286 Indonesia Satu University Faculty of Creative Technology Informatics Department Bandung Campus Bandung 40253 Indonesia BinaNusantara University School of Computer Science Computer Science Department Jakarta Campus Jakarta 11480 Indonesia

ISBN: (纸本)9798331517601

This paper discusses computer vision-based sign language by utilizing landmark detection and Scikit-Learn. The objective of this research is to develop a model that proficient in the accurate recognition of hand gestures specific to sign language. Pictures of hand gestures are taken using a camera and hand landmark are detected by utilizing MediaPipe. Following to landmark detection, a machine learning model is developed using Scikit-Learn employing features extracted from these landmarks. Evaluation of the model using the Indonesian sing language (SIBI) yielded competitive result, proving the efficacy of the proposed model without specific hardware. This work significantly improves the communication technology for the deaf, leveraging computer vision to broaden the research opportunity in sign language recognition utilizing available equipment. The accuracy obtained from this model is 100% which means the classification is correct, and reinforces the effectiveness of the approach to the recognition and classification of hand movements in sign language. © 2024 IEEE.

关键词： computer Vision Landmark Detection MediaPipe Scikit-learn Sign Language

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A Novel Cross Domain Iterative Detection Based on the Interplay Between SPA and LMMSE

A Novel Cross Domain Iterative Detection Based on the Interp...

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IEEE International Symposium on Information Theory

作者： Shuangyang Li Giuseppe Caire Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we propose a novel cross domain iterative detection for unitary modulated symbols transmissions, e.g., OFDM. Particularly, signal spaces before and after the unitary modulation are conceptualized as two domains and the proposed scheme performs iterations across these two domains for signal detection. More specifically, a tunable-sized linear minimum mean square error (LMMSE) estimator is adopted in one domain, complemented by a reduced-complexity sum-product algorithm (SPA) in the other. Heuristic state evolution of the proposed scheme is derived, which reveals that a reduced-sized LMMSE estimator will introduce performance degradation that cannot be compensated by the cross domain iteration. However, it is advantageous for complexity reduction. Our numerical results verify our conclusions and show that the proposed scheme can achieve error performance comparable to that of the standard SPA while requiring lower complexity.

关键词： OFDM Symbols Modulation Detectors Mean square error methods Complexity theory Iterative methods

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An Automated Approach for Analysing Students Feedback Using Sentiment Analysis Techniques 5th

An Automated Approach for Analysing Students Feedback Using...

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5th Mediterranean Conference on Pattern Recognition and Artificial Intelligence, MedPRAI 2021

作者： Sadriu, Shpetim Nuci, Krenare Pireva Imran, Ali Shariq Uddin, Imran Sajjad, Muhammad Faculty of Computer Science and Engineering University for Business and Technology Pristina Kosovo Gjøvik2815 Norway Department of Computer Science Islamia College University Peshawar Peshawar25000 Pakistan

ISBN: (纸本)9783031041112

Conducting and evaluating continuous student feedback is essential for any quality enhancement cell (QEC) within an education institution. Students’ feedback based on their personal opinions can play a vital role in ensuring quality education. However, students’ subjective opinions are often ignored due to time constraints or a lack of adequate analysis strategies. Therefore, to automate the quality assurance process, two classification models (i.e., based on Monkey learn API and SentiWord using TextBlob) are proposed to analyze students’ feedback data. The results shows that the model employing MonkeyLearn performs nearly 22% points better than the Textblob on the Albanian language dataset obtained from 114 students’ responses, achieving 72.12% accuracy. © 2022, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Performance Analysis of an Artificial Intelligence Nanosystem with Biological Internet of Nano Things

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computer Modeling in engineering & sciences 2022年第10期133卷 111-131页

作者： Saied M.Abd El-atty Nancy A.Arafa Atef Abouelazm Osama Alfarraj Konstantinos A.Lizos Farid Shawki Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt Computer Science Department Community CollegeKing Saud UniversityRiyadhSaudi Arabia Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway

Artificial intelligence(AI)has recently been used in nanomedical applications,in which implanted intelligent nanosystems inside the human body were used to diagnose and treat a variety of ailments with the help of the Internet of biological Nano Things(IoBNT).Biological circuit engineering or nanomaterial-based architectures can be used to approach the *** nanomedical applications,the blood vascular medium serves as a communication channel,demonstrating a molecular communication system based on flow and *** paper presents a performance study of the channel capacity for flow-based-diffusive molecular communication nanosystems that takes into account the ligand-receptor binding *** earlier studies,we take into account the effects of biological physical characteristics such as blood pressure,blood viscosity,and vascular diameter on channel ***,in terms of drug transmission error probability,the inter-symbol interference(ISI)phenomenon is applied to the proposed *** numerical results show that the proposed AI nanosystems-based IoBNT technology can be successfully implemented in future nanomedicine.

关键词： Nanosystem AI Internet of biological Nano Things(IoBNT) channel capacity

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Transforming Education with Data Mining: Opportunities, Applications, and Challenges 8

Transforming Education with Data Mining: Opportunities, Appl...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Raut, Amey Hajare, Shital Department of Computer Science & Engineering Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798331512248

Educational Data Mining (EDM), a scientific subject that emphasizes employing data analysis to improve instruction and learning, was created because of the increased usage of data in education. Through the application of predictive analytics, clustering, and personalized learning systems EDM helps the educators to understand the student performance, interest and requirement. Identifying problem areas that require attention, allocating resources as efficiently as feasible, and developing learning experiences that are customized to each student's needs are all made possible by the data generated by EDM. Although EDM has the power to change the face of education for the better, there are issues such as data privacy, ethical issues and absence of guidelines that still pose a threat. These challenges may be hailed as future advancements in the fields of artificial intelligence, blockchain and other related technologies could also solve these challenges. With the growth of the digital education EDM is likely to transform the way teaching and learning is done by making it more customized, barrier free and effective in the near future. © 2025 IEEE.

关键词： Applications of Data Mining in Education CHALLENGES and Future Directions CONCLUSION Enhancing Learning with Predictive Analytics Global Trends and OPPORTUNITIES References Techniques in Educational Data MINING

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