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10th International Conference on Web Research, ICWR 2024

作者： Nazarizadeh, Ali Samavati, Taha Mansouri, Sina Sayyadpour, Minoo Islamic Azad University Central Tehran Branch Department of Computer Engineering Tehran Iran Iran University of Science and Technology Faculty of Computer Engineering Tehran Iran K. N. Toosi University of Technology Faculty of Computer Engineering Tehran Iran Kharazmi University Dept. Math and Computer Science Tehran Iran

ISBN: (纸本)9798350394986

Today, data is more valuable to us than gold. When observing the environment, a substantial amount of data, particularly textual information, can be identified, tagged, prepared, and published in the form of a corpus or datasets. The primary objective of our paper is to gather, prepare, tag, and develop a vast dataset of Fidibo users' opinions regarding educational content and e-books. This dataset enables in-depth analysis of emotions and opinion mining, particularly within the educational content realm. A common flaw in nearly all similar datasets in the Farsi language is their restriction to user opinions on services and products available on online platforms. The dataset we refer to as LDPSA (A Large Dataset of Persian Sentiment Analysis) offers several advantages over comparable datasets in the Persian language. Notably, this dataset consists of 253,368 comments, each categorized into 5 classes. LDPSA represents the sole extensive Iranian dataset suitable for scrutinizing educational content and e-books. Moreover, significant insights were gleaned from data analysis. For example, during the COVID-19 pandemic, Iranian individuals dedicated more time to studying and engaging with educational platforms significantly. Nearly 80% of users expressed favorable opinions concerning the informational materials available on the Fidibo website. Users' inclination towards utilizing audio books has escalated, along with other analysis referenced in the paper. © 2024 IEEE.

关键词： Sentiment analysis

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Investigating the Ethical Implications of Artificial Intelligence and Establishing Guidelines for Responsible AI Development

Investigating the Ethical Implications of Artificial Intelli...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Sukumar, R. Gambhir, Vinima Seth, Jyoti Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Atlas SkillTech University Department of Isme Mumbai India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350388800

Now more than ever, artificial intelligence (AI) presents both unprecedented potential and difficulties. This article delves deeply into the ethical concerns raised by AI and lays forth principles that should be followed in the creation of ethical AI systems. In the opening, we recognize the revolutionary potential of AI as well as the ethical quandaries it poses. In order to guarantee that AI technologies are created and implemented in a responsible and fair way, a deep ethical examination is required in light of the proliferation of autonomous systems. To address these ethical difficulties, we suggest the adoption of an Ethical Analysis Framework (EAF). The Ethical Assessment Framework (EAF) provides a framework for systematically assessing the fairness, transparency, privacy, accountability, and bias of AI systems. It's a cornerstone for the community of AI ethics researchers, developers, and policymakers working together to keep AI honest. Our approach places special importance on the gathering and preparation of data. We emphasize how important ethically sound data is in defining the moral implications of AI. We found that when compared to other approaches to assessing the morality of AI, our suggested technique consistently outperformed the others. The suggested technique establishes itself as an innovative and effective way of navigating the growing ethical environment by providing a systematic approach to addressing AI's ethical implications and supporting responsible AI research. For instance, as compared to more conventional methods, our technique increased fairness evaluations by an average of 15%. In conclusion, there is a wide range of concerns related to the morality of AI that must be constantly monitored and addressed via joint effort. Our method places an emphasis on making sure AI is progressing in a way that is responsible, ethical, and fair to all members of society. © 2024 IEEE.

关键词： Ethical technology

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Distance-based mutual congestion feature selection with genetic algorithm for high-dimensional medical datasets

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Neural Computing and Applications 2025年第8期37卷 6217-6232页

作者： Nematzadeh, Hossein Mani, Joseph Nematzadeh, Zahra Akbari, Ebrahim Mohamad, Radziah Modern College of Business and Science Muscat Oman Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States Otolaryngology Department School of Medicine Emory University AtlantaGA30322 United States Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Faculty of Computing Universiti Teknologi Malaysia Skudai81310 Malaysia

Abstract: Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature Selection

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Two-Stage Fine-Tuning For Low-resource Englishbased Creole with Pre-Trained LLMs

Two-Stage Fine-Tuning For Low-resource Englishbased Creole w...

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Amanda Tan, Shu Hui Aung, Eint Sandi Yamana, Hayato Department of Computer Science and Communications Engineering Waseda University Tokyo Japan Waseda University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9798350341072

Sentiment analysis adopts natural language processing (NLP) techniques to determine the emotional tone of the text. Sentiment analysis research has predominantly been conducted on commonly spoken languages, such as English and Mandarin Chinese. However, less widely spoken languages, such as Englishbased Creoles, have received limited attention due to the need for large-scale labeled datasets. English-based Creoles are derived from English, resulting in many similarities with English. However, English-based Creoles differ from English in some aspects, making the Creole unintelligible to other English speakers. In sentiment analysis research conducted on Singapore English-based Creole (Singlish), most existing research does not leverage the capabilities of large-language models, such as pretrained BERT. To our knowledge, only Gotera et al.'s work relates to a large-language model;however, their contribution is creating a new pre-trained model for Singlish. However, creating a new pre-trained model is computationally expensive, and pre-training a less widely spoken language will result in ineffective because of the small dataset. Hence, we propose a new two-stage fine-tuning framework for pre-trained models, targeting a low-resource English-based Creole, Singlish. Our proposed framework initially clusters the dataset into two based on each data point's percentage of English words. Then, two-stage fine-tuning is performed by transferring the pre-trained BERT model onto the clustered dataset with the higher English percentage in the first stage. The model is further transferred onto the clustered dataset with the lower English percentage in the second stage. Our proposed framework outperforms the traditional fine-tuning framework, achieving a weighted F1 score of 0.8344 for sentiment analysis. © 2023 IEEE.

关键词： Sentiment analysis

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A novel deep learning approach for arrhythmia prediction on ECG classification using recurrent CNN with GWO

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International Journal of Information Technology (Singapore) 2024年第1期16卷 577-585页

作者： Singh, Prem Narayan Mahapatra, Rajendra Prasad Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology NCR Campus Delhi-NCR Campus Delhi-Meerut Road Modinagar Uttar Pradesh Ghaziabad 201204 India

In recent years, one of the most active research areas has been examining and categorizing heartbeats and brain traces connected with various types of arrhythmia and seizure. In this paper, we examine various classification methods that can be used to study and categorize electrocardiogram (ECG) signals. The proposed research evaluates the ECG signals and the Recurrent Convolutional Neural Network classification technique. The PTB Diagnostic ECG and MIT-BIH arrhythmias Databases are employed for the training and testing. The Recurrent CNN (RCNN) and the Grey Wolf Optimization (GWO) methods improve the performance matrices of machine learning compared to other techniques in the literature. The outcome performs superior to the norms currently being utilized for classification. The system's accuracy is 98%, higher than the results obtained by similar machine learning and supervised machine learning methods. The proposed research has been compared with Decision Tree, K-Nearest Neighbors algorithm, Random Forest, Support Vector Machines algorithm and Logistic Regression, and it is seen that the proposed method has the highest accuracy. Although the accuracies of the other algorithms are above 90%, they are not as efficient as the proposed algorithm. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Arrhythmia prediction ECG Classification GWO Optimization RCNN

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Robust Human Interaction Recognition Using Extended Kalman Filter

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computers, Materials & Continua 2024年第11期81卷 2987-3002页

作者： Tanvir Fatima Naik Bukht Abdulwahab Alazeb Naif Al Mudawi Bayan Alabdullah Khaled Alnowaiser Ahmad Jalal Hui Liu Faculty of Computing&Artificial Intelligence Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

In the field of computer vision and pattern recognition,knowledge based on images of human activity has gained popularity as a research *** recognition is the process of determining human behavior based on an *** implemented an Extended Kalman filter to create an activity recognition system *** proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the *** minimize noise,we use Gaussian *** of silhouette using the statistical *** use Binary Robust Invariant Scalable Keypoints(BRISK)and SIFT for feature *** next step is to perform feature discrimination using Gray *** that,the features are input into the Extended Kalman filter and classified into relevant human activities according to their definitive *** experimental procedure uses the SUB-Interaction and HMDB51 datasets to a 0.88%and 0.86%recognition rate.

关键词： Pattern recognition geometric features activity recognition full-body texture

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Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 111-136页

作者： SwornaKokila, M.L. Venkatarathinam, R. Rose Bindu, Joseph P. Manivasagam, M.A. Kishore, Kakarla Hari Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology Sri BalajiChockalingam Engineering College A.C.S. Nagar Irumbedu Thiruvannamalai District Tamil Nadu Arni India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Computer Science and Engineering Siddartha Institute of Science and Technology Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time ( © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Smart power grids

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Embedded Coded Relay System for Molecular Communications

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computers, Materials & Continua 2022年第8期72卷 2729-2748页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Farid Shawki Imran Khan Ki-Il Kim Department of Electrical Engineering Faculty of EngineeringMinia UniversityMiniaEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)Norway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Electrical Engineering University of Engineering&TechnologyPeshawar814Pakistan Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

With the emergence of the COVID-19 pandemic,the World Health Organization(WHO)has urged scientists and industrialists to exploremodern information and communication technology(ICT)as a means to reduce or even eliminate *** World Health Organization recently reported that the virus may infect the organism through any organ in the living body,such as the respiratory,the immunity,the nervous,the digestive,or the cardiovascular *** the abovementioned goal,we envision an implanted nanosystem embedded in the intra living-body *** main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system(i.e.,delivery of the therapeutic drug to the diseased tissue or targeted cell).The communication among the nanomachines is accomplished via communication-based molecular *** control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things(IoBNT).The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward(DF)principle to ensure reliable drug delivery to the targeted ***,both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into *** this paper,a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate(BER)performance and high signal-to-noise ratio(SNR),while the detection process is based on maximum likelihood(ML)probability and minimum error probability(MEP).The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position,number of released molecules,relay and receiver *** results are validated through simulation and demonstrate that the proposed scheme can

关键词： Molecular communication nanonetwork internet of bio-nano things coded relay scheme coding

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Fake News Classification Using LSTM Algorithm on Kompas Platform 6

Fake News Classification Using LSTM Algorithm on Kompas Plat...

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6th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2024

作者： Idhom, Muhammad Nurhalizah, Cesaria Deby Wardani, Firly Setya Krisnanik, Erly Surjohadi Susrama Mas Diyasa, I Gede Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Surabaya Indonesia Universitas Pembangunan Nasional 'Veteran' Jakarta Faculty of Computer Science Jakarta Indonesia University of Yos Sudarso Surabaya Faculty of Engineering Department of Industrial Engineering Surabaya Indonesia

ISBN: (纸本)9798331533243

Fake news or popularly known as 'hoax' has become an increasingly disturbing problem in today's digital era. With the rapid development of information technology, the spread of fake news has become easier and faster, threatening the integrity of information presented by leading news platforms such as Kompas. Therefore, this study aims to develop an LSTM (Long Short-Term Memory)-based algorithm in classifying fake news spread through the Kompas platform. The approach used in this study involves an in-depth text analysis process using the LSTM architecture, which is known to be effective in handling sequential data model problems such as text. The dataset used consists of a collection of news that has been labeled whether the news is included in the fake news category or not. Algorithm development is carried out through the stages of text processing, LSTM model creation, and model performance evaluation. This model achieves an accuracy of 98.13% on training data and 94.80% on test data, demonstrating the model's ability to recognize patterns well and generalize to new data. © 2024 IEEE.

关键词： Sorting

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Equivariant Frames and the Impossibility of Continuous Canonicalization 41

Equivariant Frames and the Impossibility of Continuous Canon...

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41st International Conference on Machine Learning, ICML 2024

作者： Dym, Nadav Lawrence, Hannah Siegel, Jonathan W. Faculty of Mathematics Faculty of Computer Science Technion Israel Department of Electrical Engineering and Computer Science MIT MA United States Department of Mathematics Texas A&M University TX United States

Canonicalization provides an architecture-agnostic method for enforcing equivariance, with generalizations such as frame-averaging recently gaining prominence as a lightweight and flexible alternative to equivariant architectures. Recent works have found an empirical benefit to using probabilistic frames instead, which learn weighted distributions over group elements. In this work, we provide strong theoretical justification for this phenomenon: for commonly-used groups, there is no efficiently computable choice of frame that preserves continuity of the function being averaged. In other words, unweighted frame-averaging can turn a smooth, non-symmetric function into a discontinuous, symmetric function. To address this fundamental robustness problem, we formally define and construct weighted frames, which provably preserve continuity, and demonstrate their utility by constructing efficient and continuous weighted frames for the actions of SO(d), O(d), and Sn on point clouds. Copyright 2024 by the author(s)

关键词： Probability distributions

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