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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Xiaocan Yin, Qilin Liu, Jiarui Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China Alibaba Group Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

ISBN: (纸本)157735897X

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Public risks

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Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

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computer Systems science & engineering 2023年第8期46卷 1981-1997页

作者： Pham Huy Thong Florentin Smarandache Phung The Huan Tran Manh Tuan Tran Thi Ngan Vu Duc Thai Nguyen Long Giang Le Hoang Son Graduate University of Science and Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam Institute of Information Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam VNU Information Technology Institute Vietnam National UniversityHanoi100000Vietnam Department of Mathematics University of New MexicoGallup87301New MexicoUSA University of Information and Communication Technology Thai Nguyen UniversityThai Nguyen250000Vietnam Faculty of Computer Science and Engineering Thuyloi UniversityHanoi100000Vietnam

Clustering is a crucial method for deciphering data structure and producing new *** to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive *** with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering *** data can lead to incorrect object recognition and *** research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled *** effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI *** experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.

关键词： Safe semi-supervised fuzzy clustering picture fuzzy set neutrosophic set data partition with noises fuzzy clustering

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Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

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computers, Materials & Continua 2023年第1期74卷 2261-2278页

作者： Hanadi AlZaabi Khaled Shaalan Taher M.Ghazal Muhammad A.Khan Sagheer Abbas Beenu Mago Mohsen A.A.Tomh Munir Ahmad Faculty of Engineering and IT The British University in DubaiUnited Arab Emirates Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)Bangi43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Faculty of Computer Science NCBA&ELahore54660Pakistan

Energy is essential to practically all exercises and is imperative for the development of personal ***,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current ***,there is a shortage of energy,as the energy required is higher than that *** new plans are being designed to meet the consumer’s energy *** many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future *** overcome the challenges of energy consumption optimization,in this study,we apply an energy management *** fusion has recently attracted much energy efficiency in buildings,where numerous types of information are *** proposed research developed a data fusion model to predict energy consumption for accuracy and miss *** results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

关键词： Energy consumption intelligent machine learning technique smart homes prediction

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Wireless Liberty: Sensors Measuring Pressure and Temperature

Wireless Liberty: Sensors Measuring Pressure and Temperature

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Chaudhary, Pavan Vanithalakshmi, M. Raja Praveen, K.N. Maharishi University of Information Technology School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

The continuous collection of physiological data, especially temperature and pressure, throughout the human body, using wireless, skin-adherent detectors based on NFC (near-field communication) technology, is presented in this research study. To better understand human health, forecast illnesses, and avoid problems like pressure ulcers, the article stresses the significance of monitoring skin temperature as well as pressure in diverse anatomical locations. This study proposes a novel NFC-based method for continuous physiological monitoring. It provides hitherto unattainable tracking capabilities for pressure and temperature throughout the human body and has the potential to significantly enhance healthcare results. In order to increase the radius and sensitivities of these sensors, future research will examine enlarged clinical investigations and developments in wireless technology. © 2023 IEEE.

关键词： Continuous monitoring and Health outcomes Healthcare NFC technology Physiological monitoring Pressure sensing element Pressure ulcers Skin temperature Temperature measurement Wireless sensors

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Exploring the Use of Particle and Kalman Filters for Obstacle Detection in Mobile Robots

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2023年第4期67卷 384-393页

作者： Gyenes, Zoltán Bölöni, Ladislau Szádeczky-Kardoss, Emese Gincsainé Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Muegyetem rkp. 3. BudapestH-1111 Hungary Department of Computer Science University of Central Florida 4328 Scorpius St OrlandoFL32816-2362 United States

The present study aims to explore the adaptation of estimation methodologies, specifically Particle filters and Kalman filters, for the purpose of determining the position and velocity vector of obstacles within the operational workspace of mobile robots. These algorithms are commonly employed in the motion planning tasks of mobile robots for the estimation of their own position. The proposed methodology utilizes LiDAR sensor data to estimate the position vectors and calculate the velocity vectors of obstacles. Additionally, an uncertainty parameter can be determined using the introduced perception method. The performance of the newly adapted algorithms is evaluated through comparison of the absolute error in position and velocity vector estimations. © 2023 Budapest University of technology and Economics. All rights reserved.

关键词： Mobile robots

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CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

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computers, Materials & Continua 2022年第12期73卷 4657-4675页

作者： Adnan Ahmed Rafique Yazeed Yasin Ghadi Suliman AAlsuhibany Samia Allaoua Chelloug Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene *** sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist *** visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene *** this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an ***,after acquisition and preprocessing,the image is segmented by using ***,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are *** the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion ***,to select theminimal set of features,genetic algorithm-based feature selection is *** order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is *** objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation ***,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the *** experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.

关键词： Convolutional neural network decision tree feature fusion neurofuzzy system

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Early-Stage Cervical Cancerous Cell Detection from Cervix Images Using YOLOv5

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computers, Materials & Continua 2023年第2期74卷 3727-3741页

作者： Md Zahid Hasan Ontor Md Mamun Ali Kawsar Ahmed Francis M.Bui Fahad Ahmed Al-Zahrani S.M.Hasan Mahmud Sami Azam Department of Software Engineering(SWE) Daffodil International University(DIU)SukrabadDhaka1207Bangladesh Group of Biophotomatiχ Department of Information and Communication TechnologyMawlana Bhashani Science and Technology UniversitySantoshTangail1902Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan57 Campus DriveSaskatoonS7N5A9SKCanada Computer Engineering Department Umm Al-Qura UniversityMecca24381Saudi Arabia Department of Computer Science American International University-Bangladesh(AIUB)KuratoliDhaka1229Bangladesh College of Engineering IT and EnvironmentCharles Darwin UniversityCasuarina0909NTAustralia

Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing *** this many women have ***,it is curable if it can be diagnosed and detected at an early stage and taken proper *** the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early *** solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell *** system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning *** build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and *** the YOLOv5 models were applied to the labeled dataset to train the *** versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early *** of the model’s variations performed *** model can effectively detect cervical cancerous cell,according to the findings of the *** the medical field,our study will be quite *** can be a good option for radiologists and help them make the best selections possible.

关键词： Cervical cancer pap-smear deep learning cancerous cell YOLOv5 model

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Arabic Offense Text Detection in Social Networks using Collaborative Machine Learning 6

Arabic Offense Text Detection in Social Networks using Colla...

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6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024

作者： Alabdulrahman, Maria Alotaibi, Lara Latif, Ghazanfar Alghazo, Jaafar Prince Mohammad Bin Fahd University Software Engineering Department Khobar Saudi Arabia Prince Mohammad bin Fahd University Department of Computer Science Al-Khobar Saudi Arabia University of Minnesota Software Engineering and Information Technology Management Departments CrookstonMN56716 United States

ISBN: (纸本)9798331529987

In an era of abundant social media use and digital communication, the classification of Arabic social media content and user comments into relevant categories poses a significant challenge. The content in social network platforms often encompasses a wide range of topics, sentiments, and societal concerns, making it essential to develop effective methods for discerning and categorizing these expressions. In this research, a new dataset for Arabic offensive text is built along with the methodology proposed for classifying Arabic content into five categories collected from different social networking sites. Leveraging a diverse array of text representation techniques, including TF-IDF, Bag of Words (BoW), custom embeddings, and pre-trained embeddings from models such as Arabert and Mbert, a comprehensive evaluation is conducted based, shedding light on the optimal approach for tackling this complex problem. The findings reveal that the TF-IDF and BoW methods stand out with an impressive 89% accuracy, emphasizing their robustness in addressing the nuances of Arabic tweet classification across these sensitive topics. This study offers valuable insights into the challenges inherent in categorizing Arabic social media content, emphasizing the significance of the TF-IDF approach as an effective tool for this task. © 2024 IEEE.

关键词： Adversarial machine learning

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Correction to: Automated facial expression recognition using exemplar hybrid deep feature generation technique (Soft Computing, (2023), 27, 13, (8721-8737), 10.1007/s00500-023-08230-9)

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Soft Computing 2024年 1-2页

作者： Baygin, Mehmet Tuncer, Ilknur Dogan, Sengul Barua, Prabal Datta Tuncer, Turker Cheong, Kang Hao Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Interior Ministry Elazig Turkey Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Science Mathematics and Technology Cluster Singapore University of Technology and Design Singapore487372 Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

The author found that the below references were not included in the published article. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc-4th IEEE int conf autom face gesture recognition, FG 2000, pp 46–53. Lucey P, Cohn JF, Kanade T et al (2010) The extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE comput soc conf comput vis pattern recognit-work CVPRW 2010, pp 94–101. Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. In: IEEE int conf multimed expo, ICME 2005, pp 317–321. Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2852–2861 Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. In: FGR 2006 proc 7th int conf autom face gesture recognit 2006, pp 211–216. Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59–63. Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). Lyons MJ (2021) "Excavating AI" re-excavated: debunking a fallacious account of the JAFFE Dataset. Lundqvist D, Flykt A, Ohman A (1998) The Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2–2 Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607–619. Goldberger J, Hinton GE, Roweis S, Salakhutdinov R

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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