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Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

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Prediction of Heart Disease using Data Mining and Machine Learning

Prediction of Heart Disease using Data Mining and Machine Le...

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2024 International Visualization, Informatics and technology Conference, IVIT 2024

作者： Waseemullah Manzoor, Saba Baig, Mirza Adnan Ned University of Engineering and Technology Department of Computer Science and Information Technology Karachi Pakistan Iqra University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798350354447

In this new generation, cardiac disease is expanding abruptly. The main reason for spreading this disease is the way of life of human beings, such as poor nutrition, absence of activities, medication, smoking, and so on. Today's heart disease is normally happening in individuals, and 85% of people die due to heart disease. There is an enormous amount of data created every single day in the discipline of medication, which is put away in a clinical data bank. This dataset consists of rough data that consists of conflicting and excess information. There is no question about the well-being care framework;they are extremely wealthy in keeping information and yet exceptionally poor in bringing information. Data mining techniques may help in separating significant information by using data mining procedures such as classification, regression method, clustering, and so forth. Later, when the information gathered in the dataset increases and becomes more complicated, data mining algorithms are utilized. The target of this paper is to investigate the different data mining procedures, specifically Random Forest, logistic regression, and SVM, by using an approved dataset for the prediction of heart disease, which includes various characteristics such as age, sexual orientation, chest pain, blood pressure, hyperglycemia, and so on. © 2024 IEEE.

关键词： ANN Classification Dataset Logistic Regression Random Forest SVM

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Extracting Facial Features to Detect Deepfake Videos Using Machine Learning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 834-842页

作者： Aslam, Ayesha Mir, Jamaluddin Zaman, Gohar Rahman, Atta Salam, Asiya Abdus Ali, Farhan Alhiyafi, Jamal Bakry, Aghiad Gul, Mustafa Jamal Gollapalli, Mohammed Mahmud, Maqsood Department of Computer Science Abbottabad University of Science and Technology Havelian Pakistan Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Computer Science Kettering University FlintMI United States Department of Business Administration University of York York YO10 5DD Heslington United Kingdom Department of Information Technology & Engineering Sydney Met SydneyNSW2000 Australia School of Computing Ulster University Northern Ireland Belfast United Kingdom

Generative adversarial networks (GANs) have gained popularity for their ability to synthesize images from random inputs in deep learning models. One of the notable applications of this technology is the creation of realistic videos known as deepfakes, which have been misused on social media platforms. The difficulty lies in distinguishing these fake videos from real ones with the naked eye, leading to significant concerns. This study proposes a supervised machine learning approach to effectively differentiate between real and counterfeit videos by detecting visual artifacts. To achieve this, two facial features are extracted: eye blinking and nose position, utilizing landmark detection techniques. Both features were trained on supervised machine learning classifiers and evaluated using the publicly available UADFV and Celeb-DF deepfake datasets. The experiments successfully demonstrate that the proposed method achieves a promising and superior performance, with an area under the curve (AUC) of 97% for deepfake detection in contrast to state-of-the-art methods investigating the same datasets. © (2025), (science and Information Organization). All rights reserved.

关键词： Support vector machines

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Next-Generation Consumer Electronics Data Auditing Scheme Toward Cloud-Edge Distributed and Resilient Machine Learning

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 2244-2256页

作者： Li, Yi Shen, Jian Vijayakumar, Pandi Lai, Chin-Feng Sivaraman, Audithan Sharma, Pradip Kumar Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Tindivanam604001 India National Cheng Kung University Department of Engineering Science Tainan701 Taiwan Netaji Subhas University of Technology Department of Information Technology New Delhi110078 India University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

Distributed and resilient machine learning (DRML) endues next-generation consumer electronics with AI function. Intuitively, AI provides innovative, humanized, convenient applications based on the data extended by next-generation consumer electronics. Cloud-edge computing is an ideal undertaken architecture of DRML due to its distributed property. However, as one of the core elements driving AI applications, data could be lost or corrupted owing to damaged electronics, unstable communication, and even cloud providers' malicious behavior. It is essential to ensure the data integrity of next-generation electronics before AI applications. To this end, we proposed a privacy-protection distributed data auditing scheme for cloud-edge DRML. An efficient data integrity verification method that only uses algebraic operation is constructed. Then, Function Secret Share (FSS) extends the data integrity verification method to protect consumer privacy. Besides, a consensus for data auditing results is designed among the edge servers. Finally, we present an abundance of theoretical analyses and experimental findings to substantiate and validate the efficiency and effectiveness of our proposed scheme. © 1975-2011 IEEE.

关键词： Edge computing

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FOID: A Feature-Optimized Intrusion Detection System for Securing IoMT Healthcare Networks 18

FOID: A Feature-Optimized Intrusion Detection System for Sec...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Shaikh, Jamshed Ali Wang, Chengliang Owais, Muhammad Zia, Uzair Sima, Muhammad Wajeeh Us Arshad, Muhammad Chongqing University Department of Computer Science and Technology Chongqing400044 China Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798331508692

The rapid integration of interconnected healthcare devices, software, operating systems, and networks within the Internet of Medical Things (IoMT) has greatly advanced healthcare delivery. However, this interconnectivity also presents significant security challenges. Many IoMT devices lack robust security mechanisms, making them vulnerable to intrusion and cyber-attacks. To address these vulnerabilities, we propose FOID, a feature-optimized intrusion detection scheme (IDS) designed specifically for securing IoMT healthcare systems. FOID enhances the security and reliability of healthcare networks by efficiently detecting and mitigating security breaches, particularly unauthorized access attempts. The scheme utilizes recursive feature elimination (RFE) alongside advanced machine learning models and integrates the SHapley Additive exPlanations (SHAP) mechanism to provide transparency. This comprehensive approach ensures both the integrity and confidentiality of healthcare systems, empowering healthcare professionals with actionable insights to proactively secure IoMT infrastructures. Evaluation of FOID on key IoMT cybersecurity datasets demonstrates its high efficacy, with overall accuracy rates of 98.53% and 97.91% achieved on the ICU and WUSTL-EHMS 2020 datasets, respectively, underscoring its potential as a reliable solution for anomaly detection in IoMT environments. © 2024 IEEE.

关键词： Network intrusion

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Automatic text summarization using deep reinforced model coupling contextualized word representation and attention mechanism

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Multimedia Tools and Applications 2024年第1期83卷 733-762页

作者： Aliakbarpour, Hassan Manzuri, Mohammad Taghi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

With the rapid and unprecedented growth of textual data in recent years, there is a remarkable need for automatic text summarization models to retrieve useful information from these large numbers of textual documents without human intervention within a reasonable time. Text summarization is commonly performed based on extractive and abstractive paradigms. Although different machine learning and deep learning based methods have been proposed for the task of text summarization during the last decades, they are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a new summarization model is proposed in this paper which takes advantage of both extractive and abstractive text summarization models as a single unified model based on the strategy gradient of reinforcement learning. The proposed model also employs the combination of convolutional neural network and gated recurrent unit in both extraction and abstraction modules besides attention mechanism. Moreover, language models, namely Word2Vec and BERT, are used as the backbone of the proposed model to better express sentence semantics as a word vector. We conducted our experiments on widely-studied text summarization datasets (CNN\Daily Mail and DUC-2004) and according to the empirical results, not only the proposed model achieved higher accuracy compared to both extractive and abstractive summarization models in terms of ROUGE metric but also its generated summaries presented higher saliency and readability based on human evaluation. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Reinforcement learning

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Cyber-attack detection based on a deep chaotic invasive weed kernel optimized machine learning classifier in cloud computing

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Soft Computing 2025年第4期29卷 2015-2030页

作者： Indrasena Reddy, M. Siva Kumar, A.P. Subba Reddy, K. Department of Computer Science and Engineering BVRIT Hyderabad College of Engineering for Women Hyderabad500090 India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Ananthapuramu Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Rajeev Gandhi Memorial College of Engineering and Technology Andhra Pradesh Nandyala India

Nowadays, Cloud Computing has attracted a lot of interest from both individual users and organization. However, cloud computing applications face certain security issues, such as data integrity, user privacy, and service availability. The only better solution to such issues is to identify and prevent cyber threats before they can cause seriously harm the cloud computing system. Several methods have been suggested so far to detect the attack in cloud computing (CC), but no one method attains satisfactory results. To overwhelm these drawbacks, a Cyber Security Attack Detection using Deep Kernel Machine Learning optimized with Chaotic Invasive Weed Optimization algorithm is proposed in this paper for Cloud Computing. Here, the data is amassed from CSE-CIC-IDS2018 and Bot-IoT datasets. Subsequently, a pre-processing step involving redundancy reduction and missing value replacement for uncertainty removal is achieved through the Developed Random Forest with Local Least Squares (DRFLLS) method. By utilizing Entropy-Kurtosis based feature selection approach, the pre-processing data is given to the feature selection to identify the optimal features. The selected features are supplied to the Deep Kernel Machine Learning classifier (DK-ML), which is optimized using the Chaotic Invasive Weed Optimization algorithm (Chaotic-IWOA) for classification. This classification process classifies the data as normal or anomalous categories with anomalies encompassing, like DoS, DDoS, Theft attacks, and normal attacks. The proposed technique is activated in MATLAB. The proposed technique achieves 24.88%, 17.98%, 45.65%, 35.95% better accuracy for CSE-CIC-IDS2018 dataset, and 23.93%, 13.94%, 32.94%, and 29.04% better accuracy for Bot-IoT dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Cyber attacks

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Short Term Load Forecasting and Anomaly Detection in Residential Power Consumption

Short Term Load Forecasting and Anomaly Detection in Residen...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Francis, Anil Jose, Bijoy A. Cochin University of Science and Technology Department of Computer Science Kerala Kochi India

ISBN: (纸本)9798350376135

Load forecasting constitutes a fundamental component of the data analytics framework within the widely implemented advanced metering infrastructure. When compared with the aggregated load from a substation, forecasting the significantly fluctuating residential power consumption presents a formidable challenge. Various machine learning methodologies employed to address the general time series forecasting problem can be broadly categorized into statistical, probabilistic, hybrid, and deep learning models. Predominantly, load forecasting paradigms incorporate numerous extrinsic factors such as meteorological conditions, calendar events, and other temporally lagged attributes, which prove difficult to procure and archive at an individual site level. Our research undertakes a comparative analysis of contemporary transformer-based models and cutting-edge models like N-BEATS, N-HiTS, and TimesNet for the day-Ahead prediction of the highly erratic residential power usage, drawing exclusively on historical load data, devoid of external features. Furthermore, given the impracticality of manual energy consumption monitoring, our study introduces a framework that amalgamates load forecasting with a rule-based anomaly detection mechanism. The outcomes of the regression models are systematically compiled, indicating that the TimesNet model surpasses other deep learning models in forecasting accuracy, notwithstanding its extensive training duration. This investigation was conducted utilizing publicly available authentic smart meter data from a residential setting. © 2024 IEEE.

关键词： Time series

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Research on Key Technologies of Network Security Situational Awareness Based on Big Data 2

Research on Key Technologies of Network Security Situational...

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2nd IEEE International Conference on Electrical, Automation and computer Engineering, ICEACE 2024

作者： Li, Yuxiang Guangdong University of Science and Technology Department of Computer Science Dongguan523083 China

ISBN: (纸本)9798350368208

This paper delves into the critical technologies for cybersecurity situational awareness in big data environments and proposes a comprehensive framework that integrates feature extraction, assessment and prediction. Firstly, in the feature extraction phase, autoencoders are employed to efficiently extract features from complex network data and identify potential security anomalies. Secondly, an improved Dempster-Shafer (D-S) evidence theory is used to assess cybersecurity threats. By introducing an expectation deviation function for uncertainty variables, the credibility of experts is optimized, and local conflicts among evidence are resolved. Finally, a hybrid model combining uncertain evidence theory with deep learning methods is constructed to predict potential attack scenarios. The model's predictive capabilities and computational efficiency are enhanced through optimization. The research results demonstrate that this framework significantly improves the accuracy and real-time performance of cybersecurity situation assessment, offering substantial social value in addressing cybersecurity challenges in big data environments. © 2024 IEEE.

关键词： Cybersecurity

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English Kashmiri Machine Translation System related to Tourism Domain 18

English Kashmiri Machine Translation System related to Touri...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Giri, Kaiser J. Lone, Nawaz Ali Bashir, Rumaan Bhat, Javaid Iqbal Islamic University of Science & Technology Kashmir Department of Computer Science Awantipora India

ISBN: (纸本)9789380544519

Machine translation, being an emerging area of research has received significant attention from the research community over the years and accordingly a lot of research work in the said area has been carried out so far. This work mainly pertains to international languages such as English, German, and Japanese, etc. The work related to native Indian languages, though very less, mainly pertains to Urdu, Bangla, Punjabi, etc. There are however many Indian languages that have either been least researched or not researched at all and therefore, need to be taken up for research in terms of their NLP resources and subsequent computational efforts to bring them at par with other national & international languages to meet contemporary technological challenges. The Kashmiri language, a most prominent language of the South Asian region is also among these languages. This paper presents a domain specific machine translation system related to the Kashmiri language. Since Kashmiri is a highly resource scare language in terms of digital resources, the data set used for training and testing of the said system has been created from the scratch and mainly pertains to the tourism domain. The work presented is amid the initial attempts towards machine translation of the Kashmiri language and has demonstrated good results along different evaluation metrics © 2024 Bharati Vidyapeeth, New Delhi.

关键词： BLEU Kashmiri Machine translation Recurrent Neural Networks Tourism

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