Article
Engineering, Multidisciplinary
Serhat Hizlisoy, Serdar Yildirim, Zekeriya Tufekci
Summary: An approach for music emotion recognition based on CLDNN architecture is proposed, with a new Turkish emotional music database constructed for evaluation. The method shows significant improvement in accuracy using feature combination and LSTM + DNN classifier, indicating its potential in music emotion recognition.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2021)
Article
Computer Science, Artificial Intelligence
Ismail Shahin, Noor Hindawi, Ali Bou Nassif, Adi Alhudhaif, Kemal Polat
Summary: Recent research in speech emotion recognition has shown significant advancements by using MFCC's spectrogram features and novel classifier algorithms such as CapsNet. The proposed DC-LSTM COMP-CapsNet algorithm achieves a higher accuracy in emotion recognition compared to other known methods and classical classifiers, with an average accuracy of 89.3% in recognizing Arabic Emirati-accented speech.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xiangbo Shu, Jinhui Tang, Guo-Jun Qi, Wei Liu, Jian Yang
Summary: This work proposes a novel Hierarchical Long Short-Term Concurrent Memory (H-LSTCM) model for recognizing human interactions in videos by combining individual dynamics and group dynamics to capture the long-term inter-related dynamics of human interactions. Experimental results validate the effectiveness of the proposed model.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Automation & Control Systems
Amir Bidokhti, Shahrokh Ghaemmaghami
Summary: This paper introduces a graph-based neural memory module that can be trained using differentiable mechanisms to solve tasks with long-term dependencies. Inspired by the human memory system, this module performs better than traditional methods in terms of convergence speed and final error.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Psychology, Multidisciplinary
Baitao Liu
Summary: This study focuses on the method of emotion analysis in the application of psychoanalysis based on sentiment recognition. The improved C-BiL model is applied to the sentiment recognition module, and it effectively realizes the function of sentiment recognition. The experimental results show that the C-BiL model designed in this study achieves relatively high accuracy in different datasets.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Shiming Xiang, Bo Tang
Summary: The article introduces a variation of DNC architecture called CSLM-DNC, which includes convertible short-term and long-term memory to improve memory efficiency. Inspired by the human brain, this new scheme improves learning performance through different memory locations importance and memory transformation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Elissaios Sarmas, Evangelos Spiliotis, Efstathios Stamatopoulos, Vangelis Marinakis, Haris Doukas
Summary: This paper proposes a meta-learning method to improve short-term deterministic forecasts of PV systems by blending the base forecasts of multiple DL models. Results indicate that different base models perform best at different PV plants, and meta-learning can improve accuracy by up to 5% over the most accurate base model per plant.
Article
Engineering, Civil
Guanglong Du, Zhiyao Wang, Boyu Gao, Shahid Mumtaz, Khamael M. Abualnaja, Cuifeng Du
Summary: The paper proposes a new deep learning framework CBLNN for real-time recognition of driver emotions by extracting facial information and heart rate data. Tested and proven to be able to quickly and steadily identify happiness, anger, sadness, fear, and neutrality in real time.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Syed Altan Haider, Muhammad Sajid, Hassan Sajid, Emad Uddin, Yasar Ayaz
Summary: This study utilizes statistical and Deep Learning techniques to forecast solar Global Horizontal Irradiance in Islamabad, Pakistan, aiming to promote renewable energy development for tackling global climate change. The research finds that ANN, CNN, and LSTM perform best for short-term forecasts, while SARIMAX and Prophet are efficient for long-term forecasts.
Article
Computer Science, Artificial Intelligence
Zhuang Ye, Jianbo Yu
Summary: Machine health assessment is crucial for prognostics and health management, and the proposed LSTMCAE demonstrates effectiveness in feature learning and generating health index using multivariate Gaussian distribution. Experimental results show the superiority of LSTMCAE in machine health assessment compared to other unsupervised learning methods.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Interdisciplinary Applications
Haonan He, Liangyu Chen, Shanyong Wang
Summary: Accurate demand forecasting is crucial for airlines to cope with competition and increase revenue. This paper presents adaptive frameworks based on LSTM network for predicting flight booking demand, achieving superior performance and handling irregular data patterns effectively.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Jane Oruh, Serestina Viriri, Adekanmi Adegun
Summary: In this study, an enhanced deep learning LSTM recurrent neural network (RNN) model was proposed to address the limitation of traditional LSTM in processing continuous input streams. The proposed model incorporates RNN as a forget gate in the memory block to reset cell states, enabling more efficient processing of continuous input streams.
Article
Computer Science, Information Systems
Ch Sumalakshmi, P. Vasuki
Summary: This work proposes a face emotion recognition system based on AGOA-LSTM, which utilizes CNN for feature extraction and classifies seven basic human emotions. Experimental results show that the system achieves high recognition accuracy on the YALE face database, with significantly better performance measures compared to the system without AGOA.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Ning Jin, Yongkang Zeng, Ke Yan, Zhiwei Ji
Summary: Artificial intelligence-based air quality index (AQI) forecasting is a hot research topic, and the proposed multiple nested long short term memory network (MTMC-NLSTM) model performs superior in accurate AQI forecasting.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Energy & Fuels
Neethu Elizabeth Michael, Shazia Hasan, Ahmed Al-Durra, Manohar Mishra
Summary: Accurate forecasting is crucial for integrating solar renewables and minimizing intermittent effects. This study proposes an optimized deep learning model for predicting solar time series data, which demonstrates high accuracy and reliability.
Article
Computer Science, Hardware & Architecture
Hager Saleh, Eman M. G. Younis, Radhya Sahal, Abdelmgeid A. Ali
Summary: This paper introduces a system that can predict systolic blood pressure in real-time, using deep learning models and real-time data to prevent health problems caused by high blood pressure.
MOBILE NETWORKS & APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Basma Ramdan Gamal Elshoky, Eman M. G. Younis, Abdelmgeid Amin Ali, Osman Ali Sadek Ibrahim
Summary: This study investigates the use of machine learning methods to build predictive models for diagnosing autism spectrum disorder (ASD) in children using facial images. Automated diagnosis of ASD is important for early treatment and family support.
Article
Computer Science, Artificial Intelligence
Osman Ali Sadek Ibrahim, Eman M. G. Younis
Summary: This paper compares the application of offline and online LTR in information retrieval and proposes a new offline ranking strategy. It also introduces a hybrid approach that combines online and offline LTR techniques and demonstrates through experiments that offline LTR outperforms online LTR.
KNOWLEDGE AND INFORMATION SYSTEMS
(2022)
Article
Chemistry, Analytical
Eman M. G. Younis, Someya Mohsen Zaki, Eiman Kanjo, Essam H. Houssein
Summary: Automatic recognition of human emotions is a complex process that is influenced by various factors. This study successfully developed a subject-independent multi-modal emotion prediction model using real-time sensor data. The use of ensemble learning techniques improved the accuracy of emotion recognition.
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mosa E. Hosney, Waleed M. Mohamed, Abdelmgeid A. Ali, Eman M. G. Younis
Summary: Feature selection is an important step in data preprocessing for data mining and machine learning. Traditional methods often fail to find the optimal solution due to the large search space, leading to the development of hybrid techniques. This study proposes a modified hunger games search algorithm (mHGS) to address optimization and feature selection problems.
NEURAL COMPUTING & APPLICATIONS
(2023)
Editorial Material
Computer Science, Cybernetics
Chee Siang Ang, Panote Siriaraya, Luma Tabbaa, Francesca Falzarano, Eiman Kanjo, Holly Prigerson
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES
(2023)
Article
Computer Science, Artificial Intelligence
Osman Ali Sadek Ibrahim, Eman M. G. Younis
Summary: This study proposes a new variable neighborhood search (VNS) algorithm with adaptation based on an objective function for the learning to rank (LTR) problem. The algorithm explores better neighbor solutions by varying mutation step sizes and evaluates the quality of evolved solutions using a fitness function. Experimental results demonstrate that the proposed algorithm outperforms other studies in the field of learning to rank.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Thomas Johnson, Eiman Kanjo
Summary: In this article, the development and evaluation of the Urban Wellbeing mobile application is presented. It employs real-time assessment of the environment and its relationship to well-being using multimodal sensor data and self-report well-being. By utilizing mobile technology and on-board sensors, the application collects real-time data such as environment type, location, image, and noise level, which are fused with perceived mental well-being. The results of an extensive assessment demonstrate the association between busy, polluted, and green spaces and their impact on well-being.
IEEE SENSORS LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Mosa E. Hosney, Diego Oliva, Eman M. G. Younis, Abdelmgeid A. Ali, Waleed M. Mohamed
Summary: This paper proposes a wrapper feature selection approach that combines the rat swarm optimization algorithm with genetic operators to improve classification accuracy and reduce the number of features. The approach converts the continuous search space into a discrete space using transfer functions, achieving a balance between local and global search. Experimental results demonstrate the efficiency and effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Environmental Sciences
Hager M. Elhalafawy, Abdelmgeid A. Ali, Eman M. G. Younis, Khalid elBahnasy, M. Medhat Mokhtar
Summary: In the era of Mega LEO satellite constellation, efficient networking through ISLs is crucial for mission success. The intermittent nature of ISLs due to the dynamic topology is a major challenge. DTN protocols are proposed to alleviate intermittence. This paper presents a technique using NS3 for simulating LEO constellation networks and assesses their performance in terms of delay and delivery ratio.
EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES
(2023)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Kieran Woodward, Eiman Kanjo, Kyle Taylor, John A. Hunt
Summary: With the increasing elderly population, human activity trackers can monitor daily physical activities of the elderly, improving independent living and quality of life. Limited research has explored the use of multiple Inertial Measurement Units (IMU) to capture simple and complex human activities, which may not effectively monitor the complexity of changes in the elderly population. This study proposes a multi-sensor approach using acceleration and quaternion values to recognize daily living activities, achieving high performance with the LSTM model.
PROCEEDINGS OF THE 2022 EMERGING DEVICES FOR DIGITAL BIOMARKERS, DIGIBIOM 2022
(2022)
Article
Computer Science, Artificial Intelligence
Kieran Woodward, Eiman Kanjo, David J. Brown, T. M. McGinnity, Becky Inkster, Donald J. Macintyre, Athanasios Tsanas
Summary: Mental health problems are increasing globally, putting pressure on national healthcare systems. Mental disorders are often linked to stigma, financial burden, and lack of resources. Technology for mental well-being has attractive properties, allowing for advanced clinical monitoring. However, challenges such as data collection, privacy, and battery life need to be carefully addressed.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Kieran Woodward, Eiman Kanjo, David J. Brown, T. M. McGinnity
Summary: The utilization of time series data is crucial for measuring mental wellbeing, but individual differences hinder the generalizability of deep learning models. To address this challenge, a Transfer Learning approach is proposed for personalized affective models, significantly improving model performance.
2021 IEEE INTERNATIONAL SMART CITIES CONFERENCE (ISC2)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Thomas Johnson, Eiman Kanjo
Summary: This study explores the impact of environmental factors on mental wellbeing by collecting urban environmental factors, body reactions, and users' perceived responses through a multi-sensor fusion approach. Using data visualization and spatial data analysis algorithms, the study highlights the potential opportunities to understand how the environment can affect mental wellbeing.
2021 IEEE INTERNATIONAL SMART CITIES CONFERENCE (ISC2)
(2021)
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)