Article
Computer Science, Information Systems
Jiaming Wang, Zhenfeng Shao, Xiao Huang, Tao Lu, Ruiqian Zhang, Xitong Chen
Summary: In this paper, an end-to-end locally linear embedding super-resolution network called LLE-Net is proposed to improve the quality of super-resolution results by addressing improper linear fitting and reusing hierarchical features based on the assumption that the local geometric relationship exists in the high-level feature space. Experimental results demonstrate that the proposed method outperforms other methods in various tasks.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Mohamed Nabih Ali, Alessio Brutti, Daniele Falavigna
Summary: In recent years, the development of deep learning algorithms has achieved significant milestones in speech processing. Pre-trained feature extraction models have greatly simplified the development of speech classification and recognition algorithms. However, environmental noise and reverberation still negatively impact performance, making noise robustness crucial in real-world applications. This paper explores enhancing speech embeddings directly, using Wav2Vec and WavLM models, and investigates various training approaches for the integration of a speech enhancement front-end with a classification/recognition back-end.
COMPUTER SPEECH AND LANGUAGE
(2023)
Article
Computer Science, Information Systems
F. Dornaika, A. Baradaaji, Y. El Traboulsi
Summary: This paper introduces a new semi-supervised framework for simultaneous linear feature extraction and label propagation, aiming to leverage both labeled and unlabeled data to learn more discriminative information. The experimental results show that the proposed method's performance can be better than many state-of-the-art graph-based semi-supervised algorithms.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Mikel Penagarikano, Amparo Varona, German Bordel, Luis Javier Rodriguez-Fuentes
Summary: This paper presents a semisupervised speech data extraction method for creating a new dataset for bilingual Automatic Speech Recognition systems. The dataset is derived from Basque Parliament plenary sessions with frequent code switchings. The method effectively reduces Word Error Rate in the training dataset.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Chandran Savithri Anoop, Angarai Ganesan Ramakrishnan
Summary: This work evaluates the performance of syllable-based modeling units in end-to-end speech recognition for several Indian languages. It compares the performances of various tokenization methods, including character, byte-pair encoding (BPE), and unigram language modeling (ULM), in both monolingual training and cross-lingual transfer learning. The results show that syllable-based BPE/ULM subword units can achieve promising results in the monolingual setup, especially for Sanskrit, while SLP1-character units are better for cross-lingual transfer learning.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Analytical
Lukasz Lepak, Kacper Radzikowski, Robert Nowak, Karol J. Piczak
Summary: Models for keyword spotting in continuous recordings can improve navigation experience in vast audio libraries, but face challenges in low-resource languages like Polish. Shifting focus from full speech-to-text conversion to acoustic similarity matching may help reduce data annotation demands.
Article
Computer Science, Artificial Intelligence
Jianyu Miao, Tiejun Yang, Lijun Sun, Xuan Fei, Lingfeng Niu, Yong Shi
Summary: Unsupervised Feature Selection (UFS) has gained popularity for improving learning performance and reducing computational costs. This paper proposes a novel UFS approach GLLE, integrating local linear embedding and manifold regularization in the feature subspace, achieving more promising results on real-world benchmark datasets.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Information Systems
Kavya Manohar, A. R. Jayan, Rajeev Rajan
Summary: This article presents the design and development of Mlphon, a knowledge-based computational linguistic tool for Malayalam language. Mlphon performs various functions including grapheme to phoneme conversions, syllabification, and phonetic feature analysis. It is evaluated on a manually crafted gold standard lexicon and achieves high accuracy in tasks such as orthographic syllabification and grapheme to phoneme conversion.
Article
Chemistry, Analytical
Chongchong Yu, Jiaqi Yu, Zhaopeng Qian, Yuchen Tan
Summary: The paper proposes an AVSR approach based on LSTM-Transformer, which reduces the speaker dependence and data quantity by fusing audio and visual information. Experimental results show that AVSR outperforms traditional models and lip reading in terms of accuracy, with a decrease of 16.9% and 11.8% respectively. Furthermore, AVSR demonstrates better generalization across different speakers, reducing the error rate by as much as 17.2%.
Article
Computer Science, Artificial Intelligence
Ganesh S. Mirishkar, Vishnu Vidyadhara V. Raju, Meher Dinesh Naroju, Sudhamay Maity, Prakash Yalla, Anil Kumar Vuppala
Summary: Due to the lack of large annotated speech corpus, low-resource Indian languages find it difficult to utilize the recent advancements in deep neural network architectures for Automatic Speech Recognition (ASR) tasks. In this work, the authors present the International Institute of Information Technology Hyderabad-Crowd Sourced Telugu Database (IIITH-CSTD), a Telugu corpus collected through crowdsourcing, to mitigate the low-resource problem for Telugu. The article includes the sources, crowdsourcing pipeline, protocols used, and experimental results of the collected corpus on ASR tasks.
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Zebiao Hu, Haishuang Yin, Yuanhong Liu
Summary: A novel filter method integrated with LLE, named LLE vote, is proposed for feature selection algorithms. Extensive experiments demonstrate the effectiveness of the proposed method and indicate that it outperforms the existing state-of-art methods.
Article
Computer Science, Artificial Intelligence
Zeqian Li, Xinlu He, Jacob Whitehill
Summary: We propose a new clustering task that considers compositional relationships between clusters. Our algorithms, Compositional Affinity Propagation (CAP), Compositional k-means (CKM), and Greedy Compositional Reassignment (GCR), can effectively partition examples into coherent groups and infer the compositional structure among them. Compared to popular algorithms like Gaussian mixtures, Fuzzy c-means, and Agglomerative Clustering, our methods show promising results on the OmniGlot and LibriSpeech datasets. This work has practical applications in open-world multi-label object recognition and speaker identification & diarization with simultaneous speech from multiple speakers.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Fadi Dornaika, Abdullah Baradaaji, Youssof El Traboulsi
Summary: This article introduces a new framework for semisupervised learning that utilizes both labeled and unlabeled data to estimate discriminant transformation, enabling the learning of more discriminant models.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Zhao-Min Chen, Quan Cui, Xiu-Shen Wei, Xin Jin, Yanwen Guo
Summary: The paper presents a unified deep learning framework DER for multi-label image recognition, aiming to explicitly model the correlations between labels. Through operations such as embedding, ranking, and disentangling, our model consistently outperforms previous competitive methods on three different datasets.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Artificial Intelligence
Yuanhong Liu, Zebiao Hu, Yansheng Zhang
Summary: This study introduces two LLE-based multi-structure fusion methods to extract significant features by integrating least squares and sparse structures, with the solution of coefficient fusion being a subset of the one of the function fusion. Extensive experiments demonstrate the superior performance of the proposed multi-structure methods compared to existing state-of-art related methods.
Article
Computer Science, Information Systems
Sardar Khaliq uz Zaman, Ali Imran Jehangiri, Tahir Maqsood, Nuhman ul Haq, Arif Iqbal Umar, Junaid Shuja, Zulfiqar Ahmad, Imed Ben Dhaou, Mohammed F. Alsharekh
Summary: The proliferation of mobile devices has led to the emergence of various services, but delivering task offloading results to users in the MEC environment is challenging, especially when user mobility is high. Traditional techniques handle computation offloading and mobility management separately, without considering real-time mobility factors, resulting in sub-optimal solutions. The LiMPO framework offloads compute-intensive tasks to user locations predicted by artificial neural networks and optimizes latency and energy consumption with a multi-objective genetic algorithm-based server selection technique.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Ehzaz Mustafa, Junaid Shuja, Kashif Bilal, Saad Mustafa, Tahir Maqsood, Faisal Rehman, Atta ur Rehman Khan
Summary: This article proposes a reinforcement learning-based intelligent online offloading framework, which can effectively make decisions between local or remote computation in a wireless powered MEC system, achieving optimal performance.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Sonia Bashir, Saad Mustafa, Raja Wasim Ahmad, Junaid Shuja, Tahir Maqsood, Abdullah Alourani
Summary: Cloud computing consumes a large amount of energy, leading to high expenditure, greenhouse gas emissions, and CO2 emissions. Existing energy-efficient techniques only consider the energy consumption of the CPU during task placement and ignore the energy consumption of memory and SLA violations. To address these issues, we propose two novel nature-inspired techniques based on artificial bee colony and particle swarm optimization, which consider the energy consumption of both CPU and memory during VM placement. We also provide SLA-aware variants to reduce SLA violations resulting from excessive task consolidation.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Sana Nasim Karam, Kashif Bilal, Junaid Shuja, Faisal Rehman, Tahira Yasmin, Akhtar Jamil
Summary: Unmanned aerial vehicles (UAVs) have great potential in the oil and gas industry, especially in situations where human lives are at risk. They offer cost-effective and efficient monitoring solutions through carrying sensors and cameras. However, there are specific challenges to be addressed for the effective use of UAVs in the industry.
JOURNAL OF ELECTRONIC IMAGING
(2023)
Article
Computer Science, Hardware & Architecture
Mingxuan Zhang, Muhammad Umair Hassan, Dongmei Niu, Xiuyang Zhao, Raheel Nawaz, Ibrahim A. Hameed, Saeed-Ul Hassan
Summary: This paper presents an automatic dense correspondence method for matching the mesh vertices of two 3D shapes under near-isometric and non-rigid deformations. The method combines three types of graphic structure information and includes three major steps: describing the vertices based on three types of graphical information, formulating the match as an optimization problem, and resolving the optimal solution using the projected descent optimization procedure. The method achieves superior performance to existing methods in quantitative and qualitative evaluations on challenging 3D shape matching datasets.
Article
Computer Science, Artificial Intelligence
Mohammad Ali Humayun, Hayati Yassin, Junaid Shuja, Abdullah Alourani, Pg Emeroylariffion Abas
Summary: Online medical consultation can improve the efficiency of primary health care. This paper proposes a fine-tuning strategy to identify the social origin of text authors, which can assist in selecting medical consultants for efficient communication. The proposed method achieves a 0.54% higher overall accuracy compared to the previous best result in the experiments.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Physics, Multidisciplinary
Ibrahim A. Hameed, Luay Hashem Abbud, Jaafar Ahmed Abdulsaheb, Ahmad Taher Azar, Mohanad Mezher, Anwar Ja'afar Mohamad Jawad, Wameedh Riyadh Abdul-Adheem, Ibraheem Kasim Ibraheem, Nashwa Ahmad Kamal
Summary: A disturbance estimation and rejection technique based on the improved active disturbance rejection control (IADRC) approach is proposed and verified on a ground two-wheel differential drive mobile robot. The IADRC is adopted to eliminate the effect of system uncertainties and external torque disturbance on both wheels. A novel nonlinear sliding mode extended state observer (NSMESO) is used to observe and cancel the generalized disturbance in real-time. Numerical simulations show a significant reduction in the ITAE index for both wheels, validating the efficacy of the proposed dynamic speed controller in damping the chattering phenomena and providing high insusceptibility to torque disturbance.
Article
Chemistry, Analytical
Muhammad Umair Hassan, Ole-Martin Hagen Steinnes, Eirik Gribbestad Gustafsson, Sivert Loken, Ibrahim A. A. Hameed
Summary: Industry 4.0 has revolutionized the use of physical and digital systems, especially in the digitalization of maintenance plans for physical assets. In this study, we developed a predictive maintenance approach using pre-trained deep learning models to effectively detect and classify different types of road damage. Our approach allows us to prioritize maintenance decisions based on the severity and occurrence of damage, providing a framework for efficient road maintenance. The evaluation of our proposed framework showed significant performance in various measures.
Article
Multidisciplinary Sciences
Oleg Sergiyenko, Alexey Zhirabok, Ibrahim A. Hameed, Ahmad Taher Azar, Alexander Zuev, Vladimir Filaretov, Vera Tyrsa, Ibraheem Kasim Ibraheem
Summary: This study investigates the problem of designing virtual sensors for nonlinear systems under disturbance. Two different mathematical techniques, algebra of functions and logic-dynamic approach, are used to solve the problem. The first technique provides a general solution, while the second technique uses linear algebra methods to find a solution specifically for nonlinear systems. The virtual sensors are designed to be robust against disturbance by utilizing invariant functions and estimating the prescribed function of the original system state vector. A practical example is provided to illustrate the theoretical results.
Article
Chemistry, Multidisciplinary
Haitham El-Hussieny, Ibrahim A. Hameed, Ahmed B. Zaky
Summary: Soft growing robots, inspired by plant growth, excel in navigating tight and distant environments due to their flexibility and extendable lengths. However, controlling their tip position is challenging due to a lack of precise measurement methods. This paper proposes optimization-based approaches to achieve superior performance in point stabilization, trajectory tracking, and obstacle avoidance.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Multidisciplinary
Ahmad Taher Azar, Drai Ahmed Smait, Sami Muhsen, Moayad Abdullah Jassim, Asaad Abdul Malik Madhloom AL-Salih, Ibrahim A. Hameed, Anwar Ja'afar Mohamad Jawad, Wameedh Riyadh Abdul-Adheem, Vincent Cocquempot, Mouayad A. Sahib, Nashwa Ahmad Kamal, Ibraheem Kasim Ibraheem
Summary: In this paper, a Nonlinear Higher Order Extended State Observer (NHOESO) is proposed to replace the Linear Extended State Observer (LESO) in Conventional Active Disturbance Rejection Control (C-ADRC) solutions. The NHOESO extends the standard LESO by incorporating a two-term smooth nonlinear function with saturation-like characteristics. It allows for precise observation of generalized disturbances with higher-order derivatives. The stability of the NHOESO is analyzed using the Lyapunov method. Simulation results on an uncertain nonlinear Single-Input-Single-Output (SISO) system with time-varying external disturbances demonstrate the effectiveness of the proposed NHOESO in handling generalized disturbances compared to other ESOs.
APPLIED SCIENCES-BASEL
(2023)
Article
Green & Sustainable Science & Technology
Abduallah Gamal, Mohamed Abdel-Basset, Ibrahim M. Hezam, Karam M. Sallam, Ibrahim A. Hameed
Summary: The autonomous vehicle (AV) has the potential to restructure transportation infrastructure and improve traffic congestion, quality of life, and traffic safety. This research applies a multi-criteria decision-making approach to selecting the optimal AV for logistics planning, handling uncertainty using type-2 neutrosophic numbers (T2NN). Results indicate that the velocity criterion is the most influential in selecting an intelligent AV. Rating: 9/10
Article
Computer Science, Artificial Intelligence
Syed Hammad Hussain Shah, Anniken Susanne T. Karlsen, Mads Solberg, Ibrahim A. Hameed
Summary: Aging poses challenges to elderly individuals' social lives due to declining physical abilities, but group exercise in long-term care facilities is crucial for maintaining their physical and social well-being. However, accommodating these needs can be difficult due to staff shortages and lacking resources. To address this, a robotic exercise coach could be helpful. However, accurate and efficient human activity recognition is necessary for intelligent human-robot interaction in this context.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Sardar Khaliq Uz Zaman, Tahir Maqsood, Azra Ramzan, Faisal Rehman, Saad Mustafa, Junaid Shuja
Summary: With the increasing demand for affordable and accessible broadband and mobile internet, the field of Ubiquitous Mobile Edge Computing (UMEC) has become highly dynamic. This study focuses on optimizing reliability in UMEC by considering latency and offloading failure probability. The proposed deadline-aware heuristic algorithm effectively reduces task failure ratio and achieves remarkable total latency, outperforming the state-of-the-art technique.
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
(2023)
Review
Computer Science, Hardware & Architecture
Syed Umaid Ahmed, Junaid Shuja, Muhammad Atif Tahir
Summary: This paper examines commonly used and publicly accessible datasets for plant classification. Through the exploration of over 200 research papers, the advancements and developments in leaf classification, as well as new techniques and approaches, are discussed. The coherence and gaps in algorithms are highlighted for the benefit of future researchers.
SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
(2023)