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Acoustic modelling using deep learning for Quran recitation assistance

International Journal of Speech technology 2023年第1期26卷 113-121页

作者： Ghori, Ammar Farid Waheed, Aisha Waqas, Maria Mehmood, Aqsa Ali, Syed Abbas Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Recent advancements in computer science and engineering have significantly boosted interest in and success rate of speech recognition systems. Sophisticated speech recognition systems are being developed for a variety of languages having wide-spread applications. Arabic being one of the most widely used languages in the world, augmented by religious importance, is a potential candidate in this domain, however rich vocabulary and multiple dialects open up new challenges for development of Arabic speech recognition processes. This paper focuses on development of an Arabic isolated speech recognition system for the vocabulary of Holy Quran using Mel frequency cepstral coefficients and deep neural networks. The proposed system is capable of identifying individual words from a recited verse with reasonable accuracy. It targets the 362 unique words of the first and the last 19 chapters of the Holy Quran and uses 14 hours of audio data to demonstrate the working prototype of the system. A user-friendly, web-based application has also been developed for transcription of recitations. The software is specified to make an effort in spreading the right way of recitation of the Holy Quran. The presented architecture can be extended to a complete automatic speech recognition system to help people practice recitations, mapping speech to transcription and then matching the transcribed utterance with standard templates of correct recitation to identify reciter’s short comings. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep neural networks

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Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud Computing Environment for Efficient Resource Scheduling for Distributed Overloading

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Journal of Systems Science and Systems engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

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Smart automated highway lighting system using IoT: a survey

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Energy Informatics 2024年第1期7卷 76页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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ElHBiAt: Electra pre-training network Hybrid of BiLSTM, the Attention layer to Aspect-Based Sentiment Analysis

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IEEE Access 2025年 13卷 88342-88370页

作者： Ghanee, Amin Jahanbin, Kia Yamchi, Abolfazl Razzaghi Shahid Beheshti University Department of Computer Engineering Tehran Iran Yazd University Department of Computer Engineering Yazd Iran Shiraz University Department of Computer Science Engineering and Information Technology Shiraz Iran

Aspect-based sentiment analysis is one of the famous and practical subjects in natural language processing. Traditional sentiment analysis assigns a polarity to the whole text or document and does not consider the aspects of the text, so it cannot correctly show the author’s opinion. Extracting aspects and determining their polarity has many challenges, such as the lack of rich datasets, negative transfer in cross-domain datasets, and fine-tuning of deep models of the ratio of texts with different contexts. In this research, a model has been proposed for aspect-based sentiment analysis with different contexts. This model uses the Electra as the backbone of the architecture, which is trained with different cross-domain datasets to transfer knowledge to the target domain. Also, in the proposed model of ElHBiAt, the resampling method is used to deal with imbalanced datasets, which causes the neural model to find less orientation than the polarities in the majority. In this model, two layers of attention are used to extract the essential aspects of the text, and the deep neural network BiLSMT is used to process the text in a two-way. The results show that the ElHBiAt model has been able to improve the efficiency by an average of 5.78% in the SemEval dataset, by 2% in the SEntFiN dataset, and by an average of 4.3% in the Drug dataset in terms of the F1-score. © 2013 IEEE.

关键词： Emotion Recognition

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Insider Attack Prevention:LAPUP—Lightweight Authentication Protocol Using PUF

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Journal of Communications and information Networks 2024年第2期9卷 192-206页

作者： Siranjeevi Rajamanickam Satyanarayana Vollala N.Ramasubramanian Department of Computer Engineering Government Polytechnic CollegeTiruchirappalli 620022India Department of Computer Science and Engineering Dr.Shyama Prasad Mukherjee International Institute of Information Technology-Naya RaipurChhattisgarh 493661India Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli 620015India

The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.

关键词： physically unclonable function(PUF) insiderattacks authenticationkeymanagementserver(AKMS) security

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An enhanced AMBTC for color image compression using color palette

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Multimedia Tools and Applications 2024年第11期83卷 31783-31803页

作者： Xiong, Lizhi Zhang, Mengtao Yang, Ching-Nung Kim, Cheonshik School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Computer Engineering Sejong University Seoul Korea Republic of

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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Leveraging Concise Concepts with Probabilistic Modeling for Interpretable Visual Recognition

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IEEE Transactions on Multimedia 2025年 27卷 3117-3131页

作者： Zhang, Yixuan Liu, Chuanbin Liu, Yizhi Gao, Yifan Lu, Zhiying Xie, Hongtao Zhang, Yongdong University of Science and Technology of China School of Information Science and Technology China Hunan University of Science and Technology Department of Computer Science and Engineering China

Interpretable visual recognition is essential for decision-making in high-stakes situations. Recent advancements have automated the construction of interpretable models by leveraging Visual Language Models (VLMs) and Large Language Models (LLMs) with Concept Bottleneck Models (CBMs), which process a bottleneck layer associated with human-understandable concepts. However, existing methods suffer from two main problems: a) the collected concepts from LLMs could be redundant with task-irrelevant descriptions, resulting in an inferior concept space with potential mismatch. b) VLMs directly map the global deterministic image embeddings with fine-grained concepts results in an ambiguous process with imprecise mapping results. To address the above two issues, we propose a novel solution for CBMs with Concise Concept and Probabilistic Modeling (CCPM) that can achieve superior classification performance via high-quality concepts and precise mapping strategy. Fisrt, we leverage in-context examples as category-related clues to guide LLM concept generation process. To mitigate redundancy in the concept space, we propose a Relation-Aware Selection (RAS) module to obtain a concise concept set that is discriminative and relevant based on image-concept and inter-concept relationships. Second, for precise mapping, we employ a Probabilistic Distribution Adapter (PDA) that estimates the inherent ambiguity of the image embeddings of pre-trained VLMs to capture the complex relationships with concepts. Extensive experiments indicate that our model achieves state-of-the-art results with a 5.48% improvement in classification accuracy on eight mainstream recognition benchmarks as well as reliable explainability through interpretable analysis. © 1999-2012 IEEE.

关键词： Decision making

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A bilateral assessment of human activity recognition using grid search based nonlinear multi-task least squares twin support vector machine

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Multimedia Tools and Applications 2024年第36期83卷 84121-84140页

作者： Thakur, Ujwala Prajapati, Amarjeet Vidyarthi, Ankit Department of Computer Science Engineering & amp Information Technology Jaypee Institute of Information Technology Noida Sector-62 Uttar Pradesh Noida201309 India

The recognition of individual activity has proven its importance in many application areas. Even after the pandemic crisis worldwide, the remote monitoring of human actions and their activities has increased a lot. In such situations, the smart sensors are utilized to capture the details remotely, share, and process intelligently using the computer-assisted machine. This paper introduces the new variant of the machine learning classifier, named Grid Search Based Nonlinear Multi-task Least Squares Twin Support Vector Machine (GMLT-SVM), to recognize human actions using sensory data. The proposed approach uses the grid search for hyperparameter tuning with a Nonlinear kernel for Multi-task classification in a twin support vector machine. This not only increases the efficacy of the model but also reduces the computation time for finding the optimal values of the hyper-parameters. The proposed approach when experimented with the sensory data of Human Action Recognition (HAR) gives an accuracy of 96.8%. The comparative analysis of the proposed method with existing other ML classifiers gives an increase in the accuracy of 94.2±2.6%. The experimental results suggest that the gained results are statistically significant enough to recognize human actions with sensory data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Visual quality-enhanced reversible data hiding scheme based on interpolation and variant Sudoku matrics

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Multimedia Tools and Applications 2024年第18期84卷 1-29页

作者： Yuan, Longfei Lin, Chia-Chen Chang, Chin-Chen Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

Utilizing interpolation techniques (IT) within reversible data hiding (RDH) algorithms presents the advantage of a substantial embedding capacity. Nevertheless, prevalent algorithms often straightforwardly embed confidential data into non-reference pixels without accounting for the relationships between neighboring pixels. This practice results in suboptimal visual quality in the stego images. To rectify this issue, we have introduced the augmentation and modification interpolation (AMI). Additionally, we have devised two variant Sudoku matrices to facilitate distinct data-hiding strategies tailored for different types of reference pixels: Single type and Paired type. By incorporating these innovative designs, the interpolation operation now preserves the pattern-level information of the block in the original image, rather than focusing solely on the pixel level. In assessing the effectiveness of our proposed scheme, we performed experiments utilizing two representative image datasets. The outcomes of these experiments validate that our scheme attains an average PSNR of 44.11 dB and an average hiding capacity of 1.63 bpp. Consequently, it is affirmed that the advantage offered by our proposed scheme is well-suited for scenarios demanding high-quality stego images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Interpolation

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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