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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Benxue Lu Kaizhou Gao Peiyong Duan Adam Slowik School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Information and Automation Engineering Qilu University of TechnologyJinan 250353China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy ***,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical ***,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_***,fourteen local search operators are employed to search for better *** different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration ***,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different *** experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned *** study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.

关键词： hybrid flow shop scheduling Q-learning lot streaming local search

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Improved security with novel M-Log chaos steganography algorithm for huffman compressed english text

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Multimedia Tools and Applications 2025年第2期84卷 665-688页

作者： Raghuvanshi, Devvrat Joshi, Kamaldeep Nandal, Rainu Sehrawat, Harkesh Singh, Surender Singh, Srishti Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Haryana India

This paper addresses the underexplored landscape of chaotic functions in steganography, existing literature when examined under PRISMA-ScR framework it was realized that most of the studies predominantly focuses on utilizing chaotic functions for either concealing or diffusing messages within cover images. This study explored a different direction of using chaotic function as guards for concealed data by proposing the M-Log chaos algorithm which employs logarithmic chaotic function to drive a unique bomb generator equation, strategically placing bomb bits in the image. These bombs act as sentinels, safeguarding the concealed message. Successful retrieval involves diffusing the bombs before extracting data from the rest of the image. Failure to defuse these bombs prior to access results in irreversible damage to concealed information, thwarting unauthorized access. Our proposed method demonstrates notable advancements across quantitative performance metrics, including a 40% increase in payload capacity, a mean PSNR of 45.8981, and a mean SSIM of 0.9931. Future avenues include exploring multi-dimensional chaotic functions and employing nature-based searching algorithms such as genetic algorithms or particle swarm optimization for optimal chaos bomb placement. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Steganography

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Automatic hate speech detection in audio using machine learning algorithms

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International Journal of Speech technology 2024年第2期27卷 447-469页

作者： Imbwaga, Joan L. Chittaragi, Nagatatna B. Koolagudi, Shashidhar G. Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India Information Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur572103 India

Even though every individual is entitled to freedom of speech, some limitations exist when this freedom is used to target and harm another individual or a group of people, as it translates to hate speech. In this study, the proposed research deals with detection of hate speech for English and Kiswahili languages from audio. The dataset used in this work was collected manually from YouTube videos and then converted to audio. Audio-based features namely spectral, temporal, prosodic and excitation source features were extracted and used to train various machine learning classifiers. Initial experiments were conducted for English language and later on for Kiswahili language. However, it is observed from literature that research activities on Kiswahili language is comparatively lesser. The scores calculated for accuracy, recall, precision, auc and f1 score in detecting hate speech, suggest that Random Forest classifier performed better for English language while the Extreme Gradient Boosting classifier performed better for Kiswahili language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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Video forgery localization using inter-frame denoising and intra-frame segmentation

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Multimedia Tools and Applications 2025年 1-17页

作者： Banerjee, Debanik Chittaragi, Nagaratna B. Koolagudi, Shashidhar G. Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India Department of Information Science and Engineering Siddaganaga Institute of Technology Karnataka Tumkur572103 India

Video forgery detection has been necessary with recent spurt in fake videos like Deepfakes and doctored videos from multiple video capturing devices. In this paper, we provide a novel technique of detecting fake videos by creating an ensemble network, based on statistical and deep learning methods to detect interframe forgery and intraframe forgery in forged videos separately. In this paper, Noise signature extraction of a particular image capturing sensor and an Autoencoder-based Convolutional Neural Network model (CNN) are used to localize the forged regions. We have trained the model to localize Deepfake video forgeries as well as copy-paste forgeries with effective results in the test data. The proposed fake video detector can be applied at the back-end of on-line video aggregating services and check their authenticity to verify the genuineness of videos. The results achieved have shown better performances in detecting fake videos compared to existing methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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An enhanced two-level data sanitisation and elliptic curve cryptography encryption model for securing electronic healthcare data in a hybrid cloud platform

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International Journal of Cloud Computing 2025年第1期14卷 86-114页

作者： Ambica, V. Mary, A. Viji Amutha Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Vignana Bharathi Institute of Technology India Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

A secure framework for storing EHR in the hybrid cloud platform is implemented. At first, the required medical data is gathered from the database of the hospitals and split into sensitive and insensitive parts. The sensitive part is first encrypted with the data sanitisation method. The keys obtained are optimally chosen by the modified uniform number-based red fox optimisation (MUN-RFO). Then, the insensitive part is encrypted by optimal key-based data encryption scheme (OKDES), in which the same MUN-RFO algorithm is utilised to choose the optimal keys. The sensitive and insensitive data is combined and stored in the Hybrid Cloud. From the result analysis, the cost function of the MUN-RFO-OKDES is lower by 0.022% of HBA-OKDES, 0.011% of DHOA-OKDES, 0.0025% of EFO-OKDES and 0.001% of RFO-OKDES at 5th iteration for dataset 2. Numerous simulations are carried out to prove the security provided by the given data storage model in a hybrid cloud. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Cost functions

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Gaussian Blur Masked ResNet2.0 Architecture for Diabetic Retinopathy Detection

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computers, Materials & Continua 2023年第4期75卷 927-942页

作者： Swagata Boruah Archit Dehloo Prajul Gupta Manas Ranjan Prusty A.Balasundaram School of Electronics Engineering Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India

Diabetic Retinopathy (DR) is a serious hazard that can result inirreversible blindness if not addressed in a timely manner. Hence, numeroustechniques have been proposed for the accurate and timely detection ofthis disease. Out of these, Deep Learning (DL) and computer Vision (CV)methods for multiclass categorization of color fundus images diagnosed withDiabetic Retinopathy have sparked considerable attention. In this paper,we attempt to develop an extended ResNet152V2 architecture-based DeepLearning model, named ResNet2.0 to aid the timely detection of DR. TheAPTOS-2019 datasetwas used to train the model. This consists of 3662 fundusimages belonging to five different stages of DR: no DR (Class 0), mild DR(Class 1), moderate DR (Class 2), severe DR (Class 3), and proliferativeDR (Class 4). The model was gauged based on ability to detect stage-wiseDR. The images were pre-processed using negative and positive weightedGaussian-based masks as feature engineering to further enhance the qualityof the fundus images by removing the noise and normalizing the images. Upsamplingand data augmentation methods were used to address the skewnessof the original dataset. The proposed model achieved an overall accuracyof 91% and an area under the receiver-operating characteristic curve (AUC)score of 95.1%, outperforming existing Deep Learning models by around 10%.Furthermore, the class-wise F1 score for No DR was 92%, Mild DR was 82%,Moderate DR was 66%, Severe was DR 89% and Proliferative DR was 80%.

关键词： Diabetic retinopathy deep learning transfer learning image processing image classification

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Deep learning-based software engineering: progress,challenges, and opportunities

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science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Explainable hate speech detection using LIME

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International Journal of Speech technology 2024年第3期27卷 793-815页

作者： Imbwaga, Joan L. Chittaragi, Nagaratna B. Koolagudi, Shashidhar G. Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India Information Science and Engineering Siddaganga Institute of Technology Karnataka Tumkur572103 India

Free speech is essential, but it can conflict with protecting marginalized groups from harm caused by hate speech. Social media platforms have become breeding grounds for this harmful content. While studies exist to detect hate speech, there are significant research gaps. First, most studies used text data instead of other modalities such as videos or audio. Second, most studies explored traditional machine learning algorithms. However, due to the increase in complexities of computational tasks, there is need to employ complex techniques and methodologies. Third, majority of the research studies have either been evaluated using very few evaluation metrics or not statistically evaluated at all. Lastly, due to the opaque, black-box nature of the complex classifiers, there is need to use explainability techniques. This research aims to address these gaps by detecting hate speech in English and Kiswahili languages using videos manually collected from YouTube. The videos were converted to text and used to train various classifiers. The performance of these classifiers was evaluated using various evaluation and statistical measurements. The experimental results suggest that the random forest classifier achieved the highest results for both languages across all evaluation measurements compared to all classifiers used. The results for English language were: accuracy 98%, AUC 96%, precision 99%, recall 97%, F1 98%, specificity 98% and MCC 96% while the results for Kiswahili language were: accuracy 90%, AUC 94%, precision 93%, recall 92%, F1 94%, specificity 87% and MCC 75%. These results suggest that the random forest classifier is robust, effective and efficient in detecting hate speech in any language. This also implies that the classifier is reliable in detecting hate speech and other related problems in social media. However, to understand the classifiers’ decision-making process, we used the Local Interpretable Model-agnostic Explanations (LIME) technique to explain the

关键词： Random forests

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Hybrid RMDL-CNN for speech recognition from unclear speech signal

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International Journal of Speech technology 2025年第1期28卷 195-217页

作者： Bhargava, Raja Arivazhagan, N. Babu, Kunchala Suresh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Potti Sriramulu Chaluvadi Mallikarjuna Rao college of Engineering and Technology Andhra Pradesh Vijayawada India

ASR is an effectual approach, which converts human speech into computer actions or text format. It involves extracting and determining the noise feature, the audio model, and the language model. The extraction and determination of the noise feature is a crucial aspect of speech recognition, serving as both a process of information compression and signal deconvolution. ASR schemes are mostly employed in smart homes, smart appliances, and biometric schemes. Yet, traditional approaches offer very low performance because of a noisy environment. Moreover, local differences and accents negatively influence the ASR scheme execution during the conversion of the speech signals. This paper introduces a hybrid RMDL-CNN method to address these challenges. At first, the input of unclear speech is carried out by the dataset. Then, signal pre-processing is done by employing a Gaussian filter. After that, voice enhancement is accomplished by employing nonlinear spectral subtraction. Later, the speech word is segmented from the enhanced output based on the Attentional Encoder-Decoder approach and finally, the speech is recognized using the proposed RMDL-CNN. The RMDL-CNN method is devised by the combination of RMDL and CNN. Furthermore, the established RMDL-CNN is accessed for its efficiency based on several values of k-group value, as well as learning data. In addition, the introduced RMDL-CNN approach for speech recognition achieved better accuracy, PPV, as well as NPV of 0.909, 0.947, and 0.917 for dataset 1. Moreover, the RMDL-CNN has achieved the highest accuracy of 0.909, PPV of 0.926 and NPV of 0.888 for dataset 2. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework

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Web Intelligence 2025年第Pre-press期Pre-press卷 1-26页

作者： Ramasamy, Mekala Elangovan, Mohanraj Department of Information Science and Engineering Bannari Amman Institute of Technology India Department of Computer Science and Engineering K.S. Rangasamy College of Technology India

The recent development of communication technologies made it possible for people to share opinions on various social media platforms. The opinion of the people is converted into small-sized textual data. Aspect Based Sentiment Analysis (ABSA) is a process used by businesses and other organizations to assess these textual data in order to comprehend people’s opinions about the services or products offered by them. The majority of earlier Sentiment Analysis (SA) research uses lexicons, word frequencies, or black box techniques to obtain the sentiment in the text. It should be highlighted that these methods disregard the relationships and interdependence between words in terms of semantics. Hence, an efficient ABSA framework to determine the sentiment from the textual reviews of the customers is developed in this work. Initially, the raw text review data is collected from the standard benchmark datasets. The gathered text reviews undergo text pre-processing to neglect the unwanted words and characters from the input text document. The pre-processed data is directly provided to the feature extraction phase in which the seq2seq network and transformer network are employed. Further, the optimal features from the two resultant features are chosen by utilizing the proposed Modified Bird Swarm-Ladybug Beetle Optimization (MBS-LBO). After obtaining optimal features, these features are fused together and given to the final detection model. Consequently, the Optimized Cascaded Long Short Term Memory (OCas-LSTM) is proposed for predicting the sentiments from the given review by the users. Here, the parameters are tuned optimally by the MBS-LBO algorithm, and also it is utilized for enhancing the performance rate. The experimental evaluation is made to reveal the excellent performance of the developed SA model by contrasting it with conventional models. © 2024 – IOS Press. All rights reserved.

关键词： Emotion Recognition

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