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Low-light image enhancement using the illumination boost algorithm along with the SKWGIF method

Multimedia Tools and Applications 2024年第17期84卷 1-35页

作者： Radmand, Elnaz Saberi, Erfan Sorkhi, Ali Ghanbari Pirgazi, Jamshid Department of Computer Engineering University of Science and Technology of Mazandaran Behshahr Iran

Low-light image enhancement is highly desirable for outdoor image processing and computer vision applications. Research conducted in recent years has shown that images taken in low-light conditions often pose two main problems, the first of which is low visibility (i.e., small pixel intensities). Secondly, due to the low signal-to-noise ratio, noise also becomes prominent and obscures the image content. For this reason, images with low noise are usually employed in this application, a practice not possible in the real world. In this regard, a hybrid method is proposed which is based on the illumination boost algorithm (IBA) and weighted guided image filtering with steering kernel (SKWGIF). IBA is used to raise the values of low and medium intensity pixels while preventing excessive increases in high-value pixels. SKWGIF is employed to denoise and augment image details by adjusting its parameters based on the output of the previous step. The proposed method utilizes the LOL v1, LOL v2 and ExDark datasets Our hybrid method’s comprehensive approach to low-light image enhancement produced better outcomes than previous methods. Quantitative results have shown that the proposed model outperforms state-of-the-art (SOTA) models on the LOL v1 dataset, achieving a PSNR value of 18.80. Through the successful resolution of visibility and noise reduction issues, our approach led to notable gains in image quality measures, including SSIM, PSNR, BRISQUE, and NIQE. IBA’s ability to selectively boost intensity improved image visibility without producing overexposure artifacts, and SKWGIF effectively reduced noise while enhancing image details. The combined effect produced improved image quality that was superior to that of single techniques or already-used hybrid approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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An optimized deep network-based intermediate code generation for the mathematical expression

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Multimedia Tools and Applications 2024年第17期84卷 1-24页

作者： Sharada, H.N. Anami, Basavaraj Allagi, Shridhar Department of Computer Science & amp Engineering SDM College of Engineering and Technology Karnataka Dharwad580002 India Department of Computer Science & amp Engineering KLE Institute of Technology Karnataka Hubli580030 India

In recent times, the system's mathematical expression and operation have gained greater reach in engineering and mathematics. It is vital to solving more complex expressions and equations in a short time. The most existing methodology was degraded in the recognition performance and provided inaccurate output. Therefore, a novel Squirrel-based Spiking Neural Model (SbSNM) was presented for the intermediate code generation from the mathematical expression relation data. Initially, the input ME relation data was preprocessed to remove noise and tokenized by the spiking neural features. The tokenized expressions were arranged into a syntax tree structure, and then intermediate code was generated by the squirrel functions. Finally, the differentiation was calculated and given as output. Subsequently, the performance parameters were evaluated for the presented SbSNM. The overall accuracy obtained by the presented model is 99.25% which is higher than the other existing techniques and also brought a higher precision and recall rate and f1-score. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neural networks

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Multimedia Tools and Applications 2024年 1-21页

作者： Gorai, Joy Shaw, Dilip Kumar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

As internet use in communication networks has grown, fake news has become a big problem. The misleading heading of the news loses the trust of the reader. Many techniques have emerged, but they fail because fraudsters or exploiters find new ways to deceive them. Semantic analysis and machine learning techniques play a significant role in fake news detection. We must semantically assess terms used in the headline and main content before filtration because words can have different meanings in different contexts. In the paper, a method for determining fake news is introduced by calculating the dissimilarity between the title and content of the news. Vector distance calculators are used to extract semantic dissimilarities, which were then utilized as an additional feature. Initially, Term frequency-inverse document frequency (Tf-Idf) and Mutual Information (MI) are employed for text Feature Extraction (FE) on the ‘title’ and ’content’ of the news articles. Subsequently, four different vector distance calculators are used to extract vector distance-based features. The resulting distance values are used to train various machine learning classifiers, achieving the highest accuracy of 99%. Our method provides a comprehensive analysis by capturing diverse aspects of semantic dissimilarity from various distance calculators. The proposed method is then compared with previous techniques to demonstrate its effectiveness. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Rain Attenuation Modelling Based on Symbolic Regression and Differential Evolution for 5G mmWave Wireless Communication Networks

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Progress in Electromagnetics Research B 2025年 111卷 45-58页

作者： Matondo, Sandra Bazebo Owolawi, Pius Adewale Department of Computer Systems Engineering Tshwane University of Technology Pretoria South Africa

The microphysical structure of rain has a significant impact on the quality of radio signal transmission in the upcoming deployment of 5G millimetre-wave wireless communications in South Africa. To address this, mitigation techniques that integrate rain attenuation prediction models into network management systems are essential. This study uses a machine learning technique, symbolic regression coupled with differential evolution, to predict the rain attenuation in urban and rural 5G scenarios. Symbolic regression derives the mathematical models characterizing attenuation, while differential evolution optimizes the model coefficients. The models’ accuracies are validated through predictive performance metrics, including Mean Absolute Error (MAE) and Mean Squared Error (MSE). The urban model showed excellent accuracy, and the rural model improved significantly after optimization. The interpretability of the models provides valuable insights into rain-induced attenuation and supports better design and optimization of 5G mmWave communication systems. © (2025), (Electromagnetics Academy). All rights reserved.

关键词： Mean square error

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A Comprehensive Survey on Advanced Persistent Threat (APT) Detection Techniques

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computers, Materials & Continua 2024年第8期80卷 2675-2719页

作者： Singamaneni Krishnapriya Sukhvinder Singh Department of Computer Science School of Engineering and TechnologyPondicherry UniversityKalapet605014India

The increase in number of people using the Internet leads to increased cyberattack *** Persistent Threats,or APTs,are among the most dangerous targeted *** attacks utilize various advanced tools and techniques for attacking targets with specific *** countries with advanced technologies,like the US,Russia,the UK,and India,are susceptible to this targeted *** is a sophisticated attack that involves multiple stages and specific ***,TTP(Tools,Techniques,and Procedures)involved in the APT attack are commonly new and developed by an attacker to evade the security ***,APTs are generally implemented in multiple *** one of the stages is detected,we may apply a defense mechanism for subsequent stages,leading to the entire APT attack *** detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing *** survey paper will provide knowledge about APT attacks and their essential *** follows the case study of known APT attacks,which will give clear information about the APT attack process—in later sections,highlighting the various detection methods defined by different researchers along with the limitations of the *** used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.

关键词： Advanced persistent threats APT cyber security intrusion detection cyber attacks

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A collision proof energy efficient lightweight hybrid cryptosystem for fog

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International Journal of Information and computer Security 2025年第3期26卷 179-203页

作者： Kumar, Sandeep Garg, Ritu Department of Computer Engineering National Institute of Technology Kurukshetra India

Fog computing extends cloud capabilities to the network edge, aiding IoT and users. It mitigates cloud issues like latency and reliability. However, fog’s limited resources pose security vulnerabilities like data theft and unauthorised access. To tackle this, we proposed a lightweight, energy-efficient hybrid cryptosystem with dynamic key changes. This optimises fog node energy usage and the proposed security system is collision proof and uses proportional offloading to ensure the delivery of subscribed data to fog/cloud. We have utilised chosen-ciphertext attribute-based encryption scheme to produce the keys and principles of ECC-Elgamal are considered for encryption/decryption of data. Finally, the performance evaluation and security attacks analysis depicts that our security system performs better in terms of optimal energy utilisation, overall computation overhead as well as mitigating the attacks in contrast with other state of art methodologies. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Authorization

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Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

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Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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Maximizing aged photovoltaic array power:a computer modelling study

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Clean Energy 2024年第1期8卷 171-187页

作者： Fadi N.Sibai Electrical&Computer Engineering Department Gulf University for Science and TechnologyMishrefKuwait

Photovoltaic(PV)modules age with time for various reasons such as corroded joints and terminals and glass coating defects,and their ageing degrades the PV array *** the help of the PV array numerical model,this paper explores the effects of PV module ageing on the PV array power,and the power gains and costs of rearranging and recabling aged PV modules in a PV *** numerical PV array model is first revised to account for module ageing,rearrangement and recabling,with the relevant equations presented *** updated numerical model is then used to obtain the array powers for seven different PV *** power results are then analysed in view of the attributes of the seven PV array examples.A guiding method to recommend recabling after rearranging aged modules is then proposed,leading to further significant power gains,while eliminating intra-row *** certain conditions are met,it was shown that recabling PV modules after rearranging them may lead to further significant power gains,reaching 57%and 98%in two considered PV array *** gains are possible in other arrays.A cost-benefit analysis weighing annual power gains versus estimated recabling costs is also given for the seven considered PV array examples to guide recabling decisions based on technical and economic *** the considered examples,recabling costs can be recovered in<4 *** with the powers of the aged arrays,power gains due to our proposed rearranging and recabling the PV arrays ranged between 73%and 131%in the considered examples—well over the gains reported in the ***,the cost of our static module rearrangement and recabling method outshines the costs of dynamic reconfiguration methods recently published in the literature.

关键词： solar energy optimizing array power photovoltaic array modelling photovoltaic modules ageing photovoltaic module rearrangement module recabling and reconfiguration PV array mismatch

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Hybrid optimization assisted deep ensemble classification framework for skin cancer detection

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Multimedia Tools and Applications 2025年第1期84卷 63-94页

作者： Pukhta, Irfan Rashid Rout, Ranjeet Kumar Department of Computer Science and Engineering National Institute of Technology Srinagar190006 India

In 2018, there were 1 million occurrences of non-melanoma cancer and 288,000 occurrences of malignant skin cancer (MM) recorded worldwide. Given the aging of the population and limited resources for medical care, a complete strategy to prevent skin cancer must include both practical detection and accurate diagnosis. Dermatologists and family physicians may find it challenging to identify skin cancer, particularly MM, at an early phase. The purpose of this paper is to suggest a paradigm for skin cancer detection, where pre-processing is the initial step for which median filter (MF) is employed. The pre-processed image undergoes segmentation, which is done by an Improved DBSCAN approach. Once the segmentation is over, features like "GLCM, Local Binary pattern (LBP), Local Vector Pattern (LVP), and Improved Center Symmetric Local Ternary Pattern (CSLTP)" are extracted. This is the final phase, where an ensemble classifier (EC) that combines the models like "Convolutional Neural network (CNN), Long Short Term Memory (LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU)" is deployed. Further, weights of CNN, Bi-GRU, and LSTM are tuned by Hawks Updated Blue Monkey Algorithm with Gaussian Map Randomness (HUBM-GMR) based hybrid optimization. The study will ultimately validate the HUBM-GMR model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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A novel Cosine-Cosine chaotic map-based video encryption scheme

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Journal of engineering and Applied Science 2024年第1期71卷 36页

作者： Kumari, Sweta Dua, Mohit Dua, Shelza Dhingra, Deepti Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics and Communication Engineering National Institute of Technology Kurukshetra India

The surge in online activities has led to the increasing popularity of sharing video data across diverse applications, including online education tutorials, social networking, video calling, and OTT platforms. Encryption prevents unauthorized access to the transmitted data over unreliable channels. The well-known features of chaos theory such as random behaviour, unpredictability, and initial parameters dependency facilitate its use in cryptography. Many security issues are faced by chaos-based cryptosystems because of their less complexity. Hence, a new Cosine-Cosine chaotic map characterized by intricate chaotic behaviour is designed in the current study. Additionally, we formulate an original video encryption scheme employing this Cosine-Cosine chaotic map. The encryption process involves five steps, beginning with the segmentation of the original video into frames based on its frame rate. In the second phase, a 384 bits pseudorandom key is generated that is further divided into three subkeys of 128 bits each. The novel Cosine-Cosine chaotic map-based sequence is generated. In the fourth step, red, green, and blue components are encrypted using the pseudorandom key and the chaotic sequence. In the last step, we combine encrypted frames to get cipher video. The security analysis validates that the proposed encryption protects against eavesdropping. © The Author(s) 2024.

关键词： Cryptography

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