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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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Prediction of strength properties of concrete under the influence of recycled aggregate using machine learning models

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Interactions 2024年第1期245卷 1-21页

作者： Ashwathi, R. Soundariya, R.S. Tharsanee, R.M. Yuvaraj, S. Ramya, R. Department of Civil Engineering Bannari Amman Institute of Technology Erode India Department of Computer Technology Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Civil Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Dr NGP Institute of Technology Coimbatore India

The upsurge in urbanization is depleting natural resources due to the wide usage of aggregates in construction, posing a menace to further progress. If the contemporary state persists, it becomes a threat for further progress. As an initiative to preserve the natural resources, recycled aggregate is employed as a partial additive of fine aggregate, this practice endorses sustainability but demands a cautious investigation of its effect on concrete strength. Predicting the strength characteristics of concrete becomes crucial in this situation. Machine Learning (ML) algorithms estimate these properties, enabling engineers to evaluate and enhance the concrete mix with recycled materials, confirming durability and performance without solely depending on outdated testing methods The proposed work attempts how ML algorithms have been utilized to forecast the strength properties of concrete. Input samples were collected from the experimental setup for three grades of concrete (M15, M20, M25) with four various proportions (5%, 10%, 15% and 20%) at 7, 14, 21 and 28 ages of curing excluding the conventional mix. The ML models are developed by training the collected laboratory samples with respect to four parameters– cement, fine aggregate (FA), coarse aggregate (CA) and recycled aggregate (RA). The trained models predict the three properties of concrete– compressive strength, split tensile strength and flexural strength. The test outcomes are further validated using statistical measures such as correlation coefficient– R2, Mean Square Error (MSE) and Mean Absolute Error (MAE) to assess the performance of the model. The performance evaluation of the proposed approach shows that The Support Vector Regressor outperforms the other ML models, achieving an R² value of about 90% and showing a reduced error rate in terms of MSE and MAE. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile testing

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Multilevel chaotic encryption model with cyclic redundancy check for medical data

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Neural Computing and Applications 2025年第5期37卷 3581-3603页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

In telemedicine applications, it is crucial to ensure the authentication, confidentiality, and privacy of medical data due to its sensitive nature and the importance of the patient information it contains. Communication through open networks is insecure and has many vulnerabilities, making it susceptible to unauthorized access and misuse. Encryption models are used to secure medical data from unauthorized access. In this work, we propose a bit-level encryption model having three phases: preprocessing, confusion, and diffusion. This model is designed for different types of medical data including patient information, clinical data, medical signals, and images of different modalities. Also, the proposed model is effectively implemented for grayscale and color images with varying aspect ratios. Preprocessing has been applied based on the type of medical data. A random permutation has been used to scramble the data values to remove the correlation, and multilevel chaotic maps are fused with the cyclic redundancy check method. A circular shift is used in the diffusion phase to increase randomness and security, providing protection against potential attacks. The CRC method is further used at the receiver side for error detection. The performance efficiency of the proposed encryption model is proved in terms of histogram analysis, information entropy, correlation analysis, signal-to-noise ratio, peak signal-to-noise ratio, number of pixels changing rate, and unified average changing intensity. The proposed bit-level encryption model therefore achieves information entropy values ranging from 7.9669 to 8.000, which is close to the desired value of 8. Correlation coefficient values of the encrypted data approach to zero or are negative, indicating minimal correlation in encrypted data. Resistance against differential attacks is demonstrated by NPCR and UACI values exceeding 0.9960 and 0.3340, respectively. The key space of the proposed model is 1096, which is substantially mor

关键词： Telemedicine

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An Efficient Approach for EEG Seizure Detection using CNN with Feature Extraction 7

An Efficient Approach for EEG Seizure Detection using CNN wi...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Joseph, Liya Izudheen, Sminu Narayanana, Uma Nair, Abhinav R Ashok Gopal, K.A. Rajagiri School of Engineering & Technology Department of Computer Science and Engineering Ernakulam India CVV Institute of Science and Technology Department of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350372816

Epileptic seizures are a significant agony for those who suffer from them. Epileptic studies primarily focus on understanding the abnormal behavior of brain signals. Detecting seizures in EEG signals manually is a very chaotic and challenging task. Therefore, we propose an automatic seizure detection system that drastically reduces the manual effort required for seizure detection while providing accurate results. The proposed method involves extracting twelve statistical features from segmented data and adjusting labels to facilitate binary classification using a Convolutional Neural Network (CNN). The dataset is normalized and balanced using SMOTE to address class imbalance. Evaluation metrics such as accuracy, precision and recall are computed on the test set with results visualized through accuracy and loss curves. User input allows for predictions on specific test data, determining the presence of seizures based on model predictions. This structured approach ensures reliable seizure detection through the use of advanced deep learning techniques and comprehensive model evaluation. Furthermore, a comparative study was conducted to assess the effectiveness of the proposed model against conventional machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Gradient Boosting and Decision Tree. Our analysis reveals that CNN consistently demonstrates superior performance compared to these methods. © 2024 IEEE.

关键词： Convolutional neural networks

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High Dynamic Range Imaging for Dynamic Scenes Based on Multi-Level Spike Camera

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5394-5406页

作者： Zhu, Zhenkun Xiong, Ruiqin Zhao, Jing Zhao, Rui Fan, Xiaopeng Zhu, Shuyuan Huang, Tiejun Peking University School of Computer Science Beijing100871 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

Spike camera is a retina-inspired neuromorphic camera which can capture dynamic scenes of high-speed motion by firing a continuous stream of spikes at an extremely high temporal resolution. The limitation in the current design is that each spike only represents the arrival of a fixed amount of photons. It can not deal with strong light areas in which the amount of accumulated photons reaches the pre-specified threshold multiple times within a single readout interval. In this paper, we propose a new spike camera model of high-speed imaging for high dynamic range scenarios. In this scheme, each pixel accumulates the incoming photons persistently and generates a new type of spike stream in which each spike symbol may be associated with different levels, indicating the arrival of different amounts of photons since the last readout. This enables the camera to support dynamic scenes with wider dynamic range. To achieve this, we propose a two-level buffer mechanism, one for photon accumulation and one for spike-firing encoding. We use a register to hold the number of spike-firings which has not been read out yet. At each readout time, the major part in the counter is read out via a carefully designed exponential encoding and the counter is updated. Such encoding and readout strategy enables a very efficient expansion of the dynamic range using a small number of encoding bits. Furthermore, we propose an image reconstruction scheme for the proposed camera, utilizing both spike intervals and spike levels to recover the light intensity. We incorporate Mamba and propose a temporal-spatial selective scan mechanism to extract temporal-spatial correlation within spike streams. We employ a pyramid adaptive filtering and alignment module to achieve coarse-to-fine feature alignment. Experimental results show that the proposed scheme can achieve better imaging quality and outperform the existing spike camera in high dynamic range scenarios. © 1991-2012 IEEE.

关键词： Adaptive filtering

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CLASSIFICATION OF ROYAL DELICIOUS APPLES USING HYBRID FEATURE SELECTION AND FEATURE WEIGHTING METHOD BASED ON SVM CLASSIFIER

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Scalable Computing 2025年第2期26卷 940-949页

作者： Kaur, Sandeep Sachan, Manoj Kumar Aggarwal, Ashwani Kumar Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal148106 India

Fruit safety is a critical component of the global economy, particularly within the agricultural sector. There has been a recent surge in the incidence of diseases affecting fruits, leading to economic setbacks in agriculture worldwide. Conventional manual assessment methods are laborious, prompting the exploration of automated computerized techniques for evaluating fruit quality. This research presents a novel method for assessing the quality of golden delicious apples. A dataset comprising 1256 apple images was gathered under controlled conditions. Afterward Feature extraction focuses on texture features like LBP, GLCM, GLDM, DTF, and Gabor features, color features, and shape and size features. A total of 18654 features are extracted and normalized using z-score. A hybrid method for feature selection and weighting involves the mRMR algorithm to eliminate redundant features and the Sine Cosine Optimization Algorithm for feature weighting, enhancing classification performance. The SVM machine learning technique, augmented with optimized features, yielded a 10.53% improvement in accuracy compared to SVM alone. Validation against state-of-the-art methods using Friedman’s mean rank test underscored the statistical significance of this approach across various metrics. © 2025 SCPE.

关键词： Fruits

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Efficient Saliency Map Detection for Low-Light Images Based on Image Gradient

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IEEE Transactions on Circuits and Systems for Video technology 2024年第2期34卷 852-865页

作者： Lin, Chun-Yi Haq, Muhamad Amirul Chen, Jiun-Han Ruan, Shanq-Jang Naroska, Edwin National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Hochschule Niederrhein University of Applied Science Faculty of Electrical Engineering and Computer Science Krefeld47805 Germany

Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as face recognition, vehicle recognition, and license plate recognition. However, conventional methods for object recognition may not be appropriate for low-light image recognition due to information loss in the dark regions and unexpected noise that can impair object quality. Therefore, the development of specialized techniques for low-light image enhancement has become a major research focus for object detection. This paper proposed a gradient-based saliency map detection method with an improved ResNet architecture that outperforms previous works in detecting multiple or large objects. Additionally, the proposed method enhances images with the object as the center and emphasizes foreground-background differences. Compared with previous works, this paper achieves1.28× improvements in the parameters and 1.32× faster inference speed than the original ResNet architecture. © 1991-2012 IEEE.

关键词： Neural networks

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An empirical analysis of hyperparameter tuning impact on ensemble machine learning algorithm for earthquake damage prediction

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Asian Journal of Civil engineering 2024年第4期25卷 3521-3547页

作者： Feroz, Shejuti Binte Sharmin, Nusrat Sevas, Muhammad Samee Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Earthquake damage prediction is crucial for ensuring the safety of building occupants and preventing substantial financial losses. Because it enables robust structural design, financial readiness, and well-timed expenditures in preventive measures, anticipating seismic impacts promotes sustainability and long-term building. Machine learning (ML) have transformed building damage prediction, providing efficient methodologies for assessing structural vulnerabilities and risks. ML analyzes multifaceted datasets, handling complex spatial and temporal data, enhancing accuracy in forecasting damage probabilities and enabling proactive monitoring for timely interventions. However, ensemble machine learning and the fine-tuning of such algorithms with the hyperparameter optimization with the earthquake damage prediction have not been explored in the literature yet. Hyperparameter optimization in machine learning enhances model performance and generalization capacity. Skillful adjustment of hyperparameters significantly improves predictive accuracy, resilience, and training convergence, ensuring optimal model performance across diverse datasets and real-world scenarios. This study focuses on improving earthquake damage prediction accuracy through an extensive analysis of the earthquake dataset on ensemble machine learning with hyperparameter tuning. Utilizing various hyperparameter tuning algorithms and examining five ensemble machine learning algorithms, combined with six distinct hyperparameter tuning techniques, significantly enhanced accuracy. The paper’s main contributions include exploring novel hyperparameter tuning algorithms for earthquake damage prediction and filling a knowledge gap in the field. The thorough dataset analysis revealed a scarcity of existing literature, suggesting opportunities for further research. The study underscores the critical role of hyperparameter analysis in machine learning and proposes potential applications beyond earthquake prediction,

关键词： Earthquake prediction Ensemble machine learning Hyperparameter optimization

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Modeling Task Engagement to Regulate Reinforcement Learning-Based Decoding for Online Brain Control

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IEEE Transactions on Cognitive and Developmental Systems 2025年第3期17卷 606-614页

作者： Zhang, Xiang Shen, Xiang Wang, Yiwen Hong Kong University of Science and Technology Department of Electronic and Computer Engineering 999077 Hong Kong Guangdong Institute of Intelligence Science and Technology Zhuhai519031 China Hong Kong University of Science and Technology Department of Chemical and Biological Engineering 999077 Hong Kong

Brain–machine interfaces (BMIs) offer significant promise for enabling paralyzed individuals to control external devices using their brain signals. One challenge is that during the online brain control (BC) process, subjects may not be completely immersed in the task, particularly when multiple steps are needed to achieve a goal. The decoder indiscriminately takes the less engaged trials as training data, which might decrease the decoding accuracy. In this article, we propose an alternative kernel RL-based decoder that trains online with continuous parameter update. We model neural activity from the medial prefrontal cortex (mPFC), a reward-related brain region, to represent task engagement. This information is incorporated into a stochastic learning rate using an exponential model, which measures the relevancy of neural data. The proposed algorithm was evaluated in the experiment where rats performed a cursor-reaching BC task. We found the neural activities from mPFC contained the engagement information which was negatively correlated with trial response time. Moreover, compared to the RL method without task engagement modeling, our proposed method enhanced the training efficiency. It used half of the training data to achieve the same reconstruction accuracy of the cursor trajectory. The results demonstrate the potential of our RL framework for improving online BC tasks. © 2016 IEEE.

关键词： Reinforcement learning

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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