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IensNet: A novel and efficient approach for iris spoof detection via ensemble of deep models

Multimedia Tools and Applications 2025年 1-30页

作者： Sharma, Deepika Selwal, Arvind School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India Department of Computer Science and Information Technology Central University of Jammu Samba India

Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to diversity in spoof or presentation attacks (PAs) that fails to assure consistency while adopting them in real life scenarios. Hence, iris PAs are the growing concerns that gained significant attention in recent past decade. To alleviate these attacks or recognize presentation attack instruments (PAIs), iris presentation attacks detection (IPAD) algorithms are designed to distinguish a real and fabricated iris trait. Aiming at the efficient iris spoof detection mechanism, in this research work we expound a novel ensemble learning-enabled model (IensNet) that learns three pre-trained and fined-tuned deep models (i.e. DenseNet161, ResNet and VGGNet) for better accuracy and generalized performance. The novel IensNet approach offers several merits (i.e. consolidated strengths of multiple models, improved generalization ability, etc.) as compared to a simple transfer learning strategy where the knowledge is drawn from single pre-trained model. Finally, our approach learns a novel fully-connected dual layer classifier via outcome of three fine-tuned models to yield a final classification result as bonafide or spoof iris trait. Our approach is evaluated on Notre Dame LivDet iris 2017 and Notre Dame contact lenses 2015 anti-spoofing datasets. The experimental analysis of IensNet offers outstanding performance with a lower ACER of 0.2% and 1.4% for Iris-LivDet-2017 and Notre Dame contact lenses 2015 dataset respectively. Besides, IensNet exhibit promising results in cross-dataset environment with an ACA of 91.46%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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ProxaDyn: A Proximity-Aware Dynamic Caching Approach for Named Data Networks

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IEEE Transactions on Network science and engineering 2025年第3期12卷 2360-2372页

作者： Kumari, Matta Krishna Tripathi, Nikhil Joshi, Piyush Indian Institute of Information Technology Computer Science and Engineering Group Sri City517 646 India Department of Computer Science and Engineering Dhanbad826 004 India

Named Data Network (NDN), a future Internet architecture is introduced to address the shortcomings of the current Internet architecture. NDN supports in-network caching to facilitate scalable content distribution and enhance overall network performance. However, the known NDN caching strategies suffer from a few common drawbacks, such as inefficient cache utilization, high content redundancy, and overhead due to lookup repetition. To address these issues, in this paper, we propose a novel caching strategy called ProxaDyn for efficient content lookup, placement, and replacement. During the content lookup phase, ProxaDyn interacts exclusively with the router responsible for caching a particular content. This eliminates interaction with other intermediate routers, thereby significantly reducing content access latency. For content placement, ProxaDyn strategically selects an on-path router based on content popularity. Popular content is placed in the cache of a router closer to the consumer, while less popular content is cached in a router away from the consumer. This approach significantly improves the cache hits and reduces the access latency. We test ProxaDyn over a diverse range of real-world network topologies. Using extensive experiments, we show that ProxaDyn could achieve significantly better results compared to the state-of-the-art NDN caching strategies. © 2013 IEEE.

关键词： Data communication systems

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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A lightweight and efficient model for botnet detection in IoT using stacked ensemble learning

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Soft Computing 2025年第1期29卷 89-101页

作者： Esmaeilyfard, Rasool Shoaei, Zohre Javidan, Reza Department of Computer and Information Technology Engineering Shiraz University of Technology Shiraz Iran

Efficient botnet detection is of great security importance and has been the focus of researchers in recent years. Botnet detection is also a difficult task due to the difficulty in distinguishing it from normal traffic. At the same time, detecting these botnets require a lot of computation resources using traditional methods and this limitation makes it even more difficult to detect them on Internet of Things (IoT) devices. Considering the massive IoT data, an efficient and lightweight approach for detecting and predicting IoT botnet attacks is required. In this paper, multiple lightweight machine learning methods including a deep Multilayer Perceptron (MLP) method and a Random Forest (RF) method are integrated into a stacked ensemble learning model to detect botnet attacks in IoT devices. This integration is based on applying lasso regression on features and utilizing a logistic regression in ensemble learning, which leads to increasing the accuracy of botnet detection and reducing its computational complexity. The performance evaluation of the proposed model is examined from two perspectives: accuracy and lightweight characteristics;and for this purpose, a real-world UNSW (BoT-IoT) dataset is used. A comparative study with competitive neural network methods demonstrates that our approach delivers a better outcome. Experimental results reveal that this method has a higher efficiency in all metrics than competing methods including accuracy, precision, recall, and F1 score with values of 99.3%, 99.2%, 99%, and 99.1%, respectively. Besides, the results showed that the proposed method requires at least 36% less CPU and 38% less memory compared to the competing methods, which makes the proposed method to be suitable for IoT devises with limited resources. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Bot (Internet)

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Dynamic Chaotic Encryption Using Bit Division Multiplexing and Geometric Shaping

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IEEE Photonics technology Letters 2025年第12期37卷 647-650页

作者： Lei, Yi Ding, Yinchen Yin, Xuran Chen, Bin Guo, Zhongyi Hefei University of Technology School of Computer Science and Information Engineering China

Optical wireless communication (OWC) techniques offer ultra-broad spectrum to mitigate the bandwidth-hungry in radio frequency for wireless connections. However, security risk is a critical issue that should receive significant attention. In this Letter, a dynamic chaotic encryption scheme is proposed for OWC systems to improve physical-layer security. In this scheme, the dynamic key is generated from the transmitted information, which is used to intercept the static chaotic sequence to encrypt the information at the two levels: bit and symbol. Further, bit division multiplexing in combination with geometric shaping is proposed to co-transmit the encrypted information and dynamic key, where the optimization objective is to minimize the bit-error ratio of the decrypted information. The effectiveness of the proposed dynamic chaotic encryption scheme is demonstrated via a 64-point constellation transmission, where each symbol carries 2-bit dynamic key and 4-bit encrypted information for transmission. © 2025 IEEE. All rights reserved.

关键词： Optical communication

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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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MOHSQO: Multi objective harmony search algorithm based QoS optimization in delay tolerant networks

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Multimedia Tools and Applications 2025年 1-26页

作者： Sonkar, Nidhi Pandey, Sudhakar Kumar, Sanjay Department of Computer Science and Engineering National Institute of Technology Warangal Hanamkonda India Department of Information Technology National Institute of Technology Raipur Raipur India

Delay Tolerant Networks (DTNs) have the ability to make communication possible without end-to-end connectivity using store-carry-forward technique. Efficient data dissemination in DTNs is very challenging problem due to compromising in Quality-of-Service (QoS) metrics such as delivery ratio, end-to-end delay and overhead ratio. That means, if an algorithm is design to find highest delivery ratio, then the algorithm does not bother about delay and overhead ratio. Researchers tried to improve the performance of network but, none of them had given satisfactory results to make a balance between all QoS metrics. In this paper, we have proposed a novel approach to optimize the performance of the network using Multi objective harmony search algorithm by creating a balance between all QoS metrics. Harmony search algorithm considers number of QoS parameters simultaneously. We have used characteristics of the node (Speed, residual energy, buffer capacity and encounters) to select the optimum forwarder node so that we can balance the performance metrics for network optimization. We have used ONE (Opportunistic Network Environment) simulator to simulate the proposed method and compared with exiting techniques. By analyzing the results, we can say that proposed method has the optimum performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Delay tolerant networks

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An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

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Multimedia Tools and Applications 2025年第4期84卷 1697-1724页

作者： Shukla, Preeti Chandanan, Amit Kumar Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India Department of Computer Science & Engineering Guru Ghasidas Vishwavidyalaya CG Bilaspur India

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2

关键词： Long short-term memory

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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