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A Comprehensive Multi-modal Approach for Enhanced Product Recommendations Based on Customer Habits

Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1537-1545页

作者： Bodapati, Jyostna Devi Veeranjaneyulu, N. Yenduri, Lakshmana Kumar Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India Department of IT Vignan’s Foundation for Science Technology and Research Vadlamudi522213 India San Francisco Bay Area United States

In response to the evolving demands and challenges in recommendation systems, a comprehensive and innovative approach is proposed for delivering personalized and accurate product recommendations while mitigating existing limitations. This approach combines cutting-edge methodologies from collaborative filtering, content-based recommendation systems, and state-of-the-art deep learning architectures to enhance the standard of recommendations and improve user contentment. The hybrid approach encompasses various modules, including data preprocessing, feature engineering, deep learning model development, recommendation generation processing, and post-processing. Utilizing the H &M Fashion dataset, a rich collection of product images and metadata, for experimentation, the dataset undergoes preprocessing to ensure data quality, and features are extracted from both text and images using transfer learning approaches. These features are then integrated into a unified representation using fusion techniques. Subsequently, a deep learning model is developed to generate personalized recommendations based on the integrated features. Empirical findings on benchmark H &M dataset demonstrate the superiority of the proposed hybrid approach over traditional content-based and collaborative-based models, with improvements in accuracy, recall, and F1 score. The hybrid model effectively combines different recommendation techniques, offering robustness, adaptability, and enhanced recommendation quality. This research contributes to the advancement of recommendation systems, offering insights and solutions for delivering tailored recommendations in the fashion retail industry. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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A Comparative Study on the Application of Text Mining in Cybersecurity

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Recent Advances in computer science and Communications 2023年第3期16卷 80-93页

作者： Barik, Kousik Misra, Sanjay Konar, Karabi Kaushik, Manju Ahuja, Ravin Department of Computer Science JIS Institute of Advanced Studies & Research JIS University Kolkata India Department of Computer Science and Communication Ostfold University Halden Norway Department of Computer Engineering Amity University Jaipur India Center of ICT/ICE Research CUCRID Covenant University Ota Nigeria

Aims: This paper aims to conduct a Systematic Literature Review (SLR) of the relative applications of text mining in cybersecurity. Objectives: The amount of data generated worldwide has been attributed to a change in different activities associated with cyber security, and demands a high automation level. Methods: In the cyber security domain, text mining is an alternative for improving the usefulness of various activities that entail unstructured data. This study searched databases of 516 papers from 2015 to 2021. Out of which, 75 papers are selected for analysis. A detailed evaluation of the selected studies employs sources, techniques, and information extraction on cyber security applications. Results: This study extends gaps for future studies, such as text processing, availability of datasets, innovative methods, and intelligent text mining. Conclusion: This study concludes with interesting findings of employing text mining in cybersecurity applications;the researchers need to exploit all related techniques and algorithms in text mining to detect and protect the organization from Cybersecurity applications. © 2023 Bentham science Publishers.

关键词： Data mining

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Capacitor voltage unbalance minimization for three-phase five-level diode-clamped inverter using hexagonal hysteresis space vector modulation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 661-671页

作者： Ramkumar, R. Singh, Manmohan Ramkumar, R. Usha, S. Thentral, T. M. Thamizh Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computer Engineering and Application GLA University Mathura India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Renewable Energy University of Maroua National Advanced School of Engineering of Maroua Maroua Cameroon

This article suggests a method for diminishing the voltage unbalance in a three-phase five-level diode-clamped inverter (DCI) through the use of hexagonal hysteresis space vector modulation (HHSVM). Capacitor voltage balancing leads to enhanced system efficiency, reduced stress on components, enhanced performance, abridged electromagnetic interference, and reduced total harmonic distortion. The proposed modulation technique and its implementation are thoroughly examined in this study, along with modeling and experiment data that show how efficient the method is at lowering the capacitor voltage unbalance in the proposed five-level DCI. Capacitor voltage unbalance is reduced with the use of this HHSVM approach to 0.95%, which is a superior reduction compared to traditional PWM methods. The paper also discusses the advantages of the proposed method over other existing methods, making it a promising solution for practical applications in power electronics systems.

关键词： Diode-clamped inverter (DCI) Capacitor voltage balancing Hexagonal hysteresis space vector modulation (HHSVM) Total harmonic distortion

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An Efficient Approach for Semantic Segmentation of Salt Domes in Seismic Images Using Improved UNET Architecture

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Journal of The Institution of Engineers (India): Series B 2023年第3期104卷 569-578页

作者： Bodapati, Jyostna Devi Sajja, RamaKrishna Naralasetti, Veeranjaneyulu Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi India IAENG Life Member Andhra Pradesh Guntur India Department of Information Technology and Computer Applications Vignan’s Foundation for Science Technology and Research Vadlamudi India

Many areas of Earth’s surface with large accumulations of gas and oil even have huge deposits of salt under the surface. Exploring such deposits helps many countries to increase the storage capacity of their Petroleum reserves and explore new ones. But finding such deposits is a herculean task. Expert seismic imaging requires human interpretation of salt bodies. But this leads to very biased and highly variable translations. So the idea behind this paper is to build an approach that accurately and automatically identifies if the seismic image contains any region of salt deposit or not. If a surface is found to have salt deposits, then it may contain the accumulations of oil or gas and even the salt domes or caverns can be used as a storage site for already available petroleum or oil. Since semantic segmentation classifies every pixel in the given image to its class label, this can be used to segment the salt deposits from the provided seismic images. In this paper, we introduce a variation of UNet, a popular segmentation model, for seismic image segmentation. We have added a batch normalization layer following every convolution layer as a deeper network helps extract better features which turned out to be true. Some interesting findings of this work are that the augmentation works well as this avoids over-fitting of the model. Sharpening as a post-processing technique has come up with a considerable amount of rise in the performance. Here, we prefer to use the metric as Intersection over Union (IoU) instead of accuracy as it is not as affected by the class imbalances that are inherent in foreground/background segmentation tasks. With the proposed methodology, we achieve an averaged IoU of 85.6 which is far better compared to the IoU achieved with the Segnet approach which stands at 77. © 2023, The Institution of Engineers (India).

关键词： Image enhancement

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Tensorial Evolutionary Computation for Spatial Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 154-166页

作者： Si-Chao, Lei Xiaolin, Xiao Yue-Jiao, Gong Yun, Li Jun, Zhang the School of Computer Science and Engineering South China University of Technology Guangzhou510006 China the Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen610056 China the School of Computer Science South China Normal University Guangzhou510006 China the Department of Electrical and Electronic Engineering Hanyang University Seoul15588 Korea Republic of

—Spatial optimization problems (SOPs) refer to a class of problems where the decision variables require spatial organization. Existing methods based on evolutionary algorithms (EAs) fit conventional evolutionary operators by flattening the spatial representations of SOPs to one dimension, which hence loses crucial spatial structures of the potential solutions. To address this issue, this article proposes a tensorial evolutionary algorithm (TEA) by formulating a tensor space of the SOPs, where the population of candidate solutions is represented as a third-order tensor. Accordingly, we design a tensor-product crossover operator and a two-phase mutation consisting of a local convolutional mutation operator and a global Gaussian mutation operator in the tensor space. These operators are all implemented based on tensor operations. Under the consideration of spatial context on individual solutions, we devise three types of evolutionary tensors, namely, the crossover tensor, the convolutional mutant tensor, and the Gaussian mutant tensor for the three tensorial evolutionary operators correspondingly. The resultant TEA is tested using two typical real-world SOPs, namely, the facility layout problem and the image steganography problem. Comparative experimental studies validate the effectiveness and robustness of TEA. © 2024 IEEE.

关键词： Evolutionary algorithms

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Adaptive Fine-Grained Feature Mining and RoI Feature Interaction Network for Small Object Detection in Aerial Images

Adaptive Fine-Grained Feature Mining and RoI Feature Interac...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xie, Fangmin Xiong, Zhan Wang, Zhuoyue Yang, Kun Xiao, Guoqiang College of Computer and Information Science Southwest University Chongqing China Institute of Advanced Computer Science Leiden University Netherlands Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley United States

ISBN: (纸本)9798350368741

Object detection in drone view images remains challenging due to small-scale objects distributed non-uniformly and exhibiting weak semantic features. Additionally, external factors such as lighting conditions, viewing angles, and background clutter further complicate the detection of small objects. To address these issues, we propose a small object detector called Adaptive Fine-grained Feature Mining Network (AFMNet) based on an anchor-free framework. Firstly, an Adaptive Fine-grained Feature Mining (AFFM) module is designed to generate Regions of Interest (RoI) on high-resolution feature maps and mine fine-grained information that benefits the representation of small objects. Secondly, an Inter-RoI Feature Interaction (IRFI) block is developed to efficiently integrate complementary cues-namely, deep layer semantic information and shallow layer spatial details-and enhance RoI features. Experiments on the VisDrone dataset demonstrate that our method achieves state-of-the-art performance. The source code of our AFMNet is available at https://***/xiefangmin/AFMNet. © 2025 IEEE.

关键词： Drone-View Image Feature Fusion Fine-grained Features Region of Interest Small object detection

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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence

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Federated Ensemble for Lung Pneumonia Detection 15

Federated Ensemble for Lung Pneumonia Detection

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Vishwash Challa, VenkataHemant Kumar Reddy Pranavi, Pasupuleti Singh, Tripty Nair, Rekha R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Department of Computer Science and Enginnering Bengaluru India

ISBN: (纸本)9798350370249

In the realm of medical imaging, the detection of lung pneumonia through automated methods has garnered significant attention due to its potential to augment diagnostic processes. This study proposes a novel approach, termed Federated CNN Ensemble, aiming to enhance the accuracy and robustness of pneumonia detection from chest X -ray images through federated learning and ensemble techniques. The proposed method leverages convolutional neural networks trained locally at distributed medical institutions, thereby preserving data privacy and security while harnessing collectiveintelligence. By aggregating the learned models through an ensemble strategy, our approach achieves superior performance in pneumonia detection compared to standalone CNN models. This research contributes to the advancement of automated medical diagnosis systems, offering a promising avenue for scalable and collaborative disease detection in healthcare settings. © 2024 IEEE.

关键词： DeepLearning Ensemble models Federated Learning

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From image classification to segmentation: a comprehensive empirical review

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Iran Journal of computer science 2025年 1-31页

作者： Ruksana Jim, Jamin Rahman Hossain, Md Jonair Das, Aritra Kabir, Md Mohsin Mridha, M.F. Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science and Engineering American International University-Bangladesh Dhaka 1229 Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207 Bangladesh Department of Computer Science and Engineering Bangladesh Open University Gazipur 1700 Bangladesh

With the development of the machine learning age, deep learning techniques for computer vision tasks use real-world data to analyze problems across various fields. In particular, it has achieved significant success in image classification, segmentation, and detection on different datasets. As the area of deep learning expands, annotated datasets can be challenging owing to various factors such as the labeling process or difficulty in correctly classifying data, even for experts. Therefore, an extensive survey of machine learning techniques is introduced. Through empirical analysis, several deep learning architectures were applied to benchmark datasets, such as ImageNet, CIFAR-10, COCO, and MNIST. This study includes Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variants in these datasets. Additionally, this work reviews the experimental results from modern studies and explains the methodological nuances and comparative performance. In addition to examining the relevance of model performance to preprocessing techniques, various evaluation metrics and promising challenges with future directions for new frontiers are also discussed. Subsequently, this research investigation addresses challenges such as dataset characteristics, model complexity, and computational efficiency and provides insights into the practical implementation of deep learning solutions for real-world applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： CIFAR-10 Classification COCO Convolutional neural networks Deep learning Detection ImageNET MNIST Segmentation

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Joint Power Control and Multi-Path Routing for Internet of Underwater Things in Varying Environments

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IEEE Internet of Things Journal 2025年第11期12卷 15197-15210页

作者： Xu, Cangzhu Song, Shanshan Liu, Jun Pan, Miao Xu, Gaochao Cui, Jun-Hong Jilin University Department of Computer Science and Technology Changchun130012 China Beihang University School of Electronic and Information Engineering Beijing100191 China Smart Ocean Technology Co. Ltd Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Chengdu China Jilin University College of Computer Science and Technology Changchun130012 China

Internet of Underwater Thing (IoUT) stands as promising technology facilitating diverse underwater applications. Nevertheless, IoUT across vast marine regions is challenged by highly diverse and fluctuating channel environments, which results in unreliable point-to-point (PTP) transmissions. Moreover, its multi-hop nature exacerbates severe unreliable end-to-end (ETE) transmissions. Existing methods utilize routing protocols to address the above challenges by independently power control for PTP reliability or multi-path transmission for ETE reliability. However, these methods ignore the interdependencies between power control and multi-path transmission, which fail to guarantee high energy-efficient reliability in resource-constrained and harsh underwater environments. To this end, we propose a joint power Control And Multi-Path routing (CAMP) protocol for IoUTs in varying environments. Specifically, we develop PTP and ETE reliability models by analyzing the interrelation between power control and multi-path routing, incorporating historical, current, and predictive information. A hybrid routing strategy is designed based on the reliability models to accommodate changing environmental conditions, residual energy, and link quality. This strategy initiates multi-path routing at the source and single-path forwarding at relay nodes, combined with power control. Extensive simulations demonstrate that CAMP achieves superior reliability (packet delivery rate) and energy efficiency, while simultaneously improving network performance in terms of latency and throughput. © 2014 IEEE.

关键词： Routing Reliability Power control Routing protocols Energy efficiency Relays Internet of Things Sensors Reliability engineering computer network reliability

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