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Enhancing Object Detection in Assistive technology for the Visually Impaired: A DETR-Based Approach

IEEE Access 2025年 13卷 71647-71661页

作者： Ikram, Sunnia Sarwar Bajwa, Imran Gyawali, Sujan Ikram, Amna Alsubaie, Najah The Islamia University of Bahawalpur Department of Computer Science Bahawalpur63100 Pakistan Lamar University Department of Computer Science BeaumontTX77705 United States Department of Computer Science Government Sadiq College Women University Bahawalpur63100 Pakistan College of Computer and Information Sciences Department of Computer Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia

This paper presents a real-time obstacle detection and recognition system designed to enhance navigation for visually impaired individuals through assistive technology. The system integrates a mobile application equipped with a mini camera for real-time image capture and employs advanced deep learning techniques for object detection and classification. A comparative evaluation of YOLOv8, Faster R-CNN and DETR (Detection Transformer) is conducted based on precision, Recall, F1-score, confidence score and processing efficiency. DETR demonstrates superior performance, achieving a 99% confidence score, 98% precision and a processing speed of 40ms per frame. While faster R-CNN and YOLOv8 provide competitive results, they offer a trade-off between accuracy and computational efficiency. The system follows a structured a structured workflow, including real-time acquisition, preprocessing, innovative data augmentation and optimization for edge devices using TensorFlow Lite for efficiency deployment. It classifies 80 types of obstacles, such as pedestrians, vehicles and traffic signal and provides immediate audio feedback to ensure safe navigation. The model trained over 20 epochs achieves an accuracy of 98% in the final epoch. This study introduces a scalable and practical solution integrating IoT and real-time image processing, empowering visually impaired users with enhanced mobility and safety. © 2013 IEEE.

关键词： Traffic signals

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Speaker diarization based on X vector extracted from time-delay neural networks (TDNN) using agglomerative hierarchical clustering in noisy environment

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International Journal of Speech technology 2025年第1期28卷 13-26页

作者： Khadar, K. V. Aljinu Sunil Kumar, R.K. Sameer, V.V. Department of Computer Science University of Calicut Tenhippalam Kerala Malappuram673635 India Department of Information Technology Kannur University Mangattuparamba Kerala Kannur670567 India Department of Computer Science Farook College Kerala Kozhikode673 632 India

This paper introduces a speaker diarization system using speaker embedding parameters, specifically the x-vector. By incorporating auto-correlated MFCC features for x-vector extraction using a pre-trained time delay neural network, the system exhibits enhanced adaptability to noise variations. Speaker clustering is accomplished through agglomerative clustering with PLDA scoring as the distance metric, making the system particularly valuable for potential speaker identification, especially in forensic applications. The system’s noise adaptability is thoroughly evaluated by integrating various types of noise such as Red, Pink, and white noise across a wide range of signal-to-noise ratios (20 dB to − 20 dB). Additionally, the system’s performance is comprehensively assessed by varying speech duration and adjusting the number of speakers, highlighting its robustness and effectiveness in real-world scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vectors

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The Confluence of Big Data and Cloud Computing in SME Adoption Strategies

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IEEE Access 2025年 13卷 37789-37811页

作者： Ahmad El Skafi, Mahmoud Yunis, Manal M. Zekri, Ahmed Bu Daher, Julie Department of Mathematics and Computer Science Beirut1107-2809 Lebanon Department of Information Technology and Operations Management Beirut1102-2801 Lebanon Alexandria University Faculty of Science Department of Mathematics and Computer Science Alexandria21526 Egypt

This paper explores the adoption of cloud computing and big data technologies in small and medium enterprises (SMEs), focusing on synthesizing key factors influencing adoption. By conducting a comprehensive literature review and analyzing theoretical frameworks such as the technology Acceptance Model (TAM) and the technology-Organization-Environment (TOE) framework, this study proposes a novel conceptual model tailored to the unique socio-economic context of SMEs in the MENA region. The methodology includes an analysis of 30 research articles, highlighting enablers and barriers to adoption. Key contributions include a taxonomy of factors and actionable insights for policymakers and practitioners. This research addresses gaps in existing studies by providing a region-specific perspective on cloud computing and big data technologies adoption for SMEs. This strong tendency towards services provided by cloud computing is very clear, and to shape their future IT, it's worth highlighting the existence of such technology. A synthesis of the literature on cloud computing adoption was presented in this paper classifying reviews based on factors that play an important role in taking the adoption decision incorporating the theoretical frameworks. Moreover, the relationship between big data and cloud computing has been explored. In addition to proposing a synthesized findings model, this paper includes various factors derived from the literature and related theoretical frameworks. In this paper we select research articles from broadly recognized research databases, as well as undertaking a comprehensive review of these selected articles focusing on the adoption of cloud computing and big data. It also includes a bibliography containing the most relevant publications in these domains, from 2011 to 2024. By examining 30 articles, the objectives of the paper are to analyze data derived from articles as well as to study the academic frameworks used in both developing and developed

关键词： Cloud platforms

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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M-Bi-GRU-CNN: a hybrid deep learning model with optimized feature selection for enhanced crop yield prediction

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Multimedia Tools and Applications 2025年 1-25页

作者： J, Madhuri M, Indiramma N, Nagarathna Department of Computer Science and Engineering Bangalore Institute of Technology Karnataka Bengaluru560004 India Department of Computer Science and Engineering BMS College of Engineering Karnataka Bengaluru560019 India

Weather variability significantly impacts crop yield, posing challenges for large-scale agricultural operations. This study introduces a deep learning-based approach to enhance crop yield prediction accuracy. A Multi-Head Attention-based Bi-Directional Gated Recurrent Unit (M-Bi-GRU) integrated with a Convolutional Neural Network (CNN) is proposed. The publicly available Crop Recommendation dataset is pre-processed using data cleaning and Min–Max normalization to eliminate inconsistencies. Then, the Adaptive Reptile Search Optimization (ARSO) algorithm is used to identify the most relevant features for the prediction task. The M-Bi-GRU-CNN model uses these selected features to predict crop yields. The implementation is carried out in Python, and the performance is measured using a variety of metrics. Experimental results demonstrate that the proposed model outperforms existing methods, achieving a Mean Absolute Error (MAE) of 0.45, Mean Absolute Percentage Error (MAPE) of 0.07, and Root Mean Square Error (RMSE) of 0.28. © 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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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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Empowering Sentiment Analysis in Resource-Constrained Environments:Leveraging Lightweight Pre-trained Models for Optimal Performance

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Journal of Harbin Institute of technology(New Series) 2025年第1期32卷 76-84页

作者： V.Prema V.Elavazhahan Department of Computer and Information Science Annamalai UniversityChidambaram 608002India Department of Computer Science Government Arts and Science CollegeVadalur 607303India

Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various ***,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative ***-constrained environments encompass scenarios where computing resources,memory,and energy availability are *** empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained *** models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these *** distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource *** paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment.

关键词： sentiment analysis light weight models resource⁃constrained environment pre⁃trained models

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DEXO: A Secure and Fair Exchange Mechanism for Decentralized IoT Data Markets

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IEEE Internet of Things Journal 2025年第11期12卷 16095-16111页

作者： Li, Yue Alom, Ifteher Sun, Wenhai Xiao, Yang University of Kentucky Department of Computer Science LexingtonKY United States Purdue University Department of Computer and Information Technology West LafayetteIN United States

Opening up data produced by the Internet of Things (IoT) and mobile devices for public utilization can maximize their economic value. Challenges remain in the trustworthiness of the data sources and the security of the trading process, particularly when there is no trust between the data providers and consumers. In this paper, we propose DEXO, a decentralized data exchange mechanism that facilitates secure and fair data exchange between data consumers and distributed IoT/mobile data providers at scale, allowing the consumer to verify the data generation process and the providers to be compensated for providing authentic data, with correctness guarantees from the exchange platform. To realize this, DEXO extends the decentralized oracle network model that has been successful in the blockchain applications domain to incorporate novel hardware-cryptographic co-design that harmonizes trusted execution environment, secret sharing, and smart contract-assisted fair exchange. For the first time, DEXO ensures end-to-end data confidentiality, source verifiability, and fairness of the exchange process with strong resilience against participant collusion. We implemented a prototype of the DEXO system to demonstrate feasibility. The evaluation shows a moderate deployment cost and significantly improved blockchain operation efficiency compared to a popular data exchange mechanism. © 2025 IEEE.

关键词： Internet of Things Blockchains Smart contracts Soft sensors Security Servers Resilience Decentralized applications Data models Oceans

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Diffusion models for 3D generation: A survey

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Computational Visual Media 2025年第1期11卷 1-28页

作者： Chen Wang Hao-Yang Peng Ying-Tian Liu Jiatao Gu Shi-Min Hu Department of Computer and Information Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Machine Learning Research Apple AI/MLNew YorkUSA.E-mail:jiatao@***

Denoising diffusion models have demonstrated tremendous success in modeling data distributions and synthesizing high-quality *** the 2D image domain,they have become the state-of-the-art and are capable of generating photo-realistic images with high *** recently,researchers have begun to explore how to utilize diffusion models to generate 3D data,as doing so has more potential in real-world *** requires careful design choices in two key ways:identifying a suitable 3D representation and determining how to apply the diffusion *** this survey,we provide the first comprehensive review of diffusion models for manipulating 3D content,including 3D generation,reconstruction,and 3D-aware image *** classify existing methods into three major categories:2D space diffusion with pretrained models,2D space diffusion without pretrained models,and 3D space *** also summarize popular datasets used for 3D generation with diffusion *** with this survey,we maintain a repository https://***/cwchenwang/awesome-3d-diffusion to track the latest relevant papers and ***,we pose current challenges for diffusion models for 3D generation,and suggest future research directions.

关键词： diffusion models 3D generation generative models AIG

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Hybrid CatBoost and SVR Model for Earthquake Prediction Using the LANL Earthquake Dataset

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 93-110页

作者： Kaushal, Arush Gupta, Ashok Kumar Sehgal, Vivek Kumar Department of Computer Science and Information Technology Jaypee University of Information Technology Solan171234 India Department of Civil Engineering Jaypee University of Information Technology Solan171234 India

Earthquakes have the potential to cause catastrophic structural and economic damage. This research explores the application of machine learning for earthquake prediction using LANL (Los Alamos National Laboratory) dataset. The data, obtained from a laboratory stick-slip friction experiment, simulate real earthquakes through digitized acoustic signals recorded against the time to failure of a granular layer. We introduced a hybrid model combining CatBoost and Support Vector Regression (SVR) to predict the time of the next earthquake, evaluating its performance against individual CatBoost and SVR models. The hybrid model demonstrated superior accuracy with a Mean Absolute Error (MAE) of 0.0825, outperforming the individual models. We implemented feature engineering to optimize the predictive capability of the models. Additionally, we compared our hybrid model's performance with previous studies to validate its efficacy. Our findings underscore the potential of machine learning, particularly hybrid models, in enhancing earthquake prediction accuracy. This study highlights the robustness and effectiveness of the hybrid CatBoost-SVR model, paving the way for advanced AI algorithms in seismology and contributing to improved disaster preparedness and mitigation strategies. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Stick-slip

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