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DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 1-13页

作者： S, Balasubramani D, John Aravindhar Renjith, P.N. Ramesh, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment. © 2023

关键词： K-means clustering

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Modern Machine Learning Solution for Electricity Consumption Management in Smart Buildings

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IEEE engineering Management Review 2025年第1期53卷 54-62页

作者： Gautam, Sandeep Kumar Shrivastava, Vinayak Udmale, Sandeep S. Singh, Amit Kumar Singh, Sanjay Kumar Department of Computer Science and Engineering Varanasi221005 India Department of Computer Engineering and Information Technology Mumbai400019 India National Institute of Technology Department of Computer Science and Engineering Patna800005 India

Effective management of electricity consumption (EC) in smart buildings (SBs) is crucial for optimizing operational efficiency, cost savings, and ensuring sustainable resource utilization. Accurate EC prediction enables proactive decision-making, ensuring that resources are allocated efficiently to meet actual demand levels while maintaining occupant comfort. Population growth, building expansion, and technology usage swiftly escalate electricity demand, thus necessitating economical EC management strategies and assist consumers to better understand and strategically plan their EC. To address these challenges, this article proposes a novel approach based on a hybrid prediction model combining temporal convolutional networks (TCNs) and gated recurrent units (GRU). This approach capitalizes on the strengths of both TCN and GRU. TCN is adept at efficiently identifying diverse patterns, particularly within the complex working environments of SBs, by effectively capturing high- and low-frequency information. Subsequently, GRU is leveraged to address the long-term dependencies within the data, enhancing the accuracy of EC prediction. In this article, the results demonstrate the effectiveness of the proposed hybrid model, outperforming competitive methods with an impressive mean absolute error score. This underscores the potential of this approach to improve energy management practices significantly within SB environments, ultimately enhancing both operational efficiency and occupant satisfaction. © 1973-2011 IEEE.

关键词： Forecasting

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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Multimedia Tools and Applications 2025年第12期84卷 10527-10550页

作者： Banerjee, Anasua Swain, Satyajit Rout, Minakhi Bandyopadhyay, Mainak School of Computer Engineering Kalinga Institute of Industrial Technology-Deemed-to-be-University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Silchar India

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for Indian Pines, 99.92% accuracy for Pavia University, and 99.99% accuracy for Salinas Valley dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

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computers, Materials & Continua 2024年第5期79卷 3111-3138页

作者： Sujithra Sankar S.Sathyalakshmi Department of Computer Applications Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia Department of Computer Engineering Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable *** predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce ***,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and *** paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present *** study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction *** the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the *** original dataset is used in trainingmachine learning models,and further used in generating SHAP values *** the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based *** new integrated dataset is used in re-training the machine learning *** new SHAP values generated from these models help in validating the contributions of feature sets in predicting *** conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making *** this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the *** study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of *** proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area un

关键词： Explainable AI machine learning clinical decision support systems thyroid cancer association-rule based framework SHAP values classification and prediction

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An enhanced framework for smart automated evaluations of answer scripts using NLP and deep learning methods

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Multimedia Tools and Applications 2025年第11期84卷 8491-8513页

作者： G, Mohanraj R.K, Nadesh M, Marimuthu V, Sathiyapriya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Knowledge Institute of Technology Salem637504 India

The manual process of evaluating answer scripts is strenuous. Evaluators use the answer key to assess the answers in the answer scripts. Advancements in technology and the introduction of new learning paradigms need automation of the evaluation process. This work aims to develop an enhanced novel hybrid framework that can evaluate answer scripts and automatically assign marks for different type of questions based on keywords, grammar, symbols, special keywords, and the given factors. First, the proposed system uses Optical Character Recognition (OCR) to convert image answer scripts into an editable text format. Second, the sentence transformers, the Natural Language Processing (NLP) technique flips the answer script and answers key texts into word embedding vectors. To find similarity measures, these vectors are matched using BERT encoding, spearmanś rank-order correlation, and fuzzy search. At last, the proposed model is trained using Deep Columnar Convolutional Neural Network (DCCNN) in the third step with MINST and Kaggle handwritten mathematical symbols and tested with the segmented mathematical equations to find the similarity. The performance of proposed model is measured using precision, recall, accuracy, and F1-score, and its gives highest accuracy of 93% and 95% when compared to the existing methodologies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optical character recognition

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NeurDB: an AI-powered autonomous data system

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science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of Computer Science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Circular RNAs (circRNAs) RNA binding proteins Convolutional neural network Temporal convolutional network Encoder-decoder network

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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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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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Utilizing attention mechanism with exemplar memory for improving domain adaptive person re-identification

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Multimedia Tools and Applications 2025年第16期84卷 16809-16834页

作者： Bhunia, Sugam Kr. Bakshi, Sambit Mukherjee, Imon Computer Science and Engineering Indian Institute of Information Technology Kalyani West Bengal Kalyani741235 India Computer Science and Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India

In an Unsupervised Domain Adaptation (UDA) task, extracted features from the entire image lead to a negative transfer of irrelevant knowledge. An attention mechanism may highlight the suitable transferable region of an image. Here in this paper, the representative power of the deep re-identification (re-ID) network of an exemplar memory-based domain adaptive unsupervised Person Re-identification (PRId) model is increased with our chosen optimal Bottleneck Attention Module (BAM). Performance enhancement of the deep re-ID network itself has been attempted by very few research works, suffering from a misinterpretation of global features or non-determination of local features due to the presence of occlusions, background interference, view-point variations, low camera resolution, etc. The proposed approach shows remarkable performance improvement in these specific challenging scenarios. Our attention-enabled unsupervised PRId model is evaluated in a cross-platform environment by labeled training in the DukeMTMC-reID dataset and unlabeled target testing in the Market-1501 dataset. The proposed method also enhances the overall performance of our baseline architecture. It provides a 2.12% relative increment in mean Average Precision (mAP) value, 0.93% increment in Top-1 value, 0.33% increment in Top-10 value, and 0.53% increment in Top-20 value respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Statistical tests

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