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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Naveen Kumar, N. Santhosh, V.K. Veena, K. Rayen, Sonia Jenifer Sridevi, N. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

The development of autonomous vehicles is a top goal for automakers and research institutions. Advanced Driving Assistance Systems (ADAS) have been included in mass-produced automobiles in recent years. RADAR, LIDAR, and vision are a few of the technologies on which ADAS is built. For Automatic Cruise Control (ACC) systems, for instance, RADAR is centralized in highway applications. But, in urban scenarios, where precise scene detection is necessary, vision is consolidated due to its low cost and plenty of available data. Free space detection is crucial for other autonomous navigational tasks, like path planning. Urban settings present unique challenges because of the wide range of roadway layouts and environmental factors. For instance, only modest curbs can limit the amount of driveable space, and view of the roadway is a common contributor to head-on collisions and accidents involving single vehicles. There may have been a lot fewer of these mishaps if lateral safety nets had been put in place. Most automobile accidents happen when a driver veers too near or crosses the lane line. Lane Support System (LSS) can "scan" the route borders and notify the motorist if the vehicle is getting too close to the edge of the lane. Though the technology is thought to be ready, there is still a lot of mystery around what kinds of video surveillance are required for "understanding" the field, and there are only so many data points accessible from actual road tests. This research reports on testing of LSS performances on two-lane country roads with varying geometric connections and road marker circumstances. About 2% of roads have LSS problems that could be seen throughout the day with flat ground. The root of the problems and their relative significance in the process were dissected using a decision tree. Cast shadows on the road are a well-known obstacle for driver assistance systems with vision, which makes simple tasks like lane and road detections challenging. A new set of s

关键词： Motion planning

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Integrative Approach for Automated Epileptic Seizure Detection: EEG-MRI Hybrid Model Utilizing Deep Learning Techniques 5

Integrative Approach for Automated Epileptic Seizure Detecti...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Dharani, V. Lakshmanan, L. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Epilepsy remains a significant neurological challenge, impacting millions worldwide with recurrent seizures and profound lifestyle implications. Accurate diagnosis of epilepsy at appropriate timing is critical for effective management. This research proposes a novel hybrid approach for automated epileptic seizure detection, integrating Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data through advanced deep learning techniques. The methodology involves EEG-based deep learning model training, integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for feature extraction and classification. Additionally, a MRI-guided fusion model is developed, incorporating structural and functional MRI data to enhance diagnostic accuracy. The hybrid model combines EEG and MRI features, offering a comprehensive understanding of seizure dynamics. Evaluation is conducted on publicly available dataset and Performance metrics including sensitivity, specificity, accuracy, precision, recall and F1-score were utilized to assess the efficacy of the proposed methodology. Results demonstrate superior performance of the hybrid model compared to other methods, highlighting its potential for accurate and efficient epileptic seizure detection. © 2024 IEEE.

关键词： Electroencephalography

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Automated garden-insect recognition using improved lightweight convolution network

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information Processing in Agriculture 2023年第2期10卷 256-266页

作者： Zhankui Yang Xinting Yang Ming Li Wenyong Li College of Computer Science and Technology Beijing University of TechnologyBeijing 100124China National Engineering Research Center for Information Technology in Agriculture Beijing 100089China National Engineering Laboratory for Quality and Safety Traceability Technology and Application of Agricultural Products Beijing 100089China

Automated recognition of insect category,which currently is performed mainly by agriculture experts,is a challenging problem that has received increasing attention in recent *** goal of the present research is to develop an intelligent mobile-terminal recognition system based on deep neural networks to recognize garden insects in a device that can be conveniently deployed in mobile ***-of-the-art lightweight convolutional neural networks(such as SqueezeNet and ShuffleNet)have the same accuracy as classical convolutional neural networks such as AlexNet but fewer parameters,thereby not only requiring communication across servers during distributed training but also being more feasible to deploy on mobile terminals and other hardware with limited *** this research,we connect with the rich details of the low-level network features and the rich semantic information of the high-level network features to construct more rich semantic information feature maps which can effectively improve SqueezeNet model with a small computational *** addition,we developed an off-line insect recognition software that can be deployed on the mobile terminal to solve no network and the timedelay problems in the *** demonstrate that the proposed method is promising for recognition while remaining within a limited computational budget and delivers a much higher recognition accuracy of 91.64%with less training time relative to other classical convolutional neural *** have also verified the results that the improved SqueezeNet model has a 2.3%higher than of the original model in the open insect data IP102.

关键词： Insect classification Mobile-terminal recognition SqueezeNet model Deep lightweight convolution network

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Enhanced Genomic Prediction for Maize Breeding Using Deep Learning and K-mer-based Sequence Encoding 5

Enhanced Genomic Prediction for Maize Breeding Using Deep Le...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kudiyarasudevi, C. Suresh, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram India

ISBN: (纸本)9798331518097

Maize (Zea mays L.) plays a pivotal role in global food security due to its substantial economic and nutritional value. With advancements in agricultural technologies, crop yields have surged, but challenges remain in optimizing maize breeding for even higher productivity. This research explores cutting-edge approaches in maize breeding, focusing on novel genomic prediction tools that enhance crop yield. While numerous studies have demonstrated the potential of genomic prediction in accelerating maize yields, current models often suffer from suboptimal data quality and lengthy processing times due to the use of raw data for genotype prediction. To address these challenges, this study introduces a deep learning- based model that leverages polymorphism data, employs k-mer methods to shorten sequences, and applies one-hot encoding for preprocessing. This data is then processed by a convolutional neural network (CNN) to predict genotypes efficiently. The proposed model aims to achieve a 98% accuracy in genomic prediction, outperforming existing models in both speed and accuracy, thereby contributing to the generation of superior maize traits. © 2024 IEEE.

关键词： Prediction models

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A Predictive and Adaptive Mobile/Web Application for Alzheimers Disease Using Machine Learning 9

A Predictive and Adaptive Mobile/Web Application for Alzheim...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Balamuralikrishnan, G. Boomika, S. Sri Nandhini, R. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

A neurodegenerative condition, Alzheimer's disease (AD) affects millions of individuals globally, that causes memory loss and cognitive impairment. Improving patient outcomes and delaying the course of disease need early detection and continuous treatment. The paper proposes a predictive and adaptive mobile/web application for Alzheimer's disease management, leveraging transfer learning. In this research, we utilize transfer learning techniques, specifically with pre-trained deep learning architectures like ResNet and VGG. These models, originally trained on large datasets, are fine-tuned with Alzheimer's disease-specific data to enhance diagnostic accuracy and efficiency within our mobile/web application. By offering cognitive activities, remote health tracking, and personalized care, the platform improves patient involvement and aids medical professionals in managing patient conditions. Transfer learning increases diagnostic precision while requiring less data. © 2024 IEEE.

关键词： Transfer learning

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans

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Multimedia Tools and Applications 2024年第28期83卷 71885-71908页

作者： Das, Suchismita Dubey, Rajni Jena, Biswajit Tsai, Lung-Wen Saxena, Sanjay Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei111031 Taiwan Department of Computer Science and Engineering Institute of Technical Education and Research SOA Deemed to Be University Bhubaneswar India Department of Medicine Research Taipei Medical University Hospital Taipei111031 Taiwan Department of Information Technology Office Taipei Medical University Hospital Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

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Intelligent Automation & Soft Computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

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Towards Improving the Quality of Requirement and Testing Process in Agile Software Development:An Empirical Study

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computers, Materials & Continua 2024年第9期80卷 3761-3784页

作者： Irum Ilays Yaser Hafeez Nabil Almashfi Sadia Ali Mamoona Humayun Muhammad Aqib Ghadah Alwakid University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia School of Arts Humanities and Social Sciences University of RoehamptonLondonSW155PJUK Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing ***,it is difficult to identify all faults in *** requirement changes continuously,it increases the irrelevancy and redundancy during *** to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements *** this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based *** research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change *** compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based ***,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection ***,the proposed model was evaluated by conducting *** results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user *** predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user ***,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier ***,it provides guidelines for practitioners and researchers in the modern *** the future,we will provide the working prototype of this model for proof of concept.

关键词： Requirement prediction software testing agile software development semantic analysis case-based reasoning

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Self-supervised extracted contrast network for facial expression recognition

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Multimedia Tools and Applications 2025年第15期84卷 14977-14996页

作者： Yan, Lingyu Yang, Jinquan Xia, Jinyao Gao, Rong Zhang, Li Wan, Jun Tang, Yuanyan School of Computer Science Hubei University of Technology Wuhan430068 China Department of Computer Science Royal Holloway University of London SurreyTW20 0EX United Kingdom School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Zhuhai UM Science and Technology Research Institute Zhuhai519085 China

Self-supervised Contrastive learning has recently demonstrated significant performance in Facial Expression Recognition (FER). However, existing methods fail to address inherent challenges such as similar and blurred facial expressions, and low-quality semantic pairs from image cropping. To tackle these challenges, this paper proposes a Lightweight Self-supervised Distilled Contrastive network for FER (LSDC-FER), which consists of a center suppressed cropping module and a multi-scale feature joint distillation module. The former improves the quality of face expression semantic pairs by face image localization and center suppressed cropping. The latter learns fine-grained features using knowledge distillation and explores multi-scale contextual information to improve feature learning. Experimental results show that LSDC-FER achieves 75.65% and 61.16% recognition accuracy on RAF-DB and FER-2013 public facial expression datasets, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Distillation

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