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

作者： Tejaswi, S. Babu, Tina Tejaswi, K. Seshadripuram Degree College Department of Computer Science Mysuru India School of Engineering Department of Computer Science and Engineering Alliance University Bengaluru India JSS Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798331518097

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions. © 2024 IEEE.

关键词： Intelligent systems

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Sentiment Analysis on Covid-19 data Using BERT Model

Sentiment Analysis on Covid-19 data Using BERT Model

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： ArunKarthi, T. Shanthi, S. Nirmaladevi, K. Ashwinth, K. Dev, R.B. Rajkumar, N. Computer Science And Engineering Kongu Engineering College Erode India Psna College Of Engineering And Technology Department Of Computer Applications Tamilnadu Dindigul India

ISBN: (纸本)9798350371567

In a world where microblogging has become an integral part of modern life., the sudden outbreak of COVID-19 swiftly infiltrated public opinion on various social networking platforms. With individuals adhering to social distancing measures and self-isolation protocols to combat the coronavirus, expressing their fears and concerns on social media became their primary outlet. In order to obtain a thorough comprehension of the pandemic's effects on individuals' lives and the environments in which they live, we must recognize the critical function of social media as a repository of emotions. This paper delves into the realm of sentiment analysis, focusing on the opinions disseminated through Twitter. Our primary dataset comprises these tweets, which we meticulously processed and converted into tokens for analysis. We performed fine-tuning and added an additional classifier layer to BERT, a pre-trained model, in order to classify these feelings into three categories: positive, negative, and neutral. Subsequently, we scrutinized BERT's performance in carrying out this task. Impressively, the BERT model demonstrated an outstanding accuracy rate of 94 percent on our dataset, supplemented by commendable scores in various evaluation metrics. © 2024 IEEE.

关键词： Sentiment analysis

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Enhancing Road Safety: A Smart Accident Alert System for Rapid Emergency Response 18

Enhancing Road Safety: A Smart Accident Alert System for Rap...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kishore, S. Nair, Rekha R. Babu, Tina Nipe Nitte University Department of Computer Applications Mangalore India Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9789380544519

Millions of lives are at risk annually due to traffic accidents, and the need for a proactive and efficient accident detection system is paramount. The traditional accident detection system faces some challenges such as limited scalability, connectivity, power consumption, and reliability of sensors. So proposed an accident detection system with Internet of Things (IoT) technologies to enhance road safety and emergency response mechanisms. The proposed system integrates advanced components such as Global Positioning System(GPS) modules, and Global System for Mobile Communication(GSM) modules to enable real-time monitoring, precise location tracking, and immediate communication with emergency services. The novelty of this work lies in the implementation of a cost-effective accident detection system that accurately provides alerts to the intended party. The system delivers alert messages containing longitude and latitude coordinates. Additionally, it initiates alerts in the form of phone calls, fostering timely communication with accident victims. This prompt interaction has the potential to save lives by facilitating on-demand treatment and immediate assistance to those in need. This methodology has the potential for future applications in industries such as Traffic Management and Analysis, Road Safety Enhancement, and Emergency Response Optimization. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Emergency Response Optimization Global Positioning System Global System for Mobile Communication Internet of Things ThingSpeak

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Road Extraction From Remote Sensing Images Using U-Net

Road Extraction From Remote Sensing Images Using U-Net

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sai Kumar, D. Lokesh Jyothi, V. Esther Vardhani, Chennu Meghana, Bayarla Priya, Bobbili Hema Sharika, Alladi Computer Science And Engineering Pvp Siddhartha Engineering College Vijayawada India Vr Siddhartha Engineering College Department Of Computer Applications Vijayawada India

ISBN: (纸本)9798350371567

The enhancement of the transportation database through updated road networks is a critical aspect with a wide range of applications. Traditional methods rely on onsite measurements or human interpretation of remote sensing images for mapping purposes. However, these methods have significant drawbacks, including high expenses, lengthy processing times, and a substantial workforce requirement. To overcome this challenge, we propose a method leveraging the U-Net architecture for road extraction from multispectral satellite images. The satellite imagery used is sampled from the DigitalGlobe Images dataset, covering images captured over Thailand, Indonesia, and India, with a ground resolution of 50 cm/pixel. The U-Net model is trained on both original and augmented data, employing convolutional layers, max-pooling, and up-sampling to extract and reconstruct road masks. Performance evaluation is conducted using custom validation sets, measuring metrics such as IOU and loss. Results demonstrate the effectiveness of the proposed system, achieving high accuracy and validation scores. The extracted road networks contribute to enhancing transportation databases, offering valuable insights for urban planning and management. © 2024 IEEE.

关键词： Roads and streets

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Spider monkey ınspired optimization algorithm based clustering for face recognition

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International Journal of Information Technology (Singapore) 2024年 1-13页

作者： Gopal, Veni devi Devi, G. Shree Geetha, Angelina Department of Computer Science and Engineering B S Abdur Rahman Institute of Science & amp Technology Chennai India Department of Computer Science and Engineering Hindustan Institute of Science and Technology Chennai India Dept of Computer Applications BSA Crescent Institute of Science and Technology Chennai India

Face recognition is a fast-growing technology that is widely used in forensics such as criminal identification, secure access, and prison *** contrasts from other classification issues in that there are normally a more number of classes in face recognition. Thus, the validation accuracy of the conventional subsystem face recognition is unsuitable. In order to overcome the problems that are likely to be encountered, the proposed method utilizes the Spider Monkey inspired Optimization (SMO) algorithm for face clustering and recognition. At first feature reduction is done by Advance Principal Component Analysis (APCA) which is a hybridization of PCA and linear discriminant analysis (LDA). After reducing the features, the selected features are grouped and faces are recognized by means of SMO algorithm. From the food search behavior of spider monkeys, SMO algorithm is developed. The performance of the proposed method is evaluated by means of sensitivity, specificity, and accuracy. Here the proposed method accomplishes 98.66% accuracy. Using the MATLAB platform, the recommended technique is implemented. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Accuracy Linear discriminant analysis Principal component analysis Sensitivity Specificity Spider monkey optimization

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Analysis of Eye Disease classification by Fundus Images using Different Machine/Deep/Transfer Learning Techniques 4

Analysis of Eye Disease classification by Fundus Images usin...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Sharma, Himanshu Wasim, Javed Sharma, Pankaj Mangalayatan University Computer Science & Engineering Uttar Pradesh India Mangalayatan University Department of Computer Engineering and Applications Uttar Pradesh India Eshan College of Engineering Computer Science & Engineering Mathura India

ISBN: (纸本)9798350307757

The fundus image is a crucial diagnostic tool for diseases like diabetic retinopathy (DR) because it catches a retina, a back of an eye. This issue is most prevalent among diabetics and a leading contributor to blindness, particularly in young adults and those engaged in the workforce. Detecting DR in its early stages is crucial in reducing the likelihood of blindness in patients. An ophthalmologist performed DR screening and looked for lesions on the fundus imaging. Intelligent systems have the ability to make early identification of DR more efficient, especially with the recent advancements in machine learning in healthcare. This study compares and contrasts many methods for detecting DR in fundus images. This study lays out methods for improving upon pre-trained architectures such that they can correctly classify fundus images, allowing for early detection and prediction of diabetes. A concatenate model is presented in this comparative study as a method for classifying fundus images using ML and deep/transfer learning. Various model architectures are used, including SVM, Gradient Boosting, Efficient Net, Efficient-Net B4 and B7, MLP, CNN, ResNet, VGG-19, and DNN, among others. The models' accuracy, F1-score, recall, and precision are measured in experimental findings that compare them. Our strategies outperform a single model with results ranging from 84% to 96% accuracy, respectively. They found that our ML- DL-TL methods work well for automated DR classification employing fundus image data in this experiment. © 2024 IEEE.

关键词： Intelligent systems

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GD-STFA: Gradient Descent Sea Turtle Foraging Algorithm enabled Deep Q Network for Diabetic Retinopathy Detection

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Multimedia Tools and applications 2024年第18期83卷 53817-53836页

作者： Sabeena, A.S. Jeyakumar, M.K. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil - 629180 Tamilnadu Thuckalay India Department of Computer Applications Noorul Islam Centre for Higher Education Kumaracoil - 629180 Tamilnadu Thuckalay India

Diabetic Retinopathy (DR) refers to a retinal disease that happens due to high glucose levels in the blood, and it leads to blindness globally. In order to delay or avoid vision loss and degradation, early detection, and treatment are necessary. Hence, the conception of an automated technique for precise DR recognition is necessary. Thus, a Deep Q network (DQN) with a Gradient Descent-Sea Turtle Foraging Algorithm (GD-STFA) is proposed that inherits Gradient Descent (GD) in Sea Turtle Foraging Algorithm (STFA), to detect DR from images. This method involves various phases, like pre-processing, segmentation, as well as DR detection. Here, the DR image quality is enhanced in the pre-processing phase using a median filter. After that, the segmentation is done by utilizing Swin-UNet and it is trained using the proposed GD-STFA algorithm. Finally, the DQN trained with GD-STFA finds if a person has DR or normal from his retinal images. The experimentation is performed in detail and validated on the Indian Diabetic Retinopathy Image Dataset benchmark dataset. Ultimately, the experimental outcomes demonstrate that the model is better than traditional techniques regarding optimal accuracy of 0.98, sensitivity of 0.984 and specificity of 0.971. Also, regarding the segmentation accuracy, the proposed model is around 0.906 accurate with a Dice coefficient of 0.963. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2023.

关键词： Median filters

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Artificial intelligence-inspired comprehensive framework for desertification prediction

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Improving recommendations utilizing users’ demographic information

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Quality and Quantity 2024年第6期58卷 5559-5575页

作者： Dey, Avick Kumar Dutta Pramanik, Pijush Kanti Singh, Pradeep Kumar Choudhury, Prasenjit Department of Computer Applications DSMS College West Bengal Durgapur India School of Computer Science & amp Engineering Galgotias University Greater Noida India Department of Computer Applications & amp Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science & amp Engineering National Institute of Technology Durgapur West Bengal India

The exponential increase in digital data has increased the amount of available online information. This complicates the user’s decision-making. Most online merchants and service providers utilize recommendation systems to solve this problem and meet customer needs. The traditional collaborative filtering based approach faces enormous challenges in providing potential personalized recommendation results. The demographic information of users may improve personalized recommendation results. This research proposes an improved recommendation approach based on users’ demographic information. Compared with traditional collaborative filtering-based approaches, this approach provides improved results. The experimental results show the enhanced prediction accuracy of the proposed approach and significantly lower errors when experimenting with the MovieLens dataset. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Collaborative filtering Demographic information MAE Recommendation system RMSE

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