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检索条件"机构=Computer Science and Engineering Panimalar Engineering College"

共 112488 条记录，以下是21-30 订阅

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Contour Edge Mask Filtered Residual Network based Egg Classification 8

Contour Edge Mask Filtered Residual Network based Egg Classi...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Devi, M. Shyamala Sangeetha, V. Reshmi, R. Saima Nooreen, S. Rajeshwari, C. Sai Pranavi, N. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331505462

In agricultural and food safety, classifying eggs is a crucial activity that requires great precision in distinguishing various kinds of egg. Traditional approaches can be expensive and error-prone since they frequently rely on manual examination or simple image processing techniques. In order to improve classification performance, this research proposes Contour Edge Mask Filtered ResNet (CEM-ResNet), which combines advanced deep learning techniques with edge-detection methods. The Egg Dataset containing 2000 egg images that was used for implementation. The suggested CEM-ResNet model uses contour edge masking to eliminate extraneous background noise and highlights key structural information while utilizing ResNet architecture to capture essential details of eggs. The suggested CEM-ResNet model uses contour edge masking to eliminate extraneous background noise and highlights key structural information while utilizing ResNet architecture to capture essential details of eggs. Initially, the CEM-ResNet model organizes the egg into two groups as Chicken egg and Duck egg. The organized eggs are performed with labeling and then subjected to data augmentation to end up with 42,000 images. The augmented egg images are converted to grayscale images. The grayscale images are processed with edge detection model to finalize the contour filtered egg images and ResNet model. The contour filtered egg images are refined to create the edges and then the mask was created to generate Contour Edge Mask Filtered (CEM) egg images. The ResNet model was refined to process the CEM egg images. With a high accuracy of 99.64%, the implementation reveals that the proposed CEM-ResNet model outperforms towards the classification task. © 2024 IEEE.

关键词： Deep learning

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Predicting Accident-Prone Zones Using Machine Learning: A Web Application Approach 1

Predicting Accident-Prone Zones Using Machine Learning: A We...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Devi, M. Shyamala Roshni, K. Shamili, R. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350376517

The increasing incidence of road accidents poses significant challenges to public safety and urban planning. Effective risk reduction techniques are necessary because traffic accidents represent a serious danger to public safety. In order to identify accident-prone areas, this research analyses historical traffic data, which includes information on accident instances, environmental factors, road characteristics, and traffic flow patterns. The goal of this project is to create an intuitive online tool that helps policymakers, traffic authorities, and urban planners visualize danger zones while collecting useful information. This paper presents a machine learning-based web application that predicts accident-prone zones and sends real-time notifications to users navigating these areas. With the application's dynamic user interface, users may see high-risk areas on a map and obtain comprehensive analytics to help them make wise decisions. By integrating historical accident data with geographic information, the application employs a Random Forest model to analyze and forecast risk zones, thereby enhancing road safety. User feedback demonstrates the application's efficacy and usability, revealing an accuracy of 97.63% in predictions and providing a proactive approach to accident awareness. Stakeholders may highlight initiatives by using the data, which identify important variables contributing to accidents. The objective of this tool is to improve road safety and lower the frequency of traffic accidents by enabling data-driven decision-making. Future work will be directed towards the integration of real-time traffic data and the enlargement of the model's domain to encompass diverse geographical areas. © 2024 IEEE.

关键词： Decision trees

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Continuous Monitoring of Web Server Assaults Using Machine Learning 2

Continuous Monitoring of Web Server Assaults Using Machine L...

引用

2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Vishali, M. Mirudhula, A. Priya, A. Iswarya, M. Subramanian, Kavitha Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

Because of the widespread accessibility of the weband its ease of use, every organisation nowadays uses web apps to expand its company. With the growth of Due to growing online usage, attack riskhas grown as well. An effective monitoring tools that can recognise these threats Timely is necessary. Java script, PHP, and HTML are utilisedfor website development and database use of SQL management in depth. most typical web server assaults include XSS, DOS (Denial of Service), and SQL injection Cascading Style Sheets. IDS with rules work with keywords and patterns but fail to recognise unidentified assaults. In this article, ML based model is used for leveraging web server logsto identify intrusions. It identifies the sort of assault as well as if a given log is normal or malicious. By establishing a private network with the aid of an Apache WAMP server, web server logs are produced and gathered. © 2024 IEEE.

关键词： Machine learning

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Deep Masked Input UNet Framework based Lips Segmentation prediction for Gender Classification 2

Deep Masked Input UNet Framework based Lips Segmentation pre...

引用

2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Devi, M. Shyamala Hepzhibha Rachel, S. Janani, G. Irene, B. Joy Praisy, E. Humaira, S. Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350383867

Assessments have demonstrated that the human lip and its motions provide a wealth of knowledge about the identity and substance of communication. However, due to large differences in illumination condition, head perspective, and background, obtaining strong and precise lip image segmentation in natural settings remains difficult. This paper recommends Deep Masked Input UNet that categorizes the Gender based on the lip segmentation with high precision. For implementation, 10,132 face images from the KAGGLE Lip Segmentation Dataset were used. The dataset includes 5066 images of the face and 5066 segmented lip images. The proposed Deep Masked Input UNet starts by masking the original picture with a segmented lip image to create masked face images with lips segments. Utilizing the Relu Activation function, Deep Masked Input UNet with contracting and expansion was enabled. The masked lip images are fitted to the proposed Deep Masked Input UNet and traditional CNN models. The results show that the suggested Deep Masked Input UNet model performs better in lip segmentation and gender classification, with a high accuracy of 98.95%. © 2024 IEEE.

关键词： Image segmentation

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ResNet Blended Five Dense Layered Deep Fully Connected Neural Network based Road Damage Classification 2

ResNet Blended Five Dense Layered Deep Fully Connected Neura...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Keerthivasan, R. Jai Harshan, H. Kamalesh, P. Harishraj, B. Hibban Rashid, M.D. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

Operating a vehicle on a deteriorated roadway causes an enormous threat to safety, particularly for self-driving automobiles. Deep learning-based image processing technology has made it possible to study models that assess roadway conditions. Disparity of roadway damage and complicated backgrounds in roadway shots makes it an extremely difficult challenge classifying the road damage. To address this problem, this paper recommends ResNet Blended Five Dense Layered Deep Fully Connected Neural Network (RB-5DFCNN) that classifies the road damage with high efficiency. The KAGGLE Road Damage dataset, which consists of 2074 road snapshots, is used by the proposed RB-5DFCNN model. In order to classify road damage, the RB-5DFCNN model first preprocesses the images before fitting ResNet CNN, which consists of five steps of convolutional block and identity block processing. After the ResNet model completed its task, five dense layers were applied to the DFCNN in order to classify the state of road damage. The classification of road damage condition and the determination of the necessary number of dense layers to identify road damage with high accuracy are the innovative aspects of the proposed RB-5DFCNN. To test the effectiveness of the road damage categorization, the Road Damage dataset was applied to both the proposed RB-5DFCNN model and the current CNN approaches. Python execution on a GTX Geforce Tesla V100 Nvidia graphics card server over 64 and 150 training intervals shows that the suggested RB-5DFCNN model projects an accuracy of 98.77% in comparison to the state-of-the-art CNN models. © 2024 IEEE.

关键词： Statistical tests

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Auxiliary Dense Layered LeNet Deep Learning based Mango Variety Classification 2

Auxiliary Dense Layered LeNet Deep Learning based Mango Vari...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Janani, G. Fernandes, Jonita Maria Harshini, T. Nivetha, R. Joshika Rao, O.R. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

In the agriculture sector, physical classification of fruits is a costly process that can produce inconsistent outcomes due to human negligence. Fruit categorization from snapshots is an extremely difficult venture, especially for mango fruits because various species of mango fruits share significant resemblance. This paper proposes Auxiliary Dense Layered LeNet (ADL-LeNet) that classifies the mango type with high accuracy. The Mango Varieties Classification and Grading Dataset, which includes 1200 mango pictures was used for this work. The Mango image dataset is applied with the existing CNN model to select the best CNN model. The LeNet outperforms with the accuracy of 92.77% when compared to the other CNN model. Now LeNet is selected for finetuning the number of dense layers. Generally, the LeNet is composed of three convolutions, two subsampling followed by two dense layers. Now, the LeNet is modified by adding one extra dense layer at the end that compress the output further into vector size of 42 to suggest the proposed model ADL-LeNet. The ADL-LeNet composed of three convolutional, two subsampling and three dense layers ending with the final output layer. The Mango Image Dataset has been applied with the existing CNN and proposed ADL-LeNet. The implementation shows that the proposed ADL-LeNet model outperforms with the high accuracy of 97.6% in mango type classification. © 2024 IEEE.

关键词： Fruits

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Boosting Software Quality: A Machine Learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 IEEE.

关键词： Data reliability

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AI Based Anomaly Detection for Bank Security Against Theft 3

AI Based Anomaly Detection for Bank Security Against Theft

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Ramesh Prabhakaran, R. Quamar, Danish Guhan, P. Angel Maanu, P. Sherine, W.B. Raj, Aman Mizoram University Department of Computer Engineering Mizoram India Panimalar Engineering College Department of Electrical and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331528201

A critical component of video surveillance research and real-world applications is the detection of anomalous events. In order to improve public safety, more and more surveillance cameras are being installed in public spaces like roadways, crosswalks, banks, and shopping centers. Identification of odd occurrences, such as car accidents, crimes, or illicit activity, is a crucial duty in video surveillance. Because anomalous events are less often than normal activities, the goal of an efficient anomaly detection system is to quickly identify departures from the average and identify the abnormality's temporal span. One way to think of anomaly detection is as an early stage of video analysis that separates abnormalities from normal patterns. Using classification techniques, an anomaly can be further classified into particular activities once it has been discovered. An overview of anomaly detection with an emphasis on banking operations is given in this paper. Many every day and recurring transactions in the banking industry affect a number of stakeholders, including staff members, clients, debtors, and outside parties. Early detection of these anomalies can reduce or even eliminate any potential bad effects. In order to distinguish between normal and abnormal events, this work employs an anomaly detection technique based on machine learning. © 2024 IEEE.

关键词： Banking

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Watch Product Categorization through Conv2D Sequential Covolutional Neural Network with Truncated Normal Kernel Initializer 2

Watch Product Categorization through Conv2D Sequential Covol...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Harivishnu, K.C. Jamal Mohammed, R. Jagan, S. Gurucharan, G. Mohamed Arshad, M. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

For the sake of customer satisfaction and high profitable revenue, the products are designed in the Ecommerce application along with the product category. Based on the product category, prices may differ in a broad range. So, the application must be able to categorize the products before releasing the price voucher to the customers. The absence of effective product categorization, made it challenging to provide deep learning algorithm that results in the usage of modern tools towards product classification. This paper proposes Truncated Normal Kernel Initializer Conv2D (TNKIC) Sequential model for categorizing the watch product. The suggested TNKIC model includes the Watche Image Dataset from KAGGLE which consists of 2000 pictures divided into 10 groups. To classify watch goods, pictures are preprocessed and fitted using the TNKIC model. Three convolutional layers, a max pooling layer, and three dense layers that are weight initialized using a Truncated Normal Kernel initializer are preprocessed on the watch pictures in order to classify the watch products. The watch images are splitted into training and testing images. The TNKIC model is examined to determine how well the suggested model works in the watch product categorization than the current conV2D models after being trained with watch images. When compared to current conV2D models, implementation observations using Python on a Gtx Geforce Tesla V100 Nvidia GPU workstation utilizing 64 and 250 training intervals show that the suggested TNKIC model classifies the watch product category with the accuracy of 97.88%. © 2024 IEEE.

关键词： Deep learning

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Encrypt Decrypt ReLU Activated UNet Prototype based Prediction of Leaf Disease Segmentation

Encrypt Decrypt ReLU Activated UNet Prototype based Predicti...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Shyamala Devi, M. Eswar, R. Hibban Rashid, M.D. Jai Harshan, J.H. Hari, P. Hemanth, S. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798331540685

The issues of sustainable agriculture and climate change are intimately linked to the problem of effective leaf disease prevention. In practice, the primary method for identifying and detecting plant diseases is expert observation with the unaided eye. However, this necessitates for the ongoing supervision of specialists, which can be too costly for big farms. The automatic identification of leaf diseases is a significant area of research because it might be useful for monitoring vast agricultural fields and identify diseases based only on symptoms that occur on the leaves of the plants. This paper recommends ReLU Activated UNet (RAA-UNet) that categorizes the leaf diseases with high precision. The KAGGLE Leaf disease Dataset that contains 2094 leaf images were used for implementation. The dataset consists of 1047 leaf image and 1047 respective leaf images that have been segmented. The proposed ReLU Activated UNet initiates by creating masked leaf images by masking the original image with a segmented leaf image. To classify the leaf disease, the masked leaf images are assigned to ReLU Activated UNet with contracting and expansion which was activated using Relu Activation function. The proposed ReLU Activated UNet and conventional CNN models are fitted to the masked leaf images. As demonstrated by the experiments, the proposed ReLU Activated UNet model works better with a high accuracy of 98.85% in the classification of leaf disease like leaf spot, blight and powdery mildew. © 2024 IEEE.

关键词： Accuracy Activation CNN ReLU Segmentation UNet

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