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An Efficient Attendance Management with Deep Learning

An Efficient Attendance Management with Deep Learning

2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Shivanna Hema, H.P. Janardhana, D.R. Srinivas, P.M. Sahyadri College of Engineering and Management Department of Computer Science and Engineering Mangaluru India D S Dinakar Polytechnic College Department of Electronics and Communication Engineering Shivamogga India Nitte Meenakshi Institute of Technology Department of Computer Scince and Engineering Bengaluru India

ISBN: (纸本)9798331517892

Accurate attendance recording is critical in educational institutions to ensure students' and teachers' consistency and discipline. Traditional manual attendance systems are frequently inefficient and prone to errors, such as proxy attendance and missed entries, which may impact the accuracy of attendance records. To address these challenges, an automatic attendance system that uses camera-based facial recognition technology is proposed. The latest technology records accurate attendance by taking visuals of students as soon as the class starts and comparing them to an already-existing database. Face recognition happens through human detection. The concept used here is deep learning. For students, this automated solution streamlines attendance management by ensuring that records are accurate and up to date without the need for manual entry. The system efficiently keeps track of the presence of teachers, notifying the Head of Department (HOD) right away in the case of an excused absence, therefore preventing disruptions during scheduled lessons. This technology significantly increases the reliability and efficiency of attendance tracking by automating the process. Enhancing record accuracy and accountability leads to a more regulated and effective learning environment that benefits teachers and students alike. This technological method offers greater precision and oversight, leading to improved institutional administration and educational outcomes. © 2024 IEEE.

关键词： Students

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An incremental clustering using bat-spotted hyena optimiser with spark framework

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International Journal of Intelligent Information and Database Systems 2023年第2期16卷 167-195页

作者： Vidyadhari, Ch. Sandhya, N. Ramakrishnaiah, N. IT Department Department of Computer Science and Engineering JNTUK Kakinada India VNR Vignana Jyothi Institute of Engineering and Technology Telangana 500090 India CSE Department University College of Engineering JNTUK Kakinada India

Recently, clustering techniques gained more importance due to huge range of applications in the field of data mining, pattern recognition, data clustering, bio informatics and many other applications. In this paper, a new approach called spotted hyena bat algorithm (SHBA)-based incremental clustering with spark framework is proposed. The SHBA algorithm is derived by integrating the spotted hyena optimiser (SHO) and bat algorithm (BA), that is highly desirable for handling high dimensional data and provides a unique solution with high satisfactory results. The process of incremental clustering is performed in a spark framework by considering the master and the slave nodes. The proposed approach effectively clusters the data, especially high dimensional data and is more robust against various attacks and provides more unified solution. Moreover, the proposed SHBA achieves higher performance by considering the evaluation metrics, such as Jaccard coefficient, rand coefficient, and clustering accuracy of 0.950, 0.943, and 0.962. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Clustering algorithms

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Facial Emotion Recognition for Virtual Customer Service Agents

Facial Emotion Recognition for Virtual Customer Service Agen...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Gupta, Shaswat Saxena, Srishti Verma, Vandana Nishad, D.K. Galgotias College Of Engineering. & Technology Department Of Computer Science And Engineering Greater Noida India Dsmnru Department Of Electrical Engineering UP Lucknow India

ISBN: (纸本)9798350366846

Due to the rapidly changing customer support environment, firms are using virtual agents to improve customer interactions, reduce response times, and deliver better solutions to customer questions, concerns, and issues. Virtual agents must understand and respond to user emotions to enable more human-like interactions. Face recognition, or Facial Expression Recognition (FER) holds significant importance in the field of computer vision and artificial intelligence to understand the emotions of the eyes. Deep learning is used to classify face expressions into emotion categories in this study. We train and test our models on the CK+ dataset for high accuracy. We also offer an interactive online interface for real-time emotion recognition using the Streamlet library. FER improves virtual customer care agents' emotional intelligence and user experience, as shown by our studies. © 2024 IEEE.

关键词： Emotion Recognition

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Optimization and Representation of Non-Slicing VLSI Floorplanning 4

Optimization and Representation of Non-Slicing VLSI Floorpla...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Jeyarohini, R. Britto, K. R. Aravind Ramkumar, M.P. PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350300888

The recent advances in the semiconductor nano technologies increase the complexity of very large-scale integration circuits. With the fabrication technology entering the deep Nanoscale era more demand at greater huge complication of integrated chip design arises. The development of eccentric floor plan without over-lapping is the intention of VLSI floor planning. The major objective of VLSI is concerned with floor plan area minimization and wire length optimization. Floor planning is the important step that creates an optimal layout solution for the VLSI circuits. This article attempted to collect the necessary information in reducing the floor plan layout by means of reducing the wire length, white space area with the employment of computer aided metaheuristic algorithms. In order to ease the process of execution the best local search algorithms like a Particle Swarm Optimization and Simulated Annealing algorithms is compared. © 2023 IEEE.

关键词： Simulated annealing

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Mixture Kernel based Least Square Support Vector Machine for Personality Prediction on Social Media 1

Mixture Kernel based Least Square Support Vector Machine for...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Rani, M. Jamuna Adnan, Myasar Mundher Saranya, N. Naga Karuna, Gotlur Margarat, G. Simi Sona College of Technology Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

The prediction of the human's activities and the personality from the social media platforms attains significant growth between the researchers. The statistical data over the human activities represented through the social media platforms is important benefit for the analysis in the predictions of the personalities and other attitudes of the humans. However, the challenges in personality prediction are to introduce effective approach which determines an individual's personality traits from various behavioural data. Hence, to tackle these issues, this research proposes the Mixture Kernel based Least Square Support Vector Machine (MK-LSSVM) approach for the personality prediction. The utilization of mixture kernels in SVM, enables for modelling the challenging relationships in the data, capturing various types of input features and enhancing the prediction accuracy. The standard datasets such as Essay and Myers-Briggs Type Indicator (MBTI) are utilized to implement the MK-LSSVM approach. The Term Frequency and Inverse Gravity Moment (TF-IGM) approach is utilized for the feature extraction, which determines the relationship between the feature sets and features from the datasets. The implemented results demonstrates that the MK-LSSVM approach reaches the superior average accuracy of 89.72% and average precision of 0.89 as compared to the CatBoost algorithm. © 2024 IEEE.

关键词： Prediction models

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Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM

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computer Systems science & engineering 2022年第12期43卷 1057-1068页

作者： S.Priyadarsini Carlos Andrés Tavera Romero Abolfazl Mehbodniya P.Vidya Sagar Sudhakar Sengan Department of Computer Science and Engineering P.S.R.Engineering CollegeSivakasiTamil Nadu626123India COMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de CaliCali76001Colombia Department of Electronics and Communications Engineering Kuwait College of Science and TechnologyKuwait Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAndhra Pradesh522502India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveliTamil Nadu627152India

In the recent days, the segmentation of Liver Tumor (LT) has beendemanding and challenging. The process of segmenting the liver and accuratelyspotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of theliver create difficulties during liver segmentation. The manual segmentation doesnot provide an accurate segmentation because the results provided by differentmedical experts can vary. Also, this manual technique requires a large numberof image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-AngleTexture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from itsneighbouring soft tissues with related strength. Then, the filtered images are givenas the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection(CED) technique, which segments the Liver Region (LR) from the given CTimages. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error(VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applyingthe MAT-ACM, which accurately segments the LR from the segmented LRs. Theevaluation of the proposed method is compared with the existing LTS methodsusing various performance measures to prove the superiority of the proposedMAT-ACM method.

关键词： Computed tomography contrast enhancement adaptive edge modeling multi-angle texture active contour liver tumor segmentation

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AD-OCT: An Early Alzheimer Disease Detection Method via OCT Images using Advanced Feature Extraction and Clustering Techniques 2

AD-OCT: An Early Alzheimer Disease Detection Method via OCT ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mahendran, Rakesh Kumar Kalaiyarasi, B. Krithik Karan, K. Thirunavukkarasu, Usharani Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of BioMedical Engineering Chennai India Saveetha University Saveetha School of Engineering SIMATS School of Engineering Department of BioMedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331534950

for efficient management and treatment whereas the researchers focus on the various approaches including biomarkers, imaging and techniques like Magnetic resonance imaging (MRI), Optical Coherence Tomography (OCT) and advanced Deep learning (DL) algorithms to identify the early signs of the disease before significant cognitive decline occurs. DL holds noteworthy promise in proceeding Alzheimer's research and patient care. Conventional models have traditionally faced challenges in capturing the intricate details and subtle features within OCT images that are crucial for early detection of AD, leading to superior performance. Imbalanced datasets, where positive cases of Alzheimer's are far fewer than negatives, often bias models toward the majority class, which hampers accurate detection. Moreover, current models often struggle to accurately pinpoint pathological structures within OCT images, which are essential for dependable diagnosis, presenting a significant hurdle and furthermore, limiting the generalization of models to unseen data and affecting their clinical utility. By overcoming these issues, Authors have approached an Advanced Feature Extraction and Integration Techniques by this method Authors can perceive the AD earlier. Initially Authors provide the OCT images in the Integrated Residual Enhanced Attention Module (IREAM) which combines the strengths of residual learning and attention mechanisms and the extracted features are now refined and clustered using the Cluster Feature refinement (CFR) and Clustered Feature Fusion (C2F) which improves the quality and the extracted features are merged to get a unified set. Finally, Author's model is compared with the existing model and Authors calculate the balance accuracy, Recall, AUC, Dice coefficient and F1-Score of the proposed model. © 2024 IEEE.

关键词： Optical coherence tomography

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Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation

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Chinese Journal of Electronics 2023年第4期32卷 896-907页

作者： ZHANG Zhe WANG Bilin YU Zhezhou ZHAO Fengzhi College of Computer Science and Technology Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry

Weakly supervised semantic segmentation using only image-level labels is critical since it alleviates the need for expensive pixel-level labels. Most cuttingedge methods adopt two-step solutions that learn to produce pseudo-ground-truth using only image-level labels and then train off-the-shelf fully supervised semantic segmentation network with these pseudo labels. Although these methods have made significant progress, they also increase the complexity of the model and training. In this paper, we propose a one-step approach for weakly supervised image semantic segmentation—attention guided enhancement network(AGEN), which produces pseudopixel-level labels under the supervision of image-level labels and trains the network to generate segmentation masks in an end-to-end manner. Particularly, we employ class activation maps(CAM) produced by different layers of the classification branch to guide the segmentation branch to learn spatial and semantic ***, the CAM produced by the lower layer can capture the complete object region but with many ***, the self-attention module is proposed to enhance object regions adaptively and suppress irrelevant object regions, further boosting the segmentation *** on the Pascal VOC 2012 dataset demonstrate that AGEN outperforms alternative state-of-the-art weakly supervised semantic segmentation methods exclusively relying on image-level labels.

关键词： Training Annotations Semantic segmentation Scalability Semantics Benchmark testing Boosting

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Enhancement of Photovoltaic Dust Detection Using Deep Learning Technique

Enhancement of Photovoltaic Dust Detection Using Deep Learni...

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

作者： Gracia, R. Lillian Priyadharsini, C. Caroline, B.Elizabeth Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Ifet College Of Engineering Division Of Electronics And Communication Engineering Villupuram India

ISBN: (纸本)9798350371567

The dust collection on solar panels situated in solar parks is a common problem faced by PV system operators, particularly in arid and dusty regions. Dust deposition reduces the transparency of the panel surface, leading to decreased sunlight absorption and thus reduced energy conversion efficiency. Traditional methods for detecting dust accumulation involve manual inspection or periodic cleaning schedules, both of which are labor-intensive, time-consuming, and often inefficient. Recent developments in deep learning and machine vision have made autonomous dust detection techniquesfeasible. Numerous techniques have been employed to identify and measure dust on solar panels, such as machine learning models and image processing algorithms. In this paper, the dataset of clean and dusty images of the solar panels utilized wasapplied to four state of the art models and then the EfficientnetB0 model was proposed to detect the dust over the solar panels. On comparing the EfficientnetB0 model with the other state of the art models, EfficientnetB0 yielded an impressive accuracy rate of approximately 85.92%. Further research and development in this field hold the potential to revolutionize the maintenance and operation of solar power plants, contributing to a greener and more sustainable future. © 2024 IEEE.

关键词： Solar panels

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Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly Detection 3

Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly De...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Krishnan, R. Santhana Gopikumar, S. Muthu, A. Essaki Raj, J. Relin Francis Kumari, D. Abitha Malar, P. Stella Rose Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical AndTechnical Sciences Department of Electronics and Communication Engineering Chennai India Rmk Engineering College Department of Computer Science and Engineering Thiruvallur India J.P. College of Engineering Department of Electronics and Communication Engineering Thenkasi India

ISBN: (纸本)9798350375190

In various regions, the maintenance of manholes is imperative, given the potential consequences for public health and safety. Neglecting this responsibility can result in severe outcomes, including loss of lives and the spread of diseases within the community. To address these challenges, an advanced anomaly detection system is proposed, leveraging deep learning with Autoencoders. Notably, the entire system operates on solar power, aligning with a commitment to environmentally sustainable practices. At the core of the system's functionality is the Autoencoder, a deep learning model to differentiate complex manhole conditions. The Autoencoder is trained on normative sensor data, capturing nuanced patterns inherent in routine manhole states. This training enables the Autoencoder to perform real-time anomaly detection by swiftly identifying deviations such as the presence of noxious gases, irregular sewage levels, or unauthorized attempts at access. When anomalies are detected, signaling potential hazards, the Autoencoder triggers alert messages disseminated to municipal authorities via a cloud server. This proactive approach empowers timely intervention, particularly critical in scenarios involving toxic gases, ensuring the safeguarding of maintenance personnel. Additionally, the system integrates an NFC reader, streamlining authorized access for designated personnel while upholding robust security measures. In essence, the incorporation of Autoencoders into the system furnishes an intelligent anomaly detection infrastructure for manhole surveillance. By continually adapting to normative patterns, the Autoencoder enhances the system's capacity to identify and promptly communicate potential risks, thereby improving the overall safety and efficiency of manhole maintenance practices. © 2024 IEEE.

关键词： Internet of things

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