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检索条件"机构=Computer Applications in Science and Engineering"

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Deep Learning Techniques for Human Resource Management Optimization

Deep Learning Techniques for Human Resource Management Optim...

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

作者： Ranawat, Pratyaksha Kumar, Manish Karthik, A. Asha, V. Nagpal, Amandeep Shaik, Bazani Jecrc University Computer Science Engineering India Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Computer Applications Bangalore India Lovely Professional University Phagwara India Ramachandra College Of Engineering Department Of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350366846

Because deep learning and machine learning (ML) techniques have the potential to totally change how firms manage their human capital, there has been a lot of interest in developing HRM procedures that use these techniques. Strong DSS (Decision Support System) technology must be incorporated into the HRM (Human Resource Management) profession in order to make judgements that are effective in today's competitive environment. This study focuses on the problem of prediction, decision making, prediction, and testing assistance in a HRM system. The paper discusses a creative decision support system for HR procedures. Machine learning and deep learning techniques have been offered as fundamental instruments for tracking various HR indicators in the systems developed and implemented analytical process. The description of the suggested methodology and a discussion of a outcomes from the experiments are included in the paper. © 2024 IEEE.

关键词： Decision making

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Dynamic Key Distribution and Update in Sensor Network 2

Dynamic Key Distribution and Update in Sensor Network

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2nd International Conference on Pervasive Computing Advances and applications, PerCAA 2024

作者： Vishal Dutt, Krishan Srivastava, Animesh Dutta, Neha Bali, Milan Deep Kaur Komal School of Computer Applications Lovely Professional University Punjab Jalandhar India Department of Computer Science & Engineering Graphic Era Hill University Dehradun India Department of Computer Science & Engineering Chandigarh University Chandigarh India Department of Computer Science & Engineering Chandigarh Group of Colleges Janjheri India

ISBN: (纸本)9781837242627

Key distribution in sensor nodes is one of most complex process to create a secure network. There are many innovations used for secure key distribution among the nodes. But main constraints come in computation, storage and mutual authentication using heavy communication protocols. problem exist in decision for generation and distribution of keys. Master nodes have large key pools, still problem exists in selection and distribution of key rings among large number of nodes in particular deployment areas. Dynamic deployment and revocation of keys is fundamental problem that exit in network. © The Institution of engineering & Technology 2024.

关键词： Key Distribution Mutual authentication Network topology Wireless Sensor Network

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Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Hybrid Approach for the Classification and Detection of Brain Tumor 2

Hybrid Approach for the Classification and Detection of Brai...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Rekha, S. Geetha, K. Gireesh Gokulavasan, B. Patil, Harshal Maranan, Ramya Psg College of Arts and Science Department of Computer Science Coimbatore India Sri Krishna Adithya College of Arts and Science Department of Computer Applications Coimbatore India Mohan Babu University Department of Electronics and Communication Engineering Tirupati India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350373110

Brain tumors (BT) are a leading cause of global mortality, affecting all ages, with early detection being crucial for successful treatment. Traditional biopsy-based categorization is invasive and often occurs post-surgery, highlighting the need for non-invasive diagnostic methods. This study introduces a hybrid machine learning (ML) model combining Support Vector Machine (SVM) and Gaussian Naive Bayes (GNB) to classify glioma, meningioma, pituitary tumors, and normal brain tissue from MRI scans. Trained on MRI images from Kaggle, the hybrid model's performance was compared to standalone models (SVM, GNB, Linear Discriminant Analysis, and Random Forest), achieving superior accuracy (96.19%), TPR (95.83%), TNR (96.53%), precision (96.06%), and F1-score (98.50%). BTs are notoriously aggressive, and misdiagnosis can significantly reduce patient lifespans. The hybrid model addresses the limitations of traditional ML models, enhancing diagnostic accuracy, reducing unnecessary surgeries, and expediting treatment initiation, thereby improving patient outcomes. © 2024 IEEE.

关键词： Support vector machines

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Deep Learning-Based Object Recognition in AR-VR Environment

Deep Learning-Based Object Recognition in AR-VR Environment

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Sharma, Suchi Diwakar, Manoj Kumar, Priyansh Joshi, Aditya Singh, Prabhishek Mehra, Gunjan Graphic Era Deemed to be University Department of Cse Dehradun India Graphic Era Deemed to be University Department of Computer Applications Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331527495

Artificial intelligence (AI), AR-VR (Augmented Reality and Virtual Reality) have recently made significant strides, paving the way for innovation and change in a wide range of fields and industries. The quick advancements in AR-VR and computer vision (CV) made it easier to comprehend and analyze the surroundings. In this study, works that combined augmented, virtual reality (AR-VR) and deep learning for object recognition throughout the recent years are thoroughly reviewed and presented. The three primary focuses of this paper are: 1) List out the object identification techniques used in AR-VR in detail, 2) Describe the techniques, tools, and frameworks that are being utilized to create various types of AR- VR object detection, 3) Discussing object detection in AR-VR in detail and 4) Concluding the problems and identifying possible study fields in the AR-VR environment. © 2025 IEEE.

关键词： Deep learning

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Predicting Pest Infestation Patterns with Graph Convolutional Networks (GCN) in Precision Farming 3

Predicting Pest Infestation Patterns with Graph Convolutiona...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Sireesha, Marri Hussein, Laith Manju, M. Johri, Prashant Kuppuraj, T. Aanandha Saravanan, K. Department of Computer Science and Engineering CMR Technical Campus Hyderabad India College of technical engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Kongu Arts and Science College Department of Computer Science TamilNadu Erode India School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798331542375

Due to the increases pest impacts on agricultural productivity, effective pest prediction models are required for efficient pest control in precision farming. The framework outlined in this research uses a Graph Convolutional Network (GCN) to estimate pest infestation by exploiting spatial and temporal relationships present in agri-ecosystems. The methodology combines weather, soil, pest, and crop health data into a graph-based representation of spatial units, and the directed connections between them that reflect their proximity and climatic similarity. The GCN converts these graphs to learn such complex relationship as the infestation prediction at various severity levels. The proposed model has produced a better accuracy of 94.8%, better precision of 95.2%, better recall of 94.5% and better F1-score of 94.8% as compared from Random forest, SVM, LSTM, CNN as per the comparison of table 3. Comparing them focuses on graph construction approaches and temporal aspects, mixed P/S and temporal emb has improved the model performance. This framework is practical for further pest management and employing the result without harming the environment. The results show the effectiveness of GCNs for use in pest prediction in precision farming and the opportunity to develop sustainable and scalable data-based agriculture. © 2025 IEEE.

关键词： Weed control

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Predictions of Basketball Match Results using Autoencoder with Gated Recurrent Unit Algorithm 1

Predictions of Basketball Match Results using Autoencoder wi...

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

作者： Kavitha, K.R. Almusawi, Muntather Vybhavi, Gummagatta Yajaman Edwin Prabhakar, P.B. Saranya, N. Naga 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 Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350352931

This research explores basketball match predictions within the field of sports analytics utilized Deep Learning (DL) technique to analyse the outcomes of National Basketball Association (NBA) games based on player performance and team statistics. The challenge in predicting NBA games outcomes and performance analysis various factor, as the high dimensionality of the data and uncertainty surrounding player due to difficult to predictions of basketball game. This paper, proposed Autoencoder with Gated Recurrent Unit (AE-GRU) algorithm to efficiently analyse and predict NBA game outcomes. The data was initially collected from NBA games between 2009 - 2021, focusing on five teams and pre-processing involved min-max normalization to uniformly scale the feature vectors. The Principal Component Analysis (PCA) utilized for feature selection, effectively reducing the high dimensionality of the NBA game data across various teams. The AE-GRU technique then captured the complex feature of basketball game data and handled uncertainty data and enhances performance. The performance of DL model evaluated the various metrics such as accuracy, precision, recall and f1-score metrics on NBA game. © 2024 IEEE.

关键词： Deep learning

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Hindi News Article's Headline Generation based on Abstractive Text Summarization

Hindi News Article's Headline Generation based on Abstractiv...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Kumar, Jeetendra Shekhar, Shashi Gupta, Rashmi Atal Bihari Vajpayee University Dept. of Computer Science and Application Bilaspur India Gla University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350300826

Text summarization is a technique that converts large text into shorter ones by keeping its important information. Most of the available summarizers, summarize English language text. The availability of summarizers in other languages is rare. In the proposed work, abstractive text summarization (ATS) for the Hindi language has been used to generate headlines from news text. To build the model, a Hindi news dataset has been used that contains different news articles related to sports, politics, crime, etc. After pre-processing the text, Word2Vec method has been used for word embedding. ATS model has been built using the Seq2Seq model with attention. After that, we have calculated evaluation metrics like ROUGE and BLEU scores of the result summaries. We have also fine-tuned two pre-trained models, indicBART and multi-lingual variant of T5. After result comparison, it was found that the indicBART perform better than the other two models, considered for the experiment. © 2023 IEEE.

关键词： Text processing

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Deep Learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep Learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. This improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion Recognition

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Improved Contextual Understanding and Emotion Detection in Large-Scale Text Data with Hybrid Deep Learning Models 5

Improved Contextual Understanding and Emotion Detection in L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Thilakavathy, P. Manikandan, G. Deepa, R. Jayalakshmi, V. Surendran, R. Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of science and technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

With the growth of large-scale text datasets from reviews, social media, and other online sources, sentiment analysis is essential for understanding public opinion. Traditional models struggle to understand complicated linguistic sentiments and interdependencies. This research could improve sentiment classification in customer feedback analysis, market sentiment predictions, and social media monitoring. Addressed issues include ambiguous language, sentence context flipping, and the need for robust models that can manage massive datasets. Contextual Detection Text Analysis Using Deep Learning (CDTA-DL) improves contextual knowledge and emotion identification in this research. RNNs and CNNs are used in the CDTA-DL approach to extract spatial and sequential information from text. CDTA-DL helps large-scale sentiment analysis detect sarcasm, implicit emotions, and many expressions. In a comprehensive simulation investigation employing open-access sentiment analysis datasets, CDTA-DL surpassed standard deep learning models in accuracy, recall, and F1 scores. The recommended strategy may increase emotion recognition and contextual understanding, making it ideal for large-scale sentiment analysis in various domains. © 2024 IEEE.

关键词： Contrastive Learning

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