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检索条件"机构=Dept. of Computer Engineering and Application"

共 251 条记录，以下是11-20 订阅

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360° Security in Cameras Using Linear Binary Pattern Histogram 5

360° Security in Cameras Using Linear Binary Pattern Histog...

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5th International Conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Faujdar, Neetu Chaudhary, Ankur Gla University Dept. of Computer Engineering and Application Mathura India

ISBN: (纸本)9798350321425

This research study proposes a novel approach for enhancing the security of surveillance cameras by using a combination of panoramic cameras and linear binary pattern (LBP) histograms. The proposed method can detect and track intruders in a 360° field of view, which is achieved by stitching together multiple images captured by the panoramic camera. The LBP histogram is then used to extract features from the stitched image, which are fed into a support vector machine (SVM) classifier for intruder detection. The proposed method is compared to several state-of-the-art methods, and experimental results show that the proposed method outperforms them in terms of accuracy and detection rate. Furthermore, the proposed method is shown to be robust to lighting changes and occlusions, making it suitable for real-world applications and the approach is then evaluated on a dataset of 360° images captured by cameras and achieves high accuracy in detecting abnormal events such as intrusion and theft. The results demonstrate the effectiveness of the proposed approach in enhancing the security of cameras and can be useful in various applications such as surveillance systems, smart homes, and malls. © 2023 IEEE.

关键词： Cameras

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Re-Cognition I: An Inspection of Learner's Cognitive Well-Being, Pursuant to Contemporary Period, Facilitates the Academic Life Cycle 6

Re-Cognition I: An Inspection of Learner's Cognitive Well-Be...

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ganguly, Souvik Pati Mandal, Kailash Guha, Debasis Dr. B.C. Roy Engineering College Dept. of Computer Application Durgapur India National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350306613

Recommender systems enhance the user experience by integrating factual and behavioral practices, enhancing learning strategies, and granting academic opportunities based on learners subjective interests. Cognitive intelligence, a learner's capability to interpret and synthesize cognitive abilities, is crucial to assemble effective recommendations. However, a student encounters diverse adversities in his or her academic life, whether it is his or her academic pacers or outward circumstances. As a result, a learner consequently experienced severe academic setbacks. Thus, this paper proposes a novel approach to identify cognitive deficits of learners in a school or college before recommending any scholastic content or pertaining in any academic activity. We have employed a modified Bi-directional Gated Recurrent Unit (Bi-GRU) method in conjunction with Shuffled Frog Leaping Algorithm (SFLA). Our approach overcomes some of the shortcomings of conventional Bi-GRU method in choosing the right scaling parameters during training. Bi-GRU method is opted for detecting the cognitive deficits of a learner and SFLA is applied over Bi-GRU in order to hyper-Tune the parameters. The proposed method is compared with a few cutting-edge methods, and the experimental result outperforms the other methods in training, validation, and testing accuracies, achieving 99.34%, 98.98%, and 99.19% respectively. © 2024 IEEE.

关键词： Bi-directional GRU Cognitive Deficits Cognitive Equilibrium Learner's Mental State Meta-heuristic Optimization SFLA

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ResNet-50: The Deep Networks for Automated Breast Cancer Classification using MR Images 2

ResNet-50: The Deep Networks for Automated Breast Cancer Cla...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Tejaswini Nayak, Debasish Swapnesh Kumar Kar, Anindita Jena, Lambodar Swarnkar, Tripti University Dept. of Computer Application FET-ITER Bhubaneswar India University FET-ITER Dept. of Computer Science and Engineering Bhubaneswar India Bhubaneswar India

ISBN: (纸本)9798350370188

One in every four cancer cases in women is breast cancer, which is the most prevalent malignancy in this group globally. In 2020, breast cancer will account for one in every eight new instances of cancer, according to projections of 2.3 million new cases. Breast cancer identification, both early and accurate, is critical to improve patient outcomes and survival rates. Magnetic resonance imaging (MRI) has developed as a valuable method for diagnosing breast cancer. Deep learning algorithms, notably convolutional neural networks (CNNs), have demonstrated exceptional effectiveness in a variety of medical image processing applications, including breast cancer classification. The ResNet-50 architecture has received a lot of interest in this context because of its excellent performance in image recognition tasks. ResNet-50 is a deep residual network that introduces skip connections and residual learning to solve the difficulty of training very deep neural networks. A total number of 1480 cancer MRI samples are collected from the Kaggle database, having two classes, which include healthy and malignant scans. It is observed that our proposed model with the data pre-processing techniques has achieved a classification accuracy of 92.01%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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LSB based Image Steganography with Password-Protected SMTP Transmission 2

LSB based Image Steganography with Password-Protected SMTP T...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Prakash, Mangali Bhanu Kumar, E. Ravi Anitha, Banavath Bansilal, Boda Mukkamall, Babu Reddy Sanjana, Gudipati Pranaya Vardhaman College Of Engineering Dept. of Information Technology Hyderabad India Krishna University Machilipatnam Dept. of Computer Application Machilipatnam India

ISBN: (纸本)9798350388916

Picture steganography plays a crucial role in secure communication by discreetly altering images for various purposes such as data authentication, privacy protection, and watermarking. However, traditional methods often lack resistance to manipulation. To address this, we propose a solution that merges automated password generation with the Least Significant Bit (LSB) approach. This method involves embedding text within images and automatically generating an encryption password to safeguard data security. By employing Simple Mail Transfer Protocol (SMTP) credentials, encrypted photos can be directly sent to recipients, significantly expediting the process. This automated approach eliminates the necessity for separate transfers of the image and decryption password, thereby enhancing simplicity and security. This new technology seamlessly integrates encryption, ensuring a trustworthy and secure communication channel. With this innovative solution, users can transmit sensitive information confidently, knowing that it is shielded from unauthorized access or manipulation. By combining the strengths of automated password generation, LSB steganography, and SMTP transmission, our approach provides a robust framework for safeguarding digital communication in various context. © 2024 IEEE.

关键词： Authentication Protocol

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Smart Navigation: Enhancing Vehicle Trajectory Forecasting with Deep Learning 1

Smart Navigation: Enhancing Vehicle Trajectory Forecasting w...

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

作者： Hashim, Muneera Nadera Beevi, S. Apj Abdul Kalam Technological University Tkm College of Engineering Dept. of Computer Application Kollam India

ISBN: (纸本)9798350376517

Methods for accurately predicting the trajectories of vehicles have always been sought after by Intelligent Transportation Systems (ITS) to improve travel efficiency and traffic management. Advances in deep learning and the availability of extensive trajectory datasets have made such predictions feasible with low error rates. The paper presents a vehicle trajectory prediction model by leveraging the power of stacked Long Short-Term Memory (LSTM) networks and a highly refined dataset. A novel approach for feature extraction and selection is introduced focusing on trajectory properties such as entropy, elongation and elongation angle. The most relevant predictors are identified through a combination of similarity measures like cosine similarity, Tversky index and Pearson correlation coefficient. The model demonstrates superior performance compared to existing methods achieving a low Mean Squared Error (MSE) of 0.004 and Mean Absolute Error (MAE) of 0.01. The model has potential applications in manual, hybrid and autonomous driving environments. © 2024 IEEE.

关键词： Prediction models

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IoT based dual health monitoring device for liquid infusion and urine level measurement of urine bag 3

IoT based dual health monitoring device for liquid infusion ...

引用

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

作者： Kumar, Amit Pathak, Pooja Dwivedi, Vijay Kumar Sharma, Dilip Kumar Gla University Upcst Mathura India Gla University Dept. of Computer Engineering and Application Mathura India Gla University Dept. of Mechanical Engineering Mathura India

ISBN: (纸本)9798331528201

Advanced healthcare monitoring devices that improve patient care through real-time data collecting, remote monitoring, and individualized therapy have been developed as a result of the integration of IoT technology in the healthcare industry. Vital indications including heart rate, blood pressure, glucose levels, and oxygen saturation may be continuously monitored thanks to these IoT-enabled gadgets, which include wearable sensors, smart implants, and remote monitoring systems. These devices assist in the early detection of potential health issues, enabling timely intervention. They help reduce hospital readmission rates by monitoring patients remotely. Additionally, they enhance chronic disease management by transmitting real-time data to healthcare specialists. Furthermore, by providing feedback and reminders, IoT-based healthcare devices encourage patient participation and adherence to treatment programs. Despite the many advantages, IoT technology's role in healthcare monitoring is about to change patient outcomes as it develops. The progress in technology over the years has made it possible to use smaller devices, such as smartwatches mobiles, for health monitoring and disease detection. © 2024 IEEE.

关键词： Blood pressure

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Advancing Phishing Detection with Random Forest: A Machine Learning Approach for Enhanced Cybersecurity 3

Advancing Phishing Detection with Random Forest: A Machine L...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chandra, Sucheta Das, Priyanka Mandal, Ankita Chakroborty, Sriparno Chowdhury, Ishika Ghosh, Kathamrita University of Engineering & Management Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798331523893

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection. © 2025 IEEE.

关键词： Random forests

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An Optimized Grid Computing based Feature Selection approaches from Brain Image dataset using Pretrained models 1

An Optimized Grid Computing based Feature Selection approach...

引用

1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Nayak, Sradhanjali Biswal, Pradyut Kumar Mishra, Pravakar Pradhan, Sateesh Kumar Niis Institute of Business Administration Dept. of Computer Science Bhubaneswar India Iiit Bhubaneswar Dept. of Etc Bhubaneswar India Niis Institute of Business Administration Dept. of Computer Science and Engineering Bhubaneswar India Utkal University Dept. of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9798350355093

The outcome of simulated experiments expressed the efficiency of grid-based algorithm in the domain of image processing in the context of Brain MRI Images for Tumor Detection. It provides a faster approach in detecting object and achieving higher rate of detection in comparison with existing algorithms for the similar purpose. The foremost goal of this work is improving the rate of detection by using grid based feature fusion approach with Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Scalp Swarm Optimization Algorithm. This technique produces efficient result due to hybrid fusion of gridded Images and optimization for exhaustive and precise image analysis. In the initial phase Each image is segmented into 4 grids. Pretrained models like Inception-V2, Resnet 50, Mobnetv2, VGG-16 are used to extract features before and after the images are gridded. The extracted features are analysed with classifiers such as Decision tree, Support vector Machine, Multiplayer and Self Attention Transformer that not only chooses the best set of features as well as best feature extractor. Among all these models the VGG-16 gives best result in terms of Accuracy, sensitivity, specificity and F-score. Hence VGG-16 is utilized as evaluation functions in the optimization Algorithms. The performance of the approach is measured by the values produced against the terms as precision, Recall, F-score, and accuracy rate. In the first phase of iteration, features extracted from each grid are assigned with initial weight. Optimization algorithms are used for optimizing the weight values in the combined features to bring best accuracy ratio. Grid based segmentation of Image stabilize the computation time thereby reduces the time complexity. Weighted sum Scalp Swarm Optimisation assigns best weighted values to segmentation parameters to get maximum performance. The experimental outcomes proves the efficiency of the proposed strategy. © 2024 IEEE.

关键词： Genetic algorithms

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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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Examining driver sleepiness based on his visual actions with ML Techniques

Examining driver sleepiness based on his visual actions with...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Rawat, Vandana Patwa, Kaushal Negi, Harendra Singh Sharma Ghai, Anupriya Graphic Era Deemed to be University Dept. Of Computer Scienceand Engineering Dehradun India Graphic Era Deemed to be University Dept. Of Computer Application Dehradun India Graphic Era Hill University Dept. Of School of Computing Dehradun India

ISBN: (纸本)9798350378092

Driver drowsiness is a significant risk factor for road accidents. It is essential for road safety to develop efficient sleepiness detection systems. This work concentrate around analyzing driver sluggishness in view of visual activities, expecting to recognize solid marks of To determine the driver's level of alertness, the proposed strategy involves keeping an eye on the driver's visual actions, such as eye movements, eyelid closure, and head movements. Relevant features are drowsiness utilizing PC vision methods. To determine the driver's level of alertness, the proposed strategy involves keeping an eye on the driver's visual actions, such as eye movements, eyelid closure, and head movements. Relevant features are extracted from real-time video footage captured by an onboard camera using a combination of image and video processing algorithms. To prepare and assess the sluggishness location model, a different dataset of drivers in different conditions of languor is gathered, including both reproduced and genuine situations. The dataset is clarified with ground truth marks showing various degrees of languor, got through physiological estimations, driver self-reports, and master assessments. © 2024 IEEE.

关键词： Eye movements

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