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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

共 1229 条记录，以下是181-190 订阅

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ClassScan: Simplifying Classroom Attendance using Three-Dimensional Dense Face Alignment and Face Recognition 7

ClassScan: Simplifying Classroom Attendance using Three-Dime...

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7th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Salvi, Sanket Pramod, Jain S. A. School of Computer Engineering Technology MIT-World Peace University Maharashtra Pune411038 India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bangalore574110 India

ISBN: (纸本)9798350343144

Managing attendance in classrooms is a critical task that often relies on manual processes, leading to inefficiencies and potential inaccuracies. In this study, we address this challenge by proposing a web-based face recognition attendance system. Our solution encompasses key functionalities such as student registration, face extraction, and detection, student and teacher login, attendance tracking, and reporting. By using a face recognition method that generates a 3D face using TDDFA algorithm, our system showcased an accuracy rate of 95.0%. Furthermore, the average time required for attendance marking is significantly reduced to just 2.3 seconds/student, offering a rapid and streamlined process compared to traditional manual methods. User feedback from faculty members highlights a high level of satisfaction, with an average usability rating of 4.2 out of 5. The proposed face recognition attendance system provides an efficient solution and thus addresses the challenges associated with manual attendance management in classrooms. © 2023 IEEE.

关键词： Face recognition

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Prediction of Crop Production Using Analysis Algorithms 6th

Prediction of Crop Production Using Analysis Algorithms

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

作者： Tomar, Arun Pratap Nalini, N. Computer Science and Engineering Nitte Meenakshi Institute of Technology NMIT Campus Govindapura Yelahanka Bangalore560064 India

ISBN: (纸本)9789811613371

India is a nation which is known worldwide for its agriculture which helps in developing socio-economic conditions of the nation. This sector helps for the nation's Gross Domestic Product (GDP) as it is providing employment to half of the nation’s population. Due to the development in different sectors, the contribution of the agriculture sector towards the Indian economy is decreasing year by year which is leading towards the descending lifestyle of farmers of India. This predictive model for crop production using different machine learning algorithms is aimed to help farmers to grow different kinds of crops taking into account the weather, statistics of crop production in past years in specific regions such as state-wise or district wise. This model will be using different classification algorithms and showing which crop is most favorable to grow to give proper payback for the crops for the farmers. It will take into account the year in which the crop is to grow, season, area, state, district and will provide the result as to how many percentage of chances that in given crop production will be more that will help the farmers to decide that particular crop. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Recognition and Classification of Apple and Sugarcane Plant Leaf Diseases using SVM with DAE Models 2

Recognition and Classification of Apple and Sugarcane Plant ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sunil Kumar, V. Jyothi, A.P. Sumanth, S. Nagamani, H.S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Peenya Bengaluru India Department of Computer Science and Computer Applications Kolar India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350382952

For this leaf disease study, apple and sugarcane plants are identified and classified using a mix of character features and in-depth automated coding. Farm sugar cane leaves were copied for the online apple Plant Village Dataset. Pre-process images to reduce size and improve HLG filters. Image segmentation using DWT and GLCM feature extraction follows. Like the Low-Low (LL) band, DWT images produce 4-orthogonal subbands. Deep Auto Encoder and SVM algorithms retrieved subgroup functions and identified diseases from training data. If classification findings are ignored, the division stage receives data with HL, LH, and HH bands to explain approach consistency. Deep Auto encoder classifies illness using training data after extracting sub-band features. The Auto Encoder and SVM classifier demonstrated that the proposed model outperformed alternatives in LL band. Other subbands performed worse than the LL band. This study found that LL sub band data provided fine image data for better sickness categorization than HH, HL, and LH sub band data. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Hybrid Optimization Based BERT Model for Drug Detection in NLP 3

Hybrid Optimization Based BERT Model for Drug Detection in N...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Kruthika, C.G. Namitha Devi, N.N. Madlur, Vijayananda V Asha, K. Aruna, T.M. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Vidya Vikas Institute of Engineering and Technology Department of Computer Science and Engineering Mysuru India Mangalore Institute of Technology and Engineering Department of Computer Science and Engineering Moodabidri India Reva University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350340358

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and *** and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model's hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-The-Art models. © 2023 IEEE.

关键词： Embeddings

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A Patient-Centric Blockchain Framework for Transparent and Secure Medical Data Sharing using Modified AES 2

A Patient-Centric Blockchain Framework for Transparent and S...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Pawar, Priyanka Pramod Kumar, Deepak Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

Due to its decentralised, accessible, and secured structure, blockchain-a framework that has historically been viewed with suspicion-has developed into a groundbreaking breakthrough. Reliable automated scripting and static data records are also a part of this system. The intricacy and expertise required to operate with blockchain technology is one of its disadvantages. Machine Learning (ML) frameworks are the key fixing this problem. It has the potential to make Blockchain more user-friendly. In this research paper, we reviewed the current state of blockchain-based cloud computing, machine learning, and safe data exchange. In this study, we suggest using a blockchain technology to provide a safe space where health entities can share medical information transparently. A modified advanced encryption standard (MAES) based blockchain encryption model is developed by us. This method is based on engineering and AI principles, and its main objective is to design useful artefacts that improve their respective domains. © 2024 IEEE.

关键词： Electronic health record

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Securing Digital Governance: A Deep Learning and Blockchain Framework for Malware Detection in IoT Networks 2

Securing Digital Governance: A Deep Learning and Blockchain ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

ISBN: (纸本)9798331504960

This research uses deep learning and blockchain frameworks to provide a safe platform that promotes digital governance data exchange and interoperability. Use the bonobo optimization algorithm to start a blockchain-based smart city data authentication approach. This paper presents a Blockchain-based malware detection method and framework that uses AI to account for multiple distributed conditions. An upgraded greedy search algorithm and the XGBoost decision tree construct a two-layer extreme gradient boosting (XGBoost) classification model that detects attacks. Three pre-existing XGBoost significance indices were split and merged based on the model's leaf nodes' tree traversal structural features. The augmented greedy search technique retrieved and imported spectral band variables into the XGBoost model's second layer. Bat method was used to optimize XGBoost modeling parameters. The deployed model increased power consumption per device by 13.5%, while Raspberry Pi devices used 0.2 GB and NVIDIA Jetson devices used 0.42 GB. ML models had 93% f1-scores and 95% detection accuracy on both datasets. Our technology detects malware and attacks in Smart Environments efficiently and accurately, as shown by the models. © 2024 IEEE.

关键词： Decision trees

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Smart Agriculture in the Era of Big Data: IoTAssisted Pest Forecasting and Resource Optimization for Sustainable Farming 2

Smart Agriculture in the Era of Big Data: IoTAssisted Pest F...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Kumar, Deepak Pawar, Priyanka Pramod Meesala, Mohan Kumar Pareek, Piyush Kumar Addula, Santosh Reddy Shwetha, K.S. Department of Information Technology University of the Cumberlands WilliamsburgKY United States Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331504960

The goal was to facilitate quick development and revitalisation of rural areas by utilising IoT knowledge to accomplish smart agriculture against the backdrop of big data. Depleted soil fertility, increased pest attacks, and diseases are some of the climate-related risks that the agricultural sector is battling. Predicting Utilising directly sensed environmental conditions as input, a machine learning model incorporating Random Forest and the Catboost algorithm determines, based on the current environmental conditions, whether pest populations are present or not. Next, Hybrid Weighted Ant Colony Optimisation (HWACO) is used to fine-tune the Catboost algorithm. This method evaluates the machine learning model's accuracy and precision in making predictions using test data. Predictions have been made using data from 2028 to 2022, a span of five years. Weekly forecasts are produced by the pest prediction model. With high F-measure, Precision, and Recall, the weekly predictions have an overall accuracy of 93%. Over time, the precision of the pest forecast steadily rises. Predictions for the next week are made by averaging all weather data from the past seven days. For immediate defences against pests, the weekly forecasts are crucial. The findings support sustainable farming methods and food security by showing that the device can maximise crop yields while decreasing resource consumption. © 2024 IEEE.

关键词： Smart agriculture

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Sleep Quality Prediction and Estimation by using Deep Belief Networks-Bayesian Optimization 1

Sleep Quality Prediction and Estimation by using Deep Belief...

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

作者： Mamatha Bai, B.G. Koti, Valli Madhavi Abbas, Haider Mohammed Sivakumar, V. Jansirani, D. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Giet Degree College Department of Computer Science and Engineering Rajahmundry India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Department of Information Technology Virudhunagar India New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350352931

Sufficient sleep is important for individuals who are suffering with chronic diseases, as sleep scarcity severely impacts emotional, physical and mental well-being, worsening health complications. The existing convolutional network for sleep quality prediction and estimation has computational complexity and over fitting by which the model didn't effectively predict the quality of sleep. Therefore, this research proposes Deep Belief Networks-Bayesian Optimization (DBN-BO) for predicting and estimating the quality of sleep. Initially, Sleep study data is used as input and this data contains outliers which are preprocessed by using local outlier factor and effectively reduced impact of noise while detecting the outliers. Then, the sleep quality is predicted by using DBN which learns the complex features from raw data. After that, BO is used for estimating the quality of sleep and explores the various factors related to sleep. This proposed DBN-BO effectively reduced the computational complexity and over fitting by training one layer at a time, proposed DBN-BO achieved better results in terms of accuracy 9 4. 4 5 %, sensitivity 9 7. 0 9 %, and specificity of 9 3. 4 5 %and F1-Score of 91.12 %when compared with existing convolutional layers. © 2024 IEEE.

关键词： Sleep research

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Enhancing Peer-to-Peer Network Security using 3D Convolutional Autoencoder based Anomaly Detection 1

Enhancing Peer-to-Peer Network Security using 3D Convolution...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Ajznblasm, Zaid Satish, E.G. Adlin Sylvia, A. Devi, M. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

In this era, Peer-to-Peer (P2P) networks have developed as a revolutionized network for data distribution and alternate to traditional client-server network. The features of P2P networks allow a distributed group of users to stake resources in the form of digital content or information. However, P2P networks lack in controlling access and it has limited security. In this research, Three Dimensional-Convolutional Autoencoder (3D-CA) is proposed to improve the security in P2P network. Initially, the data is obtained from University of New South Wales Network Behavior 2015 (UNSW-NB15) dataset and then preprocessed with data cleaning to ensure data quality. After that, the P2P autoencoder architecture is employed to learn the normal patterns which employs the detection in P2P network. Finally, the proposed 3D-CA is trained to detect the anomalies based on reconstruction error between input node and reconstructed pattern. From the results, the proposed 3D-CA model attained better outcomes compared to existing K-means algorithm in terms of accuracy, precision as well as F1-score by achieving 0.764, 0.90 and 0.80 respectively. © 2025 IEEE.

关键词： Peer to peer networks

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Shop Cheap - A Less Costly and More Convenient Shopping Method with Machine Learning Techniques 2

Shop Cheap - A Less Costly and More Convenient Shopping Meth...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Gopinath, M.P. Gobinath, C. Aarthy, S. Srikanteswara, Ramya Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The world is now seeing a cosmic scale of online buying and mobile shopping, both of which have offered enormous opportunity for buyers and sellers to do business through a variety of channels. Because there are so many different shopping platforms available, it may be time-consuming to search through all of them for a certain item, get the cheapest price, look for discount coupons, and so on. This issue is exacerbated by the proliferation of shopping platforms. The Model "Shop Cheap"is an application that will reduce the efforts and time spent by a user on various platforms by providing them with the best deals, offers, and coupons related to any product from all of the platforms collectively. This will allow the user to spend less time and effort on the various platforms they use. The users of the recommendation system will have products suggested to them based on their shopping habits, budget marks, and discount rates from which they regularly buy, and the users will also be suggested new deals and offer and will be notified of the latest discount rates on their favourite items based on their shopping history. The recommendation system will be able to do this because their shopping history will be analysed. © 2022 IEEE.

关键词： Recommender systems

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