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检索条件"机构=Dept. of Data Science and Computer Applications Manipal Institute of Technology"

共 402 条记录，以下是91-100 订阅

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Evaluating the Effectiveness of Object Detection Algorithms for Autonomous Parking

Evaluating the Effectiveness of Object Detection Algorithms ...

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computer, Electronics & Electrical Engineering & their applications (IC2E3), International Conference on

作者： Utkarsh Sharma Krishnan Subramonian Aaron Dsouza Sughosh P Savitha G Girisha S Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Civil Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

In response to the challenges posed by increasing urbanization, traditional parking systems face numerous issues, such as inaccurate slot detection and manual tracking, which exacerbate traffic congestion and environmental strain. This paper presents an autonomous parking system as a solution to these challenges. By leveraging advanced object detection algorithms, including YOLO-NAS, YOLOv8, and DETR models, this system revolutionizes parking space identification, freeing drivers from the task of manual searching. Our research focused on identifying the most suitable object detection algorithm for our dataset and objectives. Utilizing the ‘PKLot’ dataset, we trained and fine-tuned our models, ensuring consistency in hyperparameters across algorithms to isolate their impact on performance. We evaluated algorithmic efficacy using metrics like mean average precision (mAP) and latency, measuring accuracy in discerning occupied and vacant spaces alongside real-time processing efficiency. Our evaluation results highlight the performance differences among algorithms, guiding future research. This research advocates for an intelligent, autonomous approach to parking management, aligning with goals of optimizing operations and promoting sustainable urban development.

关键词： Training Accuracy Urban areas Training data Object detection Manuals Object recognition Traffic congestion Testing Strain

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Indian Sign Language Recognition Using Scale-Invariant Feature Transform by dept. Sensor 5

Indian Sign Language Recognition Using Scale-Invariant Featu...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning applications, ICCCMLA 2023

作者： Gangrade, Jayesh Gangrade, Shweta Mishra, Ashish Kumar, Gautam Gunjan, Vinit Kumar Manipal University Jaipur Dept. of Artificial Intelligence & Machine Learning Jaipur India Manipal University Jaipur Dept of Information Technology Jaipur India Jaypee Institute of Information Technology Dept of Computer Science & Engineering Noida India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350338287

Deaf and hard of hearing people in the Indian community now have a way to communicate with society because to Indian Sign Language (ISL). The hand is accorded significant importance in the framework of the ISL. ISL is composed of both static and dynamic sign hand gestures. Both static and dynamic signs are made up of three components: hand movement, hand orientation, and hand position. The proposed method can recognize static ISL signs. The technique that was suggested makes use of the Microsoft Kinect sensor to locate and separate a hand from its background in a complex scene. SIFT, which stands for 'scale-invariant feature transform,' was used to extract the feature. The SIFT algorithm is invariant under changes in perspective, orientation, and lighting. Recognize the 10 ISL digit movements with an accuracy of 94.9% using a multi support vector machine (MSVM). © 2023 IEEE.

关键词： Support vector machines

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Enhancing Quantum Operations: Random Forests for Anomaly Detection and Alert Generation

Enhancing Quantum Operations: Random Forests for Anomaly Det...

引用

International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： M. Anoop B. Nithya Dept. of Computer Science and Applications SRM Institute of Science and Technology Chennai India Dept. of Computer Applications Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

A promising approach for enhancing the dependability and security of quantum processes is the integration of Random Forests, a traditional machine learning technique, with quantum anomaly detection and alert production. This approach involves training Random Forest models to inspect quantum data and spot abnormalities such as variations in quantum states, entanglement patterns, and the results of quantum processes. When anomalies are found, alarms are activated, categorized by severity, and sent out via several notification channels. Operators of quantum systems can act quickly by applying techniques for adaptive control, error correction, or system reinitialization. The rigorous preservation of historical data makes post-incident study and ongoing system improvement possible. This novel strategy strengthens security while simultaneously improving the performance of quantum systems, particularly in situations involving quantum communication. To guarantee its efficiency in the continually changing quantum landscape, careful design and ongoing monitoring are essential.

关键词： Quantum system Adaptation models Quantum computing Quantum entanglement data models Error correction Security

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Siamese Network Based Anomaly Detection Framework for Robust Federated Learning

Siamese Network Based Anomaly Detection Framework for Robust...

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Intelligent Technologies (CONIT), International Conference on

作者： Karthik Shenoy K Manohara M. M. Pai Radhika M. Pai Department of Information and Communication Technology Manipal Institute of Technology Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal India

Federated learning is a decentralized approach to machine learning that has increased in popularity in recent years. It enables several participants to train a common model without revealing their data. This strategy is, however, susceptible to attacks from malicious clients who can launch targeted model poisoning attacks and reduce learning performance by delivering false model updates to the server. It is necessary to identify and eliminate such fraudulent updates and the attackers behind them to preserve the robustness and security of the shared model. In this paper, a novel Siamese network-based architecture for robust federated learning is proposed. which can identify and eliminate harmful updates. Our method is assessed and compared with other approaches for adversarial detection in image classification tasks in a federated setting, using a CNN model. Experimental findings demonstrate that the system offers reliable federated learning that is resistant to both targeted model poisoning and untargeted Byzantine attacks. Overall, the study advances the development of secure federated learning systems, by suggesting a novel method for identifying and deleting fraudulent updates in federated learning,

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Anomaly detection using Federated Learning: A Performance Based Parameter Aggregation Approach

Anomaly detection using Federated Learning: A Performance Ba...

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Intelligent Technologies (CONIT), International Conference on

作者： Pushkar Bhat Manohara Pai M M Radhika M Pai Department of Information and Communication Technology Manipal Institute of Technology Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal India

Anomaly detection is a significant challenge that exists in multiple industries, including healthcare, finance, and manufacturing. Machine Learning techniques are extensively being used to solve these problems. However, data privacy concerns and regulations are making acquiring the necessary data for model training increasingly challenging, particularly in sensitive sectors. Federated learning has emerged as a promising solution, allowing local models to be trained on edge devices using local datasets and model parameters to be shared with a central server for aggregation. This allows the models to reap the benefits of the distributed data without actually sharing any data. However, the current aggregation method used in federated learning is susceptible to models trained on bad data, which can impact the accuracy and reliability of the resulting model. To address this issue, the study proposes a new filtered aggregation algorithm that greatly mitigates the effect of models trained on bad data. The proposed algorithm accounts for models that cannot detect anomalous patterns and gives them much less representation in the final model, resulting in improved accuracy and reliability.

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Video key concept extraction using Convolution Neural Network 3

Video key concept extraction using Convolution Neural Networ...

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3rd IEEE International Conference on AI in Cybersecurity, ICAIC 2024

作者： Sardar, Tanvir H Amin Hazarika, Ruhul Pandey, Bishwajeet Guru Prasad, M.S. Mahmudul Hassan, Sk Dodmane, Radhakrishna Gohel, Hardik Dept. of Computer Science & Engg Bengaluru India Manipal Institute of Technology Department of Information Technology Bengaluru India Astana It University Department of Intelligent System and Cyber Security Kazakhstan Dept. of Computer Science & Engg. Dehradun India VIT-AP School of Computer Science and Engineering India Nmam Institute of Technology Department of Computer Science & Engg. Nitte India University of Houston Department of Cse Victoria United States

ISBN: (纸本)9798350381856

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video. © 2024 IEEE.

关键词： Speech recognition

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Parallelizing Exponential Search Algorithms with MPI and CUDA for High-Performance Computing

Parallelizing Exponential Search Algorithms with MPI and CUD...

引用

Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Vedant Shreeyansh Abhay S. Gad B. Ashwath Rao N. Gopalakrishna Kini Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Acadmey of Higher Education Manipal India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Exponential search is an algorithm for searching unbounded taken care of lists. The exponential seek approach can be used to find the vicinity of a specified value in a taken care of array. Parallel index lookups in Exponential seek can enhance their performance, specifically for massive lists. This work provides an example of the exponential seek set of rules, which employ CUDA to enforce GPU parallelism and MPI to achieve parallel memories. We've mentioned a method to evaluate exponential studies and exhibit development in performance. The effects show that we can acquire relatively quicker outcomes than sequential techniques, specifically for big arrays.

关键词： Technological innovation Search methods Message passing High performance computing Graphics processing units Information processing Parallel processing data processing Computational efficiency Indexes

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Deep Analysis for Speech Emotion Recognization 2

Deep Analysis for Speech Emotion Recognization

引用

2nd International Conference on computer science, Engineering and applications, ICCSEA 2022

作者： Bhattacharya, Sudipta Borah, Samarjeet Mishra, Brojo Kishor Das, Nabanita India Sikkim Manipal Institute of Technology Sikkim Manipal University Dept. of Computer Applications Sikkim India Maulana Abul Kalam Azad University of Technology Bengal Institute of Technology India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Emotions play a vital part in human interactions and are important determinants of user happiness and consumer sentiment. The creation of human-computer interaction (HCI) systems depends on speech emotion recognition (SER) modules. Systems for analysing and classifying speech signals in order to identify the emotions they contain are known as speech emotion recognition (SER) systems. SER is a well-established and fast developing branch of signal processing. More data will be available to learn from as speech emotion detection systems become more popular, helping them to improve their performance. This study also includes a discussion of certain common machine learning approaches, such as ensemble methods, deep learning methods, and classifier enhancement methods. Attention-based deep neural networks (DNNs) are also highlighted in this paper, which is getting a lot of attention in this domain. The findings of this study show that this field of study is being investigated steadily. The scientific community in this area could benefit from this analysis's inspiration and motivation. © 2022 IEEE.

关键词： Emotion Recognition

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Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce

Simplified Micropayment Mechanism to Eliminate the Risk of D...

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2023 International Conference on Advances in Intelligent Computing and applications, AICAPS 2023

作者： Chahar, Narendra Kumar Singh, Krishan Pal Hussain, Muzzammil Central University of Rajasthan Dept. of Data Science and Analytics Ajmer India Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India Central University of Rajasthan Dept. of Computer Science and Engineering Ajmer India

ISBN: (纸本)9798350333817

In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digital payment systems are in transition and promise amazing advancements, but they also pose many risks and as the number of online transactions is increasing tremendously, we need a security system that follows all security norms. In this paper, we study digital transaction systems and evaluate various components of E-commerce plat-forms to address the security of these services. We evaluate the attributes that affect the security of digital payment processes and identify several barriers that hinder their performance to propose a simplified payment mechanism for micro-payments that eliminates the double payment problem. © 2023 IEEE.

关键词： Online systems

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Predictive Analysis of Diabetes Mellitus Using Decision Tree Approach 2

Predictive Analysis of Diabetes Mellitus Using Decision Tree...

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2nd Asian Conference on Innovation in technology, ASIANCON 2022

作者： Thotad, Puneeth Bharamagoudar, Geeta R. Anami, Basavaraj S. Kle Institute of Technology Dept. of Master of Computer Applications Hubballi India Kle Institute of Technology Dept. of Computer Science & Engineering Hubballi India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Diabetes Mellitus is one of the Non-Communicable Disease. It is characterized by hyperglycemia. It may be reason for numerous health difficulties. According to the statistics in coming years, in 2030, worldwide it reaches up to 643 million counts of diabetic patients. The various factors such as Bad Diet, High Blood Pressure, Obesity, Age, Lack of Exercise etc. can led to Diabetes Mellitus. The various diseases like nerve damage, heart problem, eye problem, kidney disease, stroke in many people caused by high risk of Diabetes Mellitus. Machine Learning plays vital role in healthcare sectors. Using various Machine Learning techniques, a study can be done on large datasets and discover unseen facts, unseen patterns to realize awareness from the data and predict results consequently. In this work, the PIMA Indian Diabetes dataset is considered for Predictive Analysis. The Decision Tree algorithm, namely ID3 (Iterative Dichotomize 3) is used for a predictive analysis. The major phases include preprocessing, feature extraction, classification, and prediction. The classification accuracies of 79.76% and 76.3% are obtained for balanced and unbalanced datasets respectively. The maximum specificity and sensitivity obtained are 79.75% and 79.78% respectively. The ROC curve gives 79.57% overall classification accuracy. The work assists physicians as a second opinion. It can help various health industries to predict the diabetes disease in a better way. © 2022 IEEE.

关键词： Decision trees

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