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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

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Data-Driven Approaches for Early Detection and Prediction of Chronic Kidney Disease Using Machine Learning 5

Data-Driven Approaches for Early Detection and Prediction of...

引用

5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Mathai, Shibi Thirunavukkarasu, K.S. Department of Computer Science Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798400709418

In recent years, the application of machine learning (ML) techniques for medical diagnostics has shown promising advancements. This study introduces a distinctive method for predicting chronic kidney disease (CKD) harnessing the prowess of ML. Our methodology encompasses an innovative data preprocessing approach, intricate feature engineering, and an amalgamation of ensemble techniques for model training. By evaluating our model on a dataset sourced from Kaggle, comprising 400 samples, we achieved an impressive accuracy of 98%, outperforming traditional methods. The findings underscore the potential of ML in revolutionizing CKD diagnostics, laying a foundation for further exploration in this domain. © 2023 ACM.

关键词： Diagnosis

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Named Entity Recognition for Protecting Sensitive Data using Hybrid CNN 5

Named Entity Recognition for Protecting Sensitive Data using...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Gowr, P. Sheela Kumar, N. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

Named entity recognition is a natural language processing technique that effectively recognizes and categorizes named entities in a document. The named entity recognition helps to bring out dynamic information about a document or gather critical data to store in a database. Deep learning helps to develop over time, while NLP examines the structure and standards of language and produces an automated system that can discern meaning from text. Mining the essential entities in a text helps identify related data, which is vital when functioning with enormous datasets. The proposed system has a feature that can retrieve and identify sensitive data such as PAN numbers, bank account numbers, and Aadhar numbers from unstructured text data.. The proposed model is designed using Hybrid CNN and it attains 95% F1-Score, 97.5% precision, and 98.3% recall. © 2023 IEEE.

关键词： Natural language processing systems

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Deep Learning-Based Alzheimer's Disease Classification using Transfer Learning and Data Augmentation 3

Deep Learning-Based Alzheimer's Disease Classification using...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Anand, Deva Gowr, P. Sheela Logesh, S. Anand, S.M. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350338607

Alzheimer's disease is a pressing healthcare concern worldwide, highlighting the importance of early and accurate detection. Deep learning models, particularly those utilizing transfer learning, have shown promise for AD classification. This research study explores the effectiveness of transfer learning in training models based on deep learning for the classification of Alzheimer's condition. We trained several transfer models using DenseNet121, InceptionV3, Xception, and ResNet101. Based on performance evaluation, InceptionV3 was selected as the base model, outperforming in comparison to the accuracy of the other models. To enhance the accuracy of the InceptionV3 base model, we added capacity, tuned hyperparameters using Keras tuner, and utilized data augmentation techniques. The final model was trained on the Kaggle Alzheimer's Dataset, consisting of 4 classes of images, and achieving an AUC value of 87%. Our research demonstrates that transfer learning and other data augmentation approaches are useful in improving the accuracy of deep learning models for the categorization of Alzheimer's condition. This research has practical implications for improving public health outcomes, facilitating timely intervention and effective treatment. It contributes to the development of more accurate diagnostic tools for AD and can help address the challenges associated with this disease. © 2023 IEEE.

关键词： Neurodegenerative diseases

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A Review of Blockchain Technology in Smart Applications: Research Issues and Open Challenges

A Review of Blockchain Technology in Smart Applications: Res...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Samuel, B. Kasturi, K. VISTAS Vels Institute of Science Technology & Advanced Studies School of Computing Sciences Dept.of Computer Science Pallavaram Chennai India

ISBN: (纸本)9798350348217

The increasing urbanization of the worldwide people has a significant impact on societal attitudes, particularly life expectancy, as a consequence of a variety of macroeconomic, sociological, and environmental changes. The idea of a 'smart city' creates opportunities for addressing important urban problems. Smart cities strive to provide exceptional services to the populace, more efficiently use public funds, and advance general welfare. Technology tools are essential to the development of ubiquitous computing. Trustlessness, traceability, anonymity, democracy, automation, decentralization, and confidentiality are just a few advantages of blockchain *** development of IoT technology and the upgrading of intelligent devices are both facilitated by these cryptocurrency capabilities. In this post, we will provide a thorough assessment of published literature on decentralized blockchain systems in wide variety of applications. We start with talking about the core ideas behind distributed ledger technology before moving on to the various fields it has been used in, like the Internet of Things, agribusiness, financial, as well as other businesses. Finally, we examined several security issues facing the healthcare sector and thoroughly examined blockchain technology's trustworthiness. © 2023 IEEE.

关键词： Blockchain

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Comparative study of DNN Models for Skin Cancer Detection 4

Comparative study of DNN Models for Skin Cancer Detection

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Kavitha, P. Jayalakshmi, V. Department of Computer Science Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Pallavaram Chennai India Department of Computer Applications Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Pallavaram Chennai India

ISBN: (纸本)9781665401180

Malignant melanoma, the most dangerous form of skin cancer, has become more common in recent years. The persistent global increase in this cancer, as well as the high medical costs and death rate, have made early detection of this malignancy a priority. Melanoma survival and cure are directly proportional to its thickness;if it can be found early, the survival rate will be enhanced. Despite the fact that significant progress has been achieved in the identification of skin malignancies, there are still significant issues. In comparison to the naked eye, computer-based detection systems can enhance the diagnosis rate of melanoma by 5-30 percent. Because visual perception frequently contains flaws, the need for a second viewpoint with greater precision and consistency is underlined. On the other side, it minimizes the tasks and responsibilities that physicians are responsible for. Many studies have been conducted in the area of automated melanoma detection. The potential benefits of such investigations are enormous and unquantifiable. Furthermore, the challenges are numerous, and fresh contributions in this field are greatly valued. However, it is widely known that more reliable and consistent detection methods necessitate a higher level of accuracy. The goal of this paper is to provide a skin cancer diagnosis system that can automatically identify lesions as malignant or benign. Preprocessing, segmentation, feature extraction and selection, and classification are some of the methods used in an automated skin cancer diagnosis. © 2022 IEEE

关键词： Deep neural networks

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Reinforcement Learning Technique Based Automated Feature Analysis of Gene Expression Data

Reinforcement Learning Technique Based Automated Feature Ana...

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2023 International Conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Jayakrishnan, R. Meera, S. Vels Institute of Science Technology and Advanced Studies Computer Science & Engineering Tamil Nadu Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350333244

Immunohistochemistry and reverse transcription polymerase chain reaction, it is feasible to find molecular markers that are specific to cancer;nevertheless, there are no pathognomonic molecular markers available for the vast majority of solid tumors at this time. In this paper, we develop a Reinforcement learning Technique based automated feature analysis of gene expression data. This provides support for the idea that, on average, all the validation findings and training data from each epoch were utilized. This proposed method helps to acquiring knowledge at a more leisurely pace is, in the end, what makes episodic deep RL possible as a potential choice for students to pursue. To put it another way, the slow but steady accumulation of knowledge paves the path for the rapid extension of one existing body of information. The proposed methods provide 98 percent of the accuracy result than existing ANN and DNN. The proposed framework outperformed existing methods in terms of increase accuracy rate and reduced execution time than other methods © 2023 IEEE.

关键词： Reinforcement learning

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Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural science 3

Quantum Machine Learning Techniques based on Nurturing Agri-...

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

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Kone Elevator India Technology & Engineering Centre Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards. © 2024 IEEE.

关键词： Ontology

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Optimization of Resource Allocation in the Cloud Environment using Regression Models 5

Optimization of Resource Allocation in the Cloud Environment...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Nathiya, R. Preethika, S. K. Piramu VEL'S Institute of Science Technology and Advanced Studies School of Computing Sciences Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331504403

Cloud computing is a developing technology that separates computations from infrastructures making it far more effective to traditional self-hosted computing methods. It saves operating costs and has a positive environmental impact by limiting resource waste, excessive provisioning, and consumption of energy. It also helps to improve the quality of service and user experience by decreasing delays, boosting throughput, and preventing bottlenecks. This study investigates the effectiveness of using regression models to direct cloud resource allocation. The cloud environment is well-equipped to both minimize cloud costs more efficiently and respond to changing demands by making decisions based on real-time data. © 2024 IEEE.

关键词： Cloud platforms

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Malicious Webpage Detection Based on Feature Fusion Using Natural Language Processing and Machine Learning 2

Malicious Webpage Detection Based on Feature Fusion Using Na...

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Pradeepa, G. Devi, R. Vels Institute of Science Technology & Advanced Studies Department of Computer Applications Chennai India

ISBN: (纸本)9798350347579

Malicious websites are purposefully designed to deceive internet users to steal sensitive personal information, infect the victim's system with malware, cause financial losses, and damage the victim's reputation. Finding these pages or links is hard for internet users. Such websites are discovered using detection tools. The majority of detection techniques use blacklisting or whitelisting strategies to find and prevent malicious websites. However, compiling such a sizable list of website links is a time-consuming job that is challenging to update regularly. Therefore, the researchers employ machine learning-based methods to identify these fraudulent connections. These methods are based on the features taken from URLs or web pages. Additionally, features such as DNS details, webpage reputation, and visual similarity data are used. However, these features are few and do not fully utilize the URLs or website contents. This work focuses on merging URL lexical features and content-based features for malicious webpage detection in order to fully exploit the dataset's potential. Natural language processing methods like Hashing, Count, and Term Frequency - Inverse Document Frequency (TF-IDF) vectorizers are employed to extract features from the content of Web pages. The suggested approach's efficiency is evaluated by using the most well-known machine learning methods. The outcome shows that the Count vectorizer with Random Forest achieves a higher accuracy of 91.17% with 500 features. © 2023 IEEE.

关键词： Websites

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