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

作者： Nair, Rekha R Babu, Tina Kishore, S. Poornima, J. Sambasivudu, Madduru Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Applications Mangalore India Brindavan College of Engineering Department of Information Science and Engineering Bengaluru India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331530013

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields. © 2024 IEEE.

关键词： Optical character recognition

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Deep Neural Networks for Comprehensive Multimodal Emotion Recognition

Deep Neural Networks for Comprehensive Multimodal Emotion Re...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Tiwari, Ashutosh Kumar, Satyam Mehrotra, Tushar Singh, Rajneesh Kumar Sharda University School of Engineering and Technology Department of Computer Science and Engineering India Sharda University School of Engineering and Technology Department of Computer Science & Applications India

ISBN: (纸本)9798350323887

Emotions may be expressed in many different ways, making automatic affect recognition challenging. Several industries may benefit from this technology, including audiovisual search and human- machine interface. Recently, neural networks have been developed to assess emotional states with unprecedented accuracy. We provide an approach to emotion identification that makes use of both visual and aural signals. It's crucial to isolate relevant features in order to accurately represent the nuanced emotions conveyed in a wide range of speech patterns. We do this by using a Convolutional Neural Network (CNN) to parse the audio track for feature extraction and a 50-layer deep ResNet to process the visual track. Machine learning algorithms, in addition to needing to extract the characteristics, should also be robust against outliers and reflective of their surroundings. To solve this problem, LSTM networks are used. We train the system from the ground up, using the RECOLA datasets from the AVEC 2016 emotion recognition research challenge, and we demonstrate that our method is superior to prior approaches that relied on manually constructed aural and visual cues for identifying genuine emotional states. It has been demonstrated that the visual modality predicts valence more accurately than arousal. The best results for the valence dimension from the RECOLA dataset are shown in Table III below. © 2023 IEEE.

关键词： Emotion Recognition

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NL2Code: A Hybrid NLP and Model-Driven Framework for Automated Code Generation from Natural Language and UML

NL2Code: A Hybrid NLP and Model-Driven Framework for Automat...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Nair, Reshma P Thushara, M.G. Amrita School of Computing Amrita Department of Computer Science & Engineering Kerala Vishwa Vidyapeetham Amritapuri Kollam India Amrita School of Computing Amrita Department of Computer Science & Applications Kerala Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (纸本)9798331529833

The process of converting natural language requirements and visual models into executable software code remains an ongoing challenge in software engineering. We developed an intelligent system that adopts Natural Language Processing (NLP) and Model-Driven Engineering (MDE) to generate code from natural language requirements and UML diagrams. It then applies advanced NLP techniques for natural language specification, such as tokenization, POS tagging, semantic analysis, and information extraction. This information can be transformed into computer language construct and design patterns with the help of MDE concepts. It generates code templates, skeleton code, and real code and incorporates key principles of software engineering and technological specializations. Case studies explain how the system can improve the software development lifecycle for a development company by automatically translating the natural language and the visual models into code. The system synthesized code with 92-95 % accuracy and 92-93 % completeness in three particular cases of use, proving the efficiency of the translation process of natural language requirements and UML diagrams into executable code. The present work enriches software engineering, NLP, as well as the integration of MDE, and code generation, reducing time consumption, manual efforts, and fast communication in the Software Development Process. © 2025 IEEE.

关键词： Semantics

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Study of Power Optimization Algorithms for Secure Communication with Untrusted AF Relays 1

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World Conference on Information Systems for Business Management, ISBM 2023

作者： Shemi, P.M. Sabu, M.K. Department of Electronics MES College Marampally Aluva India Department of Computer Applications Cochin University of Science and Technology Kochi India

ISBN: (数字)9789819983469

ISBN: (纸本)9789819983452

This paper compares various power optimization algorithms for secure transmission in an amplify-and-forward (AF) network with multiple untrusted relay nodes and an external eavesdropper. In order to prevent the untrusted nodes from intercepting the source message and to achieve a positive secrecy rate, source-based-jamming (SBJ) is used. The optimization algorithms employed for secrecy rate maximization include the Nelder–Mead, Broyden–Fletcher–Goldfarb–Shanno, and Conjugate-Gradient methods. The performance of the untrusted relay is compared to the worst-case situation, in which it is assumed that all nodes cooperate with each other. The two-dimensional exhaustive search, derivative test, and equal power allocation (EPA) approaches are used as performance comparison benchmark systems. The analysis is done for symmetric and asymmetric relay positions and with different numbers of untrusted nodes. The complexity of the optimization algorithms has also been derived. Numerical results reveal that the optimal technique for secrecy rate maximization depends on the position of the selected relay. Also, there exists a trade-off between the complexity and convergence of the algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Jamming

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Blockchain Technology for Agricultural Supply Chain Management to Enhance Farming 4

Blockchain Technology for Agricultural Supply Chain Manageme...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Sivakumar, Dhachayani Nagarajan, Srideivanai Maruthi, R. Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350300888

Blockchain (BC) technology provides secure record storage and digital transactions in terms of security, transparency and efficiency. BC verifies the authenticity of the products and maintains records from the origin to the entire supply chain. Agricultural blockchain supply chain management (BCSCM) facilitates the automated delivery of agricultural products to the farmers. The agricultural supply chain involves too many intermediaries and the lack of transactions leads to too many manual interventions of handling the commodities in the supply chain. BC technology is one such innovation to overcome this problem and it maintains a record of immutable transactions to automate the agricultural supply chain. The proposed study explores the mechanisms and methods for agricultural blockchain supply chain to develop a secure and reliable supply chain management (SCM). © 2023 IEEE.

关键词： Blockchain

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Artificial Intelligence Technique for Rice Seed Disease and Quality Assessment 3

Artificial Intelligence Technique for Rice Seed Disease and ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Hassan, B. Nazia Somashekara, M.T. Government First Grade College Vijayanagar Department of Computer Science Karnataka Vijayanagar India Bangalore University Department of Computer Science and Applications Karnataka Bangalore India

ISBN: (纸本)9798350319125

Ensuring high-quality rice seeds is vital for agricultural productivity. This research explores existing methods for assessing rice seed quality, land suitability, and seed disease in rice. A survey of the literature reveals a diverse range of techniques used for these purposes. In particular, this study delves into the application of machine learning (ML) classifiers and deep learning (DL) techniques for assessing the rice seed quality. This paper, exhaustively covers all major work based on ML and DL techniques. Through extensive analysis, the research assesses these methods in terms of accuracy, precision, recall, F1-score, error rates, and the kappa coefficient. The results demonstrate that both ML and DL exhibit superior performance, showcasing their potential for enhancing rice seed quality assessment and disease detection. This research contributes valuable insights into the application of advanced technologies in agriculture, with implications for improving crop yield and food security. This paper serves as aid for researchers working in this area. © 2023 IEEE.

关键词： computer Vision Deep Learning Machine Learning Rice Seed Quality

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Environment Sound Recognition Systems-A Case Study 8

Environment Sound Recognition Systems-A Case Study

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Nair, Jayashree Thandil, Rizwana Kallooravi Gouri, S. Praveena, P.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India Sullamussalam Science College Department of Computer Science Areekode India Amrita Vishwa Vidyapeetham Department of Electronics and Computer Amritapuri India

ISBN: (纸本)9798350396638

There are various sounds in the environment, which are produced by multiple sources such as birds, animals, machinery, etc. An Environment sound recognition system (ESRS) detects and categorizes these environmental sounds. ESRS plays a major role in different applications with sound processing such as noise removal, IoT-based systems for sound monitoring, etc. The paper presents simple ESRS models trained on an existing standard environmental sound dataset, freely accessible via the Freesound project. This dataset contains 250000 unlabeled auditory extracts and 2000 short clips representing 50 different basic sound events. Various feature extraction techniques are applied to sound data;the extracted features are then represented using various vectorization techniques so that they can be incorporated into the models. ESRS models are built based on standard Machine Learning(ML) algorithms. Following that, these models are evaluated, tested, and compared. © 2023 IEEE.

关键词： Classification (of information)

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A Predictive Model for Road Traffic Data Analysis and Visualization to Detect Accident Zones 9

A Predictive Model for Road Traffic Data Analysis and Visual...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Subhashini, S. Maruthi, R. Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350397376

The usage of vehicles were increased throughout the world, the count of accidents is also increased globally. One of the world's major concerns is recognized to be the number of traffic accidents. In each year, there are increasingly more traffic accidents. Consequently, it has a significant impact on a nation's society, economy, and progress. As accidents occur in various places and at various times, identifying where they are more likely to occur can be complicated. This paper presents a Predictive Model to analyze and visualize road traffic accident zones in Tamil Nadu. The zones are categorized by four different attributes such as low, medium, high and very high. Data analysis is done in order to make a determination. The Real time data are acquired and interpreted using Latent Class Clustering Analysis (LCA). The outcome is represented through Cartogram visualization technique. Accident analysis is done to identify the reason or causes of an accident in attempt to prevent similar instances from occurring repeatedly. As a result, it is simpler to recognize the zones so the data is easier for the users to absorb. © 2023 IEEE.

关键词： Data visualization

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A Comprehensive Analysis of Techniques and applications in Multimodal Deep Learning 1

A Comprehensive Analysis of Techniques and Applications in M...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tomar, Vivek Sharma, Swati Arora, Sangeeta Tyagi, Aditya Dayal University Computer Science & Engineering Department Dehradun India KIET Group of Institutions Delhi-NCR Computer Science & Engineering Department Ghaziabad India KIET Group of Institutions Delhi-NCR Computer Applications Department Ghaziabad India Sharda University CSA Department Greater Noida India

ISBN: (纸本)9798350353648

In recent years, deep learning has gained popularity and been used in many different applications. Models that can process and link data utilizing several modalities are the aim of multimodal deep learning (MMDL). Even with all the advancements made, unimodal learning is still unable to fully capture the entirety of human learning. Multimodal learning facilitates a better understanding and analysis of situations in which information is processed through multiple senses. The various modalities covered in this article include picture, video, text, audio, flow, RGB, stance, depth, face expressions, body motions, and point clouds. There has been a thorough examination of the most current developments in multimodal deep learning applications in the last few years, as well as a detailed investigation of the baseline methodologies. A detailed taxonomy of distinct multimodal deep learning techniques is presented, providing further details on specific applications. Finally, key issues are outlined for each domain separately, along with potential avenues for further research. © 2024 IEEE.

关键词： Artificial Intelligence Deep Learning Machine Learning Multimodal Natural Language Processing (NLP) UniModal

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A robust method for malware analysis using stacking classifiers and dendrogram visualization

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International Journal of Information Technology (Singapore) 2024年 1-16页

作者： Naveen Kumar, N. Balamurugan, S. Maruthamuthu, R. Devi, P.P. Seshu Kumar, P. Department of Computer Applications Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Avadi Tamil Nadu Chennai India

Malware analysis is a vital and challenging task in the ever-changing cyber threat landscape. Traditional signature-based methods cannot keep up with the fast-paced evolution of malware variants. This underscores the need for developing a more effective malware classification tool helping in exploring new research directions with solutions. In this research work firstly, we present a comprehensive assessment on malware classification using dendrogram clustering techniques. This work proposes, a robust approach to malware analysis by constructing dendrogram that groups malware samples which works based on their ancestral relationships. Semantic analysis of the code also provides deeper insights into the malware’s behavior and functionality, enabling more precise identification. These methods help to detect new variants as well as enhance the recognition of existing malware families. Our proposed model results shows that, the robustness in accuracy and False Positive Rate when compared with other existing models. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Autoencoder decoder Dendrogram Malware classification Semantic analysis

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