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Deep Learning Driven Multilingual System for Recognition and Categorization of Medicinal Plants with Extensive Elucidation 9

Deep Learning Driven Multilingual System for Recognition and...

引用

9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Azhaguramyaa, V.R. Janet, J. Hariharan, A. Kamaleshwaran, A. Albert Joseph, A. Computer Science And Engineering Sri Krishna College of Engineering And Technology Tamil Nadu Coimbatore India Sri Krishna College of Engineering And Technology Dept. of Computer Science And Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350365092

Medicinal plant use is widespread throughout many cultures and geographical areas, frequently entwined with customs and knowledge. The use of technology, especially deep learning, to improve the identification and classification of these plants has gained popularity in recent years. This research introduces a new multilingual system with deep learning capabilities that can identify and classify medicinal plants with a great deal of explanation. The term 'deep learning-driven multilingual system for recognition and categorization of medicinal plants with extensive elucidation' describes an advanced technology that carries out several tasks by utilizing deep learning, one of the most advanced machine learning techniques. Based on pictures, the system can recognize and classify a variety of therapeutic plants. Because users may communicate with the system, ask questions, and get information in their native tongue, it is usable by a wide variety of people across the globe. Apart from identification and classification, the method offers comprehensive clarifications or in-dept. accounts of the recognized therapeutic herbs. © 2024 IEEE.

关键词： Deep learning

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License Plate Detection using YOLO v8 and Performance Evaluation of EasyOCR, PaddleOCR and Tesseract 15

License Plate Detection using YOLO v8 and Performance Evalua...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, P Praneeth Shruthi, P Sai Himanshu, P. Singh, Tripty Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

License plate recognition is critical to the functioning of the control and surveillance systems. Vehicle identification is based on number plates, which are made up of a certain arrangement of letters and digits. However, manually identifying every number plate is a laborious and difficult task. The majority of earlier automatic number plate recognition (ANPR) systems were restricted in how they could operate. These conditions included being limited to fixed illumination, background noise in the vehicle image. The primary goal of this project is to develop a reliable number plate recognition model that functions with the help of YOLO v8 for license plate segmentation followed with a comparison model of EasyOCR, PaddleOCR, Tesseract models based on best model for character recognition of license plate. The steps involved include processing of vehicle image, segmentation of license plate from the image, followed by internal segmentation of characters on the plate and pattern matching. Data pre-processing is finally performed in order to read and process the license plate. Problems like background noise in image like characters apart from the license plate and number-based characters being wrongly predicted as alphabets have been tackled. © 2024 IEEE.

关键词： Image segmentation

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Enhanced Task Scheduling in Cloud Computing: A Comparative Analysis and Hybrid Algorithm Implementation Using CloudSim 15

Enhanced Task Scheduling in Cloud Computing: A Comparative A...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, Padigela Srinithya Trisha, Kariveda Sree, Nichenametla Hima Bhaskaran, Sreebha Sampath, Nalini Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Task scheduling is crucial in cloud environments to optimize resource allocation and performance. This research presents a comprehensive comparative analysis of task scheduling algorithms, that includes First-Come-First Serve (FCFS), Shortest Job First (SJF), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). FCFS schedules tasks based on their arrival order, SJF prioritizes tasks based on the shortest execution time, and GA and PSO use metaheuristic approaches that use evolutionary methods for optimization. The hybrid algorithm is proposed combining SJF and GA to improve scheduling efficiency. The main objective is to improve the performance of task-scheduling algorithms in cloud environments. The performance of the individual algorithms and of the proposed hybrid algorithm are analyzed under static and dynamic workload scenarios. Metrics such as makespan and response time are evaluated using simulations. © 2024 IEEE.

关键词： Cloud Genetic Algorithm Makespan Shortest Job Task Scheduling

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Vision Enhancer: A Voice-Driven System For Obstacle Detection

Vision Enhancer: A Voice-Driven System For Obstacle Detectio...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Supraja, P. Ganapathi, Ananya Ram, K. Sushmitha Vaishnavi, V. Jyotsna, C. Dept. of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331505134

The eyes serve as the primary sensory organ for individuals, providing vital visual information that aids in perception, recognition, and understanding of the surrounding environment. However, for those with visual impairments, accessing visual information poses significant challenges, limiting their freedom, particularly in navigating through obstacles. The paper attempts to address the obstacles faced by visually impaired individuals by leveraging object detection techniques such as TensorFlow to identify obstacles in their surroundings. By incorporating dept. estimation, the system aims to determine the distance to these obstacles, providing crucial spatial awareness. The system generates warnings through voice feedback, aiding users in making informed decisions to navigate safely. The implementation of this detection system is active on handheld devices, predominantly utilizing webcams or mobile devices. Through this approach, the system achieves a commendable accuracy rate of 98%, thereby enhancing the autonomy and safety of individuals with visual impairments in their daily lives. © 2024 IEEE.

关键词： Feature extraction

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Unmasking Emotions: Deep Neural Networks for Image-Based Emotion Recognition 2

Unmasking Emotions: Deep Neural Networks for Image-Based Emo...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Sohel, Mohammad Aman Madhukar, Raghupatruni Sai Batchu, Sai Muralidhar Jyotsna, C. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350327533

Recognizing human emotions is a multifaced field that uses the technology, especially deep learning and Convolutional neural networks, to recognize and understand emotions such as happiness, sadness, anger, disgust and fear. Convolutional Neural Networks are designed for visual data explorations and they are effectively capture facial expressions for emotion recognition. The process involves the collection of labeled data, and also preprocesses it, creating an appropriate deep neural network, training the model with constant updates, and assessing its performance using metrics on a testing dataset. Proposed work tackles emotion detection in irregularly distributed datasets of preprocessed 48x48 grayscale emotion images. Significant performance gains are achieved through targeted data augmentation for marginalized classes, giving valuable insights for robust emotion recognition models. Optimizing hyperparameters enhances the model, enabling its deployment in real-world scenarios by ensuring integration, data handling, and real-Time functionality. The proposed model primarily focuses on offline image and video capturing for emotion detection, and highlighting on video-emotion recognition. © 2024 IEEE.

关键词： Emotion Recognition

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An IoT-Based Sustainable Automation System for the Garments Industry in Bangladesh 3

An IoT-Based Sustainable Automation System for the Garments ...

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3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2023

作者： Ahmed, Khandakar Rabbi Salam, Towfika Rabbu, Mohammad Mahmudul Haque, F.M. Hasan, Nazmul Miraz, Shafiun Rana, Md. Masud Mansur, Maliha Tabassum, Humayra Shayer, Khandokar Shadman Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Computer Science & Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical & Electronic Engineering Dhaka Bangladesh Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh American International University-Bangladesh Dept. of Electrical & Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350307399

The Readymade Garments sector accelerates Bangladesh's development. The 'Made in Bangladesh' brand has also helped to boost Bangladesh's reputation and brand recognition on a global scale. Despite its limited resources, the country has maintained a 6% annual GDP growth rate while attaining extraordinary social and human development. On the other hand, the phrase 'Internet of Things' (IoT) refers to a broad concept that can be summarized as an ecosystem of intelligent gadgets equipped with networks, sensors, and processing tools. IoT is a new technological platform that enables the many objects in our environment to connect via sensors and the Internet to improve our quality of life. Manufacturing and the industrial sector, agriculture, the health sector, smart cities, security, and emergency services, to name a few. IoT implementation in the garment sector would open a new door to growth in production and worker safety. Implementing home automation and safety systems in the garment sector will help in production and ensure the safety of the workers. The suggested IoT -based sustainable automation system for the Bangladeshi garments sector is efficient, eco-friendly, secure, reliable, and user-friendly. The system's user-friendly interface makes it straightforward to use and manage. The Bangladeshi garments industry's IoT -based sustainable automation system is a comprehensive and integrated solution that addresses the industry's primary difficulties and provides a sustainable and viable future. © 2023 IEEE.

关键词： Internet of things

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Cyberbullying Detection on Multiclass Data Using Machine Learning and A Hybrid CNN-BiLSTM Architecture

Cyberbullying Detection on Multiclass Data Using Machine Lea...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Balaji, Peddi Gowtham Katariya, Priyanka Prakash Sruthi, S. Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Cyberbullying has emerged as a significantly concerning online societal issue in the past few years with the proliferation of social media platforms. To address and overcome this pressing communal concern and successfully detect cyberbullying across various categories, this study proposes a novel approach that integrates Convolutional Neural Networks and Bidirectional Long Short-Term Memory Networks, alongside six traditional machine learning techniques, driven by the purpose of enhancing the accuracy and robustness of cyberbullying detection across diverse cultural and textual landscapes. To highlight the multiclass aspect, the model is trained and tested on a dataset containing instances categorized into Age, Religion, Ethnicity, Gender, Other Cyberbullying, and Not Cyberbullying. Through rigorous experimentation, the proposed deep learning model's proficiency results in a 0.83 accuracy and F measure, showcasing its efficacy in capturing the nuances of various categories. Meanwhile, among the six machine learning models employed, Random Forest emerges as the top performer with values 0.94 and 0.93 for the accuracy and F measure respectively. This comprehensive methodology underscores its ability to effectively discern between the different classes and offers a promising solution to combat cyberbullying across diverse demographics and thematic dimensions. © 2024 IEEE.

关键词： Adversarial machine learning

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Emotion Recognition in Images: CNN-based Classification of Happy and Sad States 15

Emotion Recognition in Images: CNN-based Classification of H...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Tripty Duraisamy, Prakash Reddy, Karukonda Nithin Reddy, Mettukuru Tharun Yeswanth, Kathi Venkata Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The difficulty of recognizing emotions from pictures is a real problem for mental health evaluation and human-computer interaction. In this study, it is suggested that the use of Convolutional Neural Networks (CNNs) can help identify emotions in photographs focusing on classifying happy and sad states. The CNN architecture was trained using a dataset made up of images depicting people in various emotional states ranging from joy to sadness. After extensive testing and analysis, our model discerns between happiness and sadness. So, we investigate how the emotion recognition system performance could be maximized by trying out different CNN designs, data augmentation techniques as well as hyperparameters. Moreover, part of this chapter looks at the interpretability of the model's decisions and also discusses implications and further research directions into emotion recognition with deep learning mechanisms. On the whole, our work advances emotion recognition technology and highlights how CNN-based techniques can aid in understanding human emotions through visual cues. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Evaluation of AES, DES, RSA, and Paillier Homomorphic for Image Security 9

Performance Evaluation of AES, DES, RSA, and Paillier Homomo...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bhavitha, Medikonda Rakshitha, K. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the current digital era, it is crucial to preserve sensitive data, particularly when it comes to images. Encrypting images is essential for ensuring the confidentiality and integrity of image data both in transit and storage. To address this need, this exploration design delves into the operation of four important encryption ways Advanced Encryption Standard (AES), Data Encryption Standard (DES), Rivest-Shamir-Adleman (RSA), and the Paillier homomorphic algorithm. Regarding the security of image data, each of these approaches has a distinct set of benefits and features. This study aims to explore their use in image encryption and decryption and assess their performance, security, and practicality in safeguarding images from unauthorized access and malicious threats. © 2024 IEEE.

关键词： Cryptography

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A Hybrid Prediction Model for Disseminating the Factors Related to Anomaly Detection in Software Defined Networks 15

A Hybrid Prediction Model for Disseminating the Factors Rela...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Darshan, S. Radhika, N. Radhika, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

The domain of network traffic and anomaly reporting has its highlights in recent days towards real-time applications. The utilization of network data with its correlating attributes and its measures must be considered before deriving a pattern or decision making process. SDN(Software Define Networks) in turn has been facilitating more towards the efficient and reliable mechanism for data control and network management system. This research focus on the development of a hybrid prediction model for disseminating the factors that contribute towards anomaly detection in SDN. The experimentation can be made by hybridizing different classification schemes with tuned parametric components upon statistical modelling and evaluation. The proposed hybrid model is suggested up with the hybridization of SVM, NN along with Ada Boost algorithm with tuned parameter metrics. From the proposed model it is evident that the selected features will play a significant key role in anomaly prediction in a given network. © 2024 IEEE.

关键词： Prediction models

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