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Adding Constructs to 'C' Through Flex and Bison 8

Adding Constructs to 'C' Through Flex and Bison

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Arroju, Nachiketh Vamsi, Nallabothula Reddy, N. Yeshwanth Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Developing a unique language for coding if and for loops involves using creatively designed markers such as 'ifff' and '4'. Meeting the three acceptance criteria requires coding loops using these markers, where 'ifff' signifies an if loop and '4' represents a for loop. Coding loops conventionally with '0' and '4' is considered unacceptable. Additionally, the language requires modifiers for loops and their respective lengths to be placed on one line. The language also encompasses formatting rules, such as combining multiple lowercase letters and ensuring that the letter 'c' appears three times for successful compilation. Repeated occurrence of certain letters is allowed and accepted within the language. Furthermore, specific responses like 'np' for 'no problem,' 'sry' for 'sorry,' and 'fq' for 'thank you' have been formulated based on input. © 2024 IEEE.

关键词： Codes (symbols)

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YoloV8-Powered Surveillance: Safeguarding Crops from Wildlife and Fire Hazards 15

YoloV8-Powered Surveillance: Safeguarding Crops from Wildlif...

引用

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

作者： Nishanth, K. Pranav, Shri S. Jayasuriyaa, E. Lalithamani, N. Prasand, Ajey M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350370249

Agriculture is the backbone of India's economy. However, the farmers are facing crop damage due to wild animals encounter and fire. The study has mainly intended to provide a solution for the prevention of wildlife encounters into agricultural field and fire using Object Detection. This study mainly focuses on detection of fire and four distinct animals: Elephant, Wild Boar, Deer and Cow. The dataset contains 7000 images with annotated five categories of Object. We trained five YOLOv8 models (nano - YOLOv8n, small - YOLOv8s, medium - YOLOv8m, large - YOLOv8l, extra-large – YOLOv8x) on our dataset. Raspberry Pi equipped with a Camera captures video of the crop field and applies Yolov8 (Object Detection Algorithm) to detect fire and wildlife encounters and plays some loud noises to deter wild animals. The Farmer is notified with a photograph of Farm and class name of the animal detection made through a Telegram Bot. The trained medium YOLOv8m model has attained a mAP of 92.2%, proving its efficacy in identifying fire and wild animals. By integrating this device across farm field, real-time automated monitoring and alerts can be provided to farmers, minimizing crop losses. © 2024 IEEE.

关键词： Fire hazards

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Enhancing Village Ranking: Leveraging Cluster Analysis and Machine Learning 5

Enhancing Village Ranking: Leveraging Cluster Analysis and M...

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Karthikeya, Moturi Madhan, S. Sha, Akhbar Megha, R. Dhanush Krishna, R. Gopakumar, G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The research presented in this paper addresses the critical challenge of ranking Indian villages based on their potential for socioeconomic growth, inspired by the objectives of the Shyam Prasad Mukerji Rurban Mission (SPMRM). Leveraging a novel algorithm known as ClusterRank, an extension of the PageRank algorithm, we endeavor to provide a timely, efficient, and accurate solution for village ranking. The ClusterRank algorithm, an extension of the well-known PageRank algorithm, serves as the cornerstone of our approach. It excels in its ability to efficiently and accurately rank villages, outperforming alternative ranking methods. Notably, our findings reveal the superior convergence speed of the ClusterRank algorithm, affirming its capacity to produce rapid results. To assess the efficacy of ClusterRank, we employ a range of statistical ranking coefficients, including the Spearman, Kendall, and Point Biserial correlation coefficients. Our analysis demonstrates a strong positive correlation, with a Spearman correlation coefficient of 0.89, between the rankings generated by ClusterRank and the ground truth rankings provided by SPMRM. This correlation underscores the effectiveness of ClusterRank in identifying villages with high growth potential and highlights its alignment with the objectives of the SPMRM. This research presents a compelling case for the adoption of the ClusterRank algorithm by SPMRM as a valuable tool in the identification of rural areas poised for rurban development. Such adoption not only promises substantial time and resource savings but also directs the focus of SPMRM towards the implementation of targeted interventions and initiatives to uplift rural communities. Moreover, our supplementary analyses using Kendall and Point Biserial correlation coefficients further validate the robustness of the ClusterRank algorithm. © 2024 The Authors. Published by Elsevier B.V.

关键词： Cluster analysis

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E-Waste Alchemy: A Unified Solution for Recycling and Sustainability 5

E-Waste Alchemy: A Unified Solution for Recycling and Sustai...

引用

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

作者： Sharma, Vansh Sameer, Md Sindhuja, M. Nagadevi, S. Anand, M. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331530013

Through user-centric solutions and advanced features, E-Waste Alchemy is an innovative platform that aims to transform the administration of electronic waste (e-waste). The Recycle Centre on the platform is comprehensive, allowing users to specify the brand and model of laptops, smartphones, and a variety of accessories in order to schedule and select the retrieval. E-Waste Alchemy further improves user convenience by enabling real-time tracking of nearby recyclers based on the user's current location. Furthermore, the platform employs predictive analytics to determine the market values of the components contained within each device, including plastic, aluminum, and nickel. The responsible disposal of e-waste is promoted by this predictive feature, which also contributes to environmental sustainability by optimizing recycling processes. E-Waste Alchemy is a forward-thinking and adaptable solution that facilitates the administration of e-waste and promotes a circular economy. © 2024 IEEE.

关键词： Circular economy

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Smart Refrigerator for Enhancing User Convenience and Food Management 2

Smart Refrigerator for Enhancing User Convenience and Food M...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Nandini, Bandaru Jaya Sahithi, Mamidi Leha Harshika, Siddareddy Gari Thangam, S. Gurupriya, M. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798350372892

Many individuals face challenges in effectively managing their household food storage due to limitations in traditional refrigerator technology. These limitations include challenges in monitoring the weights of food, receiving timely reminders for restocking, and incorporating location-based services. Existing solutions lack real-time inventory information, fail to provide timely restocking reminders, and lack integration with location-based services. The proposed Smart Refrigerator solution offers accurate inventory management, remote monitoring and control, location-based notifications, Peltier cooling system, and enhanced user experience through IoT technology and innovative sensor systems. A mobile application complements the system, providing users with remote monitoring, control, and locationbased notifications. Societal benefits include reduced food waste, improved food safety, enhanced user experience, and efficient household inventory management, contributing to sustainability and consumer well-being. The proposed Smart Refrigerator results demonstrated significant improvements in inventory management accuracy and user convenience with DS18B20 temperature sensors, load cells, and gas sensors, reducing food *** remote monitoring, control capabilities, and location-based notifications contributed to increased user satisfaction. © 2024 IEEE.

关键词： Location based services

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Exploring the Essence of Prompt Tone: A Comparative Study of Large Language Model Security 3

Exploring the Essence of Prompt Tone: A Comparative Study of...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Nair, Aiswarya Vithayathil, Jose Nambiar, Sandeep Prem Snehasree, S. Murali, Shalu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350369083

Chatbots powered by Large Language Model(LLM) can be manipulated by malicious prompts, generating harmful content and biased responses which would raise security concerns. Growing dependence on chatbots demands robust security for ethical development and user trust, which makes the work relevant in today's world. The motivation behind the work is to let the user have a safe experience with no negative responses being displayed while using the chatbot, which paved the way to arrive at the goal of developing a security filter that could be integrated into any LLM feature integrated application to mitigate the risk of having security vulnerabilities like prompt injection and jailbreaking, which could be achieved by converting malicious prompt into safer prompts by the method of eliminating negative sentiment phrases. The work focuses on building and implementing the security filters to popular in-production LLMs like Large Language Model Meta AI-2 (LLaMA2) and Generative Pre-trained Transformer - 3.5 turbo (GPT-3.5) to see how they handle against prompt injection and jailbreaking before and after the security filter being integrated. A large database of 200,000 prompts has been collected and preprocessed to train on a machine learning model using binary classification algorithm having 99.7% accuracy for classification of prompts into malicious or non-malicious and further checks are being done by breaking the prompt into smaller phrases and individually analyzing their compound sentiment score using Natural Language Toolkit (NLTK) Valence Aware Dictionary for Sentiment Reasoning (VADER) algorithm to detect and drop the negative sentiment phrases for the modification of the user prompt to eliminate the possibility of malicious prompt being passed to LLM. It is difficult to determine the sentiment of prompts in a detailed way and convert it into an efficient design that will perform well with models. Once this hurdle is overcome, chatbots will become even more reliable,

关键词： Economic and social effects

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Emotion Recognition Using Meta-Learning based on Facial Expressions 5

Emotion Recognition Using Meta-Learning based on Facial Expr...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Prashanth, V.J. Sri Naga Jathin, P. Gopinath, Suraj Udith, S. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Emotion recognition is essential in many real-life problems and applications such as computer-human interaction, health monitoring, etc. In this project, we propose a meta-learning approach for developing a computer visionbased emotion recognition system. The aim of the work is to find human emotions from visual inputs and sort them accordingly. Meta learning techniques are used to allow the model to quickly adapt itself to emotion patterns using little training data that is short and compact. Therefore, meta learning technique is important since it allows the algorithm to perform well among different datasets with diverse data sets including unseen emotions (unknown emotions). Emotion recognition should address generalization problems while having ability to handle adaptation issues. It therefore seeks transparency in emotion recognition in addition to overcoming challenges of adaptability. Its objective is to offer answers to practical cases where emotion detection can be applied within dynamic environments. Thus at the end of this project we expect a reliable, faster, more dynamic way of classifying and recognizing human emotions based on visual inputs which highly valuable in understanding human emotional state for multiple other applications. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Computation Offloading In Wireless-Powered Mobile-Edge computing Networks With Deep Reinforcement Learning For Online Optimization 4

Enhancing Computation Offloading In Wireless-Powered Mobile-...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kumar, R. Prasanna Spandana, Chereddy Srisurya, Ippatapu Venkata Priyadharshini, A.R. Krithika, S. Sai, Gudivada Dhyana Venkatraman, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Edge computing at the mobile frontier, enhanced by the integration of wireless energy, represents a cutting-edge strategy to boost processing performance in networks with limited energy resources, such as wireless sensor networks and the Internet of Things (IoT). This study investigates a mobile edge computing (MEC) framework powered by wireless energy, employing a dual-mode offloading scheme. In this paradigm, tasks from wireless devices (WD) may either be processed on-device or entirely shifted to an MEC server. In this approach, tasks from a wireless device (WD) are either processed locally or completely transferred to an MEC server. The objective is to create an online algorithm that can adjust task offloading and wireless resource allocation adaptively according to the variable conditions of the wireless channel. Conventional numerical optimization methods are inadequate due to the swift variations within the channel's coherence time. Our aim is to develop an algorithm that operates online and can dynamically adjust both offloading and resource distribution in response to the fluctuating state of the wireless channel. Traditional numerical optimization approaches fall short because they cannot swiftly adapt to the rapid changes in the channel's coherence. The solution we propose is a framework based on Deep Reinforcement Learning for Online Offloading that utilizes deep neural networks to incrementally learn from offloading decisions, thereby circumventing the need for complex combinatorial optimization. This leads to a significant reduction in the computational load, particularly in expansive networks. We've further enhanced this system with a method that enables real-time modification of the DROO algorithm's parameters. Our experiments demonstrate that this novel algorithm nearly achieves optimal efficiency and significantly reduces computation times - by more than ten times relative to existing techniques. For example, in a network with 30 users, DROO achiev

关键词： access points component Mobile-edge computing radio-frequency reinforcement learning resource allocation Wireless Devices WPT

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Advanced Hieroglyphic Braille Transpixelation (AHBT): A Novel Approach to Secure Image Steganography 5

Advanced Hieroglyphic Braille Transpixelation (AHBT): A Nove...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Tripathy, Aayushi Jeeban Ashwani, Siddhant Rao, Subhiksha Suresh Mishra, Sahil Rajagopal, Shinu M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Modern correspondence protection is highly in demand for the protection of information exchange, mainly by means of cryptography and steganography. In this paper, a new method called Advanced Hieroglyphic Braille Transpixelation, or AHBT, is proposed and introduced. AHBT amalgamates Braille and hieroglyphs into a very complex algorithm of information hiding in digital images. The plaintext message will first be translated into hieroglyphics and Braille, then encrypted into 8-bit and 6-bit binary formats, correspondingly. These are camouflaged with columnar transposition and hidden inside the 2 LSB of pixel components (r, g, b) using LSB steganography, storing 6 bits of data per pixel. Experimental results have proven AHBT to improve in data security and visual quality compared to conventional methods, evidenced through higher PSNR rates. This technique is a revolution in personal information protection, as it has used multilevel encryption, steganography, and ancient scripts in combination for better security and privacy. © 2024 IEEE.

关键词： Pixels

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Emotion and Cause Detection in Dialogues Using Capsule Networks 1

Emotion and Cause Detection in Dialogues Using Capsule Netwo...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Ganesh, Shyam K. Lavanya, G. Priya, S Baghavathi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518943

Identifying the sources of emotions expressed in text-to-text conversational dialogs significantly contributes to improving applications such as virtual assistants, customer support, and interventions aimed at improving mental well-being. Traditional approaches to emotion analysis are primarily focused on classification procedures rather than consideration of the underlying causes responsible for triggering specific emotional behaviors in various contexts. This paper has presented a new model that identifies emotions in conversational data but simultaneously enables the determination of the root causes of such emotions. The framework uses DistilBERT to generate subtle contextual embeddings, which incorporate the richness of meaning for each utterance. For that purpose, it uses a Capsule Network for the efficient modeling of sequential dependencies and hierarchical relationships that exist in dialog. This altogether makes this system decode more complex emotional triggers with greater accuracy. Furthermore, the model is designed to be scalable and efficient, meaning it can comfortably be deployed in any real-time environment with minimal computational resources. The Experimental Results, integrated A two-step model consisting of Distil BERT and Capsule Network greatly enhances the accuracy of emotion cause detection and can be a strong solution for applications requiring minute emotions and causal inferences from conversational data. © 2024 IEEE.

关键词： Emotion Recognition

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