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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Madasanal, Kartik Gudnavar, Anand Nagarmunoli, Sreedevi Madagouda, Basavaraj Naregal, Keerti Visvesaraya Technological University Jain College of Engineering and Research Department of Computer Science and Engineering Belagavi India Graphic Era University Department of Computer Science and Engineering Dehradun India S.G.Balekundri Institute of Technology Visvesaraya Technological University Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798331513894

This paper develops a Smart Public Transportation System using RFID technology, IoT integration, and a MERN-based web application for increasing the efficiency, accessibility, and user experience of urban transit systems. It addresses problems such as unreliable bus tracking, lack of support for multilingualism, and ineffective communication between operators and passengers. The key hardware components include RFID tags for bus identification, and IoT-enabled bus stations with RFID scanners, speakers, GSM modules for data transmission, and LCD displays. These ensure accurate real-time updates about the bus locations. On the software side, it is supported by a MERN stack, including MongoDB, ***, ***, and ***, providing a responsive web interface that's complemented by *** for dynamic route visualization and WebSockets for live updates. Multilingual support through i18next makes it inclusive for various user groups. The system is efficient in that it only updates when RFID scanners capture bus tags at certain checkpoints, thereby reducing server load much more than GPS-based systems. Additional features include route tracking, interactive maps, ticket pricing, and anonymous passenger chats. Crowd-sourced feedback mechanisms and an admin panel for bus management ensure the reliability and scalability of the system. This work shows the feasibility of cost-effective, scalable, and user-centric modernization of public transportation systems, paving the way for further enhancements such as integrated payment systems and AI-based route optimization. © 2025 IEEE.

关键词： Liquid crystal displays

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An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis 8

An Enhanced Product Quality Evaluation using Hybrid LSTM-FCN...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Raj Kumar, V.S. Kumaresan, T. Kanna, P. Rajesh Jagadeesan, S. Showmiya, P. Nithin, P. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Technology Department of Computer Science - Cyber Security Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India

ISBN: (纸本)9798350367904

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model's parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO's computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis. © 2024 IEEE.

关键词： Tweets

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Sign Language Recognition System Using Convolutional Neural Network

Sign Language Recognition System Using Convolutional Neural ...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Kumar, Pavan Chanakya, Pasam Velusamy, Saranya Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798350382693

The various uses of hand gesture detection systems and their capacity to facilitate effective machine-human interaction have garnered significant interest in recent years. An overview of current hand gesture recognition systems is provided in this study. The challenges of the gesture system are highlighted with key issues of the hand gesture recognition system. Additionally, a review of the recent motions and gesture detection technology is provided. A synopsis of the research findings for databases, hand gesture techniques, and a comparison of the key stages of gesture recognition are also provided. Finally, the benefits and cons of the systems under discussion are clarified. Though there is a large social group that could benefit from it, the concept of using technology to recognize sign language is not widely used. There are numerous technologies at this disposal that could be useful in creating a connection between this social group and the outside world. Knowing sign language is one of the keystones to empowering users to interact with the general public. computers are able to comprehend sign language through the application of machine learning and picture classification, which people can then interpret. This study uses convolutional neural networks to identify sign language motions. The RGB camera was used to capture static sign language gestures for the image collection. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Cost Efficient VBIR Technique Using ICA and IVCA

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SN computer Science 2024年第5期5卷 560页

作者： Pradeep Kumar, B.P. Srinidhi, N.N. Shiva Darshan, S.L. Naresh, E. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

With an emphasis on early-stage contrast agent transit through tumour vasculature, this study presents Adaptive Complex Independent Components Analysis (ACICA) as a unique method for evaluating intravascular responsiveness in prostatic tissue. Furthermore, a new SVM clustering method is introduced that outperforms the conventional k-means clustering for image retrieval based on vision. The study emphasises how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be improved in conjunction with quantitative analysis. Within the field of DCE-MRI, ACICA stands out as a unique intravascular attention measurer. Utilising the spatial independence of extravascular and intravascular magnetic resonance imaging (MR) data, ACICA offers a strong foundation for DCE-MRI image analysis. It incorporates pharmacokinetic modelling to optimise the time lag, especially useful for arterial curves, and a reference region (RR)-based technique to adjust the intravascular concentration curve. The model's evaluation yields outstanding results, with recall and accuracy ranging from 83 to 99% and 82.8% to 99.6%, respectively. The average recall and precision across datasets are 92.86% and 92.82%).All things considered, this study demonstrates the effectiveness of ACICA in evaluating intravascular responsiveness and presents viable paths for enhancing clinical results and diagnostic precision in the treatment of prostate cancer. © The Author(s) 2024.

关键词： Adaptive complex independent components analysis Computed tomography Dynamic contrast enhancement Magnetic resonance imaging Visual-based image retrieval

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Personalised Emotion Detection from Text Using Machine Learning 6th

Personalised Emotion Detection from Text Using Machine Learn...

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6th EAI International Conference on computer Science and engineering on Digital Transformation in Organizations: New Challenges, COMPSE 2022

作者： Bhavya, A.V. Dhanush, R.H. Sangeetha, J. Jose, Arun Cyril Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031347498

This research discusses an emotion recognition system, which is an important component of many effective computing technologies for natural language processing, based on open-source platforms with automatic speech recognition (ASR) and text analysis, and which is used for user-based customised sentiment analysis. PocketSphinx as ASR and Word2vec model, K-means clustering, and TfidfVectoriser for text automatic analysis are used to design the proposed framework. Further, the dataset that is used for testing and training the model is from International Survey on Emotion Antecedents and Reactions (ISEAR). This research yields a user-dependent system that will function as a tailored assistant for identifying emotional responses and discovering innovative applications. The suggested model greatly outperforms the prior models, with an efficiency of 81% and an f-measure of 89%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Emotion Recognition

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Food Classification using Machine Learning 1

Food Classification using Machine Learning

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Santhosh Krishna, B.V. Mai, P. Kiran Muthumanickam, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350365405

The use of machine learning techniques in food categorization has received a lot of interest since it has the potential to transform a variety of industries. Food categorization has developed as a critical application of machine learning techniques in recent years, providing potential answers to a variety of problems in the food business, health monitoring, and nutritional evaluation. This paper presents a comprehensive review of methodologies, datasets, and challenges associated with food classification using machine learning. We will have an overview of linear regression. The publicly available datasets are commonly used for training and evaluation purposes, along with challenges such as data imbalance, handling missing values, and limited annotated data. We will perform EDA, finding products by categories and checking missing values and duplicates from the dataset. Finally, we identify future research directions aimed at improving the accuracy, scalability, and real-world applicability of machine learning-based food classification systems. © 2024 IEEE.

关键词： Adversarial machine learning

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The Demonstration of Android Vulnerabilities

The Demonstration of Android Vulnerabilities

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Vinay, Mallela David Hemanth Kumar, Mandavyapuram Hemanth, Banoth Tomar, Deepak Singh Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350348460

The development of mobile applications is increasing rapidly. Client-server architectures are common in many of these systems. IOS and Android are the most commonly used operating systems for the client. Smartphones can keep any type of valuable files, including contacts, call logs, emails, location history, photographs, vedios and messages. Users' private information kept on the device can be accessed by malicious programs. Android application vulnerabilities like SQL injection, XSS etc. analyze source code of security patches of widely used applications written in weak and strong types of languages. In accordance with OWASP mobile vulnerabilities, this system demonstrates the most common vulnerabilities and attacks, namely session management, broken authentication, SQL injection and insecure data storage. Android security has a lot of flaws that may be exploited, and these vulnerabilities or attacks are quite efficient and vital for any application. A detailed explanation of low security and high security is given when an attack is performed in the application system. The research results demonstrate that security is only associated with just a few of software failure kinds that impact a select number of cases. By conducting a detailed analysis, the system will be able to identify existing holes in the Android operating system and make the most informed decisions to make it more secure. © 2024 IEEE.

关键词： Android Vulnerabilities Broken Authentication Insecure Data Storage Security Session Management SQL Injection

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Image Recognition technology Supported Environment Monitoring Smart Fan 7

Image Recognition Technology Supported Environment Monitorin...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Yang, Sheng-Yuan Lee, Hsiu-Chia Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan

ISBN: (纸本)9798350307207

Owing to the development of the Internet of Things (IoT), smart electrical appliances bring convenience to life. However, smart home appliances are too cumbersome as their interface operations are mostly not intuitive with less practicability than expected. Taiwan has an island climate, and the summer weather is scorching. An easy-to-use and practical electric fan is indispensable for cooling off the heat, which is the research object of this study to make users feel comfortable. In this study, image recognition technology was introduced. The user turns on the fan manually or through the mobile APP, and selects the tracking mode through the mode function. The camera with the fan detects the user's position and then starts to track the user. The device is equipped with a smoke sensor and a camera to monitor the user's home. The fan proves the feasibility of the conceptual architecture. © 2024 IEEE.

关键词： Image recognition

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An Efficient Security Solution for IoT and Cloud Security Using Lattice-Based Cryptography

An Efficient Security Solution for IoT and Cloud Security Us...

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2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Bagla, Piyush Sharma, Ravi Mishra, Amit Kumar Tripathi, Neha Dumka, Ankur Pandey, Neeraj Kumar National Institute of Technology Department of Computer Science & Engineering Punjab Jalandhar India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Department of Computer Science & Engineering Dehradun India Women Institute of Technology Department of Computer Science & Engineering Dehradun India Dehradun India

ISBN: (纸本)9798350314878

This study introduces lattice-based cryptography to enhance security in IoT and cloud systems. It focuses on two fundamental challenges in lattice-based encryption. The paper compares the advantages and disadvantages of lattice-based encryption with traditional RSA and elliptic curve cryptography. Furthermore, it presents a novel approach called 'Lattice-based secure data aggregation,' specifically designed for IoT and cloud systems, ensuring confidentiality and integrity during data aggregation. The paper covers various aspects, such as data encryption, aggregation, decryption, and implementation on resource-constrained devices. Performance and security evaluations compare the proposed algorithm with other data aggregation techniques, demonstrating its effectiveness against attacks, computational efficiency, and low memory requirements. The paper also addresses the approach's limitations and proposes potential areas for future research. Overall, the proposed lattice-based cryptography offers a promising solution for privacy and security-sensitive IoT and cloud systems, making it a viable option for widespread adoption. © 2023 IEEE.

关键词： Computational efficiency

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Tracking Waste Disposal and Generation of Fine to control and promote proper waste segregation

Tracking Waste Disposal and Generation of Fine to control an...

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

作者： Ashok, K.N. Kavyashree, K.S. Keerthana, S. Nagashree, C.R. Navya, L. S J C Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

The metropolitan cities in India alone produce about 10 million tons of waste. That is, if left untreated, the waste materials will cause several types of pollution which are water, soil and air that destroy our ecosystem completely. People can avoid this madness by being waste conscious at an individual level. Lack of awareness and weak imposition of rules by authorities on separate household garbage pick-up are some reasons why many policies have failed to accomplish this. Thus, we aim to monitor and fine them whenever they don't obey the rules in segregating their household wastes. This project mainly deals with using web camera images to determine the percentage of non-segregating biodegradable from non-biodegradable waste during its collection by the municipal bodies for determining the fines based on improper disposal methods of waste products. One way of analysing the proportion of nonsegregated garbage is through Convolution Neural Networks (CNN), a deep learning algorithm. This groundbreaking solution advances responsible trash disposal methods that enhance a cleaner living environment for all people and future generations alike © 2024 IEEE.

关键词： Convolutional neural networks

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