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Two-Stage Classification of Offensive Meme Content and Analysis 8

Two-Stage Classification of Offensive Meme Content and Analy...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Sivaananth, S.V. Sivagireeswaran, S. Ravindran, Sindhu Nabi, Fizza Ghulam Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India University of Punjab Dept. of Industrial Engineering and Management Lahore Pakistan

ISBN: (纸本)9798331531539

Memes are generally understood as a blend of visual and textual elements and have become an effective medium of communication on social media. However, this ease of communication has also led to challenges in content moderation, particularly with the spread of hate speech, aggressive content, and cyberbullying. Traditional moderation tools often rely on models that focus solely on text or image data, which is inadequate for the multimodal nature of memes. This research proposes a multimodal meme classifier that leverages Stacked LSTM for textual analysis and VGG-16 for visual features, integrating both modalities to enhance classification accuracy. By integrating lexical and visual information, the model captures the full context of memes, enabling it to effectively distinguish between offensive and non-offensive content. The dual-pathway approach offers a significant improvement over single-pathway methods, demonstrating promising classification performance on a curated meme dataset. This solution shows great potential for real-time integration in content moderation systems, enabling efficient detection of offensive memes. Its multimodal framework is very promising on the growing demand for robust systems capable of moderating multimodal content in harmful online activities. © 2024 IEEE.

关键词： Image fusion

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Optimizing Skin Cancer Detection Through Hybrid Convolutional and Ensemble Learning Methods

Optimizing Skin Cancer Detection Through Hybrid Convolutiona...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sahithi, Chalamala Teja Basha Shaik, Arif Pandala, Madhavi Latha Dept. of Information Technology Vijayawada India Vit Bhopal School of Computing Science and Engineering Kothrikalan India

ISBN: (纸本)9798331529833

One of the most prevalent types of cancer is skin cancer and millions of people suffer from it across the globe with melanoma being the most dangerous type if not treated at an early stage. Accurate and timely diagnosis is crucial in the management of skin cancer but traditional diagnosis methods like dermoscopy, is largely dependent on the skills of qualified dermatologists which makes it subjective and susceptible to variation among observers. In this sense, deep learning and machine learning approaches have a lot of potential in automating skin cancer classification and enhancing its accuracy for better early intervention and patient outcomes. This paper presents the analysis of the performance of several models, i.e., CNN + Random Forest, DenseNet201, MobileNetV2 and ConvoMixer + CatBoost on HAM10000, ISIC and PAD-UFES-20 datasets. The experimental results indicate that DenseNet201 achieved an accuracy of 91.8%, MobileNetV2 reached 87%, and the combination of ConvoMixer and CatBoost obtained 66% However, the CNN + RF model outperformed all of the other models achieving 96% accuracy revealing the effectiveness of hybrid approaches for skin cancer classification on a reliable and scalable manner. Future work could include investigating transformer-based models, such as Vision Transformers (Suns et al.) and Swin Transformers (Liao et al.), which are able to allow better global context within the data. Also federated learning may be used to enable the use of multi institutional databases without compromising the privacy of patients. In addition, self supervised learning approaches could also help alleviate the reliance in a substantial amount of annotated datasets thereby increasing accuracy and robustness in practical and clinical situations. © 2025 IEEE.

关键词： Federated learning

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Low-Cost Network Jammer for Wi-Fi Network in Civilian Areas 5

Low-Cost Network Jammer for Wi-Fi Network in Civilian Areas

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

作者： Thangam, S. Gupta, Pradeep Kumar Kartthikeyan, N. Mukil, L.D. Niranjan, D.K. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331504403

This application presents a novel approach to address the challenge of intercepting unmanned aerial vehicles (UAVs) operating within the 2.4 GHz frequency band. Jamming device operating at 2.4 GHz, with an Arduino Nano microcontroller and nRF24L01 module included. The device is designed to accurately search available wireless networks in a specific frequency range, focused on intercepting connections between drones in this spectrum Real-time system identifies networks and selects the channels, thus disconnecting targeted frequency devices Security measures in the presence of counter-drone activities are required This technology is an important tool for growth. Using Arduino Nano and nRF24L01 modules, the jamming device offers a flexible and scalable solution designed for network security professionals. The 2.4 GHz jammer provides precise interference, allowing targeted disruption of drones operating in a specific frequency band. In addition, the paper provides a detailed introduction to well-known drones operating in the 2.4 GHz frequency range, such as the DJI Phantom series and the Parrot Bebop series, and understanding these drones is crucial for planning effective countermeasures. The paper includes implementation data and results of experimental tests, providing insights into the efficiency and feasibility of a 2.4 GHz jammer designed to combat interference in UAV networks in a specific frequency band to be used. © 2024 IEEE.

关键词： Antennas

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Development of a Comprehensive Learning Management System using Full Stack Technology: A Modern Approach to Educational Platforms 5

Development of a Comprehensive Learning Management System us...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Supriya, M. Rathinavel, Keshav Radhakrishnan, Divya Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

Using the advantages of modern web technologies, this project utilizes powerful data modelling characteristics of Prisma and dynamic front-end capacities of *** to design a Learning Management System (LMS). The aim of this project is to create a comprehensive platform that would effectively facilitate the organization of educational resources and assignments, as well as student cooperation, ensuring scalability and performance at the same time. Many software engineering practices are utilized during this project for code maintainability, security, and performance. The full cycle of software development consists of testing and iterative adaptation of the developed systems based on the opinions of end-users. Ultimately, the LMS is improved through thorough testing and feedback from the users in an iterative and progressive process. The findings of this research are pertinent to the future developments of educational technology that will seek to implement modern, full-featured, and user-friendly LMS. © 2024 IEEE.

关键词： Software testing

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Neuroimaging Analysis for Early Diagnosis of ADNI Progression with EfficientNet 10

Neuroimaging Analysis for Early Diagnosis of ADNI Progressio...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Jeyalakshmi, J. Lokesh Kumar, J. Vishnu, S. Amrita Vishwa Vidyapeetham Dept. of Cse Amrita School of Computing Chennai India Rajalakshmi Engineering College Dept. of Cse Chennai India

ISBN: (纸本)9798350353068

This study focuses on the early detection of Alzheimer's disease progression using data from the ADNI Program. Our study resolved how structural MRI and functional MRI are affected together to identify small changes in neurophysiology and function that are indicative of early Alzheimer's disease -using deep learning and feature extraction approaches. Longitudinal data analysis tracks changes over time, providing insight into disease processes. Furthermore, we explore the synergistic potential of different approaches to combine to improve the early detection of A D. This study utilises cutting-edge deep learning techniques on multimodal neuroimaging data from ADNI, aiming to find the early stages of AD progression. This research aims to improve understanding of the progression of Alzheimer's disease, paving the way for effective diagnostic tools and treatments tailored to people at risk of the disease. This study can potentially revolutionise early AD intervention techniques and substantially influence clinical practice by utilising the complimentary information supplied by several neuroimaging modalities. © 2024 IEEE.

关键词： Neuroimaging

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Development of a Robust Emergency Communication System Utilizing LoRa and GPS with Multi-Hop Routing 2

Development of a Robust Emergency Communication System Utili...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Kumaran, U. Umah, B. Reddy, P. Praneeth Shruthi, P. Sai Tharneesh, P. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368413

Conventional communication methods such as mobile phones, satellite communication, and Wi-Fi face significant challenges in remote or underserved areas. Mobile phones often fail due to low signals or lack of towers, satellite communication is prohibitively expensive, and Wi-Fi cannot cover large distances. This research explores the use of LoRa (Long Range) technology combined with a multi-hop communication technique to address these issues. LoRa's long-range capabilities and low power consumption make it ideal for reliable communication in challenging environments and in contrast to GSM which calls for a subscription and is based on mobile infrastructure, LoRa operates independently and is cost-effective. By implementing a multi-hop strategy, messages are relayed through multiple nodes, significantly extending the communication range. This paper also addresses and resolves issues such as infinite looping in message passing by incorporating unique identifiers and timestamps to ensure message integrity. Performance evaluations demonstrate substantial improvements in communication reliability and range extension, establishing this method as a cost-effective and efficient alternative to traditional communication technologies. © 2024 IEEE.

关键词： Wi-Fi

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IoT-Driven Smart Car Parking System: Optimizing Urban Mobility and Reducing Environmental Impact 3

IoT-Driven Smart Car Parking System: Optimizing Urban Mobili...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tanvi, Y. Lakshmi Sowmya, B. Prashanth, B.N. Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid increase in urban vehicle numbers has significantly worsened traffic congestion, particularly in public parking spaces, where conventional parking systems often prove inefficient, leading to wasted time, excessive fuel consumption, and increased environmental pollution. A Smart Car Parking System is proposed to address these challenges by integrating advanced technologies, including the Internet of Things (IoT), to optimize parking operations. The system employs sensors embedded in parking spaces to detect vehicle presence and provide real-time data on availability. This enables drivers to locate free parking spaces efficiently, improving traffic flow. Additionally, the system offers valuable insights to city planners and facility managers, supporting optimal space allocation and dynamic pricing models. By reducing vehicle idling and unnecessary driving while searching for parking, the system helps minimize emissions, contributing to environmental sustainability. The study builds an automated smart car parking system for parking slot monitoring and gate control. The system ensures correct slot occupancy reading, status displayed in real time, and smooth operation of an opening and closing system. It manages additional features such as real-time sensor calibration and effective gate control with a high degree of accuracy and accountability. The proposed Smart Car Parking System represents a key innovation in urban mobility, offering a user-friendly, efficient, and ecofriendly solution to one of the most persistent challenges faced by modern cities. © 2025 IEEE.

关键词： Traffic congestion

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Real Time Accident Alert System in Hilly Region using LoRa Technology 5

Real Time Accident Alert System in Hilly Region using LoRa T...

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

作者： Thangam, S. Gurupriya, M. Kothari, Osho Sah, Subodh Narayan Shah, Kriti Priya Dharmaraj, Kadam Prajwal Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331504403

In hilly regions, ensuring road safety is particularly challenging due to the unique terrain and the potential for more severe consequences in the event of accidents. This study aims to improve road safety in hilly regions by utilizing three distinct accident detection parameters. The first parameter, tilt sensors (MPU6050), monitors vehicle inclination, triggering an alert system when sudden changes in the tilt angle indicate a rollover or collision. The second parameter, vibration sensors, identifies abrupt jolts or impacts, triggering the alert system when unusual vibrations occur. The third parameter, GPS technology, provides accurate positioning and location data for notifying nearby helping stations and first responders, facilitating rapid and precise responses in emergency situations. To ensure efficient communication of accident alerts over long distances, LoRa (Long-Range Radio) technology is integrated into the system. This communication enables swift transmission of accident alerts to the nearest helping station. The central interface for accident detection and alerting is the Blynk mobile app, accessible to a wide range of users, including vehicle operators and first responders. This study presents a significant advancement in enhancing road safety and emergency response in hilly regions, where accidents can have serious consequences. Additionally, we plan to incorporate sending an emergency alert message to the receiver's number, nearby hospital, and police station using a GSM module to further enhance emergency response. © 2024 IEEE.

关键词： Global positioning system

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Enhancing Data Security through the Synergy of AI and AES Encryption: A Comprehensive Study and Implementation

Enhancing Data Security through the Synergy of AI and AES En...

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2024 MIT Art, Design and Technology School of computing International Conference, MITADTSoCiCon 2024

作者： Budhewar, Anupama Bhumgara, Sayonee Tekavade, Abhishek Nandkar, Jayditya Zanwar, Atharva MIT ADT University MIT School of Computing Dept. of Computer Science Engineering Pune India

ISBN: (纸本)9798350362879

In today's digital era, safeguarding data is crucial. This paper examines the combination of Artificial Intelligence (AI) and Advanced Encryption Standard (AES) for enhanced data security. We integrate the k-Nearest Neighbors (k-NN) algorithm with AES encryption to fortify data protection across various domains. This study highlights the strengths and limitations of both AI and AES, and demonstrates how k-NN's pattern recognition capabilities enhance AES's robustness against evolving threats. The practical implementation for secure file transmissions and confidential data storage is also discussed. © 2024 IEEE.

关键词： Cryptography

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Design and Implementation of an Android-Driven Smart Farming Assistant 15

Design and Implementation of an Android-Driven Smart Farming...

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

作者： Chandra, Hothur Sai Pavan, Goli Sai Sri Sai, Kadiyala Karthik Sri Chandra, Karuturi Prudhvi Sai Sampath, Nalini Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering India

ISBN: (纸本)9798350370249

The Smart Farming Assistant project is a helpful friend for farmers, using modern technologies to make farming easier and smarter. It's an Android app that uses advanced technologies like deep learning to give farmers important insights and help them manage their crops better. One of its main features is the ability to recognize diseases in paddy fields, helping farmers identify and diagnose issues through analyzing pictures. Additionally, the app provides access to many helpful articles and resources, offering educational content to improve farmer's knowledge and practices. These resources include tips and tricks for cultivation, fertilizer calculator, ensuring farmers have the latest information and tools to improve their farming methods. © 2024 IEEE.

关键词： Smart agriculture

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