Identifying Toxic Content in Social Media Using NLP and Machine Learning

With the fast-paced development of social media platforms, it has become easy for individuals to spread hate speech messages. Controlling it is quite difficult as huge amounts of data is generated on every second. The administration, educational institutions and social media companies realize the need for restraining hate speech. That\u27s why, policies and guidelines have been developed by these bodies for stopping such activities. However, bringing these guidelines into action is quite challenging due to the vast number of users and rapidly changing behavior of the internet. The system uses Natural Language Processing. It uses TF-IDF to understand the words and then uses models like Logistic Regression, Support Vector Machine, Random Forest, Naïve Bayes, CatBoost and Stacking Classifier to decide whether it is hate speech or not. The system also included SMOTE-Tomek to make sure it is fair. The results show that the Support Vector Machine model is the best at finding hate speech as it gets it right 87.6% of the time. The user interface of the system is made with Streamlit so that the user can check for the text sentiment in real-time. It is better than having people look at everything because that takes lot of time and is not very good at catching everything. This system is a solution for social media platforms to stop hate speech

The Survey of Developing Peer-to-Peer (P2P) Multimedia Algorithm for Live Streaming to Enhance QoS in Cloud Environment

In this survey, I examined the latest developments in Peer-to-Peer (P2P) multimedia algorithms, particularly how they enhance live streaming performance when integrated with modern technologies like cloud computing, artificial intelligence [AI], block chain, 5G, and edge computing. My focus is on how these technologies work together to address key challenges in Quality of Service (QoS), scalability, latency, and system reliability. I reviewed a range of current system architectures, especially hybrid models that combine P2P with cloud or content delivery networks (CDNs). These setups are evaluated based on how well they manage real-time resources, deliver adaptive video quality, ensure secure content sharing, and make efficient use of peer contributions. I also studied algorithms driven by reinforcement learning, trust-based decentralization, and edge-level computation. From this analysis, I found that hybrid systems offer a strong balance between the stability of cloud services and the flexibility of peer networks. AI plays a valuable role in adapting to changing conditions, while block chain helps secure content delivery although it may sometimes slow the system down. The combination of 5G and edge computing shows real promise in lowering delays and improving real-time responsiveness. However, several challenges remain. These include unpredictable peer behavior, fluctuating network quality, high implementation costs, and persistent security concerns in decentralized environments. In conclusion, I point to future research opportunities focused on building smarter, more reliable, and highly scalable streaming solutions. By connecting multiple emerging technologies, this work provides meaningful guidance for researchers and developers aiming to advance the next generation of decentralized live streaming platforms

A Voice based assistant using todays AI

The prime objective of this research is to fill the gap between content generation and image synthesis within a single unified platform using flutter. A single unified platform which uses the natural language understanding capabilities of Chat-GPT and creative power of Dall-E. The application\u27s architecture is also presented in this article. It uses a modular client-server design with a Flutter-based user interface, secure API connection, and integration of ChatGPT and DALL·E APIs for AI interaction. Issues like managing API limitations, reducing response times, attaining precise speech recognition, and guaranteeing cross-platform compatibility were resolved during development. An optimized system that can produce synchronized textual and visual material with voice commands is the outcome of these efforts

The REM-Sleep Inspired Creative Replay for Continual Learning in Deep Neural Networks: Artificial Intelligence and Continual Learning Systems

This paper proposes DREAMLOOP (Dream Replay with Enhanced Anchoring and Memory Loop Optimization), a REM-sleep-inspired continual learning framework that extends generative replay with feature-level stabilization mechanisms. The framework is evaluated on the Split-MNIST benchmark consisting of five sequential tasks using a three-layer Multi-Layer Perceptron (SimpleMLP) classifier, where the 128-dimensional intermediate feature layer serves as the anchor representation. DREAMLOOP integrates a class-conditional variational autoencoder (CVAE) for generative replay and a feature distillation constraint that preserves internal representations across tasks. We compare against the Naive Finetuning, Experience Replay (ER) with a fixed raw-sample buffer, and standard Generative Replay (GR) without stored real data. Performance is measured using accuracy matrices, final average accuracy, backward transfer (forgetting), and memory footprint analysis. Results show that DREAMLOOP substantially improves over pure generative replay by stabilizing intermediate feature representations, achieving 67.8% final average accuracy with a reduced memory budget (M=500), thereby demonstrating favorable trade-off between retention and storage efficiency

Intelligent Load Balancing Framework for Optimal Resource Utilization in Fog-enabled IoMT Environment

The rapid adoption of Internet of Things (IoT) technologies in healthcare has given rise to the Internet of Medical Things (IoMT), which has transformed patient care and medical services. The IoMT, when combined with Fog Computing, provides a powerful paradigm for processing and analyzing healthcare data at the network edge. This paper proposes an innovative intelligent load balancing framework designed specifically for fog-enabled IoMT environments for optimizing resource utilization, improving system performance, and ensuring timely and efficient healthcare service delivery. The framework dynamically distributes computing tasks among fog nodes based on real-time parameters such as node capacity, latency, and workload. By combining machine learning (ML) models and data analytics, the system adapts to changing patterns in medical data, ensuring adaptive load distribution and faster response times. The proposed framework addresses the unique challenges facing healthcare applications, such as low latency and energy consumption in data transmission

Towards a Decentralized Social Layer: Experiments with Farcaster Frames: Exploring Trust-Minimized Interactions and Viral Growth in Decentralized Social Networks

The rise of decentralized social protocols presents a unique opportunity to reimagine user interaction, trust, and ownership on the internet. This paper explores the capabilities of Farcaster, a sufficiently decentralized social messaging protocol, as a foundation for building next-generation social applications using Farcaster Frames and MiniApps. By leveraging identity primitives like FIDs, casts, and onchain assets, we demonstrate how Farcaster can be used to create engaging, permissionless, and composable experiences. We present a series of applications developed on Farcaster, including NFT minting Frames, faucet distribution tools, a social game, and an Omegle-style social dApp. These experiments showcase how developers can use Farcaster to enable novel interaction models without relying on centralized servers or app stores. We further analyze user behavior, system limitations, and opportunities for scaling such social primitives. This paper highlights the benefits, limitations, and future directions of building social applications directly on top of decentralized communication layers like Farcaster, revealing patterns of virality, interoperability, and low-friction UX that can power the next wave of internet-native experience

Frontend Frameworks with Voice Recogni-tion for E-commerce in Remote Areas

According to recent trend, explosive growth in HTML5 is emerging as a global web consortium and spearheading the front-end evolution of the internet\u27s history. Among the many front-end development frameworks available are React, Angular, and Vue.js. Determining the optimal way to build up an e-commerce site while also offering the finest user experience is the primary goal of web development. The most well-known front-end development frameworks and libraries are introduced in this article, which then assesses how well they function with online services. The benefits and drawbacks of each framework and library will be discussed in this article depending on a number of variables, including business consideration. The paper concludes with a summary of the contributions and future predictions for front-end programming in e-business

Empirical Study of Image Steganography algorithm for secure communication

In today’s digital era, secure communication is a critical challenge due to the increas- ing risks of data interception and unauthorized access. Traditional encryption meth- ods, while effective in protecting the content, do not conceal the existence of the communication itself, making encrypted data easily identifiable and potentially vul- nerable. This presents a need for techniques that not only secure the data but also hide the fact that communication is occurring. To address this problem, this research proposes a secure communication system using image steganography in combination with encryption techniques. The system consists of four core modules: Encryption, Hide, Seek, and Decryption. In the encryption phase, the plaintext message is first en- crypted using Advanced Encryption Standard (AES) followed by an additional custom key-based encryption layer, providing enhanced security. The resulting cipher text is then embedded into a digital image using the Least Significant Bit (LSB) steganogra- phy technique, which subtly modifies image pixel values without altering its appearance. The Seek module is used to extract the hidden message, which is then decrypted using the reverse of the encryption process in the Decryption module. The system is implemented in Java, following the Iterative Waterfall Software Development Model. Experimental results confirm the effectiveness of the system in securely hiding and retrieving messages while maintaining the quality and integrity of the image. Testing further demonstrates the robustness of the dual encryption strategy, validating the system\u27s potential for secure, private, and undetectable communication

Real-Time Video Communications Web APP

This was one such project that I worked on which basically develops an anonymous video calling platform using the modern web technologies such as TypeScript, Vanilla JavaScript, WebRTC, and Socket.IO. The three key objectives set for the development of the platform were communication of users in low latency, maintaining user privacy, and most importantly, simplicity. While it was taking care in the implementation of all these technologies, it was thus possible to use them to meet all these objectives, hence availing the users with an efficient way of engaging in real-time video calls with other random peers. Among all, the prime characteristic of this system is that it has peer-to-peer architecture from WebRTC. This architecture allows direct communication between users by purely speaking about smooth audio and video transmission. WebRTC\u27s adaptive bitrate functionality also helps adapt to varying network conditions to some extent. The role of Socket.IO comes into play as signaling management, which helps the user attain a peer in the quickest time and establish connections with the least latency possible. This fast pairing process allows for an uninterrupted user experience, while the simplicity of the user interface was aimed at making the system easy to use. The onestep setup lays much interest in its ease of use; a setup that might appeal not only to technical users but everyone else

An Intelligent Particle Filter with Neural Network for Fault Location and Classification in Microgrid

Microgrid concept is initiated due to increasing involvement of distributed generation resources with the utility grid. Microgrid provide reliable and sustainable power but the protection of microgrid become challenging due to bidirectional power flow, dual mode of operation (grid connected and islanded mode). Faults in the microgrid reduce its stability and efficiency. Identification, classification, and location of faults are crit-ical for rapid restoration and microgrid protection. This research proposes a neural network-based intelligent particle filter for microgrid fault detection and classifica-tion. Even with low fault current, which is typical of inverter-based DGs, the suggest-ed method seeks to precisely identify fault kinds, locations, and directions. The fea-tures are extracted from data using S-Transform, then extracted features are esti-mated using particle filter. A neural network is then used for classification and finali-zation of location. The proposed scheme provides extremely precise fault detection, ensuring that the classification and location of the fault are promptly identified for effective protection and service restoration