Efficient Agent Based Priority Scheduling and LoadBalancing Using Fuzzy Logic in Grid Computing
A Survey of Various Task Scheduling Algorithms In Cloud Computing
Integrated Atlas Based Localisation Features in Lungs Images
A Computational Intelligence Technique for Effective Medical Diagnosis Using Decision Tree Algorithm
A Viable Solution to Prevent SQL Injection Attack Using SQL Injection
In this research an advanced web-based solution is designed to transform scholarly publishing by leveraging the computational power of modern computer systems. It automates submission, evaluation, and publication workflows, enabling contributors worldwide to submit articles seamlessly. Manuscripts are distributed electronically for evaluation, streamlining the assessment process and eliminating the need for physical handling. Contributors receive feedback and evaluation forms online, allowing for prompt revisions and corrections. The web-based structure ensures global access to published content while addressing usability enhancements based on user feedback, thereby improving the efficiency and accessibility of the online evaluation system.
Misrepresentation identification in monetary exchanges, especially in Visa utilization, is a basic use of AI that plans to distinguish deceitful exercises from huge measures of exchange information. This paper investigates different AI strategies and techniques utilized in distinguishing charge card misrepresentation. Key methodologies include supervised learning models such as logistic regression, decision trees, random forests, gradient boosting, and neural networks, which are trained on labeled datasets to identify fraudulent and legitimate transactions. Unaided learning strategies, like grouping and peculiarity discovery, are utilized to recognize examples and abnormalities in unlabeled information. Feature engineering plays a crucial role in extracting meaningful attributes from raw transaction data to enhance model performance. Model evaluation is conducted using metrics such as precision, recall, F1-score, and ROC-AUC, with particular attention to the class imbalance problem common in fraud detection datasets. Challenges addressed include evolving fraud tactics, the need for continuous model updates, and balancing detection accuracy with customer satisfaction. The integration of these AI techniques into a robust detection pipeline is presented, offering a comprehensive solution for effective and efficient credit card fraud detection.
Sentiment analysis enables quick assessment of sentence content, allowing for easy identification of the emotional tone or polarity of text. Whether applied to social media comments, product reviews, or other forms of text data, it serves as a valuable tool. This paper focuses on developing a sentiment analysis system specifically tailored to the unique characteristics of WhatsApp data. The system employs machine learning and deep learning techniques to analyze the sentiment of text messages, detecting positive, negative, or neutral tones. The analysis helps in understanding user behavior, emotional patterns, and communication trends in various contexts, such as personal interactions, social groups, or customer service.
In the rapidly evolving digital landscape, chatbots play a vital role in enhancing human-computer interaction, automating customer support, and improving information accessibility. This paper presents a medical chatbot designed using Natural Language Processing (NLP) and Deep Learning techniques to respond to user queries related to medical information. Developed with the Django framework, the chatbot supports both text and voice interactions. It employs TF-IDF vectorization for query matching and an LSTM-based encoder-decoder model for dynamic response generation. The model is trained on a COVID-19 dataset comprising 88 records across 21 intents. Speech recognition is enabled through the Google Speech API, allowing voice-based communication, while multilingual capabilities are integrated using the Google Translate API. User data and chat history are managed through a MySQL database, and the platform supports user authentication. This web-based application aims to improve access to healthcare information by offering an intelligent, interactive, and user-friendly solution.
The fusion of traditional medicinal knowledge with modern technology opens new doors for preserving and making age- old practices more accessible. Herb Quest is a digital platform designed to serve as a comprehensive repository of medicinal plants used in AYUSH (Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homeopathy). The platform combines textual, visual, and interactive elements, providing an engaging and immersive experience. It features high- quality images of medicinal plants, complemented by audio and video descriptions to cater to a wide range of users. The use of 3D panoramic views, developed with WebGL, adds a unique interactive touch, allowing users to explore plants in a virtual environment. Built with HTML, CSS, and JavaScript, the platform offers a responsive and user-friendly interface. Modern sharing features make it easy to spread knowledge, further enhancing its utility. Beyond documenting medicinal plant resources, it acts as an educational bridge between traditional medicine and contemporary digital innovation. By leveraging advanced web technologies, Herb Quest plays a vital role in preserving and promoting AYUSH practices, making them accessible to audiences around the world.
