Word Sense Disambiguation of Regional Language using Deep Learning
Air Quality Prediction and Analysis using Machine Learning
Unraveling Currency Depreciation
Revolutionizing Healthcare Monitoring: An Adaptive Wearable Framework for Real-Time Decision Support
Web-Based Implementation of a Logistic Regression Model for Rapid FNA Cytopathology Image Analysis in Breast Cancer Detection
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
India is widely renowned for having many different languages. Indians must be quite proud of the diversity of languages in our country. As a result, there are numerous languages and a large number of meaningful words. There are terms that have synonyms in every language. There will be a time when learning a new language is necessary. Natural language processing (NLP) is helpful in achieving such. It has been around for more than 50 years, and the roots of NLP may be found in the study of language. It is used in a variety of fields, including corporate intelligence, search engines, and medical research. Ambiguity is the quality of being subject to multiple interpretations, and is frequently referred to as the imprecision of a word's meaning. The method for fixing this problem is called disambiguation. This study used a large-scale dataset that included words with many meanings and senses. Additionally, the dataset is in Telugu, the regional language of Andhra Pradesh. Some of the Telugu words in this dataset are unclear when used in various contexts. Deep neural networks are utilized to do this. Two algorithms namely Bidirectional Long Short Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (RBGRU) are used in order to obtain the noticeable sense of given ambiguous word. Accurate word sense prediction achieved an accuracy of 86.30% for word sense disambiguation for regional language results. This outcome is remarkable when compared with other approaches of word sense prediction of various regional languages. : India is widely renowned for having many different languages. Indians must be quite proud of the diversity of languages in our country. As a result, there are numerous languages and a large number of meaningful words. There are terms that have synonyms in every language. There will be a time when learning a new language is necessary. Natural language processing (NLP) is helpful in achieving such. It has been around for more than 50 years, and the roots of NLP may be found in the study of language. It is used in a variety of fields, including corporate intelligence, search engines, and medical research. Ambiguity is the quality of being subject to multiple interpretations, and is frequently referred to as the imprecision of a word's meaning. The method for fixing this problem is called disambiguation. This study used a large-scale dataset that included words with many meanings and senses. Additionally, the dataset is in Telugu, the regional language of Andhra Pradesh. Some of the Telugu words in this dataset are unclear when used in various contexts. Deep neural networks are utilized to do this. Two algorithms namely Bidirectional Long Short Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (RBGRU) are used in order to obtain the noticeable sense of given ambiguous word. Accurate word sense prediction achieved an accuracy of 86.30% for word sense disambiguation for regional language results. This outcome is remarkable when compared with other approaches of word sense prediction of various regional languages.
This paper presents an advanced Air Quality Prediction and Analysis using Machine Learning technologies to detect and respond to hazardous environmental conditions. The system incorporates an MQ135 sensor for CO2 readings, an MQ2 sensor for CO readings,an MQ9 sensor and a DHT11 sensor for temperature and humidity measurements. These sensors continuously monitor the air quality and transmit data to a machine learning model via serial communication. The model, trained on relevant datasets, predicts the presence of dangerous gas levels and abnormal temperature conditions. When the system detects elevated CO2 or CO levels, it triggers multiple alerts: a buzzer sounds, an LCD displays "Gases Detected," and an SMS notification is sent via a GSM module. Similarly, if the temperature exceeds a predefined threshold, the system activates the buzzer, displays "Abnormal Temperature Detected" on the LCD, and sends an SMS alert. Additionally, all sensor data is uploaded to the ThingSpeak IoT platform for real-time monitoring and historical analysis.
Currency depreciation, also known as devaluation, plays an important role in international finance and economics The unraveling currency depreciation system involves a multifaceted interaction between market, political, and economic factors. It happens when a country's currency depreciates in value in the foreign exchange market in relation to other currencies. This phenomenon is caused by a number of reasons, such as interest rates, inflation, trade imbalances, geopolitical events, and market speculation. Policymakers, companies, and investors must comprehend the underlying causes of currency devaluation. Currency depreciation has a variety of effects. A weaker home currency can help exporters compete more successfully in global markets, which could increase export volumes. On the other hand, when importers buy products and services valued in foreign currencies, their expenses go up. The rising cost of imported goods may also put inflationary pressure on consumers. Moreover, governments and businesses in nations where there is currency depreciation may find it difficult to repay international debt that is valued in stronger currencies.
This paper presents a comprehensive methodology for wearable health monitoring systems, emphasizing the Wearable Health Monitoring and Feedback Algorithm (WHMFA). By leveraging advanced data preprocessing techniques, real-time health assessments, and adaptive learning mechanisms, the WHMFA ensures accurate health status classifications, personalized feedback, and robust data security. The system incorporates noise filtering, machine learning-based predictions, and encrypted data transmission, ensuring reliability and privacy in healthcare monitoring. Simulation results demonstrate superior performance in accuracy, latency, and resource efficiency compared to existing systems, showcasing WHMFA's potential for enhancing patient outcomes and promoting real-time health management. This work addresses critical challenges in sensor reliability, privacy, and adaptability, contributing to the advancement of wearable health technologies.
Early detection of breast cancer significantly improves patient prognosis. Fine Needle Aspiration (FNA) cytopathology is a common diagnostic procedure, but its interpretation can be subjective and time-consuming. Machine learning (ML) models have shown promise in aiding this process. This paper details the design and implementation of a user-friendly, web-based application that leverages a pre-trained Logistic Regression model for the preliminary analysis of FNA cytopathology images. The system accepts a JPG image of an FNA slide, performs automated image segmentation and feature extraction, and subsequently feeds these parameters into the classification model to predict whether the sample indicates a benign or malignant tumor. The underlying Logistic Regression model, trained on the Wisconsin Breast Cancer Dataset, demonstrated a high accuracy of 97.07% on its test set. The web application, with its frontend deployed on GitHub Pages and backend on Render, provides an accessible platform for demonstrating the potential of ML in cytopathology, simplifying the input process to a single image upload and providing an immediate, interpretable result. This work highlights the practical application of existing ML research, making sophisticated analysis tools more accessible for educational, demonstrational, or preliminary assessment purposes. The complete source code is publicly available on GitHub.
