I Venkata Dwaraka Srihith

Alliance University · Department of Computer Science and Engineering

This study focuses on using cutting-edge machine learning and image processing techniques to better understand mental health within the human body. Unlike earlier systems that lacked real-time monitoring or personalized care, this approach takes things a step further by offering live detection and regular health check-ins for employees. It not only...

This paper introduces Li-Fi technology to improve communication between vehicles, giving drivers real-time updates about nearby vehicles. By providing clear and timely information about the surroundings, it enhances driver awareness and helps reduce accidents. The system is designed to be cost-effective and adaptable, making it suitable for all typ...

Federated Learning (FL) is innovative machine learning approach that lets multiple devices work together to train models without sharing sensitive data. By keeping data on the device, FL not only boosts privacy and security but also helps improve models collectively. Recent research looked into how Blockchain technology could strengthen FL, tacklin...

This paper delves into how healthcare stakeholders interact with intelligent medical systems and how these systems affect healthcare delivery. Sparked by the growing interest and investment in intelligent agents such as those developed by Siemens, the study looks at the pros and cons of these technologies, along with the ethical issues they raise....

Agriculture is a vital part of the global economy, and recent breakthroughs in deep learning technology are showing great potential to revolutionize this industry. This paper takes a close look at the expanding research on how deep learning and machine learning are being used to tackle agricultural challenges, especially through image processing an...

With more and more motorcycles on the roads, we're seeing more accidents, often because riders aren't wearing helmets. We can use CCTV footage from nearby buildings or crosswalks to check if people are wearing helmets. It's really important to use this technology to catch those who aren't following the rules. The idea is to use computers to automat...

This paper encapsulates the project 'Emotion Detection From Voice With Machine Learning Techniques', emphasizing the construction of a reliable system adept at discerning and delineating emotions conveyed within audio recordings. Employing sophisticated machine learning methodologies, notably Convolutional Neural Networks (CNNs), the endeavours see...

Explore the realm of stock market predictions with a focus on Netflix, a leading OTT streaming giant. Renowned for its diverse content and proprietary productions, Netflix draws the attention of avid investors. This article delves into the methodology of predicting Netflix stock prices using Machine Learning and Python. For those keen on leveraging...

This paper presents a comprehensive analysis of regression models for predicting Netflix stock prices. Employing Linear Regression, Decision Tree, Random Forest, and Support Vector Regressor, the models were evaluated based on Mean Squared Error (MSE). The Support Vector Regressor outperformed others with the lowest MSE, indicating superior predict...

This comprehensive review undertakes a rigorous evaluation of contemporary methodologies deployed in brain MRI image classification, with a specific focus on tumour detection. Leveraging an exhaustive survey of the pertinent literature, we dissect and compare the effectiveness of a diverse spectrum of approaches, encompassing both established machi...

This research paper explores the development of a Restaurant Recommendation System using Python. Motivated by the challenges users face in selecting suitable dining establishments, the study employs collaborative filtering algorithms for personalized suggestions. Leveraging Python libraries such as Surprise and scikit-learn, the system is implement...

In this paper, we explore the fascinating realm of data manipulation using Python. Data manipulation is a fundamental skill for anyone working with data, and Python offers powerful tools and libraries like Pandas for this purpose. We begin by creating a sample dataset and then delve into various operations such as filtering, sorting, grouping, and...

The provided Python code offers a comprehensive framework for conducting a comparative analysis of stocks in the financial market. Using the Yahoo Finance API, it fetches historical stock price data for specified stocks (e.g., Apple and Microsoft) within a defined timeframe. The script calculates and visualizes key metrics, including daily returns...

Data Resampling is a pivotal Data Science method for tailoring dataset size and distribution. It entails the alteration of an existing dataset by either augmenting or diminishing data points. If you seek guidance on dataset resampling, this article is your essential resource. Within this comprehensive guide, we will explore the intricacies of Data...

The text discusses the essence of Data Science as the application of data and computer science to address business challenges. It highlights the prevalence of diverse problems encountered by data professionals in their daily work. The text serves as an introduction to an article that promises to provide a comprehensive exploration of the various pr...

Data Visualization plays a pivotal role in data science and analytics, involving the representation of data through graphical means like charts, graphs, and maps. These visualizations serve as powerful tools to unveil hidden patterns and insights within intricate datasets. As a Data Science professional, familiarity with diverse data visualization...

Audio Feature Extraction and Visualization in Python" presents a comprehensive framework for uncovering valuable insights from audio data. Leveraging the power of the `librosa` library, this project demonstrates the extraction of essential audio features, including MFCCs, Chroma, Spectral Contrast, Zero Crossing Rate, and Spectral Centroid. Through...

This analysis delves into the dynamic world of mobile applications within the Google Play Store. Recognizing the ubiquity and profitability of apps, we examine over ten thousand Android apps spanning various categories. Our comprehensive exploration aims to extract valuable insights from the data, enabling us to formulate strategies for fostering b...

Machine learning has evolved into a priceless asset for tackling complex obstacles across a wide range of disciplines, including Computer Vision(CV), Natural Language Processing(NLP), healthcare, and finance. At the core of machine learning lies the training process, wherein model parameters are optimized to make precise predictions on unseen data....

Python has gained immense popularity in the fields of data science and machine learning due to its extensive libraries and efficient coding capabilities, enabling time-saving solutions. This article presents a comprehensive tutorial on Automatic Exploratory Data Analysis (EDA) using Python. By leveraging Python libraries, we can swiftly extract val...

This abstract highlights the transformative impact of Graphics Processing Units (GPUs) on the speed and efficiency of machine learning models. GPUs have emerged as indispensable tools in the field of artificial intelligence, revolutionizing the way we approach complex computations. By parallelizing tasks and harnessing thousands of cores, GPUs can...

In data preprocessing for machine learning, converting categorical features to numerical values is a crucial step. Python offers various techniques to achieve this transformation. One common approach is Label Encoding, where each category is assigned a unique integer. This method is suitable when there's a meaningful ordinal relationship between ca...

AutoTS is a powerful automatic machine learning library designed specifically for automatic time series forecasting in Python. With its intuitive functionality and versatility, this library empowers users to effortlessly engage in various time series forecasting tasks, such as predicting stock prices for a specified number of days ahead. In this ar...

Decision trees are fundamental in machine learning due to their interpretability and versatility. They are hierarchical structures used for classification and regression tasks, making decisions by recursively splitting data based on features. This abstract explores decision tree algorithms, tree construction, pruning to prevent overfitting, and ens...

Topic Modeling is a crucial technique in natural language processing that involves assigning topic labels to a set of text documents. The primary objective of topic modeling is to unveil the latent themes or subjects present within the textual data. This article serves as a comprehensive guide for individuals seeking to acquire knowledge on perform...

The primary objective of this project is to explore the complex connection between area and population density through the application of data science techniques. By harnessing diverse datasets and employing advanced statistical models, we thoroughly examine the dynamics of density and reveal underlying patterns, emerging trends, and valuable insig...

The categorization of social media advertisements is of paramount importance in gauging the probability of engaging the intended audience and driving product purchases. Leveraging data science in marketing for this purpose holds tremendous promise in refining ad targeting strategies. This article seeks to shed light on the analysis of social media...

This article provides a comprehensive guide to outlier detection in the context of machine learning. Outliers are data points that deviate significantly from the general pattern within a dataset and are of particular importance to analysts and data scientists due to their potential to distort analysis results. By delving into the concept of outlier...

The classification of social media ads plays a crucial role in determining the likelihood of target audience engagement and product purchases. This application of data science in marketing offers significant potential for improving ad targeting strategies. This article aims to provide insights into the analysis of social media ads for effective aud...

This project aims to analyze the intricate relationship between area and population density using data science techniques. By leveraging various datasets and employing statistical models, we delve into the dynamics of density and uncover patterns, trends, and insights. Through this analysis, we shed light on the intricate interplay between the spat...

Feature selection and dimensionality reduction are crucial techniques in the field of data analysis and machine learning. They aim to identify and retain the most informative and relevant features while discarding redundant or noisy ones. This short review delves into the concepts, methods, and benefits of feature selection and dimensionality reduc...

The applications of mathematics in computer science have significantly impacted the development of modern technology. Mathematics provides a foundation for the analysis, design, and implementation of algorithms, which are the building blocks of computer programs. Mathematics is used extensively in cryptography and network security, data compression...

In recent years, machine learning has revolutionized many industries, from healthcare to finance to entertainment. However, as the use of machine learning has grown, so too has the risk of data leakage, which can have serious consequences for individuals and organizations alike. In this article/presentation, we take a deep dive into data leakage in...

The Rise of Algorithmic Trading" explores the increasing role of algorithms in financial markets and their impact on trading practices. Algorithmic trading refers to the use of computer programs to execute trades automatically, without human intervention. The paper discusses the history of algorithmic trading, its advantages and disadvantages compa...

This article covers the basic concepts and definitions of trees, as well as the principles, advantages, and limitations of various traversal techniques such as Pre-order, In-order, Post-order, Breadth-first, and Depth-first. Recursive and Iterative methods are presented and compared, along with their respective time and space complexity analysis. F...

Matlab has become a popular choice for researchers across various fields due to its versatility, ease of use, and powerful analytical capabilities. In this paper, we explore the role of Matlab as the ultimate solution for research challenges. We first discuss the benefits of using Matlab in research, including its ability to handle complex mathemat...

The convergence of the Internet of Things (IoT) and blockchain technology is revolutionizing the field of security. IoT devices are becoming increasingly prevalent in our daily lives, from smart homes to wearable technology, but they also pose a significant risk to cybersecurity. Blockchain technology offers a decentralized, tamper-proof network th...

Datafication has emerged as a key driver of the digital economy, enabling businesses, governments, and individuals to extract value from the growing flood of data. In this comprehensive survey, we explore the various dimensions of datafication, including the technologies, practices, and challenges involved in turning information into structured dat...

As cities around the world are growing at an unprecedented rate, there is a pressing need for smarter, more sustainable, and efficient urban infrastructure. In recent years, blockchain technology has emerged as a potential solution for various challenges faced by smart cities. Blockchain's unique features such as decentralization, immutability, and...

The global economy is no stranger to recessions, which can have severe impacts on businesses, individuals, and entire nations. As a result, it is essential to analyze economic data to identify and understand recession patterns, allowing for effective policy-making and planning. In this paper, we explore the use of Python as a tool for recession ana...

The Internet of Things (IoT) has brought about significant changes to various domains such as healthcare, transportation, and manufacturing. However, security remains a critical challenge in IoT due to the large number of connected devices with varying processing capabilities and memory constraints. Traditional cryptographic algorithms are not well...

As the need for machine learning models continues to grow, concerns about data privacy and security become increasingly important. Federated learning, a decentralized machine learning approach, has emerged as a promising solution that allows multiple parties to collaborate and build models without sharing sensitive data. In this comprehensive surve...

Firmware attacks on IoT devices have become a growing concern in recent years. These attacks exploit vulnerabilities in the firmware, which is the low-level software that controls the hardware of the device, to gain access to sensitive data or control the device remotely. Firmware attacks are particularly dangerous because they can be difficult to...

Data structures are the foundation of computing, providing efficient ways to store and manipulate data. They are essential for designing and implementing algorithms that can handle large amounts of information quickly and accurately. In this paper, we explore the world of data structures, examining their basic principles, properties, and uses. We s...

The Internet of Things (IoT) is rapidly growing and becoming an integral part of our daily lives. However, the increasing use of IoT devices also raises significant security concerns. One of the most pressing threats to IoT security is the blackhole attack, where an attacker can selectively drop or discard packets to disrupt communication between I...

With the increasing volume of data being generated every day, the need for data security has become more crucial than ever. Encryption is one of the most effective techniques for protecting sensitive data from unauthorized access or theft. This comparative survey aims to provide a comprehensive analysis of different data encryption methods in the c...

Artificial Intelligence (AI) has been around for a long time, but it's only recently become a mainstream concern. When it comes to cutting-edge research and development, At the moment, AI is at the top of the list. Over the next few decades, we will see a rise in the use of automated and cognitive technology. To be sure, this will occur. This study...

The goals of "smart cities" include relieving the burden of growing urbanisation, cutting down on energy use, protecting the environment, boosting the local economy and people's standard of living, and facilitating wider access to and use of cutting-edge Information and Communication Technologies (ICT). In smart cities, ICT is essential for policym...