AI Assistant using LLM on Raspberry Pi

This project introduces an offline AI assistant system built on Raspberry Pi, integrating speech-to-text (STT), large language model (LLM) inference, and text-to-speech (TTS) components into a unified, low-latency pipeline. The assistant operates entirely without internet connectivity, thereby preserving user privacy while enabling real-time, human-like voice interaction. This approach addresses critical limitations in existing cloud-based voice assistants, such as latency, privacy risks, and dependence on reliable network infrastructure.The system is activated via wake word detection and leverages open-source technologiesto perform each stage of the interaction. Whisper and Vosk are employed for accurate and lightweight speech recognition; LLaMA 3.2, running locally via the Ollama framework, powers the natural language understanding; and RHVoice generates natural-sounding voice responses. This pipeline ensures modularity and adaptability across a range of edge devices.Designed to run efficiently on resource-constrained hardware like Raspberry Pi 5, the assistant is optimized for offline use cases in smart homes, remote environments, and privacy-sensitive domains such as education and healthcare. Experimental evaluationdemonstrates a word error rate (WER) of approximately 2.5%, a response latency of 2.2 seconds, and a Mean Opinion Score (MOS) of 3.2 for speech synthesis quality. These results confirm the system’s feasibility for real world applications, offering a scalable and cost-effective alternative to commercial AI assistants. By combining the capabilities of LLMs with edge computing, this work contributes a practical framework for developing privacy first conversational agents that are accessible, affordable, and highly customizable. The system sets a precedent for future developments in offline AI-powered interaction across diverse use cases

The Art of T20: Visualizing the 2024 World Cup with Tableau

This paper presents a detailed analysis of the ICC Men's T20 World Cup 2024, leveraging a comprehensive dataset encompassing 55 matches. The dataset includes match details such as dates, venues, stages, team performances, toss outcomes, innings scores, player achievements, and officiating information. Advanced data visualization techniques using Tableau were employed to identify key patterns and performance trends. The study highlights the pivotal role of first-innings scores, the strategic importance of toss decisions, and the influence of venue-specific conditions on match outcomes. India emerged as the tournament's most dominant team, achieving the highest scores and the greatest number of wins. Through the use of visual analytics, critical insights were drawn for team management, analysts, and cricket enthusiasts, emphasizing the growing role of data-driven strategies in modern T20 cricket. The findings offer a foundation for predictive modeling, performance optimization, and strategic planning in future tournaments

DATA VISUALIZATION FOR AMAZON SALES REPORT

The Amazon Sales Report Dataset offers a structured overview of e-commerce transactions, including product details, pricing, quantities sold, order dates, shipping status, and regional customer data. This dataset enables comprehensive analysis of sales performance, customer behavior, and market trends. It supports business insights such as identifying best-selling products, seasonal sales patterns, and delivery efficiency. Additionally, it is well-suited for data science applications, including sales forecasting, customer segmentation, and inventory optimization. With its rich and varied attributes, the dataset serves as a valuable resource for driving data-informed strategies in the online retail domain

Brain Tumor Detection Using Deep Learning

The Using medical imaging to segment brain tumorsis a particularly challenging but crucial process. Thisis because of the potential for erroneous prognosis anddiagnosis as a result of manual classification. Workingwith vast volumes of data also requires a lot of work.Because of their similar appearances and wide rangeof traits, It might be difficult to tell a brain tumororiginating from healthy tissue in photographs. In thisstudy, fuzzy C-Means was used to remove brain tumorsfrom two-dimensional MRI images. CNN and classicalclassifiers were then used.The study’s dataset includedtumors of various sizes, shapes, and intensities. KNN,MLP, LR, SVM, and Naive.The typical classifier of thescikit-learn module included popular machine learningmethods including Bayes, Random Forest, and others.Abrain tumor is a condition brought on by aberrant braincell proliferation. Brain tumors can be classified as eitherbenign or malignant, with the former being classified as acancerous brain tumor. It is difficult to forecast the survivalrate of a patient who has a tumor because of the rarityand variety of tumor kinds. Fifteen out of one hundredindividuals with brain cancer had a possibility of survivingfor ten years or longer after diagnosis, per a UK cancerstudy. The type of tumor, the location of the tumor inthe brain, the abnormalities of the cells, and Additionalelements all influence how a patient with a brain tumor istreated

A Network Approach To Predict GDM Risk In Pregnant Women

High blood sugar during pregnancy known as gestationaldiabetes mellitus (GDM) can cause difficulties forboth the mother and the unborn child. Particularly inplaces where prenatal care is scarce, early detection andmanagement are essential. This study suggests a combinedmachine learning prediction model to determine whichexpectant mothers are susceptible to gestational diabetesmellitus. We examined eight distinct models, incorporatingdeep learning methodologies.(Artificial Neural Networks)and conventional machine learning algorithms (SupportVector Machine, Naive Bayes, Random Forest, and LogisticRegression), using a dataset of 3526 pregnant womenfrom Kaggle’s Gestational Diabetes Mellitus dataset. Withaccuracy rates ranging from 87% to 97%, these modelsdemonstrate the immense potential of machine learning toenhance GDM screening and management, especially inresource-constrained environments

Plant Disease Detection using Deep Convolution Network

The global rise in population has led to a shortage of raw materials and food supplies. The agricultural sector has become the primary and most vital source to overcome this particular constraint. However, the industry itself is facing the challenge of pests and various crop diseases. Battling this has been the significant focus of the sector for decades. Still, due to  the technology gap that existed earlier, there existed a constraint on identifying the diseased crops on a massive scale. Nevertheless, today, with the improvement of technologies such as drones, IoT devices, and higher processing speeds combined with data analysis and machine learning, the problem of identification can be resolved quickly. This paper aims to provide a brief description of existing solutions that have been published and focuses on the more efficient machine learning model based on conventional neural networks (CNN) that we have developed. This machinelearning model can be deployed on IoT devices, mobile phones, and drones and cameras that farmers can utilize to identify the diseased crops on a massive scale and take the necessary precautions not to let the disease spread and affect the supply produced

Digital Transformation in Maritime Supply Chains: A Systematic Review of DIS Platforms

Digital transformation plays a key role in improving information sharing and information processing in supply chains. Specifically, maritime supply chains require numerous data and document exchanges and can significantly benefit from digital information sharing (DIS). This notable potential has attracted attention and has resulted in a growing number of studies on blockchain platforms, cloud-based platforms, and other digital technology platforms. However, DIS adoption and execution is a complex process as it depends on various success factors and barriers and affects numerous capabilities and performance outcomes. Moreover, various information systems and management theories can be utilised to underpin these relationships. Our study aims to conduct a systematic literature review that uncovers dynamic capabilities, barriers, enablers and outcomes of DIS with blockchain and cloud-based platforms, illustrates the relationship between them, and discloses methods and theories applied in supply chains. We discuss different use cases of blockchain and cloud-based platforms for DIS in various business functions in supply chains. Particularly, we reveal six DIS-powered capabilities, five performance outcomes improved by the DIS, eight main barriers, and nine enablers of DIS implementation. The lack of theoretical underpinning and causal empirical studies is identified as an important gap in the literature. This study also presents precise future research directions that can help address these gap

Identifying Early Anemia Using Machine Learning Algorithm

This study explores the relationship between between Interleukin-6 (IL6) and Interleukin-8 (IL8) cytokines and auto-immune reactions in Sickle Cell Anemia (SCA) patients, aiming to predict their presence based on genetic factors like Haptoglobin alleles using artificial neural networks. The study, conducted on 60 SCA patients and 74 healthy individuals, found a significant association between Haptoglobin alleles and IL6/IL8 production, achieving an accuracy of 90.9% and an r-squared value of 0.88. Concurrently, the broader context of anemia as a global health issue, particularly affecting mothers and children, underscores the importance of non-invasive detection methods,like those based on machine learning and deep learning techniques. These methods, exemplifiedby convolutional neural networks (CNNs) in blood analysis, offer efficient and cost-effective avenues for early diagnosis and treatment of anemia, highlighting the pivotal role of artificial intelligence in healthcare advancements

Heart Attack Analysis Using Ensemble Machine Learning

Heart disease remains one of the leading causes of mortality worldwide, emphasizing the urgent need for accurate and timely diagnosis. While traditional diagnostic methods have proven effective, advancements in machine learning (ML) offer promising avenues for enhanced detection and prevention strategies. This paper presents a comprehensive review of existing ML-based approaches for heart disease detection, ranging from classical statistical methods to cutting-edge deep learning techniques. We begin by outlining the various risk factors associated with heart disease, including hypertension, cholesterol levels, and lifestyle choices. Subsequently, we delve into the evolution of ML in healthcare, highlighting its transformative impact on diagnostic accuracy and patient care. Leveraging large-scale datasets and feature engineering, traditional ML algorithms such as Support Vector Machines (SVM) and Random Forests have demonstrated notable success in identifying cardiac abnormalities. However, recent breakthroughs in deep learning, particularly Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have revolutionized heart disease detection by extracting intricate patterns and temporal dependencies from raw data sources

Vehicle Insurance Fraud Detection Using Machine Learning

There are thousands of companies in the insurance industry globally, and collect premiums totaling more than $1 trillioneachyear.Insurance fraud occurs when a person or organisation submits a fake insurance claim in an effort to collect money or benefitsto which they are not legally entitled. An insurance fraud is thought to have a total financial impact of over$40 billion. Deterringinsurance fraud is thus a difficult issue for the insurance sector. The established method for detecting fraud is focused on creat-ing heuristics around fraud indicators. The most prevalent form of  insurance fraud is auto fraud, which is accomplished by filing false accident claims. This essay focuses on finding auto-vehicle fraud