Machine Learning

The dataset encompasses an extensive collection of patient information, delving into their comprehensive medical background, encompassing a myriad of features that encapsulate not only the physical but also the mental and emotional states. Furthermore, the dataset is enriched with invaluable ECG data derived from the patients. Moreover, our dataset boasts additional features meticulously extracted from the ECG records, thereby enhancing the potential for our machine learning model to undergo more effective training with our rich and diverse data.


The choice of the dataset is the key for OCR systems. Unfortunately, there are very few works on Telugu character datasets. The work by Pramod et al has 500 words and an average of 50 images with 50 fonts in four styles for training data each image of size 48x48 per category. They used the most frequently occurring words in Telugu but were unable to cover all the words in Telugu. Later works were based on character level. The dataset by Hastie has 460 classes and 160 samples per class which is made up of 500 images.


We define personal risk detection as the timely identification of when someone is in the midst of a dangerous situation, for example, a health crisis or a car accident, events that may jeopardize a person’s physical integrity. We work under the hypothesis that a risk-prone situation produces sudden and significant deviations in standard physiological and behavioural user patterns. These changes can be captured by a group of sensors, such as the accelerometer, gyroscope, and heart rate.


Nasal cytology is a medicine field that focuses on the examination of nasal mucosa cells with the objective of recognizing changes in the epithelium, which is frequently subjected to acute or chronic irritation and inflammation caused by viruses, bacteria, or fungi; in the last decade, nasal cytology is becoming increasingly critical in diagnosing nasal conditions.


The Internet Graphs (IGraphs) dataset is a substantial collection of real intra-AS (Autonomous System) graphs sourced from the Internet Topology Data Kit (ITDK) project. Comprising a total of 90,326 graphs, each ranging from 12 to 250 nodes, this dataset provides a diverse and extensive resource for the exploration and analysis of network structures within autonomous systems.


This data set comes from the MetaFilter website. The question ID data of the askme section is obtained through the official dump data. After selecting a specific category, the corresponding other data is obtained using the ID, including the question title, description, questioner, tags, and all comments.


Object tracking systems within closed environments employ light detection and ranging (LiDAR) to address privacy and confidentiality. Data collection occurred in two distinct scenarios. The goal of scenario one is to detect the locations of multiple objects from various locations on a flat surface in a closed environment. The second scenario describes the effectiveness of the technique in detecting multiple objects by using LiDAR data obtained from a single, fixed location.

Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities.

The UCI dataset is a data repository maintained and made available by the University of California, Irvine that is widely used for machine learning and data mining research. The dataset covers a wide range of fields and topics, including but not limited to medicine, biology, social sciences, physics, engineering, and more. The uniqueness of this dataset is that it contains data from multiple different domains and sources, allowing researchers to explore and analyze the data from different perspectives and contexts.


Smart contract vulnerabilities have led to substantial disruptions, ranging from the DAO attack
to the recent Poolz Finance arithmetic overflow incident. While historically, the definition of smart contract
vulnerabilities lacked standardization, even with the current advancements in Solidity smart contracts, the
potential for deploying malicious contracts to exploit legitimate ones persists.
The abstract Syntax Tree (AST ), Opcodes, and Control Flow Graph (CFG) are the intermediate representa-