Generic Home Automation System Using IoT Gateway Based on Wi-Fi and ant+ Sensor Network

This research article explores the use of internet of things (IoT) technology in home automation, including cloud computing and sensor networks to improve quality of life, and the increasing affordability through mobile connectivity. In this proposed smart home system, our main objective is to build a home automation system for the common consumer, which can help him to use home appliances with confidence and control at a low cost. The paper describes the building of an IoT gateway using the ANT multi-hop wireless network protocol and the Wi-Fi protocol, specifically utilizing the nRF24L01 and Esp8266 chips. Various sensor nodes, such as a water tank level sensor, human presence sensor, smart LED door sensor, and smart switch, will be integrated into the system. The main goal of the research is to develop an affordable solution for smart home technology for everyday consumers

Lung Cancer Detection and Classification using Machine Learning Algorithms

Lung cancer is a clump of cells in the lung that are multiplying uncontrollably and improperly. Lung cancer is the deadliest disease, and its cure should be the primary focus of all scientific research. Although it cannot be prevented, we can lessen the danger. Thus, a patient's chance of life depends on the early identification of lung cancer. Several machine learning methods, such as Support Vector Machine, Logistic Regression, Artificial Neural Networks, and Naive Bayes, have been used for the investigation and prognosis of lung cancer. In this paper, Lung cancer prediction is finished by gathering the dataset from the survey and applying machine learning methods such as Support Vector Machine, Nave Bayes, K-Nearest Neighbors, Decision Tree, and Random Forest. With this result, it is revealed that Decision Tree attained the maximum accuracy of 100% as compared to the others

New Measure Routing Algorithm for PEGASIS Wireless Sensor Network

The sensor node spends the maximum energy in data transmission for maintaining this issue we proposed a new routing protocol for sensor nodes in WSN. A sensor network is organized into many sets and all sets collect the data with minimum edge weight in a parallel way so doing this all sets create a minimum edge weight chain. Further, the sets make a head node that is near the base station. All sets have a unique chain in this round after that all set again to select a head node on the basis of high residual energy, less mobility, and minimum distance to the base station that the head node sends the data to the base station. Further, our proposed routing algorithms save energy. We simulated the proposed model in MATLAB all simulation result is better than both routing algorithms

Forecasting Liver Disorders with Machine Learning Models

Liver disorders encompass a spectrum of ailments that impact the liver, a crucial organ responsible for a variety of vital bodily functions. These functions encompass metabolic processes, detoxification, protein synthesis, and the production of bile. Liver maladies can arise from various sources, such as viral infections (e.g., hepatitis), excessive alcohol consumption, conditions related to obesity (like non-alcoholic fatty liver disease), autoimmune conditions, genetic predisposition, or exposure to toxins. Common signs and symptoms may encompass fatigue, jaundice, abdominal discomfort, and digestive problems. In our study, we gather data and employ five distinct machine learning classification algorithms: Random Forest, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and XG Boost. After constructing models and evaluating their performance, we observed that XG Boost achieved an impressive accuracy rate of 99.8%

IoT and Machine Learning-Based Prediction of Smart Soil Moisture Monitoring and Irrigation System

Abstract— A country like India faces an acute water shortage, with 35 million people lacking access to safe water. India is the world's largest groundwater user, as tube wells, the main source of irrigation for Indians, provide 46% of water for irrigation. IoT and machine learning can be vital in overcoming acute water shortages and achieving optimum water resource utilization. This paper aims to present an ML model to estimate the soil moisture level and IoT to act upon it. We are introducing a working plan to collect data on soil moisture, temperature, and humidity, utilizing sensor nodes deployed in the agricultural field to gather various sensor data. The gathered data is forwarded through IoT and stored in a cloud-based database like MongoDB. This data applies to machine learning techniques for classification. Several models, such as Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine models (SVM), are utilized. The experimental results, with accuracy rates of 98.8%, 99.0%, and 99.3% for Naive Bayes, Logistic Regression, and Support Vector Machine models respectively. The combination of IoT and machine learning helps to achieve environmental goals efficiently in water resource utilization and better crop yield

Approaches to the synthesis of dolastatin 11.

A synthetic route to a stereoisomer of dolastatin 11, a potent antineoplastic agent from the sea hare Dolabella auricularia, is explored. Synthons for the three unusual acids were prepared as follows: (1) Hmp was prepared by a reaction used previously, but the yield was doubled and the isolation procedure greatly simplified. (2) An indirect procedure was developed for putting in the Ibu unit, with the gem-dimethyls added later. (3) Asymmetric syntheses of two Map stereoisomers established the two configurations in this unit in dolastatin 11 and in the related substances dolastatin 12, majusculamide C and 57-normajusculamide C. The nine pieces required for the 3R, 4S, 12S-stereoisomer of dolastatin 11 were assembled in a convergent synthesis to give a product which probably contains a stereoisomer of 1. An analogous route starting from the correct Map stereoisomer will very likely yield dolastatin 11 itself

Synthesis of β-lactamase inhibitors

Twenty-two compounds of the type R¹C₆H₄CH₂(CO)NHCHR²(CO)OCHR³COOH, where R¹ = H or OH, R² = H or Me, and R³ = H, Me, Et, i-Pr, Ph, or CH₂COOH, have been synthesized for testing as β-lactamase inhibitors. All of the intermediates and products were characterized by NMR, and mass spectra were run on some of the final products