To classify rice crops, the paper applies deep learning to agricultural crop images to classify rice crops. The collection includes images of wheat, rice, sugarcane, jute, and maize. We improved variety by horizontally flipping, rotating, and shifting rice image data sets. A CNN structure classifies rice and non-rice crops. The model has 100% accuracy on training and testing datasets; however, the classification report shows label imbalance problems for precision, recall, and F-score. Deep learning can help classify crops as well as make decisions in agriculture based on research. The study recommends more studies and improvements to enhance model performance and address dataset concerns. The research advances agricultural technology and emphasizes machine learning for crop management and production.

The scale of data streaming in social networks, such as Twitter, is increasing exponentially. Twitter is one of the most important and suitable big data sources for machine learning research in terms of analysis, prediction, extract knowledge, and opinions. People use Twitter platform daily to express their opinion which is a fundamental fact that influence their behaviors. In recent years, the flow of Iraqi dialect has been increased, especially on the Twitter platform. Sentiment analysis for different dialects and opinion mining has become a hot topic in data science researches. In this paper, we will attempt to develop a real-time analytic model for sentiment analysis and opinion mining to Iraqi tweets using spark streaming, also create a dataset for researcher in this field. The Twitter handle Bassam AlRawi is the case study here. The new method is more suitable in the current day machine learning applications and fast online prediction.

A gas leaks lead to personal and financial damage. Much effort has been dedicated to preventing such leaks and developing reliable techniques for leak detection and leakage localization using sensors. These sensors usually sound an alarm after detecting a dangerous gas in its vicinity. This paper describes a system for detecting a gas leakage from cylinders which notifies the user via the GSM network. The system consists of an LPG gas leakage detector which sends a warning signal to Arduino Uno Microcontroller. The system uses the GSM network to send notifications, a liquid crystal display (LCD) monitor to display the warning message and buzzer to sound the alert.

Several internal and external dangers might threaten a laboratory, the institution, and the public if not addressed properly. These hazards are addressed by our extensive laboratory monitoring system. This device detects elevated temperatures, gas leaks, fires, and solution pH. Elements of this system include an Esp32 microcontroller, LCD, Arduino Uno, mq2 gas sensor, DS18B20 temperature sensor, flame sensor, relay, water pump, and pH sensor for acidity measurement. The Arduino IDE controls sensors. Once the temperature reaches 50 degrees Celsius, an alarm, red LED, and mobile phone notification activate. Gas leaks activate a green LED, an alarm, and a message. Flame sensors notify and start water pumps when fires occur. Gas leaks and fires activate a buzzer. The IoT offers essential incident reporting and remote laboratory monitoring. The code is written in Arduino C++.

The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naïve Bayes for reducing data dimensionality.

This study looks at how API (Application Programming Interface) wrapper technology can make it easier to use complex functions by putting together a lot of API calls. These packages have non-real-time interfaces that are hard to use. ChatGPT is a chatbot-specific GPT-3 language paradigm. It lets developers create chatbots that respond intelligently to natural language user input, creating a more engaging user experience. This article shows that ChatGPT, Python, and API wrapper technology may be used to develop a smart chatbot. We show how to use the OpenAI API library to add ChatGPT to Python programs. This makes it easier for developers to make chatbots that sound and act more like real people when they talk. Our contribution to this field is showing that it is possible to make smart chatbots with ChatGPT and API wrapper technology. To reach this goal, we use a system that combines the OpenAI API with ChatGPT and Python. This gives us valuable information about how to make smart chatbots. The efficiency of the system has been tested many times while applying it to different environments, and the results are satisfactory.