From Human Intelligence to Artificial Intelligence Chatbots: Modern Day Writing
The purpose of the project was to find out whether humans can tell the difference between human-generated text and artificial intelligence (AI) chatbots-generated text and to identify how Al-generated text differs from human-generated text. The dependent variable was the results given by the participants (whether the paragraphs were Al-generated texts or human-generated text). The Independent variable was the participants in the experiment and the controlled variable was the type of paragraphs (both the Al-generated texts and human-generated texts) and time used to test each participant. The hypothesis for this experiment was that the participants were not going to be able to differentiate between AI-generated text and human-generated text. In this descriptive and mixed-method study, participants were presented with questionnaires. Each participant needed to state whether they thought each paragraph was human-generated or AI-generated. At the end of the questionnaire, the participants were asked to briefly explain what assisted them in differentiating between the two. They were given 60 seconds to decide. A stopwatch was used to time them. A sample of 456 participants took part in this project. They were not told how many AI-generated passages and human-generated passages there were in the selection. They only knew the total number of passages. Over 99% of the participants could not correctly differentiate between AI-generated text and human-generated text in all passages. Only four participants were able to get 100% of the questions correct. All four learners attend schools located in urban areas. After I did my analysis, I discovered that my hypothesis was incorrect. Four of the participants were able to get 100% of the questions correct this indicates that not all the participants were not able to tell the difference between Al-generated text and human-generated text. This rejects my hypothesis. However, the chances of humans differentiating between the two are very low. All four learners attend schools located in urban areas; this indicates that it is easier for learners attending urban area schools to recognise AI-generated texts than learners attending schools located in rural areas.
King's Power - The Utilization of Agricultural Waste in the Production of Sustainable Dry Cells
The idea of dramatically reducing the cost of the production dry cell, reducing its carbon footprint, and being able to be an alternative to current materials such as biochars really propels the interest of performing this project research. Biochars from durian husk, bamboo and coconut shell are promising alternative chemical materials of the anodes in the dry cell due to their eco-friendly traits and availability in the trophic areas which covers about 40% of the land on earth. Using the technique of pyrolysis, the latest and the best technique to produce a high carbon content biochars, the dry cell uses the potassium hydroxide as the electrolyte and manganese dioxide as the catalysts that make the biochar mixture to produce maximum voltage of 65% from the dry cell sold in the current market. The voltage analysis of the biochar dry cell was done in our school science laboratory and then, characterization tests analysis was carried out on the products from the specific biomass namely the SEM/EDX analysis, at the Material Characterization Laboratory (MCL), Department of Chemical and Environmental Engineering, Faculty of Engineering, University Putra 馬來西亞. Based on our research, the biochar obtained from the raw materials (Durian Husk, Bamboo and Coconut Shell) had shown different characteristics. The bamboo biochar had shown the most amount of carbon content which is 86.64% more than the durian husk biochar (72.77%) and coconut shell biochar (65.57%). On the other hand, based on the micrograph, we observed that the durian husk biochar had shown much created pores rather than bamboo biochar and coconut shell biochar. In our study, we found out that the average voltage produced by the three different biochars have shown that Durian Husk char dry cell produced the highest voltage which is 0.97V, more than the bamboo char (0.62V) and coconut shell char (0.73V). In conclusion, the biochar dry cell produced are much cheaper in term of its production as our biochar dry cell uses biomass that are freely available and comes from renewable source of energy, the best ingredient for Green Technology.
Breaking a Caesar Cipher / Vigenère Cipher Encryption for Secure Data Communication
This project had one purpose: creating almost unbreakable encryption by breaking a Caesar – and Vigenère Cipher and getting familiar with how they work. Created a program to encrypt and decrypt messages with a Caesar Cipher and Vigenère Cipher encryption. Breaking these encryptions in these programs will help to identify the factors that contribute to strong and weak encryption systems. A program was created to encrypt messages using Caesar Cipher with a key from 1 to 25 and decrypt messages without knowing the original key by doing different types of “attacks” on the system: a brute force and frequency analysis attack. Created another program to encrypt messages using Vigenère Cipher with a keyword or keyphrase and decrypted messages whilst knowing that original keyword. Tested and compared the two different cyphers when being attacked. This helped identify factors that influenced the strength of encryption and identified the advantages and disadvantages of each Cipher as well as the weaknesses in each attack. Through testing and breaking a Caesar and Vigenère Cipher successfully, multiple factors were identified that influenced the strength of the encryption system. These were used to ensure the new encryption created will be as strong as can be. Comparing the success rate of the different attacks on each Cipher, the similarities, weaknesses and strengths in the Brute Force and Frequency Analysis attacks were found.
SAFE_MEDICATION - A STUDY OF USING ARTIFICIAL INTELLIGENCE TO RECOGNISE MEDICATION ERRORS
Medication errors in patients are a global problem. They can negatively affect patients and be costly for hospitals and medical clinics. In 2021, a 28-year-old man with heart problems was admitted to a hospital in Porto Alegre. Due to a pharmacy error and insufficient monitoring in the administration, he received a dose 10 times higher than prescribed. This caused serious and probably irreversible damage to the patient. Reading the news and following the case in the media has encouraged research in scientific databases, searching for information and data on medication errors, as well as emerging technologies to reduce the occurrence of adverse medication events. Based on the findings of an English study that proved that errors occur at the drug prescription stage, the first stage of this research focused on drug dosage errors. The aim of this study is to develop an application based on artificial intelligence that can recognise these errors and help prevent them. The application uses a neural network to analyse prescriptions and warn of possible cases of incorrect dosage. The computer program was developed using a neural network and the drug dosage error recognition system using Python and Keras. The system was trained with 10 drugs and correct and incorrect dosage cases. A graphical interface was created to input and display new case data. Neural networks with different configurations were tested to obtain high accuracy with the training and validation data. A confusion matrix was used to assess the accuracy of the network for cases not used for training. The accuracy was approximately 96%, but problems were found in certain intervals. The errors are due to the need for more training, higher processing capacity and a cloud server. The results of the first stage of the research indicate the feasibility of using a neural network to recognise medication dosage errors and thus preventing the associated risks. Such a method could prevent cases like the one in Porto Alegre. Future studies could incorporate more types of drugs, allergies, drug interactions, pre-existing illnesses and other relevant factors into the system.