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Exploring Factors Influencing AI Sentiment-Analysis Engine Robot Use - Surveying Students in Social Science College
Chin-Liang Hung,
Chui-Yu Chiu
Issue:
Volume 7, Issue 1, June 2023
Pages:
1-5
Received:
28 January 2023
Accepted:
13 February 2023
Published:
27 February 2023
Abstract: To understand the factors influencing AI engine robot use, this study developed a conceptual model along the lines of previous research. The research model hypothesized that personal innovativeness in Information Technology (new software, new hardware, new AI engine, new chatbot, use new technology), perceived usefulness of Information Technology and perceived ease of use of Information Technology positively affect attitude toward AI engine robots, which in turn facilitates AI engine robot intention and AI engine robot use. By collecting data from 55 surveys from college Student’s respondents who have employed AI engine robots, we applied statistics to test the relationships in the model. Our findings demonstrate a positive effect of personal innovativeness in Information Technology, perceived usefulness of Information Technology and perceived ease of use of Information Technology on attitude toward AI engine robots. In addition, attitude toward AI engine robots has a positive effect on AI engine robot intention and AI engine robot use. Students use the AI engine to score the natural language description of the copywriting. Future research in the field of natural language processing and natural language understanding, using several AI engines for mathematical operations and applications. Quantitative research on natural language processing and natural language understanding. Used to solve more complex NLP and NLU and sentiment analysis problems.
Abstract: To understand the factors influencing AI engine robot use, this study developed a conceptual model along the lines of previous research. The research model hypothesized that personal innovativeness in Information Technology (new software, new hardware, new AI engine, new chatbot, use new technology), perceived usefulness of Information Technology a...
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Evaluation of Machine Learning Techniques Towards Early Detection of Cardiovascular Diseases
Issue:
Volume 7, Issue 1, June 2023
Pages:
6-16
Received:
20 January 2023
Accepted:
13 February 2023
Published:
15 April 2023
Abstract: The effectiveness of three Machine Learning (ML) algorithms: Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbour (KNN) techniques for the early diagnosis of heart diseases were evaluated. Heart disease’ dataset collected from kaggle.com data repository, which comprised of 303 data points with 13 features and a target variable were used and data preprocessing by data shuffling and dimension reduction were performed. The new dimension of the dataset was chosen such that 85.03% of the original information is retained. The preprocessed dataset was partitioned into 70% of the training set and 30% of the testing set. The ML algorithms were trained and tested for the diagnosis of cardiovascular diseases (CVD). The training performances of these models were evaluated with a k-fold cross-validation algorithm using 10 folds. The k-fold accuracy shows KNN with an accuracy of 0.837662, RF with an accuracy of 0.834091, and SVM with an accuracy of 0.814935. The test results also show KNN with an accuracy of 0.8, SVM with an accuracy of 0.7889, and RF with an accuracy of 0.7667. KNN emerged the best model both in training and test’s performances and is recommended for the early diagnosis of CVD.
Abstract: The effectiveness of three Machine Learning (ML) algorithms: Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbour (KNN) techniques for the early diagnosis of heart diseases were evaluated. Heart disease’ dataset collected from kaggle.com data repository, which comprised of 303 data points with 13 features and a target variable w...
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Police Use of Facial Recognition Technology and Racial Bias – An Assessment of Criticisms of Its Current Use
Issue:
Volume 7, Issue 1, June 2023
Pages:
17-23
Received:
3 May 2023
Accepted:
25 May 2023
Published:
15 June 2023
Abstract: Law enforcement has transformed drastically by advances in technology. Law enforcement bodies around the world have adopted facial recognition capabilities powered by artificial intelligence and contend that facial recognition technology is an effective tool in preventing, disrupting, investigating, and responding to crime. As the practice has grown, so have criticisms of its use and policing outcomes. Criticisms relate to the violation of civil liberties, namely the potential for abuse, propensity for inaccuracies, and improper use. In an effort to assess the validity of these criticisms, this paper examines the link between facial recognition technology and racial bias through an analysis of existing research and the use of a case study of an American municipality that has banned the use of facial recognition technology by police. Studies to date demonstrate a propensity for algorithms to mirror the biases of the datasets on which they are trained, including racial and gender biases; rates of match inaccuracy were consistently seen in relation to black persons, particularly black females. In addition to academic research, multiple examples of misidentifications of black citizens in the United States, along with related commentary from human rights and civil liberties groups, suggests that these concerns are translating into real world injustices. This paper validates concerns with the use of facial recognition technology for law enforcement purposes in the absence of adequate governance mechanisms.
Abstract: Law enforcement has transformed drastically by advances in technology. Law enforcement bodies around the world have adopted facial recognition capabilities powered by artificial intelligence and contend that facial recognition technology is an effective tool in preventing, disrupting, investigating, and responding to crime. As the practice has grow...
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A Comparative Study of Business Intelligence and Artificial Intelligence with Big Data Analytics
Issue:
Volume 7, Issue 1, June 2023
Pages:
24-30
Received:
7 May 2023
Accepted:
27 May 2023
Published:
27 June 2023
Abstract: Business intelligence systems give important and competitive information to business planners and decision-makers by combining operational and historical data with analytical tools. Business intelligence (BI) aims to increase the timeliness and quality of data, allowing managers to better comprehend their company's position with rivals. For example, changes in market share, consumer behavior and spending patterns, customer preferences, corporate capabilities, and market circumstances may be analyzed using business intelligence tools and technology. In addition, analysts and managers may utilize business intelligence to determine which changes are most likely to adapt to shifting trends. The nontrivial extraction of implicit, previously unknown, and possibly beneficial information from data is known as data mining. Clustering, data summarization, learning classification rules, discovering dependency networks, analyzing changes, and detecting anomalies are all examples of technological techniques. The introduction of the data warehouse as a repository, advancements in data purification, better hardware and software capabilities, and the emergence of web architecture have all combined to produce a richer business intelligence environment than previously accessible. This document tries to give a framework for developing a business intelligence system. AI has been used to find and investigate security flaws. Manipulation and movement When given a limited static environment, AI robots can readily detect and map their surroundings.
Abstract: Business intelligence systems give important and competitive information to business planners and decision-makers by combining operational and historical data with analytical tools. Business intelligence (BI) aims to increase the timeliness and quality of data, allowing managers to better comprehend their company's position with rivals. For example...
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