220,451 research outputs found
Cognitive Factors in Students' Academic Performance Evaluation using Artificial Neural Networks
Performance evaluation based on some cognitive factors especially Students’ Intelligent Quotient rating (IQR), Confidence Level (CoL) and Time Management ability gives an equal platform for better evaluation of students’ performance using Artificial Neural Network. Artificial Neural Networks (ANN) models, which has the advantage of being trained, offers a more robust methodology and tool for predicting, forecasting and modeling phenomena to ascertain conformance to desired standards as well as assist in decision making. This work employs Machine Learning and cognitive science which uses Artificial Neural networks (ANNs) to evaluated students’ academic performance in the Department of Computer Science, Akwa Ibom State University. It presents a survey of the design, building and functionalities of Artificial Neural Network for the evaluation of students’ academic performance using cognitive factors that could affect student’s performances. Keywords: Cognitive, Intelligent Quotient Rating, Machine Learning, Artificial Neural Network.
An Analysis of Applying Artificial Neural Networks for Employee Selection
This paper describes the research and development of an artificial neural network system as a decision aid for employee selection. The ability of the artificial neural network to recognize patterns even using noisy data for employee selection and performance evaluation suggests this framework has significant potential advantage over traditional statistical models, such as regression analysis. Further, the neural model eliminates several methodological problems associated with the use of multiple regression, including non- linearity, incorrect function form specification, and heteroskedasticity
A Neural-CBR System for Real Property Valuation
In recent times, the application of artificial intelligence (AI) techniques for real property valuation has been on the
increase. Some expert systems that leveraged on machine intelligence concepts include rule-based reasoning, case-based
reasoning and artificial neural networks. These approaches have proved reliable thus far and in certain cases outperformed
the use of statistical predictive models such as hedonic regression, logistic regression, and discriminant analysis. However,
individual artificial intelligence approaches have their inherent limitations. These limitations hamper the quality of
decision support they proffer when used alone for real property valuation. In this paper, we present a Neural-CBR system
for real property valuation, which is based on a hybrid architecture that combines Artificial Neural Networks and Case-
Based Reasoning techniques. An evaluation of the system was conducted and the experimental results revealed that the
system has higher satisfactory level of performance when compared with individual Artificial Neural Network and Case-
Based Reasoning systems
Development and evaluation of the effectiveness of an algorithm for automatic classification of network events
With the increasing volume of network traffic and security threats, automatic classification of network events has become vital. This paper presents the development and evaluation of a machine learning-based algorithm for network event classification. The algorithm extracts statistical and payload-based features from network packets and applies feature selection techniques. Supervised learning models such as decision trees, random forest and neural networks are trained on the filtered feature sets. The algorithm is evaluated on NSL-KDD and UNSW-NB15 datasets using metrics like accuracy, precision and recall. Experimental results show that the random forest classifier achieves the best performance with over 95% accuracy on both datasets. The proposed algorithm demonstrates high effectiveness in classifying network events into benign and attack categories in real-time
The Neural Testbed: Evaluating Joint Predictions
Predictive distributions quantify uncertainties ignored by point estimates.
This paper introduces The Neural Testbed: an open-source benchmark for
controlled and principled evaluation of agents that generate such predictions.
Crucially, the testbed assesses agents not only on the quality of their
marginal predictions per input, but also on their joint predictions across many
inputs. We evaluate a range of agents using a simple neural network data
generating process. Our results indicate that some popular Bayesian deep
learning agents do not fare well with joint predictions, even when they can
produce accurate marginal predictions. We also show that the quality of joint
predictions drives performance in downstream decision tasks. We find these
results are robust across choice a wide range of generative models, and
highlight the practical importance of joint predictions to the community
The Assessment of Machine Learning Model Performance for Predicting Alluvial Deposits Distribution
This paper discusses the development and evaluation of distribution models for predicting alluvial mineral potential mapping. A number of existing models includes Weight of Evidence, Knowledge-driven Fuzzy, Data-driven Fuzzy, Neural-Network, Bayesian Classifier and Geostatistical Kriging. We offer classification models developed in our laboratory, where point pattern analysis was used to identify presence or absence of a known secondary alluvial (cassiterite) deposits in the Nigerian Younger Granite Region (NYGR) and the model performance assessed. We focused on the training and testing data split using longitudinal spatial data splitting (strips and halves) to ensure predictive attribute's independence. The spatial data split runs counter to the traditional random sample data selection as a procedure for checking overfitting of models mainly due to spatial data autocorrelation. Specifically, we used classification algorithms such as; Naive Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree Bagging and Discriminant Analysis algorithms for training and testing. We analysed the model's performance results using model predictive accuracy and ROC curve values in two different approaches that improve spatial data independence among predictive attributes to give a meaningful model performance
Optimizing Capture in Pressure Swing Adsorption Units: A Deep Neural Network Approach with Optimality Evaluation and Operating Maps for Decision-Making
This study presents a methodology for surrogate optimization of cyclic
adsorption processes, focusing on enhancing Pressure Swing Adsorption units for
carbon dioxide () capture. We developed and implemented a
multiple-input, single-output (MISO) framework comprising two deep neural
network (DNN) models, predicting key process performance indicators. These
models were then integrated into an optimization framework, leveraging particle
swarm optimization (PSO) and statistical analysis to generate a comprehensive
Pareto front representation. This approach delineated feasible operational
regions (FORs) and highlighted the spectrum of optimal decision-making
scenarios. A key aspect of our methodology was the evaluation of optimization
effectiveness. This was accomplished by testing decision variables derived from
the Pareto front against a phenomenological model, affirming the surrogate
models reliability. Subsequently, the study delved into analyzing the feasible
operational domains of these decision variables. A detailed correlation map was
constructed to elucidate the interplay between these variables, thereby
uncovering the most impactful factors influencing process behavior. The study
offers a practical, insightful operational map that aids operators in
pinpointing the optimal process location and prioritizing specific operational
goals
- …