Online First Articles
Online First Articles; Ahead of issue publication
On Comparative Study for Two Selective Ant Colony's Optimal Decisions Versus Reconstruction Problem Solving by a Mouse Inside Figure of Eight (8) Maze
Hassan M. H. Mustafa, Fadhel Ben Tourkia
Computer Engineering Department, Al-Baha Private College of Sciences Al-Baha, Kingdom of Saudi Arabia
DOI:10.14738/tmlai.63.4479
Abstract
This piece of research addresses an interdisciplinary comparative study of two environmental challenging phenomenal issues. Both were associated to two nonhuman creatures characterized by their behavioral intelligent performance concerned with optimal diverse decisions paradigms. More specifically, this paper deals with the comparison study for analogical behavioral learning of social insects (Ants) colony performance, versus performed behavioral learning achievement by an animal considering a mouse inside Figure of eight (8) maze via its brain hippocampus "time cells" neurons.
In more precise details, this paper firstly have demonstrated for Ant Colony System (ACS) the two effective optimal selectivity decisions for : a) The best source location between two food sources that are equidistantly sited away from the original home nest, based upon pheromone trails and following the tandem running regulation & b) The balanced selection performance with the migration speed, in order to minimize exposure to a hostile environment to avoid vulnerability to presumable danger. Secondly, the optimal decisional issue is demonstrated related to mouse's behavioral learning intelligent approach which observed in practice following its active sequential trials aiming to reach the optimal solution for a reconstruction problem during its movement inside figure of eight (8) maze. Finally, after running of realistic Artificial Neural Networks' (ANNS) simulation programs. Interestingly, the obtained results considering models of both of suggested intelligent behavioral learning issues characterized by relevant functional analogy considering changed number of artificial ant agents versus the various number of neurons inside mouse's hippocampus brain area.
Keywords- Artificial neural network modeling, Swarm Intelligence, Tandem Running, House-HuntingAnts, Nature Inspired Computing.
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Application of an Artificial Neural Network for Early and Accurate Diagnosis of Parkinson's disease
Akshar Agarwal
Hamden High School, Connecticut, United States
DOI: 10.14738/tmlai.63.4528
ABSTRACT
Parkinson’s Disease (PD) is one of the most common neurological disorders, affecting more than ten million people globally. The hallmark symptoms of PD are tremors, limb rigidity, and imbalance. PD shares many of these symptoms with other disorders, making it difficult to diagnose. Furthermore, due to the lack of definitive laboratory tests, PD is poorly diagnosed with subjective examinations such as family history evaluations, resulting in high misdiagnosis rates. Recent research shows that an additional symptom, dysphonia, is uniquely present in over 80% of PD patients. Dysphonia is a speaking disorder caused by involuntary muscle movement and other neurological factors in PD. In this project, that unique symptom. A cross-validated neural network was programmed to deliver rapid and accurate diagnoses using biomedical voice data from 195 patients of varying statuses. This automated, machine-learning based PD diagnostic tool was successfully created and functions with over 95% accuracy. This rate includes nearly zero false negatives and few false positives, showing significant improvement over previous attempts which had misdiagnosis rates of nearly 20%. A low probability of false negatives is favorable. The neural network was designed such that overfitting is avoided, and more features/data would further improve the algorithm’s accuracy. An early and accurate diagnosis is critical for treating PD patients, and this project proposes a way to achieve that.
Keywords: Machine learning, Artificial intelligence, Parkinson's Disease, neural network, dysphonia
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An Ocean of Opportunities in Artificial Human Optimization Field
Satish Gajawada
Alumnus, Indian Institute of Technology Roorkee, INDIA
Founder, Artificial Human Optimization – A New Field
DOI: 10.14738/tmlai.63.4529
ABSTRACT
Global Optimization Techniques like Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization and other optimization techniques were used in literature to solve complex optimization problems. Many optimization algorithms were proposed in literature by taking the behavior of Birds, Ants, Fishes, Chromosomes etc. as inspiration. Recently, a new trend has begun in Evolutionary Computing Domain where optimization algorithms have been created by taking Human Behavior as inspiration. The focus of this paper is on optimization algorithms that were and are being created based on the behavior of Artificial Humans. In December 2016, a new field titled “Artificial Human Optimization” was proposed in literature. This paper is strongly meant to popularize “Artificial Human Optimization” field like never before by showing an Ocean of Opportunities that exists in this new and interesting area of research. A new field titled “Artificial Economics Optimization” is proposed at the end of paper.
Keywords: Artificial Intelligence, Machine Learning, Evolutionary Computing, Bio-Inspired Computing, Nature Inspired Computing, Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Artificial Economics Optimization, Artificial Human Optimization
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