5 research outputs found
Dermatological Detection and Classification using Machine Learning Techniques
Dermatology is the medical field that focuses on the study and treatment of skin conditions. It is a specialized branch of medicine that encompasses both diagnostic and surgical procedures related to the skin. It is a widespread disease among them. The researchers have shows a lot of attention to the early detection of lesions. Because of their proliferation ability to other parts of the body, death rates are quite high. A system that can distinguish between benign and malignant lesions is essential because melanoma can be cured with an early and accurate diagnosis. Dermoscopic skin lesion images are first segmented using data mining techniques, to identify the area of interest of the lesion part. When compared to individual classifier algorithms, dermatology datasets benefit from the various data mining techniques and feature selection methods. The SVM provides more accurate and effective skin disease prediction in terms of accuracy, precision, and Specificity
Swarm intelligence algorithms adaptation for various search spaces
U današnje vrijeme postoji mnogo algoritama inteligencije rojeva koji se
uspiješno koriste za rešavanje raznih teških problema optimizacije. Zajednicki elementi
svih ovih algoritama su operator za lokalnu pretragu (eksploataciju) oko prona enih
obecavajucih rješenja i operator globalne pretrage (eksploracije) koji pomaže u bijegu
iz lokalnih optimuma. Algoritmi inteligencije rojeva obicno se inicijalno testiraju
na neogranicenim, ogranicenim ili visoko-dimenzionalnim skupovima standardnih
test funkcija. Nadalje, mogu se poboljšati, prilagoditi, izmijeniti, hibridizirati,
kombinirati s lokalnom pretragom. Konacna svrha je korištenje takve metaheuristike
za optimizaciju problema iz stvarnog svijeta. Domeni rješenja odnosno prostori
pretrage prakticnih teških problema optimizacije mogu biti razliciti. Rješenja mogu
biti vektori iz skupa realnih brojeva, cijelih brojeva ali mogu biti i kompleksnije
strukture. Algoritmi inteligencije rojeva moraju se prilagoditi za razlicite prostore
pretrage što može biti jednostavno podešavanje parametera algoritma ili prilagodba
za cjelobrojna rješenja jednostavnim zaokruživanjem dobivenih realnih rješenja ali
za pojedine prostore pretrage potrebnao je skoro kompletno prepravljanja algoritma
ukljucujuci i operatore ekploatacije i ekploracije zadržavajuci samo proces vo enja
odnosno inteligenciju roja.
U disertaciji je predstavljeno nekoliko algoritama inteligencije rojeva i njihova
prilagodba za razlicite prostore pretrage i primjena na prakticne probleme. Ova
disertacija ima za cilj analizirati i prilagoditi, u zavisnosti od funkcije cilja i prostora
rješenja, algoritme inteligencije rojeva. Predmet disertacije ukljucuje sveobuhvatan
pregled postojecih implementacija algoritama inteligencije rojeva. Disertacija tako er
obuhvaca komparativnu analizu, prikaz slabosti i snaga jednih algoritama u odnosu
na druge zajedno s istraživanjem prilagodbi algoritama inteligencije rojeva za razlicite
prostore pretrage i njihova primjena na prakticne problem. Razmatrani su problemi
sa realnim rješenjima kao što su optimizacija stroja potpornih vektora, grupiranje
podataka, sa cijelobrojnim rješenjima kao što je slucaj problema segmentacije digitalnih
slika i za probleme gdje su rješenja posebne strukture kao što su problemi
planiranja putanje robota i triangulacije minimalne težine.
Modificirani i prilago eni algoritmi inteligencije rojeva za razlicite prostore pretrage
i primjenih na prakticne probleme testirani su na standardnim skupovima test
podataka i uspore eni s drugim suvremenim metodama za rješavanje promatranih
problema iz literature. Pokazane su uspješne prilagodbe algoritama inteligencije
rojeva za razne prostore pretrage. Ovako prilago eni algoritmi su u svim slucajevima
postigli bolje rezultate u usporedbi sa metodama iz literature, što dovodi do zakljucka
da je moguce prilagoditi algoritme inteligencije rojeva za razne prostore pretrage
ukljucujuci i kompleksne strukture i postici bolje rezultate u usporedbi sa metodama
iz literature
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications