Video deepfake detection using Particle Swarm Optimization improved deep neural networks

As complexity and capabilities of Artificial Intelligence technologies increase, so does its potential for misuse. Deepfake videos are an example. They are created with generative models which produce media that replicates the voices and faces of real people. Deepfake videos may be entertaining, but they may also put privacy and security at risk. A criminal may forge a video of a politician or another notable person in order to affect public opinions or deceive others. Approaches for detecting and protecting against these types of forgery must evolve as well as the methods of generation to ensure that proper information is supplied and to mitigate the risks associated with the fast evolution of deepfakes. This research exploits the effectiveness of deepfake detection algorithms with the application of a Particle Swarm Optimization (PSO) variant for hyperparameter selection. Since Convolutional Neural Networks excel in recognizing objects and patterns in visual data while Recurrent Neural Networks are proficient at handling sequential data, in this research, we propose a hybrid EfficientNet-Gated Recurrent Unit (GRU) network as well as EfficientNet-B0-based transfer learning for video forgery classification. A new PSO algorithm is proposed for hyperparameter search, which incorporates composite leaders and reinforcement learning-based search strategy allocation to mitigate premature convergence. To assess whether an image or a video is manipulated, both models are trained on datasets containing deepfake and genuine photographs and videos. The empirical results indicate that the proposed PSO-based EfficientNet-GRU and EfficientNet-B0 networks outperform the counterparts with manual and optimal learning configurations yielded by other search methods for several deepfake datasets

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

An applıcatıon based on the tunıcate swarm algorıthm for predıctıon the energy demand of Turkey

Enerji talebinin tahmini, her ülke için oldukça önemli bir konudur. Çünkü bir ülkenin ekonomisi, enerji talebinden doğrudan etkilenmektedir. Bu nedenle, yapılan bu çalışmada, Türkiye’nin gelecekteki enerji talebini tahmin etmek için tulumlular sürü algoritması (TSA) tabanlı doğrusal bir yaklaşım önerilmektedir. Doğrusal modelin elde edilmesi aşamasında, bir ülkenin gayri safi yurtiçi hasıla, nüfus, ithalat ve ihracat verileri modelin giriş parametreleri olarak alınmıştır. Daha sonra bu parametrelerin optimum ağırlık katsayılarını bulmak amacıyla optimizasyon problemlerinin çözümü için önerilmiş olan popülasyon tabanlı TSA algoritması kullanılmıştır. Önerilen modelin eğitim ve test aşaması için Türkiye’nin 1979-2011 arasındaki yıllara ait olan veri seti kullanılmıştır. Doğrusal model oluşturulduktan sonra, Türkiye’nin 2012’den 2030’a kadar olacak şekilde yaklaşık olarak 20 yıllık bir süre için enerji talebi, üç farklı muhtemel senaryo için tahmin edilmiştir. Daha sonra ise önerilen model ile elde edilen deneysel sonuçlar, Türkiye’nin enerji talebini için literatürde önerilmiş olan diğer algoritmaların elde ettiği deneysel sonuçlar ile karşılaştırılmıştır. Deneysel sonuçlar ve karşılaştırmalar değerlendirildiğinde, bu çalışma kapsamında önerilmiş olan TSA tabanlı model, Türkiye’nin geleceğe dönük enerji talebini tahmin etmek için rekabetçi ve başarılı sonuçlar elde etmiştir

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

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Abstracts of manuscripts submitted in 1990 for publication

This volume contans the abstracts of manuscripts submitted for publication during calendar year 1990 by the staff and students of the Woods Hole Oceanographic Institution. We identify the journal of those manuscripts which are in press or have been published. The volume is intended to be informative, but not a bibliography. The abstracts are listed by title in the Table of Contents and are grouped into one of our five deparments, Marine Policy Center, Coastal Research Center, or the student category. An author index is presented in the back to facilitate locating specific papers