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"Computational Intelligence"
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Swarm intelligence and bio-inspired computation : theory and applications
Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades.Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase.
eBook
The Atlas of AI
The hidden costs of artificial intelligence, from natural
resources and labor to privacy and freedom What happens
when artificial intelligence saturates political life and depletes
the planet? How is AI shaping our understanding of ourselves and
our societies? In this book Kate Crawford reveals how this
planetary network is fueling a shift toward undemocratic governance
and increased inequality. Drawing on more than a decade of
research, award-winning science, and technology, Crawford reveals
how AI is a technology of extraction: from the energy and minerals
needed to build and sustain its infrastructure, to the exploited
workers behind \"automated\" services, to the data AI collects from
us. Rather than taking a narrow focus on code and algorithms,
Crawford offers us a political and a material perspective on what
it takes to make artificial intelligence and where it goes wrong.
While technical systems present a veneer of objectivity, they are
always systems of power. This is an urgent account of what is at
stake as technology companies use artificial intelligence to
reshape the world.
eBook
A hybrid method for fire detection based on spatial and temporal patterns
Fire detection is a vital task for social, economic and environmental reasons. Early identification of fire outbreaks is crucial in order to limit the damage that will be sustained. In open areas, this task is typically performed by humans, e.g., security guards, who are responsible for watching out for possible occurrences. However, people may get distracted, or may not have enough eyesight, which can result in considerable delays in identifying a fire, after much damage has occurred. Thus, the idea of having machines to automatically detect fires has long been considered an interesting possibility. Over the years, different approaches for fire detection have been developed using computer vision. Currently, the most promising ones are based on convolutional neural networks (CNNs). However, smoke and fire, the main visual indicators of wildfires, present additional difficulties for the vast majority of such learning systems. Both smoke and fire have a high intra-class variance, assuming different shapes, colors and textures, which makes the learning process more complicated than for well-defined objects. This work proposes an automatic fire detection method based on both spatial (visual) and temporal patterns. This hybrid method works in two stages: (i) detection of probable fire events by a CNN based on visual patterns (spatial processing) and (ii) analysis of the dynamics of these events over time (temporal processing). Experiments performed on our surveillance video database show that cascading these two stages can reduce the false positive rate with no significant impact either on the true positive rate or the processing time.
Journal Article
Implementation of the BERT-derived architectures to tackle disinformation challenges
Recent progress in the area of modern technologies confirms that information is not only a commodity but can also become a tool for competition and rivalry among governments and corporations, or can be applied by ill-willed people to use it in their hate speech practices. The impact of information is overpowering and can lead to many socially undesirable phenomena, such as panic or political instability. To eliminate the threats of fake news publishing, modern computer security systems need flexible and intelligent tools. The design of models meeting the above-mentioned criteria is enabled by artificial intelligence and, above all, by the state-of-the-art neural network architectures, applied in NLP tasks. The BERT neural network belongs to this type of architectures. This paper presents Transformer-based hybrid architectures applied to create models for detecting fake news.
Journal Article
Massive open online course recommendation system based on a reinforcement learning algorithm
Massive open online courses (MOOCs) are open online courses designed on the basis of the teaching progress. Videos and learning exercises are used as learning materials in these courses, which are open to numerous users. However, determining the prerequisite knowledge and learning progress of learners is difficult. On the basis of learners’ online learning trajectory, we designed a set of practice questions for a recommendation system for MOOCs, provided suitable practice questions to students through the LINE chatbot (a type of social media software), and used mobile devices to encourage participation in MOOCs. Reinforcement learning, which involves reward function design and iterative solution improvement, was used to set task goals, including those related to course learning and practice question difficulty. The proposed system encouraged certain learning behaviors among students. Students who used the system exhibited an exercise completion rate of 89.97%, which was higher than that of students who did not use the system (47.23%). The system also increased the students’ overall learning effectiveness. Students who used and did not use the proposed system exhibited average midterm scores of 64.73 and 58.21, respectively. We also collected 227 online questionnaires from students. The results of the questionnaires indicated that 90% of the students were satisfied with the system and hoped to continue using it.
Journal Article