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"The term "Artificial Intelligence" has been around since the 1950s, but a lot has changed since then. Today, AI is referenced in the news, books, movies, and TV shows, and the exact definition is often misinterpreted. Artificial Intelligence For Dummies provides a clear introduction to AI and how it's being used today. Inside, you'll get a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field."

"ABSTRACTPengenalan ekspresi wajah telah menjadi tantangan dalam ilmu digital selama bertahun-tahun. Dengan pertumbuhan baru-baru ini di dalam bidang machine learning, sistem pengenalan ekspresi wajah secara real-time dengan machine learning dapat berguna untuk sistem monitoring emosi untuk interaksi manusia-komputer (HCI). Model yang penulis ajukan dirancang dengan model Convolutional Neural Network (CNN) dan menggunakannya untuk melatih dan menguji gambar ekspresi wajah dengan TensorFlow. Sistem ini memiliki dua bagian, sebuah recognizer untuk validasi dan model pelatihan data untuk data training. recognizer berisi detektor wajah dan pengenal ekspresi wajah. Detektor wajah mengekstrak gambar wajah dari frame video dan pengenal ekspresi wajah mengklasifikasikan gambar yang diekstrak. Model pelatihan data menggunakan CNN untuk melatih data. Sistem pengenal juga menggunakan CNN untuk memantau keadaan emosi dari pengguna melalui ekspresi wajah mereka. Sistem ini mengklasifikasikan emosi dalam enam kelas universal, marah, jijik, senang, terkejut, sedih dan takut, ditambah dengan emosi netral.

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ABSTRACTThe introduction of facial expressions has been a challenge in digital science for many years. With the recent growth in machine learning, a real-time facial recognition recognition system with machine learning can be useful for emotional monitoring systems for human-computer interaction (HCI). The model the author proposes is designed with the Convolutional Neural Network (CNN) model and uses it to train and test facial expression images with TensorFlow. The system has two parts, a recognizer for validation and a data training model for training data. The recognizer contains face detector and facial recognition. The face detector extracts the face image from the video frame and facial expression identifiers classify the extracted image. The data training model uses CNN to train data. The identification system also uses CNN to monitor the emotional state of the user through their facial expressions. This system classifies emotions in six universal classes, anger, disgust, pleasure, shock, sadness and fear, coupled with neutral emotions."

"This book constitutes the thoroughly refereed post-proceedings of the 21st International Conference on Inductive Logic Programming, ILP 2011, held in Windsor Great Park, UK, in July/August 2011. The 24 revised full papers were carefully reviewed and selected from 66 submissions. Also included are five extended abstracts and three invited talks. The papers represent the diversity and vitality in present ILP research including ILP theory, implementations, probabilistic ILP, biological applications, sub-group discovery, grammatical inference, relational kernels, learning of Petri nets, spatial learning, graph-based learning, and learning of action models."

"The book, gathering the proceedings of the Future of Information and Communication Conference (FICC) 2018, is a remarkable collection of chapters covering a wide range of topics in areas of information and communication technologies and their applications to the real world. It includes 104 papers and posters by pioneering academic researchers, scientists, industrial engineers, and students from all around the world, which contribute to our understanding of relevant trends of current research on communication, data science, ambient intelligence, networking, computing, security and Internet of Things."