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"Just like electricity, Machine Learning will revolutionize our life in many ways-some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with."

"The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise."

"The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses."

"A successful understanding on how to make computers learn would open up many new uses of computers and new levels of competence and customization. A detailed understanding on inform-ation- processing algorithms for machine learning might lead to a better understanding of human learning abilities and disabilities. There are many type of machine learning that we know, which includes Backpropagation (BP), Extreme Learning Machine (ELM), and Support Vector Machine (SVM). This research uses five data that have several characteristics. The result of this research is all the three investigated models offer comparable classification accuracies. This research has three type conclusions, the best performance in accuracy is BP, the best performance in stability is SVM and the best performance in CPU time is ELM for bioinformatics data.

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Keberhasilan pemahaman tentang bagaimana membuat komputer belajar akan membuka banyak manfaat baru dari komputer. Sebuah pemahaman yang rinci tentang algoritma pengolahan informasi untuk pembelajaran mesin dapat membuat pemahaman yang sebaik kemampuan belajar manusia. Banyak jenis pembelajaran mesin yang kita tahu, beberapa diantaranya adalah Backpropagation (BP), Extreme Learning Machine (ELM), dan Support Vector Machine (SVM). Penelitian ini menggunakan lima data yang memiliki beberapa karakteristik. Hasil penelitian ini, dari ketiga model yang diamati memberikan akurasi klasifikasi yang sebanding. Penelitian ini memiliki tiga kesimpulan, yang terbaik dalam akurasi adalah BP, yang terbaik dalam stabilitas adalah SVM dan CPU time terbaik adalah ELM untuk data bioinformatika."

"Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race."

"Banyaknya konferensi menyulitkan peneliti memilih konferensi berkualitas. Kemungkinan peneliti tertipu dengan konferensi predator merupakan ancaman nyata yang perlu diperhatikan. Penilaian konferensi umumnya menggunakan pakar yang membutuhkan waktu dan biaya yang tinggi. Penelitian ini fokus untuk menganalisis jika h-indeks, impact factor, jumlah dokumen, dan SJR dapat menghasilkan penilaian kualitas yang sesuai dengan penilaian manual pakar dari beberapa situs penilaian konferensi serta membandingkan hasil performanya dengan penilaian jurnal. Data yang digunakan dikumpulkan dari empat sumber situs web yang mengkalkulasi kualitas konferensi luar negeri, yaitu CORE, ERA/QUALIS, AMiner, dan ScimagoJR. Data untuk penilaian jurnal didapatkan dari Guide2Research. Variabel yang digunakan untuk penilaian adalah h-indeks, jumlah dokumen, impact factor, dan SJR. Penelitian ini menggunakan metode K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, dan Decision Tree (DT). KNN menghasilkan nilai akurasi tertinggi sebesar 72,22% dan f1 score senilai 63,06% menggunakan data Qualis dengan faktor h-indeks, IF, dan SJR.

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The number of conferences makes it difficult for researchers to choose quality conferences. The possibility of researchers being fooled by predatory conferences is a real threat that deserves attention. Conference assessments generally use experts who require time and money to evaluate the conferences. This study focuses on analyzing whether h-index, impact factor, number of documents, and SJR can produce quality assessments in accordance with expert manual assessments from several conference assessment sites and compare the resulting performance with journal assessments. The data used were collected from four website sources that calculate the quality of overseas conferences, namely CORE, ERA/QUALIS, AMiner, and ScimagoJR. Data for journal assessments were obtained from Guide2Research. The variables used for the assessment are h-index, number of documents, impact factor, and SJR. This research used K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree (DT). KNN produced the highest accuracy value of 72.22% and the f1 score of 63.06% using Qualis data with the h-index, IF, and SJR factors."

"Penelitian ini menganalisis akurasi peramalan permintaan produk barang konsumsi cepat (FMCG) menggunakan model Machine Learning, yaitu LSTM (Long Short-Term Memory) dan SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors), dengan data sekunder dari April 2021 hingga April 2024 yang terdiri dari 36 observasi bulanan. Variabel dependen adalah total penjualan, sementara variabel eksogen mencakup pengeluaran per kapita, adopsi produk, proporsi penjualan dari promosi, jumlah toko yang menjual produk, dan pangsa pasar produk. Hasil menunjukkan model LSTM memiliki akurasi lebih tinggi dalam memprediksi nilai penjualan dibandingkan SARIMAX, dengan nilai Mean Absolute Percentage Error (MAPE) yang lebih rendah pada sebagian besar sampel. Analisis korelasi mengungkapkan variabel jumlah toko yang menjual produk dan adopsi produk berpengaruh signifikan terhadap nilai penjualan dalam model LSTM, sedangkan SARIMAX unggul dalam menangkap pola musiman namun memiliki MAPE lebih tinggi. Penelitian ini menyarankan penggunaan model LSTM untuk data time series yang kompleks dan tidak stasioner, sementara SARIMAX lebih cocok untuk data dengan komponen musiman yang kuat. Pemilihan model harus mempertimbangkan karakteristik data dan tujuan analisis.

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This study analyzes the forecasting accuracy of fast-moving consumer goods (FMCG) demand using Machine Learning models, namely LSTM (Long Short-Term Memory) and SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors), utilizing secondary data from April 2021 to April 2024 with a total of 36 monthly observations. The dependent variable is sales value, while the exogenous variables include spend per buyer, product penetration, promo % of value, the number of stores selling, and market share. The results indicate that the LSTM model has higher accuracy in predicting sales value compared to the SARIMAX model, with a lower Mean Absolute Percentage Error (MAPE) for most samples. Correlation analysis reveals that the variables number of stores selling and product penetration significantly influence sales value in the LSTM model, whereas SARIMAX excels in capturing seasonal patterns but has a higher MAPE. This study recommends using the LSTM model for complex and non-stationary time series data, while SARIMAX is more suitable for data with strong seasonal components. Model selection should consider the characteristics of the data and the objectives of the analysis."

"ABSTRAKSynthesis loop merupakan salah satu sistem kritis di pabrik amoniak. Oleh karena itu, ada urgensi untuk menjaga reliability dan availability pada sistem ini. Sebagian besar peristiwa shutdown di pabrik amoniak terjadi tiba-tiba setelah alarm tercapai. Jadi, perlu ada sistem deteksi dini untuk memastikan masalah anomali ditangkap oleh operator sebelum menyentuh set point alarm. Implementasi algoritma machine learning dalam membuat model deteksi potensi kegagalan telah digunakan di berbagai industri dan objek sebagai penelitian. Algoritma yang digunakan adalah classifier dasar dan ensemble untuk membandingkan algoritma mana yang menghasilkan hasil klasifikasi terbaik. Penelitian ini dapat memberikan ide dan perspektif baru ke dalam industri pabrik amoniak untuk mencegah terjadinya shutdown yang tidak terjadwal dengan memanfaatkan data menggunakan algoritma machine learning.

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ABSTRACTSynthesis loop is one of the critical systems in ammonia plant. Therefore, there is urgency for maintaining the reliability and availability of this system. Most of the shutdown events occur suddenly after the alarm is reached. So, there needs to be an early detection system to ensure anomaly problem captured by the operator beforetouching the alarm settings. The implementation of machine learning algorithms in making fault detection models has been used in various industries and objects. The algorithm used is the basic and ensemble classifier to compare which algorithms generate the best classification results. This research can provide a new idea and perspective into ammonia plant industry to prevent unscheduled shutdown by utilizingdata using machine learning algorithm."