Federated learning is a machine learning method that supports training models on decentralized devices or servers, where each holds its local data, removing the need for data exchange. This approach is especially useful in healthcare, as it enables training on sensitive data without needing to share them. The nature of federated learning necessitates robust security precautions due to data leakage concerns during communication. To address this issue, we propose a new approach that employs selective encryption, homomorphic encryption, differential privacy, and bit-wise scrambling to minimize data leakage while achieving good execution performance. Our technique , FAS (fast and secure federated learning) is used to train deep learning models on medical imaging data. We implemented our technique using the Flower framework and compared with a state-of-the-art federated learning approach that also uses selective homomorphic encryption. Our experiments were run in a cluster of eleven physical machines to create a real-world federated learning scenario on different datasets. We observed that our approach is up to 90\% faster than applying fully homomorphic encryption on the model weights. In addition, we can avoid the pretraining step that is required by our competitor and can save up to 20\% in terms of total execution time. While our approach was faster, it obtained similar security results as the competitor.

Feature selection is a crucial preprocessing step in machine learning, impacting model performance, interpretability, and computational efficiency. This study introduces a novel hybrid approach, the Forward Recursive Adaptive Model Extraction Technique (FRAME), which combines Forward Selection and Recursive Feature Elimination (RFE) to enhance feature selection across diverse datasets. FRAME integrates the strengths of both methods, balancing exploration and exploitation of features to optimize selection. A comprehensive evaluation of FRAME was conducted against traditional methods such as SelectKBest and Lasso Regression, using high-dimensional, noisy, and heterogeneous datasets. The results demonstrate that FRAME consistently delivers superior predictive performance based on downstream machine learning evaluation metrics. It effectively reduces dimensionality while maintaining robust model performance, making it particularly valuable for applications requiring interpretable and accurate predictions, such as biomedical diagnostics. This study highlights the importance of assessing feature selection methods across varied datasets to ensure their robustness and generalizability. The findings suggest that FRAME has significant potential for further enhancement, particularly through integration with deep learning architectures for adaptive and real-time feature selection in dynamic environments. By advancing feature selection methodologies, FRAME offers a practical and effective solution to improve machine learning applications across multiple domains.

Example: 'I want to read a book' In the above example there are 6 tokens which are- ('I', 'want, 'to', 'read', 'a' and'book') A type is the class of all tokens containing the same character sequence. In the above example, there are only 5 types which are - 'can, 'you', 'a, 'as' and'canner' as'can', 'as' and'a' are being repeated. In the above example, by deleting period and hyphens between the characters and words we are normalizing the type by making it a term. So the term in the above example is: 'USA' and'antiinflammatory' Example: "Hello everyone.Welcome to the course." The tokens for the given sentence will be -- ['Hello','everyone', 'Welcome', 'to', 'the', 'course'] Welcome to the Natural Language Processing course.

Understanding machine learning (ML) systems is a critical task for data scientists and non-technical profiles alike as organizations aim to integrate AI applications on an enterprise-wide level. In this ebook, we explore practices to identify cutting-edge and responsible strategies for managing high-impact AI systems and work to understand the concepts and techniques of model interpretability and explainability.

Cyberspace occupies a large portion of people's life in the age of modern technology, and while there are those who utilize it for good, there are also those who do not. Malware is an application whose construction was not motivated by a benign goal and it can harm, steal, or even alter personal information and secure applications and software. Thus, there are numerous techniques to avoid malware, one of which is to develop samples of malware so that the system can be updated with the growing number of malwares, allowing it to recognize when malwares attempt to enter. The Generative Adversarial Network (GAN) sampling technique has been used in this study to generate new malware samples. GANs have multiple variants, and in order to determine which variant is optimal for a given dataset sample, their parameters must be modified. This study employs Optuna, an autonomous hyperparameter tuning algorithm, to determine the optimal settings for the dataset under consideration. In this study, the architecture of the Optuna Optimized GAN (OOG) method is shown, along with scores of 98.06%, 99.00%, 97.23%, and 98.04% for accuracy, precision, recall and f1 score respectively. After tweaking the hyperparameters of five supervised boosting algorithms, XGBoost, LightGBM, CatBoost, Extra Trees Classifier, and Gradient Boosting Classifier, the methodology of this paper additionally employs the weighted ensemble technique to acquire this result. In addition to comparing existing efforts in this domain, the study demonstrates how promising GAN is in comparison to other sampling techniques such as SMOTE.

In a paper recently published in the open-access journal Energies, researchers reviewed and summarized published articles to determine the most promising approach for artificial intelligence (AI) applications in environmental and energy engineering systems. AI is a computer science tool that works on creating intelligent devices, machines, and systems that carry out operations akin to human decision-making and learning. It can comprehend external data and learn from it, and adapt to its learning with practice. Combining AI with the internet of things (IoT) technologies could be another promising approach as this combination can harvest large amounts of data, and the AI can study data patterns to enable task automation for several business benefits. AI has been widely utilized in agriculture, focusing particularly on rice diseases, crop and pest management, product monitoring, and yield prediction. Medical and healthcare applications of AI include the understanding of diseases such as cancer as well as brain and heart disorders.

Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires orchestrating a cluster of GPUs to perform a single synchronized calculation. As cluster and model sizes have grown, machine learning practitioners have developed an increasing variety of techniques to parallelize model training over many GPUs. At first glance, understanding these parallelism techniques may seem daunting, but with only a few assumptions about the structure of the computation these techniques become much more clear--at that point, you're just shuttling around opaque bits from A to B like a network switch shuttles around packets. Each color refers to one layer and dashed lines separate different GPUs. Training a neural network is an iterative process.

Today is the day you build a Neural Network in Javascript.Deep Learning is ushering in a sea change in the way we build software. Andrew Ng famously refers to AI as the "New Electricity": a change destined to become as ubiquitous as electricity, imbued in every product around us, that will revolutionize how we interact with technology.Deep Learning has traditionally required vast server farms of specialized GPU chips, a PhD degree, and huge petabytes of data. Recently, however, - just this year, in fact - its become feasible to deploy and train cutting-edge Neural Networks in your browser, using Javascript. Deep Learning In Javascript will teach you how to build a Neural Network in Javascript in your browser, today.The Future of Deep Learning Is In Edge DevicesConsider: 1) Apple's new NPU chip - specialized for Deep Learning - features a 60x increase over the 2017 model. We're just in the opening rounds of specialized hardware bringing AI to your computer and phone.2) Consumers are more conscious of privacy than ever before. Techniques that can keep Deep Learning on-device, without ever hitting a remote server, allow you to leverage Deep Learning techniques without handling people's data.3) Many types of sensor data - video, audio, or cutting-edge AR and VR techniques - are too big and slow to send back and forth to a remote server for realtime processing. Leveraging Deep Learning in the browser lets you handle sensor data in realtime with no lag.Deep Learning is coming to the computer on your desk and the phone in your pocket. And guess which technology is well positioned to take advantage of this change? You guessed it: Javascript.What This Book CoversThis book is aimed at teaching Javascript developers how to leverage Deep Learning in the browser today. It's aimed at hackers looking to jump in quickly and learn through coding.This book includes:* An overview of how Deep Learning works, various approaches and when to use them* Techniques for manipulating, cleaning, and processing datasets, and how to effectively work with smaller datasets* How Image Recognition works, and how to interpret what a Neural Network "sees" when it looks at an image* How to effectively train a model in your browser, and tune it for better performance* How to take models built by others and leverage them in your apps, tweaking them for your specific use case* A step-by-step walkthrough of how to build an Image Classifier in your browser, from scratchToday is the day you build a Neural Network in Javascript.FAQWhat happens after I purchase?You'll get an email delivery with the PDF, Kindle (.mobi), and .epub files. You'll also be subscribed to receive future updates of the book for free.Do I need a math or statistics background to use this book?No! Math or Statistics background is not required. We will touch on theory as it applies to the Deep Learning models you will build, but there will be little-to-no math or statistics.Do I need to know Javascript to use this book?We'll be using modern Javascript to demonstrate techniques and build the Neural Networks and spending little time delving into Javascript. However, a passing familiarity should be all you need.What if this book is too advanced for me?Unlimited money-back guarantee: if you're not happy with your purchase, email returns@dljsbook.com and you will get your money back, no questions asked (well, I will ask you how the book could be improved!)What if this book is not advanced enough for me?Take advantage of the unlimited money-back guarantee!What if I buy this book today, and next year it's out of date?Buying the book today guarantees you unlimited access to future updates in digital format.Also, though the tools will change, the basics of building a Neural Network and techniques for training and tuning will stay the same.

Over the past several years, groundbreaking developments in machine learning and artificial intelligence have reshaped the world around us. There are various deep learning algorithms that bring Machine Learning to a new level, allowing robots to learn to discriminate tasks utilizing the human brain's neural network. Our smartphones and TV remotes have voice control because of this.

Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them. By contrast, companies with strong basic analytics -- such as sales data and market trends -- make breakthroughs in complex and critical areas after layering in artificial intelligence. For example, one telecommunications company we worked with can now predict with 75 times more accuracy whether its customers are about to bolt using machine learning.