Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning.

Machine Learning algorithms have had a profound impact on the field of computer science over the past few decades. These algorithms performance is greatly influenced by the representations that are derived from the data in the learning process. The representations learned in a successful learning process should be concise, discrete, meaningful, and able to be applied across a variety of tasks. A recent effort has been directed toward developing Deep Learning models, which have proven to be particularly effective at capturing high-dimensional, non-linear, and multi-modal characteristics. In this work, we discuss the principles and developments that have been made in the process of learning representations, and converting them into desirable applications. In addition, for each framework or model, the key issues and open challenges, as well as the advantages, are examined.

The advancements in deep learning-based methods for visual perception tasks have seen astounding growth in the last decade, with widespread adoption in a plethora of application areas from autonomous driving to clinical decision support systems. Despite their impressive performance, these deep learning-based models remain fairly opaque in their decision-making process, making their deployment in human-critical tasks a risky endeavor. This in turn makes understanding the decisions made by these models crucial for their reliable deployment. Explainable AI (XAI) methods attempt to address this by offering explanations for such black-box deep learning methods. In this paper, we provide a comprehensive survey of attribution-based XAI methods in computer vision and review the existing literature for gradient-based, perturbation-based, and contrastive methods for XAI, and provide insights on the key challenges in developing and evaluating robust XAI methods.

Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.

Recent technological advancements in space, air and ground components have made possible a new network paradigm called "space-air-ground integrated network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, the real-world deployment of a SAGIN becomes a major barrier for realizing such SAGINs. Compared to the space and terrestrial components, UAVs are expected to meet performance requirements with high flexibility and dynamics using limited resources. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. In this paper, we provide a comprehensive review of recent learning-based algorithmic approaches. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit (MAB), particle swarm optimization (PSO) and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, which can be applicable to various UAV-assisted missions on a SAGIN using these algorithms. We simulate users and environments according to real-world scenarios and compare the learning-based and PSO-based methods in terms of throughput, load, fairness, computation time, etc. We also implement and evaluate the 2-dimensional (2D) and 3-dimensional (3D) variations of these algorithms to reflect different deployment cases. Our simulation suggests that the $3$D satisfaction-based learning algorithm outperforms the other approaches for various metrics in most cases. We discuss some open challenges at the end and our findings aim to provide design guidelines for algorithm selections while optimizing the deployment of UAV-assisted SAGINs.

In terms of artificial intelligence, there are several security and privacy deficiencies in the traditional centralized training methods of machine learning models by a server. To address this limitation, federated learning (FL) has been proposed and is known for breaking down ``data silos" and protecting the privacy of users. However, FL has not yet gained popularity in the industry, mainly due to its security, privacy, and high cost of communication. For the purpose of advancing the research in this field, building a robust FL system, and realizing the wide application of FL, this paper sorts out the possible attacks and corresponding defenses of the current FL system systematically. Firstly, this paper briefly introduces the basic workflow of FL and related knowledge of attacks and defenses. It reviews a great deal of research about privacy theft and malicious attacks that have been studied in recent years. Most importantly, in view of the current three classification criteria, namely the three stages of machine learning, the three different roles in federated learning, and the CIA (Confidentiality, Integrity, and Availability) guidelines on privacy protection, we divide attack approaches into two categories according to the training stage and the prediction stage in machine learning. Furthermore, we also identify the CIA property violated for each attack method and potential attack role. Various defense mechanisms are then analyzed separately from the level of privacy and security. Finally, we summarize the possible challenges in the application of FL from the aspect of attacks and defenses and discuss the future development direction of FL systems. In this way, the designed FL system has the ability to resist different attacks and is more secure and stable.

FREMONT, CA: As we progress into the technological world and digitalization, we must comprehend the advantages of what is available to us. Machine Learning (ML) and Artificial Intelligence (AI) are the foundations of an altogether new approach to operating our organizations. From countering cyber threats to boosting consumer marketing, we can now embrace this digital horizon. Training robots to execute human jobs is referred to as artificial intelligence. This growing technology tries to imitate how our human brains interact and receive information from the environment around us.

Artificial intelligence (AI) was seen as one of the top current investment priorities and was thought to continue to attract investment in the healthcare sector in the upcoming two years, according to GlobalData's latest report'Digital Transformation and Emerging Technology in the Healthcare Industry – 2022 Edition'. In this survey-based report tracker, digital media was prioritised as a top current investment target, with 53% of surveyed respondents confirming that their companies are currently investing in this technology. It was followed by AI, social media and big data (Figure 1). Compared with last year's data, digital media saw the biggest increase in current investment, up by 22% from last year. AI ( 9% from 2021), social media ( 8%) and big data ( 5%) have also gained since last year, besides trending as very popular technologies for investment priorities for several years.

Screening for lung cancer--the second-most common type of cancer worldwide--is a complex process. Doctors use Low-Dose Computed Tomography (LDCT) to scan patients and produce hundreds of 2D images. Physicians review them to identify the location and volume of tumors, which they then evaluate in context of the patient's medical history, lab work, biopsies, and other information, all of which help determine the stage of the illness and the best course of treatment. LDCT is an essential tool in fighting the deadly disease, but it's also a slow, painstaking process that leaves room for manual error. A new approach uses edge processing, AI, and secure data sharing to help doctors arrive at an accurate diagnosis much faster and start treatment sooner.

Summary The AI in healthcare market size was valued at US$7,679.39 million in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 39.05% during 2022-2027. The key to the growth has been increasing investment and development in AI and increasing strategic moves by market players are stimulating. Additionally, key strategic partnerships and mergers and acquisitions are expected to accelerate market growth. Healthcare, including pharma, medical devices, healthcare providers, and payers, is a highly regulated industry, and therefore can be slow to adopt new technologies and modernize.However, the healthcare industry is realizing the benefits artificial intelligence (AI) can bring, and it is now being used in different areas across the entire value chain. Additionally, its use in the healthcare space is expected to continue to increase in the next five years. The integration of software with artificial intelligence is creating growth avenues for the global artificial intelligence in healthcare market.Integration of software with artificial intelligence offers immediate decision support and best results to diagnose diseases.