Machine learning (ML), a subset of artificial intelligence (AI), isrevolutionizing industries by leveraging statistical algorithms that learn from data andexperiences. Unlike traditional programs following predetermined sequences, MLalgorithms discern patterns and predict outcomes through extensive datasets. Thistransformative technology has profoundly impacted diverse sectors, includingmanufacturing, finance, retail, transportation, entertainment, and healthcare. Theinfluence of ML is amplified by the accessibility of extensive datasets and theescalating computational prowess of modern systems. As ML algorithms progress, theyare fundamentally reshaping business operations, streamlining processes, enhancingdecision-making, and fuelling innovation across sectors. The impact of machinelearning algorithms on healthcare applications and the usage of diverse data sources,such as electronic health records, medical imaging, wearable devices, and genomicdata, is discussed in this chapter.

Deep learning (DL) is a specialized area within the field of machine learning (ML) that focuses on training models using deep neural networks. Specifically, deep learning with convolutional neural networks (CNN) incorporates convolutional layers on top of neural networks to effectively extract spatial features from images, making them suitable for tasks such as image classification and object detection. The availability of abundant computational power and vast amounts of data has led to the successful training of deep CNN models for accurate image classification. Consequently, the utilization of deep CNN in the healthcare sector has significantly influenced various aspects, including disease diagnosis, aiding physicians in clinical decision-making, continuous patient monitoring, and the development of personalized treatment approaches. In this chapter, we will explore several use cases of deep CNN networks in the healthcare industry, assessing their impact and considering the associated ethical considerations.

In the digital era, low-cost hardware like sensors and cameras has led to the creation of numerous image databases for various applications. This has led to the need for retrieval systems that rely on visual content, and these types of systems are called content-based image retrieval (CBIR) systems. It’s a method utilized to locate and extract digital images from extensive databases by considering their visual attributes, as opposed to relying exclusively on metadata or written descriptions. In order to obtain appropriate images from the database, features including colour histograms, texture patterns, and shape descriptors are being used to determine similarities between the images. Over the course of the last twenty years, efforts have been directed towards creating hand-crafted features tailored for CBIR systems. However, depending solely on distance-based retrieval methods is a formidable task. Hence, this study strives to leverage the capabilities of classifiers as well for the purpose of retrieval. So, the proposed CBIR paradigm uses not only the hand-crafted features but also the strength of the classifier with weighted distance metricTherefore, the proposed CBIR paradigm is designed in a way that it uses the strength of the NaiveBayes classifier to compute weighted distance using hand-crafted wavelet features to get similar images from the database. The performance of the proposed method is evaluated on three most popular texture datasets and found to be better among all the methods reported in this work

With rapid growth of social media platforms and widespread use of handheld devices such as mobile phones and video cameras, the number of videos being captured and shared over the internet has increased significantly. However, due to the lack of organization, most of these videos lack semantic context. Traditional methods of video retrieval involve searching for relevant videos using attached semantics. which has led to the need for content-based video retrieval, where video contents are utilized for searching, whether by video or text queries.The primary goal of our system is to provide relevant videos from a database. Our proposed approach in this paper employs Pearson’s coefficient of correlation (PCC) for key frame extraction from videos, subsequently building a feature vector that represents the video’s content. We have also experimented with linear binary pattern (LBP) and Colour moments (CM). We have used precision metric for evaluating performance. For conducting experiments, we utilized the UCF101 dataset, comprising 13,320 videos across 101 categories

The era of intelligent algorithms has arrived, and machine learning is one of the most promising technologies to revolutionize healthcare. Until recently, manufacturing, transportation, and administration were the primary industries where machine learning algorithms had a significant impact. However, even formerly impervious industries like healthcare are suddenly being affected by these algorithms. While machine learning has been around for quite some time, its use in healthcare is continuously increasing alongside the availability of data. It is a statistical method that allows computers to learn from past data. They are able to identify patterns and come to conclusions or judgments depending on the information that they are presented with. Machine learning (ML) has numerous prospective applications within the healthcare industry. They extend from drug discovery to clinical decision-making and diagnosis. There are petabytes of healthcare-related data that require analysis. For instance, the human genome is an example of this, which is approximately 100 gigabytes per person. Furthermore, carry-and-wear devices generate a large quantity of data, including heart rate, blood pressure, and walking pattern. Therefore, on the basis of these data, ML techniques can be used to predict diseases and develop personalized treatments. Moreover, X-ray and MRI image classification techniques can be used to construct an ML algorithm for potential disease diagnosing, thereby reducing the burden on clinicians. Likewise, in drug discovery and development, ML algorithms have been utilized to help identify novel therapeutic targets, design new drug candidates, and predict drug toxicity. ML techniques can be used to create predictive models for patient outcomes like mortality, readmission, and disease progression. ML algorithms can be put to use to analyze electronic health record (EHR) data to facilitate clinical decision-making, such as predicting patient readmission rates or identifying patients who may benefit from a specific treatment. Therefore, ML has the potential to revolutionize the healthcare industry by providing methods to cluster, classify, predict, and assist clinicians in making informed decisions. Consequently, this chapter will investigate the current state of machine learning (ML) in the healthcare industry, as well as the challenges it faces and its future development potential.

During the COVID-19 pandemic time, educational institutions have really played a good role in imparting online education to students. Their career and academic tenure were not affected as contrary to the past pandemics throughout world history. All this has been possible through long sessions of classes, quizzes, assignments, discussions, chat interactions, and examinations through online video-based learning using computer interactive measures. The students were privileged to utilize digital technologies for longer durations for learning purposes. However, these long stretches have adversely affected their body postures, and physical and mental health as they majorly remain confined to chairs with restricted levels of physical activities. Thus, there is a need to have a model which can act as an insight for parents, doctors (pediatricians), and academic policymakers to decide on maximum hours for online teaching and related activities during future pandemics. The novel model proposed in this work helps to predict the impact of enhanced students’ computer interactions on their physical and mental health. The method proposed uses a novel model which is advanced and computationally strong. The model follows a two-step methodology, where at the first level, a variant of already existing machine learning algorithm is proposed and at the next level, it is optimized further using a hybrid bio-inspired optimization algorithm. The model consists of proposing a variant of XGBoost model (step1 optimization) followed by a hybrid bio-inspired algorithm (step2 optimization). The work considers a humongous dataset with varied age groups of students with more than 10 attributes. The proposed model is highly efficient in making predictions with 98.07% accuracy level and 98.43% F1-score. The time complexity of the model obtained is also of order of “n” where “n” depicts the number of input variables. Strong empirical results for other parameters also like specificity (95.63%) and sensitivity (96.74%) ascertain the enhanced predictive power generated using the proposed model. An extensive comparative study with other machine learning models ascertains the elevated accuracy and predictive power using the proposed model. Till now none of the researchers have proposed any such pioneering tool for parents, doctors, and academicians using advanced machine learning algorithms.

A viable, robust, and highly accurate additive hybrid model employing autoregressive fractionally integrated moving average (ARFIMA) and support vector machine (SVM) with functionally expanded inputs (Additive-ARFIMA-SVM) is presented for forecasting the air quality index (AQI). Additionally, thirteen additive and multiplicative hybrid models are introduced. Several alternatives in feature engineering employing functional expansion of inputs are incorporated to boost the performance of hybrid models. Furthermore, a gradient whale optimization algorithm with group best leader strategy (GWOA-GBL) based meta-heuristic algorithm is proposed. The missing values are imputed and a variable weight ensemble forecasting model is developed using the proposed GWOA-GBL algorithm. To evaluate the effectiveness of the proposed Additive-ARFIMA-SVM forecasting model with functionally expanded inputs, comparisons are made with sixteen machine learning models, including long short-term memory (LSTM), five statistical models, seventeen hybrid models, and ten variable weight ensemble models. Extensive statistical analyses are carried out on the obtained results considering four accuracy measures that show the statistical supremacy of the proposed Additive-ARFIMA-SVM model and GWOA-GBL algorithm in predicting the AQI time series. The proposed Additive-ARFIMA-SVM model with functionally expanded inputs improves the AQI forecasting performance by 16.34% than autoregressive integrated moving average, 14.47% than ARFIMA, 33.96% than XGBoost, 43.47% than SVM, 49.39% than LSTM, 8.64% than Multiplicative-ARIMA-SVM model considering symmetric mean absolute percentage error. The proposed Additive-ARFIMA-SVM model is so efficient and reliable that it can be applied to forecast other time series like stock price, electricity load, crude oil price, sunspot number, stream flow, flood, drought etc.

Content-Based Remote Sensing Image Retrieval (CBRSIR) is used to find relevant images from large collections of remote sensing images. CBRSIR works by indexing each image in the database with a feature vector. Deep semantic features generated using convolutional neural networks (CNNs) are more powerful than low-level features for CBRSIR tasks because they can comprehend the context and content within an image. However, the major problem with the deep features is its large vector size which in turn can impact the performance of the retrieval system and are more susceptible to noise and outlier data. Therefore, in this work, a modified ResNet50 architecture is proposed that serves as a powerful feature extractor, benefiting from its deep learning capabilities. Specific modifications are introduced to enhance its discriminative power and generalization ability, enabling it to extract more robust deep features for image indexing. The proposed method achieves a mean average precision (mAP) of 0.899 surpassing the popular competing methods ResNet50 and GoogleNet by a substantial margin of 22.02%, 26.79% respectively. Moreover, to address the curse of dimensionality, this study also proposes a novel approach that combines a modified ResNet50 architecture with Linear Discriminant Analysis (LDA) and Maximum Relevance and Minimum Redundancy (MRMR) technique. The proposed approach achieves 85.45% reduction in size of the feature vector using MRMR and 98.19% using LDA, thereby improving retrieval efficiency without impacting the performance.

In recent years, medical image transmission using a multi-cloud system has played a significant role in e-Healthcare infrastructure. It allows medical practitioners to easily store, retrieve, and share patients’ medical information across multiple stakeholders. However, multi-cloud image transmission may be vulnerable to multiple security breaches, such as authentication, confidentiality, and security issues. Motivated by these issues, this paper proposes a data-hiding scheme for secure medical image transmission in a multi-cloud environment. The proposed scheme ensures imperceptible robustness and watermark security at a low computational cost. Here, the medical image is divided into a number of shares using Neighbor Mean Interpolation (NMI). To achieve confidentiality, Electronic Patient Healthcare Record (EPHR) is encrypted using Double Scan Pixel Position Shuffling (DSPPS) approach. Then, the encrypted EPHR is divided into shares and embedded in the cover medical image shares. Finally, a minimum of 50% of watermarked image shares are utilized to retrieve the original medical image and encrypted EPHR, consequently reducing multi-cloud latency and computational burden. Experimental results show that the proposed scheme shows high imperceptibility, robustness, and watermark security at a low computational cost. Comparative analysis with some of the recent popular data hiding schemes shows that the proposed scheme has improved imperceptibility and robustness by 10%–15% (approximately) with higher watermark security at a low computational cost.

Image classification stands as a fundamental task in computer vision, and Convolutional Neural Networks (CNNs) have emerged as highly proficient tools, demonstrating remarkable accuracy and performance. However, with the increasing complexity and diversity of image datasets, there is a growing need to improve the robustness and generalization of CNN-based classifiers. One promising approach to address this challenge is the ensembling of CNNs. Ensembling involves combining the outputs of multiple CNNs to enhance classification performance. This technique leverages the strength and diversity of individual models to achieve superior results compared to using a single model alone. Therefore, GLS-NET, an ensemble framework is proposed which uses three parallel ResNet50 CNNs and takes different features as input so as to induce the diversity in data which in turn can learn discriminative features to produce high accuracy. The proposed framework is evaluated on the most popular dataset, EMNIST, and achieved good performance improvement in accuracy. EMNIST is the most popular dataset used extensively in evaluating the performance of many deep learning techniques.

Due to the availability of various photo editing tools, intruders can tamper with an image very easily. So, various watermarking and tamper detection approaches have been proposed by researchers. Basically, tamper detection techniques focus on embedding the watermark, extracting the water mark, and identifying the tampered regions. But it is very important that the tampered pixels should also be corrected. We bring forward an image watermarking technique for tamper detection and correction using remainder value differencing (RVD) and extended Hamming code (EHC). It operates on a pixel group of size 2 × 2. Watermark bits (WBs) are generated from four most significant bits of the pixels in a pixel group by EHC and concealed in four lower bit planes by the principle of RVD. The WBs are extracted at the receiver along with the identification of tampered pixels. The tampered pixels are corrected by the developed correction logic. As the principle of RVD is used, precautions are taken to avoid the fall-off boundary problem. The efficacy of this technique is accessed through various quality metrics. It is noted that it performs better than the existing techniques. The recorded peak signal-to-noise ratio value is 45.49 dB with structural similarity value 0.9889. The tampered pixels are identified and corrected.

This contribution applies tools from the information theory and soft computing (SC) paradigms to the embedding and extraction of watermarks in aerial remote sensing (RS) images to protect copyright. By the time 5G came along, Internet usage had already grown exponentially. Regarding copyright protection, the most important responsibility of the digital image watermarking (DIW) approach is to provide authentication and security for digital content. In this paper, our main goal is to provide authentication and security to aerial RS images transmitted over the Internet by the proposal of a hybrid approach using both the redundant discrete wavelet transform (RDWT) and the singular value decomposition (SVD) schemes for DIW. Specifically, SC is adopted in this work for the numerical optimization of critical parameters. Moreover, 1-level RDWT and SVD are applied on digital cover image and singular matrices of LH and HL sub-bands are selected for watermark embedding. Further selected singular matrices (Formula presented.) and (Formula presented.) are split into (Formula presented.) non-overlapping blocks, and diagonal positions are used for watermark embedding. Three-level symmetric encryption with low computational cost is used to ensure higher watermark security. A hybrid grasshopper–BAT (G-BAT) SC-based optimization algorithm is also proposed in order to achieve high quality DIW outcomes, and a broad comparison against other methods in the state-of-the-art is provided. The experimental results have demonstrated that our proposal provides high levels of imperceptibility, robustness, embedding capacity and security when dealing with DIW of aerial RS images, even higher than the state-of-the-art methods.

Radio Frequency Identification (RFID) is primarily used to resolve the problems of taking care of the majority of nodes perceived and tracking tags related to the items. Utilizing contactless radio frequency identification data can be communicated distantly using electromagnetic fields. In this paper, the comparison and analysis made between the Clustered RFID with existing protocols Ad hoc On-demand Multicast Distance Vector Secure Adjacent Position Trust Verification (AOMDV_SAPTV) and Optimal Distance-Based Clustering (ODBC) protocols based on the network attributes of accuracy, vulnerability and success rate, delay and throughput while handling the huge nodes of communication. In the RFID Network, the clustering mechanism was implemented to enhance the performance of the network when scaling nodes. Multicast routing was used to handle the large number of nodes involved in the transmission of particular network communication. While scaling up the network, existing methods may be compromised with their efficiency. However, the Clustered RFID method will give better performance without compromising efficiency. Here, Clustered RFID gives 93% performance, AOMDV_SAPTV can achieve 79%, and ODBC can reach 85% of performance. Clustered RFID gives 14% better performance than AOMDV_SAPTV and 8% better performance than ODBC for handling a huge range of nodes.

The article describes enhancements in retrieval performance of content-based image retrieval (CBIR) system using the fuzzy class membership-based retrieval (CMR) framework. The CMR approach explores the CBIR as a classifier-based retrieval problem using a neural network classifier, accompanied by a simple distance-based retrieval method. The fuzzy class membership-based approach is known to enhance the retrieval performance along with slight variation without any constraint on the feature set to be used. Despite that, its efficacy is not known for color and multi-band textures. We have proposed several advancements in a fuzzy class membership-based retrieval framework for improved retrieval. The main contributions are the simplification of vital threshold selection process and effective use of membership values to encourage the use of appropriate classifiers, investigation of the role of the cost function in neural network and distance weighting functions for improved retrieval, a way to adapt a new classifier in fuzzy class membership-based retrieval framework in place of neural network. Experimental analysis of all proposed advancements are evaluated using benchmark gray-scale texture databases viz. three versions of Broadtz and Outex database. The p-value analysis is carried out to check if the improvements are statistically significant. The proposed method is further tested with the Describable texture database (DTD) and Multi-band texture (MBT) database to check its applicability on color textures. The comparison with recent methods using gray-scale image databases viz. AT&T face database, MIT VisTex database, Broadatz texture database, and natural-color image databases viz. Corel-1K and Corel-10K showcase the efficacy of the proposed method.

This paper provides a method to understand the underlying semantics of Bharatnatyam dance motion and classifies it. Each dance performance is audio-driven and spans over space and time. The dance is captured and analyzed, which is helpful in cultural heritage preservation, and tutoring systems to assist the naive learner. This paper attempts to solve the fundamental problem; recognizing the motions during a dance performance based on motion-pattern. The used dataset is the video recordings of an Indian Classical Dance form known as Bharatanatyam. The different Adavu s (The basic unit of Bharatanatyam) of Bharatanatyam dance are captured using Kinect. We choose RGB from various forms of captured data (RGB, Depth, and Skeleton). Motion History Image (MHI) and Histogram of Gradient of MHI (HoGMHI) are computed for each motion and used as an input for the Machine Learning (ML) algorithms to recognize motion. The paper explores two ML techniques; Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The overall accuracy of both the classifiers is more than 90%. The novelties of the work are (a) analysing all possible involved motions based on the motion-patterns rather than the joint velocities or pose, (b)exploring the impact of training data and the different features on the classifiers' accuracy, (c) not restricting the number of frames in a motion during recognition and formulate a method to deal with the variable number of frames in the motions.

-

In the modern digital era, with the availability of low-cost hardware like sensors and cameras, a huge amount of image databases are being created for diverse applications. These databases give rise to the need of developing efficient content-based image retrieval (CBIR) systems. Major efforts have been put over the past two decades to develop different global and low-level texture features to build efficient CBIR systems. However, designing texture features that are suitable for distance-based retrieval is always a challenging task. Recently, Convolution Neural Networks have shown promising results for object detection and classification. CNNs are also applied to build classifier-based retrieval systems. However, the classifier-based retrieval methods can retrieve images only from the predicted class. Therefore, the performance of such system greatly depends on classification performance of the classifier. This paper proposes a method that exploits the strength of the Convolutional Neural Networks for predicting the class membership of the query image for all output classes and retrieve images using a modified distance function in the wavelet feature space. The performance of the proposed method is evaluated using three popular texture datasets of varying complexity and found to be superior to all competing methods considered.

Content-based Image Retrieval (CBIR) is a technique that can exploit the wealth of the data stored in a repository and help radiologists in decision making by providing references to the image in hand. A CBIR system for High-Resolution Computed Tomography (HRCT) lung images depicting signs of Interstitial Lung Diseases (ILDs) can be built and used as a self-learning tool for budding radiologists. The study of a few lung image retrieval systems available in the literature identifies some important issues that need to be taken care of. In most of the works, the creation of the reference database involves painstaking manual activity, which is time-consuming and needs skilled labor. A lot of human interventions are required, particularly for the proper delineation of the region of interest (ROI) that represents pathology in each of the images in a database. In most cases, the size of the ROIs representing different disease findings are fixed (i.e., either a fixed size square or circle), which at times may not be a proper representation of the disease pattern and as a consequence, it might limit the system’s performance. Until date, a few learning-based approaches have been developed for content-based image retrieval of HRCT lung images, which either learn the similarity using a classifier or get trained through relevance feedback. For medical image analysis, the availability of labelled data for learning makes these learning-based retrieval systems meaningful as it enhances their performance in contrast to their simple distance-based counterpart. The objective of this paper is to develop a CBIR system for ILDs that is reliable and needs minimal human intervention. The paper evaluates the performance of three popular segmentation algorithms. It identifies the best for the effective and automated delineation of an arbitrary region of interest (AROI) depicting the sign of ILDs on HRCT images of the thorax in contrast to the manual delineation of fixed size ROI. This minimizes the manual effort for the creation and maintenance of the reference database, as well as the manual delineation of AROI during query formation. Moreover, AROI created through the automated clustering is found to have a better representation of disease patterns. Three recently proposed general-purpose learning based CBIR techniques are implemented and tested for retrieval of HRCT lung images depicting the sign of ILDs. The best method is suggested after careful evaluation of all the competing techniques.

Unsupervised learning algorithmic techniques are applied in grouping the data depending upon similar attributes, most similar patterns, or relationships amongst the dataset points or values. These Machine learning models are also referred to as self-organizing models which operate on clustering technique. Distinct approaches are employed on every other algorithm in splitting up data into clusters. Unsupervised machine learning uncovers previously unknown patterns in data. Unsupervised machine learning algorithms are applied in case of data insufficiency. Few applications of unsupervised machine learning techniques include: Clustering, anomaly detection. Clustering algorithms in bioinformatics are mostly used to decrypt the salient data in gene expression which is used to acknowledge biological processes in an organism. These models aid in drug design through gene expression profiling. Self organising maps are used in data reduction which provides a better understanding of genomics. Various clustering algorithms are deployed in microarray analysis which is useful in clinical research in keeping track of gene expression data. To define in simpler terms unsupervised learning is a technique which works on the input data to produce the output which is hidden or undetermined. This chapter presents various unsupervised algorithms used for knowledge exploration in the field of bioinformatics and highlights several novel works reported in the recent literature.

Identifying k ey frames is the first and necessary step before solving the variety of other B haratanatyam problems. The paper aims to partition the momentarily stationary frames (key frame s) from this dance video's motion frames. The proposed key frame s (KFs) localization is novel, simple, and effective compared to the existing dance video analysis methods. It is distinctive from standard KFs detection algorithms as used in other human motion videos. In the dance's basic structure, the occurrence of KFs during performances is often not completely stationary and varies with the dance form and the performer. Hence, it is not easy to decide a global threshold (on the quantum of motion) to work across dancers and performances. The earlier approaches try to compute the threshold iteratively. However, the novelty of the paper is: (a) formulating an adaptive threshold, (b) adopting Machine Learning (ML) approach and, (c) generating the effective feature by combining three frame differencing and bit-plane technique for the KF detection. In ML, we use Support Vector Machine (SVM) and Convolutional Neural Network (CNN) as the classifiers. The proposed approaches are also compared and analyzed with the earlier approaches. Finally, the proposed ML techniques emerge as a winner with around 90% accuracy.

This work introduced a modified ranking function which produced crisp linear programming (CLP) problems. A fully fuzzy linear programming (FFLP) problem in its balanced form having all the parameters and variables are triangular fuzzy numbers is taken into account during this study. Within the literature of the sector, the prevailing proposed approaches have many shortcomings, i.e., incorporate the rule of surplus variable and does not satisfy the constraints. Here, we bearing in mind of the prevailing shortcomings, a constructive solution is approached to beat the restrictions. For better exactness of the answer which is proposed by us, we use fuzzy number. Two numerical examples are illustrated and compared with the pre-existing methods.

-

Content-based image retrieval (CBIR) is a technique that may help radiologists in their daily clinical practice by providing reference images against a given subject in hand for diagnosis. Several special purpose medical CBIR systems are built for the diagnosis of interstitial lung diseases (ILDs). Texture is used as a primitive feature to build such systems due to the texture-like appearance of ILD patterns. Therefore, it is necessary to evaluate the efficacy of promising texture feature descriptors proposed recently for building the CBIR system for ILDs. This paper presents an effective and exhaustive evaluation of five such recently proposed texture feature descriptors (viz. local binary pattern (LBP), orthogonal combination of local binary pattern (OC-LBP), center-symmetric local binary pattern (CS-LBP), local neighborhood difference pattern (LNDP), and combination of LNDP and LBP) for the design and development of CBIR system for ILDs. The performance of each method is compared using the most used performance metrics such as precision, recall, and F-score. The LNDP descriptor is found to be the best performer and therefore can be considered as a descriptor for ILD patterns for the design and development of CBIR system.

-

-

A substantial amount of the energy produced globally is utilized for household utilities, for example to maintain air-conditioning in buildings for personal comfort and essential weathering necessities, in tropical or cold climate geographic regions. The development of innovative functional materials in combination with cutting edge technology is vital for sustainable urban solutions. The progress and scaling-up of new technology for urban solution is necessary to addresses key concerns like improved energy efficacies, zero energy building (ZEB), recyclability, waste management, reduce carbon footprints, de-carbonization, etc. The building energy consumption can be controlled by adopting specialized cloaking technologies using materials or nanoadditives to create high reflective coatings/surfaces. However, large number of possible configurations and physical experiments that includes complexity of nanoadditives to achieve optimized materials performance and optical properties are time consuming as well as very expensive. In remedies, physical experiments and computational modeling methods have been utilized to develop optimized functional properties of the materials. Progression in materials research and innovation is critical for the requirement of futuristic sustainable solution, for example, green electricity and energy saving needs. Experimental techniques and computational modeling are time consuming; hence, it is much desirable to develop new methods to accelerate the materials development technologies, design optimization and implementation. This chapter aims to introduce the basics of machine learning for material technologies and list out major work carried out in this domain recently.

Automated lung tissue characterization of Interstitial Lung Disease is one of the most important aspects of the Computer Aided Disease diagnosis system. The problem remains challenging, even though there has been much research in this area. While deep learning has produced brilliant success in image applications over the past few years, the majority of training is with sub-optimal parameters, requiring unnecessary long training time, setting up hyper parameters. In this paper, we explore the classification of lung tissue pattern affected with interstitial lung disease (ILD) in high resolution computed tomography (HRCT) scans and evaluated different CNN architectures with and without transfer learning. The effect of cyclical learning rates, the hyper-parameters tuning and data augmentation on classification performance are studied using a popular publicly available dataset called MedGift dataset.

Real-time roadway-environment perception is one of the primary applications of IoT based autonomous driving to improve road safety. Roadway-environment insights include on-road detection of any type of moving vehicles, non-vehicle (persons, animals, etc.), curves and lanes. There have been various studies that provided Artificial Intelligence (AI)-based detection approaches, however, most of the methods are atomistic which are not well suited for such real-time autonomous driving owing to high detection latency and low accuracy. Therefore, in this paper, we propose a holistic AI-based roadway-environment learning system for simultaneous real-time detection of various on-road objects with high accuracy (more than 90%) at reduced computation complexity.

The development of novel Information and Communication Technology (ICT) based solutions becomes essential to meet the ever increasing rate of global urbanization in order to satiate the constraint in resources. The popular ‘smart city paradigm is characterized by ubiquitous cyber provisions for the monitoring and control of such city's critical infrastructures, encompassing healthcare, environment, transportation and utilities among others. In order to manage the numerous services keeping their Quality of Service (QoS) demands upright, it is imperative to employ context aware computing as well as fog computing simultaneously. This paper investigates the feasibility of energy minimization at the fog layer through intelligent sleep and wake-up cycles of the fog nodes which are context-aware. It proposes a virtual machine management approach for effectively allocating service requests with a minimal number of active fog nodes using a genetic algorithm (GA); and thereafter, a reinforcement learning (RL) approach is incorporated to optimize the period of fog nodes’ duty cycle. Simulations are carried out using MATLAB and the results demonstrate that the proposed scheme improves energy consumption of the fog layer by approximately 11–21% when compared to existing context sharing based algorithms.

-

-