Content-Based Video Retrieval (CBVR) systems identify videos similar to a query by directly analyzing visual content, avoiding dependence on textual descriptions. In this paper, we propose a CBVR system that introduces a novel adaptive keyframe selection strategy aimed to preserve the most informative visual content. Unlike existing methods that employ fixed similarity thresholds and a uniform number of keyframes across all videos, the proposed approach dynamically determines both the similarity threshold and the number of keyframes for each video based on its content, enabling video-specific adaptability. First, a novel keyframe selection pipeline based on ResNet50 is used to extract deep features from individual video frames. Redundant frames are removed by retaining only those with minimal feature similarity, where the similarity threshold is automatically determined for each video using the Elbow method. Second, an optical flow–based frame selection strategy is applied to refine the selected keyframes by incorporating motion information. Deep spatiotemporal features are then extracted from both using a 3D deep learning model, and their fusion into a unified video-level representation constitutes an additional novel contribution, as it jointly exploits complementary content and motion cues rather than relying on a single information source. The proposed system achieved an 18.5% F1-score gain on the SumME dataset for video summarization and improved video retrieval performance on UCF101 and HMDB51, with Top-10 accuracy gains of 20.4% and 34.2%, Top-20 mAP gains of 46.7% and 68%, respectively, and a 1.6% Top-10 precision on UCF101 over the closest competing methods.

The exponential growth of information and communication technologies has led to an unprecedented surge in digital data, with a significant portion comprising unstructured visual media, such as images and videos that often lack metadata. This absence of structured metadata presents a major challenge in efficiently managing and extracting value from these vast repositories, rendering traditional search and retrieval methods ineffective. Content-based video retrieval (CBVR) has emerged as a crucial solution, transforming fields such as traffic analysis, video surveillance, medicine, and sports. Unlike static images, videos consist of objects that continuously move and undergo appearance changes over time, making it challenging to effectively capture both spatial details and temporal dynamics. This paper proposes a method that leverages the strengths of DenseNet-151, a densely connected convolutional neural network designed for extracting rich spatial features, and Long Short-Term Memory (LSTM), a recurrent neural network specialized in capturing temporal dependencies. Additionally, a modified distance function incorporating fuzzy class membership is employed to enhance the retrieval of similar videos from the database, leading to improved retrieval performance. The effectiveness of the proposed method is evaluated on the UCF101 Human Action Recognition dataset, demonstrating a 6% improvement in Precision, a 5% increase in Recall, a 5% enhancement in F1-Score, and an 8% boost in AUC compared to the most competitive approaches in the literature.

Imagine searching for a specific scene in a movie, not by remembering its title or actors, but by describing the action itself. This is the essence of content-based video retrieval (CBVR), a technique that searches for a video based on what's inside it, rather than relying solely on manually assigned labels. Unlike traditional methods, which can be time-consuming, error-prone, and struggle with vast datasets, CBVR offers a more efficient and accurate approach.Our proposed system leverages the strong capability of deep learning, a subset of artificial intelligence, to analyze videos and extract their key characteristics. This process occurs in two stages: offline and online. Through the first stage, important features are extracted from all videos in the dataset and stored for future use. When a user submits a query video, its features are extracted in real-time (online) and compared to the stored features of all videos. The videos with features most similar to the query, essentially those with the 'closest match,' are then presented to the user.To capture the full essence of a video, our system employs a two-stream neural network architecture. This innovative approach allows us to extract both temporal features, which capture the changes and motion patterns within the video (think: someone running or jumping), and spatial features, which pivot about the static visual content of each individual frame (think: the objects and scene depicted).By utilizing a pre-trained neural network called ResNet-60, our system benefits from existing knowledge and can efficiently extract meaningful features from videos. To evaluate its effectiveness, we tested our system on the UCF101 dataset, a widely used benchmark consisting of 101 categorized videos. Our approach obtained accuracy 93,7% for top 5 retrieval and 95.95% for top 10 retrieval. The outcomes illustrate that our approach obtains superior accuracy compared to other state-of-the-art video retrieval methods.

Machine learning (ML), a subset of artificial intelligence (AI), is revolutionizing industries by leveraging statistical algorithms that learn from data and experiences. Unlike traditional programs following predetermined sequences, ML algorithms discern patterns and predict outcomes through extensive datasets. This transformative technology has profoundly impacted diverse sectors, including manufacturing, finance, retail, transportation, entertainment, and healthcare. The influence of ML is amplified by the accessibility of extensive datasets and the escalating computational prowess of modern systems. As ML algorithms progress, they are fundamentally reshaping business operations, streamlining processes, enhancing decision-making, and fuelling innovation across sectors. The impact of machine learning algorithms on healthcare applications and the usage of diverse data sources, such as electronic health records, medical imaging, wearable devices, and genomic data, is discussed in this chapter.

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.

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.

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 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.