The significance of an improved user experience in recommender systems will continue to grow as tourism advances. Most such systems generally offer information to users that benefits third-party firms' commercial interests, often involving the implicit compromise of user privacy. In the proposed work, a tourism recommendation system is developed by the authors to deliver personal recommendations to users. For designing the proposed recommendation system, the authors have considered parameters such as ratings, user activity, time utilized by the user, certifications, and other relevant attributes. Four methods: Memory-based Collaborative Filtering (MCF), Matrix factorization Collaborative Filtering (MFCF), Cosine Similarity Content Filtering (CSCF), and Term Frequency-Inverse Document Frequency (TF-IDF) are used by the authors for designing a tourist recommendation system. For the assessment of the work, the authors have calculated Mean Average Error (MAE) and Root Mean Square Error (RMSE).

Advanced Information Retrieval System: Theoretical and Experimental Perspective blends foundational theory with practicality to provide an integrative exploration of modern information retrieval (IR) systems. This volume examines a wide range of IR methodologies, from classical indexing and ranking techniques to cutting-edge AI-driven approaches, demonstrating how these systems can be applied across diverse domains, including web search, recommendation systems, sentiment analysis, and multimedia retrieval. The book takes a structured approach towards guiding readers from traditional IR models to advanced, hybrid frameworks. The early chapters focus on classical and modern retrieval techniques with comparative analyses of different methods. Subsequent chapters focus on applied scenarios such as tourism recommender systems, sentiment mining from YouTube comments, book and medicine recommendation engines, and image-audio-based retrieval systems. Advanced topics include semantic role classification using BERT, hybrid filtering methods, personalised web crawlers, and experimental studies on smoothing techniques. Real-world case studies and experimental evaluations illustrate how theoretical models translate into effective, domain-specific IR applications. Key Features: Comprehensive coverage of traditional, modern, and hybrid IR techniques Practical frameworks for recommendation systems, sentiment analysis, and web crawling Integration of AI and machine learning methods, including BERT and TF-IDF models Experimental evaluations and comparative analyses across multiple domains Real-world applications spanning tourism, healthcare, fashion, and multimedia retrieval.

Due to advancements in computer vision techniques, medical healthcare data is available to users in a very large amount. With the use of this data, an intelligent medicine recommendation system can be prepared. Authors in this chapter utilized Term Frequency-Inverse Document Frequency (TF-IDF) and Machine Learning (ML) for the development of an intelligent medicine recommendation system. The recommendation system recommends proper medicines by exploring the symptoms of patients and their medical history. The TF-IDF technique retrieves suitable features from the dataset and then applies machine learning for the classification of diseases and the recommendation of medicine to patients. The proposed recommendation system provides valuable and accurate suggestions to users, adding flexibility to the healthcare system.

The user engagement in YouTube comments could be utilized for sentiment analysis. The data is collected through web scraping from the YouTube comments. Natural Language Processing (NLP) and Bidirectional Encoder Representations from Transformers (BERT) are utilized by authors to analyze the sentiment of commenters. The current trends and user engagement in the market can be analyzed through YouTube comments. The thematic patterns are generated with the help of word clouds and sentiment distribution charts. Based on the outcome of the proposed work, it can be concluded that YouTube comments provide valuable feedback to researchers, which in turn can be used for sentiment analysis.

This chapter presents the classification and analysis of fashion data, which consists of 90 images belonging to one class, using deep learning techniques. Data augmentation is done to pre-process the dataset. Features are retrieved using Convolutional Neural Networks (CNNs), VGG16, and ResNet50. These modes are trained on styles and patterns of images so that recognition can be done. For the styles and subtitles, another dataset of 144 audio files has been utilized. Voice is converted into text by using Machine Learning (ML) and Natural Language Processing (NLP) techniques. Pre-processing of audio files has been performed using Mel-Frequency Cepstral Coefficients (MFCC) along with normalization to reduce noise. The Recurrent Neural Networks (RNNs) technique converts the audio file into a text file. The proposed work is evaluated based on accuracy, reliability, and adaptability.

Due to the digital era, there is a flood of information available online . Recommendation systems can be utilized to improve user experience. Authors in this chapter proposed to develop a hybrid book recommendation system using Collaborative Filtering (CF) and Content-based Filtering (CBF) techniques. The content filtering method identifies the features of the object, and then, based on those features, a decision can be made. Collaborative filtering works based on user interaction patterns. The proposed method overcomes the drawbacks of data sparsity in collaborative filtering and the cold-start problem in content-based systems. To overcome these issues, the dataset is pre-processed to find the popular books, ratings, and a list of active users. Authors have applied k-Nearest Neighbors (k-NN) for feature and metric selection so that the recommendation engine can work properly. The performance of the proposed model is measured in terms of performance metrics, and the outcome has proved that it is a precise and accurate recommendations model with high recall, precision, F1-Score, and accuracy metrics.

Accurate fruit detection is a fundamental task in computer vision with significant applications in agriculture, retail automation, and assistive technologies. In the proposed work authors utilized Convolutional Neural Network (CNN) model for fruit detection from the images. For training the CNN model Fruit-360 dataset from Kaggle has been utilized. The CNN technique is capable of retrieving deep feature and from these features it learns discriminative visual patterns. The model is capable of identifying and classifying fruit images into many categories. The dataset used contain verity of labeled images which are taken under controlled conditions. The proposed work is evaluated based on performance metrics including: accuracy, precision, recall and F1-score. With an accuracy of 74% on the test dataset, precision of 75.6%, and recall of 74%, experimental results confirm the effectiveness of the suggested model and are deemed adequate for real-world uses. The web app is also developed for the real-time detection and classification of fruit images. In future authors will integrate CNN model with neutral language processing for answering the questions of the users.

In the digital era, sentence interpretation is crucial for understanding the meaning of sentences. As a key component of Natural Language Processing (NLP), it helps identify relationships between words and determine their roles within a sentence. Semantic Role Classification (SRC) assigns semantic roles to different actions in a sentence, enabling deeper language comprehension. This study analyses various SRC techniques, with a particular focus on transformer models. It provides a summary of existing SRC methods, highlighting their advantages and incorporating a Continuous Integration/Continuous Delivery (CI/CD) pipeline for seamless deployment. The effectiveness of the proposed approach is evaluated based on the accuracy achieved.

Corn leaf diseases affect agriculture worldwide which leads to low production and economic concerns. Timely detection of diseases in corn leaves could improve the growth of corn. Timely detection could also optimize resource uses, decreases overall cost and confirm high-quality of the crop. Deep learning techniques are playing important role in detection and classification of different leaf diseases. You Only Look Once (YOLO) models also follow the concept of deep learning for accurately detection and classification of the diseases in corn leaves. In the proposed work, YOLOv5, YOLO6, YOLOv8, YOLOv9, YOLOv10 and YOLOv11 models are trained on dataset from Kaggle for detection of diseases in corn leaves. Data augmentation techniques such as flip vertical, rotation between-15 degree to +15 degree, 90% clockwise rotation, shearing +/- 0 degree horizontal to +/- 15 degree vertical are applied on dataset at the time of training. The model is evaluated by applying the metrics: Accuracy, Recall, Precision, mAP@50, mAP@50-95, and time taken. To validate the experimental results authors utilized 6 versions of YOLO. YOLOv9 performed best among proposed version and it is justified by the experimental results as well.

The healthcare research field is struggling with several ongoing problems like ensuring data is accurate, being open and clear, connecting different systems, keeping information safe, and building trust. Things such as broken health records, studies that can't be repeated, people changing data without permission, and patients not having control over their medical information are making it harder to move forward with scientific research and new ideas. In this situation, blockchain technology has come up as a big change that can really improve healthcare research by offering ways to manage data that are spread out, can't be changed, and are transparent. This paper looks at how blockchain can change the way healthcare research is done, help people work together securely, and build trust between those involved. Blockchain uses a system where data is stored across a network of computers, making it almost impossible to change or fake. This helps keep research data honest and makes it easier to check and track throughout the research process.

The rapid expansion of scientific literature has made it challenging for researchers to find relevant studies on specific topics efficiently. The traditional search methods require extensive time and manual effort to identify, filter, and extract essential information from numerous sources. This paper proposes an automated keyword-based web crawling system aimed at streamlining the retrieval of research papers and patents from sources, IEEE and Google patents, respectively. In the proposed work, the authors type the keyword of the research paper or patent, and then the system provides the results accordingly. The details of a patent or research paper may include details like the name of the author, the DOI, the publisher, the title of the article, publication date, and many more. The proposed technique decreases manual work and enhances the accuracy of collecting the required data.

Sign language recognition and translation have emerged as crucial areas of research to support the deaf and hard-of-hearing communities by enabling effective communication with the wider population. This paper presents a detailed review and comparative study of existing techniques for sign language recognition and translation, with a focus on both American Sign Language (ASL) and Indian Sign Language (ISL). The research explores a variety of approaches, including sensor based methods, computer vision techniques, Convolutional Neural Network (CNN), for translating signs into text or speech gestures, which are essential for accurate semantic understanding. For the implementation of the proposed work authors utilized convolution neural network. Dataset contains images of hand gestures signifying the sign language alphabet. The proposed research work is evaluated based on confusion matrix, accuracy and loss. Authors also built the Streamlit app which is helpful in showing the results on real-time data. In future Natural Language Processing (NLP) can be utilized for supporting communication between hearing-impaired and hearing individuals.