Nutrition Meets AI: Opportunities in Recommender Systems for Healthier Choices Arvind Mewada, Mohd Aquib Ansari, Ruchi Jayaswal, Shahnawaz Ahmad, Dushyant Kumar Singh, Nagendra Singh, Rupesh Kumar Dewang, Mohd Nazim Advancing Equity in Global Healthcare Systems, 2026 This chapter examines the personalization of nutrition using AI-powered recommender systems, focusing on how they can improve health goals and encourage sustainable eating habits. It incorporates the latest developments in machine learning, including matrix factorization, convolutional neural networks, reinforcement learning, and graph neural networks in devising automated food identification, meal planning, and interactive dietary recommendations. The chapter describes how these systems, using biometric data like glucose levels and gut microbiome profiles, behavior analytics such as eating and preference patterns, and contextual information like time, location, and cultural understanding, provide personalized recommendations tailored to individuals' unique health profiles. The practical applications range from dealing with chronic illnesses and obesity to sensitive dieting, demonstrating their practicality. Actionable recommendations are derived from the comparative analysis of benchmark datasets Nutrition5k, MyFoodRepo, and FoodKG, as well as the models' performance evaluations.
An in-depth exploration of structural pose estimation strategies and datasets Ruchi Jayaswal, Mohd. Aquib Ansari, Arvind Mewada, Preksha Pareek, Shahnawaz Ahmad Discover Computing, 2025 Human Pose Estimation (HPE) refers to detecting the major joints (skeleton) of a human and their motion in order to recognize any action. This task is important because of its serious implications, such as surveillance, autonomous vehicles, sports analytics, human-computer applications, animation, and medical tracking. This article discusses various methods used to survey HPE, ranging from the fundamental principles of computer vision to advanced deep learning (DL) models. We investigate various structural pose estimation strategies for 2D and 3D domains and explore volumetric, planar, and kinematic models. Not only does this paper investigate methodology, but we also evaluate popular benchmark datasets used to test these methods, looking into their pros, cons, and overall usefulness for various HPE problems. Depth ambiguity, viewpoint variation, occlusion, and the absence of any annotated data are described as some of the many critical challenges to overcome. We further investigate what can be done to assist in the use of robotics and surveillance in real-time, such as self-supervised learning and adapting to specific task domains, aiming to drive future efforts forward.
Advances in facial expression recognition technologies for emotion analysis Ruchi Jayaswal, Mohd. Aquib Ansari, Manish Dixit, Dushyant Kumar Singh, Shahnawaz Ahmad Discover Computing, 2025 With the rapid evolution of technology, automated facial expression recognition (AFER) is one of the emerging research frontiers, gaining increasing attention from computer vision scientists. Facial emotion recognition (FER) enables a wide range of human–machine interaction applications, including mental health monitoring, behavioral analysis, and virtual learning environments. This paper comprehensively reviews recent advancements in AFER, examining both traditional and deep learning (DL) based approaches. The traditional section outlines methods involving feature extraction techniques such as LBP and Gabor filters, along with classifiers like Support Vector Machine (SVM), Principal Component Analysis (PCA), and k-nearest Neighbors (k-NN). In contrast, the DL section explores models such as Convolutional Neural Networks (CNNs) and hybrid networks that offer enhanced accuracy and adaptability. Various public FER datasets, including JAFFE, CK + , and FER2013, are analyzed to demonstrate the scope of existing benchmarks. Furthermore, this study identifies key gaps in the current FER literature, such as limited robustness in real-world conditions, challenges in recognizing emotions, and the lack of standardized evaluation protocols across datasets. We also highlight recent improvements in accuracy, feature extraction, and real-time applicability. This review aims to assist both emerging and established researchers by offering insights into current trends, performance metrics, and unresolved challenges in FER, ultimately contributing to the development of more robust and adaptive emotion recognition systems.
Fine-grained temporal–spatial cues for theft recognition in surveillance videos Mohd. Aquib Ansari, Arvind Mewada, Ambrish Kumar, Ruchi Jayaswal, Amrendra Singh Yadav, Lalit Kumar, Deepika Bansal Scientific Reports, 2025 Surveillance systems play a crucial role in detecting suspicious human activities, including attacks, violence, and abductions, in public spaces. This study presents a human intervention-free, hybrid framework that utilizes deep neural networks for real-time theft activity recognition. The proposed methodology employs a dual stream fusion network, combining appearance and motion features, to accurately identify theft actions. Specifically, a modified InceptionV3 model extracts relevant body pose features through keypoint transfer, feeding two separate deep neural network pipelines for appearance and motion analysis. Long-Short-Term Memory network then models temporal relationships between the extracted features across consecutive frames. The novelty of this research lies in the proposed dual-stream fusion architecture, which aims to capture fine-grained temporal and spatial cues for theft detection. A new lab-lifting dataset has also been developed to reflect subtle theft behaviors in academic settings. The framework's performance is evaluated on a dataset comprising normal and theft activities. The results demonstrate a recognition accuracy of 91.86% , surpassing that of other methods.
Using postural data and recurrent learning to monitor shoplifting activities in megastores Mohd Aquib Ansari, Dushyant Kumar Singh, Ruchi Jayaswal Concurrency and Computation Practice and Experience, 2024 SummaryRecently, researchers have placed a great deal of emphasis on modeling activity patterns to better understand human behavior. Several approaches have been researched so far to develop automatic human activity recognition systems that infer detailed semantics from visual images, aiming to understand real human behavior patterns. However, there is still a need for a cost effective solution to distinguish human actions in the real‐world environment. With this encouragement, a novel approach is proposed to recognize shoplifting acts by examining the posture evidence of the human being. This approach begins by obtaining the two‐dimensional pose reflecting human's body joints as a skeleton from the recorded frames. Subsequently, a preprocessing step is used to preprocess skeleton data, which can handle the occlusion too. Postural feature generation is then used to extract pertinent features from such preprocessed skeletons. Finally, feature deduction is performed to downsize the derived features to a smaller dimension, and activity classification is performed on such reduced features to identify shoplifting behaviors in real time. A synthetic shoplifting dataset and real store recorded videos are used to conduct the experiments, the findings of which appear more promising than those obtained using other cutting‐edge methods, with an accuracy of 97.36% and 91.66% for synthesized and real store recorded inputs.
Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans Aniket Jhariya, Dhvani Parekh, Joshua Lobo, Anupkumar Bongale, Ruchi Jayaswal, Prachi Kadam, Shruti Patil, Tanupriya Choudhury Eai Endorsed Transactions on Pervasive Health and Technology, 2024 INTRODUCTION: Compression of MRI images while maintaining essential information, makes it easier to distinguish between different types of brain tumors. It also assesses the effect of PCA on picture representation modification and distance analysis between tumor classes.OBJECTIVES: The objective of this work is to enhance the interpretability and classification accuracy of highdimensional MRI scans of patients with brain tumors by utilising Principle Component Analysis (PCA) to reduce their complexity.METHODS:This study uses PCA to compress high-dimensional MRI scans of patients with brain tumors, focusing on improving classification using dimensionality reduction approaches and making the scans easier to understand.RESULTS: PCA efficiently reduced MRI data, enabling better discrimination between different types of brain tumors and significant changes in distance matrices, which emphasize structural changes in the data.CONCLUSION: PCA is crucial for improving the interpretability of MRI data.
Analysing the landscape of Deep Fake Detection: A Survey International Journal of Intelligent Systems and Applications in Engineering, 2024
