Recent studies state that, for a person with autism spectrum disorder, learning and improvement is often seen in environments where technological tools are involved. A robot is an excellent tool to be used in therapy and teaching. It can transform teaching methods, not just in the classrooms but also in the in-house clinical practices. With the rapid advancement in deep learning techniques, robots became more capable of handling human behaviour. In this paper, we present a cost-efficient, socially designed robot called ‘Tinku’, developed to assist in teaching special needs children. ‘Tinku’ is low cost but is full of features and has the ability to produce human-like expressions. Its design is inspired by the widely accepted animated character ‘WALL-E’. Its capabilities include offline speech processing and computer vision—we used light object detection models, such as Yolo v3-tiny and single shot detector (SSD)—for obstacle avoidance, non-verbal communication, expressing emotions in an anthropomorphic way, etc. It uses an onboard deep learning technique to localize the objects in the scene and uses the information for semantic perception. We have developed several lessons for training using these features. A sample lesson about brushing is discussed to show the robot’s capabilities. Tinku is cute, and loaded with lots of features, and the management of all the processes is mind-blowing. It is developed in the supervision of clinical experts and its condition for application is taken care of. A small survey on the appearance is also discussed. More importantly, it is tested on small children for the acceptance of the technology and compatibility in terms of voice interaction. It helps autistic kids using state-of-the-art deep learning models. Autism Spectral disorders are being increasingly identified today’s world. The studies show that children are prone to interact with technology more comfortably than a with human instructor. To fulfil this demand, we presented a cost-effective solution in the form of a robot with some common lessons for the training of an autism-affected child.

Deepfake detection has become a critical challenge nowadays with the rise of sophisticated generative techniques that manipulate audio-visual data. Existing methods primarily focus on lip movement synchronization using audio and visual features, often relying on local feature extraction with Convolutional Neural Networks (CNNs). In this work, we propose an enhanced multimodal framework that integrates with local and global features for advanced deepfake detection. Our approach extends traditional pipelines by introducing additional visual features such as eye movement and facial regions, combined with audio features to model cross-modal dependencies. While CNNs capture local features, Vision Transformers (ViTs) extract global contextual relationships from both visual and audio modalities. The diffusion models are incorporated as pre-processors to refine noisy data and generate realistic augmentations, ensuring high-quality feature representation. The proposed framework achieves state-of-the-art performance, with accuracy scores of 0.9987, 0.9825, 0.9915, and 0.9812 on the FakeAVCeleb, AV-Deepfake1M, TVIL, and LAV-DF datasets, respectively. These results demonstrate significant improvements over existing methods, highlighting the framework’s superior generalization and robustness in detecting subtle inconsistencies across manipulated audio-visual data.

This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. The study evaluates model performance using key regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results indicate that CNN and GRU models generally outperform the others, depending on the specific stock, and demonstrate superior capabilities in forecasting stock price movements. This investigation provides insights into the strengths and limitations of each model while highlighting potential avenues for improvement through feature engineering and hyperparameter optimization.

This survey explores the application of multimodal machine learning techniques in predictive healthcare analytics. By integrating various data modalities, such as medical imaging, clinical text, time-series signals, and structured tabular data, these approaches aim to emulate clinical reasoning and enhance diagnostic and prognostic accuracy. Across studies, multimodal ML consistently outperformed unimodal baselines, with intermediate fusion employed in 60% of cases and achieving average AUC improvements of 5–12% over single-modality models. Oncology and neurology emerged as the leading domains, where combining imaging with genomic and cognitive data significantly improved cancer survival and Alzheimer’s detection. Despite progress, key challenges persist, including modality misalignment (23%), missing data (18%), and limited external validation (12%). Recent trends highlight transformer-based cross-modal attention, self-supervised learning for data-scarce settings, and hybrid fusion architectures in critical care. While multimodal ML offers clear clinical advantages, regulatory constraints and interoperability gaps continue to hinder deployment. This survey contributes to the existing literature by providing a comprehensive synthesis of multimodal ML applications across healthcare domains. It documents comparative fusion strategies, modelling approaches, and empirical performance outcomes. The paper’s primary contribution lies in identifying intermediate fusion as the most effective integration strategy and revealing systematic gaps in external validation and model transparency that must be addressed for clinically trustworthy multimodal systems.

Advanced data augmentation techniques have demonstrated great success in deep learning algorithms. Among these techniques, single-image-based data augmentation (SIBDA), in which a single image’s regions are randomly erased in different ways, has shown promising results. However, randomly erasing image regions in SIBDA can cause a loss of the key discriminating features, consequently misleading neural networks and lowering their performance. To alleviate this issue, in this paper, we propose the random slices mixing data augmentation (RSMDA) technique, in which slices of one image are placed onto another image to create a third image that enriches the diversity of the data. RSMDA also mixes the labels of the original images to create an augmented label for the new image to exploit label smoothing. Furthermore, we propose and investigate three strategies for RSMDA: (i) the vertical slices mixing strategy, (ii) the horizontal slices mixing strategy, and (iii) a random mix of both strategies. Of these strategies, the horizontal slice mixing strategy shows the best performance. To validate the proposed technique, we perform several experiments using different neural networks across four datasets: fashion-MNIST, CIFAR10, CIFAR100, and STL10. The experimental results of the image classification with RSMDA showed better accuracy and robustness than the state-of-the-art (SOTA) single-image-based and multi-image-based methods. Finally, class activation maps are employed to visualize the focus of the neural network and compare maps using the SOTA data augmentation methods.

Paddy (Oryza sativa) is one of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much effort and expertise. The grain quality of the product is heavily affected by the weather conditions, irrigation frequency, and many other factors. However, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for rice grain quality assessment, noting that the key characteristics of paddy and rice are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliable ML-based IoT paddy grain quality assessment system utilizing affordable sensors. It involves a specific data collection procedure followed by image processing with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios. To our knowledge, it is the first automated system to precisely provide an overall quality metric. The main feature of our system is its explainability in terms of utilized features and fuzzy rules, which increases the confidence and trustworthiness of the public toward its use. The grain variety used for experiments majorly belonged to the Indian Subcontinent, but it covered a significant variation in the shape and size of the grain.