In the current scenario, the recommendation system is challenging to maintain due to three key requirements: the need for real-time user behavior analysis, the inability to explain why recommendations are made, and struggles to handle new users/items. In this article, our objective is to develop a hybrid recommendation system that solves the challenges of traditional approaaches. Our framework combined real-time learning, agentic rules, as well as sensor compatibility in a dynamic environment. We develop a novel framework called SAFIRE (Sensor-Aware Framework for Intelligent Recommendations and Explainable Hybrid Techniques), where the 8 traditional algorithms (User-Based CF, Item-Based CF, KNNWithMeans, KNNBaseline, SVD, SVD++, NMF, BaselineOnly), a Hybrid ensemble, and Explainable AI are used to recommend it. From our experimental work, it reveals that the accuracy of BaselineOnly provides an RMSE score of 5-fold RMSE of 0.5156, and MAE is 0.34055. Similarly, 10-fold CV of RMSE is 0.51558, and MAE is 0.34069. The lowest MAE of the 5-fold is 0.29913. For 10-fold, NMF MAE is 0.30144. This study also conducted the statistical test and found that Memory-Based CF (KNN variants, UserCF, ItemCF), having 10-fold CV, performs slightly better than 5-fold., p-values are significant.NMF, the mean difference is −0.00248 very small improvement in 10-fold CV, and p-values < 0.05, which is significant. Model-based techniques like BaselineOnly, NMF, and SVD show little variation (mean difference < 0.003) and hold up well during CV folds.

This study investigates the role of leaf shape in detecting disease in tomato plants, grounded
in the observation that plant leaves often undergo structural changes in response to infection.
Healthy and diseased tomato leaves are characterized by extracting shape signature
features from images and analyzing their spectral characteristics. Leaf images were captured
using a Sony ZV-E10 Mark II mirrorless camera equipped with a Sigma 16mm f/1.4
DC DN lens. Each leaf was placed flat on a matte white surface under a controlled overhead
photography setup. The camera was mounted at a fixed height on a tripod, and
uniform illumination was achieved using two symmetrically positioned LED spotlight
lamps, minimizing shadows and glare. The dataset comprises 200 samples: 100 healthy
and 100 diseased tomato leaves, representing a range of morphological and pathological
variations. Three primary shape metrics were extracted from the images to characterize the
structural differences. (1) The Centroid Contour Distance measured the radial distances
from the leaf centroid to its outer contour, (2) The Hausdorff Distance quantified the geometric
dissimilarity between contours, and (3) Dice Similarity Index assessed the degree of
overlap. In addition, spectral characteristics were derived from the RGB channels: mean
intensities of red, green, blue, and the Excess Green Index. Results show that both shape
and spectral features are valuable for detecting plant diseases: PCA show clustering patterns
between the two classes of leaves and correlation analysis highlights the relationship
between several pairs of geometric and color features. In conclusion, shape is an essential
aspect of plant health as it reflects the structural changes that occur as a result of disease.
When combined with spectral data, can form the basis for an effective, automated disease
detection system.

In contrast to the existing Wi-Fi-based systems, we have developed a GSM-based, cellu-lar-Internet of Things (C-IoT) enabled, portable air quality monitoring system that collects the critical air quality parameters through advanced sensors as well as location of the de-vice using Global Positioning System (GPS). Our system utilizes sensors to monitor tem-perature, humidity, carbon dioxide, and Particulate Matter concentrations along with the location information. These sensors are integrated with ESP32 microcontroller which is interfaced with the GPS module for location information as well as with the GSM module to transmit the sensor data and location information to a central IoT gateway using exist-ing cellular infrastructure. The developed C-IoT sensor node is powered through a porta-ble power bank allowing complete mobility of the developed sensor node within the cellu-lar coverage in the entire city. The data collected through the mobile C-IoT system is dis-played in a live dashboard as well as over a live map for location-aware air quality moni-toring. As a pilot run, we collected localized environmental data through developed node by moving around a pre-defined urban area to create a pollution map of the area.

This research presents a novel, real-time Pakistani Sign Language (PSL) recognition sys-tem utilizing a custom-designed sensory glove integrated with advanced machine learn-ing techniques. The system aims to bridge communication gaps for individuals with hearing and speech impairments by translating hand gestures into readable text. At the core of this work is a smart glove engineered with five resistive flex sensors for precise finger flexion detection and a 9-DOF Inertial Measurement Unit (IMU) for capturing hand orientation and movement. The glove is powered by a compact microcontroller, which processes the analog and digital sensor inputs and transmits the data wirelessly to a host computer. A rechargeable 3.7 V Li-Po battery ensures portability, while a dynamic dataset comprising both static alphabet gestures and dynamic PSL phrases was recorded using this setup. The collected data was used to train two models: a Support Vector Machine with feature extraction (SVM-FE) and a Long Short-Term Memory (LSTM) deep learning network. The LSTM model outperformed traditional methods, achieving an accuracy of 98.6% in real-time gesture recognition. The proposed system demonstrates robust perfor-mance and offers practical applications in smart home interfaces, virtual and augmented reality, gaming, and assistive technologies. By combining ergonomic hardware with intel-ligent algorithms, this research takes a significant step toward inclusive communication and more natural human-machine interaction.

Autonomous vehicles need to explain their actions to improve reliability and build user trust. This study focuses on enhancing the transparency and explainability of the decision-making process in such systems. A module named XAI-Interpreter is developed to identify and highlight the most influential factors in driving decisions. The module combines two complementary methods: Learned Attention Weights (LAW) and Object-Level Attention (OLA). In the LAW method, images captured from the ego vehicle’s front and rear cameras in the CARLA simulation environment are processed using the Faster R-CNN model for object detection. GRAD-CAM is then applied to generate visual attention heatmaps, showing which regions and objects in the images affect the model’s decisions. The OLA method analyzes nearby dynamic objects, such as other vehicles, based on their size, speed, position, and orientation relative to the ego vehicle. Each object receives a normalized attention score between 0 and 1, indicating its influence on the vehicle’s behavior. These scores can be used in downstream modules such as planning, control, and safety. The module is currently tested in simulation. Future work will involve deploying the system on real vehicles. By helping the vehicle focus on the most critical elements in its surroundings, the Explainable Artificial Intelligence (XAI)-Interpreter supports more transparent and explainable autonomous driving systems.

Human gait has proved to be one of the standard biometrics for human identification. It is a non-invasive biometric method that uses human walking patterns specific for each human being. In most of the traditional methods, we use handcrafted features of simple convolutional models for gait analysis in human identification. Here we may face challenges addressing complex temporal dependencies in gait sequences. This study proposes a novel deep learning framework that applies multi-feature input representations. It combines Gait Energy Images (GEI), Frame Difference Gait Images (FDGI), and Histogram of Oriented Gradients (HOG) features. This is proposed for enhancing the accuracy of human identification. The proposed work implements a CNN-based feature extractor with an attention mechanism for gait recognition. The model is trained and validated on a labeled dataset, showcasing its ability to learn discriminative gait representations with improved accuracy. The proposed pipeline of activities include preprocessing and converting gait sequences into frames, organizing them using folder-based numerical extraction, followed by the training of an attention-enhanced convolutional network. The proposed model was found to perform better than existing methods on public datasets and works well even with different camera angles and clothing styles.

Low-cost sensor-fusion system combining a 10.525 GHz CW Doppler microwave sensor with an 8 × 8 Time-of-Flight (ToF) infrared sensor for short-range object tracking. Data are acquired and processed in a sequential fusion pipeline: ToF-based CNNs estimate object presence, coordinates, and cross-section, while Doppler histograms yield radial velocity; outputs are then fused at the decision level. A dataset of 31,367 frames was collected. The system tracks objects (≥35 cm²) at speeds up to 10 m/s within 5–250 cm, achieving 98% detection and 84% positioning accuracy. This approach offers radar-like capabilities at reduced cost, enabling applications in industrial, and consumer-electronics domains.

In the Philippines, chunks of calcium carbide are usually used as a ripening agent for mango samples, while chlorpyrifos is used to control pests and diseases in banana fruits. However, both of these agents can cause harm to human health. The acetylene gas from the calcium carbide produced during the ripening process of mango and the chlorpyrifos residue in the banana fruit can be inhaled and touched by humans. Likewise, considering the country’s economic standing, the development of low-cost and portable instruments is encouraged. To address these issues, a multi-spectral near-infrared light-emitting diode-based spectrometer was developed. This study aims to determine the calcium carbide residue and chlorpyrifos detection capability in mango and banana fruit, respectively, using the developed spectrometer prototype. The prototype is a spectrometer system that uses a graphical user interface, a DC power supply, and a black box. Fruit samples were scanned inside the black box and irradiated by the near-infrared lights from the circuit board. Partial least square regression and linear discriminant analysis showed 81.33% calcium carbide residue prediction capability and 88.9% correct classification, while it shows 74% chlorpyrifos residue prediction capability and 80% correct classification. Therefore, the multispectral near-infrared light-emitting diode-based spectrometer prototype has the chance to detect calcium carbide chemical residue in mango fruit and chlorpyrifos in banana fruit non-destructively.

A game-changing strategy for increasing crop productivity while preserving vital resources is precision agriculture. The development of cloud computing and the Internet of Things (IoT) has made it possible and efficient to monitor soil and environmental data in real time. In order to monitor temperature, soil moisture, humidity, and light intensity, this work proposes an inexpensive, IoT-enabled smart agriculture system that uses low-cost sensors. The real-time data is wirelessly transmitted by an ESP32 edge computing device and stored and analyzed on cloud platforms like Firebase or Thing Speak. A rule-based algorithm generates alerts when sensor values surpass predefined thresholds, enabling prompt and informed decision-making. Field experiments reveal that the proposed system is accurate, economical, and energy-efficient, making it ideal for automation and remote monitoring in precision agriculture. A user-friendly dashboard allows farmers to easily visualize data trends and receive timely notifications. The system supports scalability and can be adapted to different crop types and soil conditions with minimal effort. Moreover, by optimizing water and resource usage, the system contributes to sustainable farming practices and environmental conservation. This deployable solution offers a practical and affordable pathway for small and medium-sized farmers to adopt smart agriculture technologies and improve crop yield outcomes efficiently.

Precision agriculture is dependent on precise crop identification to maximize resource utilization and enhance yield forecasting. This paper investigates the use of Vision Transformers (ViTs) for crop classification from high-resolution satellite images. In contrast to traditional deep learning models, ViTs use self-attention mechanisms to capture intricate spatial relationships and improve feature representation. The envisioned framework combines preprocessed multispectral satellite imagery with a Vision Transformer model that is optimized to classify heterogeneous crop types more accurately. Experimental outcomes confirm that ViTs are superior to conventional Convolutional Neural Networks (CNNs) in processing big agricultural datasets, yielding better classification accuracy. The proposed model was tested on a multispectral satellite image from Sentinel-2 and Landsat-8. The results shows that ViTs efficiently captured long-range dependencies and intricate spatial patterns and attained a high classification accuracy of 94.6% and a Cohen’s kappa coefficient of 0.91. The incorporation of multispectral characteristics like NDVI and EVI also improved model performance, allowing for improved discrimination between crops with comparable spectral signatures. The results point out the applicability of Vision Transformers in remote sensing for sustainable and data-centric precision agriculture. Even with the improvements made in this study, issues like high computational expense, data annotation needs, and environmental fluctuations are still major hurdles to widespread deployment.