The integration of Internet of Things (IoT) technology in precision agriculture offers substantial potential for improving productivity and crop yield. However, challenges persist in optimizing wireless solutions for outdoor smart farms, where diverse data types and environmental obstacles can impede efficiency, causing issues such as data loss and connectivity disruptions. The present study undertakes an investigation into the comparative efficacy of three primary IoT communication protocols, namely Wi-Fi (Wireless Fidelity), Zigbee, and Long-Range Wide Area Network (LoRaWAN), within the context of agricultural monitoring scenarios. This inquiry is substantiated by a series of comprehensive experiments conducted across diverse environmental conditions. Our findings underscore Wi-Fi as the optimal candidate for integration into agricultural monitoring systems, with a focus on paramount factors such as connectivity, data transmission precision, and wireless coverage. This study reveals how data recovery is implemented in communication systems used in agriculture in various scenarios. The methods are deliberate to recover lost data caused by failures in both node networks, thereby notably improving the accuracy of communication solutions for farmers. Additionally, this study provides reference for farmers and stakeholders on leveraging technology in smart agriculture to inflate efficiency and productivity.

Accurate sales forecasting is vital for balancing demand and supply and enhancing profitability in the retail sector. Deep learning (DL) models have shown promise in this area; however, most either handle temporal or spatial patterns in isolation. Moreover, many studies rely on synthetic datasets or omit critical contextual variables, reducing real-world accuracy. This study proposes a hybrid convolutional neural network (CNN)-long short-term memory (LSTM) model for retail sales forecasting using real-world data enhanced with environmental and demographic variables in term of holidays, salary days, protests, and weather conditions. CNNs capture spatial patterns, while LSTMs model temporal dependencies, making the hybrid architecture well-suited for multivariate forecasting tasks. Our model demonstrates a significant improvement in predictive performance, achieving a mean absolute percentage error (MAPE) of 4.16%, outperforming traditional and standalone neural models. By incorporating external factors, the proposed approach enables more reliable forecasting and supports informed decision-making in retail operations.

This research presents a novel, real-time Pakistani Sign Language (PSL) recognition system utilizing a custom-designed sensory glove integrated with advanced machine learning 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 performance and offers practical applications in smart home interfaces, virtual and augmented reality, gaming, and assistive technologies. By combining ergonomic hardware with intelligent algorithms, this research takes a significant step toward inclusive communication and more natural human–machine interaction.