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In this paper, a miniaturized design of dual transmission frequency selective rasorber (FSR) with absorption-transmission-absorption-transmission (A-T-A-T) feature is proposed. Initially, lowfrequency resonators are incorporated on the square ring shaped lossy resistive layer for expanding the broad absorption towards the lower spectrum of frequency. Then the dual bandpass frequency selective surface (FSS) with transmission bands lying within the absorption spectrum is integrated with the resistive layer. Further, a cross-loop resonator is integrated inside the square ring of the resistive layer due to which a dual transmission pole is achieved through the resistive layer aligning with the dual operating frequencies of bandpass FSS. The proposed structure exhibits two broad absorption bands ranging from 3.7.10.5 GHz (95.7%) and 12.6.14.6 GHz (14.70%). The two transmission bands are at 10.7 GHz (8.62%) and 16.0 GHz (8.75%) with minimum insertion loss of 1.0 dB and 0.7 dB, respectively. The proposed FSR is polarization-insensitive and a compact design with an electrical size of $0.015\lambda^{2}$ and a thickness of $0.08\lambda$ along with a wide angular stability up to 50o incidence. The working process of the proposed design is illustrated by studying an equivalent circuit model (ECM). Further, a prototype of 23 × 23 array is fabricated and the measurements are carried out for both normal and oblique incidences. The close resemblance observed between the measured and simulated response experimentally validates the proposed FSR design.

Emotion Recognition is a critical area of research including healthcare, human-computer interaction, and psychology. While traditional methods mainly rely on facial expressions and textual analysis, they also have inherent flaws and cannot be reliable. Facial expression-based emotion recognition assumes that it represents genuine internal emotions that may be inaccurate. Similarly, textual analysis depends on the available data and needs help accurately capturing subtle emotions in text. However, electroencephalography (EEG) has emerged as a rising alternative for objective and real-time emotion recognition. Unlike facial and textual methods, EEG directly measures brain activity and provides a reliable result. To address this researchers have used basic machine learning methods that need manual feature extraction, which might miss essential data and make the process slow and less accurate. In this study, we propose a comprehensive methodology for EEG-based emotion recognition that addresses the limitations of traditional methods and basic machine learning techniques. Our approach involves preprocessing EEG signals using a butter-worth bandpass filter to eliminate noise, followed by feature extraction techniques. We then employ Principal Component Analysis (PCA) for dimensionality reduction, ensuring efficient data representation. To further enhance the model performance we explore machine learning classifiers(GaussianNB, SVM, Random Forest) and proposed an EEG-LSTM and GRU model with an accuracy of 97% and 96% respectively, that gives better results than the basic machine learning models.

By providing food to billions of people, agriculture contributes significantly to the global economy. Plant ailments, however, can reduce crop yields and result in financial losses. An automated artificial intelligence (AI)-based method for the automatic identification of plant diseases using resource-constrained Internet of Things (IoT) devices has been presented to solve this issue. However, the deployment of state-of-the-art convolution neural networks (CNNs) and Vision Transformers (ViT) on IoT devices is not feasible due to their large number of trainable parameters. To overcome this limitation, a meta-ensemble of lightweight MLP-Mixer and faster Long Short-Term Memory (LSTM) models has been proposed for plant disease detection on low-powered micro-controllers (MCUs) of IoT devices. The MLP Mixer model is based on a simple multi-layer perceptron network. The proposed meta-ensemble consists of two levels: predictions made by the trained models at the first level are used to train the machine learning classifier at the next level, resulting in further improvement of categorisation accuracy. The proposed meta-ensemble has been tested on three diverse datasets of varying sizes and plant species, including Maize, Cotton, and a dataset derived from the Plant Village(PV) dataset. On the Maize, Cotton, and derived PV datasets, respectively, experimental results demonstrate that the suggested technique obtained classification performance of 94.27%, 98.43%, and 97.45%. Moreover, prediction time of the proposed meta-ensemble is low, and it has considerably fewer trainable parameters than CNN and other transformer-based architectures. Therefore, the proposed meta-ensemble is an efficient and effective solution for plant disease detection with limited resources.

The growth of the tourism industry has greatly boosted the Point-of-Interest (POI) recom- mendation tasks using Location-based Social Networks (LBSNs). The ever-evolving nature of user preferences poses a major problem. To address this, we propose a Long-Term Preference Mining (LTPM) approach that utilizes the Temporal Recency (TR) measure in the visits along with the location-aware recommendation based on Spatial Proximity (SP) to the user’s location. The temporal dynamics and changing preferences are exploited based on the modified Long Short-term Memory (LSTM) that utilizes the time decay. The spatial considerations are modeled in two aspects: geographical proximity based on enhanced representation learning using orthogonal mapping. Second, the Region-of-Interest (ROI) is based on spatial griding and metric learning to capture the spatial relationships between POIs to enhance the metric space representation. The final recommendations are based on a multi-head attention mechanism that allocates the weights to different features. The combination of three models, called, LTPM-TRSP approach captures the user-POI, POI-POI, and POI-time relationships by focusing on the informative representation of sequential and spatial data. The category-aware final recommendations based on comprehensive historical behavior and geographical context are quite efficacious. The experimentation on three real-world datasets, Gowalla, Foursquare, and Weeplaces, also suggests the potency compared to other state-of-the-art approaches.