The accumulation of salt through natural causes and human artifice, such as saline inundation or mineral weathering, is marked as salinization, but the hindrance toward spatial mapping of soil salinity has somewhat remained a consistent riddle despite decades of efforts. The purpose of the current study is the spatial mapping of soil salinity in Kot Addu (situated in the south of the Punjab province, Pakistan) using Landsat 8 data in five advanced machine learning regression models, i.e., Random Forest Regressor, AdaBoost Regressor, Decision Tree Regressor, Partial Least Squares Regression and Ridge Regressor. For this purpose, spectral data were obtained between 20 and 27 of January 2017 and a field survey was carried out to gather a total of fifty-five soil samples. To evaluate and compare the model’s performances, the coefficient of determination (R²), Mean Squared Error (MSE), Mean Absolute Error (MAE) and the Root-Mean-Squared Error (RMSE) were used. Spectral data of band values, salinity indices and vegetation indices were employed to study the salinity of soil. The results revealed that the Random Forest Regressor outperformed the other models in terms of prediction, achieving an R² of 0.94, MAE of 1.42 dS/m, MSE of 3.58 dS/m and RMSE of 1.89 dS/m when using the Differential Vegetation Index (DVI). Alternatively, when using the Soil Adjusted Vegetation Index (SAVI), the Random Forest Regressor achieved an R² of 0.93, MAE of 1.46 dS/m, MSE of 3.90 dS/m and RMSE of 1.97 dS/m. Hence, remote sensing technology with machine learning models is an efficient method for the assessment of soil salinity at local scales. This study will contribute to mitigating osmotic stress and minimizing the risk of soil erosion by providing early warnings regarding soil salinity. Additionally, it will assist agriculture officers in estimating soil salinity levels within a shorter time frame and at a reduced cost, enabling effective resource allocation.

In the digital era, the proliferation of online reviews through videos has been meteoric and driven by recent technological advancements. The sentiments expressed in these videos drive consumer reliance, decision-making and may shape perception building of general public. The sentiment classification of videos is emerging as a vital area of research in Natural Language Processing (NLP) and is challenging due to complexity of emotions and temporal aspects of modalities. These challenges become more complex for low-resourced languages such as Urdu. Addressing the need for a comprehensive study in this realm, we introduce a generic framework, Urdu Multimodal Sentiment Analysis (UMSA), for emotion detection and sentiment classification of videos. Our fusion-based approach highlights that incorporating additional modalities and cross-modal interactions significantly enhances the analysis. Our research culminates in the creation of the Urdu Sentiments Dataset (USD), a comprehensive collection of Urdu video reviews. In this study, we classify videos using a two-phase analysis approach that incorporates early fusion and ensembling. After fusion, we perform ensembling of two models for each modality: audio, text, and frames. We utilize Long Short-Term Memory (LSTM) networks and Random Forest Classifier for audio. Text-based analysis is conducted using Logistic Regression and the Bidirectional Encoder Representations from Transformers (BERT) model. For frames, we employ Random Forest and Convolutional Neural Networks (CNN). Afterwards, we implement model ensembling across the three modalities. This multimodal integration proves essential in providing a clearer and more comprehensive understanding of the sentiments conveyed and achieved more than 80% accuracy. The validation of this framework is reinforced through a comprehensive case study approach. This independent validation highlights the framework’s robustness and adaptability to real-world scenarios.

Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan’s economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models’ performances were evaluated and compared using R-squared (R${}^2$), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R${}^2$ = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning.

Species identification is a critical task for biological studies, ecological monitoring, and conservation efforts. A comprehensive comprehension of the evolutionary mechanisms that lead to biological variety is necessary while species are distinct categories of living organisms; however, naming, identifying, and differentiating between species is more complex than it may seem. Traditional methods, relying on dichotomous keys and manual observation, are time-consuming and error-prone. Precise species identification is crucial for all taxonomic investigations and biological procedures. Numerous experts are currently engaged in the task of identifying a solitary species. To address these challenges, we present a robust artificial intelligence framework for species identification using deep learning techniques, specifically leveraging the ResNet-50 Convolutional Neural Network (CNN). Our approach utilizes a ResNet-50-based CNN to accurately classify 15 species, including humans, plants, and animals, from images taken at unique locations and angles. The dataset was pre-processed and augmented to enhance training, ensuring robustness against variations in lighting, occlusion, and background clutter. Featuring 4 million trainable parameters, our modified ResNet-50 model demonstrated superior computational efficiency and accuracy. The proposed model achieved an overall accuracy of 96.5%, with class-specific accuracies of 98.25% for humans, 97.81% for animals, and 96.90% for plants. These results surpass those of existing models such as GoogleNet, VGG, SegNet, and DeepLab v3+, highlighting the efficacy of our approach. Performance was evaluated using metrics such as sensitivity, specificity, and error rate, further validating its reliability. Our findings suggest that the ResNet-50-based CNN model is highly effective for automatic species identification, offering significant improvements in accuracy and computational efficiency.

Cybercrimes frequently manifest through the utilization of the internet. Spoofing attacks epitomize an advanced facet of contemporary cybercrime. Considerable research has been undertaken in the domain of these diverse spoofing attack techniques. Recent spoofing attacks predominantly concentrate on voice spoofing, driven by the vulnerabilities of Automatic Speaker Verification systems. Such type of attacks contains significant threats, resulting in fraudulent activities including imitation of someone’s voice for financial scam, bypassing security locks, or getting sensitive information and data. To address these concerns, this research focuses on the detection of voice spoofing attacks by utilizing a hybrid VGGish-LSTM model. While previous transfer learning methodologies have essentially been applied to image classification tasks, our methodology focuses on voice classification. In comparison to VGGish, another transfer learning model i.e. YAMNet is also utilized. These models support the extraction of significant features i.e. embeddings, capable of mitigating bias and facilitating analysis with limited datasets. For classification, LSTM and 1D CNN models are applied on the extracted embeddings. The evaluation uncovers that the hybrid VGGish-LSTM model exhibits better performance than alternative configurations. We achieved a remarkable accuracy of 99.11% with an equal error rate of 1.18% on ASV Spoof 2019 LA dataset. This research helps the general public to recognize spoof attacks, thereby helping in the prevention of frauds by the detection of synthesized speech.