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The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management. [ABSTRACT FROM AUTHOR]

Cotton, a crucial cash crop in Pakistan, faces persistent threats from diseases, notably the Cotton Leaf Curl Virus (CLCuV). Detecting these diseases accurately and early is vital for effective management. This paper offers a comprehensive account of the process involved in collecting, preprocessing, and analyzing an extensive dataset of cotton leaf images. The primary aim of this dataset is to support automated disease detection systems. We delve into the data collection procedure, distribution of the dataset, preprocessing stages, feature extraction methods, and potential applications. Furthermore, we present the preliminary findings of our analyses and emphasize the significance of such datasets in advancing agricultural technology. The impact of these factors on plant growth is significant, but the intrusion of plant diseases, such as Cotton Leaf Curl Disease (CLCuD) caused by the Cotton Leaf Curl Gemini Virus (CLCuV), poses a substantial threat to cotton yield. Identifying CLCuD promptly, especially in areas lacking critical infrastructure, remains a formidable challenge. Despite the substantial research dedicated to cotton leaf diseases in agriculture, deep learning technology continues to play a vital role across various sectors. In this study, we harness the power of two deep learning models, specifically the Convolutional Neural Network (CNN). We evaluate these models using two distinct datasets: one from the publicly available Kaggle dataset and the other from our proprietary collection, encompassing a total of 1349 images capturing both healthy and disease-affected cotton leaves. Our meticulously curated dataset is categorized into five groups: Healthy, Fully Susceptible, Partially Susceptible, Fully Resistant, and Partially Resistant. Agricultural experts annotated our dataset based on their expertise in identifying abnormal growth patterns and appearances. Data augmentation enhances the precision of model performance, with deep features extracted to support both training and testing efforts. Notably, the CNN model outperforms other models, achieving an impressive accuracy rate of 99% when tested against our proprietary dataset. [ABSTRACT FROM AUTHOR]

Sign language is the predominant form of communication among a large group of society. The nature of sign languages is visual. This makes them very different from spoken languages. Unfortunately, very few able people can understand sign language making communication with the hearing-impaired extremely difficult. Research in the field of sign language recognition can help reduce the barrier between deaf and able people. A lot of work has been done on sign language recognition for numerous languages such as American sign language and Chinese sign language. Unfortunately, very little to no work has been done for Pakistan Sign Language. Any contribution in Pakistan Sign Language recognition is limited to static images instead of gestures. Furthermore, the dataset available for this language is very small in terms of the number of examples per word which makes it very difficult to train deep networks that require a considerable amount of training data. Data Augmentation techniques help the network generalize better by providing more variety in the training data. In this paper, a pipeline for the Pakistan Sign Language recognition system is proposed that incorporates an augmentation unit. To validate the effectiveness of the proposed pipeline, three deep learning models, C3D, I3D, and TSM are used. Results show that translation and rotation are the two best augmentation techniques for the Pakistan Sign Language dataset. The models trained using our data-augment-supported pipeline outperform other methods that only used the original data. The most suitable model is C3D which not only produced an accuracy of 93.33% but also has a low training time as compared to other models. [ABSTRACT FROM AUTHOR]

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data. [ABSTRACT FROM AUTHOR]

Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded with detailed experimental results along with discussion on the outcomes of sentiment analysis for real-world forecasting and approval of general elections in Pakistan. [ABSTRACT FROM AUTHOR]

Agriculture sector is the second largest sector of Pakistan, which contributes 17.9% of the total Gross Domestic Production. The productivity of major crops such as wheat, maize, rice, sugarcane, and cotton is central to the economic development of the country. Among these crops, wheat is considered as a staple crop of Pakistan, covering almost nine million hectares of the cultivated land. However, its productivity rate is severely affected by rust disease, which is an airborne fungal disease caused by a group of fungi from Pucciniales order. This disease has the ability to decrease the wheat production rate up to 30% and destroy the crop within a month after its first assault, thus posing a serious threat to food security. Owing to conventional farming practices, there is a recurring concern for an early arrest of this disease to minimize the yield losses and to feed the growing population. Several precision farming solutions are available worldwide to timely identify the rust attack, mitigate its catastrophic effects, and invoke the remedial action in a site-specific manner. In this paper, the technical review of cutting edge techniques used for detecting wheat rust attacks is presented, which include Remote Sensing, Machine Learning, Deep Learning, and Internet of Things. Additionally, the challenges and limitations associated with these techniques are discussed to highlight the practical implications of implementing these techniques. [ABSTRACT FROM AUTHOR]

Efficient agricultural water management requires accurate determination of reference evapotranspiration (ETo). Artificial intelligence has recently managed to solve several water management challenges. Acquiring suitable data sets, choosing appropriate criteria, and identifying the optimal algorithm are significant challenges in machine learning. Machine learning may be an effective tool for estimating reference evapotranspiration using minimal meteorological inputs. This work aims to estimate the amount of ETo in the Khyber Pakhtunkhwa region of Pakistan, highlighted as an important agricultural hub, using machine learning. The machine learning techniques include support vector machines based on radial basis function in conjunction with a random forest, artificial neural network, and deep learning long short-term memory systems with 15 input combinations. The findings showed that all four strategies predicted ETo values with reasonable accuracy and reliability. The support vector regression model with scenario-14 revealed strong results than the others (R2 = 0.9970). The ultimate purpose of this research is to develop the best scenarios with a reasonable accuracy rate and the least amount of input parameters to create the most suitable methodology. Hence, the second most successful models were artificial neural nets and deep learning with scenario-9 and 11, estimating ETo with a higher accuracy level (R2= 0.9768 and 0.9688) during the testing stage. In conclusion, the research confirms the beneficial effects of support vector regression and enhanced performance of machine learning algorithms in estimating ETo in water shortage tropical and semiarid zones. [ABSTRACT FROM AUTHOR]

A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart classrooms to enhance faculty efficiency based on accumulated learning outcomes and interests. Smart classroom boards, audio-visual aids, and multimedia are directly related to the Smart classroom environment. Along with these facilities, more effort is required to monitor and analyze students' outcomes, teachers' performance, attendance records, and contents delivery in on-campus classrooms. One can achieve more improvement in quality teaching and learning outcomes by developing digital twins in on-campus classrooms. In this article, we have proposed DeepClass-Rooms, a digital twin framework for attendance and course contents monitoring for the public sector schools of Punjab, Pakistan. DeepClassRooms is cost-effective and requires RFID readers and high-edge computing devices at the Fog layer for attendance monitoring and content matching, using convolution neural network for on-campus and online classes. [ABSTRACT FROM AUTHOR]

Accurate forecasting of key hydrological processes, such as rainfall, generally requires the use of auxiliary predictive hydrological variables. Data requirements can be reduced by using artificial intelligence models that are able to successfully capture the information contained in the historic observations of the target variable of interest. In this study, a novel Wavelet-coupled Multi-order Time Lagged Neural Network (WMTLNN) model is developed to accurately forecast rainfall by using previous rainfall records only. The study is conducted using daily rainfall data recorded in the period 2015–2017 at three meteorological stations (Astore, Chillas, and Gilgit) located in Upper Indus Basin (UIB), Pakistan. WMTLNN models are developed by introducing time lags up to ten days, Symlets 4 (sym4) wavelets and Daubechies wavelets with three vanishing moments (db3), and Maximal Overlap Discrete Wavelet Transformation (MODWT) to account for boundary effects in the forecasting mode. The performance of WMTLNN models is compared with that of Time Lagged Neural Network (TLNN) models, Wavelet-coupled Time Lagged Neural Network (WTLNN), and deep learning Long Short-Term Memory (LSTM) models. Comparative analysis indicates that WMTLNN models overcome the other models, with more than 80% forecasting accuracy for most of the cases, and a typical range of 0.85–0.95 accuracy in terms of NSE. The highest NSE value is 0.97 at Astore with LSTM model, 0.96 at Chillas with WMTLNN model, and 0.95 at Gilgit with WMTLNN model. Overall, wavelet transformation of time series data enhances efficiency and accuracy of rainfall forecast. [ABSTRACT FROM AUTHOR]