Background: Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Methods: Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Results: Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. Conclusions: The study concluded that it may be used to build an automated system for the detection of severity of CKD.

Market Basket Analysis (MBA) is a data mining technique assisting retailers in determining the customer's buying habits while making new marketing decisions as the buyer's desire frequently changes with expanding needs; therefore, transactional data is getting large every day. There is a demand to implement Deep Learning (DL) methods to manipulate this rapidly growing data. In previous research, many authors conducted MBA applying DL and association rules (AR) on retail datasets. AR identifies the association between items to find in which order the customer place items in the basket. AR is only used in mining frequently purchased items from retail datasets. There is a gap in classifying these rules and predicting the next basket item using DL on the transactional dataset. This work proposes a framework using AR as a feature selection while applying DL methods for classification and prediction. The experiments were conducted on two datasets, InstaCart and real-life data from Bites Bakers, which operates as a growing store with three branches and 2233 products. The AR classified at 80,20 and 70,30 splits using CNNN, Bi- LSTM, and CNN-BiLSTM. The results considering simulation at both splits show that Bi-LSTM performs with high accuracy, around 0.92 on the InstaCart dataset. In contrast, CNN-BiLSTM performs best at an accuracy of around 0.77 on Bites Bakers dataset.

Social media has attained popularity during the last few decades due to the rapid growth of online businesses and social interaction. People can interact with one another and communicate their sentiments by expressing their ideas and points of view on social media. Businesses involved in manufacturing, sales, and marketing increasingly focus on social media to get feedback on their goods and services from people worldwide. Businesses must process and analyze this feedback in the form of sentiments to gain business insights. Every day, millions of Urdu and Roman Urdu sentences are posted on social media platforms. The critical loss of this massive amount of data results from ignoring the thoughts and opinions in language with limited resources, such as Urdu and Roman Urdu in the favor of resource-rich languages, such as English. The current study focused on sentiment analysis of Roman Urdu text. Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) word embedding techniques were deployed to conduct the current study. Support Vector Machine (SVM), Linear Support Vector Machine (LSVC), Logistic Regression (LR), and Random Forest (RF) classifiers were deployed. The experiments showed that SVM showed 94.74%, while RF showed 93.13% accuracy using BoW word embedding technique

Converting a word to its original form, is called stemming, which is extremely important in the field of Natural language processing (NLP). It’s an integral part of the linguistic pre-processing of every Natural language processing application. Stemming converts inflectional word forms into their root word. Much work has been done for stemming in different national and regional languages like English, French, Arabic, German, Urdu, and Hindi. Many regional languages still need work to build digital resources using Natural language processing. Saraiki is one of the widely spoken regional languages in Pakistan. Almost eighty million people use this language for communication. There are very limited digital resources using the Saraiki language available to support advancement in Natural language processing technologies. The current research aims to propose a hybrid stemmer to stem Saraiki Work. The hybrid stemmer contains two hundred prefix and postfix rules and Long short-term memory based sequence-to-sequence model for converting Saraiki words into the stem. Firstly, Saraiki text * Corresponding Author: mubasher@isp.edu.pk was pre-processed, and a rule set was implemented. Secondly, the Long short-term memory based sequence-to-sequence model was deployed to stem the Saraiki word correctly. In the last step, The Saraiki Stemmer performance was evaluated by accurately finding stem word accuracy using a rule-set and Long short-term memory sequence to sequence model. After experiments, using the rule set correctly, stem word accuracy was 68.53%, while the Long short-term memory based sequence-to-sequence model produced 93.0% accuracy of correctly stem words. This work contributes significantly to the regional linguistic field by introducing stemmer for the Saraiki language.

Nowadays, aerial vehicles (drones) are becoming more popular. Over the past few years, Unmanned Aerial Vehicles (UAVs) have been used in various remote sensing applications. Every aerial vehicle is now either partially or completely automated. The tiniest type of aerial vehicle is the UAV. The widespread use of aerial drones requires numerous safe landing site detection techniques. The paper aims to review literature on techniques for automatic safe landing of aerial drone vehicles by detecting suitable landing sites, considering factors such as ground surfaces and using image processing methods. A drone must determine whether the landing zones are safe for automatic landing. Onboard visual sensors provide potential information on outdoor and indoor ground surfaces through signals or images. The optimal landing locations are then determined from the input data using various image processing and safe landing area detection (SLAD) methods. UAVs are acquisition systems that are quick, efficient, and adaptable. We discuss existing safe landing detection approaches and their achievements. Furthermore, we focus on possible areas for improvement, strength, and future approaches for safe landing site detection. The research addresses the increasing need for safe landing site detection techniques in the widespread use of aerial drones, allowing for automated and secure landing operations.

The Internet of Things (IoT) is an interconnected framework of computer devices, software, and sensors to work collectively to automate and monitor different tasks and procedures without human intervention. IoTs provide benefits and ease in performing operations for various industries and home and health stakeholders. Still, it faces security threats and challenges that make the IoT framework unreliable regarding security and privacy. Intrusion detection systems have been designed and deployed to overcome these security challenges. Over the past few years, for making the IDS intelligent artificial intelligence has been integrated with them. Besides this, different feature selection techniques are also being used to make the datasets appropriate for Machine Learning (ML) classifiers. This research uses a filter-based feature selection technique to select the important features from two datasets, UNSW-NB15 and NSL-KDD. After that, datasets with selected features are given to the classifiers as input. This work also evaluates different ML classifiers such as Multiple Layers Perceptron (MLP), CatBoost, Light Gradient Boosting (LGBM), Extra-Tree Classifier (ETC), Random Forest (RF), and K-nearest neighbor (KNN). Different metrics, Accuracy, Precision, Recall, F1, Detection Rate, and FAR, have been used for performance analysis. Evaluation results proved the LGBM classifier effective among other classifiers with 98.70% accuracy on the UNSW-NB15 dataset and 99.8% on the NSL-KDD dataset after feature selection with mean absolute difference (MAD) feature selection technique. Results are also compared with existing models to provide the efficiency of the proposed model.