Computing Open

In recent years, the healthcare industry has faced numerous challenges in ensuring efficient and reliable supply chain management. The emergence of novel supply chain-based frameworks has presented promising solutions to these challenges. This survey paper aims to explore and analyze the latest advancements in supply chain frameworks specifically tailored for the healthcare industry. We review the existing literature, identify key trends, and highlight the potential impact of these frameworks on improving patient care, optimizing inventory management, enhancing operational efficiency, and reducing costs. Additionally, we discuss the challenges and opportunities associated with the implementation of these frameworks and suggest future research directions.

Android operating systems have grown in popularity and are currently being increasingly used on smartphone devices. Because of the quickly expanding quantity of Android malware and the potential safeguard of vast amounts of data kept on Android devices, the identification and categorization of Android malware is regarded as a big data challenge. Researchers started using deep learning (DL) techniques for malware detection, according to the most recent state-of-the-art studies. Nevertheless, researchers and practitioners face challenges such as the choice of DL architecture, extracting and processing features and evaluating results. In this paper, several traditional and hybrid machine learning (ML) models were developed and analyzed in detail to classify Android malware. First, some state-of-the-art DL model has been developed like multi-layer perceptron (MLP), deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM), convolutional neural network (CNN) and later several hybrid models have been developed by combining DNNs with support vector machine (SVM). Second, we have focused on maximizing achievement by fine-tuning a variety of configurations to guarantee the best possible combination of the hyper-parameters and attain the maximum statistical metric value. Finally, we have accomplished performance analysis between these hybrid models and a model based on VGG-16-CNN-1D along with SVM has been proposed to enhance the accuracy and efficacy of large-scale Android malware detection which outperforms all the other developed models using Drebin Dataset. Among the DL methods, DNN achieved the highest F1 score 99.07%. After doing a comparison between hybrid models, it was found that DNN-SVM achieved 99.12% while VGG16-CNN-1D-SVM achieved 98.56% precision, 98.78% recall and 99.20% F1 score, respectively.

Since system identification is a substantial part of many control algorithms, attaining a suitable model is necessary. In this paper, as a novelty, six standard identification methods are used so as to identify the linear ARMA forms of the proposed, widely used model of stirred tank heater. We have chosen these six methods because these methods fit to our desire for the parameter estimation of the ARMA model and they are new and applicable for modeling of all fields of engineering and systems. Beyond that, the mathematics of these methods are so easy to apply and they provide enough data for identifying a wide variation of systems. Furthermore, these algorithms are brought up in a simple and concise manner and easy to implement. In addition, the mathematical form of the suggested stirred tank heater is presented. Furthermore, identification methods are compared in terms of some measures, such as rate of convergence and produced error. Finally, the effectiveness of the six presented identification methods for estimating the stirred tank heater model is illustrated.

Many efforts have already been carried out for education mining in the past. Many techniques and models are already developed to predict and identify students’ performance, learning behavior, status, and education level. However, there is no exact solution for the student’s result prediction since it is affected by the level of the student, the field of study, the location of the data collection, different sizes and nature of data, etc. Different research shows that there can be up to 10% difference in the accuracy of results with and without a feature selection process. Thus, the proposed model designs a better model for student result prediction using feature selection and deep learning techniques. The proposed dissertation task compares and analyzes Correlation-based Feature Selection (CFS), Chi-Square (${\chi }^2$), Genetic Algorithm (GA), Information Gain (IG), Maximum Relevance Minimum Redundancy (mRMR), ReliefF, and Recursive Feature Elimination (RFE) feature selection techniques with the Classification and Regression Tree (CART), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) machine learning algorithms. In the proposed model, a feature selection process CFS and prediction using CNN is recommended. The recommended model (CFS–CNN) is tested with a primary dataset collected from bachelor-level students. The recommended model provides improved performance compared to old techniques. The major contribution of the proposed dissertation is to design a better model for the prediction of students’ results using demographic data and past examination results.

Cloud computing is an on-demand availability of computing resources. The current cloud computing environment uses different algorithms for its security; RC4 is one of them. However, due to the serial implementation of RC4 over cloud computing, the process of encryption and data transmission is badly affected by the factors of poor latency. This research is proposed to cover this factor by implementing a parallel RC4 algorithm in a cloud computing environment. The parallel RC4 model has been developed in web-based architecture relevant to the cloud computing environment. The method processed data simultaneously in four parallel pipelines encrypted through the RC4 algorithm. The proposed method used RC4 algorithms’ parallel implementation to encrypt and decrypt the data. The proposed method increases the efficiency of data encryption and decryption and transmission over a cloud environment using four pipelines at once. These four pipelines receive data and encrypt them using a key and then merge those four streams into a single cipher. These four pipelines enhance the speed of communication over cloud and solve the latency issue majorly. Data sending from node devices to cloud servers is divided into four equal streams and then encrypted using RC4 distinctly but parallel. Then after the roaming, the data are again first combined and then decrypted using the RC4 algorithm. The sample data sets are evaluated on the criteria of the starting time and ending time of the encryption and decryption of data. Results show that the proposed method is able to enhance the speed of the whole process up to average 3.7% due to the implementation of parallel technique and had reduced the time notably. The future work on this research involves enhancement of security process of RC4 algorithm since it can be breached easily.

Artificial Intelligence (AI) has become increasingly integral to healthcare, aiding medical professionals in disease diagnosis. However, a critical challenge faced by practitioners lies in the trustworthiness of diagnosed results, as machine learning models often lack transparent explanations for their predictions. Explainable AI (XAI) addresses this, allowing practitioners and patients to comprehend and trust outcomes. This study employs the liver disease patient dataset to predict liver disease, vital in India where yearly, 10 lakh people face new liver cirrhosis diagnoses with high mortality. Early detection difficulties underscore the need for transparent predictions. Using Kaggle data, Random Forest, XGBoost, and Explainable Boosting Machine (EBM) classifiers are compared, with EBM outshining, achieving a 99.8% accuracy score. The study concludes that the EBM classifier is a superior and transparent liver disease predictor.

The $\epsilon$-insensitive-Least Squares Support Vector Regression ($\epsilon$-LSSVR) optimization problem with $\epsilon$-insensitive quadratic loss function is a non-smooth, piecewise quadratic complex problem. Therefore, the optimal solution of $\epsilon$-LSSVR takes longer than Classic LSSVR. Additionally, due to its unbounded influence function, $\epsilon$-LSSVR, like classical LSSVR, shows poor generalization ability in the presence of outliers. To overcome the above-mentioned drawbacks, this paper presents an $\epsilon$-insensitive Weighted Least Squares Support Vector Regression ($\epsilon$-WLSSVR) with equality constraints to increase robustness against outliers. To reduce the computational complexity and time, the optimization problem of $\epsilon$-WLSSVR is solved by the Sequential Minimal Optimization (SMO) algorithm. Extensive simulations have shown that the presented $\epsilon$-WLSSVR model combined with the SMO algorithm is more effective and efficient than LSSVR and its known sparse variants.